Optimal Distribution System Reconfiguration Research Articles

Optimal reconfiguration of distribution systems considering reliability: Introducing long-term memory component AEO algorithm

This article introduces a modified version of the Artificial Ecosystem Optimization (AEO) algorithm, called Long-term Memory Component AEO (LMAEO), for optimizing the reconfiguration of radial distribution networks. The LMAEO algorithm incorporates a long-term memory component, enabling individuals in the population to make decisions based on past experiences. This integration of long-term memory allows the algorithm to explore a wider range of potential solutions during the optimization process, potentially leading to improved performance and better exploration of the solution space. To verify the effectiveness and superiority of the LMAEO technique, it is compared with the conventional AEO algorithm and other well-known algorithms using seven benchmark functions. The proposed LMAEO algorithm successfully addresses the reconfiguration of distribution systems considering reliability for the modified 12-bus, 33-bus and 69-bus IEEE test systems. Leveraging the strengths of AEO and the long-term memory component, the LMAEO algorithm achieves efficient solutions for this problem. To assess the performance of the proposed LMAEO, a comparison is made with the original AEO algorithm. The results demonstrate that the LMAEO technique surpasses the AEO optimizer in terms of optimal reconfiguration of distribution systems jointly considering reliability, system losses and voltage deviations.

Short-circuit constrained distribution network reconfiguration considering closed-loop operation

This paper presents a novel scenario-based stochastic mixed-integer second-order cone programming model to solve the problem of optimal reconfiguration of distribution systems with renewable energy sources considering short-circuit constraints. The proposed formulation minimizes technical losses by modifying the statuses of sectionalizing and tie switches, allowing the operation of distribution networks with radial and closed-loop topologies. Since the formation of loops could impact fault current levels, short-circuit constraints are considered in the problem formulation. Numerical experiments are carried out using an 84-node system and the results demonstrate the effectiveness of the proposed formulation to reduce technical losses notably when a closed-loop operation is allowed. Additionally, it is verified that short-circuit constraints prevent the adoption of network configurations with high short-circuit values.

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

This paper presents an Enhanced Marine Predators Algorithm (EMPA) for simultaneous optimal distribution system reconfigurations (DSRs) and distributed generations (DGs) addition. The proposed EMPA recognizes the changes opportunity in environmental and climatic conditions. The EMPA handles a multi-objective model to minimize the power losses and enhance the voltage stability index (VSI) at different loading levels. The proposed EMPA is performed on IEEE 33-bus and large-scale 137-bus distribution systems (DSs) where three distinct loading conditions are beheld through light, nominal and heavy levels. For the 33-bus DS, the proposed EMPA successfully reduces the cumulative losses by 72.4% compared to 70.36% for MPA for the three-loading levels simultaneously. As a result, significant voltage improvement is achieved for heavy, nominal and light loadings to be 95.05, 97, 98.3%, respectively. For the 137-bus DS, it successfully minimizes the losses of 81.16% under small standard deviation 4.76%. Also, the 83-bus test system is considered for fair comparative between the proposed and previous techniques. The simulation outputs revealed significant improvements in the standard MPA and demonstrated the superiority and effectiveness of the proposed EMPA compared to other reported results by recent algorithms for DSRs associated with DGs integration.

Multi-objective optimization model for optimal reconfiguration of distribution networks with demand response services

Distribution networks are energy sectors that usually deal with residential energy consumers whose expectations associated with voltage quality and other relevant issues should be taken into account. There are various factors that might threaten reliable/economic performance of the distribution networks. In this paper, optimal reconfiguration of distribution systems is studied under a multi-objective optimization model to enhance reliability and minimize power losses of distribution networks. The developed multi-objective model is solved with epsilon-constrained (EPC) method and the trade-off conditions are achieved with min-max fuzzy satisfying (MMFF) technique. Demand response services (DRSs) including time-of-use (TOU) mechanism are proposed to make the energy consumers capable of revising their energy usage pattern. The proposed framework is investigated regarding the IEEE 33-bus test system in general algebraic modeling systems (GAMS) and the results validating the effectiveness of the proposed model are presented and compared. According to the obtained results, after reconfiguring the distribution system under the proposed model, loss of power in the system is reduced and reliability of a system associated with energy not supplied (ENS) index is improved. After proposing DRS and having the consumers motivated to change their consumption pattern, the obtained values for the mentioned targets are more suitable.

Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives

Abstract Optimal distribution system reconfiguration (DSR) and distribution generation (DG) allocation are viable solutions for improvement of technical and economic aspects of distribution systems. This paper proposes a stochastic multi-objective DSR (SMO-DSR) model, aims to maximize the DG owner’s profit and minimizes the distribution company’s (DisCo’s) costs. The uncertainties of wind power generation, electricity price, and demand are handled via scenario based approach. The proposed SMO-DSR model is solved via e-constraint method and the best compromise solution is selected by fuzzy satisfying criterion. The model is a mixed integer non-linear programing (MINLP) problem which is implemented on IEEE 33-bus distribution system in General Algebraic Modeling System (GAMS) environment. To show the effectiveness of the proposed SMO-DSR approach, it is studied in different cases. A sensitivity analysis is also performed to show the effect of contract price of wind energy on the objectives of DisCo and DG owner. The obtained results substantiate the interaction between the DSR and DG allocation problems. Also, it is shown that the contract price of wind energy considerably influences both DG owner and DisCo schedules. Besides, when a compromise is made between the DG owner’s profit and DisCo’s cost, the power losses of the network is reduced.

