Market participation strategy for energy communities under demand and price uncertainty

Energy communities play an important role in enhancing local energy resilience and enabling cost-effective energy management. By aggregating consumers, these communities can leverage collective market participation to achieve economic and operational benefits. This paper proposes an optimal strategy for energy communities to participate in the day-ahead and intraday markets while ensuring cost savings for community members. To address the uncertainties in intraday market prices and the energy consumption of community members, an adaptive robust optimization approach is deployed. The proposed model ensures that, in the worst-case scenario, the energy cost for community members does not exceed the cost of individual participation. The resulting formulation is a nonlinear mixed-integer min-max-min problem, which is recast as a nonlinear min-max problem using duality theory. The outer and inner minimizations determine the optimal strategy of the energy community in the day-ahead and intraday markets, respectively. The worst-case realization of uncertain parameters is captured by the middle maximization. The Big-M method and a decomposition technique are employed to linearize the nonlinear terms. Simulation results demonstrate the effectiveness of the proposed model in a community of 20 consumers, reducing the total energy cost from €515.33 under three daily tariffs to €326.64 per day. Moreover, these simulation results highlight the significant impact of seasonal variation on intraday costs, which are driven by uncertainties in energy demand and market prices.