微网购电优化、储能调度、购电策略

为充分发挥氢能的中长期存储优势，提出了一种基于场景法随机优化模型的电氢耦合微电网两阶段周优化调度策略。首先，建立微电网中电氢耦合设备的数学模型；其次，以最小化周运行成本为目标，分别设置电、氢储能的周期为周和日，建立基于周预测数据的微电网第1阶段周调度模型。然后，利用预测误差的典型场景衡量风电不确定性，以日运行期望成本与两阶段储氢罐状态偏差惩罚之和最小为目标，构建考虑不确定性的第2阶段日前调度模型，并通过滚动求解，得到最终周运行方案。最后，算例表明，所提策略能降低微电网运行成本并提高能量利用率。

针对目前并网型微电网实现资源协同优化，提高微电网运行的经济性与环保水平，建立了考虑需求响应和碳排放额度的微电网分层优化调度模型。在微电网负荷侧考虑分时电价和新能源消纳来优化负荷曲线，并采用多重指标对优化方案进行评价，在微电网发电侧考虑碳排放限额，对微电网内部分布式电源进行优化调度以实现微电网综合运行成本最低的目标。采用混沌粒子群算法（chaos particle swarm optimization, CPSO）求解该优化问题，通过算例仿真分析了柔性负荷占比0%、10%、20%和不同碳排放量额度约束下的优化结果，验证了模型与算法的有效性。

针对高渗透率分布式可再生能源并网引发的电压越限、双向潮流等问题，提出一种双层有功无功协同优化方法，实现离网型微电网有功无功协调优化调度，保证系统安全稳定运行并提升运行的经济性。下层模型基于混合整数二阶锥规划优化慢速调节离散设备，上层模型基于多智能体深度策略梯度算法优化快速调节连续设备。双层模型同时调节微电网的有功和无功潮流，能够实时观测微电网状态，在线决策调节设备的优化方案，且不依赖精确的潮流模型和复杂的通信系统。最后，在改进IEEE 33节点微电网系统中验证双层优化模型的可行性和有效性。

针对传统群智能优化算法在光储微电网调度问题中存在收敛速度慢、易陷入局部最优等问题，提出一种自适应权重改进的雁群算法。首先，以系统总运行成本最低为目标建立光储微电网调度模型，并考虑功率平衡、储能充放电和SOC约束。其次，采用改进的AWGA协调全局搜索能力和局部寻优能力。并优化当前个体位置，从而提高算法收敛性能。最后，进行仿真分析，将改进的算法与SSA、GWO和WGA进行对比，结果表明，改进算法能够降低经济成本，并在收敛速度和稳定性方面具有较好的效果。

随着国家“双碳战略”的推进，新能源在交通领域的应用与能源转型得到快速的发展。在我国西部无电网或弱电网地区，风光资源充足，可采用微电网为路域用能设施供能。但高速公路微电网存在纵向跨度大、离散分布、出力不均衡、沿线配电网建设运行成本高等问题，因此，将移动储能调度设备引入到高速公路微电网能源调度系统结构中。在此基础上，构建高速公路微电网及移动储能系统模型，提出新的调度成本机制及能量调度双层架构。同时，高速公路微电网系统具有长距离带状结构，会造成微电网子控制器调度产生通信负担。针对此问题提出交替方向乘子法分布式双层优化调度策略，该方法将全局问题拆解为局部问题进行并行优化求解，各微电网仅需与相邻微电网进行通信，相互之间交换期望能源需求信息。系统以高速公路微电网总运行成本最小作为耦合变量，通过增广拉格朗日罚函数进行松弛，将原优化问题解耦为各系统的独立子优化问题，并采用双层循环求解的方式，最终获得全局最优调度方案。本文通过数值仿真分析验证可再生能源利用率提升了15.3%，在实现高速公路用能的基础上保障了经济性。

随着能源结构转型，农村电网将朝着“以分布式新能源为主导”的新型局域电力系统方向演进。为增强新型局域电力系统的自主频率调控能力和源荷出力匹配性，首先考虑风机与储能对系统的虚拟惯量支撑作用，构建了系统惯量需求模型；其次，针对负荷峰谷期特性差异，建立了基于弃风水平的风机灵活备用模型；然后，结合农作物生长过程中的光照需求特性，分析了农作物从自然光照和人工补光中吸收的光合有效辐射，构建农业补光负荷能耗转移模型；最后，结合系统惯量需求约束、风机灵活备用约束、农业补光负荷转移约束，构建了农村独立微电网优化调度模型，并以我国某村镇的独立微电网为研究背景进行了算例分析，结果表明所建模型能够有效促进新能源消纳、降低系统运行成本。

