计及电动汽车接入的区域综合能源系统双层日前协调优化调度

doi:10.12204/j.issn.1000-7229.2023.05.003

电力建设 ›› 2023, Vol. 44 ›› Issue (5): 23-33.doi: 10.12204/j.issn.1000-7229.2023.05.003

计及电动汽车接入的区域综合能源系统双层日前协调优化调度

李玲¹(), 曹锦业¹(), Nikita Tomin²(), 杨德昌¹(), 郑颖颖¹()

1.中国农业大学信息与电气工程学院, 北京市 100083
2.俄罗斯科学院西伯利亚分院能源研究所,新西伯利亚 630090

收稿日期:2022-06-06 出版日期:2023-05-01 发布日期:2023-04-27
通讯作者: 杨德昌(1983),男,博士,副教授,主要研究方向为综合能源系统优化运行、综合能源系统状态估计,E-mail:yangdechang@cau.edu.cn。
作者简介:李玲(1998),女,硕士研究生,主要研究方向为综合能源系统优化运行,E-mail:1725813743@qq.com;
曹锦业(1999),男,硕士研究生,主要研究方向为综合能源系统优化运行,E-mail:caojinye@cau.edu.cn;
Nikita Tomin (1984),男,副研究员,主要研究方向为人工智能算法在综合能源系统中的应用,E-mail:tomin.nv@gmail.com;
郑颖颖(1987),女,博士,教授,主要研究方向为综合能源系统建模与能流优化、智能电网用户侧需求响应控制策略,E-mail:yyzheng@cau.edu.cn。
基金资助:
国家自然科学基金资助项目(51977212);交流项目(52111530048)

Bi-level Coordinated Day-ahead Optimal Dispatch of Regional Integrated Energy System Considering the Integrations of Electric Vehicles

LI Ling¹(), CAO Jinye¹(), Nikita TOMIN²(), YANG Dechang¹(), ZHENG Yingying¹()

1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Energy System Institute of Siberian of Russian Academy of Science, Novosibirsk 630090,Russia

Received:2022-06-06 Online:2023-05-01 Published:2023-04-27
Supported by:
National Natural Science Foundation of China(51977212);National Natural Science Foundation of China(52111530048)

摘要/Abstract

摘要：

规模化电动汽车(electric vehicles, EV)的接入对区域综合能源系统(regional integrated energy system,RIES)优化调度提出了新的挑战,文章提出了计及分布式电源和电动汽车源荷双向互动的RIES双层日前优化调度策略。首先,采用蒙特卡洛法模拟电动汽车无序充电,在此基础上利用分时电价引导负荷侧的电动汽车有序充电;然后,基于电力市场和碳交易市场,充分考虑源侧风光出力和负荷侧价格型需求响应等过程中的不确定性因素,以日系统经济成本和碳交易成本最低为目标进行系统优化调度,并采用改进粒子群算法进行求解;最后,通过仿真算例分析可得,文章提出的双层优化调度策略能够有效降低系统的负荷峰谷差,在实现电动汽车有序充电的同时,能够有效提升用户用电满意度、降低碳排放量以及增加系统经济效益。

关键词: 电动汽车, 有序充电, 分时电价, 需求响应, 双层日前调度策略

Abstract:

Access to large-scale electric vehicles poses new challenges for the optimal dispatching of a regional integrated energy system (RIES). This study proposes a bi-level day-ahead scheduling strategy for a RIES while considering the integration of electric vehicles. First, the Monte Carlo method was used to simulate the disordered charging of electric vehicles. On this basis, the time-of-use price was used to guide the orderly charging of electric vehicles on the load side. Next, based on the electricity and carbon trading markets and fully considering the uncertainty factors in the process of wind power output on the source side and the demand-side response on the load side, the system was optimized and scheduled to minimize the daily economic and carbon trading costs. An improved particle swarm optimization algorithm was used to solve the problem. Finally, through the analysis of simulated examples, it was concluded that the bi-level optimal scheduling strategy proposed in this study can effectively reduce the peak-valley difference of the system load, improve the electricity consumption satisfaction of users, reduce carbon emissions, and increase the economic benefits of the system, while realizing the orderly charging of electric vehicles.

Key words: electric vehicles, orderly charging, time-of-use electricity price, demand-side response, bi-level day-ahead scheduling strategy

中图分类号:

TM73

李玲, 曹锦业, Nikita Tomin, 杨德昌, 郑颖颖. 计及电动汽车接入的区域综合能源系统双层日前协调优化调度[J]. 电力建设, 2023, 44(5): 23-33.

LI Ling, CAO Jinye, Nikita TOMIN, YANG Dechang, ZHENG Yingying. Bi-level Coordinated Day-ahead Optimal Dispatch of Regional Integrated Energy System Considering the Integrations of Electric Vehicles[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(5): 23-33.

图/表 13

图1 RIES的基本结构

Fig.1 The basic structure of RIES

图2 需求响应不确定性分析

Fig.2 The uncertainty of demand-side response

表1 A、B、C拐点的含义

Table 1 Meaning of inflection point A, B, and C

图3 基于蒙特卡洛的EV无序充电仿真流程

Fig.3 EV disordered charging simulation process based on Monte Carlo

图4 双层日前调度模型

Fig.4 The bi-level optimization scheduling model

表2 价格弹性矩阵

Table 2 Price elasticity matrix of demand

表3 EV电池额定参数

Table 3 Rated battery parameters for EVs

图5 三种场景下的电动汽车充电负荷

Fig.5 The charging load for EVs in three scenarios

表4 三种场景下的用户满意度

Table 4 The consumer satisfaction degree in three scenarios

图6 三种场景下的调度结果

Fig.6 The scheduling results in three scenarios

表5 三种场景下的调度费用

Table 5 The Scheduling cost in three scenarios元

图7 三种场景下的电功率优化结果

Fig.7 The electric power optimization in three scenarios

表6 碳交易成本和经济效益

Table 6 Carbon trading cost and economic benefits元

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计及电动汽车接入的区域综合能源系统双层日前协调优化调度

Bi-level Coordinated Day-ahead Optimal Dispatch of Regional Integrated Energy System Considering the Integrations of Electric Vehicles

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