Optimization of integrated energy systems for enhanced grid flexibility using the Meerkat optimization algorithm

• Demand-side management effectively reduces peak loads on the grid. • Renewable energy and batteries facilitate the conversion of excess electricity into gas for market sale. • DG reduces energy purchases from the upstream grid, leading to a 32% cost reduction. • Highlighting the need for a unified optimization framework to enhance efficiency and profitability. • Development of an Integrated Optimization Model. As renewable energy sources and energy storage systems (ESS) become more common, effective energy management is increasingly important. Grid operators are exploring systems like power-to-gas (P2G), which convert surplus electricity from renewables or batteries into gas that can be sold back to the gas grid. The integration of distributed gas production systems and P2G creates a complex relationship between electricity and gas networks, requiring a practical approach to energy management and profit optimization. This research focuses on optimizing integrated energy systems to identify the most efficient solutions. Effective demand-side management is essential for optimal power grid operation, as it can reduce peak loads and improve profits. This paper introduces a mixed integer non-linear programming (MINLP) model to jointly optimize electricity and gas networks, incorporating distributed generation, P2G systems, storage, electric vehicles (EVs), and demand-side management. To address computational challenges, the study uses the Meerkat Optimization Algorithm (MOA). The proposed model is tested on a 33-bus distribution network, showing its effectiveness in different scenarios. Key results include: 1) demand-side management reduces peak loads on the grid, 2) renewable energy and batteries help convert excess electricity into gas for market sale, and 3) distributed generation lowers energy purchases from the upstream grid, leading to a 32 % cost reduction. Index terms: Evolutionary algorithm, joined systems, P2G, gas grid, energy system.

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Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2025-10-15
DOI
https://doi.org/10.1016/j.ijepes.2025.111263
Primary Topic
Hybrid Renewable Energy Systems
Type
article
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article

Optimization of integrated energy systems for enhanced grid flexibility using the Meerkat optimization algorithm

Mojtaba Vahedi, Reza Davarzani, Mahmoud Samiei Moghaddam, Hesamodin Hoseinpour et al.
International Journal of Electrical Power & Energy Systems
Hybrid Renewable Energy Systems
article

Optimization of integrated energy systems for enhanced grid flexibility using the Meerkat optimization algorithm

Mojtaba Vahedi, Reza Davarzani, Mahmoud Samiei Moghaddam, Hesamodin Hoseinpour, Hamidreza Jalalian
article en

Abstract

• Demand-side management effectively reduces peak loads on the grid. • Renewable energy and batteries facilitate the conversion of excess electricity into gas for market sale. • DG reduces energy purchases from the upstream grid, leading to a 32% cost reduction. • Highlighting the need for a unified optimization framework to enhance efficiency and profitability. • Development of an Integrated Optimization Model. As renewable energy sources and energy storage systems (ESS) become more common, effective energy management is increasingly important. Grid operators are exploring systems like power-to-gas (P2G), which convert surplus electricity from renewables or batteries into gas that can be sold back to the gas grid. The integration of distributed gas production systems and P2G creates a complex relationship between electricity and gas networks, requiring a practical approach to energy management and profit optimization. This research focuses on optimizing integrated energy systems to identify the most efficient solutions. Effective demand-side management is essential for optimal power grid operation, as it can reduce peak loads and improve profits. This paper introduces a mixed integer non-linear programming (MINLP) model to jointly optimize electricity and gas networks, incorporating distributed generation, P2G systems, storage, electric vehicles (EVs), and demand-side management. To address computational challenges, the study uses the Meerkat Optimization Algorithm (MOA). The proposed model is tested on a 33-bus distribution network, showing its effectiveness in different scenarios. Key results include: 1) demand-side management reduces peak loads on the grid, 2) renewable energy and batteries help convert excess electricity into gas for market sale, and 3) distributed generation lowers energy purchases from the upstream grid, leading to a 32 % cost reduction. Index terms: Evolutionary algorithm, joined systems, P2G, gas grid, energy system.

International Journal of Electrical Power & Energy SystemsVol. 172
Islamic Azad University, Shahrood (IR), Islamic Azad University, Damghan Branch (IR)
Openalex Percentile: Top 68%
Hybrid Renewable Energy Systems
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