虚拟电厂技术的市场化研究

虚拟电厂充分调用系统内的可调节资源，通过需求响应引导用户侧配合发电侧的发电调度，减少弃能电量，通过燃气轮机的开机运行解决部分时段风光出力不足的问题，通过储能在电价较 ...

随着分布式能源的快速发展，虚拟电厂作为电力市场的重要参与者，能够灵活调节和优化多种能源资源的调度，以应对电力供需的不平衡问题。本文针对虚拟电厂在偏差考核机制 ...

文献[8]提出了一种基于多链式区块链的电力交易决策模型，解决了多类型产消者的电力交易优化问题。文献[9]设计了面向虚拟电厂的隐私保护结算模型，通过区块链确保电力交易的 ...

为应对高比例可再生能源并网导致的电力系统灵活性不足问题，本研究提出一种面向虚拟电厂(VPP)的负荷基线估计与可调潜力评估方法。通过整合用户侧可调负荷，构建融合用电 ...

市场中的调频服务提供商可以分为传统发电企业(传统火电、水电等)、新型经营主体(独立储能电站、虚拟电厂等)、售电企业和电力用户，他们各自拥有不同的技术特性、运营 ...

针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型.首先,源侧基于Frank-Copula函数建立风光出力联合概率分布模型,采样约简得风光出力典型 ...

3) 商业楼虚拟电厂：将分布式电源、储能装置、可控负荷等综合能源聚合的“城市电仓”支持电力市场和电网运行，实现终端约2.05万个的需求侧响应。

市场建设方面，需重点发展虚拟电厂，完善其参与调峰、调频辅助服务的规则。通过降低准入门槛、建立基于调节性能的补偿机制，并推动参与省间现货市场，充分激发市场活力。

“局域云”主要功能为局域电网中众多由分布式电源、储能设备组成的城市配电网或社区微网提供云计算服务，例如：电能双向管理、用户电能交易、提供定制用电方案、即插即用响应和 ...

... 聚合优化管控，实现源网荷储一体化调节，并参与电网调峰调频和电力市场交易，实现发电侧与用电侧的双侧调节。聚合商通过分布式云和边缘计算可以对聚合的各类终端用户 ...

虚拟电厂(virtual power plant, VPP)是一种以电力系统为框架的协调管理系统，它集成传统能源、可再生能源、储能系统、灵活负载，并通过先进的信息通信技术实现分布式能源的 ...

在零售市场方面，德国将分布式电源和可控负荷进行整合，利用虚拟电厂保持电网平衡，其价格手段包括实时电价、分时电价以及尖峰电价三种，用户一方面可以通过储热加热器等储能 ...

针对超大城市的电力调控问题，提出以虚拟电厂技术构建交易渠道，利用大量分布式能源提供电能与辅助服务[6]。考虑低压侧负荷灵活资源参与市场交易，通过低压区域电网与 ...

含风光水的虚拟电厂与配电公司协调调度模型[J]. 电力系统自动化, 2015, 39(9): 75 ... 并网发电厂辅助服务管理暂行办法[R]. 北京: 国家电力监管委员会, 2006. [21] ...

并网模式. 削峰填谷. 参与电力市场套利. 提供辅助服务(调频、调压). 锂离子电池+ 飞轮/超级电容. 电池承担能量调度，飞轮或超级电容提供毫秒级调频响应，提升电网服务质量.

No abstract available

The declining provision of inertia by synchronous generators in modern power systems necessitates aggregating distributed energy resources (DERs) into virtual power plants (VPPs) to unlock their potential in delivering inertia and primary frequency response (IPFR) through ancillary service markets. To facilitate DER participation in the IPFR market, this paper proposes an aggregation model and market mechanism for VPPs participating in IPFR. First, an energy-reserve-IPFR market framework is developed, in which a VPP acts as an intermediary to coordinate heterogeneous DERs. Second, by taking into account the delay associated with inertial response, an optimization-based VPP aggregation method is introduced to encapsulate the IPFR process involving a variety of DERs. Third, an energy-reserve-IPFR market mechanism with VPP participation is introduced, aiming to minimize social costs, where stochastic deviations of renewable energy generation are explicitly modeled through chance-constrained reformulations, ensuring that the cleared energy, reserve, and IPFR schedules remain secure against forecast errors. Case studies on IEEE 30-bus and IEEE 118-bus systems show that the nadir and quasi-steady-state frequencies are reproduced by the VPP aggregation model with a mean absolute percentage error<= 0.03%, and the proposed market mechanism with VPP participation reduces the total system cost by approximately 40% and increases the net profit by about 30%.

No abstract available

No abstract available

This study intends to optimize the trading decision-making strategy of the new electricity market with virtual power plants and improve the transmission efficiency of electricity resources. The current problems in China’s power market are analyzed from the perspective of virtual power plants, highlighting the necessity of reforming the power industry. The generation scheduling strategy is optimized in light of the market transaction decision based on the elemental power contract to enhance the effective transfer of power resources in virtual power plants. Ultimately, value distribution is balanced through virtual power plants to maximize the economic benefits. After 4 hours of simulation, the experimental data shows that 75 MWh of electricity is generated by the thermal power system, 100 MWh by the wind power system, and 200 MWh by the dispatchable load system. Comparatively, the new electricity market transaction model based on the virtual power plant has an actual generation capacity of 250MWh. In addition, the daily load power of the models of thermal power generation, wind power generation, and virtual power plant reported here are compared and analyzed. For a 4-hour simulation run, the thermal power generation system can provide 600 MW of load power, the wind power generation system can provide 730 MW of load power, and the virtual power plant-based power generation system can provide up to 1200 MW of load power. Therefore, the power generation performance of the model reported here is better than other power models. This study can potentially encourage a revised transaction model for the power industry market.

With the acceleration of the market-oriented reform of the power system, the construction and policy support of virtual power plant (VPP) projects have increased year by year and the participation in the electricity market, grid operation, and auxiliary service market of virtual power plants has been encouraged to promote the positive role of VPP in the rational allocation of user resources, increase operating income, maintain grid stability, and improve new energy consumption. However, the coordination method for VPP to participate in different markets has not been studied deeply and effectively yet. In this context, to coordinate the electricity market with diversified demand, aiming at the maximization of comprehensive revenue in diversified market-oriented decision-making of VPPs, an optimal operational strategy of the virtual power plant to participate in the joint markets of electricity spot and auxiliary service market is innovatively proposed. The case study on a certain VPP in Liandu district, Lishui city, Zhejiang province of China verifies the effectiveness and practicability of the proposed strategy.

Countries all over the world are facing the problem of green and low-carbon transformation. Vigorously developing distributed generation is one of the important ways to achieve carbon peak and carbon neutrality. However, the large-scale promotion of distributed generation still has many difficulties, such as small single unit capacity, high cost of grid access, difficult dispatching management, strong randomness and so on. With the progress of modern communication technology and Internet technology, distributed generation is aggregated into a virtual entity, namely Virtual Power Plant (VPP), by using the network to break through the geographical limitations, which provides a new idea to solve these problems. In response to the problems caused by the current grid connection of distributed energy, drawing on the concept of VPP, taking into account the rapid development of distributed energy, the development potential of controllable loads, and future development plans, a mechanism for VPP to participate in the electricity market is proposed. Through case studies, it is proven that VPP can not only meet real-time power balance, but also bring stable benefits to distributed entities, Provide reference basis for expanding emerging entities such as VPP in the market.

No abstract available

Aiming at the problems of renewable generation consumption and waste treatment, a market participation method of virtual power plant with renewable generation and waste treatment is proposed. Wastes are incinerated in the waste treatment unit, which contains a gas tank for storing gas caused by waste incineration and can decouple the processes of power generation and flue gas treatment. According to this characteristic, flue gas treatment can be regarded as time-shiftable and adjustable load, and cooperate with renewable generation for coordinated operation of virtual power plant. In order to increase the profit of virtual power plant while reducing the curtailment of renewable generation, an environmentally economic operation model with a goal of maximum benefit of virtual power plant is established, and three kinds of incentive and loss mechanisms are introduced. Results show that the objective function value is the largest under the action of three different mechanisms. The virtual power plant with renewable generation and waste treatment can improve the utilization of renewable energy generation.

In the context of small-scale virtual power plants (VPPs), the minimum power requirement (MPR) poses a significant challenge for market participation due to the inherent volatility and uncertainty associated with renewable energy sources. To mitigate this, a VPP system can potentially enhance its profitability by utilizing an Internal MPR, which is a value derived from market prices and historical data, including actual and forecasted renewable power generation, and is typically lower than the market MPR. This study investigates several market strategies, designated as Plans II, III, and V, under hypothetical conditions, and evaluates their impact on the profitability of VPPs relative to existing strategies (Plans I and IV) within the Japan Electric Power eXchange (JEPX) market. Currently, the MPR for day-ahead (DA) and intraday (ID) markets is set at 0.1 MW per settlement period. This research involves the simulation of all five plans across 11 small-scale VPP systems, with capacities ranging from 0.05 MW to 1 MW. The simulation results indicate that reducing the market MPR to 0.04 MW for both DA and ID markets results in increased profitability for VPPs. The findings of this study suggest potential policy implications for Japan's power market, emphasizing the benefits of adjusting MPR thresholds to better accommodate the participation of small-scale VPPs and enhance overall market efficiency.

As a kind of flexible resource with energy storage characteristics, virtual power plant is more and more important in cooperating with the grid to reduce peaks and fill valleys, and consuming new energy, etc. However, it lacks of economy and high efficiency in the market operation, which directly leads to the lack of participation in the market, and an optimization strategy is proposed to achieve synergistic participation in the market for electric energy and auxiliary services. The proposed method firstly formulates a time-sharing market bidding strategy based on the fluctuation of spot electricity price and the characteristics of aggregated resources of virtual power plants; then establishes a multi-timescale optimization model based on dynamic planning to realize the synergistic clearing of spot trading of electric energy and auxiliary service of FM and the maximization of revenues; and finally compares the economic indexes of the single-market participation and joint optimization modes, and adopts the Shapley value method for the fair distribution of revenues among aggregated resources. Finally, by comparing the economic indexes under single market participation and joint optimization mode, and using the Shapley value method for the fair distribution of benefits among aggregated resources, the simulation results verify the effectiveness of this strategy in improving the virtual power plant market benefits, resource utilization efficiency and rationality of benefit distribution.

