The demand side management (DSM) technique has been widely advocated to curb peak load requirements in smart grids. Since household consumption generally constitutes a significant percentage of overall energy usage, managing residential energy consumption is thus vital for achieving better system-wide efficiency. Dynamic pricing programs have emerged as an effective DSM tool in influencing consumers' behavior regarding their energy consumption and relieving the power grid from maximum demand while maintaining its operational integrity. This paper focuses on managing residential peak load demand in Singapore and maximizing renewable energy use. To this end, a Generic Algorithm (GA)-based algorithm is proposed to shift household loads away from peak periods by providing monetary incentives to consumers while reducing daily electricity bills. Simulation results demonstrate the effectiveness of the proposed algorithm by quantifying the incurring reductions in both peak load and electricity costs.
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Optimal Residential Building Energy Management Using Genetic Algorithm
Semantic Scholar · Engineering · 2022
Abstract
The demand side management (DSM) technique has been widely advocated to curb peak load requirements in smart grids. Since household consumption generally constitutes a significant percentage of overall energy usage, managing residential energy consumption is thus vital for achieving better system-wide efficiency. Dynamic pricing programs have emerged as an effective DSM tool in influencing consumers' behavior regarding their energy consumption and relieving the power grid from maximum demand while maintaining its operational integrity. This paper focuses on managing residential peak load demand in Singapore and maximizing renewable energy use. To this end, a Generic Algorithm (GA)-based algorithm is proposed to shift household loads away from peak periods by providing monetary incentives to consumers while reducing daily electricity bills. Simulation results demonstrate the effectiveness of the proposed algorithm by quantifying the incurring reductions in both peak load and electricity costs.