基于深度强化学习算法的储能系统盈利策略研究

在高比例新能源接入下,配置储能可以辅助电力系统削峰填谷,平抑波动。然而目前储能系统成本较高,需要政府进行支持。为此,提出了一种储能盈利策略,以在电网、储能运营商和用户组成的电力市场中实现运营利润最大化。结合智能算法提出了一种考虑激励的盈利策略,为每个峰值时段的储能系统运营商提供不同权重的奖励分配。该算法一方面基于最小二乘支持向量机的深度学习,来建立价格和负荷预测模型；另一方面基于深度强化学习,考虑电网的峰值状态、用户负荷需求和储能系统运营商利润,确定最优充放电策略。最后通过案例分析,验证该策略可以显著提高储能系统运营商利润并减轻电网压力。

Study on the profitability model of energy storage system considering incentive strategy

With a high proportion of new energy access, the deployment of energy storage can assist the power system to cut peaks and fill valleys and smooth out fluctuations. However, current energy storage systems are costly and require government support. To this end, a profitability strategy for energy storage to maximise operating profit in an electricity market consisting of the grid is proposed, storage operators and customers. A profitability strategy that takes into account incentives in combination with an intelligent algorithm that provides different weighted reward allocations to the storage system operator for each peak hour is proposed. On the one hand, the algorithm is based on deep learning of least square support vector machine to establish price and load forecasting models. On the other hand, deep reinforcement learning is used to determine the optimal charging and discharging strategy considering the peak state of power grid, user load demand and the profits of energy storage system operators. Finally, a case study is conducted to verify that the strategy can significantly improve the profitability of the energy storage system operator and reduce the pressure on the grid.