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This article proposes a hierarchical distributed voltage control (HDVC) scheme for active distribution networks (ADNs) with high penetration of photovoltaics based on distributed model predictive control (DMPC) and alternating direction method of multipliers (ADMM). The reactive power outputs of several photovoltaic clusters (PVCs) and photovoltaic (PV) units within each PVC are optimally coordinated to keep PV terminal voltages and the voltages of all critical buses of ADNs within the feasible range and mitigate voltage fluctuations. In the ADN layer, a distributed reactive power control scheme based on DMPC is designed for the PVC, which regulates the voltages of all critical buses to be closed to the rated value and mitigates the reactive power variations. In the PVC layer, the reactive power outputs of PV units are optimized based on ADMM to minimize the voltage deviation of each PV terminal and track the reactive power reference from the PVC control. The proposed HDVC scheme requires communication only between neighboring PVC controller, while each PV controller only communicates with the corresponding PVC controller. This regulates the voltages in a completely decentralized manner and effectively reduces the computation burden of the PVC and PV controllers. A modified Finnish distribution network with 10 PVCs was used to validate the control performance of the proposed HDVC scheme. [ABSTRACT FROM AUTHOR]

Active distribution network is integrated with a large number of renewable energy sources and flexible loads such as electric vehicles(EVs). The unstable output of distributed generation and the random access to different phases cause three phase unbalance in the distribution network, which increase the energy loss and carbon emissions. Three phase unbalance reduction requires high real‐time dispatch and optimization of the power flow. This paper attempts to reduce the energy loss and carbon emission by using flexible multi‐state switch (FMSS) with optimal energy dispatch strategy. A multi‐objective optimization model is established based on second‐order cone relaxation to reduce the three‐phase unbalance and energy loss. EVs regulation strategy is proposed by analyzing their charging flexibility. A fast solution method is brought up based on second‐order cone model transformation. The effectiveness and rapidity of the method is verified by multiple cases. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

High penetration of uncertain wind power generation brings challenges to power system operational security and economy. Here, an adjustable box uncertainty set is first presented to characterize the spatiotemporal correlation of multiple wind power generations. Afterward, a two‐stage risk‐adjusted robust energy dispatching model is established as a min‐max‐min optimization problem for active distribution networks operating with high penetration wind power generation. Then a three‐layer Nest Column and Constraint Generation (NCCG) decomposition approach is designed to efficiently solve the proposed model. Finally, the proposed two‐stage adaptive robust optimization model and NCCG approach are validated by a distribution network with uncertain wind power generations, and the simulation results indicate the distribution network operation economy could be compromised with robustness by adjusting the conservative regulation parameter. [ABSTRACT FROM AUTHOR]

With the rapid development of active distribution networks, the "petal"‐type distribution network has become the mainstream power supply structure. Power control methods for active distribution networks should be further studied to ensure the safe and reliable power supply of a distribution system. An electromagnetic rotary power flow controller (RPFC) is a feasible solution for controlling power in active distribution networks. However, when testing the effectiveness of the PQ decoupled control method for RPFC based on instantaneous reactive power theory, difficulties were encountered with the synchronous control of the rotor position angle of two rotating‐phase transformers, and the accuracy of power control was unsatisfactory. Given this condition, PQ control is improved in three ways. First, the system's periodic oscillation problem is solved via variable speed control. Second, the servo motor‐rotary phase‐shifting transformer synchronous rotation scheme, which reduces power control error and improves stability, is designed. Third, the overshoot phenomenon in power control is improved using the variable‐domain fuzzy proportional‐integral adaptive method. Experimental results show that the proposed advanced control scheme exhibits good dynamic and static performance in power control scenarios and achieves effective improvement in RPFC. [ABSTRACT FROM AUTHOR]