As an innovative energy management model, virtual power plant has attracted much attention in the field of power system. This paper is oriented to the demand of power grid scheduling, combined with the characteristics of virtual power plant to carry out research. Firstly, based on the demand for grid control and load characteristics, the study on load classification is carried out to clarify the scope of adjustable loads and access methods; secondly, the process of virtual power plant participation in the market and the system architecture for participating in the market of auxiliary services such as peak shifting and standby are elaborated; and lastly, the overall architecture and security architecture of the virtual power plant technical support system are put forward. The research results provide an overall solution for virtual power plant access to grid scheduling, which helps to realise the overall access of virtual power plant and ensure the safe and economic operation of power grid.

This paper aims to explore the mechanism and settlement method of large-scale participation of virtual power plantsin electricity market trading. As a new mode of power resource aggregation and optimal scheduling,virtual power plant can improve energy utilization efficiency and market responsiveness by integrating distributed energyresources, fully stimulate virtual power plant's multi-time scale (medium and long term,spot) power and corresponding safety regulation capacity by market means through the design of trading mechanism suitable forlarge-scale participation, and the corresponding settlement method. However,virtual power plants are also faced with market problems such as tight supply and demand and weak power grid regulationability, which require emerging market entities to stimulate value,but lack a trading mechanism and settlement method that takes into account the corresponding output and regulatorycharacteristics of virtual power plants. In view of this,this paper proposes a trading mechanism an d settlement method that ADAPTS to the large-scale participation of virtual powerplants.

This study proposes an optimized virtual power plant (VPP) operation strategy for peak regulation ancillary services, enhancing power system flexibility and economic efficiency through coordinated management of distributed energy resources (DERs). A VPP aggregation model is developed, incorporating distributed generation, energy storage, and demand-responsive loads. An economic-maximization scheduling model is formulated using mixed-integer linear programming (MILP), accounting for peak regulation incentives, operational constraints, and electricity price variations. Simulation results demonstrate that the approach improves peak-shaving capability, reduces operating costs, and increases renewable energy utilization, offering a viable solution for VPP participation in electricity markets. The findings provide both theoretical and practical insights for real-world VPP implementations.

With the rapid development of distributed energy technologies and the widespread adoption of flexible loads such as electric vehicles (EVs), energy storage systems (ESSs), and air conditioning units (ACs), virtual power plants (VPPs) have become critical solutions for participating in spot market transactions. This paper first models electric vehicles, energy storage systems, and air conditioning as a unified virtual battery (VB) model to determine the adjustable power ranges of various resources. Second, the Minkowski sum and embedded hyperbox method are employed to aggregate and reduce the dimensionality of the adjustable power capacities of these three resource types, thereby deriving the overall adjustable power range of the VPP. Finally, a bilinear optimization model is established to account for the uncertainties in spot market prices, determining the optimal bid quantity and pricing strategy for the VPP's participation in the spot market. The results demonstrate that the proposed approach effectively enhances the market competitiveness of VPPs while optimizing the utilization of distributed resources, providing new insights into the scheduling and optimization of virtual power plants in spot market.

Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants.

Under the background of energy internet, a large influx of market entities participates in the interaction of “source network and load storage” and becomes an important resource for system regulation through the electricity marketization. Among them, the virtual power plant involves several market participators such as the distributed generation, demand side adjustable resources, distributed energy storage. The research of the operation mechanism and market-oriented trading organization for the virtual power plant becomes a popular topic in recent years. This paper proposes a novel mechanism for virtual power plants to participate in the electricity trading market, including the trading categories and auxiliary service which can be provided by the virtual power plant.

To build a comprehensive framework for virtual power plant (VPP) development aligned with market dynamics and to devise effective strategies to foster its growth, this study undertakes several key steps. Firstly, it constructs a VPP development framework based on market conditions, to drive the evolution of new power systems and facilitating energy transformation. Secondly, through a blend of theoretical analysis and model construction, the fundamental principles of VPP are systematically elucidated, and a decision model for the VPP development framework, focusing on price demand response, is formulated. Lastly, an optimal scheduling model for the new power system is developed, with its efficacy validated across three distinct scenarios. The findings underscore the critical importance of integrating energy storage technologies, particularly pumped storage hydropower systems, for achieving balance and optimization within new power systems. Model verification reveals that the incorporation of energy storage power stations significantly enhances system stability and efficiency, particularly in addressing the volatility associated with renewable energy sources. Additionally, the analysis indicates that while the adoption of energy storage technologies may marginally increase overall power generation costs, the total power generation cost declines with the integration of battery storage and pumped storage hydropower stations. This suggests that leveraging energy storage technologies not only enhances system operational reliability but also contributes to reducing the overall cost of power production to a certain extent. In summary, this study presents an economic and environmentally sustainable scheduling model for new power systems within the context of market trading environments. By offering both theoretical insights and practical guidance, it aims to support sustainable development and energy transformation initiatives. Ultimately, the study is poised to foster the adoption of clean energy, facilitate the establishment of smart grids, and bolster the sustainable utilization of energy resources, thereby advancing environmental conservation efforts.

Due to the uncertainty and volatility of renewable energy, the stable operation of the grid faces new challenges. With its unique technical characteristics, energy storage and virtual power plants (VPP) are rapidly developing and participating in the power market. As the number of energy storage connected to the grid increases, the privacy and security of these market participants must be ensured. In light of this, this study summarizes the trading framework for VPP composed of energy storage in the power market and proposes a model for VPP. Based on this, a centralized clearing model for the power market with VPP composed of energy storage participation is established. Finally, a distributed power market clearing model based on Alternating Direction Method of Multipliers (ADMM) is proposed. Simulation results verify that the proposed strategy can achieve the same effectiveness as centralized power market clearing while safeguarding the information security of market participants.

The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy.

This paper studies the settlement schemes for virtual power plants (VPPs) participating in market transactions. It first provides an overview of the construction and settlement status of virtual power plants. Virtual power plants in different regions have different development focuses. For instance, in Europe, the main focus is on aggregating distributed power sources, while in North America, it is on aggregating controllable loads. China's virtual power plants started relatively late, but in recent years, demand-side management has been carried out successively in Jiangsu, Shanghai and other places. Virtual power plant projects in regions such as North Hebei, Shenzhen and Shanxi have been put into grid dispatch operation and power market operation. The paper analyzes in detail the construction of virtual power plants in the United States and the settlement mechanism of virtual power plants in the PJM market, and discusses various mechanisms for virtual power plants to participate in market transactions, including energy trading, peak shaving trading and ancillary services. Through case analysis, the revenue situations of hybrid virtual power plants under different settlement methods are compared, and suggestions for virtual power plant settlement schemes are put forward. The research results show that the participation of virtual power plants in market transaction settlements needs to be flexibly adjusted according to different regions and market rules. The aggregation or separate settlement and the way of collecting transmission and distribution fees will all have an impact on the revenue and development of virtual power plants.

The intermittent nature of distributed energy resources (DERs) has introduced significant challenges in power system operations, particularly in terms of flexibility, efficiency, and market participation. Aggregating DERs into a virtual power plant (VPP) offers a promising solution to these challenges, but it requires effective strategies to manage the inherent uncertainties and optimize operations across multiple energy markets. This paper develops an optimal bidding strategy for an aggregated multienergy virtual power plant (MEVPP) participating in both the day‐ahead (DA) energy market and the frequency regulation reserve market (FRRM). To effectively address these uncertainties, we propose a two‐stage scenario‐oriented stochastic optimization model that aims to maximize revenue and minimize operational costs by incorporating risk management strategies. Then, a novel fast forward selection and simultaneous reduction (FFS&SR) algorithm is proposed, which efficiently generates and refines scenarios, ensuring computational feasibility without compromising accuracy. The proposed VPP’s decision‐making problem considers the VPP’s risk‐averse nature, employing the conditional value at risk (CVaR) metric as a risk‐aversion parameter. Simulation results conducted over a 24‐h planning horizon validate the model’s performance, exhibiting superior performance in the bidding market scenarios. Furthermore, the numerical findings compare the risk‐neutral VPP framework with the proposed risk‐sensitive VPP strategy, revealing a trade‐off between expected profit and CvaR, indicating that as the risk aversion parameter escalates, expected profits decline while CVaR value rises, underscoring the importance of risk management in VPP optimization.

No abstract available

Environmental concerns from the fossil-fueled generation have accelerated renewable integration, empowering prosumers but challenging grid stability. The Virtual Power Plant (VPP) aggregates distributed energy resources to enhance flexibility; however, traditional utility-dependent models face regulatory and privacy constraints. This article introduces the utility-agnostic VPP paradigm, which autonomously manages diverse DERs—solar, wind, storage, and flexible loads without relying on host utilities. We contrast its architecture with utility-supervised counterparts, highlighting decentralized control, secure low-latency communication, adaptive forecasting, and digital—twin—enabled observability. A modular testbed demonstrates real-time aggregation, capability-curve extraction, and market participation under uncertainties. Key challenges, including limited grid data access, phase imbalance, regulatory alignment, and scalability, are addressed through robust communication protocols, data-driven algorithms, and hierarchical aggregation. Essential features such as dynamic capability curves, online monitoring, and machine learning—based inference are detailed. By delineating technical requirements and forward-looking insights, this work provides a roadmap for deploying utility-agnostic VPPs that enhance system resilience, optimize DER value, and foster innovation in liberalized energy markets.

With the rapid increase in the proportion of new energy in the power system, the intermittency and volatility of its output pose significant challenges to the stable operation of the grid. There is an urgent need to incorporate the demand side into optimization strategies. Therefore, this paper proposes a virtual power plant (VPP) optimization strategy based on enhanced quasi-linear demand response (DR). Firstly, considering extreme output and extreme ramping scenarios of new energy sources such as wind and solar, an uncertainty scenario set is constructed. Secondly, the concept of load baseline is introduced, and an enhanced load quasi-linear calculation method is proposed. A response degree evaluation mechanism and market mechanism are established to improve the flexibility of adjustable resources’ participation in demand response. Finally, a two-stage dispatching model for VPP is established considering quasi-linear demand response. This model is transformed into a Markov game process, and the training efficiency is improved by introducing asynchronous episodic rounds to enhance the DDPG algorithm. Case studies demonstrate that the proposed strategy fully exploits the potential of demand-side response, reduces wind and solar curtailment, and improves the economic efficiency of the system.

