非孤岛式微网基于小区负载、光伏发电量和储能电量的分布式能源本地消纳、外网分时购电与储能充放电调控

… Energy storage is needed in PV based microgrids to cater to … storage, pumped-hydro, flywheel, compressed air storage, … energy storage (SMES) are storage options proposed for …

… PV installations, natural gas, and energy-storage devices have been supporting further expansion of dg and microgrids. … location and size for energy storage within a microgrid so that a …

… management in [9] and [10]. In this paper, optimal energy management of grid connected microgrid which comprises PV systems and BESS is presented. DP is used to minimize the …

… distributed MGs, an efficient energy management system (EMS) … grid-connected MGs' energy management. Stochastic programming is employed to account for the unpredictable solar …

Microgrids (MGs) are a growing energy industry segment and represent a paradigm shift from remote central power plants to more localized distributed generation. Controlling MGs represents a challenge mainly due to their complexity and the different properties each asset in the MG has. Various methods have been proposed to address this challenging problem of MG control. Some of these methods are considered the optimal operation of MG assets. Other works are based on a systems approach and address the scalability and simplicity of synthesizing a MG’s energy management system (EMS). <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-variables based logical control strategies, which are practical methods to model control strategies in MGs, can make the control structure more scalable. However, this method is not optimal. On the other hand, Switched Model Predictive Control (S-MPC) is an advanced method utilized to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Nevertheless, its implementation is not straightforward. Therefore, to overcome these existing problems, this paper proposes a novel systems approach method called an extended optimal <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-variable method developed by combining the <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-variable based control method with the S-MPC method. This unique method has demonstrated a significant improvement in optimizing an MG’s energy management and enhanced the adaptation and scalability of a control structure of the MG. Our results show that the proposed extended optimal <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-variable method: (i) reduces the operational cost of MG by nearly 35%; (ii) reduces the usage of the battery energy storage system by 42%, and (iii) enhances the practicality of photovoltaic (PV) usage by 28%. Our novel extended optimal <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-variable technique also increases the adaptation and scalability of the control structure of the MG significantly by translating the results of S-MPC to the <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-variable method.

Abstract With the fast development of microgrids (MGs), the microgrid community (MGC) integrating adjacent MGs has raised more and more attentions. In an MGC, each MG shares available power reciprocally to minimize the operation costs and maintain power balance. As the amount of MGs grows, the MGC becomes complicated and difficult to control. Different from centralized strategy, this paper proposes a cooperative energy management scheme based on distributed model predictive control (DMPC) for grid-connected MGC. The normal MGC is virtualized as two-level structure to simplify the internal interactions among subsystems. All the subsystems iterate in parallel adopting the proposed cooperative logarithmic-barrier method, which follows a communication protocol to derive control signals. The real-time hardware-in-the-loop (HIL) study verifies that, the optimization results of proposed DMPC approximate to Pareto solutions of centralized MPC (CMPC), whereas the computing speed is much faster than CMPC.

This paper develops an effective model for microgrid optimal scheduling with dynamic network reconfiguration. Network reconfiguration can effectively alter local power flow and thus provide an opportunity to reduce microgrid distribution network losses during grid-connected operation (supporting microgrid economic objectives) and to reduce potential load curtailments during the islanded operation (supporting microgrid reliability objectives). The proposed optimal scheduling model is decomposed into a grid-connected operation master problem and an islanded operation subproblem. A novel and highly accurate dynamic linear power flow model, with the ability of line switching, is developed and included in both problems. The optimal schedule determined in the master problem is assessed to meet the microgrid islanding feasibility in the subproblem. If infeasible, the decision variables are amended using the islanding cuts, which will accordingly revise the network reconfiguration, as well as the schedule of dispatchable units, energy storage, and adjustable loads. The simulation results on a test microgrid demonstrate the effectiveness and satisfying performance of the proposed model.

… in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power … and system economy when achieving optimal scheduling. In order to …

… In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the …

This paper introduces a new rural microgrid model, including residents and agricultural greenhouses. Based on the new model framework, the precise energy scheduling of a rural microgrid is realized by means of load classification and load forecasting. Moreover, we also adopt a new energy-storage mode, cloud energy storage (CES), as the shared energy-storage unit of rural microgrid, and analyze the service and operation mechanism of CES in detail. The shared storage characteristic and adjustable storage capacity of CES are helpful for the precise management of power dispatching. At the same time, in order to accurately implement energy scheduling, we fully consider the load characteristics of rural areas and divide the load into residential load and agricultural load. Then the extreme gradient boosting (XGBoost) algorithm is used to predict the short-term power consumption of the two types of load respectively, which can effectively alleviate the uncertainty of load power consumption and improve the accuracy of scheduling. Finally, an illustrative example of rural energy scheduling is given. The example studies the impact of energy-storage capacity on the cost of the scheduling scheme, and designs a power-dispatching scheme based on load forecasting, which accurately solves the energy charging and discharging planning and grid energy trading planning.

… In [28], storage systems are applied in microgrids to balance power, smooth out load, reduce power exchange with the main grid in the grid-connected mode, and ensure successful …

… both wind energy and solar irradiance changes in combination with load power variations. … ahead DER scheduling. A stochastic programming approach for reactive power scheduling of …

The original load control model of microgrid based on demand response lacks the factors of incentive demand response, the overall satisfaction of users is low, the degree of demand response is low, the Time Of Use (TOU) price of peak-valley filling capacity is weak, and the peak-valley difference of load curve is large. Regarding the limitations of the current microgrid demand response model, this study further optimizes the flexible load control strategy and proposes a two-objective optimization model based on price and incentive. Meanwhile, the model is solved using an improved chaotic particle group algorithm. Finally, the microgrid load data were selected for simulation analysis. The simulation results showed that the comprehensive demand response of flexible control model proposed increased the overall satisfaction of users by 9.51%, the overall operating cost of microgrid suppliers decreased by 12.975/ten thousand yuan, the peak valley difference decreased by 4.61%, and the user demand response increased by 27.24%. The model effectively improves the overall profit of the supply side of the microgrid, improves the user satisfaction, and maximizes the linkage benefits of the supply and demand of the micro grid. In addition, the model effectively reduces the phenomenon of distributed power supply in the microgrid, and realizes the supply and demand matching of the whole load in the microgrid.

Abstract Demand Side Management (DSM) is one of the ways to create interaction between the MicroGrids (MGs) and increase consumer participation in management schemes. Different algorithms and strategies have been used to execute consumption management programs which often cover a limited number of loads in several specific types. In this paper, first, the load shift method, as an optimization problem to reduce system demand peak and subscriber’s bills for various loads in smart MGs, is solved by Hybrid Particle Swarm Optimization algorithm with Sinusoidal and Cosine Acceleration Coefficient (H-PSO-SCAC). Then the study is aimed at measuring the effect of the proposed program on the generation and presence of MGs in the market for improving the level of social welfare. The results are performed on a Smart Grid (SG) consisting of three residential, commercial and industrial MGs which include different types of controllable loads. The results show that the highest percentages of peak load reduction after the implementation of the DSM program by (H-PSO-SCAC) algorithm for the three MGs are 23%, 19% and 19%, respectively. Also, the highest percentages of reduction in subscriber’s bill for the three MGs are 16.8%, 19.2% and 20.5%, respectively. The proposed algorithm has performed much better in reducing bills and peak loads than most other methods such as Logarithmic Function (LF), Multi Agent (MA), Evolutionary Algorithm (EA), and Symbiotic Organisms Search (SOS). The findings show that the proposed program can reduce peak load, reduce subscriber’s bills, save production costs, help balance the supply and demand, and improve the level of social welfare from the perspective of the distribution system operator.

