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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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article formulates the distribution generation placement and the network reconfiguration problems of distribution networks (DNs) as a composite multi‐objective optimization problem by normalizing and combining the objective functions, and suggests a new rainfall optimization (RFO) based method for solving the developed problem. The RFO, a metaheuristic optimization algorithm that models the behavior of raindrops falling down from a hill towards sea, has certain drawbacks such as removal of inactive raindrops that weakens the search process, and the possibility of choosing the same raindrop as inactive throughout the iterative process. This article also introduces modifications in the RFO for overcoming its drawbacks before adopting it in the proposed method. It considers two scenarios with seven test cases for simulation study, and compares the results of two standard DNs with existing methods for exhibiting the superior performances in respect of final solution, robustness, voltage profile, and average execution time of the proposed method. [ABSTRACT FROM AUTHOR]

As the load steadily rises in a distribution system (DS), electrical power generation is improved. Inserting Distributed Generation (DG) unit in a DS has benefits such as enhancement in voltage profile and decreased power losses. In combination with optimal size, these advantages can be obtained to inject DG in the optimal location. Reconfiguration is another framework for optimizing the voltage profile and reduction of energy loss. Furthermore, reconfiguring the distribution network and installing the DG would ultimately improve system efficiency, which is undoubtedly challenging task. In this paper, equilibrium optimizer (EO) is explored to find out how to reconfigure the distribution network and how best DG can be positioned. For the IEEE-33 bus. Reliability Analysis is also done. The suggested method is implemented in MATLAB R2020A platform software. Outcomes of the suggested Technique based on the EO is compared with the existing techniques. There is Substantial improvement in voltage profile in addition to the decline in loss of power and reliability indexes (EENS, SAIFI, and SAIDI) for different cases of System with DG placement and System. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Continuous increases in electrical energy demand and the deregulation of power systems have forced utility companies to provide high-quality and reliable services to maintain a sustainable operation and reduce electricity price. One way to continue providing the required services while simultaneously reducing operational costs is through minimizing power losses and voltage deviation in the distribution network. For this purpose, Network Reconfiguration (NR) is commonly adopted by employing the switching operation to enhance overall system performance. In the past, work proposed by researchers to attain switching sequence operation was based on hamming distance approach. This approach caused the search space to grow with the increase in total Hamming distance between the initial and the final configuration. Therefore, a method is proposed in this paper utilizing a Mixed Integer Second Order Cone Programming (MISOCP) to attain optimal NR to address this issue. The Hamming dataset approach is opted to reduce search space by considering only radial configuration solutions to achieve an optimal switching sequence. In addition, a detailed economic analysis has been performed to determine the saving after the implementation of the proposed switching sequence. The effectiveness of the proposed technique is validated through simulations on IEEE 33-bus distribution network and a practical 71-bus network in Malaysia. The result shows that the proposed method determined the optimal network configuration by minimizing the power losses for the 33 bus and 71-bus system by 34.14% and 25.5% from their initial configuration, respectively to maintain sustainable operation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Security-constrained unit commitment (SCUC) model is used for power system day-ahead scheduling. However, current SCUC model uses a static network to deliver power and meet demand optimally. A dynamic network can provide a lower optimal cost and alleviate network congestion. However, due to the computational complexity and the lack of effective algorithms, network reconfiguration has not been included in the SCUC model yet. This paper presents a novel approach to handle the computational complexity in security-constrained unit commitment (SCUC) with corrective network reconfiguration (CNR) while considering the scalability through accelerated-decomposition approach with fast screening non-critical sub-problems of SCUC-CNR. The proposed approach provides substantial computational benefits and is also applicable to SCUC. Simulation results on the IEEE 24-bus system show that the proposed methods are substantially faster without the loss in solution quality while the scalability benefits are demonstrated using larger cases: the IEEE 73-bus system, IEEE 118-bus system and Polish system. [ABSTRACT FROM AUTHOR]

