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Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (8): 1562-1571    DOI: 10.3785/j.issn.1008-973X.2020.08.015
    
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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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 wordselectric-gas comprehensive energy system      demand response      fuzzy programming theory      piecewise linearization      day-ahead scheduling     
Received: 14 November 2019      Published: 28 August 2020
CLC:  TM 761  
Corresponding Authors: Chuang-xin GUO     E-mail: 840677912@qq.com;guochuangxin@zju.edu.cn
Cite this article:

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.

URL:

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


考虑需求响应和风电不确定性的能源系统调度

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 
Fig.1 Membership function of wind power error distribution
Fig.2 Time-sharing tariff before and after considering time-sharing self elasticity demand response
Fig.3 Time-sharing gas price before and after considering time-sharing self elasticity demand response
Fig.4 Total load of energy power system before and after considering time-sharing self electricity demand response
Fig.5 Load of power system before and after considering price elasticity of electricity-gas cross
Fig.6 Electricity price before and after considering electricity-gas cross elasticity coefficient
Fig.7 Gas price before and after considering electricity-gas cross elasticity coefficient
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
Tab.1 System operation cost in different scenarios
Fig.8 Number of starting units in different scenarios
Fig.9 System operation cost under different models
Fig.10 Error of abandoned wind power under different models
Fig.11 Error of shortage of electricity under different models
Fig.12 System operation cost under different power balance and rotating reserve confidence
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