Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services

Das, Ridoy; Wang, Yue; Putrus, Ghanim; Kotter, Richard; Marzband, Mousa; Herteleer, Bert; Warmerdam, Jos · 2019 · OpenAlex-citations

DOI: 10.1016/j.apenergy.2019.113965

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Summary

This paper addresses the conflicting objectives of various stakeholders in smart grids, including end-users, electric vehicle (EV) owners, and system operators, regarding the integration of EVs and renewable energy sources. The authors propose a Multi-Objective Techno-Economic-Environmental Optimisation (MOTEEO) framework to schedule EV charging and discharging. The primary motivation is to resolve the trade-offs between economic costs, technical grid stability, environmental impact, and battery longevity, which are often addressed in isolation or with insufficient modeling of battery degradation in existing literature. The study employs a decentralized optimization approach for day-ahead scheduling within home micro-grids and distribution networks. The framework concurrently optimizes four specific objectives: minimizing household energy cost, minimizing EV battery degradation, reducing grid net exchange (grid utilization), and lowering CO2 emissions. A key methodological contribution is the inclusion of a dynamic battery degradation model that accounts for stress factors such as temperature, charging rate, and average state of charge, rather than treating degradation as a constant. The optimization utilizes the Augmented Non-Dominated ε-Constraint (ANEC) method to generate Pareto-optimal solutions. To select a final schedule from these multiple optimal solutions, the authors apply a Multi-Criteria Decision-Making (MCDM) process tailored to stakeholder priorities, using the Analytical Hierarchy Process (AHP) and utility functions. The model also incorporates scenarios for providing ancillary services, specifically firm frequency response. Results from three case studies demonstrate significant improvements over uncontrolled EV charging. The proposed MOTEEO method reduces energy costs by 88.2%, battery degradation by 67%, CO2 emissions by 34%, and grid utilization by 90%. The study further analyzes the economic implications of providing frequency regulation, finding that while it offers overall profitability for EV owners, achieving a 41.8% improvement in grid utilization requires system operators to compensate end-users and EV owners for incurred benefit losses of 27.34% and 9.7%, respectively. This highlights the necessity of financial incentives to stimulate participation in energy services. The significance of this work lies in its holistic approach to smart grid management, addressing a critical research gap by simultaneously optimizing technical, economic, and environmental factors while explicitly modeling battery wear. The findings underscore that while EVs can provide substantial benefits to the grid and environment, successful implementation requires cooperative decision-making and appropriate compensation mechanisms to align the interests of all stakeholders. The decentralized nature of the framework also supports scalability and data privacy, making it suitable for high EV penetration scenarios and peer-to-peer energy trading.

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