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Active electricity distribution systems (AEDS) play an important role on the social welfare that is highly relies on the reliability of these systems. In this regard, maintenance management tries to decrease component failures via repairing component that are more possible to be failed in operation time. In this manuscript, a stochastic MINLP maintenance scheduling model is proposed in which the reliability of AEDS's equipment is considered through employing a three-state Markov model. This model helps us to embed the uncertainty of AEDS's equipment service restoration time by considering online monitoring system for it and FLISR functions in AEDS. In this paper, minimizing the total cost of AEDS is determined as objective function considering total customer interruption cost, total cost of energy not supplied, total cost of prevented maintenance actions, total corrective maintenance actions cost implemented in the AEDS and total cost of implementing online monitoring for AEDS's component in maintenance scheduling. The proposed framework is applied in two different cases, Case I is maintenance scheduling without online monitoring system and Case II is maintenance scheduling with online monitoring system. The aforementioned cases are implemented in an IEEE Test System as well as a real scale distribution network. Moreover, several sensitivity analyses are implemented for indicating the efficiency of the proposed model.

Nowadays, the deployment of micro‐grids (MGs) is one of the important trends in modern distribution network planning. Implementing this strategy aims to improve the ability of the distribution network to withstand extreme weather conditions and supplying critical loads. While the literature has focused chiefly on operating distribution systems and partitioning them into MGs, in this work, the idea of planning a new low voltage (LV) radial distribution system based on multi MGs for a greenfield is proposed. The main contribution is providing a framework to plan a resilient LV distribution system step by step for a new town without any operating grid. In the planning process, some MGs are formed and their service area and technical specifications like the number of MGs and their sizes are determined by a multi‐objective optimization algorithm considering hardening and resiliency issues. The planning is done in a way that all predefined critical loads will be supplied. To consider the effects of extreme weather conditions, failure rates of poles and conductors due to thunderstorms are considered as a function of wind speed, and their appropriate models are used. The provided method is implemented in a test case with three major scenarios. Accordingly, planning in normal conditions considering hardening options and resilient network planning cases are evaluated. Simulation results and resiliency metrics are compared in detail.

Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.

An effective way to improve distribution system reliability is to place switches and protective equipment in the optimal location. Commonly, in the placement problem, the use of equipment in the designated location is assumed to be possible. But in practice, to establish protection coordination between the equipment, it is necessary to remove or relocate some of the equipment. This paper aims to increase distribution companies' profits and reduce customer interruption costs through a feasible solution. A new hybrid method for equipment placement has been proposed that simultaneously solves the protection coordination problem. While determining the optimal number and location of reclosers and sectionalizers, the proposed method ensures protection coordination between equipment (new and existing devices). Furthermore, to achieve a more realistic and accurate model, factors such as equipment malfunction, fault types (transient and permanent), and the relationship between these faults have been carefully considered to formulate the proposed method. In this method, to solve the optimization problems related to equipment placement and protection coordination, genetic algorithm, and linear programming in MATLAB software have been used, respectively. A real‐life distribution network has been utilized to evaluate the proposed method, and the results show the capability and robustness of this method.

Reliability improvement is a fundamental issue in electric distribution network (EDN) operation. In this regard, providing the most efficient maintenance policy can dramatically assist the electric utility companies in reducing the failure rate of EDN components. In the present study, a novel probabilistic reliability‐centred maintenance (RCM) approach is proposed in which the reliability level of the EDN components are evaluated according to the three‐state Markov model (MM). Using the MM, this study presents a trade‐off between the corrective maintenance and the preventive maintenance (PM) actions and finally determines the precedence of EDN components in PM financial resource allocation. Through the method presented in this study, the financial resources of the PM are economically allocated with regards to economic parameters such as customer outage costs and budget constraints. The efficacy of the proposed RCM approach is evaluated through the implementation of the RCM approach on bus number four of the well‐known Roy Billinton test system.

Plug-in electric vehicles (PEVs) are considered as innovative solutions to achieve sustainable development. The installation of PEV parking lots is the main requirement for using PEVs. Another requirement is the expansion of the distribution system to meet a portfolio of demands with different attributes including parking lots. In this study, an innovative framework for integration of parking lots in distribution system planning (DSP) problem is presented. The storage capability of gridable parking lots (GPLs) is used to postpone investments in distribution system expansion. To this end, first, a novel stochastic GPL allocation model is proposed in which GPLs can act as charging stations and storage elements. Then, an integrated model for DSP problem is presented considering different objectives of distribution system company and GPL owners. Finally, an efficient linearisation lemma is applied to solve the proposed DSP model. The numerical studies demonstrate the proficiency of the proposed DSP model.

Condition Based Maintenance (CBM) using inspection data is one of the available maintenance approaches for power system equipment. In Inspection Based Maintenance (IBM) as a specific type of CBM, decisions about the time and type of the maintenance tasks are made after determining the deterioration condition of the equipment. In this paper, an IBM model is proposed for yearlong transmission equipment maintenance scheduling. In order to quantify the effect of the inspection and maintenance tasks, the probability distribution of the deterioration condition is calculated using the transition probability matrix of the Markov process. The problem of system level maintenance scheduling is formulated with binary variables as weekly inspection time during the year. Inspection time of equipment is optimized by minimizing the total cost, including inspection, maintenance, redispatch and failure costs while network security constraints are considered. The proposed model is implemented for the maintenance of transformers and transmission lines in IEEE-RTS and IEEE 57-bus test systems.