Network reconfiguration and emergency power vehicles (EPVs) dispatching are widely used in distribution networks for load restoration. However, their capabilities are limited by the allocated amounts of circuit breakers and EPVs. E-taxis can also participate in the restoration as a kind of mobile energy storage using the vehicle to grid (V2G) technology. However, the uncertainty of E-taxis should be considered in the restoration. To achieve better effectiveness of the restoration and fully utilize the capability of network reconfiguration, EPVs and E-taxis, this article proposes a coordinated restoration optimization method considering the uncertain restoration capabilities of discharging stations with E-taxis. A joint probability distribution function is established based on Gaussian mixture model to describe the uncertainty of station discharging capabilities considering the correlation of user rationality, taxi state-of-charge, and transportation status. Then, a bilevel programming model embedded with the chance constraint programming is developed to optimize the coordinated dynamic restoration scheme of the network reconfiguration and EPV dispatching, with the consideration of the mobility of EPVs during the restoration. Simulations studies are performed to verify the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes an innovative integrated power and hydrogen distribution system (IPHDS) restoration model in response to multiple outages caused by natural disasters. During the restoration, repair crews and mobile battery-carried vehicles are considered to repair faulted lines and support critical power loads. Also, the network reconfiguration is taken into consideration in the restoration model to pick up loads. Besides, to address the different response time of hydrogen and power systems, the aerodynamic law-based dynamic hydrogen flow model is applied in the hydrogen system. The proposed model is presented as a mixed-integer linear program, which is verified on a 33-bus-48-node IPHDS with multiple outages. The present results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The amount of power losses in the lines of distribution topology are very significant and they make the bus voltages of the system poor. This leads to the development of smart distribution system configurations. Therefore, this paper highlights an efficient network topology approach for the optimal utilization of radial distribution systems (RDSs) by applying the network reconfiguration (NR) and allocating the distribution static compensator (D-STATCOM), distributed generation (DG) units, and electric vehicle charging stations (EVCSs). The proposed RDS problem is solved subjected to the power balance, location, and sizes of D-STATCOM and DG, and voltage constraints. Here, the binary bat algorithm (BBA) is implemented for solving DG, D-STATCOM, and EVCSs allocation in RDS as well as the network reconfiguration (NR). The developed methodology has been tested on IEEE 33 bus RDS. [ABSTRACT FROM AUTHOR]

Demand response, the reactive power output of distributed generation (DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG. With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.

As an aggregator of distributed energy resources (DERs) such as distributed generator, energy storage, and load, the virtual power plant (VPP) enables these small DERs participating in system operation. One of the critical issues is how to aggregate DERs to form VPPs appropriately. To improve the controllability and reduce the operation cost of VPP, the complementary DERs with close electrical distances should be aggregated in the same VPP. In this paper, it is formulated as an optimal network partition model for minimizing the voltage deviation inside VPPs and the fluctuation of injection power at the point of common coupling (PCC). A new convex formulation of network reconfiguration strategy is incorporated in this approach which can guarantee the components of the same VPP connected and further improve the performance of VPPs. The proposed approach is cast as an instance of mixed-integer linear programming (MILP) and can be effectively solved. Moreover, a scenario reduction method is developed to reduce the computation burden based on the k-shape algorithm. Numerical tests on the 13-bus and 70-bus distribution networks justify the effectiveness of the proposed approach.

Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration.

Distribution systems play an important role, delivering the electric power of generation system to individual consumers. Distribution system reconfiguration (DSR) is a large-scale combinatorial optimization problem. For the last 45 years, the DSR problem has been widely studied; nowadays, DSR, combined with new challenges, is being highly investigated, as researchers aim to reach a better solution. This paper presents a complete review and classification of the most significant works to date, providing a literary framework for DSR specialists. A categorization of solution methods, case studies, and novelties of the most relevant works regarding DSR are provided. In order to establish a complete background, not only traditional approaches, but also those involving uncertainty, reliability, electricity market, power quality, distributed generation, capacitor placement, and switching time in DSR are highlighted. This framework can help researches to improve previous formulations and methods and can propose more efficient models to better exploit the existing infrastructure.

The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.

