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.

Sustainable electrification calls the need for more efficient protection schemes. Synchronous-based distributed generations (SBDGs) are more susceptible to power grid faults owing to their low inertia time constants. Accordingly, fault elimination faster than the associated critical clearing time (CCT) is required to prevent unintentional disconnection of SBDGs, which inevitably makes the coordination of directional over-current relays (DOCRs) complicated than before, especially in meshed structure distribution networks (DNs). This study proposes communication assisted dual-setting DOCRs as effective solutions for providing fast-response protection in order to meet transient stability constraints of SBDGs in meshed active DNs. Dual-setting DOCRs are capable of operating in both forward and reverse directions with individual settings for each direction and thus providing flexibility in the coordination process. In this regard, time domain simulation as a reliable method is conducted to calculate CCTs. Afterwards, coupled with selectivity constraints associated with dual-setting DOCRs, transient stability constraints are all together accommodated in the optimisation process. Moreover, dual-setting DOCRs are allowed to follow user-defined time-inverse characteristics which help to alleviate clearing time of faults. The approach reveals a non-linear programming model which is tackled by seeker algorithm. Detailed numerical studies are carried out to validate the performance of the proposed approach.

The enduring life span of the machine insulation will be decided based on degradation level in motor and generator stator windings. The non-destructive diagnostic tools like dielectric loss and capacitance test and partial discharge (PD) analysis, recognized to access the deterioration in the insulation system of rotating machines. The experiments reveal various characteristic parameters such as leakage current, dielectric dissipation factor, the capacitance value, and PD magnitude. The integrity of the rotating machine can be find out by analyzing these parameters. This research study shows the hybrid method for prediction of insulation condition in the stator winding by utilizing the artificial neural network (ANN) with gravitational search algorithm in comparison with ANN and ANN–genetic algorithm. The advent of expert systems ensures the quality assurance and service life assessment of the high-voltage assets. It offers a predictive maintenance solution to personnel dealt with power utilities thereby increasing the uptime, reliability, and productivity, which in turn reducing the operating costs, downtime and unplanned outages. For testing and predicting the insulation status, several 11 kV machines are considered. The predicted results using hybrid techniques extend a close agreement with reference to the data obtained from the experiments performed. The proposed method indicate the competent and trustworthy, by the presented test results.

Ever increasing use of renewable energies beside other uncertain parameters in power systems makes it necessary to evaluate power system issues, probabilistically. One of the important studies in power systems operating is optimal power flow (OPF), which should be considered as probabilistic OPF (POPF). However, the Monte Carlo simulation (MCS) method can be efficiently used for handling any type of uncertainty but this method suffers from large calculation requirements and cannot be implemented in various studies. Especially, in evolutionary based optimization problems the use of MCS method is completely restricted. Data clustering is an alternative method which keeps the accuracy of the MCS method and requires very less calculation burden. In this study, data clustering is used for probabilistic assessment of power system in POPF problem. The proposed method is very fast, accurate and can easily handle any type of correlation between stochastic input variables. The correlated input variables are generated by the Cholesky decomposition method. The Genetic Algorithm (GA) is used as optimization tool. In order to demonstrate and validate the performance of the proposed method, IEEE 30- and 118-bus standard test systems are studied and the results are compared with a modified version of the two-point estimate method.

We present a new noise-resilient secondary control scheme for voltage and frequency synchronisation of an autonomous microgrid (MG). The communication network is an integral part of distributed secondary control structure. The communication links among the distributed generator units are assumed to be ideal in nature. However, the communication links are subjected to uncertain noises, which can significantly affect the synchronisation performance of MG control. We consider the information received over the communication link is corrupted with generalised Gaussian noise. Further, the complete non-linear model of the MG system has been considered for designing a robust distributed control scheme augmented with an auxiliary corrective control input to counterbalance the impact of noises. The performance of the proposed control scheme is evaluated by pursuing simulation of a MG test system in MATLAB/SimPoweSystem environment under load perturbation, changes in communication topology, communication link delays, data dropout, and variations in noise parameter. The results of a comparative assessment of the proposed control scheme with two approaches first a neighbourhood tracking error-based control scheme and second a noise-resilient control scheme are presented to illustrate the effectiveness of the proposed robust noise-resilient control scheme in voltage and frequency synchronisation and accurate active power-sharing.

