Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization (2311.14563v1)

Abstract: The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage leading to a need for more coordinated and grid aware EVs charging and discharging strategies. However, coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies as the complexity increases with more control variables and EVs, necessitating large optimization and decision search spaces. In this paper, we propose an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid, by utilizing EVs to store surplus energy and discharge it during periods of energy deficit. The optimization problem is addressed using Harris Hawks Optimization (HHO) considering criteria related to energy grid balancing, time usage preference, and the location of EV drivers. The EVs schedules, associated with the position of individuals from the population, are adjusted through exploration and exploitation operations, and their technical and operational feasibility is ensured, while the rabbit individual is updated with a non-dominated EV schedule selected per iteration using a roulette wheel algorithm. The solution is evaluated within the framework of an e-mobility service in Terni city. The results indicate that coordinated charging and discharging of EVs not only meet balancing service requirements but also align with user preferences with minimal deviations.

• The paper demonstrates how a multi-objective Harris Hawks Optimization effectively schedules EV charging and discharging to stabilize the grid.
• It integrates user preferences and locational factors to optimize energy storage during low demand and discharge during peak periods.
• The findings highlight the method’s potential to enhance grid reliability and support the efficient integration of renewable energy.

Electric vehicles (EVs) are increasingly integrated into the energy grid, presenting both opportunities and challenges for grid balancing, particularly with the rise of renewable energy. Effective coordination of EV charging and discharging is crucial for maintaining grid stability. The paper titled "Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization" published in November 2023 explores this complex issue using a sophisticated optimization algorithm.

Harris Hawks Optimization (HHO) is a nature-inspired algorithm that models the cooperative behavior of Harris hawks in hunting. This algorithm is applied to develop a multi-objective optimization framework that balances the energy needs of the grid with user preferences and locational factors. The proposed model ensures a reliable energy supply by scheduling EVs to store surplus energy during low demand periods and discharge it during peak times, effectively smoothing out energy consumption across the grid (Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization, 2023).

The challenge of coordinating a large number of EVs is not new. Previous studies have delved into various approaches for distributed charging control. For instance, a comprehensive survey of distributed charging control algorithms categorizes these efforts into centralized, decentralized, and hierarchical systems, each with its operational and cost-oriented perspectives (A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid, 2019). These systems aim to mitigate the potential strain on the power grid from extensive EV adoption.

Historical context also shows alternative methodologies such as game theory and hybrid decision-making frameworks. For example, the application of noncooperative Stackelberg games to manage grid-to-vehicle energy exchanges optimizes pricing strategies for both the grid and EV groups to achieve a socially optimal equilibrium (Economics of Electric Vehicle Charging: A Game Theoretic Approach, 2012). Similarly, profit-aware decentralized scheduling strategies have been proposed to enhance overall system profitability while balancing supply and demand (Profit-aware Online Vehicle-to-Grid Decentralized Scheduling under Multiple Charging Stations, 2016).

Integrated approaches, such as the one described in the hierarchical coupled routing-charging model, emphasize the need for multi-level decision-making—accounting for coordination between transportation networks and grid operations to find optimal routing and charging solutions for EVs (Hierarchical coupled routing-charging model of electric vehicles, stations and grid operators, 2020).

Overall, the integration of HHO into EV coordination represents an evolving effort to harness advanced optimization techniques for real-time grid balancing. By considering multiple objectives and leveraging natural behavioral analogies, the model proposed in (Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization, 2023) contributes to a more stable and efficient energy grid while aligning with user convenience and operational feasibility. This is indicative of the broader trend toward sophisticated, interdisciplinary approaches in managing the growing presence of EVs within smart grids.