In the quest to revitalize rural areas and reduce carbon footprints, a groundbreaking study published in *Power Construction* (Dianli jianshe) offers a promising solution: a dual-layer optimization model for configuring the capacity of rural multi-microgrid systems with biogas energy generation. Led by ZHANG Jinliang and CHENG Jia from the School of Economic and Management at North China Electric Power University in Beijing, this research addresses the critical need for efficient new energy system capacity allocation in rural settings.
The study introduces a novel approach to enhance the penetration rate of renewable energy sources while minimizing annual integrated costs. By establishing a two-layer model, the researchers aim to optimize both the capacity allocation and operation of multi-microgrid systems incorporating biogas generation. The upper-level objective focuses on minimizing the annual integrated cost of the multi-microgrid system while maximizing the utilization of renewable energy sources. In contrast, the lower-level objective targets minimizing the operating cost of the system.
“Our model not only reduces the overall capacity allocation of new energy and energy storage but also lowers the annual investment cost,” explains ZHANG Jinliang. “This approach effectively decreases the cost of purchasing external power by 9%, although the amount of power sales is correspondingly reduced.”
The researchers employed a particle swarm optimization algorithm combined with the CPLEX solver to tackle this complex problem. To ensure fair cost allocation among microgrids, they utilized the Shapley value method. The results, validated through Matlab simulations, demonstrate that introducing biogas energy as a power generation unit can significantly increase the penetration rate of new energy. Moreover, the rural inter-microgrid power interaction proposed in the study effectively reduces the overall capacity allocation of new energy and energy storage, thereby lowering the annual investment cost.
“By apportioning the costs using the Shapley value method, we observed a reduction in the actual costs of each microgrid by 6.3%, 2%, and 4.4% compared to scenarios where the microgrids operated independently,” adds CHENG Jia.
The implications of this research are far-reaching for the energy sector. By optimizing the capacity configuration of rural multi-microgrid systems, the study paves the way for more efficient and cost-effective energy solutions. The integration of biogas energy generation not only enhances the utilization of renewable energy sources but also contributes to reducing carbon emissions. This dual-layer optimization model offers a viable path towards achieving both economic and environmental goals in rural revitalization efforts.
As the energy sector continues to evolve, the findings from this study could shape future developments in rural electrification and renewable energy integration. By providing a robust framework for capacity allocation and operation optimization, the research offers valuable insights for policymakers, energy providers, and rural communities alike. The dual-layer optimization model proposed by ZHANG Jinliang, CHENG Jia, and their team represents a significant step forward in the quest for sustainable and efficient energy solutions in rural areas.