… of a microgrid. The constraints considered in this study are as follows: i) Reserve for variation in load demand ii) Reserve for variation in the power outputs of non-dispatchable DGs …

… the load of the macrogrid under five different power scheduling schemes. The original load (OL) is based on the SCE trace of a 1-day period in September, 2011. The online power …

… a scheduling framework to optimally operate the microgrid by taking the three aspects into account. This framework allows microgrids … to satisfy the power and heat load in the microgrid. …

… The dump load power rating is defined to be equal to the maximum rated output of the wind farm so it is able to handle all possible dumped energy. In the present case, wind generation …

… His areas of interest include power system load flow, optimal power flow, economic load dispatch, and soft computing/evolutionary computation applications to different power system …

Abstract The integration of renewable energy sources together with an energy storage system into a distribution network has become essential not only to maintain continuous electricity supply but also to minimise electricity costs. The operational costs of this paradigm depend highly upon the optimal use of battery energy. This paper proposes day-ahead scheduling of the battery energy while considering its degradation costs due to charging-discharging cycles. The degradation costs with respect to the depth of charge are modelled and added to the objective function to determine the actual operational costs of the system. A framework to solve the function is developed in which particle swarm optimisation, the Rainflow algorithm and scenario techniques are integrated. Uncertainties of parameters, modelled by scenario generation and reduced by scenario reduction techniques, are discussed. Simulation results demonstrate that the proposed method can reduce the operational costs by around 40% compared to the baseline method. They also reveal that uncertainty in power generation and power demand has no influence on the energy schedule of the battery, but variation in electricity prices has an impact on the outcome. Several pragmatic tests verify the effectiveness of the proposed method.

… As future study, it is suggested to look into issues such as coordination and scheduling of several microgrids, their power quality along with their participation in ancillary service markets…

… Reference [24] established a virtual real-time TOU electricity price optimization model … swarm optimization algorithm to optimize the user electricity price and minimize the total electricity …

… optimization scheme to minimize the electricity price with a framework for optimal trading of energy between sellers and buyers of the microgrid … from the electricity allocation model. Then…

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method—Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models—exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)—are developed, each reflecting a different price elasticity of demand. These models are integrated with a flexible elasticity matrix to assess the dynamic consumer response to fluctuating electricity prices. The study evaluates four operational scenarios, focusing on grid participation, DER utilization, and the impact of real-time pricing (RTP), time of use (TOU), and critical peak pricing strategies. Quantitative results demonstrate the significant cost-saving potential of integrating DRPs with microgrid operations. In the optimal scenario, the GRSO achieved a minimum generation cost of 882¥ for the base load profile. Further, when critical peak pricing (CPP) was applied, the generation cost was reduced to 746¥, representing a 15.4% reduction. For a scenario where the grid’s participation was limited, the logarithmic-based demand response model decreased the generation cost to 817¥, while full grid interaction led to higher cost reductions. Additionally, our results show a significant reduction in peak load, with load factor improvements of up to 87.7% across the studied demand profiles. Furthermore, limiting the grid’s upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in terms of both execution time and convergence, making it a viable solution for real-time microgrid optimization. In conclusion, the proposed GRSO-based framework provides an efficient approach for microgrid cost minimization, achieving up to a 15.4% reduction in operational costs and notable environmental benefits by reducing emissions. This study highlights the importance of dynamic demand response strategies and grid participation for sustainable and cost-effective microgrid management.

… formation optimization scheme based on the Rotary Power Flow Controller (RPFC), and an electricity price incentive strategy for integrating distributed resources for microgrid clusters. …

Abstract With the development of scattered energy resources in the rural areas of Sarawak (Malaysia), various operational problems due to the unplanned installation of autonomous microgrids become gradually remarkable. To address this concern, the paper proposes an optimal strategy to evaluate the performance of different hybrid microgrid configurations for the Long San Village in Sarawak. A mathematical model is presented for sizing the component of the system to meet the maximum load demand under changing weather conditions and at the lowest possible cost. The developed approach simulates different microgrid models using deterministic and stochastic optimization methods to find the exact dynamic energy price of the selected optimal configuration in the context of system uncertainties. Furthermore, the operational feasibility of the system in terms of reliability and voltage security is studied in addition to economic feasibility with a comparative analysis of the environmental impact. The results show that the optimal configuration with the lowest cost of energy and net present cost can be achieved if the installed solar PV is less than 61 kW with 85 kW h of energy storage and 11 kW of hydro generation, where such system has 55,725 (kg/year) Carbon Dioxide and 330 (kg/year) Nitrogen Oxides. The findings also indicate that the dynamic energy pricing increases to 0.71 $/kWh when the power generation from renewable resources drops to zero. Further, the dynamic analysis shows that in order to reduce the voltage drop during disturbances, it is crucial to carefully install the sources in the buses connected to high energy demand.

Abstract Eco-friendly technologies regarding electricity production are urgent need of sustainable energy development. The renewable energy resources (RERs) have brought green revolution in mitigation of greenhouse gaseous emission resulted from traditional energy resources (TERs). Moreover, the effective utilization of these resources is influenced by pricing schemes which have limitations. Therefore, this paper aims at optimization modeling for dynamic price-based demand response (DR) which includes flexible and inflexible loads along with the effective utilization of RERs i.e. photovoltaics (PVs) and wind turbines (WTs) in a microgrid (MG). The optimization problem regarding profit maximization for loads (flexible and inflexible) is solved via particle swarm optimization (PSO). Two cases are used to evaluate the performance of proposed dynamic pricing scheme. The simulation results have shown that proposed scheme is suitable in term of profit and comfort for flexible and inflexible loads as compared to fixed pricing scheme in both cases. In addition, the dynamic pricing scheme is exemplified as plug and play devices because of its easy implementation in present market structure without any modification.

