Stochastic Downside Risk-Constrained Scheduling for a Sustainable Power System

With the restrictions on non-renewable energy sources and increasing environmental pollution, attention is being drawn to renewable energy sources. But due to the variable nature of these sources, new challenges have been created in the balance between the production and consumption of power systems. An example of a sustainable energy system (SES) in this paper stores wind power with an electrical energy storage system (EESS) and uses a responsive load economic model (RLEM) to cope with the variable nature of wind generation (WG) and demand. First, since wind energy and demand face uncertainties, the Bayesian probabilistic method is applied to produce the scenario tree. Due to the many generated scenarios, the K-Means clustering algorithm is used to select only ۵ scenarios. Furthermore, the downside risk constraints (DRC) method is used to measure the risk imposed by stochastic parameters. The proposed strategy is compared with a risk-neutral strategy to investigate DRC implementation. The results are compared in two cases to demonstrate the advantages of the proposed risk evaluation method. In addition, the Pareto front can be used between risk-in-cost (RIC) and the expected operation cost (EOC) to establish an optimal risk strategy in the presence of uncertainties. The results show that the expected operating cost of the system increases slowly while the expected risk-in-cost of the system decreases significantly.

کلیدواژه ها:

sustainable energy system ، responsive load economic model ، Downside risk constraints ، Bayesian probabilistic method ، K-means clustering algorithm