In traditional power systems, the positive sequence fault component (PSFC) based criteria is often used to identify the fault directions. If the PSFC impedance angle of the inverter‐interfaced distributed generator (IIDG) is between 0 and 90°, the traditional PSFC‐based criteria will remain applicable in active distribution networks (ADNs). However, due to the diversity of the IIDG fault control mode and the uncertainty of the positive sequence voltage variation caused by faults, the PSFC impedance angle of IIDG is difficult to determine. To solve this problem, this paper proposes a two‐stage fault control scheme for IIDG. At different stages, IIDGs select different power references to meet different requirements. In the first stage, the PSFC impedance angle of IIDG is fixed to a given value, thereby maintaining the applicability of the PSFC‐based criteria in ADN. The detailed power reference calculation method of IIDG is given for this stage. In the second stage, IIDGs can select different fault control modes to support the fault voltage flexibly. By using the proposed two‐stage IIDG fault control scheme, the protection devices can accurately identify the fault direction in ADNs, which is verified by the MATLAB simulation results. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the integration of more and more renewable energy generations (REGs), the structure of traditional distribution networks is hard to accommodate the volatile power injections of REGs. As a new power electronic device, soft open point (SOP) can be installed to control both active and reactive power flow among active distribution networks (ADNs). This paper presents a comprehensive optimization method for allocating SOPs within an ADN with high penetration of REGs. In order to find proper SOP candidate locations, a selection strategy based on two technical indices is proposed. To mitigate the risk of voltage violation caused by REG forecast errors and improve the adaptiveness of allocation results, a two-stage robust optimization model for SOP allocation is formulated to minimize the total cost of SOP investment and network operation. The proposed model is converted into a mixed-integer second-order cone programming (MISCOP) problem, which is then decoupled into a master problem of planning and a subproblem of operation and solved by column and constraint generation (CCG) algorithm. Simulation results show that the proposed method can effectively find the optimal SOP allocation schemes. Comparisons with different mathematical formulation and solution methods show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This study presents a model predictive control (MPC)-based centralised control approach for maintaining the voltages of the nodes within permissible limits in the presence of high photovoltaic (PV) penetration. The proposed control scheme coordinates the actions of on-load tap changer (OLTC) and PV inverters optimally to fulfil the desired objectives. The objectives are minimisation of change in control variables, slack variables, energy loss, and voltage error. These objectives are weighted to form the overall objective function. Three rules are formulated based on the severity of voltage magnitudes. The weights of the objectives are adjusted according to these pre-defined rules. Simulations are performed in an active distribution network (ADN) integrated with and without microgrids, where both demands and generations vary hourly over the day. As power is injected by the microgrids during most of the time of the day, the excursions of node voltages are slightly higher in the microgrids integrated ADN. Moreover, the incorporation of the proposed rule-based MPC drastically reduces the energy loss due to active power loss in distribution networks, as is evident from the simulation results obtained by comparing the proposed approach with an existing MPC-based approach. [ABSTRACT FROM AUTHOR]

With the large-scale configuration of distribution automation and the widespread implementation of distributed generation (DG) and flexible load (FL), the distribution network can achieve active regulation, which makes the traditional fault restoration strategy and distribution network reliability evaluation more complicated. Given this background, a method of reliability evaluation for the active distribution network considering 'supply-grid-load' is proposed. On the supply side, a distribution network islanding model considering DG is established, with the objective of supplying power to the maximised amount of equivalent power load. On the grid side, the influence of distribution network automation on the distribution network reliability is elaborately analysed and the time-varying failure rate model of equipment which considers the life cycle characteristics is presented. On the load side, an incentive-based FL model is proposed based on the elastic coefficient matrix. Finally, simulation results of RBTS-BUS6 system demonstrate the effectiveness of the proposed model and method. [ABSTRACT FROM AUTHOR]

Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi‐objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second‐order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air‐conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak‐valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN. [ABSTRACT FROM AUTHOR]

