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ABSTRACT This paper proposes a novel approach for the optimal allocation of a depot for electric buses (EBs) charging in a specific transit service, considering the impact on the power system. The main objective is to minimize the total cost, achieved by minimizing the cost of the new cables connecting the depot station to the distribution system and the upgrade cost of existing lines to meet the additional loads. The outcomes are the optimal location of the depot, the optimal electric node bus in the distribution system to supply it, and the required system upgrades. The optimization problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and solved using the General Algebraic Modeling System (GAMS). The methodology is tested on the H16 EB service line in Barcelona, Spain, and a typical electrical distribution system. Three case studies are presented in this paper. In the first case, the impact of a single service on the distribution network is analyzed, and in the second case, three H16 EB services are assumed to serve the network. The third case handles a multi‐route, multi‐terminal bus service to allocate and supply a depot capable of accommodating all the routes. This case will also include sensitivity analysis to test the robustness and reliability of the model. Results show that for a single H16 EB service, no line upgrades were needed, and the total cost per phase was $120,000. For three H16 EB services, three lines required upgrades, and the total cost per phase increased to $718,000. In the third case, the sensitivity analysis revealed that higher demand factors lead to increased costs due to more update requirements and voltage deviations. The results demonstrate that the minimum distance between the depot and node is not always the optimal or feasible solution that would prevent the depot load installation at a weak spot and meet the power flow constraints.

Abstract The integration of seasonal loads, such as cereal baking and aquatic‐product processing loads, often leads to significant voltage deviations and severe peak loads of the distribution system during specific periods, resulting in increased network losses. Traditional approaches for reducing network losses are becoming less effective and cost‐efficient due to the spatiotemporally uneven distribution characteristics of seasonal loads. To address this issue, this study proposes an optimisation model that collaboratively integrates mobile energy storage, switching capacitors, and tie lines to minimise annual network losses in special planning scenarios affected by seasonal loads. The deployment strategies of multiple reinforcement methods are thoroughly analysed, greatly enhancing the explainability and feasibility of the collaborative deployment model. Then, the proposed model is reformulated to a mixed‐integer linear programming model using the inscribed regular dodecagon approximation approach, thereby making it trackable for state‐of‐the‐art solvers. To illustrate the effectiveness of the model, case studies are conducted on a unique 55‐bus distribution system located in East China, which contains feeders with substantial seasonal variation aquaculture loads and with general loads. The effectiveness of multiple reinforcement methods is thoroughly analysed through detailed numerical results. Furthermore, a sensitivity analysis of the investment budget is conducted.

Abstract The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems.

Abstract Unbalanced operation of distribution networks, as a crucial but inevitable aspect of power quality, brings about economic and technical issues. The increase in active power loss is more concerning due to the economic and technical implications. Economic sustainability of liberalized distribution networks is hugely tied to the availability of transparent and effective approaches to determine the stakeholders of this sort of phenomenon. This is done by awarding/penalizing them accordingly. Studies in the literature have not considered the incremental losses that unbalanced operations cause to distribution systems. Here, a mathematical approach based on vector analysis in the symmetrical components space is introduced. This approach quantifies the contribution of each unbalanced load to unbalanced operation and increased power losses. This technique discriminates between the mutual effects of different unbalanced loads on the power losses of the entire network. By means of the proposed approach, the best candidate unbalanced load(s)/location(s) to perform the corrective balancing actions and their ranking are precisely specified. Vector representation of the developed method simplifies its understanding, utilization, and expansion. For validation, the proposed method is applied to the IEEE 37‐bus test distribution network under various scenarios. The results confirm the sound results and practical merits of the developed method.

Abstract In recent years, distributed structures have been developed in distribution networks as a result of privatization as well as integration of independently operated distributed energy resources (DERs) into the grid. In this context, effective management techniques seem to be necessary to enable decentralized operation of systems that have been consisted of multiple agents. Accordingly, novel management schemes should be applied in such systems to enable the distributed reactive power management, which would finally improve voltage profile in the network. As a result, this paper develops a distributed algorithm for reactive power management in multi‐agent distribution systems with the aim of improving the voltage profile of the grid. Correspondingly, in the proposed framework, several cost functions are developed to model the effects of reactive power management on the system from the distribution system operator (DSO) as well as agents’ perspectives. Consequently, the proposed reactive power management algorithm would result in voltage profile improvement, while each agent of the system merely strives to maximize its own profits. Accordingly, the proposed scheme ensures the privacy of independent agents. Finally, the proposed scheme is applied on the modified IEEE‐37‐bus test system to investigate its effectiveness on Peer‐to‐Peer (P2P) reactive power management in multi‐agent systems.