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

… microgrid based on pure stochastic optimization. The microgrid operator performs the stochastic optimization … and responsive loads, purchasing and selling electricity in the day-ahead …

… This paper aims to minimize the generation cost of a low voltage (LV) grid-connected microgrid system using a novel hybrid whale optimization algorithm (WOA)- Sine cosine algorithm (…

… optimization problem to minimize the total electricity … electricity cost minimization problem is considered with a renewable-based DER and bidirectional power transactions in a microgrid …

The energy market is gradually changing from centralized trading to peer-to-peer trading due to the tremendous increase in a microgrid with green energy resources. When more generating units are included in the microgrid, the possibilities of more reactive power flows exist in the system that leads to high transmission loss which has to be optimized. The reactive power is one of the essential ancillary services in the microgrid towards preserving the voltage in the transmission and distribution line. The major contribution of the paper is towards managing the ancillary service in the distributed energy network economically and technically. This study aims to estimate and optimize the power loss, reactive power, and price management as well. Towards optimization, the self-balanced differential evolution algorithm (SBDE) is used in this study. A distribution system operator is involved in coordinating the sellers and buyers. The proposed layered microgrid architecture uses the blockchain technology for reactive power price management by providing transparency and security among peers. The process of converging various transactions into a block and adding in the distributed blockchain is illustrated. Multiple transactions are performed by using the proposed methodology, giving efficient energy transaction. The results show that the power loss is minimized using SBDE algorithm for different cases. Additionally, the study has demonstrated the price allocation of the optimal reactive power obtained from providers. The blockchain technology embedded in reactive power pricing will play a significant role in the evolution of traditional power distribution systems to active distribution networks.

… The proposed method is applied to a particular case of a building with integrated microgrid, … and volatile electricity pricing from the electricity distribution grid. The microgrid level MPC …

In recent years, electric vehicles (EVs) have drawn great attention due to their potential of mitigating pollution emission. Household plug-in electric vehicles (PEVs) are key devices discussed in this article, and their potentiality as mobile energy storage is brought into fully consideration. This article presents a price optimization method for the microgrid economic operation, considering across-time-and-space energy transmission of EVs in microgrids. The object of the proposed price optimization method is to minimize the total cost of microgrid economic operation. Different study cases are investigated to analyze the impact of charging/discharging prices on the microgrid economic operation. The results show that the proposed price optimization method can reduce total cost of the microgrid system as well as provide some benefits for PEV owners. Specially, the optimization method is able to be applied to the pricing of EV charging and discharging in real systems.

… In this paper inaccuracy of electricity price forecasting has been addressed using a fuzzy … To simplify, in this work we have considered only uncertainty in power price forecasting. …

Abstract Using renewable distributed generators as photovoltaic cell and wind turbine in microgrid includes green and free energy exploitation. However, uncertainty of generated energy from these resources may underestimate energy planning for load demands. The uncertainty may be from market price and load demand in addition to the renewable distributed generators. To overcome the uncertainty challenge, energy storage system and demand response program with cooperation of users on demand side are applied as solutions to plan the energy flow in microgrid to guarantee the voltage stability and essential load supporting. In this paper, price-based demand response for industrial, commercial and house loads are considered in which the effect of demand response on the cost reduction is analyzed and discussed. Electrical and heat demand are considered in the microgrid while uncertainty of renewable distributed generators, market price and load demand are modeled and estimated by point estimation method. Simulation results with three scenarios are performed with and without price-based demand response program and results are compared. As shown in simulation results, demand response has highly reduced total cost (22–28% related to the case without that) where voltage dip (maximum 1.5%) and power deviation (maximum 1.33%) are also improved in the microgird.

Economic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.

There is a rapid increase in the utilization of renewable sources such as solar and wind to provide power and electricity. The reason for this trend is to reduce costs and preserve the environment. However, the challenge is to efficiently use and store the energy from these sources. One approach is to optimize the decision when to charge or discharge a battery. The objective is to generate the greatest monetary gain. More charge/discharge cycles will reduce the life of a battery, thereby increasing the cost. A strategy to reduce the number of charge cycles while maintaining the effectiveness of electricity distribution from battery storage will improve battery life. With the inevitable proliferation of electric vehicles (EVs) in the market, strategies specific to electric vehicle battery profitability will be explored. An additional concern which threatens the financial feasibility of battery energy storage systems (BESSs) is the requirement of secure operation. There is currently little research study on strategies to detect cyberattacks on such systems. In this paper, we have presented a novel taxonomy for battery optimization, survey representative BESS utilization strategies, and classify these schemes within the taxonomy. Within our classification, we outline the battery optimization methods that have been discussed, analyze their ability to address issues that arise when implementing a BESS, and describe alternative research that could be explored. Future research could refer to this information to create unique battery optimization schemes to provide more efficiency and optimal revenue for a BESS when compared to current strategies.

Clean and renewable energy is developing to realize the sustainable utilization of energy and the harmonious development of the economy and society. Microgrids are a key technique for applying clean and renewable energy. The operation optimization of microgrids has become an important research field. This paper reviews the developments in the operation optimization of microgrids. We first summarize the system structure and provide a typical system structure, which includes an energy generation system, an energy distribution system, an energy storage system and energy end users. Then, we summarize the optimization framework for microgrid operation, which contains the optimization objective, decision variables and constraints. Next, we systematically review the optimization algorithms for microgrid operations, of which genetic algorithms and simulated annealing algorithms are the most commonly used. Lastly, a literature bibliometric analysis is provided; the results show that the operation optimization of microgrids has received increasing attention in recent years, and developing countries have shown more interest in this field than developed countries have. Finally, we highlight future research challenges for the optimization of the operation of microgrids.

In response to the growing demand for sustainable and efficient energy management, this paper introduces an innovative approach aimed at enhancing grid-connected multi-microgrid systems. The study proposes a strategy that involves the leasing of shared energy storage (SES) to establish a collaborative micro-grid coalition (MGCO), enabling active participation in the dispatching operations of active distribution networks (ADNs). A hierarchical framework wherein the ADN assumes the role of the central orchestrator is developed, while the MGCO and SES operators (SESO) function as responsive entities within a single-leader multi-follower game optimization model. The ADN leader implements time-of-use (TOU) pricing mechanisms as a strategic incentive to encourage active engagement from the MGCO and SESO followers in peak regulation dispatch activities. In response to these TOU policies, the followers implement a two-stage dispatch optimization strategy. The first stage is dedicated to optimizing energy storage utilization. Within this phase, each microgrid meticulously fine-tunes its energy storage charging and discharging strategies with the primary objectives of mitigating power fluctuations, reducing load variance, minimizing energy storage costs, and facilitating on-demand leasing of energy storage resources. Subsequently, energy storage resources are pooled and shared to harness collective benefits and enhance alliance-wide energy utilization. Emphasis is placed on prioritizing the integration of renewable energy sources, promptly responding to peak demand regulation and scheduling, maximizing overall benefits, and equitably distributing cooperative advantages among alliance members. To substantiate the effectiveness of this comprehensive methodology, the paper presents illustrative results that provide compelling evidence of its potential to facilitate the seamless integration of microgrids, shared energy storage, and active distribution networks.

The increasing share of decentralized intermittent renewable energy reinforces the necessity of balancing local production and consumption. Decentralized energy systems, powered by renewable energy technologies and incorporating storage and conversion technologies, are promising options to cope with this challenge. Many studies have evaluated their potential contributions, but an overview of the status‐quo in both academia and practice is missing. The extant literature lacks a comprehensive review of the scientific knowledge on decentralized energy systems, partially attributed to the lack of common terminology. Additionally, it remains unclear what kind of systems are already implemented today worldwide as they have not yet been thoroughly analyzed and described. However, pilot projects provide valuable insights into future applications and operational aspects. To fill these gaps, an extensive review of the current state of literature and practice is conducted. To do so, 64 publications and 56 projects were analyzed and an overview is provided using four criteria: terminology, scope/motivation, application, and technical configuration. These criteria facilitate the understanding of decentralized energy systems needed to spur their development and diffusion. Further advancements of research and practice are discussed. For example, technological learning hinges on a common terminology and on an identification of optimal technical configurations per application. There are both avenues for future research.

