Abstract
Residential sector is the biggest potential field of reducing peak demand through demand response (DR) in smart grid. Heating, ventilating, and air conditioning (HVAC) is the largest residential electricity user in house. Therefore, controlling the operation of HVAC is an effective method to implement DR in residential sector. The algorithms proposed in literature are single objective optimization algorithms that only minimize the electricity cost and could not quantify the user's comfort level. To tackle this problem, this paper proposes a comfort level indicator, builds a multi-objective scheduling model, and presents a multi-objective optimal control algorithm for HVAC based on particle swarm optimization (PSO). The algorithm controls the operation of HVAC according to electricity price, outdoor temperature forecast, and user preferences to minimize the electricity cost and maximize the user comfort level simultaneously. The proposed algorithm is verified by simulations, and the results demonstrate that it can decrease the electricity cost significantly and maintain the user comfort level effectively.
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