约束条件弹性化的月度电力市场机制设计

Monthly Electricity Market Design Based on Constraint Relaxation

大用户直购电的深入开展和大规模新能源发电上网给电力系统调峰带来严峻挑战。传统的月度机组组合方案编制主要依靠电网运行人员指定机组启停和承担深度调峰等辅助服务,这种机制容易产生调度公平性的问题。为此,该文提出基于约束条件弹性化的新型月度电力市场机制。在该机制下,市场成员除了向交易机构申报常规的报价和物理约束条件外,根据自身运行实际,向交易机构申报其约束可松弛范围及其补偿报价,利用激励机制充分挖掘机组约束弹性化的潜力,用市场成员主动参与的方式保障调度的公平公正,提升电网运行的安全经济。设计了满足激励相容原理的约束松弛补偿结算机制,让未突破自身约束条件的机组承担那些约束条件被突破机组的补偿成本,从而有效促进市场成员理性申报约束松弛补偿报价。基于IEEE-30节点系统的算例模拟计算表明,该文机制可以有效激发市场成员申报约束松弛能力,实现在更大的可行域范围内找到比传统机制更优的机组组合方案,将有利于促进新能源消纳,提高整体社会福利。

Due to the developing of large consumers direct-purchasing and large-scale renewable energy integrating into grid, the power system has encountered great challenges in peak load regulation. In traditional power system operations, ancillary services, such as generator startup/ shutdown or deep peak load regulation, rely on operator＇s own decisions which would easily cause unfairness among market participants. Thus, we designed a new monthly power market mechanism based on constraint relaxation. In this new mechanism, besides providing the routine bidding price and its generation constraints, the market participants will also provide the relaxation range of its constraints and the compensation price, considering the realistic operation status. The market participants＇ potential in relaxing their generation constraints is grasped by the incentive mechanism. The fairness and equity of power dispatch is ensured by the widely involvement of market participants. The security and economy of power grid operations is enhanced. We designed a compensation settlement which will improve the rationality of bidding by incentive compatibility principle, and let the market participants who do not relax their constraints pay the constraints relaxation cost. The new mechanism can solve the optimization in a larger feasible region and increase the renewable energy accommodation and finally increase the overall social welfare. The simulation based on the IEEE 30 bus system shows that the new mechanism can encourage market participants to apply their constraint relaxation capability and find a better result in a larger feasible region compared with the traditional mechanism.