As the scale of distributed generations connecting to the power system increases continuously, communication system considering the requirements of real‐time control is becoming much more important in active distribution system. Therefore, in order to improve the responsiveness of the distribution network during the communication interruption, an offline backup strategy is proposed considering the combined output of distributed generations and energy storage systems involving the characteristics of the active distribution system. Based on a typical set of clusters which describe the features of wind turbines and photovoltaics, the strategy analyses operational vulnerability combining structural characteristics of the active distribution system and solves the output level of each distributed generation according to the improved algorithm. A numerical case study is presented to demonstrate the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Accurately identifying the key links in active distribution network (ADN) that affect the system's safety and stability is of great significance to improve the efficiency of operation and maintenance and risk early warning for distribution network (DN). In this study, the node resilience (NR) is first proposed as an indicator for structural stability identification of DN. Then, a critical node identification method considering the security, economy and structure stability in ADN is proposed, which uses the NR, the utility risks of branch load change, node voltage deviation and line loss variation as the identification indicators. Also, combining with subjective experience and objective data, the comprehensive evaluation method of critical nodes is improved, and the multilevel and multidimensional evaluation model of critical nodes is constructed. Furthermore, the time sequence of load change and photovoltaic output is considered. Finally, the rationality and effectiveness of the proposed method are proved by taking IEEE 33-bus system and an actual ADN as examples. [ABSTRACT FROM AUTHOR]

Assessment and enhancement of transmission network capability have been one of the prime interests of power system monitoring, operation and control. Conventionally, the issue of available transfer capability (ATC) assessment has been tackled considering the transmission and distribution networks as segregated entities because they had little or no influence on each other's performance owing to the unidirectional flow of power from the transmission to distribution networks. Now, with increasing interest in smart transmission and distribution in amalgamation with a paradigm shift in sources of generation from centralised to decentralised even at distribution levels mandating the analysis of power grid while inculcating both the hierarchies simultaneously. In this study, a platform for quasi‐static operation of power transmission and distribution networks, employing a multi‐agent based system has been developed. The developed multi‐agent systembased system has been utilised for assessment of ATC considering active distribution network (ADN) whereby Modified IEEE 24 bus system at transmission and Modified IEEE 123 node system at distribution level has been considered as a test system. It has been observed that the presence of ADN considerably affects the ATC of the system. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed energy resources (DERs) in the active distribution network (ADN), how to enable joint planning of DERs under the uncertainty of distributed generations (DGs) has become a challenging problem. This study establishes a two‐stage joint planning model considering doubly‐fed induction generator, photovoltaics (PVs) with the ancillary services of PV inverter, distributed energy storage systems and different types of controllable loads in the ADN. To address the uncertainties of DGs, a two‐stage data‐driven distributionally robust planning model is constructed. The proposed model is solved in a 'master and sub‐problem' framework by column‐and‐constraint generation algorithm, where the master problem is to minimise the total cost and find the optimal planning decision under the worst probability distributions, and the sub‐problem is to find the worst probability distribution of given uncertain scenarios. Besides, the original mixed‐integer non‐linear planning problem is converted into a mixed‐integer second‐order cone programming problem through second‐order cone relaxation, Big‐M and piecewise linearisation method. The numerical results based on 33‐bus system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With the access of renewable distributed generations and the uncertainty of demand‐side response, the operation states of the active distribution network (ADN) are uncertain. This study proposes a novel optimal strategy taking active management costs of the source–network–load as the goal and considering the site selection and the incentive price for the responsive load to optimise the ADN. To deal with uncertainties, a chance‐constrained optimisation model is established and the Monte Carlo sampling method is used. Then, the uncertain model is settled by the improved backtracking search algorithm, which uses an adaptive mutation scale factor. Finally, the practicability of the proposed strategy and the performance of the algorithm are verified by the modified IEEE 33‐bus distribution system and an actual distribution system in China. [ABSTRACT FROM AUTHOR]