Stability constrained optimal distribution system reconfiguration considering uncertainties in correlated loads and distributed generations

This paper proposes a small signal stability constrained distribution system reconfiguration (DSR) methodology under uncertainties associated with the load demand and the power output of the renewable energy based distributed generation. Further, the correlation among the uncertain load demand and among the uncertain DG power output has also been considered. This DSR problem is formulated for minimising real power loss, number of switching operations as well as maximizing the voltage stability margin. This formulation takes into consideration the system’s probabilistic operational constraints such as maximum limit on line current, minimum and maximum limits on bus voltage magnitude, radiality of distribution system and probabilistic small signal stability constraint. A KnEA-PE approach, consisting of a knee point driven method and 3 point estimation method, is then utilized to solve this DSR problem. The effectiveness and feasibility of presented formulation has been tested on IEEE 33-bus, 69 bus and 119-bus distribution systems. The obtained results are compared with those obtained by the multi-objective NSGA-II-based method.

Optimal reconfiguration of distribution systems with representation of uncertainties through interval analysis

The present work presents an approach for optimal reconfiguration of electrical distribution systems (EDS) to minimize energy losses considering uncertainties in the load demand and in the wind based distributed generation (DG). The optimization algorithm applied to solve the reconfiguration problem is based on the bio-inspired metaheuristic Artificial Immune Systems (AIS). An interval power flow model is used to obtain an interval energy loss from the representation of the uncertainties. The interval loss is used to guide the AIS algorithm through the search space. Network and operational constraints as the radiality and connectivity of the network as well as different load levels are considered. Well-known test systems are used to assess the impact of the uncertainties representation in the reconfiguration problem.

Reliability enhancement using optimal distribution feeder reconfiguration

Failure statistics show that distribution networks engage the most contribution to the customer unavailability services. Optimal reconfiguration of distribution systems has many advantages like total power losses reduction, voltage profile enhancement, reliability improvement and so on. Therefore, in this paper a new multiobjective improved shuffled frog leaping algorithm (ISFLA) is proposed to investigate the distribution feeder reconfiguration (DFR) problem from the reliability enhancement point of view. Nevertheless, since the total cost of MW loss is an important and attractive subject to the electric power utilities, the total active power losses is also considered as an objective function in the investigations. Therefore, the objective functions of the problem to be optimized are system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), average energy not supplied (AENS) and the total active power losses. During the optimization process, the proposed ISFLA finds a set of non-dominated optimal solutions referred to Pareto optimal solutions that are kept in an external memory called repository. As the result of the conflicting behavior of the objective functions investigated, a fuzzy clustering technique is employed to control the size of the repository in the predetermined limits. The feasibility and the efficiency of the proposed method are examined by a standard distribution test system.

An efficient heuristic algorithm for reconfiguration based on branch power flows direction

This paper presents a meshed algorithm for optimal reconfiguration of distribution systems. In the reconfiguration problem, the final objective is to reach a radial network that optimizes some function like the network losses. Many algorithms start from a radial network where a switch closure is complemented by opening of another switch to ensure a radial network. These radial algorithms have an inherent inconvenient, that is, the final solution depends on the initial radial network selected. Other group of algorithms initially represent the distribution network as a meshed network and then open switches until a radial system is obtained. In this paper, to avoid the above aforementioned inconvenient of radial algorithms, a meshed algorithm is used. Furthermore, taking into account that breakpoint nodes are defined as the nodes where the branch power flows converge, the contribution of this paper is to provide a method for solving the problem when multiple loops are considered using an approach based on the breakpoint nodes, since it is complicated to associate each breakpoint node with its corresponding loop. On the other hand, one of the drawbacks of the reconfiguration problem is the need to solve a great number of power flow computations for calculating the losses in each stage of the algorithm. The algorithm proposed has the property that reduces the number of power flows. By this way, the execution time of the algorithm is improved. Besides, it is not necessary to check the network connectivity at each step of the procedure. To prove the effectiveness of the proposed algorithm several test systems have been used, achieving good results.

A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm

All utility companies strive to achieve the well-balanced distribution systems in order to improve system voltage regulation by means of equal load balancing of feeders and reducing power loss. Optimal reconfiguration is one of the best solutions to reach this goal. This paper presents a new combined method for optimal reconfiguration using a multi-objective function with fuzzy variables. This method considers both objectives of load balancing and loss reduction in the feeders. Since reconfiguration is a nonlinear optimization problem, the ant colony algorithm is employed for the optimized response in search space. This method has been applied on two IEEE 33-bus and 69-bus distribution systems. Simulation results confirm the effectiveness of the proposed method in comparison with other techniques for optimal reconfiguration.

Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm

This paper presents the particle swarm optimization (PSO) algorithm for solving the optimal distribution system reconfiguration problem for power loss minimization. The PSO is a relatively new and powerful intelligence evolution algorithm for solving optimization problems. It is a population-based approach. The PSO is originally inspired from the social behavior of bird flocks and fish schools. The proposed PSO algorithm in this paper is introduced with some modifications such as using an inertia weight that decreases linearly during the simulation. This setting allows the PSO to explore a large area at the start of the simulation. Also, a modification in the number of iterations and the population size is presented. Comparative studies are conducted on two test distribution systems to verify the effectiveness of the proposed PSO algorithm. The obtained results are compared with those obtained using other techniques in previous work to evaluate the performance.

Distribution system minimum loss reconfiguration in the Hyper-Cube Ant Colony Optimization framework

This paper presents an original application of the Ant Colony Optimization concepts to the optimal reconfiguration of distribution systems, with the objective of minimizing the distribution system losses in the presence of a set of structural and operational constraints. The proposed algorithm starts from the current configuration of the system and proceeds by progressively introducing variations in the configuration according to local and global heuristic rules developed within the Hyper-Cube Ant Colony Optimization framework. Results of numerical tests carried out on a classical system and on a large real urban distribution system are presented to show the effectiveness of the proposed approach.