Adaptive distributed scheduling of the resources in smart grids with considering demand response

Unit commitment (UC), a non-linear and non-convex problem, is one of the important problems in power system operation. Solving the UC problem centrally has problems such as security concerns. On the contrary, solving it in a fully distributed manner reduces performance speed. This paper presents a method for solving the UC problem that addresses both security concerns and speeds up performance. This paper intends to solve the UC problem by using a hybrid structure and with the help of accelerator and cyber security parameters of the power system. This problem is solved for the first time in a power system despite its non-linear nature and non-convexity. Also, the demand response (DR) issue is considered as a contributing factor in resource scheduling management. This is important because DR is an inherently distributed issue. The presented method uses a developed decentralized algorithm called the accelerated hybrid alternating direction method of multipliers (AHADMM). AHADMM is a combination of the ADMM framework and hybrid network architecture that its parameters are tuned. Then for decentralized optimization, the generalized Benders decomposition (GBD) algorithm and the AHADMM algorithm are utilized to speed up the computation and provide a framework to exploit the distributed nature of the problem. The proposed algorithm consists of two main problems, primal and master problems. The primal problem, which is solved in a distributed way, is responsible for providing a feasible solution such that an optimality cut can be generated. In contrast, the master problem is responsible for accumulating the optimality cuts and approximating the feasible region of the original non-linear problem. Regarding the improvement of the solution method, we have used the accelerator parameters and the limitation parameter of fusion centers in such a way as to guarantee the speed of calculations and the cyber security of the power system.