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.
Demand side management (DSM) consists of planning, executing, and controlling activities to reduce electricity consumption. By using demand response (DR), customers help reduce demand during peak times. Consumer participation in DSM depends on his behaviour. Consumer behaviour is determined by factors such as lifestyle, electricity price, electricity consumption tariff, contract type etc. Thus, factors affecting the consumer's behaviour should be considered in order to determine more accurately the amount of participation in DSM programs. This article presents a model for the optimal scheduling of distributed energy resources by taking into account factors related to consumer behaviour. To reduce the volume of the DR data while maintaining the main features, distributed principal component analysis (D_PCA) was used to reduce the volume of DR data. Also, by integrating this method with the accelerated hybrid alternating direction method of multipliers (AHADMM) algorithm, an adapted and accelerated method is achieved to realize the reliability and cyber security of the system. The case study was conducted on the IEEE 118 bus power system at different levels of demand, which verified proposed metaalgorithm improved at least between 4 to 13 iterations of energy resource convergence speed compared to similar methods and while DR is also intended.
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