Increased load demands worsen distribution system problems such as greater line losses, voltage deviation and a plethora of other concerns. This current work presents an approach stressing simultaneous optimal allocation and sizing of capacitor banks and distributed generations, as well as optimal radial distribution system (RDS) reconfiguration, to address these difficulties. The above objectives are accomplished through the maiden application of the proposed quasi-reflection-based slime mould algorithm (QRSMA). The efficacy of QRSMA is established by testing it on different benchmark functions. A new modified backward forward load flow approach is also proposed and validated by comparing its results to those obtained using MATPOWER software for IEEE 69, 85, and 118 bus RDSs. The proposed load flow technique is independent of the sequential bus numbering scheme and may be applied to any RDS network topology. The proposed QRSMA is tested on 118 bus RDS and to prove its effectiveness; its results are compared to those of other studied algorithms. The study takes into account both fixed and variable loading scenarios. A cost-benefit analysis of the strategy is also performed in order to make the methodology more realistic.

Smart self-healing is perceived as a new alternative to ensure reliability and quality of power supply with the development of intelligent communication and control technology. On the basis of multistage characteristics of self-healing control, this article proposes an integrated multistage self-healing strategy for smart distribution systems using multiagent system (MAS), in which the complex self-healing problem is decomposed into phased sub-problems and is addressed by a unified control framework composed of different algorithms of stages. In the proposed control framework, decision-making agents vary with fault points and transition between self-healing stages making the technique fully decentralized. Stressing on the coordination between stage algorithms composed of communication self-adaption, fault tolerance, fault location and isolation, service restoration and state regression, the proposed strategy features well real-time control performance and relatively complete self-healing functions. Comprehensive simulation studies are carried out on the 84-bus and 22-bus distribution systems using MATLAB and JADE and the self-healing test platform respectively, and the test results have shown the effectiveness of the proposed strategy.

This study aims to suggest a new quasi-oppositional chaotic neural network algorithm (QOCNNA) for simultaneous network reconfiguration and distributed generations allocation (SNR-DG) in radial distribution networks (RDNs). The proposed QOCNNA is developed by combining original NNA with chaotic local search (CLS) and quasi-oppositional-based learning (QOBL) approaches. Integration of QOBL helps the algorithm improve exploration of the search space and convergence speed. Meanwhile, CLS can improve exploitation of the algorithm by performing local search around the current best solution. The objective of the SNR-DG problem is to define the network configuration, settings of distributed generations to optimize active power loss and voltage stability in RDNs. The performance of QOCNNA is tested on the SNR-DG problem with 33-, 69- and 118-bus RDNs considering different scenarios. The result analysis indicates that SNR-DG implementation is effective for power loss reduction and voltage stability improvement. Notably, QOCNNA yields active power loss reductions and voltage stability index improvements associated with case multi-objective SNR-DG at nominal load condition of {70.79%, 16.36%}, {84.02%, 8.59%}, and {57.79%, 10.10%}, respectively, for 33-, 69-, and 118-bus RDNs. In comparison with previous studies, QOCNNA is more effective in improving the performance of RDNs. Compared with other methods that we have applied, QOCNNA dominates the performance in solution accuracy, convergence speed, and robustness for all case studies. Also, QOCNNA finds effective and feasible solutions for daily variable load and generation scenario with the minimized total annual energy loss. Simulated outcomes in this scenario verify the superiority of QOCNNA over analytical-based approaches and the applied methods regarding total annual energy loss reduction and cost savings as well. There are hence simulation-based evidences to state that the CLS and QOBL help QOCNNA achieve a good trade-off between exploration and exploitation. Thus, QOCNNA has proved to be a favorable method in dealing with the SNR-DG problem.

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

In the AC/DC hybrid distribution network, non-faulty areas can maintain uninterrupted power supply by adjusting the control mode of the voltage source converter (VSC) after a fault. In addition, the non-faulty areas can also be restored through network reconfiguration. To account for the impact of these two restoration methods on reliability accurately, this article proposed a reliability evaluation method for the AC/DC hybrid distribution network considering VSC restoration capability and network reconfiguration. First, a restoration optimization model of the AC/DC hybrid distribution network considering network reconfiguration and control modes of VSC is established, which can give full play to these two restoration methods while ensuring the radial constraints of the network. Then, the failure mode and effects analysis (FMEA) method is introduced and combined with the restoration optimization model to analyze the impact caused by faults on the network. Among them, the FMEA method is utilized to determine the network topology after fault isolation, and on this basis, the restoration optimization model is solved to obtain the outage time of loads in the network. Finally, the aforementioned work is combined with the sequential Monte Carlo method to evaluate the reliability of the AC/DC hybrid distribution network to adequately simulate the fluctuation characteristics of distributed generation (DG) and load. The case study shows that the proposed method can accurately evaluate the reliability of the AC/DC hybrid distribution network.