Considering the problem of cooperative operation of highly intermittent renewable generations (photovoltaic and wind power) and flexible load in active distribution network, a distributed cooperative optimisation operation strategy based on combine-then-adapt diffusion algorithm is proposed in this study. Distributed agents of distributed generators are connected by the distributed sparse network through distributed hierarchical control method. As an agent node of networked system, it interchanges information with its neighbours. In order to make the distribution generator meet the equal incremental cost principle, the adaptive adjacency matrix and fusion matrix with the weighted factor are introduced to update the state value of nodes to obtain the same convergence value. In this strategy, the coordinated source and load is applied by the active power optimisation dispatching for active distribution network. Stimulate analysis and experimental results show that the proposed combine-then-adapt diffusion algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Finally, the simulation analysis and experimental results of IEEE14-bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The realisation of large‐scale utilisation of renewable energy and two‐way interactive electricity consumption are the main goals of active distribution networks (ADNs). This study focuses on the spirit 'open, equal, cooperate, and share' the initiative distribution network and premises that users' habits of energy use will not change, establishes the optimal dispatch model of the multi‐stakeholder game. In order to effectively increase the enthusiasm and fairness of the participation of new stakeholders in the ADN to participate in grid coordination and optimisation, promote the consumption of renewable energy, the optimal relevance equilibrium strategy is found by using the multi‐stakeholder equilibrium game with improved correlated equilibrium Qλ learning CEQλ. The results of the example show that the multi‐stakeholder game coordination optimisation dispatch strategy based on this study can guide users to participate in demand response, improve the load curve and the consumption of renewable energy, so as to the peak load shifting. [ABSTRACT FROM AUTHOR]

Constrained by low capacity and volatility, the rapid growth of distributed energy resources are obviously slowdown resulting in consumption difficulty and investment obstacle. As an effective integration and management technology, virtual power plant (VPP) becomes a suitable cornerstone of renewable energy future development. Based on current scientific research, this study intends to provide a detailed review of VPP from an internal perspective (e.g. energy resources' integration and operation) to the external aspect including participation in electricity market. In accordance with market diversity, different bidding strategy optimisation problems of VPP are formulated and their corresponding mathematical solution methods are literately reviewed. To extract characteristics of VPP, a comparison between energy management techniques (e.g. VPP, microgrid, active distribution network, and load aggregator) is conducted, where advantages and defects of VPP are also concluded. Meanwhile, realistic deployments of VPP in European electricity markets are elaborated to verify its feasibility and applicability. To better accommodate future development of renewable energy, a flexible structure and effective management mechanism of VPP should be built leading to its technical innovation and management reform. Possessed with the threefold development orientation, VPP will undoubtedly improve the utilisation and management of renewable energy sources as a coordinated and operational entity. [ABSTRACT FROM AUTHOR]

In this study, a new multi‐objective optimisation model is developed to deploy phasor measurement units (PMUs) and intelligent electronic devices (IEDs) optimally in an active distribution network for state estimation. Three objective functions are considered in this optimisation problem such as the total cost of PMUs and IEDs, and the root mean square value of state estimation error. Since conflicting objectives are considered; to find the optimal locations of the devices, the meter placement problem is developed in a multi‐objective framework to get compromised solution. The best compromised solution is obtained using multi‐objective hybrid particle swarm optimisation (PSO)‐Krill Herd algorithm (KHA). Furthermore, the reliability of the proposed meter placement technique is tested under load and generators' output variations. All distributed generations (DGs) are considered as wind generators and the output of each DG is modelled using Weibull distribution function. The proposed algorithm is tested on IEEE 69‐bus system as well as on a practical Indian 85‐bus system. The obtained results have been compared with traditional PSO, KHA and also with well known non‐dominated sorting genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a second‐order cone programming (CP) method by which one can accurately calculate the interval power flow of an active distribution network with distributed generation is proposed. First, according to the interval models of the distributed wind power and solar power generation, the nonlinear programming (NLP) model for the interval power flow of the active distribution network is established. Second, based on the structure characteristics of the distribution network, the NLP model of the interval power flow is transformed into the standard second‐order CP model. Finally, an interior point method with polynomial time complexity is used to solve it. The numerical results obtained by the modified IEEE‐33 bus system show that the proposed second‐order CP method has better global convergence and a higher computational efficiency than the NLP method, and the interval for the power flow state of the distribution network can be accurately solved. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) is emerging as an effective approach for controlling converter to mimic the traditional synchronous generator (SG). VSG can provide virtual inertia, handle both reactive and active power, which is grid‐friendly for integrating distributed generations. Generally, when grid voltage is unbalanced, the negative sequence voltage components will appear. Due to traditional VSG control strategy does not consider the negative sequence components and cannot eliminate them, thus it will lead to current unbalance and power oscillations. To address these problems, a comprehensive control strategy of VSG under unbalanced voltage conditions is proposed. Herein, the basic principle of VSG and inherent reasons for VSG current unbalance and power fluctuation are demonstrated with quantitative analysis. The cascaded control framework and design process of the comprehensive control strategy are presented, integrating the traditional VSG control algorithm, novel current reference generator and typical current regulator. Additionally, related critical issues such as sequence components extraction and control parameter design are discussed. The proposed control strategy can flexibly meet different operation demands, including current balancing and suppression of active power or reactive power oscillations. The validity and effectiveness of the proposed control strategy are verified with simulation and experimental results. [ABSTRACT FROM AUTHOR]

