A Distribute Parallel Approach for Big Data Scale Optimal Power Flow with Security Constraints

This paper presents a mathematical optimization framework for security-constrained optimal power flow (SCOPF) computations. The SCOPF problem determines the optimal control of power systems under constraints arising from a set of postulated contingencies. This problem is challenging due to the significantly large problem size, the stringent real-time requirement and the variety of numerous post-contingency states. In order to solve the resultant big data scale optimization problem with manageable complexity, the alternating direction method of multipliers (ADMM) is utilized. The SCOPF is decomposed into independent subproblems correspond to each individual pre-ontingency and post-contingency case. Those subproblems are solved in parallel on distributed nodes and coordinated through dual (prices) variables. As a result, the algorithm is implemented in a distributive and parallel fashion. Numerical tests validate the effectiveness of the proposed algorithm.