Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering

Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the "clustered players" to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers.Comment: 6 pages, 4 figures, 2 tables. Accepted to the 13th IEEE PES PowerTech Conference, 23-27 June 2019, Milano, Ital

Call-options in Peer-to-Peer Energy Markets

This paper proposes the novel application of call-options for financial loss mitigation in a peer-to-peer (P2P) energy market. P2P energy markets present the opportunity for end-users to trade electricity among themselves by managing their electricity usage and production capabilities. But variability characteristics of renewable resources pose a fundamental challenge to their integration into the grid as well as participating in emerging P2P energy markets. The growing penetration of renewable supply will increase the need for tools to mitigate potential energy traders' financial losses. This paper proposes and evaluates the application of call-option contracts in P2P markets to hedge against financial losses related to power shortfall in renewable supply. A case study is presented, showing that P2P traders might have to bear financial losses when they cannot meet their market obligations, and how options can be used to mitigate such losses

Multiscale design for system-wide peer-to-peer energy trading

The integration of renewable generation and the electrification of heating and transportation are critical for the sustainable energy transition toward net-zero greenhouse gas emissions. These changes require the large-scale adoption of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has gained attention as a new approach for incentivizing the uptake and coordination of DERs, with advantages for computational scalability, prosumer autonomy, and market competitiveness. However, major unresolved challenges remain for scaling out P2P trading, including enforcing network constraints, managing uncertainty, and mediating transmission and distribution conflicts. Here, we propose a novel multiscale design framework for P2P trading, with inter-platform coordination mechanisms to align local transactions with system-level requirements, and analytical tools to enhance long-term planning and investment decisions by accounting for forecast real-time operation. By integrating P2P trading into planning and operation across spatial and temporal scales, the adoption of large-scale DERs is tenable and can create economic, environmental, and social co-benefits

Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized to be NP-hard and challenging to be solved. This paper proposes a novel evolutionary algorithm that successfully tackles the problem and reduces the solution conservativeness (i.e. operation cost) by around 50% comparing with the baseline Bonferroni Approximation. Considering the imperfect solar power forecast, we construct three data-driven ambiguity sets to model uncertain forecast error distributions. The solution is thus robust for any distribution in sets with the shared moment and shape assumptions. The proposed method is validated by robustness tests based on those sets and firmly secures the solution robustness.Comment: Accepted by IEEE Transactions on Smart Gri