Biblio

While renewable generation sources provide many economic and environmental benefits for the operation of power systems, their inherent stochastic nature introduces challenges from the perspective of reliability. Existing optimal power flow (OPF) methods must therefore be extended to consider forecast errors to mitigate in an economic manner the uncertainty that renewable generation introduces. This paper presents an AC-QP OPF solution algorithm that has been modified to include wind generation uncertainty. We solve the resulting stochastic optimization problem using a scenario based algorithm that is based on randomized methods that provide probabilistic guarantees of the solution. The proposed method produces an AC-feasible solution while satisfying reasonable reliability criteria. Test cases are included for the IEEE 14-bus network that has been augmented with 2 wind generators. The scalability, optimality and reliability achieved by the proposed method are then assessed.

Renewable generation and energy storage are playing an ever increasing role in power systems. Hence, there is a growing need for integrating these resources into the optimal power flow (OPF) problem. While storage devices are important for mitigating renewable variability, they introduce temporal coupling in the OPF constraints, resulting in a multiperiod OPF formulation. This paper explores a solution method for multiperiod AC OPF that combines a successive quadratic programming approach (AC-QP) with a second-order cone programming (SOCP) relaxation of the OPF problem. The SOCP relaxation’s solution is used to initialize the AC-QP OPF algorithm. Additionally, the lower bound on the objective value obtained from the SOCP relaxation provides a measure of solution quality. This combined method is demonstrated on several test cases with up to 4259 nodes and a time horizon of 8 time steps. A comparison of initialization schemes indicates that the SOCP-based approach offers improved convergence rate, execution time and solution quality.