… The development of local energy communities and collective self-consumption framework at … How energy sharing can be encouraged in a distributed way for a local energy community ? …

Renewable Energy Communities (REC) can play a crucial role in enhancing citizen participation in the energy transition. Current European Union legislation enshrines energy communities and mandates Member States to encourage these organizations, promoting adequate conditions for their establishment. Nevertheless, uptake has been slow, and more research is needed to optimize the associated energy sharing. Using a Portuguese case study (REC Telheiras, Lisbon), this research aims to match local generation through four photovoltaic systems (totalizing 156.5 kWp of installed capacity) with household electricity consumption while cross evaluating the Portuguese legislation for energy sharing. The latter aim compares two scenarios: (a) current legislation (generated energy must be locally self-consumed before shared) and (b) equal share for members with a fixed coefficient. The evaluation is performed according to two indexes of self-consumption (SCI) and self-sufficiency (SSI), related to the simulation of four photovoltaic systems in public buildings, their associated consumption profiles, and an average household consumption profile of community members. The results show that, while maximizing self-consumption for the same values of generation and consumption, the number of participants is considerably lower for Scenario A (SCI = 100% is achieved with at least 491 residential members in Scenario A and 583 in Scenario B), implying that legislative changes enabling energy communities to better tailor sharing schemes may be necessary for them to become more attractive. The methods and results of this research can also be applied to other types of facilities, e.g., industrial and commercial consumers, if they are members of a REC and have smart meters in their installations.

Abstract The power sector is undergoing a fundamental transition from centralized fossil-fuelled to distributed production systems. Usually, the decentralization of the energy distribution is treated by considering the interactions between prosumers and main grid, neglecting to include the rising chance for prosumers to directly exchange the produced energy, thus bypassing the grid. In this direction, this paper proposes a prosumer-centred dynamical model able to take the energy interactions among prosumers into account, along with the usual interactions established with the grid. The modelling approach takes inspiration from the methodological framework of agent-based models, particularly appropriate to deal with problems characterized by a significant number of interactions among the parts. Specifically, the developed model has been formulated with the main scope of investigating to what extent energy exchanges among prosumers reduce the supply from traditional plants. The electricity demand and production of each prosumer, the distance among buildings, operational and technological features (usage of the links, installed capacity of the energy production systems have been included in the analysis. Hourly-based simulations run on NetLogo performing a real urban area in Southern Italy as a case study for two scenarios evaluating the distribution from photovoltaic panels and small-scale cogeneration units. In addition, an a posteriori environmental analysis to evaluate the reduction of carbon emissions due to the distribution among prosumers has been conducted. Results reveal that short-range interactions among prosumers are preferred when planning to reduce the electricity supply from the main grid. In addition, the spatial configuration of the buildings within the area as well as the capacity of the installed energy production systems significantly affect the distribution. Finally, simulations highlight the noticeable impact of seasonality on both the distribution and the emissions’ reduction.

… storage systems desired to increase the self-consumption efficiency. … energy storage systems management that considers local renewable generation, local demand, and retailer energy …

As photovoltaic technologies are being promoted throughout the country, the widespread installation of distributed photovoltaic systems in rural areas in rural regions compromises the safety and stability of the distribution network. Distributed photovoltaic clusters can be configured with energy storage to increase photovoltaic local consumption and mitigate the impact of grid-connected photovoltaic modes. Against this background, this paper focuses on rural areas, combines typical operation modes of distributed photovoltaic clusters, and constructs the two-stage energy storage optimization configuration model for rural distributed photovoltaic clusters. Taking a Chinese village as an example, the proposed model is optimized with an improved particle swarm optimization algorithm. Given different combinations with and without energy storage and demand response, comparative analyses are conducted on photovoltaic local consumption and the economic benefits of independent operators in various scenarios. Simulations indicate that the photovoltaic local consumption proportion of distributed photovoltaic clusters with energy storage reaches 62.64%, which is 34.02% more than the scenario without energy storage. The results indicate that configuring energy storage for rural distributed photovoltaic clusters significantly improves the photovoltaic local consumption level. Meanwhile, implementing demand response can achieve the same photovoltaic local consumption effect while reducing the energy storage configuration, and the life-cycle economic benefits are appreciable. The simulation results show that participating in demand response can reduce the energy storage system cost by 7.15% at a photovoltaic local consumption proportion of 60%. This research expands application channels of rural distributed photovoltaic clusters and provides references for investment and operation decisions of distributed photovoltaic energy storage systems.

Large-scale renewable energy sources (RESs) have been integrated into the active distribution network (ADN). For promoting the local consumption of RESs within ADN, an optimal dispatching strategy was proposed with two-stage hierarchical energy management framework. On the spatial boundary, a two-layer energy management framework was designed with the local optimization layer and the global optimization layer. The local optimization layer was for optimal power flow in the branch feeder with the objective functions of minimizing operation costs and maximizing the consumption of RESs. The global optimization layer was for optimal power flow in the main feeder with the objective functions of minimizing power loss and the voltage deviation of nodes. On the time scale, two-stage optimal dispatching models were established, including the day-ahead optimal models and intra-day optimal models. The day-ahead optimal models identified the operation status of the controllable units, and then the intra-day optimal models were updated with the ultra-short-term forecast results. A risk indicator was introduced to quantify the uncertainty of RES, and a non-dominated sorting genetic algorithm with elite strategy was adopted to solve the multi-objective nonlinear programming problem. An actual project in northern China was used as the testing system. The results of case studies verify that the proposed strategy can effectively realize the maximum local consumption of RESs and support the economic operation of ADN.

Distributed generation and storage of energy, conceived as one of the prominent applications of the Smart Grid, has become one of the most popular ways for generation and usage of electricity. Not only does it offer environmental advantages and a more decentralized way to produce energy, but it also enables former consumers to become producers (thus turning them into prosumers). Alternatively, regular power production and consumption is still widely used in most of the world. Unfortunately, accurate business models representations and descriptive use cases for small scale facilitates, either involved in distributed energy or not, have not been provided in a descriptive enough manner. What is more, the possibilities that electricity trade and its storage and consumption activities offer for small users to obtain profits are yet to be addressed and offered to the research community in a thorough manner, so that small consumers will use them to their advantage. This paper puts forward a study on four different business models for small scale facilities and offers an economical study on how they can be deployed as a way to offer profitability for end users and new companies, while at the same time showing the required technological background to have them implemented.

… to change their consumption pattern from the times of high-energy prices to other times for … plant in distribution networks. Lee [10] explored the mechanism of distributed energy trading …

… From the technological point of view, distributed energy generation means local energy … in the design of an energy system, because the energy consumption varies from one year to …

… This paper discusses the design of a restructured electric distribution network that employs a large number of small distributed energy resources (DER) units, which can improve the …

… of new energy absorption … energy consumption. Firstly, a wind–solar diesel-storage microgrid model is established by combining data on wind energy, solar energy resources, and local …

In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO2 emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from economic and environmental perspectives and exploit a mixed integer linear programming technique to solve it. We validated the proposed approach through extensive experimental analysis. The results of the experiment show that the proposed approach can decrease both CO2 emissions and the daily energy cost.

