- The paper presents a bi-level model that minimizes IMG net costs while maximizing EV battery swapping station profits using a real-time pricing mechanism.
- It employs a hybrid JAYA-BBA algorithm that integrates real-coded JAYA and branch and bound to solve complex scheduling challenges in multi-stakeholder energy systems.
- Numerical results validate that joint optimization significantly reduces IMG costs and boosts BSS profitability compared to independent strategies.
Optimal Scheduling of Isolated Microgrid with Electric Vehicle Battery Swapping Stations: A Bi-Level Programming Approach
This paper presents a novel model for addressing the scheduling challenges between an isolated microgrid (IMG) and electric vehicle battery swapping stations (BSSs) in a multi-stakeholder environment. The core contribution of this research lies in the development of a bi-level optimal scheduling framework that enhances the participation of BSSs in optimizing the economic operations of an IMG under real-time pricing conditions.
Model and Methodology
The proposed bi-level model integrates two sub-problems: the upper level focuses on minimizing IMG net costs, while the lower level targets maximizing the BSS profits. The real-time pricing environment, shaped by demand responses from the upper-level's decisions, serves as a crucial mechanism for achieving the coordination between IMG and BSS. This price-based approach responds to dynamic supply-demand interactions, facilitating a balanced economic optimization.
To resolve the bi-level problem, the authors introduce a hybrid algorithm named JAYA-BBA. This algorithm synergizes a real/integer-coded JAYA optimization method with the branch and bound algorithm (BBA), which are, respectively, employed to address the upper- and lower-level sub-problems through iterative exchanges. This integration allows for effective solution generation that accommodates both real-time pricing dynamics and specific constraints inherent to the scheduling problem.
Numerical Results and Analysis
Simulation scenarios deployed on a modified Oak Ridge National Laboratory (ORNL) Distributed Energy Control and Communication (DECC) lab microgrid system validate the efficacy of the presented model. The results demonstrate that the proposed approach significantly enhances IMG economic performance while effectively accommodating the operational interests of the BSS. Through joint optimization, the research illustrates that a balance can be maintained between minimizing IMG costs and maximizing BSS profits. This is evidenced by the comparison of various optimization strategies, indicating that joint optimization leads to reduced net costs for the IMG and increased profitability for the BSSs relative to independent optimization approaches.
Implications and Future Work
The implications of this research are multifold. Practically, the framework supports decision-makers in energy management systems to harmonize operations between microgrids and BSSs, promoting sustainable and economically viable renewable energy use. Theoretically, the bi-level programming model serves as an innovative methodology addressing NP-hard scheduling problems in decentralized energy systems with multi-agent interactions.
The authors recognize the necessity of exploring further dimensions of this problem domain. Future work is anticipated to encompass multiple-timescale scheduling models integrating both day-ahead and real-time considerations, potentially incorporating advanced machine learning techniques for predicting EV arrival sequences to enhance operational accuracy. Additionally, expanding the model's complexity to account for correlations between load and distributed energy resources (DERs) uncertainties may further refine the model's applicability in real-world scenarios. A detailed exploration of optimal operation within integrated energy systems presents another promising avenue.
Conclusion
The integration of BSSs into IMGs through a bi-level programming approach with a real-time pricing mechanism affirms its viability as a method for navigating the scheduling complexities in multi-stakeholder energy environments. By leveraging a hybrid analytical and heuristic solution methodology, this paper provides a substantial contribution to the effective operation and management of isolated microgrids, steering advancements in sustainable energy systems and optimizing stakeholder benefits.