Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Abstract

Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastructure and remote control. The main components of this article include the use of graph theory and binary particle swarm optimization algorithm for optimal switching, the use of particle swarm optimization algorithm and forward‐backward sweep load flow for load shedding and distributed generation rescheduling, investigating the effect of self‐healing on improved reliability, power losses reduction, and load shedding reduction and the present a new approach for self‐healing optimization and a new mathematical model. The problem of self‐healing due to nonlinear and unbounded constraints is one of the nonconvex mixed integer nonlinear programming optimization problems. The optimization problem becomes a convex mixed integer linear programming optimization problem using the proposed scheme. Due to the nonlinearity and the nonconvexity of the problem, commercial solvers are not able to solve this problem. Even the Baron solver, which is used to solve nonconvex nonlinear problems, cannot achieve the optimal global solution. However, the proposed method is easily able to achieve the optimal solution by eliminating the nonlinear and nonconvex element. A modified RBTS‐4 distribution system is used as a test system. The IEEE 33‐bus distribution system is used to validate the proposed method. The results of case studies demonstrate the effectiveness of the proposed methodology.

Centralized optimal management of a smart distribution system considering the importance of load reduction based on prioritizing smart home appliances

“…While in the DR scheme, only the effects of shiftable loads are examined. In [19,22,[24][25][26], intelligent algorithms are used to solve the problem. Due to the lack of guaranteed optimal global solutions, these algorithms are not appropriate.…”

Impact of Demand Response on Reliability Enhancement in Distribution Networks

“…In [53], an approach based on Markov decision making was proposed for calculating restoration policy. In [54], a graph theory based on a particle swarm optimization (PSO) algorithm was presented for distribution system restoration. In this paper, the effects of restoration scheme and load harvest on distribution system reliability were studied.…”

“…In some articles, load restoration resources in the distribution system and network reliability were considered, but no plan was considered for pricing and participation levels of consumers (for instance, [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]). Also, in some models, the distribution network and optimal power flow were ignored (for example, [46,48,49,54]).…”

“…In the models proposed in previous studies, DR programs reduced consumption at peak times, but these studies did not consider the simultaneous effect of DR and elasticity with a load restoration framework for improving the reliability indices, and they focused mainly on profitability for the DSO ( [13][14][15][16][19][20][21]). Also, in some studies such as [46,48,54], the physical constraints of transmission lines were considered, or the effect of DSO on the energy market was neglected. Although, in some models, profitability for consumers and reduction in energy consumption costs were considered, dependency of energy consumption on the fluctuating prices of the electricity market, particularly at peak times, reduced the efficiency of these models.…”

A Petri Net-Based Power Supply Recovery Strategy for the Electric Power System of Floating Nuclear Power Plant

“…It is widely used in power system network reconstruction. Intelligent optimization methods mainly include genetic [26] and particle swarm optimization [27] algorithms. The above method is simple in operation and requires few parameters to be adjusted, so the optimal solution can be quickly searched for.…”