Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 12, Issue 5, Pages 4039-4048Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3078655
Keywords
HVAC; Microgrids; Real-time systems; Load modeling; Schedules; Optimization; Home appliances; Transactive energy; HVAC system; appliances scheduling; energy market; game theory
Categories
Funding
- FEDER (COMPETE 2020)
- FCT [POCI01-0145-FEDER-029803 (02/SAICT/2017)]
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Transactive energy (TE) is proposed as a framework to address the load-generation mismatch in high penetration renewable energy systems. The framework includes online appliance scheduling and a TE market to optimize operation schedules and reduce power consumption to tackle real-time power shortage. Extensive simulations demonstrate the efficiency of the proposed TE framework.
Transactive energy (TE) is a viable framework to tackle the load-generation mismatch in energy systems with high penetration of renewable energy resources (RERs). In this paper, we propose a TE framework for prosumers with heating, ventilation, and air conditioning (HVAC) systems to address real-time power shortage in a residential microgrid. Our framework consists of two phases. First, to mitigate load-generation mismatch, we develop an online appliance scheduling method to determine the optimal operation schedule of each prosumer's appliances. In particular, we apply receding horizon optimization (RHO) to tackle the load and renewable generation uncertainties and to better match the real-time power consumption of the appliances with the priorly-purchased power from the day-ahead market. Second, in case that there still exists power shortage at the microgrid level, a TE market based on pay-as-market clearing price (MCP) is proposed among prosumers to reduce the power consumption of their HVAC systems. We capture the competition among the participating prosumers as a non-cooperative game and develop an algorithm to achieve the Nash equilibrium, while considering prosumers' willingness to participate in the TE market. Extensive simulations are performed to demonstrate the efficiency of our proposed TE framework.
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