… distributed energy control algorithm and provide a sufficient convergence condition of the algorithm. The energy … which should be obtained by each consumer with local information. The …

… distributed energy system is an important strategic choice for all countries to develop the efficient consumption technology of abundant renewable energy. … and supply energy locally …

The distributed energy resources (DER) comprise several technologies, such as diesel engines, micro turbines, fuel cells, photovoltaic, small wind turbines, etc. The coordinated …

… connected to the power system distribution network where it is consumed by the end users. … DG takes place on two-levels: the local level and the end-point level. Local level power …

… The aim of this paper is to solve the optimal power dispatch of MMGs at the presence of SSERs such as WT, PV, FC, and CHP for a given hours. Because of the intermittent behavior of …

… In this paper, a consensus algorithm-based adaptive control for optimal power dispatch was addressed for dc microgrid with DGs. The control was fully distributed such that each …

… proposes an optimal economic dispatch of a grid connected microgrid. The microgrid consists … We thus denote P r t as the transferable power between the microgrid and the main grid at …

… In this paper, an off-line optimal power dispatching problem is introduced with the aim of minimizing global energy cost, considering the forecasts for consumption and production and …

Abstract This paper evaluates the design and optimization of an islanded hybrid microgrid for various load dispatch strategies by assessing the optimal sizing of each component, the power system responses and different cost analysis of the microgrid. Four divisions of the northern side of Bangladesh namely, Mymensingh, Rangpur, Rajshahi and Sylhet hybrid microgrids incorporating solar PV, wind turbine, battery storage, diesel generator and a load of 27.31 kW have been optimized for five different dispatch strategies: (i) Load Following, (ii) Cycle Charging, (iii) Generator Order, (iv) Combined Dispatch and (v) HOMER predictive dispatch strategy. The proposed microgrids have been optimized to reduce the Net Present Cost, CO2 emission and Levelized Cost of Energy. All the five dispatch strategies for the four microgrids have been analyzed in HOMER Pro, and subsequently, the power system responses and feasibility analysis of the microgrids have been performed in MATLAB Simulink. The results obtained in this study provide a guideline to estimate component sizes and costs for the optimal operation of the proposed microgrids under various load dispatch scenarios. The simulation results suggest that the Load Following is the best dispatch strategy having the lowest Net Present Cost of 149,794 USD, Levelized Cost of Energy of 0.204 USD/kWh, Operating cost of 3,698 USD and CO2 emission of 3,298 kg/year with a stable power system response. Combined Dispatch is found to be the worst strategy having the maximum Levelized Cost of Energy of 0.532 USD/kWh, Net Present Cost of 415,030 USD, Operating cost of 15,394 USD and Green House Gas emissions of 17,266 kg/year and comparatively poor power system responses. Finally, a brief comparative analysis has been presented between designed microgrid system and other hybrid energy systems and conventional power stations in terms of Levelized Cost of Energy, Net Present Cost, CO2 emissions and operating cost.

Coupling dc microgrids in close vicinity can minimize the total operating cost by full utilization of the generated power of the power systems. A fully distributed, consensus-based, and resilient hierarchical control scheme is proposed in this article to optimize the total generation cost of multiple dc microgrids by equalizing incremental costs of all distributed generators (DGs). The proposed scheme uses a sparse communication network in a fully distributed manner, which operates based on the neighbor-to-neighbor output feedback without requiring a central controller. In the proposed control scheme, optimal power sharing among DGs within a dc microgrid is met via the secondary controller. Additionally, the elimination of voltage deviation is accomplished in the secondary level without any influence on the optimal power sharing. The tertiary controller is responsible to realize optimal power sharing among dc microgrids. Simulation results illustrate the capability of the proposed strategy in equalizing incremental costs, not only among all DGs within a dc microgrid, but also among all dc microgrids. The performance of the proposed control scheme is evaluated by simulating multiple dc microgrids considering intermittent renewable DGs in PLECS software environment.

An AC-DC hybrid microgrid is gradually becoming popular. For economic viability and environmental sustainability, an AC-DC microgrid should be operated optimally. This study introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC-DC hybrid microgrid under load and generation uncertainties. The operational attributes of an AC-DC hybrid microgrid, and load and renewable generation uncertainties are incorporated in the optimal scheduling problem by using a customised power-flow technique, and by modelling uncertainties by Hong's two-point estimate method, respectively. The economic and environmental objectives are modelled in the fuzzy domain by fuzzy membership functions. A combination of particle swarm optimisation and fuzzy max-min technique is then employed for obtaining the optimal solution. The static active power droop constants of the dispatchable units are the control variables. Simulation results on a 6-bus AC-DC hybrid microgrid system demonstrate that optimal scheduling results in 4.26% reduction of operating cost and 13.91% reduction of emission in comparison to capacity based droop settings. Further, a comparison between the proposed method and the elitist multi-objective GA indicates that the optimised solution lies on the Pareto-front, thereby validating the proposed technique.

… In this paper, a new concept of end-user-driven microgrid is introduced, and an optimal power scheduling approach for the battery dispatch in the microgrid is illustrated. The main contri…

In recent years, microgrid (MG) deployment has significantly increased, utilizing various technologies. MGs are essential for integrating distributed generation into electric power systems. These systems’ economic dispatch (ED) aims to minimize generation costs within a specific time interval while meeting power generation constraints. By employing ED in electric MGs, the utilization of distributed energy resources becomes more flexible, enhancing energy system efficiency. Additionally, it enables the anticipation and proper utilization of operational limitations and encourages the active involvement of prosumers in the electricity market. However, implementing controllers and algorithms for optimizing ED requires the independent handling of constraints. Numerous algorithms and solutions have been proposed for the ED of MGs. These contributions suggest utilizing techniques such as particle swarm optimization (PSO), mixed-integer linear programming (MILP), CPLEX, and MATLAB. This paper presents an investigation of the use of model predictive control (MPC) as an optimal management tool for MGs. MPC has proven effective in ED by allowing the prediction of environmental or dynamic models within the system. This study aims to review MGs’ management strategies, specifically focusing on MPC techniques. It analyzes how MPC has been applied to optimize ED while considering MGs’ unique characteristics and requirements. This review aims to enhance the understanding of MPC’s role in efficient MG management, guiding future research and applications in this field.

This paper proposes a fully distributed fixed-time control approach fulfilling the economic operation of DC microgrids (MGs) and considering time delays. A distributed cost optimizer is developed for maintaining MG’s economical operation through equalizing DGs’ incremental costs (ICs) while addressing DGs’ capacity limits within a fixed settling time unrelated to the initial values. Besides, a fixed-time voltage regulator is presented to restore MG’s average voltage for preserving generations-demands power balance. For further improvement of the system stability to oppose time delays, Artstein’s reduction technique is employed for transforming the delayed system to a delay-free one. Accordingly, the proposed controller has an improved dynamic performance and lower integral squared error (ISE) than the existing fixed-time controller, especially with high time delays. Comprehensive convergence analysis confirms that the proposed controller is fixed-time stable regardless of the initial conditions. Extensive simulation case studies are carried out to verify the superiority of the developed controller.

