Microgrid Aggregator Research Articles

Port berth allocation and microgrid cluster joint optimization scheduling based on master-slave game

The port microgrid cluster, which integrates berth allocation and energy scheduling for joint optimization, is a highly interconnected system of logistics and energy closely coupled microgrids. It can reduce port load fluctuations and ensure the stable and reliable operation of the port energy system. A port berth allocation and microgrid cluster joint optimization scheduling method based on master-slave game is proposed to address issues such as large fluctuations in current port loads, vulnerability of port power supply system to the impact of vessel loading and unloading operations, and conflicts of interest among various participants in the power market. To ensure power quality while achieving peak shaving and valley filling, a two-stage optimization model for microgrid cluster was constructed, enabling joint optimization of berth allocation and energy scheduling. In the first stage, considering the impact of random events during vessel navigation, berth allocation is conducted with the objectives of maximizing peak shaving and valley filling rates and minimizing overall costs, thereby achieving preliminary load leveling while ensuring power supply system quality. In the second stage, dispatching is performed for power generation equipment and adjustable loads, aiming to further achieve peak shaving and valley filling while maintaining the economic viability of the microgrids. Addressing conflicting interests among stakeholders in the port's electricity market, a master-slave game strategy has been established. This strategy aims to balance the interests between the electricity trading agent and the microgrid aggregator composed of multiple microgrid models. By solving the second-order partial derivatives of each optimization variable with respect to the objective function, we demonstrate the existence and uniqueness of the equilibrium solution in the master-slave game. In response to the challenge of difficulties in solving the multi-objective optimization model due to conflicting objective functions, a Dung Beetle Optimization (DBO) algorithm incorporating non-dominated sorting strategy and crowding distance was introduced. This resulted in a novel multi-objective optimization algorithm termed Non-Dominated Sorting Dung Beetle Optimizer (NSDBO). The NSDBO algorithm was demonstrated to possess favorable characteristics in terms of distribution, convergence, and uniformity. Using a northern Chinese port as an example, berth allocation and energy scheduling for three port areas were jointly optimized, and various scenarios were compared. The results indicated that, compared to Scenario 1, which did not consider random events, the proposed joint optimization method increased the peak shaving and valley filling rate by 8.46 %, reduced network losses by 42 kW, and increased the revenue of the microgrid aggregation merchant by 2.11 %. Compared to Scenario 2, which only considered energy scheduling, the joint optimization increased the peak shaving and valley filling rate by 36.89 %, reduced network losses by 18 kW, and increased the revenue of the microgrid aggregation merchant by 2.29 %. Compared to Scenario 3, which was individually optimized, the joint optimization increased the peak shaving and valley filling rate by 48.42 %, reduced network losses by 24 kW, and increased the revenue of the microgrid aggregation merchant by 2.73 %. This validates the superiority of the proposed joint optimization method in mitigating the impact of random events, reducing network losses, stabilizing port load fluctuations, and enhancing the revenue of the microgrid cluster.

The economics of renewable energy portfolio management in solar based microgrids: A comparative study of smart strategies in the market

In this study, an energy-management approach based on digital twin is developed to manage a microgrid (MG) based on the energy generated by renewable sources of solar and wind, diesel generators, energy storage, and a variety of loads consumers in the market. The MG is assumed to participate in the pool market and schedule its controllable sources accordingly as a means of maximizing profits. This paper proposes a risk-limited scenario-driven stochastic programming strategy that uses the conditional value at risk approach for handling different types of uncertainty. Double stochastic optimization levels are used to solve the proposed scheme. The day-ahead market can be optimized at one level by submitting the best bids at hourly intervals based on forecasts. As a second level of optimization, scenario-driven stochastic information has been used for determining the best schedule. Also, shark optimization algorithm is proposed to solve the optimization problem. Aside from being advantageous for MGs and their users, the suggested energy management system likewise takes advantage of the MG's aggregators and non-real power plants. This model is shown to be valid based on the outcomes and would be beneficial for the market owners.

Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory

In this paper, a bi-level energy management framework based on Conditional Value at Risk (CVaR) and game theory is presented in the context of different ownership of multiple microgrid systems (MMGS) and microgrid aggregators (MAs). The energy interaction between MMGS and MAs can be regarded as a master–slave game, where microgrid aggregators as the leaders set the differentiated tariff for each MG to maximize its benefits, and MMGS as the follower responds to the tariff decision specified by the leader through peer-to-peer (P2P) energy sharing. The P2P energy sharing of MMGS can be regarded as a co-operative game, employing asymmetric Nash bargaining theory to allocate the co-operative surplus. The Conditional Value at Risk model was used to characterize the expected losses by microgrid aggregators due to the uncertainties of renewable energy resources. The Karush–Kuhn–Tucker conditions, Big-M method, and strong duality theory were employed to transform the bi-level nonlinear model of energy management into a single-level mixed integer linear programming model. The simulation results show that when MGs adopt the P2P energy-sharing operation mode, the total operating cost of MMGS can be reduced by 7.82%. The simulation results show that the proposed co-operative optimization framework can make the multiple microgrid systems obtain extra benefits and improve the risk resistance of microgrid aggregators.

A Risk-Averse Energy Sharing Market Game for Renewable Energy Microgrid Aggregators

Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency.

Game Theory-Based Bidding Strategy in the Three-Level Optimal Operation of an Aggregated Microgrid in an Oligopoly Market

This paper proposes a new framework for the optimal operation of a microgrid aggregator (MGA) that participates in an oligopoly electricity market. This aggregator obtains an optimal bidding (power and price) strategy for a multigrid (MG) system, i.e., a community MG. Consequently, the granted quantity, i.e., power in the electricity market, is deployed to optimally schedule the MG’s resources to meet demand. As such, as per three-level optimization, the independent system operator (ISO) clears the market with the goal of maximizing the social welfare in the first stage and determining the hourly market price as well as players’ credited power. In the second-level optimization process, the players select the optimal coefficient supply function equilibrium according to the power granted from the market. In third-level optimization, an optimal scheduling for MGs’ resources and demand would be obtained according to the won power in the market to maximize the aggregator profit. In addition, a price-taker MGA is simulated for comparison with the price–maker MGA to highlight the advantage of the proposed technique. Furthermore, a bidding strategy based on game theory is proposed to obtain the optimal price and power of the oligopoly market players and maximize all players’ profits. Finally, a test system including three generators is created to evaluate the performance of the devised bidding strategy. The results show that the proposed bidding strategy can optimally calculate the focal point of the Nash equilibrium (NE) in the oligopoly electricity market.

A tie-line power smoothing via a novel dynamic real-time pricing mechanism in MMGs

In a Multi-Microgrid (MMG), the microgrids (MGs) are generally scheduled individually. Uncoordinated operations in the MMG may result in tie-line power congestion, especially in peak-load hours. Previous studies have rarely reported how to coordinate MGs operation by implementing the acquired optimal day-ahead scheduling in a competitive environment. To solve this problem, a new entity- called a microgrid aggregator has been presented to manage trading power between the MMG and the main grid. In this paper, a bi-level optimization model is proposed to control the tie-line power. On the lower level, each MG optimizes its energy trading with the main grid individually and sends the result to the upper level. On the upper level, an aggregator checks the total trading power between the MMG and the grid. In case of any violation, the aggregator activates demand response programs through a represented dynamic real-time pricing. Subsequently, a new rescheduling is implemented by the local controller (LC) in each MG to achieve maximum profit. This process is a cooperative method. Thus, the aggregated profit should be fairly distributed among the microgrids. In this paper, sharing profit between MGs is implemented based on the core concept. The performance of the proposed method is assessed by simulating various scenarios in different seasons and days, which demonstrates the effectiveness and robustness of the proposed method.

A novel microgrid support management system based on stochastic mixed-integer linear programming

This paper focuses on a support management system for the management and operation planning of a microgrid by the new electricity market agent, the microgrid aggregator. The aggregator performs the management of microturbines, wind and photovoltaic systems, energy storage, electric vehicles, and usage of energy aiming at having the best participation in the market. Nowadays, the electricity market participation entails making decisions aided by a support and information system, which is an important part of a microgrid support management system. The microgrid support management system developed in this paper has a formulation based on a stochastic mixed-integer linear programming problem that depends on knowledge of the stochastic processes that describe the uncertain parameters. A set of plausible scenarios computed by Kernel Density Estimation sets the characterization of the random variables. But as commonly happen, a scenario reduction is necessary to avoid the need to have significant computational requirements due to the high degree of uncertainty. The scenario reduction carried out is a two-tier procedure, following a K-means clustering technique and a fast backward scenario reduction method.The case studies reveal the performance of the microgrid and validate the methodology basis conceived for the microgrid support management system.

