考虑EV和需求侧响应的社区微网能量管理

doi:10.19799/j.cnki.2095-4239.2020.0312

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (2): 617-623.doi: 10.19799/j.cnki.2095-4239.2020.0312

考虑EV和需求侧响应的社区微网能量管理

陆燕娟(), 陈友芹, 潘庭龙()

江南大学物联网工程学院，江苏无锡 214000

收稿日期:2020-09-08 修回日期:2020-10-17 出版日期:2021-03-05 发布日期:2021-03-05
通讯作者: 潘庭龙 E-mail:1607936314@qq.com;tlpan@jiangnan.edu.cn
作者简介:陆燕娟（1995—），女，硕士研究生，从事微网能量管理工作，E-mail：1607936314@qq.com；
基金资助:
国家自然科学基金项目(61672266)

Community microgrid energy management considering electric vehicles and demand response

Yanjuan LU(), Youqin CHEN, Tinglong PAN()

The College of IoT Engineering, Jiangnan University, Wuxi 214000, Jiangsu, China

Received:2020-09-08 Revised:2020-10-17 Online:2021-03-05 Published:2021-03-05
Contact: Tinglong PAN E-mail:1607936314@qq.com;tlpan@jiangnan.edu.cn

摘要/Abstract

摘要：

本文针对独立型社区微网能量管理问题展开研究，构建了基于电动汽车充放电和需求侧响应的微网多目标优化数学模型，提出一种含有负荷级和源荷级的能量管理策略。负荷级依据车主出行习惯和分时电价引导电动汽车有序充放电以减小微网负荷峰谷差；源荷级通过需求侧响应调整居民用电方式优化负荷曲线，优先使用光、储及电动汽车出力、剩余“净负荷”由微型燃气轮机消纳以最小化微网运行成本、污染气体排放量和能源浪费率。采用带精英策略的非支配排序遗传算法对模型求解，分析不同能量管理方案典型社区微网运行情况，验证了优化模型和策略的正确性和有效性。

关键词: 电动汽车, 遗传算法, 社区微网, 能量管理, 需求侧响应

Abstract:

The energy management problem of microgrids in an independent community is investigated in this paper. A multiobjective optimization mathematical model of microgrids considering electric vehicle charging or discharging and demand response (DR) is constructed, and an energy management strategy including load and source-load levels is proposed. Charging or discharging electric vehicles is orderly guided in the load level according to the owner's travel habits and time-of-use electricity price to reduce the microgrids' peak-valley difference. The load curve is optimized at the source-load level by adjusting the residential electricity consumption method through DR. PV, battery energy system, and electric vehicles are used preferentially. The car's output and the remaining "net load" are absorbed by the micro-gas-turbine to minimize the operating cost of the microgrid, the amount of polluting gas emissions, and the energy waste rate. According to the model's multiobjective, multiconstraint, nonlinear, and other characteristics, a nondominated sorting genetic algorithm with elite strategy is used to solve, compare, and analyze the operations of typical community microgrids with different energy management schemes. The correctness and effectiveness of the optimization model and strategy are verified.

Key words: electric vehicle, genetic algorithm, community micro-grid, energy management, demand response

中图分类号:

TM 732

陆燕娟, 陈友芹, 潘庭龙. 考虑EV和需求侧响应的社区微网能量管理[J]. 储能科学与技术, 2021, 10(2): 617-623.

Yanjuan LU, Youqin CHEN, Tinglong PAN. Community microgrid energy management considering electric vehicles and demand response[J]. Energy Storage Science and Technology, 2021, 10(2): 617-623.

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类型	能量成本系数/CNY·(kW·h)^-1	运维系数/CNY·(kW·h)^-1
PV	0	0.01015
MT	0	0.04228
BES	1190	0.02898

类型	排放系数/kg·(kW·h)^-1	排放成本/CNY·kg^-1
CO₂	0.0016	0.09
SO₂	0.000008	6.64
NO_x	0.00044	27.93

电动汽车模式	峰谷差/kW·h	负荷率	峰谷差率
无序充电	187.2	66.5%	60.3%
有序充放电	99.4	84.7%	41.9%

电动汽车需求侧响应	运行成本/元	气体排放/kg	能源浪费率
考虑	360.6	3.7	29.8%
不考虑	375.3	3.85	33.2%

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考虑EV和需求侧响应的社区微网能量管理

Community microgrid energy management considering electric vehicles and demand response

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