Aiming at the nonlinear multi-objective optimization problem of distribution network reconfiguration, the weight of three objective functions, namely network loss, load balance and voltage offset, is determined by the random weight method, which is transformed into a single objective function with the same dimension, the same attribute and the same order of magnitude. The optimal reconfiguration scheme is obtained by using the adaptive multi-population fruit fly optimization algorithm (AMFOA). The node-branch matrix is used for binary coding to avoid the generation of infeasible solutions, and the dynamic step adjustment strategy is introduced to enhance the local search ability of FOA and achieve the effect of balancing global and local search, Cooperative subpopulation strategy and Chebyshev chaotic mapping are used to improve the global search ability and increase the optimization speed. Through the simulation and analysis of a typical IEEE 33-node system with DG, the effectiveness and practicability of the strategy are verified. [ABSTRACT FROM AUTHOR]

This study proposes a distributed load restoration method to effectively model and solve the load restoration problem of integrated power distribution and natural gas systems (IDGSs) after faults for resilient operation. The IDGS load restoration problem is devised as a mixed-integer second-order cone programming (MISOCP) model to minimize the total load loss cost of IDGS. This model considers distribution network reconfiguration and the coupling effect between the power distribution system (PDS) and natural gas system (NGS). Moreover, a bidirectional gas flow model is used to offer more flexibility for load restoration when the NGS is faulty. By considering the independent management and information privacy of each energy subsystem, the proposed model is solved using an alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Furthermore, an adaptive-ADMM algorithm is used for convergence acceleration. Finally, numerical studies are conducted to validate the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

Power loss reduction is a very important problem in operating the distribution network. This paper presents a method relied on artificial ecosystem-based optimization (AEO) for the network reconfiguration (NR) problem. The considered goal of the NR problem is to reduce power loss. The performance of the proposed AEO method is evaluated on the 14-node, 69-node and 136-node distribution networks. In addition, the NR approach relied on cuckoo search algorithm (CSA) is also implemented to compare with the proposed AEO method. The statistics result comparisons between AEO and CSA demonstrate that AEO outperforms CSA method with the higher success rate and better quality of the obtained solution in finding the optimal network configuration. In addition, AEO is also better than some previous approaches in the literature in term of the obtained network configuration. Consequently, AEO method is a very effective approach for the NR problem. [ABSTRACT FROM AUTHOR]

Contemporary researches offer that most researchers have concentrated on either network reconfiguration or Distributed Generation (DG) units insertion for boosting the performance of the distribution system (DS). However, very few researchers have been studied optimum simultaneous distributed generation units insertion and distribution networks reconfiguration (OSDGIR). In this paper, the stochastic meta-heuristic technique belong to swarm intelligence algorithms is proposed. Salp Swarm Algorithm (SSA) is inspired by the behavior of salps when navigating and foraging in the depth of the ocean. It utilized in solving OSDGIR. The objective function is to reduce power loss and voltage deviation in the Distribution System. The SSA is carried out on two different systems: IEEE 33-bus and local Iraqi radial (AL-Fuhood distribution network). Three cases are implemented; only reconfiguration, only DG units insertion, and OSDGIR. Promising results were obtained, where that power loss reduced by 93.1% and recovery voltage index enhanced by 5.4% for the test system and by 78.77% reduction in power loss and 8.2% improvement in recovery voltage for AL-Fuhood distribution network after applying OSDGIR using SSA. Finally, SSA proved effectiveness after an increase in test system loads by different levels in terms of reduced power loss and voltage deviation comparison with other methods.

