RESEARCH PAPER
Smart Strategies for Local Energy Grids: Optimizing Energy Management with Hybrid Electric Vehicle Integration
1
State Grid Beijing Electric Power Company, China
Submission date: 2025-02-11
Final revision date: 2025-04-20
Acceptance date: 2025-06-08
Online publication date: 2025-06-11
Publication date: 2025-06-11
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):206049
HIGHLIGHTS
- Incorporating stochastic models to manage renewable and demand uncertainties.
- Proposing a probabilistic EV strategy for reducing costs and boosting profits.
- Enabling adaptive control using MDP under real-time pricing and load changes.
- Optimizing energy management using a modified SOS for multi-objective goals.
- Enhancing grid flexibility, efficiency, and sustainability in smart energy systems.
KEYWORDS
Energy OptimizationSymbiotic Organism Search AlgorithmDemand ResponseRenewable Energy IntegrationElectric Vehicle Charging
TOPICS
ABSTRACT
This study presents a novel energy optimization framework for local energy networks, addressing the stochastic nature of renewable energy generation, demand fluctuations, and the integration of electric vehicles (EVs) and battery storage systems. The proposed methodology supports fair power allocation by considering operational constraints, dynamic pricing schemes, and demand response (DR) programs. A key contribution of this study is defining an EV's charging and discharging probabilistic model, aiming to enhance interactions with the grid while reducing operational cost and increasing economic returns. In addition, the challenge of optimization is augmented by including market-oriented constraints like real-time pricing and uncertain loading patterns, both of which are dynamically embedded into the decision-making process using the Markov Decision Process (MDP). Moreover, a modified symbiotic organism search (SOS) algorithm has been proposed to deal with the limitations entailed by multi-objective optimization.
FUNDING
This research was financially supported by scientific research project of State Grid Beijing Electric Power Company (520223230010).
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| ISSN: | 1507-2711 |
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