考虑促进清洁能源消费的多类型用户调峰潜力评价方法

Xiaoming Xiao, Ye Cai, Ying Liu, Zheng-yi Li, Chen Xue
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摘要

用户侧调峰是促进清洁能源消费的有效手段。为了充分发挥用户侧参与调峰、响应快、经济性高的优势,提高用户参与积极性,本研究提出了考虑促进清洁能源消费的用户侧参与调峰潜力评价模型。针对传统静态方法无法反映调峰潜力指数的多时间尺度波动问题,提出了一种通过提取多类型用户参与调峰的多日典型特征的动态激励评价方法。首先,构建区间权重,降低用户负荷波动对评价指标的影响;引入加速度的概念来描述各指标观测值在不同时间尺度上的变化趋势;其次,计算了各类指标优缺点的增益范围;此外,分配相应的“奖励”和“惩罚”值,可以更客观、真实地反映被评价对象的现状,从而获得动态激励指标值;最后,基于区间权值和动态激励指标值,构造了区间型决策矩阵对。对各类用户参与调峰的潜力进行了评分和排序。以某地区5类用户的负荷为例,结果表明,该方法能有效地评价不同类型负荷的调峰潜力。
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Evaluation Method of Peak Shaving Potential for Multi-type Users Considering the Promotion of Clean Energy Consumption
Peak shaving on the user side is an effective means to promote the consumption of clean energy. This study proposes a user-side participation in peak shaving potential evaluation model considering the promotion of clean energy consumption in order to give full play to the advantages of user-side participation in peak shaving, fast response and high economy and to improve user participation enthusiasm. Aiming at the problem of multi-time scale volatility that traditional static methods cannot reflect the peak shaving potential index, this study proposes a dynamic incentive evaluation method by extracting the multi-day typical characteristics of multiple types of users participating in peak shaving. First, interval weights were constructed to reduce the impact of user load fluctuations on evaluation indicators; the idea of acceleration was introduced to describe the trend of changes in the observed values of indicators at different time scales; second, the gain range of the advantages and disadvantages of each type of indicator was calculated. In addition, assigning corresponding ”reward” and ”punishment” values can reflect the current state of the evaluated object more objectively and truly, thereby obtaining dynamic incentive index values; finally, based on interval weights and dynamic incentive index values, an interval-type decision matrix pair was formed. The potential of various types of users to participate in peak shaving was scored and sorted. Taking the load of five types of users in a certain area as an example, the results show that this method can effectively evaluate the peak shaving potential of different types of loads.
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