不确定条件下城市用电负荷的预测与计算方法

S. Sh. Tavarov, A. I. Sidorov, I. F. Suvorov, A. B. Svyatykh
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摘要

该研究解决了车里雅宾斯克地区几个城市中市政消费者占主导地位的配电网络中实际用电量与计算值的一致性。根据车里雅宾斯克地区几个城市的用电量数据,为了研究法规文件规定的比用电负荷与每套公寓实际值之间的一致性,分析了2021-2022年期间特定户数的市政消费者的年平均用电量。根据SP 256.1325800.2016中概述的指导方针,使用常规方法计算给定期间的电力负荷,对所研究设施中市政消费者的年平均用电量进行对应分析。公寓实际用电负荷与现行规范性文件规定的电耗标准值之间的差异从- 48%到300%不等。对于位于车里雅宾斯克地区城市的16个考虑对象,比较了实际电力负荷与既定标准值之间的差异。对于6套公寓,这一差异从- 58%到155%不等。为了提高城市用户占主导地位的配电网的用电量预测和负荷计算的准确性,提出了采用新因子的方法。这个因素涉及到一个广义的不确定系数Ai,它的值是在考虑的时期内确定的。使用所开发的方法,预报计算的相对偏差小于或等于10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method for forecasting and calculating the electrical load of municipal consumers under uncertainty
The study addresses the conformity of actual electricity consumption to the calculated value in electric distribution networks in which municipal consumers predominate in several cities of the Chelyabinsk region. To study the conformity between the specific electrical load established by regulatory documents and the actual value per apartment according to power consumption data in several cities of the Chelyabinsk region, the average annual power consumption by municipal consumers with a specific number of apartments was analyzed over a period of 2021–2022. The correspondence analysis of the average annual electricity consumption by municipal consumers in the studied facilities was carried out using the conventional method for calculating the electrical load over the given period following the guidelines outlined in SP 256.1325800.2016. The discrepancy between the actual electrical load on the apartment and its normative value established by the acting normative documents ranged from minus 48 to 300% with respect to electricity consumption. For the considered 16 objects located in the cities of the Chelyabinsk region, the discrepancy between the actual electrical load and the established normative values was compared. For 6 apartments, this discrepancy ranged from minus 58 to 155%. To improve the accuracy of forecasting electricity consumption and calculating electrical loads in electric distribution networks with a predominance of municipal consumers, methods using a new factor were recommended. This factor involves a generalized uncertainty coefficient Ai, whose values are determined for the considered period. When using the developed methods, relative deviations in the forecast calculations are less than or equal to 10%.
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