Energy Trading among Multiple Microgrids Considering Adjustable Potential

Trustful double auction design for Peer-to-Peer energy trading between interconnected micro-grids with supply–demand imbalance

This paper proposes a model for energy sharing of interconnected microgrids (MGs), mainly where some MGs are owned by an entity, such as the government, which is the case study in Western Australia (WA). In the proposed model, MGs are able to trade energy among themselves when some of them have surplus generation, and others have lack of generations to meet their demand; however, they are obliged to pay for the use of distribution network, called network charge, and the share of network loss due to this energy transaction. In doing so, the network loss is taken into account and calculated through a power flow. The possibility of energy trading with the main grid is also considered through the wholesale electricity market. Considering the uncertainty of Photovoltaic (PV) generation and load involved, the decision making to inject or import energy to/from the main grid as well as to trade between MGs is obtained through a bi-level linear optimization. In the upper level, the distribution network operator intends to manage the energy exchange between MGs and energy trading with upstream grid, while in the lower level, each MG attempt to minimize its operational cost relating to PV and energy storage system (ESS). Finally, the proposed method is applied to a real project in Western Australia.

In this article, an agent-based transactive energy (TE) trading platform to integrate energy storage systems (ESSs) into the microgrids’ energy management system is proposed. Using this platform, two different types of energy storage market models are proposed to promote local-level (within the microgrid) and communal- or global-level ESSs’ participation in the intra- and intermicrogrid TE markets. Also, a reinforcement learning algorithm known as simulated-annealing-based $Q$ -learning is used to develop bidding strategies for ESSs to participate in the TE markets. Besides energy trading, the proposed system also accounts for the losses caused by energy transactions between ESSs and microgrids using a complex current-tracing-based loss allocation method. The overall efficacy of the proposed energy market management system is demonstrated using a modified IEEE 123-bus distribution system with multiple microgrids and ESSs. Based on simulation results, it is observed that the proposed model can effectively reinforce the balance between the supply and the demand in the microgrids using the mix of local and global ESSs.

Plummeting costs of Renewable Energy Sources (RES), Energy Storage Systems (ESS) and technological advancements in ICT has resulted in the mushroom growth of Microgrids (MG) around the world. Due to the high composition of variable, inflexible and uncertain RESs in MGs, at times of low RES output, MGs have to either unwillingly reduce consumption, turn on polluting conventional generators or purchase expensive electricity from the main grid. Conversely, at times of high RES output, there are instances where RESs have to be forcefully curtailed to maintain the stability of the network. In this paper, we introduce a novel Transactive Energy (TE) trading approach to reduce the effect of the inconvenient characteristics of RES generation in MGs. This is achieved by optimized sharing of resources across MGs through an aggregator agent, thus, further reducing the dependency on the main grid. Since resources are shared across MGs, blockchain as a technology tool is considered to accurately record transactions and remunerate participating assets.

Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms.

In this paper, a centralized model predictive control (MPC) is applied on a group of interconnected microgrids (MGs) with the main grid. The objective is to maximize the benefits for all the elements constituting the MGs in addition to the benefits of the main grid. The application of MPC in our study needs a forecasting information about energy prices, production power, and loads. The algorithm is tested on five interconnected MGs connected to the main grid. Results have shown the performance of the proposed algorithm, especially for the benefits of MG owners, the coordination between MGs while respecting of the constraints related to each one of them.

In this paper, we present a Value-Decomposition Deep Deterministic Policy Gradients (V3DPG) based Reinforcement Learning (RL) method for energy trading and management of regional interconnected microgrids (MGs). In practice, the state of an MG is time-varying and the traded energy flows continuously, which is generally neglected in researches. To address this problem, an Actor-Critic framework is adopted. Each MG has to make energy trading decision based on local observation and has no access to any knowledge of other MGs. We bring in the idea of value-decomposition in the training process to ensure the generation of feasible cooperative policies while maintaining MGs' privacy and autonomous decision-making ability. Furthermore, in light of the uncertainty and fluctuation of renewable energy generation and users' demand, a recurrent neural network (RNN) with Burn-In initialization is combined with critic network to achieve implicit predictions. Meanwhile, we also take Energy Storage System (ESS) with operational constraints into consideration and deem it as a virtual market innovatively. Experiments have been carried out under real-world data to verify the merit of the proposed method, compared to existing RL-based works.

