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In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities.

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system.

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm.

This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device’s optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model.

In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike.

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples.

Electrical Power quality in distribution systems is crucial to both utility and consumers simultaneously. The main issue that affects the quality factor of electrical distribution systems is phase load imbalance. Phase imbalance is a major issue in distribution networks in Pakistan, India, the United States, China, and other nations and regions. The distribution system in Pakistan is normally a three-phase, four-wire system, whereas our residential and commercial loads are often single-phase, resulting in an unbalanced system. These unbalanced circumstances in the system result in single-phasing, overloading, and overheating situations, and the return of current to neutral, as well as increased power system investment and operational expenses. In this paper, several methodologies for phase balance are being studied. After researching several techniques of phase balancing and building on that methodology, a simulation prototype is developed, and different unbalanced situations are studied. To analyze unbalanced conditions in practical mode, a hardware prototype is developed on the basis of simulation. Examined some unbalanced loads on the simulation prototype and then on the hardware prototype and achieved the best possible load balancing on phases.

The energy crisis and environmental concerns have accelerated the development of the active distribution network (ADN) with a high proportion of renewable energy, which poses a challenge to the operation of the power system. Moreover, using active management means to promote the consumption of renewable energy is an important task of ADN. Therefore, as an important operation means, the network reconfiguration is used to enhance the adjustable capacity of the power system at the planning stage. Firstly, a “wind–light–load” uncertain scenario set is constructed to address the uncertainty of wind speed, lighting, and load. On this basis, a robust optimization model for distributed power generation taking into account network reconstruction and in ADN is proposed. In addition, the distributed generator (DG) permeability indicator is introduced in the planning model to improve the ADN ability of absorbing renewable energy. A linearized AC power flow model is utilized to calculate the power flow. Finally, via simulation in an IEEE 33-bus system and IEEE 69-bus system, the influence of network reconfiguration and robustness on distributed generator planning, economy and reliability of ADN is analyzed, and the validity of the model is verified.

This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system’s power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem.

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.

Distributed energy resources (DERs) based on renewable power, such as photovoltaic (PV), have been increasing worldwide. To support this growth, some technologies have been developed to increase the hosting capacity (HC) of distribution networks (DNs), such as the Soft Open Point (SOP), which can replace normally open switches in DNs with the advantage of allowing power and voltage control. The benefits of SOPs in terms of increasing distributed generation (DG) hosting capacity can be enhanced by network reconfiguration (NR). In this work, an optimization-based approach is proposed for placing SOP in DN with simultaneous NR; that is, the proposed algorithm consists of a promising alternative to previous works in the literature that deal with SOP placement and NR in an iteratively way or in a two-step procedure, considering that better results can be obtained by simultaneously handling both options, as shown in the introduced case studies. The optimization problem is modeled as nonlinear mixed-integer programming, and solved by a Multi-objective Artificial Immune System (MOAIS). The proposed algorithm is applied to a well-known medium-voltage (MV) test system that is widely used for the problem at hand, and the results show the effectiveness of the proposed approach to maximize the HC by optimizing the SOP installation site in the tested system. An important outcome is that the association of SOP planning and NR in a simultaneous manner tends to provide better quality solutions, where HC can overcome 400% for multiple SOPs. Another outcome is that the proposed MOAIS is able to provide good concurrent solutions to support the decision-making of the DN planner.

The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions.

Load shedding control is an emergency control measure to maintain the frequency stability of the power system. Most of the existing load shedding methods use the extensive form of directly cutting off the outlet of the substation, featuring low control accuracy and high control cost. A network reconfiguration technique can adjust the topology of the distribution network and offers more optimization space for load shedding control. Therefore, this paper proposes a reconfiguration–load shedding coordination optimization scheme to reduce the power loss caused by load shedding control. In the proposed method, a load shedding mathematical optimization model based on distribution network reconfiguration is first established. The tie switches and segment switches in the distribution network are used to perform the reconfiguration of the distribution network, and the load switches are adopted to realize the load shedding. To improve the solving efficiency of the model, a solving strategy that combined a minimum spanning tree algorithm with an improved genetic algorithm is trailed to address the nonlinear and nonconvex terms. The application of the proposed method and model are finally verified via the IEEE 33 bus system, and the advantages in reducing the loss cost and the number of outage users are accordingly proven.