Abstract In recent years, extreme weather events, such as typhoons, have led to large‐scale power outages in distribution systems. As a result, developing strategies to bolster distribution system resilience has become imperative. This paper proposes a two‐stage stochastic programming model aimed at enhancing this resilience. Prior to a typhoon, the first stage establishes a comprehensive wind field model based on extreme value distribution for accurate wind speed predictions. Simultaneously, a refined stress–strength interference model is used to determine the likelihood of distribution line failures. Taking into account the uncertainty of line damage, repair crews and mobile emergency generators are then strategically positioned at staging depots. Following the typhoon, the second stage coordinates network reconfiguration, dispatches repair crews, and mobilizes mobile emergency generators to minimize load shedding and expedite repairs. This model was validated on the IEEE 33‐bus distribution system, coupled with a corresponding transportation network, utilizing data from the 2018 super typhoon ‘Mangkhut’ in China. Simulations indicate that our approach can effectively reduce load shedding and power outage durations, thereby enhancing the resilience of distribution systems.

Abstract Single phase‐earth faults are the most frequent faults likely to occur but hard to identify in distribution system, especially in a neutral un‐effectively grounded system. To address this issue, this paper innovatively proposes a typical distribution system structure, in which it only introduces one three‐phase isolated power transformer (PT)‐based subnetwork division mode that can effectively limit the zero‐sequence current flow from other sound lines. Second, following the qualitative discussions about the effect of the proposed structures on power supply reliability and probability of bushfires caused by high impedance earth faults (HIEFs), a novel detecting judgement criterion merely using vector‐based variation via pre‐fault and post‐fault zero‐sequence voltage is mathematically formulated. In general, the proposed structure takes into account the the fault current suppression while manifesting fault feature at the same time, and in contrast with current detecting methods, vectorized form can present a wider exploring space and a higher identifying sensitivity, especially in detecting HIEFs. Numerical simulations and field tests validate the effectiveness and adaptability of the proposed detecting approach towards single phase‐earth fault identification field.

Abstract The application of the security region methodology in a practical distribution system with large scale normally requires large computer memory and high computation time. To overcome this problem, this article proposes a decoupling and dimension reduction method, which can significantly accelerate the calculation of distribution system security region (DSSR) and is important for the application of the DSSR theory in large‐scale distribution systems. First, the definition of DSSR dimension reflecting the size of solution space and the time complexity is proposed. And the solution algorithm for DSSR dimension is also given. Second, a decoupling and dimension reduction method suitable for the analysis of DSSR is proposed. Following the method, an incidence matrix can be obtained from the DSSR expressions, which can be further divided into multiple block matrices. According to the feeder combinations of the block matrices, the distribution system can be decoupled into multiple sub‐networks for more efficient analysis. Finally, a 10kV distribution network is used in case study to validate the proposed method. The results for a time‐consuming calculation, that is, TSC curve calculation, show that the proposed method can reduce the computation time significantly, making the time‐consuming calculation suitable for the analysis of large‐scale cases.

Abstract As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy‐saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I‐GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I‐GWO has a movement strategy called dimension learning‐based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I‐GWO‐TS) algorithm can solve mixed‐integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high‐power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors’ location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system.

In the literature, many works detail the benefits of Volt‐VAr control to minimise the losses in a distribution system. However, it is also important to understand the impact of it on the life of the other assets in a distribution system. Since, distribution transformers (DTs) are the important assets of a distribution system, the main objective of this paper is to appraise the impact of Volt‐VAr control on the life of DTs. The paper presents a detailed methodology to estimate the loss of life (LoL) of the DTs under various Volt‐VAr control schemes, such as base‐case (without any control), voltage control, and Volt‐VAr control (VVC). The proposed methodology is implemented on modified IEEE 13‐bus and IIT‐Roorkee distribution systems considering the voltage‐dependent load models, seasonal load influences, and harmonics. The simulation results are presented to explore the ability of VVC in reducing the hottest‐spot temperature in the DTs during peak hours. The results reveal that the life of the DT can be extended by a considerable amount by optimally controlling the voltages and reactive power flows in the distribution system. Moreover, it gives a significant reduction in energy losses and improvement in power factor in distribution systems.

