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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (8): 1562-1571    DOI: 10.3785/j.issn.1008-973X.2020.08.015
电气工程     
考虑需求响应和风电不确定性的能源系统调度
张通1(),刘理峰2,杨才明2,张伊宁1,郭创新1,*(),谢栋2
1. 浙江大学 电气工程学院，浙江 杭州 310027
2. 绍兴供电局，浙江 绍兴 312362
Energy system scheduling considering demand response and wind power uncertainty
Tong ZHANG1(),Li-feng LIU2,Cai-ming YANG2,Yi-ning ZHANG1,Chuang-xin GUO1,*(),Dong XIE2
1. School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2. Shaoxing Power Supply Bureau, Shaoxing 312362, China
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摘要：

将价格型需求响应的影响因素分为电、气分时自弹性和电-气交叉价格弹性，两者分别考虑价格调控下能源需求在时间、类型上的转移. 依据模糊数的物理意义对风电历史数据进行拟合；考虑不同场景的发生概率对不确定性的影响，提出基于多场景的模糊优化模型. 依据模糊规划理论，考虑价格型需求响应、风电出力和系统负荷的不确定性，建立考虑风电消纳的电-气综合能源系统源荷互动日前模糊优化调度模型. 采用模糊期望约束、模糊机会约束的等效处理方法和天然气气潮流线性化方法将非线性约束转化为线性约束并求解该模型. 算例表明，通过考虑价格型需求响应和风电不确定性，可以更加准确地模拟在不确定的市场环境下，用户在不同能源间的选择行为，同时降低用能的峰谷差，提高风电的消纳能力.
关键词： 电-气综合能源系统;  需求响应;  模糊规划理论;  分段线性化;  日前调度    
Abstract:

The influencing factors of price demand response are divided into electricity, gas time-sharing self elasticity and electricity-gas cross price elasticity. The former considers the transfer of energy demand in time under price control, and the latter in type. The historical data of wind power was fitted according to the physical meaning of fuzzy number, and a fuzzy optimization model based on multi scene was proposed, considering the influence of different scene probabilities on the uncertainty. A day ahead fuzzy optimal scheduling model of the source load interaction of the electricity-gas comprehensive energy system considering the wind power consumption was established, based on the fuzzy programming theory, considering the price demand response, the uncertainty of wind power output and system load. The nonlinear constraints were transformed into linear constraints and the model was solved by using the equivalent treatment method of fuzzy expectation constraints, fuzzy opportunity constraints and the linearization method of natural gas flow. Examples show that, by considering the price demand response and wind power uncertainty, the user's choice behavior among different energy sources can be more accurately simulated in the uncertain market environment, while reducing the peak valley difference of energy consumption and improving the wind power consumption capacity.
Key words: electric-gas comprehensive energy system    demand response    fuzzy programming theory    piecewise linearization    day-ahead scheduling
收稿日期: 2019-11-14 出版日期: 2020-08-28
CLC:  TM 761  
基金资助: 国家自然基金重点资助项目(51537010)；国网浙江省电力公司集体企业科技资助项目
通讯作者: 郭创新     E-mail: 840677912@qq.com;guochuangxin@zju.edu.cn
作者简介: 张通(1995—)，男，硕士生，从事综合能源系统优化调度研究. orcid.org/0000-0001-8831-1820. E-mail： 840677912@qq.com
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张通
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引用本文:

张通,刘理峰,杨才明,张伊宁,郭创新,谢栋. 考虑需求响应和风电不确定性的能源系统调度[J]. 浙江大学学报(工学版), 2020, 54(8): 1562-1571.

Tong ZHANG,Li-feng LIU,Cai-ming YANG,Yi-ning ZHANG,Chuang-xin GUO,Dong XIE. Energy system scheduling considering demand response and wind power uncertainty. Journal of ZheJiang University (Engineering Science), 2020, 54(8): 1562-1571.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.08.015    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I8/1562

图 1  风电误差分布的隶属度函数
图 2  考虑分时自弹性需求响应前、后分时电价
图 3  考虑分时自弹性需求响应前、后分时气价
图 4  考虑分时自弹性需求响应前、后能源电力系统总负荷
图 5  考虑电-气交叉价格弹性前、后电力系统负荷
图 6  考虑电-气交叉价格弹性前、后电价
图 7  考虑电-气交叉价格弹性前、后气价
场景 GC/105 美元 PC/105 美元 WC/105 美元 TC/105 美元
场景1 2.038 2.812 2.879 7.729
场景2 2.056 2.973 2.227 7.166
表 1  不同场景下的系统运行成本
图 8  不同场景下的启动机组数
图 9  不同模型下的系统运行成本
图 10  不同模型下的弃风电量误差
图 11  不同模型下的不足电量误差
图 12  不同系统电量平衡、旋转备用置信度下的系统运行成本
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