基于数据驱动可调鲁棒的冷-热-电联供综合能源系统日前调度优化

doi:10.12204/j.issn.1000-7229.2021.01.004

摘要/Abstract

摘要：

冷-热-电联供综合能源系统(integrated energy system with combined cool, heat and power system, IES-CCHP)能够就地消纳分布式风电、光伏,也能够同时满足系统内电动汽车用户的充电需求。然而,电动汽车充电需求、风电出力、光伏出力的随机性严重影响了IES-CCHP运行的经济性。因此,采用两阶段可调鲁棒优化为IES-CCHP制定日前调度策略以提升系统运行经济性。日前阶段在观测到随机变量前制定能够应对最恶劣运行场景的日前调度策略;实时阶段在确认随机变量实际值后决策实时调度计划修正日前调度策略。优化目标为运行两阶段运行总成本最小,模型采用非精确狄利克雷模型挖掘历史数据构建不确定集描述随机变量,并进一步采用对偶理论、大M法、列与约束生成(column-and-constraint generation,C&CG)等方法,迭代求解上述两阶段模型。最后,通过算例分析证明了所提模型与方法的有效性。

关键词: 冷-热-电联供综合能源系统(IES-CCHP), 可调鲁棒优化, 数据驱动, 非精确狄利克雷模型, 分布式能源

Abstract:

Integrated energy system with combined cool, heat and power system (IES-CCHP) is able to help power system to locally consume distributed wind and solar power, while satisfying charge demand of electric vehicles. However, uncertainties in the charge demand, wind and solar power significantly affect the economy of IES-CCHP. Therefore, this paper applies two-stage adjustable robust optimization to present day-ahead economic dispatch strategy for IES-CCHP. Day-ahead stage decides day-ahead dispatch strategy that can withstand the worst-case scenario before observing value of stochastic variables; real-time stage provides strategy for correcting the day-ahead strategy after confirming the stochastic variables. The objective is to minimize the costs of the two stages. Imprecise Dirichlet model is employed to dig historical data for constructing uncertainty set for describing stochastic variables. And then duality theory, big-M method, and column-and-constraint generation (C&CG) and so on, are applied to solve the presented two-stage model. Finally, experimental cases are carried out to demonstrate the effectiveness of model and method.

Key words: integrated energy system with combined cool, heat and power system(IES-CCHP), adjustable robust optimization, data-driven, imprecise Dirichlet model, renewable energy source

中图分类号:

TM73

陈晓东, 马越, 陈贤邦, 刘洋. 基于数据驱动可调鲁棒的冷-热-电联供综合能源系统日前调度优化[J]. 电力建设, 2021, 42(1): 28-40.

CHEN Xiaodong, MA Yue, CHEN Xianbang, LIU Yang. Data-Driven Adjustable Robust Optimization of Day-ahead Economic Dispatch of Integrated Energy System with Combined Cool, Heat and Power System[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 28-40.

图/表 19

图1 冷-热-电综合能源系统拓扑结构

Fig.1 Structure of integrated energy system with combined cool, heat, and power system

图2 风电出力不确定集区间与历史数据量的关系

Fig.2 Relationship between interval of uncertainty set of wind power and number of historical data

图3 负荷与可再生能源的预测数据

Fig.3 Forecast data of load and renewable energy

表1 样本场景数量对经济性的影响

Table 1 Impact of the number of samples on economy

图4 样本场景数量与可再生能源消纳水平的关系

Fig.4 Relationship between the number of samples and consumption of renewable energy source

表2 可调鲁棒参数对经济性的影响

Table 2 Impact of adjustable robust parameter on economy 美元

图5 可调鲁棒参数与可再生能源消纳水平的关系

Fig.5 Relationship between adjustable parameter and consumption of renewable energy source

表3 不同方法的经济性对比

Table 3 Comparison of economy of different methods 美元

表4 不同方法的购售电方案对比

Table 4 Comparison of different electricity transaction strategies 美元

图6 基于确定性优化的日前调度方案

Fig.6 Day-ahead economic dispatch based on deterministic optimization

图7 基于随机规划的日前调度方案

Fig.7 Day-ahead economic dispatch based on stochastic program

图8 基于鲁棒优化的日前调度方案

Fig.8 Day-ahead economic dispatch based on robust optimization

图9 基于可调鲁棒优化的日前调度方案

Fig.9 Day-ahead economic dispatch based on adjustable robust optimization

表A1 可控机组参数

Table A1 Parameters of controllable generators

表A2 惩罚系数

Table A2 Penalty coefficients

表A3 购售电单价

Table A3 Prices of buying and selling electricity 美元/(kW·h)

表A4 电储能系统参数

Table A4 Parameters of electrical storage system

表A5 热储能系统参数

Table A5 Parameters of heat storage system

表A6 能源转换效率

Table A6 Coefficients of energy converting

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