This study proposes a method of coordinating electric vehicle charging to reduce losses in a distribution system, using only knowledge of the phase that each charger is connected to. Reducing network losses cuts costs and can be achieved through demand response mechanisms. However, directly minimising losses requires accurate values of the line impedances, which can be difficult to obtain. Flattening load over time and balancing load across phases have both been proposed as alternate solutions which indirectly reduce losses. Here, the practical differences between load flattening and explicitly minimising losses are quantified using simulations of residential charging in European style, three-phase distribution networks. Then, a new smart charging strategy, which incorporates phase balancing as a secondary objective to load flattening, is proposed. This requires only the knowledge of the phase that each load is on and achieves 30–70% of the potential reduction in losses.

This study proposes a new averaged model for steady-state and dynamic response analysis of the electric spring (ES). The accuracy of steady-state and dynamic behaviour of the ES is demonstrated. Further, the dynamic behaviour of the proposed model is described as a function of the modulating signal. The developed model is used to design a controller for bus voltage regulation in a distribution system with an intermittent renewable energy source. The ES controller design problem is formulated as an optimization problem with a time domain-based objective function to reduce the time-weighted voltage deviations using particle swarm optimization method. Different disturbances have been applied to verify the developed ES model accuracy including sudden changes in renewable source current and load variations. An experimental analysis is also performed with the detailed non-linear switching model of the ES to verify its behaviour with that of the proposed averaged model. The characteristics and performance of ES are thoroughly investigated. The effectiveness of the developed model and the proposed controller design approach is demonstrated in terms of a significant improvement in the dynamic performance of the distribution system

This paper finds its motivation from the perspective of utility planners, where the flexibility of choice while reconfiguring a distribution system into Multi-Micrgrids (MMGs) is critical. To address the topology planning problem for MMGs from this dimension, a holistic algorithm incorporating graph theory, ordered binary decision diagrams, and a modified Gauss Siedel power flow algorithm is proposed. This multi-indexed, multi-tier scheme guarantees self-adequacy and reliability of individual microgrids (MGs) by effectively solving a modified balanced partitioning problem, and ensures validation of inequality constraints through power flow studies. The available solutions are ranked according to two system-level performance indices to provide a hierarchically feasible solution list to system planner. This algorithm is presented as an alternate tool for heuristic techniques to ensure a complete search of solution space and to increase the number of available topology planning solutions for the system operator. The flexibility of choice offered by this planning algorithm, tendency to be employed as a resource assessment tool, and incorporation of power flow analysis for autonomous MGs renders it to be practically superior to its counterparts. The proposed algorithm is tested to furnish its advantages and applicability in practical utility systems.

The increasing complexity of the distribution system makes the practical distribution network operation much difficult. This paper presents an ordinal optimisation (OO) approach to solve the distribution network reconfiguration, which is an NP- hard problem with discrete control variables. OO uses crude and computationally fast model to reduce the search space. The spirit of OO is to seek the good enough solution instead of the best with high probability. The proposed approach is validated with two practical distribution systems, TPC 84 node test system. The results are compared with Optimal Flow Pattern (OFP), Common Genetic Algorithm (CGA), Partheno Genetic Algorithm with Tree Structure Encoding technology (TSE-PGA) and Second-Order Cone Programming (SOCP).

With the high-penetration distributed photovoltaic (PV) systems accessed to a distribution network, the peak active power output of PV systems is always greater than the total local load demands, which might lead to the issues of overvoltage and reverse power flow in a distribution system. In this paper, based on the reactive power output capability of distributed PV inverters, a distributed PV reactive power local control strategy is proposed. Three types of voltage reactive power local control modes of PV inverter are analysed, respectively; they are voltage-var control mode (Q(V) mode), adjustable power factor mode (PF(P) mode) and active power curtailment mode (APC mode). The applicable conditions of different control modes are analysed. A simulation example based on IEEE 33-node system is studied, where the method of determining the optimal control mode under different operating scenarios of distribution system with PV is given, and the validity of the three local control modes is verified.

In recent years, major power outages caused by extreme natural disasters have incurred large economic losses in distribution systems. This study proposes a transient simulation and verification method for resilience-enhancement strategies and proposed a graphics processing unit-based parallel simulation method. The proposed method can be used to verify the transient feasibility of the distribution system before a strategy is actually executed. Simulations are performed to verify the proposed method.