In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of jellyfish. They are divided into three categories. The jellyfish could flow towards the ocean current or between its own swarm. Meanwhile, whenever the food supply is adequate, the jellyfishes are attracted to its location. It is formulated considering the three reliability indices of SAIFI, SAIUI and TENS, simultaneously in a multi-objective model based on the weight factors. The proposed methodology based on the JFS optimizer is implemented on an IEEE 33-node distribution network. According to the numerical results, the SAIFI, SAIUI and TENS is improved by 36.44%, 34.11% and 33.35%, compared to the initial condition. For comparison purposes, tuna swarm optimizer and tunicate swarm algorithm, besides the JFS algorithm, are implemented as well. The simulation results declare the significant outperformance of the JFS algorithm compared to TUNA and TSA in terms of the obtained improvements and the regarding convergence properties.

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

The topic of power loss reduction in distribution systems has gained significant attention over recent years. Despite the efforts of the European Union towards the minimization of power losses, the decarbonization of the transport sector has raised several concerns, since charging overlaps of Electric Vehicles (EVs) can cause extensive power losses and power quality issues. Considering these, the present paper proposes a two-stage EV charging planning and Network Reconfiguration (NR) methodology, addressing the problem of power loss minimization in both Low-Voltage (LV) and Medium-Voltage (MV) Distribution Networks (DNs), respectively. In the first stage, considering the key role of the aggregator, the EV charging planning is applied to LV DN. In the second stage, the NR technique is applied to the MV DN, by taking into account the hourly power demand of LV DNs as obtained by the aggregators. The proposed methodology has been applied on a benchmarked MV network for which each node is represented by a real LV network. The results indicate that the proposed methodology could yield up to a 63.64% power loss reduction, in respect to the base scenario, i.e., no charging planning and no NR are applied.

Network reconfiguration is an effective way to avoid severe, large-scale power outages and to improve the resilience of active distribution networks (ADNs). Furthermore, the rapid development of distributed energy resources (DERs) provides new perspectives for network reconfiguration. In this paper, the effect of network reconfiguration and DER collaboration on ADN’s resilient restoration are studied. The applications of DERs are fully explored. In order to achieve a better resilient performance, a detailed multiperiod model considering both reconfiguration and multiple DERs is established. Some linearization techniques are used to simplify the proposed model. Then, we build a rolling horizon optimization framework to solve the model. The framework eliminates the adverse effect of prediction errors and speeds up the calculation. By introducing predictions into strategy determination, the framework achieves a better restoration effect than the traditional greedy method. The proposed framework is tested on a 33-bus system. The simulations verify the efficiency of our work.

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem.

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

The distribution network is the most exposed part of the electrical power system relative to different abnormal events; therefore, it reports the highest occurrence rates in terms of electrical and mechanical failures. The present project describes a strategy for restoring faulty areas after the occurrence of an abnormal event causing an outage; consequently, the proposed algorithm is not only focused on the maximization of the connected loads but also deals with the minimization of the switching operations by considering technical operational constraints. The remarked study has two stages: The first one finds an initial set of tie-line candidates using the spanning tree technique, while the second stage applies a genetic algorithm to determine the optimal solution considering all the constraints. Three cases studies have been used to test the proposed algorithm, then the simulation and results of IEEE 13, 37 and 94 node feeders depict the effectiveness of the restoration strategy.

As the interconnection of renewable-energy-based distributed generations (DGs) to the distribution system increases, the local and temporary voltage and current problems, which are difficult to resolve with the existing operation method, are becoming serious. In this study, we propose a short-term operational method that can effectively resolve voltage and current violations caused by instantaneous output fluctuations of DGs in a system with a high hosting capacity of renewable energy sources. To achieve the objectives, a modified heuristic network reconfiguration method, and a method determining the maximum power output limit of individual DGs are proposed. We propose a cooperative method for controlling the power output fluctuations of renewable-energy-based DGs, which includes voltage control, network reconfiguration, and power curtailment. The proposed algorithm was verified through case studies by using a test system implemented in MATLAB environments. It can effectively resolve violations caused by DGs while minimizing the number of switching operations and power curtailment. The proposed method is an appropriate operation method to be applied to the real system as it can cope with the instantaneous output fluctuation of DGs, which was not dealt with in the existing operation method.