A virtual power plant (VPP) faces multiple uncertainties and temporal coupled decisions when participating as an independent entity in electricity and green markets. A multi-level electricity–green coupled market framework is constructed for a VPP participating as an independent market entity. To address uncertainties in renewable energy outputs and market prices, a risk management method based on conditional value at risk entropy weight method information gap decision theory (CVaR-EIGDT) is proposed. To address the temporal coupled challenges in VPP participation across multi-level electricity–green coupled markets, a multi-stage rolling decision-making method coordinating annual, monthly, and daily scales is proposed, achieving deep coupling in the decision-making sequence of multi-level electricity–green coupled markets. Results show that the proposed model enables adaptive decision-making under varying risk preferences, with decisions exhibiting strong practical adaptability while balancing real-time adjustments and long-term planning. The multi-level electricity–green coupled market framework enhances VPP profitability and resilience, while the CVaR-EIGDT method effectively improves decision-making efficiency across multi-level electricity–green coupled markets.

The integration of large-scale renewable energy resources introduces volatility, posing heightened demands for flexible operation of power systems. Virtual Power Plant (VPP), by aggregating distributed resources, incorporate flexible ramping product (FRP) into their optimization strategies to reduce operational costs. Therefore, this paper proposes a multitimescale optimization strategy for VPP based on FRP. Firstly, a model for VPP participation in FRP market is established, considering operational characteristics of distributed resources. Secondly, scenario generation and reduction techniques are employed to address the uncertainties associated with wind power, photovoltaic generation, and load, as same as both pricebased and incentive-based demand response (DR) strategies are proposed. Finally, considering FRP and DR, a multi-timescale rolling optimization model for the VPP is constructed, and a mixed integer programming-deep Q network (MIP-DQN) is employed to solve the model. Case study results demonstrate that the proposed VPP optimization strategy exhibits excellent economic performance and flexibility.

With the development of the new power system and the continuous deepening of the power market reform, the participation modes of power demand - side resources in the market have become diversified. Virtual power plants integrate distributed demand resources and play dual roles of both buyers and sellers in the market, enabling the optimization of power resource allocation. In order to comprehensively optimize power trading, this study starts from the perspective of the power demand side and constructs a bidding model for virtual power plants participating in the day-ahead power market. It focuses on electric vehicle scheduling and demand response strategies, and analyzes their impacts on the economic performance of virtual power plants as well as the internal action mechanisms of operation strategies on the cost structure. Through case studies to optimize the bidding electricity volume and combined with flexible scheduling and demand response measures, the results show that the power trading cost can be precisely controlled. This indicates that virtual power plants have great potential in promoting the efficient and sustainable development of the power market.

With the continuous advancement of energy internet technologies, Virtual Power Plants (VPPs) have become a key technological means for the large-scale aggregation and regulation of distributed energy resources. This paper focuses on the optimization problem of VPP resource aggregation and proposes a resource aggregation planning method based on dynamic reconstruction. First, a VPP membership reward mechanism is established to encourage the participation of more Distributed Energy Resources (DERs). Second, considering the diversification and heterogeneity of numerous distributed resources, a resource feature extraction and classification method is proposed. Then, a resource membership willingness determination model is developed to accurately assess the joining capacity of each resource. Based on this, a dynamic reconstruction optimization strategy for VPPs is proposed, integrating the dynamic response characteristics of distributed resources and the demand variation patterns in the electricity market. Finally, through case study analysis, the feasibility and superiority of the proposed method in VPP resource aggregation planning are verified, demonstrating that the method can effectively improve the efficiency and operational benefits of VPP resource aggregation.

Virtual power plants gathers different power generation resources and gives play to their respective advantages in order to gain more profits in the electricity market and guarantee the interests of internal members. This paper establishes a two-layer bidding model of virtual power plants participating in spot market. The upper level of the model is the power exchange center to determine the winning power quantity of each power manufacturer with the goal of the lowest social electricity cost; the lower level is the virtual power plant operator to optimize the bidding strategy with the goal of maximizing their own income. The example analysis shows that the participation of all kinds of power generation resources in the spot market in the form of virtual power plants can improve the overall profit, and the moderate adoption of more conservative bidding strategy can further improve the profit of virtual power plants.

Under the new power system, the proportion of new energy and new entities continues to grow, new business users are diversified, and the power supply and demand sides exhibit a "double randomness" characteristic. Faced with such challenges, VPP (Virtual Power Plant), as source network load storage integrators that aggregate multiple distributed resources, provide the possibility for the supply-demand balance of the power system. At present, there is no mature operation mode for VPP in China, and they still rely mainly on aggregating load side resources for demand response. This article analyses Australia's participation, trading patterns, and profitability in the electricity market and ancillary services market, summarizes the profit and risk points of VPP in Australia, and proposes relevant suggestions for improving the business model of VPP and promoting their entry into the market in China based on the actual situation.

Operational challenges are expected when a large amount of wind and solar energy is added to the electricity system. It is necessary to introduce new technologies to allow more energy portfolio integration into power systems in order to compensate for the intermittent nature of renewable energy sources (RESs) such as wind and solar power due to their fluctuating nature. A possible solution to the problem of renewable energy integration in power market transactions is the idea of a virtual power plant (VPP). A VPP is a novel and smart approach of integrating distributed energy resources (DERs) such as demand response (DR) and energy storage system (ESS). A VPP could exploit DERs and demand-side participation to mitigate peak loads and thus sustain grid stability. This paper presents a DR strategy of a VPP for simulating energy transactions within the VPP internal electricity market. The method assesses the impact of the DR program on renewable energies integration aiming to minimize VPP operating costs over the short-term planning horizon. Stochastic programming theory is used to address the optimization problem while protecting the interests of the end users. Preliminary findings show that peak load has been reduced while the overall cost of operating has decreased.

This paper models a virtual power plant (VPP) with high-penetration of distributed energy resources (DERs) to participate in the day ahead (DA) and futures markets and bilateral contracts with the aim of maximizing its profit. A two-stage stochastic optimization problem is developed that in the first stage, the VPP operator participates in the futures market and signs bilateral contracts. The VPP will participate in the DA market and supply its electrical loads in the second stage. The uncertainty parameters of the problem, including the DA market price, wind speed, and solar radiation, are first forecasted using the Long Short Term Memory (LSTM) neural network. Then the scenario generation and reduction method is used to cover the uncertainties in the predicted data. The problem has been simulated in three different cases, which indicate a significant increase in the profit of the VPP.

In order to promote the consumption of renewable energy under electricity market mechanism in the process of achieving decarbonization, a virtual power plant (VPP) integrating distributed wind and photovoltaic generators, other dispatchable generation resources, controllable loads and energy storage systems is examined in this work, and an optimal dispatching model of the VPP considering its joint participation in electricity market and carbon allowrance trading market as a whole is presented. Firstly, in order to obtain the sources-load typical scenarios of the power system in a whole year, sources-load curves using variable copula model is constructed. At the same time, taking into account proposed cluster validity index, the generated scenarios are reduced effectively, and a representative day set is obtained, which can depict and represent extreme scenarios well. Secondly, an optimal VPP dispatching model based on representative day set is established to minimize the operating cost, comprehensively considering electricity and carbon allowance trading. Finally, cases studies including deterministic and stochastic optimization are conducted to verify the effectiveness of the proposed model and analyze the impact of multiple uncertainties, which is of guiding significance for the optimal scheduling of VPP systems in the future.

In this work we consider a virtual power plant composed of several unit types, which are characterized by different reliability values. In addition, we assume a simple model of market participation of the virtual power plant, where its income is proportional to the quantity, which can be generated with sufficient reliability by its component units and analyze the question, how a fair distribution of the resulting income among the component units may be determined. We study the problem in the framework of game theory using the concept of transferable utility cooperative games. By analyzing the characteristic function of the resulting game in the case of various examples we conclude that the most common solution concept of cooperative game theory, the Shapley value, does not necessarily results in a core-stable (i.e. fair) allocation in the case of the problem, furthermore we show that the core is not necessarily non-empty.

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources’ uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA’s own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.

To improve the visibility and controllability of distributed energy resources, an MVPP equivalent model has been designed. Firstly, the appropriate parameters are selected to describe MVPP equivalent model after analyzing the characteristics of distributed resource model. And based on a simple aggregation example, an equivalent parameter calculation method for MVPP aggregating distributed resources is derived. To improve the robustness of MVPP equivalent model, a robust calculation method of MVPP parameter deviation is further studied and proposed, in which the parameters of MVPP equivalent model are modified by the results of robust optimization. The robust optimization process is decomposed into the main and sub-problems, and solved by Column and Constraint Generation Algorithm. Finally, the results show the form of MVPP equivalent model. Through comparison, it shows that the cooperation of resources and equipment in various forms of energy can bring more flexibility to MVPP and the integrated energy system it belongs to.

No abstract available

No abstract available

The exploding deployment of distributed energy resources (DERs) brings unprecedented challenges to the optimization of large-scale power distribution networks—numerous grid-tied devices pose severe control scalability crises. Besides, the exposure of private DER data, such as energy generation and consumption profiles, is leading to prevalent customer privacy breaches. Despite the importance, research on privacy-preserving DER control in a fully scalable manner is still lacking. To fill this gap, a hierarchical DER aggregation and control framework is first developed to achieve scalability over a large DER population size. Second, a novel privacy-preserving optimization algorithm is proposed for the developed DER aggregation and control framework based on the secret sharing technique. Finally, privacy preservation guarantees of the developed algorithm are provided against honest-but-curious adversaries and external eavesdroppers. Simulations on a 13-bus test feeder demonstrate the effectiveness of the proposed approach in preserving private DER data within power distribution networks.

The integration of distributed energy resources (DERs) into renewable grids faces challenges from volatile generation, dynamic loads, and non-independent and identically distributed (non-IID) data across decentralized nodes. Existing centralized optimization methods lack scalability and privacy safeguards, while conventional federated reinforcement learning (FRL) struggles with unstable convergence under asynchronous updates. This study proposes DA-FRL (Distributed Asynchronous Federated Reinforcement Learning), fusing Asynchronous Advantage Actor-Critic (A3C) with Adaptive Proximal Policy Optimization (APPO). Innovations include: (1) an asynchronous federated aggregation mechanism combining A3C’s distributed training with APPO’s adaptive KL-divergence regularization for robust non-IID convergence; (2) a LSTM-AEPPO probabilistic forecasting model using long short-term memory (LSTM) for temporal dependencies and adaptive exploration (AEPPO) to balance exploration-exploitation trade-offs; (3) a differential privacy-preserving gradient sparsification protocol reducing communication costs via Top-k gradient compression while securing sensitive client data. Experiments on NREL wind and CDGS photovoltaic datasets show DA-FRL reduces Winkler scores by 18.7% and 21.3%, respectively, advancing secure, scalable DER coordination under dynamic grid conditions.