Load dynamics play an important role in the voltage stability. Grid-side short-circuit faults may induce severe voltage drops at motor load buses. Once the fault is cleared, depending upon its clearing time, the load bus voltage may either return to its permissible range or degrade gradually until it collapses. From the short-term voltage stability viewpoint, the former is a stable phenomenon, while the latter is an unstable circumstance and the load should be shed in order to prevent voltage instability from being spread over the grid. To cope with this problem, this study presents a new under-impedance load shedding (UILS) scheme. A quadratic distance relay is used as the load shedding element. The trajectory of load impedance is monitored by the proposed UILS relay. Following the grid-side fault clearance, if the impedance trajectory enters the load shedding zone and remains inside it for a predefined time, voltage instability is detected and the load is curtailed by the UILS relay. The proposed approach is tested on various networks, including a real power system model. The obtained results approve the efficiency of the proposed UILS scheme.

This study proposes a unique method of bidding strategy which is based on bi-level optimisation model for analysing the profits of the generation companies (GENCOs) and distribution companies in the day-ahead and real-time competitive power markets. The bi-level optimisation problem includes lower-level and upper-level problems. In the lower-level problem, market clearing model is presented, in which market clearing problem is solved, locational marginal price is minimised at load buses, net generation cost is also minimised with a simultaneous increase in profit with optimal placement of wind generator by assessing the conditional value-at-risk (CVaR). In the upper level, the profit is maximised by selecting the proper bidding strategy based on double-auctioned market mechanism. To check the effectiveness of the proposed strategy, Monte Carlo simulation and scenario-based approach are used to generate the scenarios considering the random line outages, generator outages and load variations. The CVaR is used as a risk assessment tool for the uncertainties that would inadvertently affect the profit of the GENCOs. The proposed approach is applied to the modified IEEE 30-bus system to show the effectiveness of the proposed model and its impact on the bidders and suppliers.

This study describes a methodology to solve the multi-period distribution expansion planning problem considering reliability, distributed generation (DG), capacitor and switch placement in the context of smart grids. The developed computational model is formulated as a mixed integer nonlinear optimisation problem and solved through the combination of meta-heuristics, stochastic simulation methods (Monte-Carlo simulation), and application of optimal power flow. The main objectives of this optimisation problem are defining the best choice to install power system components and determining the installation period and size of components to minimise investment cost while maximising system reliability. The proposed method was implemented in a modified version of the IEEE-RBTS bus 2 and in a 90-bus test system. The obtained results show a reduction of >20% of the operating cost and >70% of the average interruption time. In addition, it was performed scenarios not including DG, self-healing, and reliability analysis, individually. The analyses show the impact in the results by including a new variable in the expansion planning, increasing up to 40% in the fitness function value. Furthermore, it is presented with a discussion of the impact of the method to estimate the reliability indexes on the optimisation process.

Ancillary services used for active power balancing are called balancing services or operating reserves and their provision is vital for maintaining power system frequency at the nominal value. In a deregulated environment, where generation is unbundled from transmission and distribution operations, independently owned generating companies may elect to provide operating reserves. However, it is not easy to calculate the exact cost of reserve provision and, therefore, bid for it accurately. Although the cost efficiency of reserve provision can be improved by co-optimising energy and reserve markets, generating companies can still encounter monetary losses caused by the provision of reserve. Currently, these losses are compensated based on ex-post calculations. Hence, current energy and reserve prices do not adequately factor in the ex-post compensation caused by reserve provision. This study proposes an energy and reserve co-optimisation with an explicit consideration of two compensation mechanisms, i.e. lost opportunity and uplift payments. The problem is structured as a bilevel model. The upper level is a mixed-integer unit commitment problem and the lower level is a continuous economic dispatch problem. The case study shows that the proposed model increases market efficiency.

