市场环境下考虑多元不确定性的热电联合虚拟电厂竞标策略

Bidding Strategy for Combined Heat and Power Virtual Power Plant in Electricity Market Considering Multiple Uncertainties

“双碳”背景下,热电联合虚拟电厂将热电联产机组、储能设施等聚合为可控集合体以促进新能源消纳,具有广阔的发展空间。电力市场环境下,热电联合虚拟电厂的竞标策略决定了其在市场中的购售电量,对其收益具有重要影响。然而,电价和分布式新能源出力等多元不确定性的存在,给热电联合虚拟电厂竞标策略的制定带来困难。为此,提出了市场环境下考虑多元不确定性的热电联合虚拟电厂竞标策略。考虑电价与虚拟电厂内部风光的不确定性,建立热电联合虚拟电厂参与电力市场的竞标模型。利用蒙特卡洛法和后向场景缩减法,构建刻画电价不确定性的典型场景。基于此,结合随机优化方法与基于Wasserstein距离的分布鲁棒方法,将热电联合虚拟电厂竞标模型转换为分布鲁棒随机优化模型,并利用对偶转换和数据驱动方法将该模型转换为单层模型,以利于求解。最后,通过IEEE 30节点系统验证所提方法的有效性。

Under the background of the goal of “Carbon Peak and Carbon Neutralization”,the combined heat and power virtual power plant(VPP) has a broad development space,which aggregates the combined heat and power generators,the energy storage facilities,et al.into a controllable unit to promote the efficient accommodation of renewable energy.In the electricity market environment,the bidding strategy of the combined heat and power VPP determines its purchases and sales of electricity in the market,which has an important impact on its income.However,the existence of multiple uncertainties from electricity price and the distributed renewable energy brings difficulties to the formulation of the combined heat and power VPP bidding strategy.Therefore,a bidding strategy of the combined heat and power VPP considering multiple uncertainties in the market environment is proposed.Considering the uncertainties of the electricity price and the distributed renewable energy within the virtual power plant,a bidding model for the combined heat and power virtual power plant to participate in the electricity market is established.Using the Monte Carlo method and the backward scenario reduction method,the typical scenarios describing the uncertainty of electricity price are constructed.Based on this and combining the stochastic optimization method and the distributionally robust optimization based on the Wasserstein distance,the bidding model of the combined heat and power VPP is converted into a distributionally robust stochastic optimization model,which is further transformed into a single-layer model by using the dual transformation and the data-driven methods in order to facilitate the solution.Finally,the effectiveness of the proposed method is verified by the IEEE30 node system.