To effectively manage isolated DC microgrids (MGs), a control system with adaptable response time for handling the dynamic nature of renewables and changing load demands is essential. This paper introduces a novel distributed predefined time (PDT) control, which is developed for optimizing the power dispatch in islanded DC MGs. The proposed strategy incorporates a distributed PDT cost optimizer, aimed at maintaining the MGs' economical operation through synchronizing the incremental costs (ICs) of all distributed generators (DGs) concerning their capacity constraints within a prescribed convergence time. Furthermore, a PDT voltage regulator is presented to ensure MG's average voltage stability, balancing the power supply and demand within a predetermined timeframe. A notable advantage of the developed control system is its capability for modulating the convergence time utilizing a predefined parameter, irrespective of the initial states. A thorough stability analysis is introduced, confirming the controller's stability within a predefined time. Extensive simulations and experimental case studies have been presented, demonstrating the controller's effectiveness in a variety of scenarios.

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

The expansion of electric microgrids has led to the incorporation of new elements and technologies into the power grids, carrying power management challenges and the need of a well-designed control architecture to provide efficient and economic access to electricity. This paper presents the development of a flexible hourly day-ahead power dispatch architecture for distributed energy resources in microgrids, with cost-based or demand-based operation, built up in a multi-class Python environment with SQLExpress and InfluxDB databases storing the dispatcher and microgrid data, and its experimental implementation using Modbus communication. The experimental power dispatch architecture is described and each operation stage is detailed, including the considered mathematical models of the energy resources, the database management, the linear-programming optimization of power dispatch, and the Modbus setpoint writing. Validation studies of the proposed control system are presented for real-time digital-simulated devices and physical resources as a real application at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The simulated and physical microgrid characteristics are described and the hourly dispatch results for generation, storage and load devices are presented, standing out as a reliable power management architecture for economic commitment and load peak shaving in simulated and real microgrids. The proposed architecture demonstrates its readiness for present and future electrical system challenges, effectively incorporating meteorological variations, renewable energy sources, and power demand fluctuations into the control framework, with a strong dependence on the quality of the meteorological forecast.

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control (MPC) is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

This paper presents a novel model of voltage source converter (VSC) based energy routers (ERs) in microgrids. The proposed model explicitly describes the circuit laws within the ERs and captures the coupling of control variables among the AC/DC, DC/DC, DC/AC links. Therefore, the proposed model possesses unparalleled control capabilities in the operational parameters of both the AC and DC sides of ERs. By incorporating such a model into the AC-DC optimal power flow formulation, we not only optimize the power flows between the AC and DC sides of ERs, but also strategically schedule the flexible resources. To solve the ERs based AC-DC optimal power flow (ER-OPF) in a distributed fashion, we propose a hierarchical distributed optimization method that decouples the power grids into a master region and several sub-regions. In the master region, we propose a sequence of strong relaxations to transform the problem into a semidefinite program (SDP). We further show that this SDP relaxation is exact. For the sub-regions, we utilize sequential quadratic programming (SQP) to solve multiple small-scale subproblems with non-convex constraints in parallel. Case studies on the IEEE benchmark system show that fuel costs and power losses are reduced dramatically by utilizing ERs in microgrids. In addition, the proposed distributed algorithm can converge faster than alternating direction method of multipliers (ADMM).

… resulting in optimal power sharing. … , the optimal power dispatch will be reached, as Theorem 3 states. On the other hand, if the network is not fully connected, the optimal power dispatch …

The power system responsiveness may be improved by determining the ideal size of each component and performing a reliability analysis. This study evaluated the design and optimization of an islanded hybrid microgrid system with multiple dispatch algorithms. As the penetration of renewable power increases in microgrids, the importance and influence of efficient design and operation of islanded hybrid microgrids grow. The Kangaroo Island in South Australia served as the study’s test microgrid. The sizing of the Kangaroo Island hybrid microgrid system, which includes solar PV, wind, a diesel engine, and battery storage, was adjusted for four dispatch schemes. In this study, the following dispatch strategies were used: (i) load following, (ii) cycle charging, (iii) generator order, and (iv) combination dispatch. The CO2 emissions, net present cost (NPC), and energy cost of the islanded microgrid were all optimized (COE). The HOMER microgrid software platform was used to build all four dispatch algorithms, and DIgSILENT PowerFactory was used to analyze the power system’s responsiveness and dependability. The findings give a framework for estimating the generation mix and required resources for an islanded microgrid’s optimal functioning under various dispatch scenarios. According to the simulation results, load following is the optimum dispatch technique for an islanded hybrid microgrid that achieves the lowest cost of energy (COE) and net present cost (NPC).

The economic power-dispatching model of a multi-microgrid is comprehensively established in this paper, considering many factors, such as generation cost, discharge cost, power-purchase cost, power sales revenue, and environmental cost. To construct this model, power interactions between the two microgrids and those between the micro- and main grids are considered. Furthermore, the particle swarm optimization (PSO) algorithm is utilized to solve the economic power-dispatching model. To validate the effectiveness of the proposed model as well as the solution algorithm, a practical project case is studied and discussed. In the case study, the impact of multiple scenarios is first analyzed. Then, the system operation economic costs under different scenarios are described in detail. Moreover, according to the optimization power-dispatching results of the multi-microgrid, power interactions between the two microgrids and those between the micro- and main grids are fully discussed.

Due to the opening of the energy market and agreements for the reduction of pollution emissions, the use of microgrids attracts more attention in the scientific community, but the management of the distribution of electricity has new challenges. This paper considers different distributed generation systems as a main part to design a microgrid and the resources management is defined in a period through proposed dynamic economic dispatch approach. The inputs are obtained by the model predictive control algorithm considering variations of both pattern of consumption and generation systems capacity, including conventional and renewable energy sources. Furthermore, the proposed approach considers a benefits program to customers involving a demand restriction and the costs of regeneration of the pollutants produced by conventional generation systems. The dispatch strategy through a mathematical programming approach seeks to reduce to the minimum the fuel cost of conventional generators, the energy transactions, the regeneration of polluted emissions and, finally, includes the benefit in electricity demand reduction satisfying all restrictions through mathematical programming strategy. The model is implemented in LINGO 17.0 software (Lindo Systems, 1415 North Dayton Street, Chicago, IL, USA). The results exhibit the proposed approach effectiveness through a study case under different considerations.