Optimal Network Reconfiguration (NR) is a well-accepted approach to minimize power loss and enhance voltage profile in the Electrical Distribution Networks (EDN). Since the NR problem contains huge combinational search space, most researchers consider the meta-heuristic techniques to attain NR solution. However, these meta-heuristic techniques do not guarantee to obtain the optimal solution besides they require large processing time to converge. This is mainly due to (1) random initialization and updating of population and (2) the continuous verification of population during the search process. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and enhancing the voltage profile of the EDN, this work proposes a new method based on two-stage optimizations. The proposed method introduces an approach to simplify the network into simplified network graph. Then, this approach is utilized for guided initializations and generations of the population and for the proper population's codification. The proposed method is implemented using the firefly algorithm and verified on 33-bus and 118-bus test systems. The results show the ability of the proposed method to obtain the optimal solution within fast computational time and with superior consistency compared to the conventional methods.

Natural disasters and cascading events have historically caused severe disruptions of the electric power system. For instance, in 2012, hurricane Sandy left over 8 million people in darkness and caused total damage costing $65 billion. Under emergency conditions, microgrids can help the electric power system recover critical loads, such as hospitals, data centers, and water pumping stations. While the literature has mostly focused on the utilization of existing microgrids, the idea of planning for future microgrids in combination with switching operations to make the grid more resilient against devastating events is investigated in this study. This work aims to simultaneously maximize the resiliency of distribution networks - in terms of service to the critical loads - and minimize the dispatchable generation capacity of microgrids. The considered microgrid model entails dispatchable power generators, renewable energy resources, and electrical energy storage systems (ESS) to serve consumers. Considering the topological and operational limitations, the robust optimization scheme optimizes the objectives by the effective selection of the node for microgrid connection and the minimum change in its generation capacity. While the problem is modeled as a multi-objective, mixed-integer linear programming (MO-MILP) problem, the results show more than 99% accuracy compared to the exact results of an exhaustive search algorithm. Numerical tests are performed on the IEEE 37-node test feeder and the IEEE 123-node test feeder to assess the proposed method's performance. Given the accuracy, computation time efficacy, and the generic formulation of the problem, the optimization scheme can be easily applied to any real network.

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.

Network reconfiguration (NR) has received attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the feedforward control of synchronous and inverter-interfaced distributed generators (DGs) is achieved in coordination with the operations of sectionalizing and tie switches (SWs). This enables DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. An analytical dynamic model of a reconfigurable MG is developed to analyze the MG frequency response to NR and determine the desired dynamics of the feedforward controllers when integrated with feedback loops for inertial response emulation and primary and secondary frequency control. A small-signal analysis is conducted to analyze the contribution of the feedforward control to the MG frequency regulation. Simulation case studies of NR-aided load restoration are also performed. The results of the small-signal analysis and case studies confirm that the proposed strategy is effective for the improvement of the MG frequency regulation under various conditions of load demand, model parameter errors, and communication time delays. [ABSTRACT FROM AUTHOR]

Optical multilayer optimization periodically reorganizes layer 0-1-2 network elements to handle both existing and dynamic traffic requirements in the most efficient manner. This delays the need for adding new resources in order to cope with the evolution of the traffic, thus saving CAPEX. The focus of this paper is on Layer 2, i.e., on capacity reoptimization at the optical transport network (OTN) layer when routes (e.g., LSPs in MPLS networks) are making unnecessarily long detours to evade congestion. Reconfiguration into optimized routes can be achieved by re-defining the routes, one at a time, so that they use the vacant resources generated by the disappearance of services using part of a path that transits the congested section. To maintain the Quality of Service, it is desirable to operate under a Make-Before-Break (MBB) paradigm, with the minimum number of reroutings. The challenge is to determine the best rerouting order while minimizing the bandwidth requirement. We propose an exact and scalable optimization model for computing a minimum bandwidth rerouting scheme subject to MBB in the OTN layer of an optical network. Numerical results show that we can successfully apply it on networks with up to 30 nodes, a very significant improvement with respect to the state of the art. We also provide some reoptimization analysis in terms of the bandwidth requirement vs. the number of reroutings. [ABSTRACT FROM AUTHOR]

