Optimization of Uncertainty Load Restoration Strategy Based on Assessment of Conditional Value-at-Risk

With the increasing of thermostatically controlled load proportion and the accessing of renewable energy, problems caused by the uncertainty of load becoming non-negligible for load restoration. In this paper, we focus on deal with uncertainty problems of load restoration during the last stage of network reconfiguration. Primarily, Conditional Value-at- Risk (CVaR) is introduced to describe load randomness and Analytic hierarchy process (AHP) to deal with fuzzy information for load comprehensive weight evaluation. In order to determine restoration strategy, Particle Swarm Optimization (PSO) is employed to obtain the solution of the multi-constraint optimization model, the objective of which is to restore maximal outage loads in deterministic confidence level. Particularly, transient process is considered when load pick up. Furthermore, taking risk assessment into account, the value function is proposed by which can find the proper confidence level following a system uncertainty reserve for load uncertainty though evaluating strategy's comprehensive value of profit and risk. Ultimately, we can find the best strategy with a suggested system uncertainty reserve to offer advice for dispatcher. An example is provided to demonstrate the effectiveness of the method by the use of PSS/E and Python.