家庭微电网是智能配电网的重要组成部分和主要建设内容，随着智能电网的不断发展，家庭居民用电将参与电网的优化调度运行。为了适应社会的这种需要，首先以家庭可调度负荷、电动汽车和家用蓄电池工作状态作为约束条件，以用户用电成本最低和净负荷曲线平坦度为优化目标，建立了家庭微电网系统优化调度模型。其次，结合空调和热水器的工作负荷特性，提出了一种基于分时电价和需求响应的家庭微电网系统协同控制策略。对以北京市某居民小区的典型家庭微电网系统为实例的分析结果表明，采用基于所提出的优化调度模型的家庭微电网系统，可以实现对分布式光伏发电、家用蓄电池、电动汽车、家庭负荷设备的优化调度和控制。

随着新能源电动汽车的不断普及和推广,在电动汽车灵活接入电网情况下,保证电网功率波动更平滑成为目前要面对的重要问题之一。以电网运行成本最低为目标,以风力,光伏等多种发电方式为对象,微电网能量管理系统通过考虑电网分时电价及合理储能的特性,综合考虑普通负荷和电动汽车负荷的微电网能量管理系统建立模型。分别在正常天气和阴雨、无风等特殊天气的情况下,运用不同的微电网运行策略,结合上海地区每年的降水日数与晴朗日数比进行仿真。结果表明,所建模型不仅可以降低电网运行成本,而且可以减少电力资源的浪费。

微电网作为消纳清洁能源的重要途径,优化调度直接影响微电网系统的稳定性与经济性.对微电网各部分进行了详细数学建模,同时引入分时电价机制,综合考虑微电网系统运行约束,建立了多目标优化模型,并调用商业求解器cplex对优化模型进行求解.以某地微电网数据为例进行运行仿真,结果表明该优化模型在考虑环保的同时对经济性有明显提升.

随着第5代移动通信技术（5G通信）的迅速发展，5G基站数量不断增加，5G通信耗电量大、用电成本高的问题日益突出。为此，提出了考虑光伏和储能接入的5G基站光储系统优化配置方法，以提高5G基站运行的经济性。首先，考虑5G基站负荷情况和配电网分时电价，建立了5G基站光储系统的经济调度模型；然后，通过量子粒子群优化算法计算典型日内5G基站光储系统的最小综合成本，以此确定光伏和储能的最优接入容量；最后，通过算例证明合理配置光伏和储能容量可以提高5G基站系统的经济性。光伏和储能容量的配置受储能成本和峰谷电价差的影响较大，且光储系统所能带来的经济效益随峰谷电价差的增大和储能成本的降低而增大。

… the power procurement and consumption behavior of microgrid systems using Monte Carlo methods, and obtains the optimal power procurement … in the microgrid operating environment …

… management strategies for optimal scheduling of the microgrid (MG), including electric … entity’s different objective and optimize the power procurement strategy. In the day-ahead stage, …

… of local reactive power procurement through DGs and reactive power control methods such as … active power (P) requirements are met efficiently, with inherent support for reactive power …

As a renewable energy solution for remote marine environments, marine raft microgrid clusters differ from terrestrial multi-microgrid systems and traditional single-island microgrids. In the absence of large-scale grid support, these marine raft microgrids must maintain the stability and economic efficiency of power supply within a collaborative multi-microgrid context. To address this, a multi-objective optimization approach for energy scheduling is proposed. This study initially constructs a microgrid cluster system model and introduces two economic objective functions. These functions consider both inter-microgrid power scheduling and the economic benefits of power procurement. By applying the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Constraint-based Multi-objective Evolutionary Algorithm based on Decomposition (CMOEA/D) to solve the objective functions, the results indicate that the CMOEA/D algorithm demonstrates high efficiency and accuracy in pursuing economically optimal solutions. Compared to the NSGA-II algorithm, CMOEA/D outperforms in terms of the quality of optimal solutions and iteration time, thereby enhancing the economic benefits of the microgrid cluster and validating the effectiveness of the proposed model. This research provides significant theoretical and practical guidance for energy management in remote marine environments, showcasing its profound theoretical significance and application value.