Recent innovations in information and communication technologies (ICT) provide new opportunities and challenges for the integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing the integration of DERs, the novel market platform should be designed for these new market players to provide correct incentives for them to join the market and follow market rules. In this paper, a feeder-based market is proposed for local energy trading among prosumers and consumers in the distribution system to share energy with other players in the local market and with neighbourhood areas. A two-step market clearing (2SMC) mechanism is proposed for market clearing, in which in the first step, each local market is cleared independently to determine the market clearing price and in the second step, players can trade energy with neighbourhood areas. In comparison to a centralised market, the proposed method is scalable and reduces computation overheads, because instead of clearing market for a large number of players, the market is cleared for a fewer number of players. Case studies demonstrate the efficiency and effectiveness of the proposed market clearing method in increasing social welfare and reducing computation time.

In this study, a comprehensive reconfiguration and operation optimisation of distribution system with flexible DC device (FDD) is discussed. The structures of distribution network with FDD are illustrated and the exact operating of FDD set-points are discussed which realised power flow control and loop closing operation of distribution system. Then, the non-linear combinational optimisation models of reconfiguration and operation optimisation with FDD are established considering different objectives and constraints. In order to solve the complicated optimisation problems, improved particle swarm algorithm and sequential AC/DC power flow method are used. Results show that the FDD have stronger ability to promote the performance of distribution system with power flow optimal adjusting and cooperation with traditional switch.

This study proposes a novel user-centric cyber-physical framework to achieve distributed demand response (DDR) in a distribution system where local schedulers (LS) present at individual buses program both local and non-local loads to reduce the maximum load within a sparse communication setting. A COnjoint Methodology for communication and controller DESign (COMDES) has been proposed to design the topology of the communication network while considering various cyber and physical properties of the smart grid like bandwidth, load profile and voltage controller stability margin. The designed communication network provides a unified pathway for exchanging multiple types of data among different points in the grid to consummate the advanced ideas of DDR and multi-input multi-output voltage control concurrently. Concepts of Lyapunov stability and linear matrix inequalities are employed to design optimisation frameworks for tuning the values of voltage controllers which, in turn, play a vital role in pooling the loads to be scheduled by the individual LS for execution. Established algorithms like earliest deadline first, least laxity first and dynamic rate priority are used to generate switching schedules for redistributed loads and achieve peak shaving. IEEE 4 bus and 14 bus models have been used to demonstrate the efficacy of the proposed framework.

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable-based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO(2) and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro-based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi-objective optimisation method proposed in this paper for optimal integration of renewable-based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33-bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24-hr time-varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

The increasing amount of distributed energy resources installed in distribution systems imposes new challenges on the operation and control of both distribution and transmission systems. At the same time, most distributed energy resources have the potential to support grid operation, e.g. as reactive power suppliers. To exploit these capabilities, a flexible and scalable control approach capable of dealing with large numbers of heterogeneous components is required. Many approaches are either distribution or transmission oriented and neglect the interests or constraints of the other side. Furthermore, the drawbacks of classical centralised control are often ignored. This study presents SwarmGrid-X, an agent-based control concept that considers the structure, demands and capabilities of components in the distribution system and the power requirements of the transmission system at the point of common coupling. Agents control components autonomously and determine control decisions in a decentralised way. They cooperate in swarms to achieve a common system goal while information and power flow are kept as local as possible. The cooperative nature of SwarmGrid-X transforms the distribution system into a cyber-physical power system. This study explains how SwarmGrid-X enables cyber-physical interactions of components to automate electrical power distribution and support the transmission system.

As the load of the power system continues to increase, the capacity of switchgear climbs, resulting in that the overheating problems becoming more serious. In order to realise the monitoring of the temperature of the switchgear, the authors need to conduct a thorough analysis of the temperature rise principle of the switchgear firstly. For the purpose of getting the detailed temperature distribution in the switchgear, a series of temperature rise tests were carried out based on the KYN28A-12(Z) overhead line switchgear, and the data were analysed and compared in detail to summarise the factors that affect the temperature distribution in the switchgear.

The integration of the distributed power generation into a distribution system comes with several system problems. One of the teething problems related to system protection is islanding detection. Various anti-islanding techniques based on feature evaluation were proposed in the recent past. However, they overlook the need for justifying the selection of a particular detection feature among all the possible measures. In this study, a wrapper feature selection approach is proposed where a modified multi-objective differential evolution algorithm is coupled with a kernel-based extreme learning machine classifier. To select the optimum features, five standard objective functions have been considered, such as dependability, security, accuracy, F-measure, and the number of features. About 1864 cases have been generated from the designed IEEE 13 bus system to extract the sensitive features. IEEE 1547 standards have been considered while designing and testing the IEEE 13 bus system against islanding. The selected optimal features detect the islanded condition decisively for both synchronous and inverter-based distributed generators. The features also validate their performance under noisy environment accurately with lesser computational time.