4.5 Article

Home Energy Management System Embedded with a Multi-Objective Demand Response Optimization Model to Benefit Customers and Operators

Journal

ENERGIES
Volume 14, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/en14020257

Keywords

customer satisfaction; demand response; energy storage systems; electric vehicles; home energy management; loss of life; renewable energy sources; transformer aging; coincident peak

Categories

Funding

  1. Qatar National Research Fund (Qatar Foundation) [NPRP11S-1202-170052]

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This paper presents a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources, taking into account several factors to achieve the optimal demand/generation profile. Simulation results show that the system can reduce electricity costs, decrease demand peaks, and minimize transformer lifespan loss.
This paper proposes a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources. The optimal demand/generation profile is presented while considering utility price signal, customer satisfaction, and distribution transformer condition. The electricity home demand considers electric vehicles (EVs), Battery Energy Storage Systems (BESSs), and all types of non-shiftable, shiftable, and controllable appliances. Furthermore, PV-based renewable energy resources, EVs, and BESSs are utilized as sources of generated power during specific time intervals. In this model, customers can only perform Demand Response (DR) actions with contracts with utility operators. A multi-objective demand/generation response is proposed to optimize the scheduling of various loads/supplies based on the pricing schemes. The customers' behavior comfort level and a degradation cost that reflects the distribution transformer Loss-of-Life (LoL) are integrated into the multi-objective optimization problem. Simulation results demonstrate the mutual benefits that the proposed HEMS provides to customers and utility operators by minimizing electricity costs while meeting customer comfort needs and minimizing transformer LoL to enhance operators' assets. The results show that the electricity operation cost and demand peak are reduced by 31% and 18%, respectively, along with transformer LoL % which is reduced by 28% compared with the case when no DR was applied.

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