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A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances

机译:考虑不确定性和碳排放配额的虚拟电厂基于CVaR鲁棒性的多目标优化模型和三阶段求解算法

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In order to make full use of distribute energy resources and decrease the abandoned energy of clean energy, the paper aggregates wind power plant (WPP), photovoltaic power generation (PV), biomass power generation (BPG), energy storage system (ESS), conventional gas turbines (CGT) and flexible load into a virtual power plant (VPP). Firstly, the basic structure and uncertainty factors of VPP operation are analyzed, and the output model of power sources is proposed. Then, a multi-objective optimization model is proposed with three objective functions, namely, the maximum operation revenue, the minimum operation risk and the minimum carbon emissions. Furthermore, the robust optimization theory is applied to construct a risk aversion model by converting the constraint conditions into stochastic constraint conditions with uncertainty factors. Thirdly, a three-stage solution algorithm is put forward for the proposed multi-objective model including payoff table establishment, fuzzy linearization and objective weight calculation. Finally, the modified IEEE30 node system is chosen as simulation system. The results show: (1) VPP could utilize the complementary nature of different distributed energy sources to balance the revenue, risk and carbon emission. (2) Robust optimization theory and conditional value at risk could describe the uncertainty risk, and, the prediction accuracy needs to be improved for controlling the operation risk. (3) The risk attitude of decision makers would affect VPP scheduling scheme, the less uncertainty lead to greater risk when confidence degree = 0.85 and robust coefficient = 0.95 (risk extreme aversion). (3) Price-based demand response (PBDR) could smooth load demand curve and the maximum total emission allowances (MTEA) can highlight the environmentally friendly characteristics, which could improve more grid-connected space of clean energy and achieve the optimal VPP operation. Therefore, the proposed multi-objective model could obtain higher economic benefit and achieve lower carbon emissions while rationally controlling risks, which could be taken as an reliable decision support for decision makers.
机译:为了充分利用分布式能源并减少清洁能源的废弃能源,本文汇总了风电厂(WPP),光伏发电(PV),生物质发电(BPG),储能系统(ESS),常规燃气轮机(CGT)和灵活负载到虚拟电厂(VPP)。首先,分析了VPP运行的基本结构和不确定因素,提出了电源输出模型。然后,提出了具有三个目标函数的多目标优化模型,即最大经营收益,最小经营风险和最小碳排放量。此外,通过将约束条件转换为具有不确定性因素的随机约束条件,将鲁棒优化理论应用于构建风险规避模型。第三,针对所建立的多目标模型提出了三阶段求解算法,包括支付表建立,模糊线性化和目标权重计算。最后,选择改进的IEEE30节点系统作为仿真系统。结果表明:(1)VPP可以利用不同分布式能源的互补性来平衡收益,风险和碳排放。 (2)鲁棒的优化理论和风险条件值可以描述不确定性风险,控制操作风险需要提高预测精度。 (3)决策者的风险态度会影响VPP计划方案,当置信度<= 0.85和稳健系数> = 0.95(风险极度厌恶)时,不确定性越小,风险越大。 (3)基于价格的需求响应(PBDR)可以使负荷需求曲线平滑,最大总排放配额(MTEA)可以突出环保特性,可以改善更多的并网清洁能源并实现最佳VPP运行。因此,所提出的多目标模型可以在合理控制风险的同时获得较高的经济效益和较低的碳排放量,可以作为决策者的可靠决策支持。

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