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JEM 2021, 11(3): 66-77 |
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Sheibani F, Keshavarz H, Mollahassani-pour M. Estimation of Demand Side Resources Potential Regarding Climate Changes. JEM 2021; 11 (3) :66-77
URL: http://energy.kashanu.ac.ir/article-1-1287-en.html
URL: http://energy.kashanu.ac.ir/article-1-1287-en.html
Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan
Abstract: (1910 Views)
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 so-called 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 demand-side 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 short-term to long-term 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 two-stage structure is proposed to determine the potential of DRRs based upon the variation of energy consumption in comparison with the temperature along one-year 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’ potential in different years. Energy consumption as well as ambient temperature data of the city of Boston over six years (2011-2016) 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 55-66 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.
Materials and methods: Therefore, in this paper, a two-stage structure is proposed to determine the potential of DRRs based upon the variation of energy consumption in comparison with the temperature along one-year 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’ potential in different years. Energy consumption as well as ambient temperature data of the city of Boston over six years (2011-2016) 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 55-66 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.
Type of Study: Research |
Subject:
Electrical Engineering
Received: 2019/05/26 | Revised: 2022/01/2 | Accepted: 2020/11/14 | Published: 2021/11/1
Received: 2019/05/26 | Revised: 2022/01/2 | Accepted: 2020/11/14 | Published: 2021/11/1
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