The Electricity Distribution Companies (DISCOMs) in India are subjected to considerable Unscheduled Interchange (UI) penalties based on the deviation between the actual and scheduled power drawn from the grid to strictly maintain the nominal operating conditions in the system according to the Deviation Settlement Mechanism (DSM) guidelines. Therefore, it becomes essential to procure power optimally while incurring minimum penalty due to UI for the better financial viability of the DISCOMs. This study proposes a framework for the optimal procurement of power considering multiple resources while minimising UI for a grid-connected microgrid with non-shiftable or non-curtailable loads. The term virtual load is referred to account for the variations in demand, which represent the UI for each 15-minutes time interval. The problem statement is a nonlinear problem (NLP) formulated in GAMS software and solved using the Solving Constraint Integer Programs (SCIP) solver. Further, a real case study of Gujarat International Finance-Tec City Power Company Limited (GIFT PCL), which supplies power to GIFT City infrastructure, is considered in this study to validate the proposed model.

This paper deals with an optimal energy procurement strategy in a DC microgrid.The DC microgrid comprises renewable sources, storage systems, and demand response participators, who are players in the electricity market.The DC microgrid operator considers bids from all the market players and pursues an optimal energy procurement scheme for the DCMG.The objective is to maximize the financial benefit.Uncertainties of electrical load demand, renewable generation, and energy price of grid is incorporated through Hong's point estimate.A nested optimization problem is solved to realize the desired objective.Discrete dynamic programming is used to solve the problem of storage scheduling and optimal demand response participation.The procurement from renewable sources is determined using the particle swarm optimization method.Simulation studies on a six-bus DC microgrid test system reveal that the daily expected profit can be increased by ~31.62% using the proposed energy management scheme.

… with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the …

Multi-energy community microgrids (MGs) have been recognized as key enablers for harnessing distributed demand-side flexibility resources, especially when integrating storage. However, the literature on demand response-integrated community energy system design and dispatch optimization has, thus far, failed to concurrently maximize the flexibility potential in several energy carriers, thereby neglecting the potentially significant improvement opportunities of the associated business cases. In response, this paper introduces a novel Nash bargaining-based cooperative game approach for the optimal aggregator-mediated demand response scheduling of multi-energy community MGs serving electricity and thermal loads, as well as hydrogen as a transportation fuel. More specifically, the proposed approach systematically and effectively characterizes how the players share the resulting surplus from demand-side management in an equitable manner under the assumption that the interests of the players - the MG operator, sectoral demand response aggregators, and small-scale end-users - are neither completely opposed nor completely coincident. The proposed approach is then integrated into a meta-heuristic-based long-term MG planning method. A case study for a community-based residential users' aggregation scheme in Aotearoa-New Zealand demonstrates the effectiveness of the method in reducing the total discounted system cost of a multi-energy MG by ~14% (equating to US$1.9m) and ~31% (US$5.4m) respectively compared to the cases where: (i) the actors' behaviors are characterized using non-cooperative game theory under self-interestedness assumptions; and (ii) no demand response programs are implemented.

This paper investigates distributed low-carbon scheduling for interconnected multi-microgrid integrated energy systems. An electricity-heat coupled carbon-aware model is established to characterize renewable utilization, multi-energy conversion, storage dynamics, inter-microgrid energy exchanges, and carbon trading. The coordinated scheduling problem is reformulated into a distributed optimization framework with local operational constraints and exchange consistency conditions. A distributed electricity-heat coupled coordination algorithm is developed to enable scalable and privacy-preserving decisionmaking via neighbor-to-neighbor information exchange. Convergence is guaranteed under standard assumptions. Numerical results indicate that the proposed approach enhances multi-energy coordination, achieves effective carbon-emission control under emission constraints, and attains stable distributed convergence.

