A-priori multi-objective optimization for the short-term dispatch of distributed energy resources

In order to exploit the flexibility provided by distributed energy resources (DERs), a multi-objective optimization (MOO) approach is proposed to minimize the bus voltage deviations, the network losses, and the current security index. Effective linear power flow equations are included into both the objective functions and the inequality constraints of the MOO model, thus yielding benefits in terms of reduced model dimension and computational complexity. The weighted sum (WS) method with the a-priori assignment of weights is used to transform the MOO into a single-objective optimization (SOO) that directly provides the final solution on the Pareto front. Six surrogate weight methods (SWMs) are utilized to support the decision-maker in the weight assignment. A validation procedure, based on Monte Carlo simulation, is introduced to determine on a case-by-case basis the best SWM for the short-term dispatch of the DERs. The MOO is tested on a real low voltage smart grid with photovoltaic systems, battery storages, and controllable loads. The obtained results demonstrate the high accuracy and low computational effort of the proposed method, indicate the most accurate SWM in the specific application, and show the effectiveness of the proposal with respect to other MOO approaches.