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A Comparison of Priority Service versus Real-Time Pricing for Enabling Residential Demand Response
The unprecedented growth of renewable energy has led to various challenges in power system operations. Demand response can provide further flexibility to the system in order to balance the effects of the massive integration of renewable resources. This work focuses on the application of demand response to individual households and inferring the impact that results from its application on consumers’ perceived quality of service. The analysis is centered on the impact to consumers by assessing the effects of these demand response schemes on their comfort and bill. The application is realized by means of a home energy management system algorithm that schedules appliances within the house by minimizing the discomfort experienced by the consumer. The algorithm is based on reinforcement learning techniques that allow scheduling appliances online after a training period, while still accounting for changes in consumers’ behavior. The home energy router is used in order to compare two demand response schemes: real-time pricing and priority service pricing. We illustrate the concept using a simple example of a household with one appliance. It provides an end-to-end illustration of (i) how to design a priority service menu from a time series of real-time prices, (ii) how a household selects options from this menu, (iii) how devices are dispatched in the household by a home energy router, and (iv) what consumer welfare losses are relative to the golden standard of real-time pricing.
A Comparison of Priority Service versus Real-Time Pricing for Enabling Residential Demand Response
The unprecedented growth of renewable energy has led to various challenges in power system operations. Demand response can provide further flexibility to the system in order to balance the effects of the massive integration of renewable resources. This work focuses on the application of demand response to individual households and inferring the impact that results from its application on consumers’ perceived quality of service. The analysis is centered on the impact to consumers by assessing the effects of these demand response schemes on their comfort and bill. The application is realized by means of a home energy management system algorithm that schedules appliances within the house by minimizing the discomfort experienced by the consumer. The algorithm is based on reinforcement learning techniques that allow scheduling appliances online after a training period, while still accounting for changes in consumers’ behavior. The home energy router is used in order to compare two demand response schemes: real-time pricing and priority service pricing. We illustrate the concept using a simple example of a household with one appliance. It provides an end-to-end illustration of (i) how to design a priority service menu from a time series of real-time prices, (ii) how a household selects options from this menu, (iii) how devices are dispatched in the household by a home energy router, and (iv) what consumer welfare losses are relative to the golden standard of real-time pricing.
A Comparison of Priority Service versus Real-Time Pricing for Enabling Residential Demand Response
2019-01-01
Conference paper
Electronic Resource
English
DDC:
690
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