تخمین پتانسیل منابع سمت تقاضا با توجه به تغییرات آب‌وهوایی

در بستر شبکه‌های هوشمند الکتریکی، بهره‌گیری از منابع سمت تقاضا (منابع پاسخ‌گویی بار)، مدیریت مناسب‌تر تقاضای روزافزون انرژی و همچنین کاهش چشمگیر هزینه‌های متحمل‌شدۀ سیستم را فراهم می‌آورد. تعیین پتانسیل منابع سمت تقاضا به‌علت اثرگذاری بر برنامه‌ریزی‌های کوتاه‌مدت تا بلندمدت سیستم قدرت، حائز اهمیت است. مقالۀ حاضر، به ارائۀ ساختاری دومرحله‌ای به‌منظور شناسایی پتانسیل نامی منابع پاسخ‌گویی تقاضا، بر مبنای تغییرات الگوی مصرف انرژی و تغییرات دمایی در افق زمانی سالیانه می‌پردازد. در مرحلۀ اول، با استفاده از روش تخمین نمونه، آستانۀ دمایی عملکرد وسایل سرمایشی و گرمایشی تعیین می‌شود. در مرحلۀ دوم، بر اساس آستانۀ عملکردی وسایل سرمایشی و گرمایشی، داده‌های مصرف انرژی به دو بخش تفکیک می‌شوند؛ سپس با اعمال ضرایب مختلف به منحنی تقاضا متوسط روزانه و منحنی تقاضا پرباری روزانه، میزان بار انعطاف‌پذیر (پتانسیل نامی منابع سمت تقاضا) تعیین می‌شود. در پایان، با آنالیز آماری پتانسیل‌ نامی منابع سمت تقاضا به‌دست‌آمده در سال‌های مختلف، پتانسیل ‌نامی منابع مجازی سمت تقاضا در فصول گرم و سرد مشخص می‌گردد. ساختار پیشنهادی، با استفاده از داده‌های مصرف انرژی و داده‌های دمایی شهر boston مورد ارزیابی قرار می‌گیرد؛ نتایج حاکی از میزان تفاوت در پتانسیل ‌نامی با‌ توجه به تغییرات دوره‌ای دماست.

Estimation of Demand Side Resources Potential Regarding Climate Changes

Introduction: Today, growing energy consumption as well as environmental concerns arising from climate changes affects the energy policy decisions. Moreover, with the advent of smart grids, new challenges regarding power system scheduling have been considered. In smart environments, utilizing Demand Side Resources (DSRs), the socalled Demand Response Resources (DRRs), provides more suitable management for growing energy consumption as well as considerable reduction in system expenditures and emitted contaminants. Demand Response (DR) is defined as changes in electric usage by demandside resources from their normal consumption patterns in response to changes in the price of electricity or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. The estimation of DSRs potential due to affecting the power system scheduling ranging from shortterm to longterm is contemplated as a crucial issue. Energy consumption and ambient temperature data can be considered as important factors to determine the DSRs potential. Here, the presence time of cooling and heating appliances affects the power system scheduling while considerable energy savings are provided by appropriate management and controlling of these appliances.Materials and methods: Therefore, in this paper, a twostage structure is proposed to determine the potential of DRRs based upon the variation of energy consumption in comparison with the temperature along oneyear horizon time. In the proposed model, the potential of DSRs, in warm and cold weather conditions, is obtained regarding the operation temperature threshold of cooling and heating appliances and the maximum and average load profile of the grid. In the first stage, through a sample estimation method, the temperature threshold for commitment of cooling and heating appliances is specified. In the next stage, energy consumption data are clustered in two categories regarding temperature thresholds. Then, the flexible load level, DRRs potential, is determined via implementing multifarious indices to the maximum and average daily load profile. Lastly, the final potential of DSRs is determined in warm and cold weather conditions regarding the statistical analysis of computed DRRs rsquo; potential in different years. Energy consumption as well as ambient temperature data of the city of Boston over six years (20112016) is utilized to evaluate the capability of the proposed structure. First, days with temperature over 55 degrees Fahrenheit are selected to determine the commitment of cooling appliances due to more proper temperature gradient than other points. Utilizing classification algorithm and Maximum Like Lihood (MLL) algorithm, operation temperature threshold of cooling appliances is estimated from 2011 to 2016. Regarding obtained operation temperature threshold of cooling appliances per year, the appropriate temperature within the comfort range is defined between 5566 degrees Fahrenheit. As a result, temperatures lower than 55 degrees Fahrenheit are considered as operation temperature threshold of heating appliances. Now, according to the number of days in which cooling and heating appliances are committed per year, the hourly confidence interval diagram is depicted per cluster. Then, the statistical average of available data in upper limit and normal limit of the confidence interval diagram per hour are computed and contemplated as maximum and average hourly load respectively. Finally, regarding the obtained results, maximum and average daily load profiles are specified in warm and cold weather conditions. The difference between these two curves shows the nominal potential of DSRs which can be obtained by optimal management and control. Result: It can be observed that 12% to 15% of peak demand can be utilized as nominal potential of DRRs in power system scheduling studies in cold weather condition. It should be mentioned that the minimum and maximum potential of DSRs in presence of heating appliances are occurred in 2012 and 2015 respectively. Similarly, 14.5% to 17.5% of peak demand has been identified as nominal potential of DRRs in presence of cooling appliances. It is worth mentioning that the difference between nominal potential of DSRs in cold and warm weather conditions is occurred due to diverse characteristic of consumers in different ambient temperature. Discussion and Conclusion: As mentioned before, in this paper, the number of analysed periods is considered equal to 365 days in one year; however, increasing the number of samples in a year may lead to more accurate outcome. Moreover, utilizing input data such as energy price and the level of paid incentive to participating customers in DR programs provide more practical results.