Based on Improved Crayfish Optimization Algorithm: Cooperative Optimal Scheduling of Multi-Microgrid System
The integration of new energy sources in multi-microgrid (MMG) systems introduces complex interactions among various components, influencing both the system solution’s accuracy and speed. Addressing these challenges, this study presents a novel bi-level optimal scheduling Stackelberg game model, leveraging shared energy storage to enhance inter-subject interactions within MMG environments.
To solve the optimization scheduling model, an innovative Chaotic Gaussian Quantum Crayfish Optimization Algorithm (CGQCOA) was developed. This algorithm integrates four improvement techniques: Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy. The enhanced algorithm exhibits exceptional initial solutions and improved search capabilities. Compared to its predecessors, the CGQCOA shows remarkable improvements in optimization outcomes with relative errors reduced by 98%, 20.96%, 98.74%, and 16.55%, respectively. These advancements not only boost model-solving accuracy but also expedite the convergence to optimal solutions.
Simulation results underscore the model’s efficacy: revenues for Microgrid 1, Microgrid 2, and Microgrid 3 increased by 0.73%, 1.17%, and 1.04%, respectively. Moreover, pollutant emission penalties decreased by 5.9%, 11.5%, and 12.68%, coupled with a 1.91% revenue increase in shared storage. These results validate the proposed methodology’s success in enhancing subject revenues and minimizing pollutant emissions.
Energy Transition and Microgrid Evolution
With the depletion of traditional fossil resources and the worsening of environmental issues, energy transition has become a global imperative. While new energy sources like wind and solar are increasingly adopted, they also introduce challenges such as energy wastage and high transmission costs. To combat these issues, Professor Lasseter.R.H introduced the Microgrid (MG) concept, which facilitates safe, local integration of new energy into the grid. This approach not only reduces operator transmission costs but also promotes greater acceptance of renewable energy sources.
Optimizing Multi-Microgrid Scheduling
Research in MMG optimal scheduling involves developing scheduling models that integrate myriad factors, alongside robust solution algorithms. MMGs, being aggregations of multiple MGs, necessitate sophisticated scheduling to balance diverse energy demands and supply characteristics. As new energy source penetration increases, optimizing for factors like intermittency and volatility becomes critical. To this end, integrating energy storage into the scheduling model enhances system reliability and flexibility.
Traditionally, energy storage has been configured separately, which, although effective, poses high construction and operational costs. Shared energy storage offers a cost-effective alternative, preserving the benefits of traditional setups. Innovations in auction mechanisms and game-based models have facilitated improved utilization of storage capacity, enhancing both MG revenue and operational flexibility.
Incorporating Game Theory
The MMG environment is complex, with frequent energy flows and intricate capital dynamics. High penetration of renewable energy further complicates optimization scheduling. Here, game theory becomes indispensable, dissecting the competitive and cooperative interplay between subjects and devising optimal strategies amidst uncertainty.
In this light, a Stackelberg game model was established to optimize revenue for MG and user operators, among other innovative game-based frameworks. These models have mitigated the system’s reliance on centralized management and elevated MG revenue, underscoring the effectiveness of cooperative strategies in uncertain settings.
Advancements in Intelligent Optimization Algorithms
Current research also delves into developing intelligent optimization algorithms that enhance solution generation speed and precision. Previous algorithms, despite their utility, have faced challenges like limited initial populations and slow convergence.
In recent strategic developments, enhancements to algorithms, such as incorporating chaotic maps and quantum behavior, have bolstered search capabilities. However, they are sometimes hindered by reduced diversity over iterations, necessitating innovative approaches like the CGQCOA.
Conclusion and Future Directions
As the field progresses, emerging solutions like CGQCOA demonstrate the pivotal role of chaotic maps and quantum behavior in refining algorithm performance. These technologies offer substantial benefits in exploring vast solution domains, albeit at slower convergence speeds.
Overall, this study’s proposed strategies significantly improve revenue streams in MMG systems while curbing emissions, marking important strides towards sustainable energy integration.
The paper’s subsequent sections will delve deeper into MMG inter-subject interaction, the bi-level Stackelberg game model, a comprehensive introduction to CGQCOA, simulation analyses, and concluding insights.
