计及灵活性负荷资源需求响应和不确定性的楼宇微网调度双层优化模型

doi:10.12204/j.issn.1000-7229.2022.06.014

摘要/Abstract

摘要：

针对含分布式发电资源和灵活负荷资源的楼宇微网消纳可再生能源的问题,以电动汽车为灵活性资源,构建了计及需求响应和充放电不确定性的楼宇微网调度优化模型。首先,构建了灵活性负荷资源的电价型需求响应模型和激励型需求响应模型;其次,将电动汽车视为产消一体者,分别采用马尔科夫链和信息间隙决策理论(information gap decision theory, IGDT)处理充放电不确定性;最后,以净收益最大化、光伏消纳最大化、用户满意度最大化、二氧化碳排放量最小化构建确定型楼宇微网调度优化模型。通过算例分析验证了所构建优化模型的有效性,该模型不仅提高了系统的清洁能源消纳率,减少了二氧化碳排放量,还能够为用户带来一定的收益,挖掘灵活性负荷资源参与微网调度的潜力,最终实现供用双方的效益双赢。

关键词: 楼宇微网, 电动汽车, 需求响应, 不确定性, 马尔科夫链, 信息间隙决策理论(IGDT), 双层优化

Abstract:

In order to solve the problem of building microgrid consuming renewable energy where distributed generation and flexible load resources are integrated, this paper takes electric vehicles as flexible resources, and constructs a scheduling optimization model for building microgrids considering demand response and charging/discharging uncertainty. Firstly, the price-based demand-response model and incentive-based demand-response model of flexible load resources are constructed. Secondly, the electric vehicle is regarded as an integrator of production and consumption, and Markov chain and information gap decision theory (IGDT) are used to deal with the uncertainties of charging and discharging, respectively. Finally, the deterministic scheduling optimization model is constructed by maximizing the net revenue of the system, photovoltaic accommodation, users’ satisfaction, and minimizing carbon dioxide emissions. The effectiveness of the constructed optimization model is verified by example analysis. The model not only enhances the clean energy accommodation rate of the system and reduces carbon dioxide emissions, but also can bring certain benefits to users, tap the potential of flexible load resources to participate in microgrid scheduling, and realize finally the win-win benefits of both supply and demand sides.

Key words: building microgrid, electric vehicle, demand response, uncertainty, Markov chain, information gap decision theory (IGDT), two-level optimization

中图分类号:

TM73

王萍萍, 许建中, 闫庆友, 林宏宇. 计及灵活性负荷资源需求响应和不确定性的楼宇微网调度双层优化模型[J]. 电力建设, 2022, 43(6): 128-140.

WANG Pingping, XU Jianzhong, YAN Qingyou, LIN Hongyu. A Two-Level Scheduling Optimization Model for Building Microgrids Considering Demand Response and Uncertainties of Flexible Load Resources[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(6): 128-140.

图/表 18

图1 系统供能结构

Fig.1 Energy supply structure of the system

图2 IBDR阶梯型补偿机制

Fig.2 Ladder-type compensation mechanism of IBDR

图3 求解步骤

Fig.3 Solution steps

图4 优化模型结构框架

Fig.4 Structural framework of the optimization model

图5 里程拟合结果

Fig.5 Fitting result of the milage

图6 EV数量与充电负荷曲线

Fig.6 Curves of charging load and the number of EVs

表1 情景设置

Table 1 Case setup

表2 CO2排放因子

Table 2 Emission factors of CO2

图7 用电负荷及光伏发电曲线

Fig.7 Curves of load and photovoltaic power

表3 时段划分及充电价格

Table 3 Time division and charging prices

表4 EV物理参数

Table 4 Physical parameters of EVs

图8 情景1机组出力

Fig.8 Output of generators in Case 1

图9 PBDR前后充电负荷曲线及机组出力

Fig.9 Charging loads before and after PBDR and the output of generators

图10 IBDR前后充电负荷曲线及机组出力

Fig.10 Charging loads before and after IBDR and the output of generators

图11 EV放电前后充电负荷曲线及机组出力

Fig.11 Charging loads before and after EVs’ discharging and the output of generators

图12 考虑不确定性前后充电负荷及机组出力

Fig.12 Charging loads with and without considering discharge uncertainty of EVs and the output of generators

图13 收益鲁棒值和不确定性系数变化轨迹

Fig.13 Variation trajectory of revenue robustness and uncertainty coefficient

表5 各情景计算结果对比

Table 5 Calculation result comparison of the cases

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