Nowadays, intermittent distributed generators (IDGs), represented by distributed wind turbine generators and photovoltaic generators, are developing rapidly. A multi-scenario mathematical model with the objective of minimizing the annual carbon emission is proposed for the optimal allocation of IDGs in the active distribution network (ADN). The model takes into account three active measures, i.e. regulating the on-load tap changer of the transformer, curtailing the active power of IDGs, and regulating the power factor of IDGs. K-means clustering method is introduced to reduce the number of scenarios and obtain the probability of each scenario. A hybrid solving strategy combining the adaptive genetic algorithm and primal-dual interior point method is developed to solve the model. Case studies are carried out on the IEEE 33-bus ADN. The optimal allocation schemes under different cases are compared, and the potential contribution of carbon emission reduction from active measures is studied. [ABSTRACT FROM AUTHOR]

Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a new two‐stage methodology is proposed to supply critical loads based on cost‐effective improvement. In the first stage, the interruption cost is proposed as the load priority and the ON/OFF status of switches are considered as the binary decision variables. Therefore, IIO is considered as a mixed integer linear programming (MILP) problem to minimise the interruption cost. At the second stage, the power flow calculation is performed on the initial islands for the real‐time operation. The proposed method can be utilised for both long‐ and short‐term studies. In the long‐term study, the inherent uncertainty of ADN is considered in MILP by using a Monte‐Carlo simulation. This concept is used for clustering ADN into self‐sufficient microgrids. Moreover, by taking a snapshot of the ADN status and performing operational feasibility, the proposed method can be considered as a real‐time power regulation method. The proposed methodology is implemented on the IEEE 33‐bus distribution network, and the results are discussed in detail. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem for active distribution networks with distributed generation (DG) and a variety of discretely adjustable devices (e.g. on‐load tap‐changers, OLTCs) is essentially a non‐convex, non‐linear, mixed‐integer optimisation problem. In this study, the quadratic model of three‐phase OLTCs is proposed by adding branch currents as unknown variables, which guarantee a constant Hessian matrix throughout iterations. This study proposes a three‐phase OPF model for active distribution networks, considering a three‐phase DG model. The OPF model is solved by an interior point method incorporating a quadratic penalty function as opposed to a Gaussian penalty function. Furthermore, a voltage regulator is also incorporated into the OPF model to form an integrated regulation strategy. The methodology is tested and validated on the IEEE 13‐bus three‐phase unbalanced test system. [ABSTRACT FROM AUTHOR]

Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi‐period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage‐dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on‐load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two‐stage stochastic programming formulation of the multiobjective problem. The result is a set of non‐inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In active distribution networks, distributed generators are integrated into microgrids or geographically distributed subsystems. They may belong to different owners and are operated independently to preserve privacy. Therefore, fully distributed economic dispatch (ED) methods are needed and most existing algorithms show first‐order convergence. This study introduces a fully distributed dynamic ED with second‐order convergence, which is based on a parallel primal–dual interior‐point algorithm with a matrix‐splitting technique. In this method, each area optimises its own problem with limited information exchanged with its neighbours, and no central coordinator is needed. Numerical tests demonstrate that the algorithm can converge at a second‐order rate. On the basis of a peer‐to‐peer communication paradigm, this method can achieve a global optimum while the privacy of each area is obliquely protected. [ABSTRACT FROM AUTHOR]

