【Applied Energy最新原创论文】综合能源微电网多能交易：三层分布式风险规避的随机博弈方法

AEii国际应用能源 · 公众号 · · 2023-04-04 20:30

正文

原文信息：

Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

原文链接：

https://www.sciencedirect.com/science/article/pii/S0306261922015392

Highlights

• An energy trading model for networked multi-energy microgrids (MEMGs) is developed.

•Each MEMG acts as a prosumer with practical power and thermal network constraints.

•Thermal energy is treated equally to electric energy in the multi-energy markets.

•A tri-layer risk-averse stochastic Nash game method is used for effective solutions.

•A distributed alternating search procedure is used to compute the Nash equilibrium.

摘要

本文提出了在当前开放的综合能源市场中，多个并网多能微电网之间的三层最优非合作能源交易方法。通过风险规避的随机规划方法有效处理了可再生能源、能源交易价格和电能负荷的不确定性。首先，在考虑实际电网和热网约束以及电池老化的情况下，提出了单个多能源微电网的综合运行模型。其次，为了保证多能多能微网之间的公平交易，并缓解所有不确定性来源所带来的不利影响，探究了一种基于三层的Cournot纳什博弈的能源竞价方法。在第一层，即日前多能源市场，以规避风险的随机规划方式获取最佳能源投标、储能资产调度和热流模型；在第二层，即日中多能源市场，计算所有资源的最优日内能源投标和调度；在第三层，即实时多能源市场，每个多能源微电网和实时多能源市场之间的交易量和金额最终确定。同时，为了保护单个多能源微电网的隐私并减轻计算负担，采用分布式交替搜索算法来计算日前和日内市场中的纳什均衡点。最后，通过数值算例验证了该方法的有效性。从模拟结果可以推断，与文献中传统的合作博弈、确定性规划和不计风险的方法相比，我们提出的方法综合考虑了市场竞争、不确定性处理和能源交易风险，在实际应用中更实用、更经济。

更多关于"stochastic game approach"的研究请见：

https://www.sciencedirect.com/search?qs=stochastic%20game%20approach&pub=Applied%20Energy

Abstr act

This paper discusses a tri-layer non-cooperative energy trading approach among multiple grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The heterogeneous uncertainties from renewable energy, market prices, and electric energy loads are also considered via the risk-averse stochastic programming (SP) approach. First, comprehensive operation models of individual MEMGs are presented with the consideration of practical electric energy and thermal network flows as well as battery degradation. Second, to guarantee fair multi-energy trading among MEMGs and deal with adverse effects from all uncertainty sources, a tri-layer Cournot Nash game-based energy bidding method is developed and solved by the SP approach. In the first layer, i.e., day-ahead multi-energy market, optimal energy bids, dispatches of energy storage assets, and thermal flows against uncertainty scenarios are acquired in a risk-averse manner; In the second layer, i.e., intraday multi-energy market, optimal intra-day energy bids and dispatches of all resources against uncertainty realizations are sequentially calculated; In the third layer, i.e., the real-time multi-energy market, transactions between each MEMG and the wholesale multi-energy market are finalized. Third, for protecting the privacy of individual MEMGs and alleviating the computation burdens, the distributed alternating search procedure is employed to compute the Nash equilibriums in the day-ahead and intra-day markets. In the end, numerical case studies are conducted to verify the effectiveness of our method. From the simulation results, it can be inferred that compared with the traditional cooperative, deterministic and risk-natural methods in the literature, our proposed method is more practical and economical for real-world applications since it comprehensively considers the market competition, uncertainty handling, and energy trading risk.

Keywords

Multi-energy microgrids (MEMGs)

Cournot Nash game

Risk-averse stochastic

Energy market

Alternating search procedure

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