A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration

In this study, a stochastic multiobjective framework is proposed for distribution feeder reconfiguration (DFR). The proposed multiobjective framework can concurrently optimise competing objective functions including total power losses, voltage deviation and total cost. Moreover, system uncertainties including wind power generation and active and reactive load uncertainty are explicitly considered in the stochastic DFR problem. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism in conjunction with Weibull/Gaussian probability distribution function of wind/load forecast variations for random scenario generation wherein the stochastic multiobjective DFR problem is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective formulation based on the adaptive modified particle swarm optimisation (AMPSO) is implemented for each deterministic scenario of the first stage. Utilisation of the stochastic framework would capture more uncertainty spectrum of the investigated multiobjective DFR problem rather than that of the deterministic framework. Consequently, the results of the stochastic framework are more realistic and dependable. Moreover, the new adaptive optimisation algorithm (AMPSO) has much more ability than the other well-known algorithms in the area of optimisation applications.

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.

There are numerous approaches being used to reduce power losses in the distribution system. In this work, the concept of network reconfiguration is used in an effort to provide new algorithms to decrease distribution system losses. The network reconfiguration issue must be solved with effective distribution load flow (DLF). Two key components are required for the Optimal Network Reconfiguration (ONR) strategy to reduce losses: first, promising radiality for the reconfigured network, and second, providing optimal losses for the final reconfigured network. The complex computational problem of network reconfiguration is addressed using five meta-heuristic techniques: Genetic Algorithm (GA), Shuffled Frog Leap Algorithm (SFLA), Particle Swarm Optimization (PSO), Pigeon Inspired Optimization (PIO), and Jaya Optimization algorithm. These techniques take into account equality and inequality constraints to find the best network reconfiguration with the minimum power losses in the system. The IEEE 33 bus distribution system is used as a test case, and the test system is subjected to network reconfiguration. Results of different algorithms were included. Over the other algorithms, the Pigeon Inspired Optimization (PIO) algorithm outperforms them.

In order to improve the premature convergence problem of traditional shuffled frog leaping algorithm (SFLA), this paper proposed a normal cloud mutation shuffled frog leaping algorithm (NCM-SFLA) by mixing the cloud model algorithm (NCM) with SFLA algorithm, NCM is used to overcome the shortage of SFLA which is easy to fall into local optimal solution. The proposed NCM-SFLA has a good parallel characteristic, and the parallel computing can be implemented easily in multi core environment. In case study, this paper takes the Li Xianjiang cascade reservoirs in China as an instance to solve the cascade reservoirs operation optimization problem by the proposed NCM-SFLA. The results show that, compared with the Multi- dimensional Dynamic Programming (MDP), NCM-SFLA has the better global search ability and faster convergence speed, and the corresponding parallel computing can effectively shorten the run-time of NCM-SFLA. Therefore, the feasibility and rationality of the proposed NCM-SFLA and its parallel computing are effectively proved by the case study results.

PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.FindingsSimulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.Originality/valueA modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

Feeder reconfiguration is an important operational process in power distribution grids, which is implemented to enhance the system’s performance by managing the switches. Given variations of the electricity price and load pattern in smart distribution networks, the operational problems of the distribution system are largely time-dependent and very complex. To deal with these temporal dependencies, it is important to extend the problem across different time intervals. Toward this end, a multi-objective optimization model is presented in this study for dynamic feeder reconfiguration problem in the distribution system over multiple time intervals considering distributed generators, energy storage systems, and solar photovoltaic units. The demand response program including interruptible/curtailable service is proposed to enable energy consumers to rethink their energy consumption patterns based on incentive and punitive policies. The common objectives considered in the feeder reconfiguration problem are power loss and voltage deviation which are important objectives for traditional distribution systems, but less attention has been paid to distribution network voltage security. This study considers operational cost, energy loss and voltage stability index as objective functions that can meet operational and voltage security expectations. Dynamic feeder reconfiguration problem is a complex integer nonlinear program problem and hence it is difficult to solve which requires the use of appropriate optimization algorithms to converge to global optima or find close to global optima. In this paper, a modified honey bee matting optimization algorithm based on the new mating mechanism is presented to solve the multi-objective dynamic feeder reconfiguration problem. The proposed approach uses an eliminating zone concept to finds a set of non-dominated solutions during the search process. Furthermore, a fuzzy decision-maker is adopted to select the best compromise solution among the non-dominated solutions. The suggested approach is tested on the IEEE 33-node and 136-node test systems and its superiorities are shown through comparison with other evolutionary algorithms.

The rising penetration of intermittent renewable distributed generation leads to uncertainties in the planning of electric distribution networks. Fully considering the uncertainties pertinent to wind power generation, photovoltaic power generation and load demand, this paper proposes a scenariobased model for the planning of active distribution systems. The solution obtains the optimal capacities and locations of wind and photovoltaic based distributed generators in the distribution system, whilst minimizing the active and reactive power losses as well as voltage deviation. A scenario matrix is generated using the heuristic moment matching technique that captures the stochastic moments and correlation among historical wind and photovoltaic power, and electricity demand. The scenario matrix is then incorporated to propose a stochastic planning model that considers a multi-objective index for minimizing power losses and voltage deviation. Finally, the effectiveness of the proposed planning model is confirmed using case-studies in 53-bus and IEEE 123-bus distribution systems.