Electricity Distributions Networks (DNs) are changing from a once passive to an active electric power system element. This change, driven by several European Commission Directives and Regulations in the energy sector prompts the proliferated integration of new network elements, which can actively participate in network operations if adequately utilized. This paper addresses the possibility of using these active DN elements for optimization of a time-discrete network operation in terms of minimization of power losses while ensuring other operational constraints (i.e., voltage profiles and line currents). The active elements considered within the proposed optimization procedure are distributed generation units, capable of reactive power provision; remotely controlled switches for changing the network configuration; and an on-load tap changer-equipped substation, supplying the network. The proposed procedure was tested on a model of an actual medium voltage DN. The results showed that simultaneous consideration of these active elements could reduce power losses at a considered point of operation while keeping the voltage profiles within the permitted interval. Furthermore, by performing a series of consecutive optimization procedures at a given time interval, an optimization of network operations for extended periods (e.g., days, months, or years) could also be achieved.

Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software.

This study aimed to minimize energy losses in traditional distribution networks and microgrids through a network reconfiguration and phase balancing approach. To address this problem, an algorithm composed of a multi-objective function and operation constraints is proposed. Network connection matrices based on graph theory and the backward/forward sweep method are used to analyze power flow. A minimizing energy loss approach is developed for network reconfiguration and phase balancing, and the particle swarm optimization (PSO) algorithm is adopted to solve this optimal combination problem. The proposed approach is tested on the IEEE 37-bus test system and the first outdoor microgrid test bed established by the Institute of Nuclear Energy Research (INER) in Taiwan. Simulation results demonstrate that the proposed two-stage approach can be applied in network reconfiguration to minimize energy loss.

Contemporary distributions are now going to underground their overhead distribution lines due to techno-social reasons. Reliability and loss reduction are the two prime objectives for distribution system operation. Since failure rates of ungrounded cables are the function of Joules heating besides their physical lengths, the reliability evaluation of undergrounded distribution systems needs to be reviewed. This paper suggested a suitable modification in existing reliability indices in order to make them more appropriate for underground distribution systems. A multi-objective network reconfiguration problem is formulated to enhance the reliability and performance of distribution systems while duly addressing the variability and uncertainty in load demand and power generation from renewables. The application results on a standard test bench shift the paradigm of the well-known conflicting nature of reliability and network performance indices defined for overhead distribution systems.

The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.

In this study, we allocated soft open points (SOPs) and distributed generation (DG) units simultaneously with and without network reconfiguration (NR), and investigate the contribution of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to distribution systems under different loading conditions. A recent meta-heuristic optimization algorithm called the discrete-continuous hyper-spherical search algorithm is used to solve the mixed-integer nonlinear problem of SOPs and DGs allocation, along with new NR methodology to obtain radial configurations in an efficient manner without the possibility of getting trapped in local minima. Further, multi-scenario studies are conducted on an IEEE 33-node balanced benchmark distribution system and an 83-node balanced distribution system from a power company in Taiwan. The contributions of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to the system, are investigated to determine the real benefits gained from their allocation. It was clear from the results obtained that simultaneous NR, SOP, and DG allocation into a distribution system creates a hybrid configuration that merges the benefits offered by radial distribution systems and mitigates drawbacks related to losses, power quality, and voltage violations, while offering a far more efficient and optimal network operation. Also, it was found that the contribution of the internal loss of SOPs to the total loss for different numbers of installed SOPs is not dependent on the number of SOPs and that loss minimization is not always guaranteed by installing more SOPs or DGs along with NR. One of the findings of the paper demonstrates that NR with optimizing tie-lines could reduce active losses considerably. The results obtained also validate, with proper justifications, that SOPs installed for the management of constraints in LV feeders could further reduce losses and efficiently address issues related to voltage violations and network losses.

In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.

This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems.

With the development of active distribution networks, the means of controlling such networks are becoming more abundant, and simultaneously, due to the intermittency of renewable energy and the randomness of the demand-side load, the operating uncertainty is becoming serious. To solve the problem of source–network–load coordination scheduling, a multiobjective scheduling model for an active distribution network (ADN) is proposed in this paper. The operating cost, renewable energy utilization rate, and user satisfaction are considered as the optimization objectives, and the distributed generation (DG) output power, switch number, and incentive price for the responsive load are set as the decision variables. Then the probabilistic power flow based on Monte Carlo sampling and the chance-constrained programming are used to deal with the uncertainty of the ADN. Moreover, the reference point–based many-objective evolutionary algorithm (NSGA3) is used to solve this nonlinear, multiperiod, and multiobjective optimization problem. The effectiveness of the proposed method is verified in the modified IEEE 33-bus distribution system. The results show that the proposed scheduling method can effectively improve the system status.

