Electric distribution network reconfiguration for power loss reduction based on runner root algorithm

This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem

Voltage stability maximization based optimal network reconfiguration in distribution networks using integrated particle swarm optimization for marine power applications

1949-1956This paper addresses a novel method to optimize network reconfiguration problem in radial distribution network considering voltage stability maximization and power loss reduction without violating the system constraints. In nature inspired population based standard particle swarm optimization (PSO) technique, the flight path of current particle depends upon global best and particle best position. However, if the particle flies nearby to either of these positions, the guiding rule highly decreases and even vanishes. To resolve this problem and to find the global best position, integrated particle swarm optimization (IPSO) is utilized for finding the optimal reconfiguration in the radial distribution network. The performance and effectiveness of the method are validated through IEEE 33 and 69 buses distribution networks and is compared with other optimization techniques published in recent literature for optimizing network reconfiguration problem. The simulated results simulate the fact that to attain the global optima, IPSO requires less numbers of iterations as compared to the simple PSO. The present method facilitates the optimization of modern electric power systems by empowering them with voltage stability

Efficient and Risk-Aware Control of Electricity Distribution Grids

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy

Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations.  To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively. &nbsp

Optimal distribution network configuration using improved backtracking search algorithm

Optimal network configuration is one of the effective approaches for power loss reduction of the distribution network. This paper shows a network reconfiguration method using improved backtracking search algorithm (IBSA). Wherein, IBSA is improved in the process of generating randomly the initialization population. The network reconfiguration method based on IBSA is used to find the optimal network configuration for the 33-node and 69-node systems. The results are compared to the original backtracking search algorithm (BSA), particle swarm optimization (PSO), firefly algorithm (FA) and previous approaches. From the compared results, IBSA can determine the optimal network configuration with higher success rate than BSA, PSO, FA and lower power loss than other previous approaches. As a result, IBSA is an effective approach for finding the optimal network configuration

Optimum reactive power compensation for distribution system using dolphin algorithm considering different load models

The distribution system represents the connection between consumers and the entire power network. The radial structure is preferred for distribution system due to its simple design and low cost. The electrical distribution system suffers from problems of rising power losses higher than the transmission system and voltage drop. One of the important solutions to improve the voltage profile and to reduce the electrical distribution system losses is the reactive power compensation which is based on the optimum choice of position and capacitor size in the network. In this paper, different models of electrical loads such as constant power(P), constant current(I), constant impedance(Z), and composite (ZIP) model are implemented with comparisons between them in order to identify the most effective load type that produces the optimal settlement for alleged loss reduction ,enhancement of the voltage profile, and cost savings. To minimize search space, Dolphin Optimization Algorithm (DOA) is applied for selecting the size and location of capacitors. Two case studies (IEEE 16- bus and 33- bus) are employed to evaluate the different load models with optimal reactive power compensation. The results of comparison between the different load models show that ZIP model is the best to produce the optimum solution for capacitor position and size. In addition, comparison of results with literature works are done and showed that DOA is the most robust among other algorithms to achieve the optimum solution for voltage profile enhancement significant reduction of losses, and saving cost

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

Algorithms for Efficient, Resilient, and Economic Operation of Pre-Emptively Reinforced Reconfigurable Distribution Substations

Stochasticity of demand profiles at electricity distribution substations is increasing due to the proliferation of low carbon technologies; in particular mobile, bi-directional, or intermittent loads such as electric vehicles and heat pumps. The decarbonisation of heat and transport will cause a long-term increase in overall connected load, making substation reinforcement necessary, whilst planning of upgrade locations and capacities remains challenging. This project will investigate pre-emptive substation reinforcement with algorithmic topology control, to utilise the additional installed substation capacity only when required. Distribution Substation Dynamic Reconfiguration (DSDR) proposes the installation of additional transformers in parallel with the existing transformer in each substation, removing the need to scrap and replace these. Telematics-controlled switches are installed on the high- and low-voltage side of each transformer in the substation, with local agent algorithms deployed to control in real-time when each parallel transformer is brought into or taken out of service. Substation reconfiguration is thus controlled to optimise for maximum operating efficiency. The threshold algorithm most recently trialled in medium voltage parallel transformer substations is implemented as a baseline, and a novel model-based reconfiguration algorithm is proposed, implemented, and evaluated in software and hardware. This work led to a 1.34% improvement in algorithm performance on substation efficiency, over a yearly demand profile including residential and new electric vehicle load for the year 2050, equivalent to a potential saving of 2.68 TWh annually if deployed UK-wide. This approach unlocks several opportunities to operate existing substations in the smart, flexible, resilient, and efficient manner that will be required to reach the net zero target by 2050

Reliability improvement and loss reduction in radial distribution system with network reconfiguration algorithms using loss sensitivity factor

Studies on load flow in electrical distribution system have always been an area of interest for research from the previous few years. Various approaches and techniques are brought into light for load flow studies within the system and simulation tools are being used to work out on varied characteristics of system. This study concentrates on these approaches and the improvements made to the already existing techniques considering time and the algorithms complexity. Also, the paper explains the network reconfiguration (NR) techniques considered in reconfiguring radial distribution network (RDN) to reduce power losses in distribution system and delivers an approach to how various network reconfiguration techniques support loss reduction and improvement of reliability in the electrical distribution network