Distributed energy trading among energy prosumers (i.e., energy producers that also consume energy) is expected to bring significant cost benefits for the participating actors. In terms of the system architecture, physical grouping into microgrids (MG) can be further enhanced by communication infrastructure that provides support for flexible organization of prosumers into virtual MGs. However, how to manage prosumers using communication infrastructure is not widely investigated. In this paper, we propose a virtual MG architecture induced by communication constraints and consider its impact on total costs of energy trading. More precisely, we refine the distributed energy trading model considered in the recent literature with additional communication constraints and investigate impact of the resulting virtualized MG architecture on the overall energy trading costs. We show by simulations that there indeed exists an optimal energy trading architecture that achieves minimum possible energy trading cost, for any given model parameters.

During the past few years, microgrids (MGs) have been becoming more attractive as effective means to integrate different distributed energy resources (DERs). To coordinate active and reactive power sharing among DERs, conventional droop control method is widely used as a decentralized control scheme. However, sharing powers among the sources based on the units' rated capacities is not an optimal solution in terms of economy and efficiency. In this paper, a new adaptive droop-based control strategy is proposed for AC MGs to optimally share MG load between corresponding units. The mentioned control strategy is developed in two levels. The upper control level is a mixed-objective optimization algorithm that provides optimal set-points for power generations considering system's constraints and goals, while the lower control level is responsible for tracking the reference signals coming from the upper level. To demonstrate the effectiveness of the proposed control strategy under different operating scenarios, simulation results in a benchmark MG are also presented.

This paper proposes a topology-aware vehicle-to-grid (V2G) energy trading mechanism that uses charging/discharging powers of electric vehicles (EVs) to regulate voltage deviations of an active distribution system. To incentivize EVs to trade charging/discharging energy, auction theory is employed to guarantee three economic properties: 1) truthfulness; 2) individual rationality; and 3) social cost minimization. The mechanism is designed according to the topology of an active distribution system, in which multiple microgrids (MGs) are networked through a distribution network (DN). Two types of V2G auctions are proposed for the MG and DN, respectively. In each auction, the auctioneer optimizes control of EVs’ charging/discharging by solving a social cost minimization problem subject to constraints of power network topology. Further, the mechanism uses analytic target cascading framework, allowing the MG and DN to set up their own V2G auctions separately, and meanwhile enabling them to coordinate with each other to determine DN–MG power exchange. It is theoretically proved that the mechanism’s three economic properties hold in both the cases of grid-tied MGs and islanding MGs. Simulation results show that the proposed mechanism can regulate voltages of the distribution system to a secure range. Theoretic analysis of the economic properties is verified as well.

Impact of energy storage system and distributed energy resources on bidding strategy of micro-grid in deregulated environment

Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach

Based on the analysis of the characteristics of microgrid power generation, low network loss and low pollution, the microgrid power market is established based on multi-agent theory to solve the power interaction between microgrid (MG) and active power distribution network. Considering an imperfectly competitive power market, the Nash game theory is used to analyze the microgrid bidding strategy. The study found that the MG participating in the bidding can maximize the profit through the bidding strategy with high scale factor and low-cost quotation. Once the bidding strategy of a microgrid is changed, other microgrids will adjust the bidding strategy accordingly. But the electricity trading market will eventually achieve Nash equilibrium. Finally, the bidding strategy and optimal selection scheme of MG’s own profit maximization are obtained by using game theory and model.

Renewable Energies play crucial role in modern distribution systems. An active distribution system consists of several Micro-Grids (MGs) each of which has kinds of Distributed Generations (DGs) and Demand Response (DR) resources. Hence MG optimal operation is bi-level optimization problem whose upper level seeks for micro-grid owner profit and the lower level corresponds to distribution companies (DC) profit. In such a system MGs can exchange power between each other as well as distribution company. Therefore, upper level problem determines DGs generation, curtailed load, power exchange with other MGs subject to load balance constraint. Also Distribution Company (DISCO) clears retail market. On the other hand, renewable energies uncertainty and variably makes the MGs operation more complicated. In this paper, renewable energy variability handled by shift able load management and their uncertainty has been managed by curtailable load. The developed model has been solved by Particle Swarm Optimization (PSO) for upper level and then with given population in each iteration, lower level problem has been solved completely by other PSO package. This process ends when upper level PSO converge. Numerical studies have been shown that renewable management by proposed model leads to more optimal solutions.

Reliability evaluation of multi-microgrids considering optimal operation of small scale energy zones under load-generation uncertainties