Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand
This study aims to develop a mathematical modeling approach to maximize the welfare of the price-responsive customers (CUs) in a wholesale electricity market in Tokyo, Japan. The contributions made by this paper in the quest to determine the role of CUs in a demand response market are twofold. First...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722017498 |
| _version_ | 1797901888008486912 |
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| author | Ladan Malehmirchegini Hooman Farzaneh |
| author_facet | Ladan Malehmirchegini Hooman Farzaneh |
| author_sort | Ladan Malehmirchegini |
| collection | DOAJ |
| description | This study aims to develop a mathematical modeling approach to maximize the welfare of the price-responsive customers (CUs) in a wholesale electricity market in Tokyo, Japan. The contributions made by this paper in the quest to determine the role of CUs in a demand response market are twofold. First, the aversion of the CUs to the risk of choosing the Demand Response Programs (DPRs) is taken into account by considering their expected utility from consuming electricity. The proposed model is founded on the customer theory in microeconomics, using the concept of the expected utility function to model the behavior of the risk-averse CUs in response to different DPRs. Second, it introduces an hourly-based model for the short-term price elasticity of demand, considering the day-ahead price mechanism defined in the Japan Electric Power Exchange (JEPX) market. The estimated price elasticities are used in a price elasticity matrix of demand (PEMD) to precisely reflect the different response strategies, including flexible, in-flexible forward-shifting, backward shifting, and optimizing responses. An accurate day-ahead hourly load forecasting is performed, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, which is trained on four years of data provided by the Tokyo Electric Power Company (TEPCO), with a mean absolute percentage error (MAPE) of 0.94%. The developed model is used to analyze the CUs’ behaviors with different response strategies in the JEPX market in Tokyo. By applying the Time-of-Use (TOU) and Real-Time-Pricing (RTP) programs, the results reveal a peak reduction potential of 10.7% and 7.3%, respectively, for the flexible CUs. Applying the RTP program to the curtailable loads can achieve a 7.7% reduction in daily peak demand and a 1.6% reduction in daily electricity consumption. |
| first_indexed | 2024-04-10T09:09:04Z |
| format | Article |
| id | doaj.art-a75a5d9d74084fe2b4b3a3fa9fa65905 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:09:04Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-a75a5d9d74084fe2b4b3a3fa9fa659052023-02-21T05:13:23ZengElsevierEnergy Reports2352-48472022-11-0181191011926Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demandLadan Malehmirchegini0Hooman Farzaneh1Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, JapanInterdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan; Transdisciplinary Research and Education Center for Green Technologies, Kyushu University, Fukuoka, Japan; Corresponding author at: Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan.This study aims to develop a mathematical modeling approach to maximize the welfare of the price-responsive customers (CUs) in a wholesale electricity market in Tokyo, Japan. The contributions made by this paper in the quest to determine the role of CUs in a demand response market are twofold. First, the aversion of the CUs to the risk of choosing the Demand Response Programs (DPRs) is taken into account by considering their expected utility from consuming electricity. The proposed model is founded on the customer theory in microeconomics, using the concept of the expected utility function to model the behavior of the risk-averse CUs in response to different DPRs. Second, it introduces an hourly-based model for the short-term price elasticity of demand, considering the day-ahead price mechanism defined in the Japan Electric Power Exchange (JEPX) market. The estimated price elasticities are used in a price elasticity matrix of demand (PEMD) to precisely reflect the different response strategies, including flexible, in-flexible forward-shifting, backward shifting, and optimizing responses. An accurate day-ahead hourly load forecasting is performed, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, which is trained on four years of data provided by the Tokyo Electric Power Company (TEPCO), with a mean absolute percentage error (MAPE) of 0.94%. The developed model is used to analyze the CUs’ behaviors with different response strategies in the JEPX market in Tokyo. By applying the Time-of-Use (TOU) and Real-Time-Pricing (RTP) programs, the results reveal a peak reduction potential of 10.7% and 7.3%, respectively, for the flexible CUs. Applying the RTP program to the curtailable loads can achieve a 7.7% reduction in daily peak demand and a 1.6% reduction in daily electricity consumption.http://www.sciencedirect.com/science/article/pii/S2352484722017498Demand response programsPrice elasticity matrix (PEM)Time series forecastingOptimization modelingWholesale electricity market |
| spellingShingle | Ladan Malehmirchegini Hooman Farzaneh Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand Energy Reports Demand response programs Price elasticity matrix (PEM) Time series forecasting Optimization modeling Wholesale electricity market |
| title | Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand |
| title_full | Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand |
| title_fullStr | Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand |
| title_full_unstemmed | Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand |
| title_short | Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand |
| title_sort | demand response modeling in a day ahead wholesale electricity market in japan considering the impact of customer risk aversion and dynamic price elasticity of demand |
| topic | Demand response programs Price elasticity matrix (PEM) Time series forecasting Optimization modeling Wholesale electricity market |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722017498 |
| work_keys_str_mv | AT ladanmalehmirchegini demandresponsemodelinginadayaheadwholesaleelectricitymarketinjapanconsideringtheimpactofcustomerriskaversionanddynamicpriceelasticityofdemand AT hoomanfarzaneh demandresponsemodelinginadayaheadwholesaleelectricitymarketinjapanconsideringtheimpactofcustomerriskaversionanddynamicpriceelasticityofdemand |