Synchronised harmonic analysis that utilises GPS signals to synchronise harmonic measurements enables advanced power quality analysis in smart grid. However, it is difficult to put this promising technology into practice due to an extremely high requirement on data storage and processing. To overcome this problem, this study presents a novel Hadoop‐based framework for big data analysis of synchronised harmonics in distribution networks. The proposed framework facilitates harmonic data processing associated with data storage and advanced analysis based on big data techniques. Under this framework, a big matrix multiplication algorithm based on MapReduce programming model solving the harmonic state estimation problem is deployed in the stage of data processing. A MapReduce‐based harmonics distortion calculation is implemented as an advanced analysis for harmonics. Comprehensive numerical studies are carried out to analyse the characteristics of the proposed algorithm and verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Nowadays, more and more wind turbine generators ( WTGs) are being integrated into power systems. In this paper, an optimal WTG allocation model with the objective of minimizing the annual cost is proposed. The model can take into account the time-sequential correlation between the wind speed and load demand, as well as four kinds of active management measures, i.e. regulating the on-load tap changer of the transformer, curtailing the output power of WTG, regulating the power factor of WTG, and managing the demand side. The time-sequential correlation is handled by the joint probability distribution method. A hybrid solving strategy combining the fuzzy adaptive particle swarm optimization algorithm and primal-dual interior point method is developed to get the optimal solutions. Case studies carried out on the IEEE 33-bus active distribution network verify the feasibility of the developed model and the high efficiency of the proposed solving strategy. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Distribution network operators and planners usually model the load demand of plug‐in electric vehicles (PEVs) to evaluate their effects on operation and planning procedures. Increasing the PEVs' load modelling accuracy leads to more precise and reliable operation and planning approaches. This study presents a methodology for possibilistic–probabilistic‐based PEVs' load modelling in order to be employed in optimal wind distributed generation (WDG) planning. The proposed methodology considers not only the PEVs temporal uncertainty, but also the uncertain spatial effect of PEVs on WDGs as renewable‐based distributed resources. The WDG planning is considered as an optimisation problem which is solved under technical and economic constraints. A hybrid modified particle swarm optimisation/genetic algorithm is proposed for optimisation that is more robust than the conventional algorithms. The effectiveness of the proposed load modelling of PEVs and the proposed algorithm is evaluated in several scenarios. [ABSTRACT FROM AUTHOR]

In the environment of active distribution network, more distributed control devices are deployed to enhance the system reliability and flexibility. The master station of the distribution automation system is required to transfer real‐time topology information to numerous field devices for implementing the distributed operations. To reduce the computational burden of the master station and to make it more autonomous for the distributed agents to utilise the topology information, this study proposes a fully distributed solution to topology processing for the distributed applications. It reduces the grid topology to an undirected graph, divides it into subgraphs and executes a distributed adaptation of graph connectivity algorithm to solve the problem. The algorithm uses the Lagrange multiplier method to optimise the subtask allocation and neighbour‐to‐neighbour communications to compute the graph connectivity. The configuration technique and the usage are presented to illustrate the distributed approach. The algorithm is applied in a fault location, isolation and service restoration scheme to demonstrate its effectiveness. It is compared with the centralised method to highlight the improved performances. Test results show that the algorithm can recognise changes in real‐time topology correctly and has significant advantages. [ABSTRACT FROM AUTHOR]

With the rapid development of distributed generations (DGs) and interruptible loads (ILs), distribution network company can actively purchase electricity in market instead of playing as a traditional passive purchaser. This study proposes a stochastic bi‐level model‐based strategic trading model for an active distribution company (ADisCo) which operates the active distribution network (ADN) to maximise its profit in electricity market. Uncertainties pertaining to bidding and offering prices of other market rivals', the imbalance prices in the balance market and the productions of DGs are considered via stochastic programming. Besides, a linear ADN operation model is proposed to ensure ADN operation security within the stochastic programming model. The proposed model is initially formulated as a stochastic bi‐level model, where the upper‐level problem represents the maximisation of the profit of ADN operator, whereas the lower‐level model represents the maximisation of the social welfare in clearing of market from the perspective of independent system operator. On the basis of the complementarity theory, the proposed model can be transformed into a mixed integer linear programming model. Case studies demonstrate the efficiency and effectiveness of the proposed strategic trading model for an ADisCo with DGs and ILs. [ABSTRACT FROM AUTHOR]

