Incentivizing Energy Trading for Interconnected Microgrids (1609.07576v1)

Abstract: In this paper, we study the interactions among interconnected autonomous microgrids, and propose a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing. We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively. For practical implementation, we propose a decentralized solution method with minimum information exchange overhead. Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.

• The paper proposes a joint scheduling and trading model using Nash bargaining to optimize both local operations and energy exchanges.
• It introduces an incentive mechanism that ensures fair benefit sharing and reduces individual microgrid costs by up to 29.4%.
• The paper develops a decentralized ADMM-based algorithm that minimizes information exchange while supporting scalable and privacy-preserving energy trading.

Incentivizing Energy Trading for Interconnected Microgrids

The paper "Incentivizing Energy Trading for Interconnected Microgrids" by Hao Wang and Jianwei Huang presents an analytical framework for optimizing energy trading among autonomous, interconnected microgrids using a Nash bargaining approach. It provides insights into microgrid interaction and proposes a strategy that enhances the integration of renewable energy, notably improving system resilience and efficiency.

Key Contributions

The paper makes several noteworthy contributions:

1. Joint Scheduling and Trading Framework: A comprehensive model is formulated to jointly optimize the internal power scheduling within individual microgrids and the energy trading among interconnected microgrids. This dual approach addresses the coordination of local power generation, consumption, and trading opportunities.
2. Incentive Mechanism Design: Using Nash bargaining theory, the paper develops an incentive mechanism that fosters fair benefit sharing among interconnected microgrids. This mechanism seeks to leverage the diverse supply and demand profiles of different microgrids for mutual gains.
3. Decentralized Solution Method: The paper proposes a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM), thus minimizing the information exchange overhead required for practical implementations. This decentralized solution is efficient and scalable, respecting the privacy of individual microgrid operations.
4. Empirical Validation: The paper includes numerical studies grounded in realistic data, demonstrating that interconnected microgrids can achieve up to a 13.2% reduction in total operational costs via energy trading. Additionally, individual microgrids can reduce their costs by as much as 29.4%.

Theoretical and Practical Implications

The paper underscores the potential of decentralized microgrid systems to transform energy distribution networks by enhancing the use of renewable resources and reducing reliance on centralized grid power. Implementing such systems can minimize energy loss during transmission, reduce pollution, and optimize energy use reflecting the diverse generation and demand scenarios observed in practice.

From a theoretical perspective, the combination of Nash bargaining frameworks with microgrid energy trading evidences a shift towards more socially optimal energy operations in decentralized environments. The proposed models could be adapted for networked microgrids outside the specific contexts studied, offering a baseline for additional research into market-driven operations and advanced incentive designs.

Prospective Developments

Looking to the future, the incorporation of more nuanced machine learning models to predict generation and demand, adaptive to the evolving climate and socio-economic factors, could further enhance system resilience. Moreover, exploring blockchain or similar technologies for transparent and secure transaction handling could address privacy concerns in energy exchanges.

In conclusion, this paper lays substantial groundwork for advancing microgrid interconnectivity, underscoring the critical role of strategic coordination and incentives in distributed energy networks. As the grid landscape evolves with increasing concerns over sustainability and efficiency, these findings hold significant promise for the development of smarter, more autonomous energy systems.