Non-Strategic Capacity Withholding from Distributed Energy Storage within Microgrids Providing Energy and Reserve Services

Microgrids have the potential to provide security and flexibility to power systems through the integration of a wide range of resources, including distributed energy storage, usually in the form of batteries. An aggregation of microgrids can enable the participation of these resources in the main system’s energy and ancillary services market. The traditional minimum-cost operation, however, can undermine microgrid’s ability to hold reserve capacity for operation in islanded mode and can rapidly degrade distributed batteries. This paper studies the impacts of various operational strategies from distributed energy storage plants on their revenues and on market prices, considering an array of microgrids that act in a synchronized fashion. The operational model minimizes the entire electric power system cost, considering transmission-connected and distributed energy resources, and capturing capacity degradation of batteries as part of the cost function. Additionally, microgrid-based, distributed batteries can provide energy arbitrage and both system-level and microgrid-level security services. Through several case studies, we demonstrate the economic impacts of distributed energy storage providing these services, including also capacity degradation. We also demonstrate the benefits of providing reserve services in terms of extra revenue and battery lifespan. Finally, we conclude that limitations in the provision of system-level services from distributed batteries due to degradation considerations and higher microgrid-level security requirements may, counterintuitively, increase system-level revenues for storage owners, if such degradation considerations and microgrid-level security requirements are adopted, at once, by a large number of microgrids, leading to unintended, non-strategic capacity withholding by distributed storage owners.

Profit Maximization of Microgrid Aggregator Under Power Market Environment

The integration of smart microgrid on the large scale in the Indian power market has been highly captivating paradigm since the incorporation of the open access power market under the Indian Electricity Act (IEA) 2003. Efficiency and liquidity of the power market are also rising gradually after the inception of power exchanges (PXs) in 2008. This study presents a comprehensive framework to participate at PXs-based electricity trading platform and to determine the market-clearing price considering single and double side closed bidding mechanism for N bid area. Further, an “optimized algorithm” is proposed to integrate microgrids in the Indian power market by taking into account several characteristics of IEA 2003 and the 2010 power market regulation. Further, to validate the proposed work, the proposed optimization algorithm is applied to a case study considering a 2.5-MWp grid-connected solar PV-based microgrid with an average load of 2060.2 kW in the Indian power market for an hourly power trading in a financial year time horizon.

Energy management in multi-microgrids considering point of common coupling constraint

There are different models for Energy Management of Multi-Microgrids (MMGs). Generally, the owners of microgrids are not identical. In this case, due to privacy concerns and overcomes drawbacks of conventional decentralized systems, hybrid energy management system is proposed. Unlike other energy management models, in hybrid model, multi-microgrids are connected to the grid through the common line entitled Point of Common Coupling (PCC). Energy management in hybrid multi-microgrids considering optimal utilization of PCC capacity is a critical issue that has been less taken into account. In this paper, a new bi-level method is proposed for optimal energy management in hybrid MMG systems taking into account the PCC line capacity. In the first level, each microgrid implements its day-ahead scheduling based on different quantities of PCC line capacity and extracts its profit-quantity curve. This novel curve shows the variations of microgrid profits versus different PCC limits. Subsequently, a two-stage optimization problem is presented in the second level, in which in the first stage an introduced microgrid aggregator (MGA) maximizes microgrids aggregated profit and determines optimal quota of each microgrid from PCC line based on corresponding profit-quantity curves while in the second stage, this profit is fairly divided among microgrids via Shapely value. Numerical results demonstrate the efficiency and reliability of the proposed method.

Game Theory Based Profit Maximization Model for Microgrid Aggregators With Presence of EDRP Using Information Gap Decision Theory

In the competitive electricity markets, each microgrid (MG) aggregator strives to maximize its own profit, whereas their coordination is a serious challenge for distribution system operator (DSO). In addition, indefiniteness of market clearing price and pungent alterations of renewable power generation are driving this issue toward a more complicated problem. To address this problem, we proposed an unprecedented game theoretic model by means of Stackelberg strategy to peruse the competition among MGs aggregators and DSO in order to attain the equilibrium point that can maximize the profit of all players who participate in retail market. Our design objective is to distinguish the optimal power exchanges between the MGs and market simultaneously considering emergency demand response program in order to hedge the risk of participation in retail market. Furthermore, information gap decision theory has been fulfilled to cope with drastic uncertainty of the problem. Finally, Benders decomposition (BD) associated with mathematical program with equilibrium constraints and strong duality theorem are accomplished to make the proposed model computationally tractable and subsequently replaced by its Karush–Kuhn–Tucker optimality conditions, which are in turn linearized. The executed detailed and extensive results indicate the sufficiency and competency of the proposed model.

