Automation of Electric Power SystemsVol. 40, Issue 5, Pages: 45-53(2016)
作者机构:
清华大学电机工程与应用电子技术系,北京市,100084
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DOI:
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Published:2016
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WANG Jianxiao. [J]. Automation of electric power systems, 2016, 40(5): 45-53.
DOI:
WANG Jianxiao. [J]. Automation of electric power systems, 2016, 40(5): 45-53.DOI:
基于成本-效益分析的温控负荷需求响应模型与方法
摘要
传统的需求响应研究基于经济学中的需求价格弹性系数
而由于历史数据匮乏
该系数难以准确获取
难以准确反映用户的响应行为。为此
文中提出基于成本-效益分析的经济驱动需求响应模型与方法
以制冰蓄冷设备为例
研究温控负荷用户中长期需求响应的投资决策行为。根据历史运行数据
辨识用户消费粘性参数
建立经济驱动的需求响应模型
基于年度多场景用电曲线开展投资优化决策。通过场景削减技术
提取年度典型场景
有效降低模型维数
提升优化求解效率。精细分析用户参与需求响应的成本效益
生成用户的需求响应函数
从而精准判断用户的响应行为。基于某地区工业用户投资制冰蓄冷设备的算例分析表明
所提出的模型与方法能够辨识用户粘性参数
生成需求响应函数
反映用户真实响应行为
实现用户中长期需求响应投资的科学决策。
Abstract
Traditional demand response (DR) research is based on the price elasticity of demand in economics. However
the elasticity can hardly be obtained for lack of historical data
leading to the difficulty in exactly reflecting customers’ response behavior. Therefore
an economy-driven model and a method of DR for thermostatically-controlled loads based on cost-benefit analysis are proposed. Take the ice storage equipment as an example. Customers’ investment behavior with respect to mid-term and long-term DR response is studied. According to historical operation data
customers’ consumption stickiness is recognized and an economy-driven DR model is developed. The DR model is optimized in light of the annual load curve. Then the scenario reducing technique is employed to extract typical annual scenarios
thereby decreasing model dimensions and improving computational efficiency. After analyzing the cost and benefit of DR
the customer DR function is generated
by which the customers’ response behavior is accurately reflected. The case study based on an industrial customer in a province shows that the model and method proposed can recognize customers’ consumption stickiness
generate DR functions and exactly reflect customers’ response behavior
while achieving scientific decision making for mid-term and long-term DR investment. This work is supported by National Natural Science Foundation of China (No. 51537005) and State Grid Corporation of China (No. DZ71-14-042).