This paper presents a distributed energy resource and energy storage investment method under a coordination framework between transmission system operators (TSOs) and distribution system operators (DSOs), which simultaneously addresses two main aspects of the flexibility aggregation of DSOs, i.e., flexibility enhancement and dynamic flexibility provision. First, to characterize the key flexibility features of power distribution networks, a multi-port multi-period feasible region formulation is designed using the robust optimization conception while avoiding over-parameterized formulations that require extensive information exchange. Second, a two-stage robust planning model is built to enhance distribution system flexibility by maximizing the volume of feasible regions, in which the uncertainty of dispatch instructions is modeled as a decision-dependent uncertainty (DDU) set and the worst realization is identified for the operational security evaluation. The proposed two-stage robust planning model is solved via a customized column-and-constraint generation algorithm. Finally, a distributed framework for TSO-DSO coordination is proposed to enable the dynamic adjustment of feasible region provision of DSO, given the TSO’s preference, which is then solved by a DDU-based two-stage robust extension of the alternating direction method of multipliers algorithm. Numerical results verify the effectiveness of our proposed models and the scalability of the associated algorithm. Note to Practitioners—The increasing integration of renewable energy resources has stimulated the need for aggregated flexibility provided by power distribution networks (PDNs). However, there are three challenges: i) using the currently feasible region formulation of PDN requires extensive information exchange with the transmission system operator (TSO), thus not compatible with the current structure. ii) the optimal investment of various assets to leverage their cost-effectiveness in feasible region enhancement needs to be addressed. iii) lack of a proper coordination mechanism between TSO and distribution system operators (DSOs) to facilitate the optimal feasible region provision and leverage the spatiotemporal flexibility. To address the above challenges, we design an easy-to-implement feasible region formulation to characterize the key flexibility features of PDNs. Upon this, we propose a feasible region enhancement planning method based on an extended two-stage robust model to address the decision-dependent uncertainty. Furthermore, we facilitate the optimal feasible region provision through the TSO-DSO coordination process in a distributed manner. The numerical results show that the proposed planning method and coordination mechanism could effectively achieve optimal flexibility enhancement and dynamic flexibility provision. In practical application, our proposed planning method can be readily implemented with advanced analytical tools. The coordination mechanism is also compatible with the current TSO-DSO structure and can be easily integrated into it.

F1exible load resources such as electric vehicles and air conditioners, which are widely accessed on the demand side, can provide great flexibility potential for the new power system, so as to promote the full absorption of renewable energy. Firstly, based on the virtual energy storage model, the adjustable flexibility of the aggregate resources of electric vehicles and air conditioners is modeled. Then, the maximum absorptive capacity evaluation model of renewable energy is used to analyze the effect of distributed resource aggregation on the consumption of renewable energy. Finally, the typical application scenarios are analyzed on three time scales: intra-day minute level, day-day hour level and annual level, and the results verify the great improvement effect of distributed resource aggregation on renewable energy consumption.

Distributed energy resources (DERs) such as grid-responsive loads and batteries can be harnessed to provide ramping and regulation services across the grid. This paper concerns the problem of optimal allocation of different classes of DERs, where each class is an aggregation of similar DERs, to balance net-demand forecasts. The resulting resource allocation problem is solved using model-predictive control (MPC) that utilizes a rolling sequence of finite time-horizon constrained optimizations. This is based on the concept that we have more accurate estimates of the load forecast in the short term, so each optimization in the rolling sequence of optimization problems uses more accurate short term load forecasts while ensuring satisfaction of capacity and dynamical constraints. Simulations demonstrate that the MPC solution can indeed reduce the ramping required from bulk generation, while mitigating near-real time grid disturbances.

Public energy systems face simultaneous objectives: cost efficiency, reliability under component degradation, and environmental sustainability. We propose a unified framework for distributed energy resource management that couples online convex optimization with predictive maintenance and a privacy layer. The approach optimizes dispatch decisions under time-varying demand and renewable injections using a primal dual scheme with long term constraints. Maintenance actions are co optimized via a stochastic decision process calibrated on health indicators. We prove sublinear regret and constraint violation bounds, then quantify the impact of differentially private aggregation noise on the performance guarantees. Synthetic experiments on standard distribution feeders illustrate improvements in operational cost, network losses, and downtime, together with a tunable privacy utility trade off. All data and results in this article are synthetic and do not contain confidential information.

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal-dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal-dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. We evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.

This paper presents a method to calculate parameters for aggregating distributed energy resources (DERs), especially electricity consumption of customers. In DER aggregation, energy resource aggregators reward customers complying with requests of demand response. On the other hand, the aggregators need to financially offset opportunity losses of retail electricity suppliers caused by the DER aggregation. This is the negawatt compensation. In the authors' proposal, electricity trading is expressed as a problem of social welfare maximization, allowing changes in the profits of aggregators, retailers, and customers to be measured. As a result, the unit prices of monetary rebate to customers and negawatt compensation to retailers can be calculated mathematically.

Utilities around the world are increasingly integrating distributed energy resources (DERs) into their electricity grids to boost reliability, resilience, customer satisfaction, and economic benefits. This article dives into the challenges and opportunities that come with this integration, focusing on the creation of virtual power plants (VPPs) to bring together DERs. VPPs provide advantages like resource adequacy, system resilience, emission reductions, and energy justice. The article highlights three key technologies that improve the observability and controllability of behind-the-meter (BTM) DERs, enhance the functionality of distribution planning tools, and turn traditional uninterruptible power supplies (UPSs) into VPP components. These advancements are crucial for the effective management and operation of DERs, ensuring a stable and reliable electricity grid. Additionally, the article discusses the regulatory landscape, including the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which supports DER participation in energy markets. The findings emphasize the need for ongoing research, development, and demonstration of promising technologies to tackle the challenges of DER integration and unlock the full potential of VPPs in the energy transition.

No abstract available

Aggregates of distributed resources form large-scale market entities to interact with the power grid by integrating massive distributed resources. However, the energy management of large-scale distributed resources is confronted with a conflict between global optimization and local demands. Centralized optimization and regulation struggle to balance users’ electricity costs and comfort, while local user energy management lacks overall coordination with the operational status of the aggregates of distributed resources. Therefore, a lightweight user energy management strategy for aggregates of distributed resources based on cloud-edge collaboration is proposed. First, a hierarchical decision-making framework for the energy management of aggregates of distributed resources is constructed, enabling the transformation of the energy management of aggregates of distributed resources from one-way control to two-way interaction. Second, considering the limited computing resources of low-cost edge devices, a local decision-making algorithm based on sparse reinforcement learning is proposed, which utilizes adaptive topology evolution to reduce the number of trainable parameters in neural networks. Finally, case studies conducted on real-world datasets verify the effectiveness and efficiency of the proposed lightweight strategy.

To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply–demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.

In order to realize low-cost aggregation of distributed energy storage adjustable resources in the substation and improve the coordinated and optimal control effect of different energy units, an aggregation method of distributed energy storage adjustable resources in the substation based on edge controller is proposed. According to the equivalent thermal parameter state equation, the actual output state equation and the electric vehicle state of charge equation, the mathematical model of resource aggregation is constructed. The objective function is constructed with the goal of minimizing the operation cost of resource aggregation in the substation, and the fuzzy PID controller is selected as the edge control logic. The objective function is solved by fuzzy rules of controller variables and triangular membership function, and the distributed energy storage adjustable resource aggregation in the substation is realized. The experimental results show that the operating cost of resource aggregation is about 20 yuan /h, and the F1 value is 0.964 and the G value is 0.956. It is proved that this method has good comprehensive performance.

The widespread adoption of distributed energy storage resources on the distribution network side has increasingly underscored the importance of harnessing their substantial flexibility potential. Given this background, a zonotope-based aggregation and control method for distributed energy storage in networks is proposed. First, an individual operational model of distributed energy storage resources was established, and their feasible region space was characterized. Second, an approximation and efficient aggregation method for the feasible region of distributed energy storage resources based on zonotope was proposed. Then, the aggregated clusters of distributed energy storage resources were applied to the optimal regulation of distribution networks, and a power decomposition method based on the water-filling algorithm was presented. Finally, the effectiveness of the proposed aggregation and regulation method for distributed energy storage resource clusters was validated through case studies on the revised 33-bus distribution network.

In this paper, we put forward a novel DER aggregation framework, encompassing a multiagent architecture and various types of mechanisms for the effective management and efficient integration of DERs in the Grid. One critical component of our architecture is the Local Flexibility Estimators (LFEs) agents, which are key for offloading the Aggregator from serious or resource-intensive responsibilities---such as addressing privacy concerns and predicting the accuracy of DER statements regarding their offered demand response services. The proposed aggregation framework allows the formation of efficient LFE cooperatives. Our experiments verify its effectiveness for incorporating heterogeneous DERs into the Grid in an efficient manner---showing that the use of appropriate mechanisms results in higher payments for participating LFEs.

No abstract available

Different types of distributed “source load” resources can participate in power grid regulation in different time scales, corresponding to the service response time of milliseconds, seconds, minutes and hours. Distributed “source load storage” resources such as distributed power supply, industrial load, commercial buildings, energy storage participation in new energy consumption of the grid, peak cutting and valley filling, frequency modulation, voltage regulation and interruptible load control need to respond quickly and regulate accurately, while the dispatching system and virtual power plant dispatch command communication need to pass through the production control region, the management information region, the Internet region and the border protection equipment. The delay of dispatching communication network is difficult to control on demand, the multi-security protection means of mass terminals is lacking, and the user is difficult to accurately control and coordinate output. As a result, the virtual power plant's aggregated distributed resources are difficult to efficiently and real-time participate in the accurate regulation and control of the power grid. The communication network delay measurement scheme is designed, and the distributed measurement tool of network delay is developed to realize the perceptable state of the aggregated regulation and control network. It provides effective support for distributed “source load” resources to participate in accurate regulation and collaborative output of the power grid, helps the effective aggregation and deployment of large-scale distributed flexible resources, and solves the problem of insufficient flexibility adjustment capacity on the load side of the power system.