This study presents an approach for developing the dynamic equivalent model of an active distribution network (ADN), consisting of several micro‐grids, for frequency stability studies. The proposed grey‐box equivalent model relies on Prony analysis to establish stop time and load damping as the required modelling parameters. Support vector clustering (SVC) and grouping procedure are employed for aggregation and order‐reduction of ADN. This significantly decreases the sensitivity of the estimated parameters to operating point changes which, in turn, guarantees the model robustness. This is done through representing the SVC output, that is, clusters, by cluster substitutes. The final ADN dynamic equivalent model is represented by several groups, in which their mutual interactions are taken into account by a new developed mathematical‐based criterion. Simulation results reveal that the proposed model is robust which could successfully take into account the continuous and discontinuous uncertainties. [ABSTRACT FROM AUTHOR]

This study proposes long‐term dynamic planning of distributed generation (DG) units in distribution networks considering active management (AM). In the AM operation mode, the distribution network equipment is controlled in real time based on the real‐time measurements of system parameters (voltage and current). The proposed model determines the optimal size, location and time of investment of DG units. The uncertainty of energy price and rate of load growth is handled using a scenario‐based modelling approach. To demonstrate the advantages of dynamic DG planning, its results are compared with those of static model. Also the results of optimal DG planning in active network are compared with those of the passive operation mode. In addition, a technical evaluation and comparison between conventional, passive and active networks have been performed. The proposed model is successfully applied to the 33‐bus radial distribution network. The results indicate that using AM, the cost function and losses are decreased effectively compared to the passive management. Computation of the optimal DG planning in AM environment is formulated as a mixed integer non‐linear problem which can be solved using commercial optimisation packages like as GAMS. [ABSTRACT FROM AUTHOR]

Distributed generators can be integrated in geographically distributed areas and microgrids of active distribution networks (ADNs), and can operate independently. On the basis of the alternating direction method of multipliers, the authors describe an efficient fully distributed algorithm to solve multi‐area economic dispatch problems in ADNs without requiring a central coordinator. The physical interpretation of the distributed algorithm and a proof for its convergence are both given. Network losses are taken into account by exploiting the features of ADNs. Two other popular distributed algorithms, including Lagrangian relaxation and auxiliary problem principle, are also implemented and compared with numerical tests. They discuss numerical results that demonstrate the effectiveness and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Plug in hybrid electric vehicles (PHEVs) can be a strategic source to mitigate risk of supply insecurity in an active distribution network. This study proposes a new methodology to quantify the risk of supply insecurity with weather condition based operation of PHEVs in an active distribution network. The approach divides operating characteristics of PHEVs into charging, discharging and null. Operation of PHEVs with change in weather conditions, intermittent characteristics of distributed generation, sector customer demand characteristics and random outages of components are modelled on Markov‐chain Monte Carlo simulation. A set of case studies are performed considering distributed operation of PHEVs as oppose to central operation of conventional units. Results suggest that distributed operation of PHEVs can potentially mitigate risk of supply insecurity of moderately stressed networks. Highly stressed networks, which are operated with PHEVs, need supplementary supports from conventional units to mitigate risk of supply insecurity. [ABSTRACT FROM AUTHOR]

This study aims to solve for the optimal location and capacity of distributed generation (DG), taking vulnerable node identification into consideration, in active distribution networks (ADN). Vulnerable nodes exist in the distribution network. Considering that the power fluctuations of those vulnerable nodes will have a significant impact on other nodes, the allocations of the DGs should avoid such nodes. Therefore vulnerable node identification and removal from the network can greatly limit the siting range of DGs. In this study, the vulnerable nodes are identified based on the small‐world network theory, which is used as the preliminary location of the DG. Then, a genetic algorithm (GA) is proposed to finally address the optimal location and capacity for grid‐connected DG. A GA with voltage boundary constraints is utilised to effectively prevent the bus voltage from reaching its boundary. This method improves the calculation efficiency greatly and is therefore suitable for flexible distribution network topology in ADN. According to the change of the distribution network topology, the corresponding optimal location and capacity limit for the DG can be quickly calculated. Some examples validate the algorithm and prove that it has fast convergence. [ABSTRACT FROM AUTHOR]