Implementing microgrids in power systems will improve the network reliability and reduce the impact of outages on end-users. Determining the most efficient boundaries of microgrids under contingencies is one of the main challenges for utilities from reliability and economics points of view. Currently, most research works have been focused on predefined boundary or static microgrids regardless system conditions and priority or importance of customers. In this paper, a novel concept for designing and operation of flexible microgrids in order to improve the reliability of a power distribution system is proposed. Compared to current approaches, boundaries of the proposed flexible microgrids can be extended or shrunk based on generation and demand levels, technical constraints, and customers’ comfort. Furthermore, a demand response (DR) program is performed to maintain a balance between generation and consumption in the microgrid. In this paper, genetic algorithm (GA) and mixed-integer linear programming (MILP) are simultaneously applied to a model and solve two-stage optimization considering utilities’ profits and customers’ satisfaction. In planning level, GA is utilized for sitting and sizing of distributed generations and placement of switches. In operation level, MILP is used to select target switches as boundaries of optimal microgrids, model priority of customers, and determine the contribution of each load in the DR program. The case study is also presented and final results show the superiority of the proposed method compared with the traditional fixed boundaries method in microgrids.

One of the distribution network operators’ (DNOs’) responsibilities is to design distribution networks in order to reliably and efficiently meet a portfolio of demands with different attributes. The advent of plug-in electric vehicles introduces a number of new network players, such as gridable parking lot owners (GPLOs), which might have conflicting objectives with DNO’s ones. This paper presents a dynamic bi-level model for distribution network planning considering different objectives of DNO and GPLO. The power injection capability of gridable parking lots (GPLs) is employed as an alternative supply source to enhance the network flexibility in supplying loads under faulty conditions. Moreover, the proposed planning approach is developed in a way that the economic risk of the plan associated with the stochastic nature of the GPL behavior is controlled. Different test networks are used to validate the effectiveness of the proposed model.

One of the most difficult problems in the management of economically power systems is the unit commitment problem. The mathematical complexity of the problem increases with the increase in unit numbers. Given the high dimensions of this problem, meta-heuristic methods can act as one of the best ways of solving this problem. one of the challenges that meta-heuristic strategies faces is the low number of feasible answers compared to non-feasible ones while trying to satisfy the unit commitment constraints which can cause problems in the convergence of these methods. A new approach based on binary encoding in the population was proposed in this paper, using the Gray Wolf Optimization Algorithm (GWO), to solve the complexity of the unit commitment problem. The GWO is then applied to 10 thermal test units. Comprehensive numerical tests are carried out to check the GWO's efficacy using the approach proposed. The simulation results are presented and compared to numerous current heuristic methods that show the GWO's superior performance using the method proposed to solve the UC problem.

Due to the unique structure and special characteristics of electric distribution networks, along with the increase in the number and severity of the natural disasters in recent years, presenting a proper framework and procedure for evaluating the resiliency of these systems is more essential. In addition, it is obligatory to model the different capabilities and features of the smart grids components in order to provide a better knowledge about their expected performance in such circumstances. In response to these challenges, this paper addresses the concept of resiliency and its dimensions in distribution networks. A new model based on mixed-integer linear programming is proposed to properly model and evaluate the resiliency of smart distribution systems. In the proposed model, optimal formation of dynamic microgrids (MGs), their service areas, and the optimal management of different technologies such as energy storage (ES) units, demand side management programs and distributed generations (DGs) units are investigated. In addition, employing a two-stage framework based on stochastic programming, the impact of increasing penetration level of the renewable energy resources and their related uncertainties on system resiliency is examined. The efficiency and applicability of the proposed integrated model is verified by performing multiple simulations on the modified 118-bus test system and a real distribution network.

This paper presents an active distribution network expansion planning framework, which concurrently uses the renewable distributed generations and energy storage systems as capacity expansion options. In order to enhance the network reliability, the model takes into account the island mode operation of the renewable resources and energy storage systems. The proposed planning framework, which is modeled as a probabilistic bi-level optimization problem quantifies and controls the economic risk level associated with the stochastic nature of these resources. The master level is devoted to the here-and-now decisions in the planning phase, whereas the slave level, which is formulated as a two-stage model, is related to the wait-and-see decisions in the operational phase. At the first stage of the slave level, the network operational behaviour is determined by performing an optimal power flow modeled as a mixed-integer linear programming problem. At the second stage of the slave problem, the network reliability is optimized considering island mode operation and taking into account energy-limited nature of storage systems. The effectiveness of the proposed active distribution network planning model is demonstrated through several case studies. Simulation results demonstrate that the proposed approach can result in a flexible low-risk plan for the expansion of the network.