With the increasingly widespread application of distributed resources in the power distribution network, the traditional power grid dispatching methods are difficult to adapt to the diversity and uncertainty of distributed resources. Therefore, new technologies and methods need to be introduced to improve the efficiency of distributed resource dispatching and the stability of the power grid. To effectively integrate these scattered and diversified resources, this paper puts forward a distributed resources based on quantile regression forest dynamic aggregation method, the method using quantile regression forest algorithm, the real-time state of distributed resources, forecast trend and user demand for accurate analysis, according to the prediction results, dynamically aggregate DER output into a generalized energy storage model, and realize the intelligent scheduling and optimal configuration of resources. Through simulation experiment and actual case analysis, this paper proves that this method can effectively improve the accuracy of power grid dispatching, ensure the security and enhance the stability.

To facilitate the clean and economical operation of power systems, distributed energy resources (DERs) have received considerable attention. However, integrating numerous DERs into the transmission grid through local distribution networks imposes a substantial computational burden on scheduling optimization due to the need for detailed modeling of each resource and network component. To reduce this complexity and improve DER management, aggregation models for DERs are adopted. This paper presents a method for generating an aggregation power curve (APC) based on optimality condition analysis. In contrast to most existing studies that disregard distribution network and operational constraints when constructing aggregation models, this paper integrates DERs’ models, local loads, and network security constraints during the aggregation process, while also considering the optimal economic dispatch of the local network. This method can delineate the output status of each DER at every turning point of the APC, thereby improving the internal transparency of the aggregation model. Case studies conducted on IEEE 33‐bus and IEEE 141‐bus systems demonstrate the effectiveness of the proposed approach. APCs of certain scenarios are depicted clearly. It can be observed that the presence of network security constraints narrows the power output range of the local network and usually generates additional turning points in the APC.

While the interconnection among multiple distribution areas enhances the flexibility of resource allocation, it also increases the difficulty of coordinated management due to load balancing and the sharing of distributed generation. To address these issues, this study establishes an optimization model for controllable resource aggregation in interconnected distribution systems, with the synchronous alternating direction method of multipliers (SADMM) as its core. The model aims to achieve unified management and optimal scheduling of various distributed energy resources and controllable loads within the system, thereby enabling rational power allocation. The objective is to improve energy utilization efficiency across Interconnected Distribution areas, balance area loads, reduce the power drawn from upper-level grids, and enhance the economic efficiency of distribution area operations.

With the increasing penetration of high-proportion renewable energy in new-type power systems, Virtual Power Plant (VPP) has emerged as a critical enabling technology for aggregating distributed energy resources. However, it faces challenges in achieving flexible regulation of large-scale resources and dynamic adaptability to grid operations. This paper proposes a hierarchical and zonal dynamic reconfigurable resource aggregation technique for VPPs. By establishing a multi-layered architecture spanning “provincial-municipal-aggregator-end-user resources”, the method enables hierarchical-zonal dynamic aggregation and reconfiguration of virtual generating units. The effectiveness of the proposed hierarchical-zonal strategy is demonstrated through frequency regulation scenario. The research outcomes provide theoretical foundations and practical references for VPP applications in electricity markets and grid control operations.

Aiming at the problems of scattered distribution, diverse types and high aggregation difficulty of user-side adjustable resources, this paper proposes a cloud-side-end cooperative virtual power plant system architecture and terminal design. By studying the resource aggregation model and differentiated communication networking scheme for four types of typical scenarios: industrial load, building air conditioning, DC charging station and residential small and micro load, the problem of unified access and real-time regulation of heterogeneous resources is solved. On this basis, a virtual power plant terminal supporting edge intelligent computing is designed, which has the capabilities of multi-protocol compatibility, unified resource modeling, and dynamic strategy optimization, and realizes the deployment of core algorithms such as photovoltaic flexible control and energy storage frequency and voltage regulation. Pilot applications show that the program can effectively aggregate distributed power supply, energy storage, adjustable load and other resources. It significantly improves the response efficiency of source-network-load-storage interaction and the level of new energy consumption, and provides key technical support for the construction of an efficient and safe virtual power plant operation system.

The renewable energy power generation industry represented by wind power and photovoltaics (PV) is booming in China. Due to the randomness and intermittency of new energy power generation, its large-scale generation has brought major challenges to the safe and economic operation of power systems. Aggregating demand side flexible resource (DSFR) represented by decentralized electric heating, electric vehicles and distributed energy storage to participate in power grid dispatch can reduce the load peak-valley difference, which is an effective way to improve the safe and economic operation of the power system. This study selects decentralized electric heating, electric vehicles, and distributed energy storage as research objects, takes the minimum dispatch compensation cost, the minimum curtailment of wind and PV, and the minimum load variance as optimization objectives combines with various constraints of DSFR participating in the dispatching process, and establishes the multi-objective optimal dispatch model of typical DSFR. The dispatch results of DSFR and the cost-benefit of load aggregators (LAs) and DSFR users in different dispatch scenarios are compared and analyzed through the simulation example. The results show that the proposed model can effectively smooth load fluctuations, achieve peak shifting and valley filling, further promote new energy consumption, and significantly improve the economy of DSFR users and LAs. The research broadens the framework of DSFR participating in power grid operation regulation and provides references for the development of DSFR dispatching.

The uncertainty of renewable energy resource output greatly complicates power system functioning. The Virtual Power Plant (VPP) offers a feasible solution to these problems. VPPs can help to eliminate the demand for new power plants, expand market opportunities, and decentralize the power system into smaller, more intelligent units. In this study, a VPP model has been developed employing distributed renewable energy resources and small-scale synchronous machines. The local control system for the VPP is based on renewable energy converters, while the centralized control system is based on load flow. Finding the stability of our suggested VPP model under various operating situations is the primary goal of this study.

Virtual power plants, as an emerging power system technology, have the core value of aggregating dispersed energy resources through modem information technology to form a virtual entity that can participate in electricity market activities. This article will analyze and explore the application of distributed resource aggregation technology in the participation of virtual power plants in grid auxiliary services, in order to help the construction of economic dispatch in virtual power plants and promote further innovative development of resource aggregation technology.

The new types of demand-side controllable resources in recent years, such as smart home appliances, portable power stations, and electric vehicles, have introduced newfound opportunities for smart grid control. Aggregating a large volume of these resources can provide a substantial amount of ancillary services for the grid, such as voltage and frequency control. Nevertheless, the integration of such a large and highly distributed amount of controllable resources for grid services has posed significant challenges for grid operators as well as local energy community aggregators, primarily due to the uncertainties, load characteristics, computational complexity, and scalability. This paper aims to devise an efficient distributed aggregation system targeting the optimal control and integration of diverse controllable resources for power system services. This is achieved through a model-free deep reinforcement learning-based approach, which not only maximizes the total profits of the distributed resource owners to incentivize participation but also improves the control performance. The efficacy of the proposed system is validated through case studies utilizing real-world data.

A transition from a traditional power system towards a low-carbon power system requires unlocking new sources of flexibility a t t he distribution level a nd engaging distribution system users to become electricity market participants. These users are usually too small to adequately position themselves in electricity markets. Therefore, the latest package of the European Commission highly encourages aggregating them into a single market entity. Aggregation of distribution system users can be realized through different business models. The main intention of this paper is to analyze two potential business models to be used for aggregation of distribution system users. The first one illustrates the case in which the supplier also offers aggregation services in markets or auctions on behalf of its customers. The second model considers the case in which a new market participant, called the “independent aggregator”, enables distribution system users to actively participate in electricity markets or offer services to the grid operator. The paper elaborates on the advantages of both models through simplified examples by positioning them within the current European market setup.

In order to improve the trading efficiency of virtual power plants (VPPs) participating in the market of multi-type auxiliary services under the gaming environment, an initial trading price setting method based on the information of VPPs’ response characteristics and real-time supply and demand changes is proposed to accelerate the convergence speed of the game. Firstly, a master–slave game trading model is established based on the reference auxiliary service pricing, which consists of a tariff coefficient and a basic tariff. Secondly, the tariff coefficient model is constructed based on response information, including response rate, quality, and reliability. Again, the basic tariff model is constructed based on the real-time supply and demand situation and the real-time grid tariff. Finally, the effectiveness of the proposed method in accelerating the convergence speed of the game is verified by analyzing 12 VPPs under the three auxiliary service scenarios of peaking, frequency regulation, and reserve.

No abstract available

The emergence of new market entities such as distributed photovoltaics (PV) and distributed energy storage that both consume and produce electricity has profoundly impacted the operational mechanisms of power systems. So, the establishment of a reasonable value assessment system is required to assess the peak regulation value of these new entities in the electricity market. This paper constructs a comprehensive evaluation index for new entities from both peak regulation performance and green attributes, which evaluates the value of new entities in peak regulation. Considering the uncertainties in the output of new entities, kernel density estimation is utilized to describe the output range of new entities that can obtain peak regulation performance indicators of new entities at an appropriate confidence level. Secondly, the green value of new entities is comprehensively considered not only from the green certificate cost of new entities to portray the green value but the risk value of the uncertainties of new entity output. Then, a combination of the Analytic Hierarchy Process (AHP) and entropy weight method is employed for the weights of the green certificate cost and the risk value of the uncertainties. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for peak shaving value evaluation. Finally, the effectiveness of the proposed approach in the paper is verified through simulation analysis.

No abstract available

No abstract available

The penetration rate of renewable energy sources (RES) represented by wind turbines (WTs) and photovoltaics (PVs) in the power grid has been increasing in recent years. The large-scale scenarios caused by the uncertainty of the power generated by RES bring high computational complexity to the optimization analysis of power system. Virtual power plants (VPPs) can aggregate and control decentralized and diversified distributed resources, which is essential to ensure the safe and stable operation of the new power system and improve economic benefit. In this paper, Latin hypercubic sampling is used to generate stochastic scenarios of RES, and the clustering algorithm is used for reduction of the original scenarios. Considering the operation mechanism of VPP in the electricity market and the peak-regulation auxiliary service market, a day-ahead optimal dispatch model is developed. To solve the optimization model, a distributed stochastic optimization algorithm based on power flow decoupling is proposed based on alternating direction multiplier method (ADMM). The simulation results show that the VPP is capable of peaking the instructions of the grid. The reduction in the peak-to-valley difference in the load curves improves the stability of the grid. The proposed distributed optimization algorithm has good convergence performance.

