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Bidding Strategy of Thermal Units Participating in Real-Time Depth Peak Load Regulation Ancillary Service Market based on Stackelberg Game

机译:基于Stackelberg游戏的实时深度峰值负荷调节辅助服务市场的热部门竞标策略

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With the development of the peak load regulation ancillary service market and the improvement of the rules, the role of thermal power unit changed from the passive participant in peak load regulation for duty to the active bidder. The power grid dispatching agency called the depth peak load regulation (DPLR) resources on demand according to the price. In order to research the optimal bidding strategy of thermal power units in the depth peak load regulation ancillary service market, a bi-Ievel model of multiple thermal power manufacturers and single market dispatching institution is established based on stackelberg game. The upper level is the bidding strategy of thermal power manufacturers, including the income and cost of thermal power manufacturers. The lower level is the optimal scheduling scheme of the market dispatching institution. The nonlinear bi-Ievel model is solved by the particle swarm optimization algorithm. The cases show the importance and validity of the proposed model.
机译:随着峰值负荷调节辅助服务市场的发展和规则的改进,热电部门的作用从被动参与者的峰值负荷调节中的占空比达到活跃投标人的作用。电网调度机构称为深度峰值负载调节(DPLR)资源按照价格按需。为了研究深度峰值负荷调节辅助服务市场中的热功率单元的最佳探测策略,基于Stackelberg游戏建立了多个热电厂和单一市场调度机构的双Ievel模型。上层是热电厂制造商的竞标策略,包括热电厂制造商的收入和成本。较低级别是市场调度机构的最佳调度方案。非线性Bi-Ievel模型由粒子群优化算法解决。案例显示了所提出的模型的重要性和有效性。

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