Vulnerability mitigation is the solution for creating resilient distribution networks aims to prevent the uncontrollable outage propagation. This paper presents a new resilience index as a powerful tool to enhance the resilience of distribution systems against extreme hurricane events. The suggested framework provides a comparative study for optimal feeder routing problem and HV substation placement from the cost and resilience viewpoints. Once, the network is planned based on cost minimization, and then the proposed resilience index is calculated for the planned network. Finally, the network is planned based on resilience enhancement and afterward the planned network's cost is calculated. In case of resilient-based planning, the studied area is divided into small sites with different wind speed to evaluate the geospatial characteristics of hurricane and a fragility index is calculated for each distribution network component located at each sites. Both cost- and resilient- based networks are planned using deterministic and probabilistic approaches. In deterministic approach, network planning is done according to the worst prediction, the highest wind speed. But in probabilistic one, some scenarios with specified probability are defined for wind speed. Results show that, fragility index is high when considering worst prediction, so this less probable scenario needs higher investments. In this regards, scenario based planning, can lead to the best compromised solution considering cost and resilience which the obtained results validate the accuracy of the modellings and efficiency of the proposed method.

In this paper, a methodology is presented to find the optimal size, location and technology of distributed generation (DG) units considering economic, technical, and environmental issues simultaneously. This technique can be implemented to both renewable and nonrenewable DGs. From economic perspective, minimizing cost and maximizing profit are investigated separately and compared with each other. In addition, from technical and environmental viewpoints, a complete set of indices are defined. In order to find the optimal solution, multiobjective particle swarm optimization, and fuzzy decision-making techniques are utilized. Furthermore, to evaluate the proposed method more accurately, two scenarios are defined. In fact, the first scenario, which is only based on cost minimization and technical objective functions, plays an introductory role for better comprehension of the second scenario. In the second scenario, which includes the presentation of the methodologies of this paper, the effect of selecting either profit maximization or cost minimization as an economic objective function along with the minimization of technical and environmental objective functions are precisely investigated. By carrying out the simulations on the IEEE 33-bus distribution test system, it is concluded that the cost-based framework is not the most profitable one, whereas the profit-based one may well lead to a more appropriate result from economic viewpoints.

Self-healing capability as one of the most important features of smart distribution networks (SDN) causes an increase in the resiliency and flexibility of networks by responding fast and restoring service during an outage. A two-layer algorithm based on metaheuristics is proposed for the optimal operation of smart distribution network in self-healing mode considering microgrid (MG) and direct load control (DLC) programme capability in the presence of micro turbine (MT) and energy storage (ES) in the distribution network. In the first layer, a new approach based on the graph theory is proposed to find the optimal forming of MGs, while in the second layer the optimal energy management of MGs, including storages, is determined considering self-healing mode. To illustrate the advantage of the proposed method, the proposed algorithm is implemented on the modified IEEE 33 bus distribution network.

This paper introduces a three-stage resilience improvement scheme to enhance operational power system resilience against a hurricane event. As a prerequisite for implementing this scheme, the hurricane impact on the power system is modeled using a stochastic model in which a set of transmission line failure scenarios is generated. This scheme is composed of three stages: (i) Employing an approach for vulnerability assessment of each scenario to identify the scenarios in which cascading failures are likely (or not likely) to occur; (ii) Implementing optimal transmission line switching (OTLS) technique for those scenarios in which cascading failures are not much probable; (iii) Implementing preventive islanding (PI) technique for the scenarios in which cascading failures are probable. The amount of resilience improvement is assessed using a two-step risk-based framework. The applicability of the proposed methodology is examined by implementing it in the IEEE 24-bus test system.

Outage management has a crucial role in improving reliability level of distribution networks, and it is profoundly dependent on the reserve capacity margin of sub-transmission (ST) substations and load transferring capability between them. These factors enable distribution system operator (DSOs) to restore interrupted load points in out-of-service areas. In this study, the connection of reserve capacity in ST substations and load transferring capability, made by tie switches, is studied. The mentioned correlation is addressed via costumer interruption cost derived from outage management in primary distribution networks. Then, a mathematical model is developed in which optimal reserve capacity of ST substations and optimal placement of tie switches are determined simultaneously. The resulted optimisation model is formulated as a mixed-integer non-linear problem, and the genetic algorithm is used to solve the problem. Finally, the effectiveness of the proposed model is evaluated in a test distribution network.

Future distribution systems will accommodate an increasing share of distributed energy resources (DERs). Facing with this new reality, virtual power plants (VPPs) play a key role to aggregate DERs with the aim of facilitating their involvement in wholesale electricity markets. In this paper, the trading strategies of a VPP in cooperation with its neighboring VPPs are addressed. Toward this aim, a portfolio of inter-regional contracts is considered to model this cooperation and maximize the energy trade opportunities of the VPP within a medium-term horizon. To hedge against profit variability caused by market price uncertainties, two efficient risk management approaches are also implemented in the VPP decision-making problem based on the concepts of conditional value at risk (CVaR) and second-order stochastic dominance constraints (SSD). The resulting models are formulated as mixed-integer linear programming (MILP) problems that can be solved using off-the-shelf software packages. The efficiency of the proposed risk-hedging models is analyzed through a detailed case study, and thereby relevant conclusions are drawn.