Microgrid incorporating distributed renewable energy resources (RERs) is increasingly important owing to the goal to reach net-zero emissions by mid-century. This article deals with the optimal energy management of microgrid comprising RERs and battery energy storage system. In addition to considering power flow constraints and uncertainties of RERs, the importance of retaining profits of microgrid operators and the needs for providing extra supports to the main grid are rising. To meet all the requirements, a novel Lagrange multipliers-based method is proposed to deal with equality and inequality constraints simultaneously to directly obtain the optimal economic dispatch solution analytically. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the Lagrange multipliers-based method. Furthermore, the comparison of the proposed method with experience-based energy management system, Newton-particle swarm optimization, and deep Q-learning network is provided to evaluate the obtained solutions. Finally, through the hardware in the loop mechanism, which is built using OPAL-RT real-time simulator with floating-point digital signal processor, the effectiveness of proposed Lagrange multipliers-based method is verified and proven to be pragmatic.

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.

The operation of energy storage systems (ESSs) in power systems where variable renewable energy sources (VRESs) and ESSs must contribute to securing the supply, can be considered as an arbitrage against scarcity. The value of using stored energy instantly must be balanced against its potential future value and future risk of scarcity. This paper proposes a multi-stage stochastic programming model for the operation of microgrids with VRESs, ESSs and thermal generators that is divided into a short- and a long-term model. The short-term model utilizes information from forecasts updated every six hours, while the long-term model considers the value of stored energy beyond the forecast horizon. The model is solved using stochastic dual dynamic programming and Markov chains, and the results show that the significance of accounting for short- and long-term uncertainty increases for systems with a high degree of variable renewable generation and ESSs and limited dispatchable generation capacity.

… for hydrogen-based multi-energy off-grid microgrids under economics and resilience … the energy resources configuration in the first stage, and conducts long-term economic dispatch as …

… stability of the microgrid load dispatch process are discussed. The results show that the microgrid with distributed energy storage systems and centralized energy storage system can …

: As the world’s energy framework shifts towards a low-carbon model, the widespread incorporation of renewable energy (RE) sources, primarily wind power and photovoltaics (PV), into the power grid is an unavoidable development. The micro-energy grid (MEG), as an integrated system that combines distributed energy, energy storage (ES), and power loads, can achieve efficient consumption of RE by implementing multi-machine optimal allocation and unified coordinated power control for parallel operation of grid-forming energy storage (GFES). For this purpose, this paper puts forward a low-carbon and economic dispatch strategy for MEG that considers multi-machine optimal allocation of GFES and unified coordinated power control for parallel operation. The strategy constructs a multi-machine optimal allocation model for GFES in the outer layer, striving to achieve the lowest operational costs for the MEG. In the inner layer, based on the obtained optimal multi-machine allocation scheme for GFES, a unified coordinated power control model for parallel operation of GFES in the MEG is constructed, targeting the minimization of pollutant gas emissions and system voltage deviation. The plant growth simulation algorithm (PGSA) is employed to solve the established models for multi-machine optimal allocation of GFES and unified coordinated power control for parallel operation in the MEG. Through simulation analysis, it has been substantiated that the proposed method can effectively achieve multi-machine optimal allocation and unified coordinated power control for parallel operation of GFES, reduce the operational costs of the MEG system, while also decreasing pollutant gas emissions and stabilizing system operation, thereby offering robust and substantial backing for the attainment of a low-carbon economy and the pursuit of sustainable development.

… These quantities correspond to 0.97% and 0.25% of total dispatch of the microgrid for the hydrogen and battery, respectively. The difference between these values (0.72%) is indicative …

Big tech companies are rapidly deploying large GPU data centers and pursuing 24/7 carbon-neutral operation with wind and solar. Yet renewables are intermittent, AI workloads are highly variable, and inference is non-deferrable under strict SLOs, causing persistent supply–demand mismatches. We propose HES4AI, a hybrid energy storage framework to minimize the carbon footprint of AI data centers that run ML training and inference on different timescales. HES4AI consists of three major components: (1) a three-tier energy-storage hierarchy that captures fast wind fluctuations with supercapacitors (SCs) while using uninterrupted power supply (UPS) batteries and thermal energy storage (TES) to shift energy over longer horizons and couple IT and cooling; (2) a two-stage pair-then-pool routing policy that pairs renewables with loads and storage first and pools only residual imbalances, reducing the required rating and CapEx of the shared pooling interface; and (3) an online constrained linear program that jointly optimizes routing, storage dispatch, and bounded training elasticity under practical state of charge (SoC) and power limits. On a hardware testbed with Nvidia GPUs and a Maxwell supercapacitor, UPS-only consumes 1.38–1.42 × more brown energy and wastes 1.87–1.93 × more renewables than HES4AI. Across four regions in trace-driven simulations, HES4AI outperforms the conventional micro-grid, two UPS-only state of the arts methods, and a heuristic method under the same storage budget and PTP-sized pooling cap: Relative to HES4AI (1.0), baselines use 1.21–2.51 × more brown energy and waste 2.42–2.51 × more renewable energy.