A mesh type distribution network is often configured into a radial structure to offer a variety of practical benefits, such as lower short-circuit current, low operating costs, and low construction costs. This paper presented a novel technique for optimal planning of radial distribution networks based upon a combination of Graph Theory and Machine Learning (ML) approaches. A network comprises several sectionalizing and tie-type switches. The conversion of a mesh type system into a radial one involves the selection of the best set of switches on the feeder lines that are to be opened or closed. Out of all switching combinations, we have provided the optimal radial combination, consequently the maximum voltage stability of the network with less active and reactive power losses. The proposed system also preserves the system's radial nature, with out islanding any load by a simple coding scheme. To test the capabilities of the proposed technique, we carried out simulations on the 30-node test distribution system and the results are encouraging. [ABSTRACT FROM AUTHOR]

This paper proposes a service restoration model for unbalanced distribution systems and inverter-dominated microgrids (MGs), in which frequency dynamics constraints are developed to optimize the amount of load restoration and guarantee the dynamic performance of system frequency response during the restoration process. After extreme events, the damaged distribution systems can be sectionalized into several isolated MGs to restore critical loads and tripped non-black start distributed generations (DGs) by black start DGs. However, the high penetration of inverter-based DGs reduces the system inertia, which results in low-inertia issues and large frequency fluctuation during the restoration process. To address this challenge, we propose a two-level simulation-assisted sequential service restoration model, which includes a mixed integer linear programming (MILP)-based optimization model and a transient simulation model. The proposed MILP model explicitly incorporates the frequency response into constraints, by interfacing with transient simulation of inverter-dominated MGs. Numerical results on a modified IEEE 123-bus system have validated that the frequency dynamic performance of the proposed service restoration model are indeed improved. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources (DERs), the power flow (PF) in the distribution network (DN) is changed from unidirectional to bidirectional, resulting in complex control and coordinate measures. Network reconfiguration (NR) is a feasible solution for the power grid side to deal with the complex PF. This paper proposed an edge-cloud-coordinated reconfiguration framework with edge servers (ESs) in the prosumer side and cloud servers (CSs) in the utility grid side, where the edge computing (EC) technology is implemented in ES to support load forecasting (LF), while cloud computing (CC) is used in CS to reconstruct the DN. LF is implemented by the long–short-term memory network to acquire the load information in advance, and the social preference of prosumers has been considered. The NR is formulated as a complex combinatorial optimization problem with the goal of minimizing power losses, while satisfying the power flow and voltage requirement. The NR problems are solved by the proposed advanced harmony search algorithm, which can find the optimal global solution, while satisfying the complex constraints of the NR problem. Numerical results are conducted based on an IEEE 33-bus network, which shows the high accuracy of LF and demonstrates the effectiveness of the proposed framework in terms of reducing more than 40% power losses and satisfying the voltage requirement.

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.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for a wide range of diseases. Although first promising findings were obtained so far, the exact mode of action of taVNS is not fully understood yet. We recently developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks. With this schedule, we observed short-term taVNS to have a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale functional brain networks from subjects with focal epilepsies. We here expand on this study and investigate the impact of short-term taVNS on various local and global characteristics of large-scale evolving functional brain networks from a group of 30 subjects with and without central nervous system diseases. Our findings point to differential, at first glance counterintuitive, taVNS-mediated alterations of local and global topological network characteristics that result in a reconfiguration of networks and a modification of their stability and robustness properties. We propose a model of a stimulation-related stretching and compression of evolving functional brain networks that may help to better understand the mode of action of taVNS.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for a wide range of diseases. Although first promising findings were obtained so far, the exact mode of action of taVNS is not fully understood yet. We recently developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks. With this schedule, we observed short-term taVNS to have a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale functional brain networks from subjects with focal epilepsies. We here expand on this study and investigate the impact of short-term taVNS on various local and global characteristics of large-scale evolving functional brain networks from a group of 30 subjects with and without central nervous system diseases. Our findings point to differential, at first glance counterintuitive, taVNS-mediated alterations of local and global topological network characteristics that result in a reconfiguration of networks and a modification of their stability and robustness properties. We propose a model of a stimulation-related stretching and compression of evolving functional brain networks that may help to better understand the mode of action of taVNS. [ABSTRACT FROM AUTHOR]

Using the heuristic search approach, this article has attempted a methodology to reduce power (I2R) loss through optimal reconfiguration in the radial distribution system, resulting in a better and optimal design. The suggested approach is verified by testing it on IEEE 33-bus and IEEE 69-bus systems. MATLAB simulation is used to test the proposed solution and the results are presented in this paper. [ABSTRACT FROM AUTHOR]