Abstract The reliability and resilience of the electrical power grids are essential to industry, economy and society. Microgrids that are able to island from the bulk electrical grid are one technology that may vastly improve electrical power service to customer loads. To achieve these improvements, an islanded microgrid should be able to operate through the loss of one of its generators without shedding electrical load. The loss of one generator will typically result in significant additional loads, including transient overloads, being placed on the remaining generators. There is also the possibility of additional generator tripping during these processes (i.e. cascading failures), which would likely result in the collapse of the microgrid. The novelty of our work consists in incorporating dynamic models of generator controllers into a microgrid optimal dispatch formulation with the ultimate goal to avoid operational failures and ensure the “survivability” of all-inverter microgrids to generator loss and transient overloads. The integration of generator and controller dynamics into the optimal dispatch formulation significantly increases the computational complexity. As we develop algorithms to restore speed of the optimization, our method can be readily implemented into a new operational strategy capable of an unprecedented level of reliability against generator contingencies. In addition, we quantitatively illustrate the effect of the survivability constraints on the microgrid operating costs and how the related trade-off between capital and operating costs should be taken into account at the design stage. The methods developed here also apply to the dispatch of off-grid microgrids.

A dynamic programming algorithm for the optimal charge/discharge scheduling of BESS energy storage is presented. It ensures the minimisation of the electricity bill for a given battery capacity, while reducing stress on the battery and prolonging battery life. Optimal scheduling of the battery charge state is in itself unique; the methods of multipass dynamic programming are used to accomplish this. Maximum payoff for load redistribution and peak load shaving is determined while accounting for charging rate, battery voltage fluctuation and internal losses as a function of charge state. The optimal charging curve is significantly different from the curve conventionally published for BESS.

Integration of renewable energy sources, active role of consumers, and energy management systems is currently among research priorities in energy systems. This paper proposes an innovative coordinated energy scheduling for a microgrid of neighbor prosumers with different consumption patterns. All prosumers have photovoltaic generation systems, Li-ion batteries as energy storage systems, and regular household loads. A genetic algorithm is used to schedule each prosumer's battery charge/discharge, with the aim of reducing energy exchange losses by minimizing the power in the point of interconnection of the microgrid with the main grid, with several advantages compared to classical optimization objectives, and without worsening battery lifespan degradation. Individual and coordinated strategies are compared, and self-consumption and self-sufficiency of the prosumers’ set are evaluated with the aim of showing the advantage of coordination. The paper concludes that coordinated operation can contribute to improve the exploitation of energy resources in the prosumer microgrid, reducing the amount of energy interchanged with the distribution grid by approximately 13%, and, at the same time, avoiding increasing the battery cycling and consequent degradation.

… an optimal charging/discharging scheduling for BSSs … charging procedures such as the commonly-used constant current to constant voltage (CC-CV) charging method, the discharging …

Three phase battery energy storage (BES) installed in the residential low voltage (LV) distribution network can provide functions such as peak shaving and valley filling (i.e. charge when demand is low and discharge when demand is high), load balancing (i.e. charge more from phases with lower loads and discharge more to phases with higher loads) and management of distributed renewable energy generation (i.e. charge when rooftop solar photovoltaics are generating). To accrue and enable these functions an intelligent scheduling system was developed. The scheduling system can reliably schedule the charge and discharge cycles and operate the BES in real time. The scheduling system is composed of three integrated modules: (1) a load forecast system to generate next-day load profile forecasts; (2) a scheduler to derive an initial charge and discharge schedule based on load profile forecasts; and (3) an online control algorithm to mitigate forecast error through continuous schedule adjustments. The scheduling system was applied to an LV distribution network servicing 128 residential customers located in an urban region of South East Queensland, Australia.

… potential to maximize the energy efficiency of a distribution … placement, sizing, charge/discharge scheduling, and control, … contribute to BESS charging and discharging scheduling. We …

… charging/discharging schedules based on load conditions. Finally, an experimental study was conducted based on the actual load data of a certain line in Zunyi, Guizhou, China. The …

This article proposes an optimal charging and discharging schedule for a hybrid photovoltaic-battery system connected in the premises of a residential customer. The scheduling strategy is formulated to minimize the electricity bill of the customer. The proposed scheme uses the data obtained from short-term load, weather, and solar forecasting. A time-of-use tariff scheme is considered to be implemented by the utility. The proposed method is tested on real residential load and solar generation scenario. The test results verify that the implementation of the optimal battery scheduling algorithm can significantly increase the net saving in the electricity bill of the customer.

… discharge scheduling of ESS in a MG. Specifically, we optimize the amount of energy to be discharged … to solve the optimal Discharge Scheduling of Energy Storage Systems Problem (…

… This paper presents a mathematical formulation that allows battery storage systems to take part in the short-term scheduling problem considering an adaptable short-term cost model. A …

… and the net present value (NPV) of the battery storage system. The financial benefits of our … energy dispatch schedule were compared with basic off-peak charging/on-peak discharging …

… of discharge on the degradation. This paper proposes a new formulation of … battery degradation cost for the optimal scheduling of BESSs. To this end, we define (1) a one-cycle battery …

The economic and environmental benefits brought by electric vehicles (EVs) cannot be fully delivered unless these vehicles are fully or partially charged by renewable energy sources (RES) such as photovoltaic system (PVS). Nevertheless, the EV charging management problem of a parking station integrated with RES is challenging due to the uncertain nature of local RES generation. This paper aims to address these difficulties by deploying an energy storage system (ESS) in parking stations and exploiting the charging and discharging scheduling of EVs to achieve better utilization of intermittent PVS for EV charging. A real-time charging optimization scheme is also formulated, using mixed-integer linear programming (MILP) to coordinate the charging or discharging power of EVs along with the power dispatches of power grid and ESS based on the vehicles’ charging or discharging priorities and electricity price preferences. Extensive simulations show that the proposed approach not only maximizes the satisfaction of EV owners in terms of fulfilling all charging and discharging requests, but also minimizes the overall operational cost of the parking station by prioritizing the utilization of energy from PVS, ESS, and scheduling of every EV’s charging and discharging.

… Battery energy storage system (BESS) can be used to shave the peak by discharging during peak periods and charging during … In China, a MW level battery energy storage system …

Microgrid as an important part of smart grid comprises distributed generators (DGs), adjustable loads, energy storage systems (ESSs) and control units. It can be operated either connected with the external system or islanded with the support of ESSs. While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the unbalance between the daily load curve and generation curve. In this paper, a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge (SOC) of EV batteries. The optimization problem is proposed to obtain the economic operation for the microgrid based on this model. In day-ahead scheduling, with the estimated power generation and load demand, the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming. With the optimal charging/discharging scheduling of EVs, the daily load curve can better track the generation curve. The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.

… , capacities, and charge/discharge schedules of the batteries used in the ESU must be … batteries must have sufficient capacities to supply the energy demanded by the charge/discharge …

… A battery storage dispatch strategy that optimizes demand charge reduction in real-time was developed and the discharge of battery storage devices in a grid-connected, combined …

Abstract Thanks to the unique features, deployment of battery energy storage systems in distribution systems is ever-increased. Therefore, new models are needed to capture the real-life characteristics. Beside active power, the battery energy storage system can exchange reactive power with the grid due to the inverter-based connection. Although some previous works have considered this issue, a detailed linear model suitable for the realistic large scale distribution systems is not addressed adequately. In this context, this paper proposes a mixed integer linear programming model for optimal battery energy storage system operation in distribution networks. The proposed model considers various parts of the battery energy storage system including battery pack, inverter, and transformer in addition to linear modeling of the reactive power and apparent power flow limit. Moreover, a linear power flow model is used to calculate voltage magnitudes and power losses with high accuracy. The proposed model is applied to the IEEE 33-bus test case and the results prove the accuracy and efficiency of the proposed model. The results demonstrate that considering reactive capability of the batteries offers new benefits including voltage profile improvement, decreasing reactive power flow in the network, reducing network losses, and releasing network and substation capacity.