This study addresses a shuffled frog leaping algorithm for solving the multi-objective reactive power dispatch problem in a power system. Optimal Reactive Power Dispatch (ORPD) is formulated as a nonlinear, multi-modal and mixed-variable problem. The intended technique is based on the minimization of the real power loss, minimization of voltage deviation and maximization of the voltage stability margin. Generator voltages, capacitor banks and tap positions of tap changing transformers are used as optimization variables of this problem. A memetic meta-heuristic named as shuffled frog-leaping algorithm is intended to solve multi-objective optimal reactive power dispatch problems considering voltage stability margin and voltage deviation. The Shuffled Frog-Leaping Algorithm (SFLA) is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The most important benefit of this algorithm is higher speed of convergence to a better solution. The intended method is applied to ORPD problem on IEEE 57 bus power systems and compared with two versions of differential evolutionary algorithm. The simulation results show the effectiveness of the intended method.

This paper deals with solving optimal power flow problems using one of the new meta-heuristic optimizations inspired by the behavior of grey wolves (Canis lupus). Optimal power flow (OPF) faced several challenged such as increased of fuel cost of generation units, increased the real power losses at transmission lines, increased of voltage deviation of each bus, and uncontrolled of voltage stability for whole system. Therefore, in this paper, the objective functions that have been optimized include generation cost (GC), power loss (PL), voltage deviation (VD), and voltage stability index (VSI). The GWO algorithm has been applied on the Iraqi Super Grid High Voltage 400 kV (ISGHV 400 kV) to set the optimal control variables. The results obtained by this algorithm have been compared with the results obtained by the initial study case to determine the percentage of reduction rate. This comparison proved the efficiency and superiority of the GWO algorithm when applied to the power system networks. The percentages of reduction rates compared with the initial case are 45.08%, 27.28%, 70.64%, and 15.40% of generation cost (GC), power loss (PL), voltage deviation (VD), and voltage stability index (VSI), respectively.KeywordsOptimal Power Flow (OPF)Grey Wolf Optimizer (GWO)Iraqi Super Grid High Voltage (ISGHV) 400 kV

Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

Although metaheuristic techniques have recently become popular in optimization, still they are not suitable for computationally expensive real-world problems, specifically when the problems have many input variables. Among these techniques, particle swarm optimization (PSO) is one of the most well-known population-based nature-inspired algorithms which can intelligently search huge spaces of possible arrangements of design variables to solve various complex problems. The candidate solutions and accordingly the required number of evaluated particles, however, dramatically increase with the number of design variables or the dimension of the problem. This study is a major modification to an original PSO for using all previously evaluated points aiming to increase the computational efficiency. For this purpose, a metamodeling methodology appropriate for so-called high-dimensional, expensive, black-box (HEB) problems is used to efficiently generate an approximate function from all particles calculated during the optimization process. Following the metamodel construction, a term named metamodeling acceleration is added to the velocity update formula in the original PSO algorithm using the minimum of the metamodel. The proposed strategy is called the metamodel guided particle swarm optimization (MGPSO) algorithm. The superior performance of the approach is compared with original PSO using several benchmark problems with different numbers of variables. The developed algorithm is then used to optimize the aerodynamic design of a gas turbine compressor blade airfoil as a challenging HEB problem. The simulation results illustrated the MGPSO’s capability to achieve more accurate results with a considerably smaller number of function evaluations.

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

The electrical distribution system (EDS) plays a vital role in the power sector, serving as the final stage in the delivery of electrical energy from the transmission network to end users. It ensures the efficient, reliable, and safe supply of electricity to residential, commercial, and industrial consumers. Over the years, distribution systems have evolved significantly in terms of design, operation, and technology to meet the growing energy demand and improve service quality. This paper surveys existing research, technical developments, and innovations in electrical distribution systems (EDS). It provides a detailed overview of traditional systems mainly radial, ring, and interconnected networks and highlights their characteristics, advantages, and limitations. The study also emphasizes the importance of load management, voltage regulation, and protection schemes that ensure system reliability and minimize power losses. With the integration of renewable energy sources such as solar and wind, as well as the implementation of automation and smart grid technologies, modern distribution systems have become more intelligent and adaptive. The paper explores the role of technologies like Supervisory Control and Data Acquisition (SCADA), smart meters, and Internet of Things (IoT) devices in enhancing monitoring, control, and fault detection. Additionally, challenges such as aging infrastructure, power quality issues, and cybersecurity threats are discussed, along with emerging solutions like energy storage and artificial intelligence–based predictive maintenance. Overall, this survey aims to provide an in-depth understanding of both conventional and modern electrical distribution systems, their operational challenges, and future development trends, contributing to improved system performance and sustainable power distribution in the evolving energy landscape. The findings from this survey consolidate recent advancements and highlight future research opportunities in smart and sustainable distribution systems.

The distribution system is the largest segment of the electrical power system and the final stage in delivering electricity to consumers. It experiences power losses which could be technical or non-technical due to its radial configuration and connected components. Technical losses occur as energy dissipation in the form of heat in the system components, which is unavoidable. It affects the efficiency of the system and increases operational costs. Therefore, the reduction is essential for adequate power supply and improvement in utility revenue generation. This study focuses on reducing power losses on 11 kV Taiwo distribution feeder in Ilorin using the Network reconfiguration approach. The feeder was modelled, and its power losses were evaluated based on the Backward Forward Sweep method suitable for the radial network. The reconfigured network was optimised using the Particle Swarm Optimisation technique. The study shows that the values of feeder real power losses before and after reconfiguration are 176.883 kW and 121.972 kW, respectively.Further improvement was, however, achieved when PSO was used for the reconfiguration, as the power loss value stands at 98.465 kW. This is 44.33 % reduction compared to the initial power loss value and 19.273 % after reconfiguration. Furthermore, the values of the reactive power losses before and after reconfiguration are 9.474 kVar and 6.527 kVar, respectively, amounting to a 31.11 % reduction. However, PSO reduces the value to 3.101 kVar, which is 67.27 % of the initial value. Thus, radial distribution network reconfiguration using PSO has proven to be a robust method for reducing power loss in the distribution network.