Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Abstract

Simultaneous control and operation of Capacitor Banks (CBs) and Distributed Generators (DGs) with Distribution Network Reconfiguration (DNR) is the most important modern trend for distribution systems performance enhancement. The complexity and variety of the control variables of this problem represent a challenge for distribution systems planners and operators as well as interested researchers. Optimal solution can provides quantitative as well as qualitative power service to satisfy consumers' satisfaction and reduces dissipated energy. Furthermore, performance enhancement of distribution systems is very vital where improving their performance directly affects the performance of transmission and generation systems. For solving the considered problem, an improved Tunicate Swarm Algorithm (ITSA), which imitates the swarming behaviors of the marine tunicates and their jet propulsions during its navigation and foraging procedure, is proposed. In ITSA, Lévy flight distributions are emerged in the traditional Tunicate Swarm Algorithm (TSA), which improves the diversification searching abilities of the TSA and consequently avoids the stagnation possibilities. The proposed ITSA is applied and tested for optimal DNR simultaneously with optimal control of the switched CBs and dispatchable DGs taking into account daily load variations. The ITSA is compared with other techniques on the standard 33-bus, 69-bus and the large-scale 119-bus distribution systems considering different automation scenarios of the distribution systems. The simulation results reveal that significant enhancement in distribution system performance is obtained through the application of the proposed automation process using the ITSA. ITSA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literatures.

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

“…Fig. 6 demonstrates the convergence rates of the proposed MRFOA compared to TSA and ITSA [1]. This figure shows the improvement of convergence rates of MRFOA in providing higher efficacy to override the premature convergence and developing its obtained solution.…”

“…This table shows the statistical analysis of the proposed MRFOA for the optimal operation of the DGs, CBs, and tie switching simultaneously with 10 independent runs in comparison with the recent algorithm of tunicate swarm optimizer (TSA). For all load levels, the best solution is obtained by the proposed MRFOA as presented in Table 4, where a dramatic decrease in the standard deviation obtained by the proposed MRFOA than the TSA [36] and ITSA [1] is achieved. Fig.…”

“…Consequently, they are built-in adaptive values that does not affect on the MRFO performance. On the other side, the number of individuals and the maximum number of the iterations are considered 50 and 200, respectively which are the same as taken in TSA [1] in order to make fair comparison.…”

“…The rapid power demand increase with limited generation and transmission expansion is a big challenge for numerous electrical grids. Therefore, the conventional distribution systems suffer from excessive losses, poor voltage regulation, continuous overloading, unreliability, and service insecurity [1], [2]. Commonly, the enhancement of distribution systems requires addition of active and reactive power resources and controlling these injected active and reactive power.…”

Optimal allocation of biomass distributed generation in distribution systems using equilibrium algorithm

“…In Reference 23, the uncertainties of the load and the output powers of PV and wind generators were considered in the optimal reactive power dispatch and solved using a marine predator's algorithm (MPA). In Reference 24, an improved tunicate swarm algorithm has been dedicated for optimal DGs allocation considering the optimal control of the switched capacitor banks with daily load variations, but this work handled the power losses as a single minimization objective. In addition, a multi‐objective salp swarm optimizer technique 25 and coyote optimization algorithm 26 have been carried out for achieving several technical and economic benefits but the environmental dimension and load growth concerns have not been highlighted.…”