Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to address this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4)numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.
In this paper, the motivation to develop microgrids as an effective solution for the control of distribution networks with high level penetration of Distributed Energy Resources(DERs) is discussed. As many different control methods for microgrids can be found in literature, this paper proposes a classification from highly centralized to distributed peer-to-peer control architectures. A peer-to-peer control paradigm is proposed as a way to control the distribution network with a high penetration of distributed energy resources. Different control algorithms suited for the proposed peer-to-peer c…
In this paper, we study the interactions among interconnected autonomous microgrids, and propose a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We des…
Recent trends express the impact of prosumers and small energy resources and storages in distribution systems, due to the increasing uptake of renewable resources. This research studies the effect of coordination of distributed resources with the utility grid and the role of prosumers in the operation of renewable microgrids. We formulated this problem as a social welfare maximization problem followed by employing the dual decomposition method to decompose it into sub-problems of the microgrid, distributed generators, prosumers, and consumers. Moreover, the corresponding power balance mechanis…
In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm is proposed that converges to the optimal solution in a practical number of iterations and with a limited communication overhead. Furthermore, this approach allows for a very intuitive economics interpretation …
In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generali…
Peer-to-peer (P2P) energy systems have recently emerged as a promising approach for integrating renewable and distributed energy resources into energy grids to reduce carbon emissions. However, market-clearing energy price and amounts, resulted from solving optimal P2P energy management problems, might not be satisfactory for peers/agents. This is because peers/agents in practice do not know how to set their cost function parameters when participating into P2P energy markets. To resolve such drawback, this paper proposes a novel approach, in which an inverse optimization problem is formulated …
To contribute to a smart grid, the unit commitment and dispatch (UCD) is a very important problem to be revisited in the future microgrid environment. In this paper, the UCD problem is studied by taking account of both traditional thermal units and distributed generation units (DGs) while considering demand response (DR) and carbon emissions trading (CET). Then, the UCD problem is reformulated as the optimal switching problem of a hybrid system. The reformulation allows convenient handling of time-dependent start-up cost for dynamic programming (DP). Afterwards, a closed-loop hierarchical oper…
This work considers energy management in a grid-connected microgrid which consists of multiple conventional generators (CGs), renewable generators (RGs) and energy storage systems (ESSs). A two-stage optimization approach is presented to schedule the power generation, aimed at minimizing the long-term average operating cost subject to operational and service constraints. The first stage of optimization determines hourly unit commitment of the CGs via a day-ahead scheduling, and the second stage performs economic dispatch of the CGs, ESSs and energy trading via an hour-ahead scheduling. The com…
With the proliferation of distributed generations, traditional passive consumers in distribution networks are evolving into "prosumers", which can both produce and consume energy. Energy trading with the main grid or between prosumers is inevitable if the energy surplus and shortage exist. To this end, this paper investigates the peer-to-peer (P2P) energy trading market, which is formulated as a generalized Nash game. We first prove the existence and uniqueness of the generalized Nash equilibrium (GNE). Then, an distributed online algorithm is proposed to track the GNE in the time-varying envi…
With an increasing share of volatile renewables, more flexibility is needed to balance the electricity system. In order to enable local flexibility, we suggest a three-stage stochastic market clearing model using community-based peer-to-peer (P2P) trading with participation in the short term reserve markets. With this approach, internal intraday (IID) and real-time (IRT) markets are cleared where peers can trade to balance their day-ahead market position under uncertainty in load and activation of reserves. Results show increased participation of 19 and 44 % in reserve markets when P2P IID and…
This paper designs a market platform for Peer-to-Peer (P2P) energy trading in Transactive Energy (TE) systems, where prosumers and consumers actively participate in the market as seller or buyer to trade energy. An auction-based approach is used for market clearing in the proposed platform and a review of different types of auction is performed. The appropriate auction approach for market clearing in the proposed platform is designed. The proposed auction mechanism is implemented in three steps namely determination, allocation and payment. This paper identifies important P2P market clearing pe…
Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning
Department of Data Science and AI, Faculty of IT, Monash University, Australia（数据科学与人工智能系，信息科技学院，墨尔本大学，澳大利亚）；Monash Energy Institute, Monash University, Australia（墨尔本能源研究所，墨尔本大学，澳大利亚）
