Artificial intelligence and optimization: a way to speed up the security constraint optimal power flow

Optimal power flow is a widely used tool in power system planning and management. Due to the complexity of the power system both in terms of number of variables, degrees of freedom and uncertainty, there is a continuous effort to find more efficient computational methods to solve optimal power flow problems. This article presents a novel method to speed-up the solution of a security constraint optimal power flow problem. An unconventional warm start based on the training of a neural network is investigated as an option to improve the computational efficiency of the optimization problem. The principle of the method and the validity of the approach is demonstrated by different analysis performed on the IEEE14 test grid and based on a linearized mathematical formulation of the problem. The results show the effectiveness of the method in reducing the number of iterations needed to converge to global optimum.

Analysis of Market Equilibrium Based on Co-evolution in Electricity Spot Market

By solving the Nash equilibrium of the electricity market, it is possible to observe the game process of market entities under different boundary conditions and predict the future trend of the market. In order to study the state of market equilibrium in the power spot market, firstly we constructed a bi-level equilibrium model. The upper layer is the problem of maximizing the profit of power generation enterprises under the bidding constraint, and the lower layer is the security constraint economic dispatch with the goal of maximizing social welfare. The traditional solution transforms the bi-level model into MPEC or EPEC through optimal conditions, but they are generally non-convex and difficult to solve. In this regard, the coevolution algorithm is used to solve the bi-level model, and it is proved that the result of co-evolution under a limited strategy set is equivalent to the Nash equilibrium. Finally, an example of PJM 5 machine with 5 nodes is used to analyse the power market equilibrium in the spot market.