The energy storage plays an important role in the operation safety of the microgrid system. Appropriate capacity configuration of energy storage can improve the economy, safety, and renewable energy utilization of the microgrid. This study considers the uncertainty of renewable energy, and builds an energy storage capacity configuration (ESCC) in microgrid by using the distributionally robust optimization (DRO). This model co-optimizes energy storage planning, day-ahead scheduling, and renewable energy utilization of the microgrid, which derives the energy storage configuration strategy, balancing renewable energy utilization and operation economics of microgrid. The proposed model is a two-stage model with distributionally robust chance constrains. By applying decision rules, variable substitution, and duality techniques, this model is approximately transformed into a mixed integer programming problem with a second-order cone constraint, which can be directly solved. Experiments on the IEEE 33-bus system are carried out to verify the effectiveness and advantages of the proposed model.

… microgrid (MG) systems. The various fitness functions that were evaluated includes economic dispatch … methods of energy storage technologies during charging and discharging modes. …

Effective real-time energy management strategies are crucial for optimising hybrid power plants, particularly when challenged with integrating Renewable Energy Sources (RESs) and managing their intermittent nature. This paper presents a comprehensive energy management framework holding real-time optimisation for HPP. The practical implications of this research are significant, as it provides a roadmap for seamlessly integrating RESs with Battery Energy Storage Systems (BESSs) in Hybrid Power Plants (HPPs) to minimise cost while meeting daily household energy demands. Furthermore, it demonstrates how diesel generators (DGs) can be incorporated into the HPP’s energy management system while minimising carbon emissions. An Energy Dispatch Engine (EDE) is introduced to control HPPs that combine PV, BESS, DG and Pumped Hydro Storage (PHS). Two optimisation approaches are used, namely, Mixed-Integer Linear Programming (MILP) and Stochastic Dual Dynamic Programming (SDDP). The system leverages load and RES power data while considering State-of-Charge (SoC) constraints to manage battery health proactively. Optimising discharge and charge profiles of the BESS, with the overarching goal of minimising the total cost of satisfying daily load demand, is an objective. Various tariff schemes were explored to assess the presented EDE. Our testing demonstrates that the SDDP approach consistently results in lower total costs than MILP. The total cost for the MILP method, where the system with PHS incurs higher costs (219.8 <inline-formula> <tex-math notation="LaTeX">${\$}$ </tex-math></inline-formula>/24h) than the total cost for the SDDP method, where the system with PHS system (180 <inline-formula> <tex-math notation="LaTeX">${\$}$ </tex-math></inline-formula>/24h). The cost of CO2 emissions was found to be lower in the case of SDDP, amounting to 8.3 <inline-formula> <tex-math notation="LaTeX">${\$}$ </tex-math></inline-formula>/24h for a total emission of 160 kg. In contrast, the MILP approach resulted in a higher CO2 cost of 10.2 <inline-formula> <tex-math notation="LaTeX">${\$}$ </tex-math></inline-formula>/24h for a total emission of 200 kg. This suggests that SDDP is more cost-effective in terms of reducing CO2 emissions.

… share electricity with which building. A building can then purchase or sell electricity based on … to the uncertainties of generating electricity from the RE systems in the microgrid. Thus, it is …

Integrated energy microgrids (IEM) have emerged as an effective way to improve energy efficiency and promote distributed energy utilization. IEM systems acquire electricity and gas from external markets and supply electricity/heat/cold to users. In this paper, we study the optimal energy purchase strategy for IEM, considering the impact of demand response incentives. Firstly, considering the uncertainties, we construct an IEM medium-and long-term market multi-energy purchase model based on conditional value-at-risk, optimizing the portfolio of electricity and gas purchases, as well as their proportion in total energy amount. Subsequently, based on medium-and long-term daily energy supply curves and day-ahead load forecast results, a spot market energy purchase model is established to optimize the spot purchase of electricity and gas, maintaining the supply-demand balance while minimizing operating costs. Furthermore, we design demand response incentives and develop a master-slave game model between IEM and users to guide the formulation of the energy purchase strategy by incorporating corrected load data as feedback. The energy purchase strategies are resolved by the GUROBI solver, while the optimization of demand response incentives is carried out through the PSO algorithm, all based on the MATLAB platform. The adaptability of the proposed model and strategy is verified.

… electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. … the dispatch strategy in this …

… presents an optimization strategy for the bidirectional TOU electricity price for multi-microgrids (MMGs) … purchase strategy of MGOs, the TOU electricity price on the load side, and the …

… Additionally, annual electricity purchase costs from the grid and the cost of selling electricity to the grid are decreased by 7.5% and 44.6%, respectively. These improvements contribute …

Peer-to-Peer (P2P) energy sharing enables prosumers within a community microgrid to directly trade their local energy resources such as solar photovoltaic (PV) panels, small-scale wind turbines, electric vehicle battery storage among each other based on an agreed cost-sharing mechanism. This paper addresses the energy cost minimization problem associated with P2P energy sharing among smart homes which are connected in a residential community. The contribution of this paper is threefold. First, an effective Home Energy Management System (HEMS) is proposed for the smart homes equipped with local generation such as rooftop solar panels, storage and appliances to achieve the demand response (DR) objective. Second, this paper proposes a P2P pricing mechanism based on the dynamic supply-demand ratio and export-import retail prices ratio. This P2P model motivates individual customers to participate in energy trading and ensures that not a single household would be worse off. Finally, the performance of the proposed pricing mechanism, is compared with three popular P2P sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and bill sharing (BS) considering different types of peers equipped with solar panels, electric vehicle, and domestic energy storage system. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network.

… Therefore, the bidding strategies of microgrid are as follows: η s e l l M = { … purchase electricity at time t. At any time, the price of electricity purchased by large power users from microgrid …

… [29] developed and compared rule-based EMS for a PV/Battery microgrid considering four different electricity purchase and sale tariffs. In the first tariff option called flat-flat, the price for …

The exponential augmentation of electricity consumption and the restructuring of the conventional power industry resulted in the emergence of microgrids (MGs). The nondispatchability and random attributes of the integrated renewable energy resources (RERs) challenge MGs scheduling operation, for which the multi-MGs system's energy management (EM) study has been gaining paramount importance and studied in this article. This article contemplates both internal and external markets for MGs’ effective participation in energy trading, which involves the energy exchange among MGs and that with the utility grid (UG). The energy pricing considers two conflicting objectives: MGs’ goal to improve their economy by reducing their purchasing prices and reliance on UG; the distribution network operator's aim to maximize its profit from the deployed market. Also, this article implements hybrid scenario and copula-based Monte Carlo techniques to assess the intermittencies associated with load demands, plug-in hybrid vehicle charging demands, and correlated RERs generations. The EM framework is formulated as a max–min optimization problem solved by a metaheuristic fuzzified jellyfish search optimization algorithm. Several simulation outcomes under different charging scenarios are reported, suggesting a 1.5% and 3.2% reduction in the multi-MG system's operational cost compared with the particle swarm optimization algorithm considering the best charging strategy during the summer and winter seasons.

… the impact of electricity pricing strategies and demand … ) microgrids. Although numerous studies have examined individual … time-of-use (TOU), critical peak pricing (CPP), and real-time …

… This paper proposed an investor-oriented time-of-use pricing design method for distributed energy storage in microgrids considering investment return stability. A bilevel Stackelberg …

This paper investigates the construction of a microgrid energy management system and energy storage scheduling strategy considering time-of-use (TOU) tariffs and demand charges. By collecting key information such as photovoltaic power generation, load demand power, electricity price information, and the state of charge of energy storage devices, a refined power allocation and charging/discharging strategy is formulated based on a rule-driven scheduling framework, which is adaptable to both direct current (DC) and alternating current (AC) coupling configurations. A peak-shaving control mechanism based on dynamic adjustment with a sliding window is proposed, and the energy storage charging/discharging model is optimized under the TOU tariff mechanism. Meanwhile, a cost model incorporating power trading costs and demand charges is constructed, which includes models for the power grid and photovoltaic systems. The effectiveness of the proposed method is verified through simulation experiments.

… framework for ESS in microgrids, uniquely incorporating … active distribution networks and microgrids. By systematically … hybrid backup storage within self-sufficient microgrids [23] . By …