Application of Actor-Critic Learning Algorithm for Optimal Bidding Problem of a Genco
Application of Actor-Critic Learning Algorithm for Optimal Bidding Problem of a Genco
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Actor-Critic学习算法在Genco最优投标问题中的应用
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发表时间：
2002
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通讯作者：
S. Soman
中科院分区：
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作者：
G. Gajjar;S. Khaparde;P. Nagaraju;S. Soman
在放松管制的电力市场中，发电公司（GenCo）的最优竞价是一项复杂的任务。该问题是在马尔可夫决策过程 (MDP)（一种离散随机优化方法）的框架中表述的。当考虑的时间跨度为24小时时，时间差法就变得很有吸引力。跨度内的累积利润就是要优化的目标函数。采用时间差分技术和演员批评家学习算法。设计最佳策略以实现利润最大化。草案中包含了市场出清制度。考虑了三个、七个和十个参与者的模拟案例，并讨论了获得的结果。
The optimal bidding for generation companies (GenCo) in the deregulated power market is an involved task. The problem is formulated in the framework of the Markov decision process (MDP), a discrete stochastic optimization method. When the time span considered is 24 hours, the temporal difference method becomes attractive for application. The cumulative profit over the span is the objective function to be optimized. The temporal difference technique and actor-critic learning algorithm is employed. An optimal strategy is devised to maximize the profit. A market-clearing system is included in the formulation. Simulation cases of three, seven, and ten participants are considered, and the results obtained are discussed.