Dynamic thermal rating for underground cables with balanced phase currents has been extensively studied in the literature. Paradoxically, distribution feeders operate inherently unbalanced and no publications are available for unbalanced thermal rating. Because of the lack of technology, utilities operate with very conservative margins using the maximum of the three-phase currents to determine the line loading. This practice leads to the significant underutilization of underground cables. To fill the gap this paper presents a physically sound and accurate transient electro-thermal model of unbalanced three-phase distribution cables considering variations in the load and environment. Moreover, the model proposed in this paper allows harvesting the large hidden capacity of distribution systems trapped in the thermal inertia of underground cables. A convenient visual tool is developed to uncover hot spots and underutilized energy routes. This facilitates real-time network reconfiguration based on predicted load graphs and dynamic thermal conditions. The proposed model is also useful to study load growth, perhaps eliminating or deferring capital investments needed to upgrade the capacity of the existing underground feeders. The IEEE 37-bus distribution network is used as a case study. The results show that the proposed methodology has a high potential to defer investments, for instance, one could save $5M for a small system like the IEEE 37-bus distribution network which would have been necessary under current conservative practice. [ABSTRACT FROM AUTHOR]

To provide electric power supply to consumers even in abnormal situations, the equipment used in electric distribution systems, including distribution lines, have sufficient capacity margins. Maintenance and/or replacement of these lines involving network reconfigurations are routinely performed. Since the monetary costs of equipment investments are significant, downsizing the equipment is important from the standpoint of cost savings. In this paper, we propose a method to downsize distribution lines while maintaining the feasibility of the network reconfigurations. The proposed method consists of three steps. First, (1) sets of non-dominated solutions approximating the Pareto fronts of the network reconfiguration problems are found by a many-objective evolutionary algorithm. Second, (2) the distribution system operator specifies the preferred solutions among the sets found in (1). Finally, (3) a set of downsized lines is chosen from among the set of lines under the condition that the specified reconfiguration procedures remain feasible even after downsizing the lines. A computational experiment using a large-scale distribution system model demonstrates that the proposed method successfully identifies within a practical computational time those distribution lines that provide possibilities for downsizing and their downsized capacities. [ABSTRACT FROM AUTHOR]

• Impact assessment of peer-to-peer energy trading on distribution network power loss. • Coordinated approach for power loss reduction in P2P trading-enabled networks. • Simulation study with economic and environmental impact analysis of power loss. This paper examines the impact of decentralized Peer-to-Peer (P2P) energy trading on the power loss occurring in the active distribution network and presents potential solutions for the Distribution System Operator (DSO) to reduce the power loss in the presence of P2P energy trading. An Artificial Bee Colony (ABC) algorithm and backward/forward sweep power flow-based framework is presented to optimally reconfigure the distribution network and minimize the impact of P2P energy trading on power loss. The presented framework is then augmented with the voltage sensitivity indices to determine the placement and sizing of the switched capacitor bank, which helps further to minimize the power loss in the network. An effective coordinated approach combining network reconfiguration and switched capacitor bank placement is developed to diminish the power loss while respecting the network constraints. The developed framework is tested on the IEEE 33-bus radial distribution network, and a comparative analysis is presented to showcase the effectiveness of the presented mitigation techniques in reducing power loss and associated economic and environmental impact. Results show that by adopting the network reconfiguration and the switched capacitor bank placement simultaneously, the DSO can achieve a power loss reduction of around 55% in the presence of P2P energy trading. [ABSTRACT FROM AUTHOR]

