DOI: https://doi.org/10.61435/ijred.2026.61951
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Economic environmental optimization in multiple renewable energy sources with demand response based on multi-objective optimization algorithm

1School of Chemical Process Automation, Shenyang University of Technology, ShenYang 110000, LiaoNing, China

2College of Business and Trade, Shenyang University of Technology, ShenYang 110000, LiaoNing, China

Received: 11 Nov 2025; Revised: 26 Feb 2026; Accepted: 28 May 2026; Available online: 5 Aug 2026; Published: 1 Sep 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

The use of renewable energy sources in distribution networks results in considerable environmental and economic benefits, but it introduces challenges related to uncertainty, intermittency, and system stability. A complete multi-objective optimization model is developed that integrates renewable energy units, battery energy storage systems, electric vehicles, demand response programs, and hydro turbine units to solve these problems. The proposed methodology achieves cost savings and reduces carbon footprint while maintaining operational stability in the system. The optimization model includes full mathematical representations of all components including photovoltaic and wind generation systems and battery energy storage system state-of-charge dynamics and electric vehicle charging and discharging schedules and controllable hydro generation. A time-of-use demand response scheme is adopted to model demand flexibility which allows for load shifting and increased renewable utilization. The model is employed in a case study of 150 customers; the framework shows its efficiency through comparative simulations that evaluate performance under scenarios with demand response and without demand response. The results show that demand response reduces peak demand, improves storage coordination, and increases renewable integration. The demand response lowered costs to $6,300-$11,150 and emissions to 12,825-12,860 kg. The configuration of electrical vehicle and battery energy storage systems are combined to achieve peak shaving allowing customers to support the grid and the hydro turbine can provide effective back up power when the renewables are unavailable. The results indicate that coordinated optimization of renewables with storage and demand flexibility leads to improvements in cost-emission performance while enhancing sustainability and system resiliency.

Keywords: Battery Energy Storage System; Demand Response; Electric Vehicle; Hydrogen Turbine; Multi-Objective Optimization; Renewable Energy Source

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Section: Original Research Article
Language : EN
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