In the context of the smart grid, we propose in this paper an algorithm that forms coalitions of agents, called prosumers, that both produce and consume. It is designed to be used by aggregators that aim at selling aggregated surplus of production of the prosumers they control. We rely on real weather data sampled across stations of a given territory in order to simulate realistic production and consumption patterns for each prosumer. This enables us to capture geographical correlations among the agents while preserving the diversity due to different behaviors. As aggregators are bound to the market operator by a contract, they seek to maximize their offer while minimizing their risk. The proposed graph-based algorithm takes the underlying correlation structure of the agents into account and outputs coalitions with both high productivity and low variability. We show that the resulting diversified coalitions are able to generate higher benefits on a constrained energy market, and are more resilient to random failures of the agents.
Renewable energy sources continues to gain popularity. However, two major limitations exist that prevent widespread adoption: availability of the electricity generated and the cost of the equipment. Distributed generation, (DG) grid-tied photovoltaic-wind hybrid systems with centralized battery back-up, can help mitigate the variability of the renewable energy resource. The downside, however, is the cost of the equipment needed to create such a system. Thus, optimization of generation and storage in light of capital cost and variability mitigation is imperative to the financial feasibility of DC microgrid systems. PV and wind generation are both time dependent and variable but are highly correlated, which make them ideal for a dual-sourced hybrid system. This paper presents an optimization technique base on a Multi-Objective Genetic Algorithm (MOGA) which uses high temporal resolution insolation data taken at 10 seconds data rate instead of more commonly used hourly data rate. The proposed methodology employs a techno-economic approach to determine the system design optimized by considering multiple criteria including size, cost, and availability. The result is the baseline system cost necessary to meet the load requirements and which can also be used to monetize ancillary services that the smart DC microgrid can provide to the utility at the point of common coupling (PCC) such as voltage regulation. The hybrid smart DC microgrid community system optimized using high-temporal resolution data is compared to a system optimized using lower-rate temporal data to examine the effect of the temporal sampling of the renewable energy resource. Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.