Risk assessment of microgrid aggregators considering demand response and uncertain renewable energy sources

In power market environment, the growing importance of demand response (DR) and renewable energy source (RES) attracts more for-profit DR and RES aggregators to compete with each other to maximize their profit. Meanwhile, the intermittent natures of these alternative sources along with the competition add to the probable financial risk of the aggregators. The objective of the paper is to highlight this financial risk of aggregators in such uncertain environment while estimating DR magnitude and power generated by RES. This work develops DR modeling incorporating the effect of estimating power at different confidence levels and uncertain participation of customers. In this paper, two well-known risk assessment techniques, value at risk and conditional value at risk, are applied to predict the power from RES and DR programs at a particular level of risk in different scenarios generated by Monte Carlo method. To establish the linkage between financial risk taking ability of individuals, the aggregators are classified into risk neutral aggregator, risk averse aggregator and risk taking aggregator. The paper uses data from Indian Energy Exchange to produce realistic results and refers certain policies of Indian Energy Exchange to frame mathematical expressions for benefit function considering uncertainties for each type of three aggregators. Extensive results show the importance of assessing the risks involved with two unpredictable variables and possible impacts on technical and financial attributes of the microgrid energy market.

Energy management in multi‐microgrids via an aggregator to override point of common coupling congestion

Generally, the owners of microgrids are not identical; therefore, each microgrid tries to optimise its own profit and maximise its utilisation of the point of common coupling (PCC) capacity to boost its profitability, while being ignorant of its neighbouring microgrids. In the absence of information concerning neighbouring microgrids, data limitations in the PCC may be troublesome, especially in load peak hours. Hence, existence of an agent is vital to manage energy exchanges between microgrids and grid. This study introduces a bi-level hierarchical structure to manage energy in a system composed of multimicrogrids while considering PCC congestion. In the first level, each microgrid implements its day-ahead scheduling and declares its probable energy mismatch to an agent, entitled microgrid aggregator (MGA). In the second level, the MGA aims to maximise profit of microgrids considering PCC constraint through a two-stage optimisation problem. In the first stage, Karush-Kuhn-Tucker conditions are used to present a novel virtual pricing algorithm exerted by aggregator for rescheduling of microgrids. After regulating generation of microgrids through a reciprocal process, the resultant profit is fairly divided among microgrids via Shapley value in the second stage. The simulation results reveal the efficiency and reliability of the proposed method.

Impact of Microgrid Aggregator on Joint Energy and Reserve Market Based on Pure Strategy Nash Equilibrium

With the coordination of various distributed energy resources, microgrid has the capability to participate energy and reserve market simultaneously via a microgrid aggregator (MGA). The profit-maximizing offering problem of MGA satisfying a pure strategy Nash equilibrium (NE) is modelled in the paper as a bi-level programming to analyze the impact of MGA on the market equilibrium. A linearization method for pure strategy NE conditions is proposed, and then the bi-level problem is equivalently transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucher (KKT) conditions and strong duality theorem. Numerical results demonstrate the effectiveness of proposed method and present market NE prices are influenced by the scalability and location of integrated MGA.

Robust bidding strategies and scheduling of a price‐maker microgrid aggregator participating in a pool‐based electricity market

This study presents a model for the activities of the price-maker microgrid aggregator (MGA). In this model, an MGA is considered to aggregate several microgrids (MGs) and be in charge of obtaining an optimal bidding strategy for MGs as well as scheduling their resources and demand. Two price-maker strategies (the marginal and non-marginal strategies for players) are proposed and the robust scheduling and optimal transactions of a price-taker MGA are also obtained in order to analyse different bidding behaviour of MGA. A robust optimisation is used in this model in order to capture uncertainties associated with renewable generation in the worst-case situation. Accordingly, the robust solution is obtained for the optimal scheduling of an MGA participating in the pool-based day-ahead electricity market. The proposed robust bidding strategies and scheduling of a price-maker MGA are obtained considering a hypothetical test system and the results are compared with the bidding strategy and robust scheduling of a price-taker MGA. The results show that the robust scheduling and also the market prices are completely changed for different strategies of the MGA. Also, using the proposed model for the price-maker MGA increases the profits of MGs.

Demand Control Management in Microgrids: The Impact of Different Policies and Communication Network Topologies

This paper studies how the communication network between proactive consumers affects the power utilization and fairness in a simplified direct-current microgrid model, composed of three coupled layers: physical (an electric circuit that represents a microgrid), communication (a peer-to-peer network within the microgrid), and regulatory (individual decision strategies). Our results show that, for optimal power utilization and fairness, a global knowledge about the system is needed, demonstrating the importance of a microgrid aggregator to inform about the power consumption for different time periods.

