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Bidding and Cooperation Strategies for Electricity Buyers in Power Markets

机译：电力市场中购电者的竞价与合作策略

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摘要

Deregulation of electric power industries in recent years has opened many opportunities for electricity buyers. However, the strong influence of network physical constraints may result in economic decisions that adversely affect the interests of the consumers. Compared with the monopolistic economy of yesteryears, electricity buyers may actually be able to influence the market by cooperating with other buyers in the network. This paper presents a coevolutionary approach to investigate individual and cooperative strategies of buyers in a power market, taking fully into account the physical network constraints. First, the study focuses on deterministic cases, where buyers choose their bidding strategies to maximize the profits in different scenarios of playing individually or cooperatively. It is found that, by evolutionary learning, buyers can benefit from cooperation. After that, the uncertain nature of the market is modeled, where buyers find optimal cooperation strategies to hedge against the risk of low payoffs. The payoff distribution problem in cooperative game theory was linked with the optimal coalition generation problem by proving a theorem. The statistically consistent simulation results show that our approach is able to discover interesting cooperation strategies and can be easily extended to practical networks with a large number of buyers.
机译：近年来，电力行业的放松管制为购电者带来了许多机会。但是，网络物理约束的强大影响可能导致做出不利于消费者利益的经济决策。与过去的垄断经济相比，电力购买者实际上可能能够通过与网络中的其他购买者合作来影响市场。本文提出了一种协同进化方法来研究电力市场中购买者的个体和合作策略，同时充分考虑物理网络的约束。首先，研究侧重于确定性案例，在这种情况下，购买者选择了自己的出价策略，以在不同情况下单独或合作玩耍来最大化利润。发现通过进化学习，购买者可以从合作中受益。此后，对市场的不确定性进行建模，购买者在其中找到最佳的合作策略来对冲低回报风险。通过证明一个定理，将合作博弈理论中的收益分配问题与最优联盟产生问题联系起来。统计上一致的仿真结果表明，我们的方法能够发现有趣的合作策略，并且可以轻松地扩展到具有大量购买者的实用网络。

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