Transition towards a smart grid requires network modernization based on the deployment of information and communication technologies for managing network operation and coordinating distributed energy resources in distribution systems. The success of the most advanced smart grid functionalities depends on the availability and quality of communication systems. Amongst the most demanding functionalities, those related to fault isolation, location and system restoration (FLISR) to obtain a self-healing smart grid are critical and require low latency communication systems, particularly in case of application to weakly-meshed operated networks. Simulation tools capable of capturing the interaction between communication and electrical systems are of outmost utility to check proper functioning of FLISR under different utilization conditions, to assess the expected improvements of Quality of Service, and to define minimum requirements of the communication system. In this context, this paper investigates the use of public mobile telecommunication system 4G Long Term Evolution (LTE) for FLISR applications in both radially and weakly-meshed medium voltage (MV) distribution networks. This study makes use of a co-simulation software platform capable to consider power system dynamics. The results demonstrate that LTE can be used as communication medium for advanced fault location, extinction, and network reconfiguration in distribution networks. Furthermore, this paper shows that the reduction of performances with mobile background usage does not affect the system and does not cause delays higher than 100 ms, which is the maximum allowable for power system protections.

Due to the various network requirements of applications, quality of service (QoS)-aware routing plays an important role in the networks. Recently proposed resource allocation algorithms focus on the current traffic matrix, which is not applicable for dynamic networks. In this paper, we exploit an estimation of flow matrix that gives our scheme the ability to sufficiently reduce the total packet loss and simultaneously raise the network throughput. In this way, we mathematically formulate the QoS-aware resource reallocation in software-defined networking (SDN) networks based on the traffic prediction. To solve this optimization problem, two schemes are proposed: (i) exact solution; and (ii) fast suboptimal one. The proposed schemes are compared with the accuracy perspective. Moreover, the impact of prediction on resource reallocation is discussed. In this regard, it is shown that, compared with the conventional scheme, the proposed scheme decreases the packet loss and increases the throughput significantly.

An efficient distribution network must be able to supply power with good voltage profile. The main objective of the proposed work is to allocate losses of the unbalanced distribution network by the firefly algorithm in regulated and deregulated environments before and after loss minimization. Reconfiguration is one of the methods for loss reduction of unbalanced distribution network. Further, optimal placement of distributed generation and capacitor in the reconfigured unbalanced distribution network can further reduce the loss. The results of reconfigured unbalanced distribution network in regulated environment have already been reported. In this paper reconfiguration of an unbalanced distribution network in a deregulated environment is also carried out using an established Fuzzy Firefly algorithm. Loss sensitivity factor of unbalanced distribution networks is used to get the appropriate location of distributed generation and capacitor to be placed in the unbalanced distribution network. Their ratings have been found out by using bacteria foraging optimization algorithm (BFOA). The suggested loss allocation method using Firefly algorithm is implemented at first on 13 node unbalanced distribution network to check the performance of the proposed loss allocation method when compared to other available method. Finally the proposed method has been implemented on 25 node unbalanced distribution network. Both of the implementations are carried out under MATLAB environment.

With large-scale integration of distributed energy resources (DERs), distribution networks have turned into active distribution networks (ADNs). However, management risks and obstacles are caused by this in due to renewable energy generation (REG) forecasting errors. In this paper, a day-ahead active power scheduling method considering REG forecast errors is proposed to eliminate the risks, minimize the costs of distribution companies and achieve optimal power flow. A hierarchical coordination optimization model based on chance constrained programming is established to realize day-ahead optimal scheduling of active power in ADNs coordinated with network reconfiguration, achieving an optimal solution of network topologies and DER outputs. The hierarchical method includes three levels: the first level provides initial values, and multiple iterations between the second and third level are used to solve the multi-period mixed integer nonlinear optimization problem. The randomness due to REG forecast errors is tackled with chance constrained programming in the scheduling procedure. The hybrid particle swarm optimization algorithm is employed to solve the proposed model. Simulation results verify the validity of the proposed method with an improved 33 nodes distribution network.