Risk-Averse Generation Maintenance Scheduling With Microgrid Aggregators

This paper presents risk-averse long-term generation maintenance scheduling in the power systems with a considerable installed capacity of microgrids. Microgrid aggregators facilitate the participation of microgrids in the wholesale market. In this paper, the effect of microgrids as controllable demand entities on the generation maintenance scheduling practices in the power system is investigated. The uncertainties in the marginal cost of generation in microgrids, the generation capacity installed within the microgrids, and the system electricity demand are captured using respective nominal values and uncertainty intervals. Moreover, the contingencies in transmission network are addressed by introducing additional variables. A two-stage robust optimization problem is formulated to determine a trade-off among the performance and conservativeness of the procured solution in the long-term operation horizon. The problem is formulated as a mixed integer linear programming problem and column-and-constraint generation procedure is used to solve the problem. The master problem minimizes the maintenance cost of the generation units subjected to generation units’ constraints in the long-term operation horizon and the sub-problems determine the worst realization of the uncertainties and generate additional constraints in the master problem. The proposed methodology is applied to two case studies for a 6-bus and IEEE 118-bus power systems.

System-level assessment of reliability and resilience provision from microgrids

Microgrids are emerging to coordinate distributed energy resources and locally increase reliability to expected events and resilience to extreme events. Furthermore, by deploying their inherent flexibility, grid-connected microgrids are capable to provide different services at the system-level too. However, these are often assessed independently and a comprehensive integrated framework that can assess benefits for the whole power system is missing. In this outlook, this paper introduces a system-level assessment framework based on the concept of different duration’s reserve services that can be provided by microgrids to the main electricity grid in response to both credible (reliability-oriented) and extreme, possibly unforeseen (resilience-oriented) contingencies. Probabilistic capacity tables accounting for different sources of uncertainty related to both microgrids’ operation and occurrences of unfavorable events are built to assess the microgrids’ potential capacity contribution to a particular reserve service. Case studies based on representative microgrids and a British test system clearly illustrate and quantify how aggregation of microgrids could provide significant contribution to both short-term reliability and longer-duration resilience services far beyond the simple summation of the individual contributions, thus demonstrating a clear synergic effect, as well as the key role played by different forms of energy storage. The proposed framework can assist policy makers and regulators on the strategic role of microgrids for energy system planning and policy developments, including design of ancillary services markets not only to enhance system reliability but also resilience.

Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach

Energy trading mechanism for microgrids has an inherent two-layer architecture, in which the energy trading at the first layer is between a microgrid aggregator and consumers (e.g., households) within a microgrid, and the second layer is referred to as the wide area energy trading among multiple microgrids. This paper employs a two-layer game approach to achieve optimal and elastic energy trading for microgrids and improve utilization of green energy. First, a non-cooperative game is developed inside a microgrid, in which the relationship among household users is non-cooperative, and they adjust load schedules to optimize their utilities while trading energy with the microgrid aggregator. Second, a multileader-multifollower Stackelberg game is employed for the energy trading among microgrids. The role of a microgrid (as an energy buyer or seller) in the energy market is based on the result of the first game, and it can elastically adjust its energy trading strategy by charging or discharging the energy storage device. The existence and uniqueness of the equilibriums for the two games are proven. We also present algorithms that can reach the equilibriums where players achieve optimal utilities. Simulation results show that the proposed two-layer energy trading is able to significantly improve the utilization of microgrids' green energy.

Network expansion planning with microgrid aggregators under uncertainty

In this paper, a bi-objective robust model is proposed for network expansion planning (NEP) considering the integration of the microgrid aggregators. The objectives include minimization of both the expansion cost and transmission lines loading index. Forced outages of system components are taken into account as the system uncertainties. A hybrid method as the combination of gravitational search algorithm (GSA) and primal-dual interior point (PDIP) method is employed to solve the nonlinear programming (NLP) problem of the NEP. In the proposed hybrid method, the operation sub-problems are solved by the PDIP method under the worst-case single component contingencies, while the expansion plan is defined as scenario independent variables obtained by the GSA algorithm. To detect the worst-case single component contingencies with the severest effects on the system security, a subsidiary optimization problem is solved for each load level of the system. A realistic network of Qom as a part of Tehran Regional Electric Company, Iran, as well as IEEE 118-bus test system are analysed to evaluate the efficiency of the proposed method. A key conclusion is that the stochastic model may not provide sufficient security level, and a robust model is required to ensure the system security against the severe contingencies.