• Optimal voltage/VAR control for active distribution networks with distributed PVs. • Active management of branch switches, tap changers, VAR compensators, smart PVs. • Mixed integer second-order cone programming-based formulation. • Multiple network operation modes are encoded and tested. • Multiple smart PV operation modes are encoded and tested. Rapidly increasing penetration of distributed PVs brings great challenges to distribution networks. Driven by the revised IEEE 1547 standard, inverter-based smart PVs provide customized control and voltage regulation capabilities. This paper presents a comprehensive study investigating voltage/VAR control considering network reconfiguration, VAR optimization, and smart PVs. To do so, a second-order cone programming-based VAR optimization and network reconfiguration (VONR) model is formulated. The enhanced necessary radiality condition, and the extensions for multiple smart PV operation modes and multi-period operation are provided. A comprehensive case study considering various network and smart PV operation modes, and hourly VONR operation is conducted. The results show that implementing VONR with smart PV reactive power dispatching optimizes the scheduling plans, resulting in a reduction of network loss by 59.97 % and 30.37 % for the 33-bus and 123-bus test systems, respectively, compared to the no-control operation. [ABSTRACT FROM AUTHOR]

With the integration of renewable energy-based sources and demand response initiatives, the distribution network has transformed into an active network with bidirectional flows. Network Reconfiguration (NR), i.e., changing network topology, is an alternative to minimize power loss in the distribution network with sectionalizing switches (normally closed) and tie-line switches (normally open). Due to the discrete nature of switches (on or off), the NR formulation is generally a mixed-integer NP-hard nonlinear problem. This paper proposes the first NR formulation for an unbalanced active distribution network with continuous variables to indicate switches' statuses in NR while considering mutual coupling between line impedances. The obtained solution provides the optimal real power and voltage magnitude settings of dispatchable Distributed Generators (DGs) and network topology for the minimum active power loss in the network. The proposed formulation considers all practical aspects of the distribution network, i.e., mutual coupling, load unbalances, angular unbalance, and the absence of one or two phases in a line section, which, otherwise, have not been considered together in previous formulations. Further, a novel convex relaxation for bus voltage phase angles avoids explicit voltage recovery. Compared to prior formulations, numeric results for unbalanced radial distribution networks showcase the proposed formulation's effectiveness. • NR for active unbalanced mutually coupled distribution networks is formulated. • The use of continuous switch status variables reduces the problem's complexity. • A novel convex relaxation is used, where bus voltage recovery is not required. • Effect of unbalances and mutual coupling is analyzed on NR and DGs' set points. [ABSTRACT FROM AUTHOR]

This work proposes a sequential stochastic coordinated energy management scheme (SCEMS) for a multi-energy carrier zero bus microgrid (ZBMG) in the presence of distributed tri-generation plants (TGPs), renewable sources, energy storage systems (ESSs), plug-in hybrid electric vehicles (PHEVs), auxiliary boiler and chiller units, and controllable loads. The first stage of the proposed architecture deals with implementing an integrated price-based demand response program to maximize consumers' benefits and boost demand-side participation. The impacts of grid-to-vehicle and vehicle-to-grid modes of PHEV operation are investigated in this study. The second stage of this formulated problem considers the optimal power allocation of TGPs, ESS devices and auxiliary units aiming to satisfy economic, environmental, flexible, and reliability-driven objectives. Furthermore, to ensure optimal serving of heat and cooling load, novel heat and cool-following strategies are suggested. The uncertainties associated with the renewable generators, PHEV load, grid energy price, and load demands are modelled and incorporated in the SCEMS following "Hong's 2m+1 point estimate method". This stage also optimizes the distribution network structure for efficient microgrid operation. Moreover, a novel P − P Q V δ − z e r o + bus triplet approach of controlling power factor of dispatchable distributed generator is proposed. The efficacy of the framed problem is corroborated through examples. The simulation studies show that the proposed coordinated energy management strategy can: (1) economically accomplish peak shaving and valley filling for multi-energy demands; (2) reduce operation cost and electric energy loss by 8.19% ∼ 10.38% and 41.63% ∼ 47.91%, respectively; (3) enhance average flexibility index by 4.06% ∼ 5.62% but with a marginal rise in emission by 0.39% ∼ 1.68%; and (4) improve node voltage profile of the system. [Display omitted] • Sequential two-stage coordinated energy management scheme for zero bus microgrid. • Integrated price-based demand response model for demand side management of microgrid. • Heat and cool-following modes for optimal serving of heat and cooling loads. • Stochastic load estimation of electric vehicles operating in bi-directional modes. • Introduction of a novel bus triplet in the power flow algorithm. [ABSTRACT FROM AUTHOR]