Under the carbon peaking and carbon neutrality goals, the high proportion of fluctuating and intermittent renewable energy is accessed to the power grid, which brings severe challenges to the safe and stable operation of the power system. Virtual power plant (VPP), as an effective means of large-scale and normal dispatching of distributed resources (DRS), provides a new way to solve this problem. However, at present, the design of ancillary service trading varieties of VPP, the construction of market environment, and the allocation of internal benefits among DRS are far from mature. In this paper, the ancillary service market mechanism of VPP and benefit allocation method are proposed. Firstly, the ancillary service market mechanism of VPP is designed to provide ancillary services including peak-regulation service, frequency-regulation service, reservation service, and reactive power service. Secondly, the benefit allocation method among DRS is designed so as to fully arouse the enthusiasm of DRS to participate in VPP. Finally, a case is given to verify the validity of the proposed benefit allocation method which can fully guarantee the comprehensive contributions of DRS. It provides ideas and references for VPPs to participate in ancillary service market transaction and benefit allocation.

Renewable energy such as wind power and photovoltaics integrated to the power grid have brought challenges to deep peak regulation. In order to address the deep peak regulation problem, virtual power plants (VPP) are proposed to provide with deep peak regulation ancillary service. This paper designs the deep peak regulation ancillary service market mechanism considering participation of VPPs. The thermal power units and VPPs are proposed to participate jointly in the deep peak regulation ancillary service market. Two stage deep peak regulation ancillary service market clearing model are established, the day-ahead clearing model and the real-time clearing model. The deep peak regulation ancillary service market adopts the marginal clearing method. The thermal power units and VPPs can obtain profits by actual deep peak regulation capacity and clearing price. Finally, the case analysis show that the proposed market mechanism is effective and the economy of deep peak regulation is significantly improved.

A virtual power plant consists of various sources, storage devices, and responsive loads. The operator of this unit can operate it as an energy storage device and transmitter in power distribution networks by controlling the active power of the aforementioned elements. This virtual unit is also connected to the grid with an electrical inverter, which can control active and reactive power between the grid and the virtual unit. Therefore, the system operator can gain financial benefits from different markets for sources, storage devices, and responsive loads. This study presents the operation of an intelligent distribution system (IDN) as a coupling of the virtual power plant and electric inverter (CVE). CVEs participate in energy and active (flexibility market) and reactive (reactive power market) service markets simultaneously. The deterministic formulation of the proposed scheme is responsible for maximizing the profits of CVEs in the markets for reactive power and energy. In this case, the problem is limited by the AC optimal power flow equations in the network and the operation model of CVEs. A nonlinear formulation and a linear approximation model are used in this scheme to achieve the optimal solution. An adaptive robust optimization (ARO) approach is applied to model uncertainties in energy prices, renewable energy, and mobile storage device energy consumption. Since flexibility modeling requires at least two uncertainty scenarios, the formulation of CVE participation in the flexibility market is further modeled. The CVE’s objective function in this scheme is to maximize profit in each of the markets listed above, and the model is constrained by the resulting robust model and the formulation of CVE flexibility. Finally, CVEs can improve network functionality and allow access to significant profits in these markets for power sources, storage devices, and responsive loads, as demonstrated by numerical results obtained from implementing this scheme on an IEEE 69-bus IDN. The proposed design can be applied in consumption areas such as industrial, agricultural, and residential sectors, leading to increased energy efficiency.

Amid the context of a sustainable development strategy, there is a growing interest in renewable energy as an alternative to traditional energy sources. However, as the penetration rate of clean energy gradually increases, its inherent features, such as randomness and uncertainty, have led to a surging demand for flexibility and regulation in power systems, highlighting the need to enhance the flexibility of power systems in multiple dimensions. This paper proposes a method for evaluating the adjustable power capacity of a virtual power plant (VPP), which considers the high-energy-consuming industrial load in the day-ahead to real-time stages and establishes an optimization scheduling model for auxiliary service markets based on this method. Firstly, within the day-ahead phase, the VPP is categorized and modeled based on its level of load flexibility regulation. The assessable capacity is then evaluated to establish the adjustable power range of the VPP, and the capacity of the VPP is subsequently reported. Secondly, the adjustable loads inside the VPP are ranked using the performance indicator evaluation method to obtain the adjustment order of internal resources. Finally, on the real-time scale, an optimization scheduling model to minimize the net operating cost of the VPP is established based on real-time peak-shaving and frequency regulation instructions from the auxiliary service market and solved using the CPLEX solver. The case study results show that the proposed method effectively reduces the net operating cost of the VPP and improves the stability of its participation in the auxiliary service market, which verifies the effectiveness of the proposed method.

No abstract available

This article presents a novel measurement-based frequency regulation scheme that utilizes the contributions of inverter-based resources (IBRs). IBRs are assumed to be aggregated within dynamic virtual power plant (DVPP), conceptualized as a new entity in modern power systems that can be summoned by the system operator to provide dynamic ancillary services. While the participation of DVPPs in frequency regulation is outlined in ancillary service markets, the main objective is to distribute regulation signals among the resources within DVPPs. To achieve this, a power imbalance propagation paradigm is introduced, aiming to estimate the extent of power imbalance across the grid. Subsequently, the contribution factor is established for the purpose of evaluating the involvement of individual clusters of IBRs within the context of both primary and secondary frequency control commitments. The proposed mathematics-based approach is scalable and only needs to have access to few local measurments and exchanged power information between the adjacent neighboring areas to realize a real-time application. The effectiveness of the proposed methodologies is examined on two power grids.

Under the “Double Carbon” strategy, the speed of new energy installation and grid connection has been continuously improved. In order to better absorb new energy power generation and ensure the safe and stable operation of the power system, it is urgent to provide ancillary service resources such as frequency regulation and peak shaving. The virtual power plant aggregates the distributed energy scattered in the power grid through advanced communication, computing, dispatching, market and other means, making it a “power generation system” that can be uniformly dispatched, and then follow the dispatching instructions and participate in the ancillary service market. Firstly, the relevant structure and development of virtual power plant is introduced. Secondly, the relevant types of virtual power plants participating in the ancillary service market are analyzed. Thirdly, the actual situation of virtual power plants participating in ancillary service markets for frequency regulation and peak shaving is compared and analyzed with reference to the market access conditions, compensation mechanism and allocation mechanism, which lays a theoretical foundation for different provinces to further improve the mechanism of virtual power plants participating in ancillary service markets. At last, according to China’s national conditions, suggestions on the construction of market mechanism for virtual power plants to participate in ancillary services are proposed.

After the large-scale access of distributed power sources to the distribution network, significant high/low voltage problems have emerged. Using a virtual power plant to provide reactive power voltage regulation as an ancillary service effectively addresses voltage issues. However, since a third party manages the virtual power plant and contains both discrete and continuous regulation devices internally, there is a need to consider privacy protection. To address this, a training method that requires minimal boundary information and reward–penalty information for interaction between discrete and continuous action agents is proposed. This method uses distributed two-layer multi-agent deep reinforcement learning for the virtual power plant’s reactive power voltage support strategy. By utilizing actual engineering data and comparing it with the “centralized training” framework algorithm, this study proves the effectiveness of the deep reinforcement learning training method and reactive power voltage control strategy. It demonstrates advantages such as protecting the privacy of the virtual power plant and low training difficulty.

In the Northeast region of China, where the proportion of renewable energy is high and the climate is cold, there is a high demand for renewable energy integration and heating stability. Aggregating electric-thermal flexibility resources in the form of virtual power plants and participating in the ancillary service market can help address these challenges. This paper first establishes an operational benefit model for virtual power plants in cold regions based on the characteristics of their resource endowments. The model considers the compensation mechanism in the Northeast's ancillary service market. Next, considering the synergistic interaction between peak regulation in the ancillary service market and virtual power plants in cold regions, the model comprehensively considers the scheduling potential of various types of flexible adjustable devices, the demand response capability of flexible electric-heat loads, and the peak shaving and valley filling effect of energy storage devices. A two-stage robust optimization scheduling model is constructed for electric-heat integrated virtual power plants to obtain the economically optimal dispatch plan under the worst-case scenario. A robust coefficient is introduced to flexibly adjust the conservatism of the optimized dispatch plan. The C&CG algorithm and strong duality theory are used for solving. Finally, a case study is conducted using the measured data from a continuous 7-day period in December 2022 in a specific area of Jilin Province. The results demonstrate that the coordinated operation of electric-heat integrated virtual power plants can effectively reduce the peak-valley difference in the system and bring incremental operational benefits to the system.

In the ancillary service market, different ancillary service products have different requirements for the time domain response behavior of bidding resources. Therefore, when virtual power plants(VPPs) aggregate management of controllable loads with many orders and significant difference in response characteristic, In addition to considering the power characteristics of controllable loads, they also need to consider the correspondence between the time domain response performance of controllable loads and different ancillary service demand. Firstly, the definition of time-varying feasible set of virtual power plant is clarified, and the dynamic partition construction method of virtual power plant is proposed to quantitatively analyze the fit of virtual power plant to different auxiliary services; Then, taking the virtual unit formed by aggregation as the minimum unit, an auxiliary service optimization bidding strategy considering the performance difference of bidding resources is constructed. Finally, a numerical example proves that the index ranking classification method can form a better bidding strategy, and verifies the feasibility and rationality of the virtual power plant optimization bidding strategy considering the difference in response performance of controllable loads.

Changes in the generation mix will increase fluctuation of power balance in energy systems and simultaneously will decrease available resources of Ancillary services (AnS) to stabilize those fluctuations. Renewable resources (RES) are not primarily designed to provide AnS. Thus, alternative AnS resources has to be found or built to prevent a shortfall in available AnS capacities. An ideal AnS provider is independent on internal or external circumstances and provides services with high availability and reliability. Such requirements may fulfill a stand-alone hybrid powerplant designed as virtual power plant (VPP) only for AnS provision, comprised of battery energy storage system (BESS) and small spinning generators. The generators are stopped and ready to start when there is no significant demand for AnS, which challenges operation of the power plant: the service demand is unknown in term of activation time and required power, optimal scheduling of generator operation (up-time) is crucial both for cost efficiency and service provision quality. Robust control algorithm is introduced in this paper using rolling time window, demand interpreter, scheduling module and real time power adjustment module.