… system using a Lagrangian relaxation-based optimization algorithm to determine the hourly charge/discharge commitment of a battery in a utility grid. They used an eight-bus test …

… scheduling problem with a battery as an energy storage system … battery charging and discharging operations are mutually exclusive. Therefore the battery cannot charge and discharge …

… applying a model predictive control approach to the problem of efficiently optimizing microgrid … We present a control-oriented approach to microgrid modeling and high level optimization …

Abstract The development of microgrids is an advantageous option for integrating rapidly growing renewable energies. However, the stochastic nature of renewable energies and variable power demand have created many challenges like unstable voltage/frequency and complicated power management and interaction with the utility grid. Recently, predictive control with its fast transient response and flexibility to accommodate different constraints has presented huge potentials in microgrid applications. This paper provides a comprehensive review of model predictive control (MPC) in individual and interconnected microgrids, including both converter-level and grid-level control strategies applied to three layers of the hierarchical control architecture. This survey shows that MPC is at the beginning of the application in microgrids and that it emerges as a competitive alternative to conventional methods in voltage regulation, frequency control, power flow management and economic operation optimization. Also, some of the most important trends in MPC development have been highlighted and discussed as future perspectives.

… grid, in a deliberate and controlled way. The Model Predictive Control (MPC) approach is applied for achieving economic efficiency in microgrid operation management. The method is …

… electricity within microgrids, eg… microgrids. To overcome these limitations and extend the above frameworks, we propose here to use Model Predictive Control (MPC) for handling control …

Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations.

… over the prediction horizon of 24 h, usually with the sampling period of 15 min, can be computed as the solution of an optimization problem, based on the model predictive control (MPC) …

… Some properties of the microgrid make the dispatching problem difficult. For example, … model predictive control (MPC) is proposed for the power dispatching optimization of the microgrid …

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

Abstract In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.

Abstract Renewable energy sources have been increasingly deployed as distributed generators in remote areas. Meanwhile, fluctuating power generation from renewable energy sources, together with variable power demand, poses challenges in stable and reliable power supply. In this paper, a microgrid with solar photovoltaic (PV) and battery energy storage (BES) is studied. A state of charge (SOC)-oriented charging scheme is developed to control the BES to smooth the PV output. Most importantly, a sophisticated control algorithm, consisting of a model predictive voltage control (MPVC) and a model predictive power control (MPPC), is proposed for the interlinking converter. It enables stable voltage in islanded mode. Also, in grid-connected mode, flexible reactive power can be injected into the main grid for grid support according to the voltage variation level. Finally, by considering the intermittent nature of the PV and the load profile, an energy management system (EMS) is designed to ensure power balance within the system. Case studies are provided to demonstrate the effectiveness of the proposed control strategy.

… can achieve high performance through: (i) advanced control … a control-oriented approach to microgrid modeling and optimization and propose the use of Model Predictive Control (MPC) …

This paper proposes the application of a finite control set model predictive control (FCS-MPC) strategy in standalone ac microgrids (MGs). AC MGs are usually built from two or more voltage source converters (VSCs) which have the capability of regulating the voltage at the point of common coupling, while sharing the load power at the same time. Those functionalities are conventionally achieved by hierarchical linear control loops. However, they present severe limitations in terms of slow transient response and high sensitivity to parameter variations. This paper aims to mitigate these problems by first introducing an improvement of the FCS-MPC strategy for a single VSC that is based on explicit tracking of derivative of the voltage reference trajectory. Using only a single step prediction horizon, the proposed strategy exhibits very low computational expense, but provides steady-state performance comparable to carrier-based sinusoidal PWM, while its transient response and robustness to parameter variation is far superior to hierarchical linear control. These benefits are exploited in a general ac MG setting where a methodology for paralleling multiple FCS-MPC regulated VSCs is described. Such an MG is characterized by rapid transient response, inherent stability in all operating conditions, and fully decentralized operation of individual VSCs. These findings have been validated through comprehensive simulation and experimental verification.

This research seeks to enhance energy management systems (EMS) within a microgrid by focusing on the importance of accurate renewable energy prediction and its strong correlation with load curtailment. Analyzing the precision of disturbance predictions, reveals that predicting one hour in advance is more effective than immediate predictions or those made several hours beforehand. Furthermore, the study investigates scheduling load curtailment to manage peak power from renewable energy sources by comparing two distinct strategies: Case 1, which implements curtailments in both morning and afternoon, and Case 2, which focuses solely on midday curtailment. The findings indicate that Case 1 effectively aligns load management with the peak output of photovoltaic (PV) energy, thereby reducing reliance on grid power and enhancing energy efficiency. In contrast, Case 2’s focus on midday curtailment results in increased energy purchases from the grid, missing the chance to leverage abundant solar energy. A key finding of this research shows that applying Case 1 for curtailment along with accurate forecasting, improves battery coordination and alleviates stress on the supercapacitor, leading to energy purchases from the grid being reduced. This interdependent relationship between precise forecasting and effective load management not only enhances the efficiency of the hybrid energy system (battery and supercapacitor), but also relies more on renewable energy sources and storage systems, thereby lowering overall energy costs, leading to a more reliable and effective energy management system.

Abstract Managing uncertainty is key to enhancing robustness in microgrids. This study focuses on the uncertainties in aggregated electric vehicles (EVs) and establishes a two-layer model predictive control (MPC) strategy for charging EVs with a microgrid. The uncertainty in sampling results of EV connection times to the microgrid and the uncertainty in sampling results of state of charge (SOC) of each EV at the time of the initial connection are both taken into account through multi-uncertainty sampling. In order of consider the impact of extreme scenarios, as feedback from arriving EVs are combined with multi-uncertainty sampling for higher accuracy of sampling results during rolling optimization. Simulation results show that, compared to conventional strategies, the proposed strategy has better performance in reducing forecasting error and regulating the charging and discharging of aggregated EVs with uncertainties.

… STOCHASTIC MODEL PREDICTIVE CONTROL In this section, we propose an algorithm to solve the stochastic model predictive control problem described in the previous section …

In recent times, Microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of Renewable Energy Resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs’. The Model Predictive Control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors. Thus, in this review paper, the superiority of MPC to several techniques used to model uncertainties is presented for both grid-connected and islanded system. It highlights the features, strengths and incompetencies of several modeling methods for MPCs and some of its variants regarding handling of uncertainties in MGs. This survey article will help researchers and model developers to come up with more robust model predictive control algorithms and techniques to cope with the changing nature of modern energy systems, especially with the increasing level of RERs penetration.

Battery energy storage systems (BESSs) have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI) control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC) has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC) and MPC based on PI control in the outer and predictive current control (PCC) in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.