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The Impacts of Energy Customers Demand Response on Real-Time Electricity Market Participants

机译:能源客户需求响应对实时电力市场参与者的影响

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In this paper, we consider the profit-maximizing demand response of an energy customer in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy customer, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon continuous-state Markov decision process (MDP) problem. Through rigorous analysis, we show that the optimal actual demand policy exhibits a threshold structure, which can solve the MDP without the need of discretizing the state and action spaces. We demonstrate through extensive simulations that the proposed demand response strategy not only maximizes the profit of the energy customer, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed demand response strategy increases the energy customer's profit by 53.8% and saves the bills of other utilities by 80.4% comparing with the benchmark algorithms.
机译:在本文中,我们考虑了实时电力市场中能源客户的利润最大化的需求响应。在实时电力市场中,市场结算价格由日前市场中实际电力供应和需求与预测值的随机偏差确定。能源客户在一定时间内需要总量的能源,它具有随时间变化能源使用的灵活性,因此非常适合通过需求响应来利用波动的实时市场价格。我们表明,可以通过解决有限水平连续状态马尔可夫决策过程(MDP)问题来获得利润最大化的需求响应策略。通过严格的分析,我们表明最优的实际需求策略具有阈值结构,可以解决MDP,而无需离散状态和动作空间。通过广泛的仿真,我们证明了所提出的需求响应策略不仅可以最大程度地提高能源客户的利润,而且还可以缓解电网的供需不平衡,甚至可以减少其他市场参与者的费用。与基准算法相比,所提出的需求响应策略平均将能源客户的利润提高了53.8 \%,而其他公用事业的费用却节省了80.4 \%。

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