Power systems have become more exposed to frequency excursions, emphasising the importance of dynamic ancillary services such as inertial support and fast frequency response to mitigate them. Virtual power plants (VPPs) are seen as suitable candidates for provision of these services and each generation technology within the plant should contribute to the aggregated response according to its own characteristics. However, most VPP studies focus on their steady-state contribution and only a few explore their potential to provide dynamic ancillary services. Moreover, the influence of power reserves (i.e., climatological conditions, state of charge of batteries, etc.) is rarely taken into consideration. This paper presents a centralised controller for a VPP that allows the provision of dynamic ancillary services, considering different generation technologies and their power reserves. The proposed controller helps manage and select the contribution of each individual technology according to the power reserves and the type of energy source. In contrast to existing solutions that depend on continuous adjustment of control parameters, the proposed controller selects the control parameters from predefined subsets that comply with grid codes. The proposed approach is validated by performing numerical simulations.

No abstract available

The significant injections of renewable power have been leading to risks of safety and stability of power system by curtailing system inertia. Based on the requirement of ‘carbon neutralization’ policy, independent participants including energy storage (ES), load demand response (LDR) and virtual power plant (VPP) are introduced into system as innovative ancillary service methods. This paper proposes a bi-level optimization method to achieve the economic dispatch of multi-components virtual power plant consisting with gas generator, PV, wind turbine, energy storage and electricity vehicle in the day-ahead optimization, while achieving the maximum renewable power utilization in the real-time optimization by adjusting dispatch plan. This method considers constraints including solar/wind power forecasting, load level forecasting, ES charging/discharging constraints, and DC power flow limits. A case study based on practical project is present, showing that the bi-level optimization is capable of achieving diversified resources aggregation in extended region, as well as the economic optimization and green energy consumption target.

With the high proportion and penetration access of clean energy to the power grid, the difficulty of clean power consumption is intensified. The emergence of virtual power plant (VPP) with significant regulatory potential provides a new path to solve the severe problem caused by the rapid development of clean energy. However, the design of VPP transaction varieties and the construction of market mode have not been maturely established at present, which is not conducive to the long-term development of VPP. First of all, this paper designed the transaction varieties that VPP can provide in the power market and ancillary service market. Secondly, the basic framework of VPP participating in market transactions under the promotion of spot market is designed. Finally, the prospects of potential business modes of VPP including providing smart energy services, providing big data value-added services, participating in carbon trading and peer-to-peer energy-trading market are explored, which can fully mobilize the enthusiasm of market participation, realize the sustainable development of VPPs and finally promote the consumption of clean energy.

The power system is rapidly integrating renewable energy sources to move towards an energy-efficient and environment-friendly future, especially under the trend of gradual retirement of thermal generators. Renewable power generation and battery energy storage are significantly complementary when participating in energy markets and ancillary service (AS) markets simultaneously. This paper proposed a cooperative scheme for rooftop photovoltaic (PV), wind power generation and battery energy storage system (BESS) taking part in the energy market and frequency control ancillary service markets simultaneously in virtual power plant (VPP) environment and the corresponding optimal bidding strategy in multiple energy markets. The integrated system can provide not only better services to power grid but also considerable profits to each market participator. The feasibility of the cooperative scheme and optimal bidding strategies are later proved through various case studies.

This paper presents a comprehensive framework for Virtual Power Plant (VPP) participation in spot and ancillary service markets, integrating deep learning-based forecasting with rolling optimization strategies. The proposed approach combines Transformer-GRU hybrid architecture for renewable energy forecasting and LSTM networks for electricity price prediction, achieving forecast accuracies exceeding 96%. A three-stage optimization framework is developed: day-ahead forecast declaration, intra-day rolling optimization, and intra-day dispatch order optimization. The rolling optimization strategy maximizes VPP profitability by coordinating various distributed energy resources, including renewable generation, energy storage systems, and flexible loads. Case studies demonstrate that the proposed approach improves VPP profitability by 16.25% compared to non-optimized operations, while maintaining rapid response capabilities to grid dispatch orders within 0.1 seconds. The results validate the effectiveness of the proposed framework in enhancing both economic performance and operational efficiency of VPPs in modern electricity markets.

Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services act as a barrier for small scale prosumers participation forcing the implementation of new cooperative business models at the local level. This paper is proposing a fog computing infrastructure for the local management of energy systems and the creation of coalitions of prosumers able to provide ancillary services to the grid. It features an edge devices layer for energy monitoring of individual prosumers, a fog layer providing Information and Communication Technologies (ICT) techniques for managing local energy systems by implementing cooperative models, and a cloud layer where the service specific technical requirements are defined. On top, a model has been defined allowing the dynamical construction of coalitions of prosumers as Virtual Power Plants at the fog layer for the provisioning of frequency restoration reserve services while considering both the prosumers’ local constraints and the service ones as well as the constituents’ profit maximization. Simulation results show our solution effectiveness in selecting the optimal coalition of prosumers to reliably deliver the service meeting the technical constraints while featuring a low time and computation overhead being feasible to be run closer to the edge.

This study proposes a double-stage double-layer optimization model for a virtual power plant (VPP) consisting of interconnected microgrids (IMGs) with integrated renewable energy sources (RESs) and energy storage systems (ESSs) to realize demand-side ancillary service, considering intra energy sharing among the IMGs within the VPP. In particular, the first stage, day-ahead scheduling, is carried out to predict the hourly electricity consumption baseline and regulation capacity for the next day, the latter of which results in a reward from the market operator. In the second stage, real-time power consumption control is performed by following the dynamic regulation (or RegD) signal. The second stage is further divided into two layers: the upper layer distributes demand response (DR) signals from the main grid according to the electricity unit price of each microgrid (MG) and exchanges electricity among MGs based on a new energy sharing mechanism to reduce RegD-following violations. The lower layer performs real-time power consumption control for each MG to minimize operation costs. The overall goal is to maximize the reward in the day-ahead stage and minimize the RegD-following violation penalty in the real-time stage, so as to minimize the overall operation cost of the VPP. The optimization is written in five objective functions, which are solved using mixed integer linear programming (MILP) in Gurobi solvers. Extensive simulation and comparison studies are carried out, and numerical results show that compared with traditional MG operations, VPPs comprised of IMGs can reduce operation costs and provide better frequency support for the grid through superior RegD signal following performances.

A voltage regulation method for slow voltage variations at distribution level is proposed, based on a view of the loads, generators, and storage along a distribution line as point weights. The “centers of mass” of the absorbed and injected currents (loads and generation, respectively) are compensated by minimizing the distance between them, through proper redispatching of the power of the available units and interruptible loads. The technique is recursively applied to lesser parts of the distribution line to address local phenomena and is assumed to be offered as ancillary service to the system operator by a virtual power plant. The favorable results of the methodology are assessed on a distribution line of the island of Rhodes (Greece) under critical loading for numerous scenarios. Unlike previous approaches, the technique focuses specifically in the restoration of bus voltages within standard limits and may reduce the activation of on-line tap changing transformers control.

With a high proportion of renewable energy connected to the power grid, it is necessary to optimize the grouping of flexible resources on the demand side to facilitate regulation and control in order to solve the shortcomings of decentralized and poor direct control of flexible resources on the demand side and meet the demand for deep peak regulation ancillary services of the power grid. In this paper, an optimized grouping method of virtual power plant (VPP) is proposed, which takes into account the peak regulation profits balance index of VPPs and the modularity structure index based on electrical distance. Firstly, the flexibility adjustment ability of various demand side flexible resources is analyzed, and the peak regulation profits balance index is proposed and combined with the modularity index reflecting the structural characteristics of the VPP. Then, the maximum weighted sum of the two indexes is used as the objective function to establish the VPP optimized formation model, and the genetic algorithm is used to solve it. Among them, the peak regulation profits balance index is obtained by simulating the participation of VPPs in the peak regulation ancillary services market based on typical day scenarios and calculating the peak regulation profits of each VPP. Finally, the rationality and effectiveness of the proposed method are verified by an example.

Frequency regulation is one of the basic objectives in ancillary market, which involves different stages and multiple participants. There exists different techniques for this service, where the points of demarcation are the time of service, and the regulatory requirements. The paper discusses primary frequency regulation, w.r.t Italian regulations, which is provided by conventional power plants upon TSO requests. The paper demonstrates conventional technique with its limitations, and proposes use of Virtual Power Plant (VPP) for the service provision. Storage and renewables techniques are compared under VPP context, and the use of storage is motivated. Finally, technical and economic comparison amongst potential storage techniques is done.

No abstract available

No abstract available

This paper proposes a distributed Model Predictive Control (MPC) approach to coordinate flexible resources as a virtual storage plant (VSP) for delivering ancillary services to the power network with high renewable penetration. We consider VSPs comprising battery systems and Heating, Ventilation and Air Conditioning (HVAC) systems acting as storages. The proposed control framework is based on stochastic MPC and an alternating direction method of multipliers (ADMM)-based fully distributed algorithm. The main control objective is to timely track a time-varying automatic generation control signal from the area control center of the electric grid by optimally coordinating an arbitrary number of HVAC and battery units. The uncertainty is handled by randomized techniques, with a number of scenarios guaranteeing a robust constraint satisfaction of the stochastic convexified problem formulation. The effectiveness of the MPC scheme is tested through a numerical case study, where the proposed MPC framework can systematically deal with the system constraints and technical service requirements, and the procured nearly real-time unit dispatch can compensate for the impact of renewables on the network operation.

As an important platform for distributed energy aggregation and coordination, Virtual Power Plant (VPP) has attracted extensive attention in recent years. With the continuous expansion of VPP scale, optimizing the allocation of various distributed resources through VPP intelligent scheduling has become a key way to improve energy efficiency and reduce operating costs. Aiming at the problems of high resource scheduling cost and unequal income distribution of VPPs participating in the power peak load management ancillary service market, this paper proposes a two-layer game bidding model for VPPs based on blockchain technology. A dynamic resource alliance mechanism incorporating an improved Shapley value is established to quantify the real-time marginal contribution of heterogeneous resources within the VPP and dynamically adjust alliance members in response to fluctuating peak load management demands. Based on game theory, a bilevel optimization model is formulated: an outer layer employs a Stackelberg game where the market operator optimizes the unified clearing price and VPPs' awarded capacity; an inner layer utilizes the ADMM algorithm to coordinate distributed resource scheduling for cost minimization. Case analysis under a representative summer day scenario demonstrates that the proposed approach significantly enhances the daily earnings of participating VPPs while leading to a substantial reduction in the grid's peak-valley load difference. This effectively improves power resource utilization and boosts VPP operational income, providing a theoretical foundation for multi resources collaborative participation in ancillary service markets.

