A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community

Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pre-defined stochastic scenarios, without searching for the “best” scenarios and testing the optimization models with these scenarios. Moreover, little work both optimized RECs under uncertainty and distributed optimal life-cycle costs (investment and operation) among members. Thus, the objectives are: i) identifying the “best” set of stochastic scenarios of solar irradiance and user electricity demands and ii) assessing the accuracy of the “stochastic forecasts” of the total system costs and unit sizes, obtained by solving a stochastic programming model based on the “best” scenarios. The proposed novel procedure shifts the “present moment” back in time to split historical data into “past” and “future” periods used to identify the “best” scenarios and compare the “stochastic forecasts” with the utopic “perfect forecasts” based on the perfect knowledge of real data, respectively. The small errors between these forecasts in the optimal life-cycle costs (less than 2%) and sizes (3-13%) indicate good effectiveness of the suggested procedure. Also, the optimal life-cycle costs of “stochastic forecasts” are fairly distributed among users by applying the Shapley value mechanism.