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<p>Research on microgrids often overlooks the variations in price elasticity among different load categories within demand response programs (DRPs). Price elasticity indicates how the quantity demanded of a product or service changes in response to price alterations. In microgrid settings, various load types—such as residential, commercial, and industrial—demonstrate unique responses to changes in pricing. To effectively capture the dynamic responses of customers to variations in power prices, we employ a flexible elasticity model. This model is instrumental in reflecting the real-world behaviour of consumers, who may adjust their energy consumption patterns in response to price signals. By incorporating this model, we aim to provide a more realistic representation of demand-side responses, which is essential for the successful implementation of demand response strategies. Each of these models offers unique insights into how different types of loads respond to price changes, allowing for a comprehensive evaluation of their impacts on microgrid operations. The exponential model, for instance, may capture rapid increases in demand with price drops, while the hyperbolic model could illustrate more gradual changes in consumption patterns. To assess the effectiveness of these demand response programs, we conducted a series of case studies, each evaluated against a set of techno-economic performance metrics. These metrics include factors such as cost savings, energy efficiency, reliability, and customer satisfaction. By analyzing the outcomes of each case study, we were able to derive a prioritized ranking of the demand response programs based on their overall performance. The multi-criteria assessment method employed in this study allows for a holistic evaluation of the various demand response initiatives, taking into account the diverse impacts they may have on both the microgrid and its consumers. The results of numerical simulation show that energy cost and emission polluting are reduced by 15.3% and 11.3% with implementing DRPs.</p>

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Energy Management of Smart Microgrid Considering Hybrid Optimal Consumption of Demand Side

  • 期刊论文

  • 发表日期：

    2025年4月14日

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  • O. C. Candra

摘要

Research on microgrids often overlooks the variations in price elasticity among different load categories within demand response programs (DRPs). Price elasticity indicates how the quantity demanded of a product or service changes in response to price alterations. In microgrid settings, various load types—such as residential, commercial, and industrial—demonstrate unique responses to changes in pricing. To effectively capture the dynamic responses of customers to variations in power prices, we employ a flexible elasticity model. This model is instrumental in reflecting the real-world behaviour of consumers, who may adjust their energy consumption patterns in response to price signals. By incorporating this model, we aim to provide a more realistic representation of demand-side responses, which is essential for the successful implementation of demand response strategies. Each of these models offers unique insights into how different types of loads respond to price changes, allowing for a comprehensive evaluation of their impacts on microgrid operations. The exponential model, for instance, may capture rapid increases in demand with price drops, while the hyperbolic model could illustrate more gradual changes in consumption patterns. To assess the effectiveness of these demand response programs, we conducted a series of case studies, each evaluated against a set of techno-economic performance metrics. These metrics include factors such as cost savings, energy efficiency, reliability, and customer satisfaction. By analyzing the outcomes of each case study, we were able to derive a prioritized ranking of the demand response programs based on their overall performance. The multi-criteria assessment method employed in this study allows for a holistic evaluation of the various demand response initiatives, taking into account the diverse impacts they may have on both the microgrid and its consumers. The results of numerical simulation show that energy cost and emission polluting are reduced by 15.3% and 11.3% with implementing DRPs.