No abstract available

The presence of large-scale Battery Energy Storage Systems (BESSs) in the grid is increasing at a quick pace, mainly to facilitate the process of the also increasing penetration of Renewable Energy Sources (RESs). However, BESSs are not exempt from caveats and limitations. In this regard, the interest in coordinating already installed resources in the form of RES-only Virtual Power Plant (RVPP) for energy market participation and ancillary services provision is rapidly growing. This paper determines the size of BESS needed to achieve similar economic performance as an RVPP in the energy and ancillary service markets for different levels of uncertainty and weather conditions. Uncertainties related to electricity markets, as well as RVPP RES units and demands, are considered in the RVPP optimization problem.

No abstract available

No abstract available

This paper discusses an optimisation model for multi-VPP systems with adjustable resources to participate in electricity spot market transactions. Firstly, the article introduces the VPP structure, and establishes the power model and cost model of each device and adjustable resource from the supply side, demand side and energy storage side. Secondly, it proposes an optimal scheduling model of VPP system with system revenue as the objective function, and adopts the Nash game model of cooperative game theory to solve the problem of multiple VPPs participating in the electricity spot market. Finally, the impact of demand response on VPP profits is analysed through a case study of multiple VPPs participating in the electricity spot market. The results of the study show that the scenarios involving demand response and game theory lead to the highest profits Introducing adjustable resources can improve the flexibility and cost-effectiveness of electricity supply.

The consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.

With the rapid development of China’s electricity spot market, the participation of Integrated Energy Systems (IESs) with multi-energy complementarity has become an inevitable trend in future energy development. However, IESs face difficulties in effectively matching heterogeneous resource capabilities with the diverse requirements of the multi-timescale spot market. Therefore, this paper proposes an optimization strategy for integrated energy system operation based on the hierarchical dispatch of flexibility resources, aiming to enhance the adaptability of different resources to multi-period markets. Firstly, a quantitative flexibility assessment framework is established from three key dimensions—power regulation range, energy shifting capacity, and dynamic response speed—to evaluate the market adaptability of various adjustable resources. Subsequently, the flexibility assessment results are converted into dynamic market participation ratios, which are incorporated as constraints into a Model Predictive Control (MPC)-based optimization model. In the day-ahead scheduling stage, the model prioritizes meeting fundamental electricity demand while dynamically reserving a portion of flexible capacity for participation in more profitable intra-day and real-time market services. Case studies demonstrate that the proposed strategy achieves real-time computational feasibility, significantly improves the economic performance of park-level IESs, and maintains stable dispatch behavior under market uncertainties and forecast deviations. The results indicate that the proposed hierarchical flexibility-oriented dispatch framework provides a practical and scalable solution for enabling IES participation in multi-timescale electricity spot markets.

With the transformation of the energy structure, the integration of numerous small-scale, widely distributed renewable energy sources into the power grid has introduced operational safety challenges. To enhance the operational competitiveness, the virtual power plant (VPP) has emerged to aggregate and manage these distributed energy resources (DERs). However, current research on the VPP’s frequency modulation performance and bidding strategy remains insufficient in the joint market of electrical energy and frequency modulation (FM) ancillary services, with inadequate coordination of internally distributed resources. To fully leverage the flexibility of VPPs and incentivize their participation in electricity market operations, this paper investigates game-based bidding strategies and internal distributed resources allocation methods for VPPs in the joint market for electrical energy and frequency ancillary services. Firstly, the regulatory performance indicators of VPPs participating in the joint market and develops the corresponding market-clearing model. Secondly, to address the competition among distributed resources within VPPs, a master-slave game approach is innovatively employed to optimize the VPP’s trading strategies. This method ensures the rational allocation of electricity consumption among distributed energy resources within the VPP and derives the optimized bidding prices and quantities for both the VPP and its internal members. Finally, the case study shows that the proposed trading strategy provides effective bidding strategies for distributed energy resources participating in the joint market for energy and frequency regulation ancillary services. It enhances the regulatory performance of VPPs in the energy-frequency regulation market, ensures the profitability of distributed energy resources, and contributes to the economically stable operation of the market.

Virtual power plant (VPP) with a high percentage of flexibility resources has issues that need to be addressed, such as high source-load volatility and limited scope to participate in multi-market bids. Therefore, this paper proposes a VPP standby capacity setting method based on normal distribution framework and Bayesian parameter optimization. Through the marginal revenue and expenditure of standby capacity analysis, this paper constructs a two-stage optimization strategy for VPP trading in multi-market considering double uncertainty, which is solved by the Improved Multi-Objective Squirrel Search Algorithm (IMSSA). Compared to the traditional program, the VPP's participation in the day-ahead spot bidding increased by 5.97% and 2.48%, respectively, total revenue increased by 17.41% and 12.97%, respectively, reliability increased by 0.21%, and overall energy efficiency increased by 10%. Compared to Squirrel Search Algorithm and Particle Swarm Optimization Algorithm, IMSSA improves the optimal revenue by 1.03% and 1.91%, and the convergence speed by 24.24% and 38.01%, respectively.

Enabling the integration of distributed energy resources (DERs) into the wholesale market, as prompted by the FERC Order 2222, introduces substantial operational complexities. To align with the current wholesale energy market practice, regional transmission organizations (RTOs) are pursuing the aggregation of transmission-node-level DERs (T-DERs), in which each T-DER is a projected representation of all DERs in the distribution network underneath the transmission node, such as a virtual power plant (VPP). This paper advances a real-time economic dispatch (RTED) framework to facilitate the aggregation of multiple T-DERs, optimizing their participation in energy market operations. We enhance the distribution factors (DFs) based modeling to capture the impact of individual T-DERs across the transmission network. Specifically, an innovative DF updating strategy is introduced in our methodology, incorporating a tailored K-Nearest Neighbors (KNN) predictor and a discrete PI corrector, along with transmission signals that sufficiently guarantee transmission constraints. These enhancements optimize the economic efficiency of the market clearing process, allowing numerous T-DERs to be involved in current energy market practices. The proposed method is tested on modified 24-bus and 118-bus systems via a rolling RTED process with real load data. The test results show that the proposed framework demonstrates marked improvements in minimizing system operation costs and satisfying the transmission network constraints.

This paper proposes a blockchain-integrated virtual power plant (VPP) framework for day-ahead (DA) energy market participation, formulated as a two-stage mixed-integer quadratic programming model. In stage 1, the optimal scheduling of distributed energy resources, including battery energy storage systems, photovoltaic units, diesel generators, and flexible loads, is performed to generate DA bids based on forecasted prices and generation. Stage 2 undertakes a post-clearing re-dispatch to align operations with the awarded quantities and prices determined by the Independent System Operator. To ensure transparency and auditability, all forecasts, bids, schedules, and settlements are immutably recorded on an Ethereum-based smart contract. Case studies using 2025 Wholesale Electricity Market data demonstrate that the proposed framework achieves a DA profit of A$674.7, with sensitivity analysis confirming robustness to ±10% price variations. Overall, the integration of blockchain enhances transparency and reduces reliance on intermediaries in DER coordination.

To address the trading decision problem of virtual power plants in multiple markets including the spot market, the mid-to-long-term market, and ancillary services, a joint trading strategy integrating Transformer and multi-agent reinforcement learning is proposed. First, a multi-market electricity price joint prediction model based on the Transformer is constructed, which employs a self-attention mechanism to capture the coupling relationships and long-term dependencies across different markets. Second, a hierarchical multi-agent actor-critic framework is designed, modeling various distributed resources within the virtual power plant as agents, and an improved multi-agent deep deterministic policy gradient algorithm is applied to achieve coordinated optimization among resources. Finally, a shared memory mechanism and a centralized training with decentralized execution paradigm are introduced to enhance decision-making performance. Simulation results indicate that, over a 30-day test period, the proposed method achieves a total revenue of 521,490 yuan, representing improvements of 70.7%, 24.3%, and 11.3% compared with the traditional deep deterministic policy gradient, multi-agent deep deterministic policy gradient, and long short-term memory-based multi-agent reinforcement learning methods, respectively, approaching 96.3% of the theoretical upper bound obtained by mixed-integer linear programming. Under scenarios of extreme renewable energy fluctuations, the revenue decreases by only 11.4%, whereas in price spike scenarios, the revenue increases by 12.7%, demonstrating excellent economic performance and robustness.

As an emerging market participant, Virtual Power Plants (VPPs) are gradually gaining access in multiple market trading varieties. They aggregate flexible distributed resources to respond to system dispatch instructions and execute transaction results, expanding the adjustable resources for the new power system. Currently, VPPs primarily participate in the ancillary services market, with secondary involvement in the energy market. With the penetration of a high proportion of new energy sources, the transition of VPPs to the spot market becomes a crucial business expansion direction. This study explores a spot market clearing model adapted for VPPs participation. It allows VPPs to reflect their flexible regulation characteristics by introducing a flexible declaration method, aiming to enhance the matching rate between supply and demand in the electricity market. Through a comparative analysis of clearing results based on different transaction models, an assessment method for the flexibility premium of VPPs is established. This transition in pricing system allows VPPs to shift from energy pricing to a “energy + flexibility” pricing model, thereby improving the competitiveness and value recognition of flexible resource VPPs in the electricity market.

With the rapid increase in the construction of 5G base stations, the backup battery of 5G base stations will be a huge potential energy storage resource. China’s electricity market reform is constantly advancing, and the study of 5G base stations’ standby energy storage to participate in power optimization dispatch is an effective way to realize the reciprocal benefits of power grids and telecommunication operators. Considering the special characteristics of 5G base station backup energy storage to participate in the power market, the article establishes a virtual power plant of 5G base station considering the backup energy storage. Then, the 5G base station VPP is added to the operation of the power grid as an adjustable resource, and the dual-5G base station VPP optimization matrix is built. Finally, a numerical example verifies that the standby energy storage of the 5G base station contains rich schedulable capacity and that the 5G base station VPP can validly improve the economy of power grid operation.