In this study, magnetic potential balance based active power filter integrated with distribution transformer (DT-APF) is proposed for harmonic reduction in distribution systems. Instead of using a step-down transformer for voltage matching, the active power filter is connected to the taps from the secondary windings of the distribution transformer. The filtering system's integration, operating efficiency as well as reliability are significantly enhanced. The topology of the proposed DT-APF is introduced; the compensation principle, mathematical model as well as the corresponding detection method are described in detail. A non-linear controller based on passivity-based control (PBC) that ensures global asymptotic stability is proposed to obtain an excellent compensation performance. To verify the feasibility and effectiveness of the presented DT-APF and PBC in harmonic suppression, results from MATLAB simulations and experiments of a down-scaled prototype system at laboratory are demonstrated.

The penetration of wind power is increasing in distribution network for reducing reliance on fossil fuels and covering continuously increasing demand for energy. However, it is argued that the forecasting error of wind power cannot be avoided even using the best forecasting approach. Therefore, in this study, a mean-variance-skewness based expectation maximisation (MVSbEM) model has been proposed by maximisation of the mean and skewness while simultaneously minimisation of the variance to obtain the optimal trade-off relationship between the profit and risk of distribution network planning (DNP) considering uncertain wind power integrated. In the MVSbEM model, the indexes of network loss, voltage deviation, and investment cost are concurrently taken into account under several kinds of actual operation constraints. In addition, the authors have made a full investigation on the MVSbEM by considering different forecasting errors, power factors of wind power, the different forecasting wind speeds, the number of wind turbines as well as the lines and substations upgrading. Furthermore, in order to reduce the computational burden, the Latin hypercube sampling method is used to sample uncertain wind speed. The feasibility and effectiveness of the MVSbEM model have been comprehensively evaluated on a modified IEEE 33-bus system.

The increased adoption of renewable energy generation is reducing the inertial response of the Great Britain (GB) power system, which translates into larger frequency variations in both transient and pseudo-steady-state operation. To help mitigate this, National Grid, the transmission system operator in GB, has designed a control scheme called enhanced frequency response (EFR) specifically aimed at energy storage systems (ESSs). This study proposes a control system that enables the provision of EFR services from a multi-electrical ESS and at the same time allows the management of the state of charge (SOC) of each ESS. The proposed control system uses a Fuzzy Logic Controller to maintain the SOC as near as possible to the desired SOC of each ESS while providing EFR. The performance of the proposed controller is validated in transient and steady-state domains. Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller. These benefits include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.

Emerging distribution systems with a proliferation of distributed energy resources are facing with new challenges, such as voltage collapse and power flow congestion in unsymmetrical network configurations. As a fundamental tool that could help quantify these new challenges and further mitigate their impacts on the secure and economic operation of distribution systems, effective AC optimal power flow (ACOPF) models and solution approaches are in urgent need. This study focuses on ACOPF of three-phase four-conductor configured distribution systems, in which neutral conductors and ground resistances are modelled explicitly to reflect practical situation. In addition, by leveraging the Kirchhoff's current law (KCL) theorem and the effect of zero injections, voltage variables of neutrals and zero-injection phases can be effectively eliminated. The ACOPF problem is formulated as a convex semidefinite programming (SDP) relaxation model in complex domain. In recognising possible solution inexactness of SDP relaxation model, a Karush–Kuhn–Tucker condition based process is further proposed to effectively recover feasible solutions to the original ACOPF problem by calculating a set of computational-inexpensive non-linear equations. Numerical studies on a modified IEEE 123-bus system show the effectiveness of the proposed SDP relaxation model with variable reductions and the feasible solution recovery process for three-phase four-conductor configured distribution systems.

This study determines the optimal capacity of various components of an AC mini-grid hybrid power system in a remote area of South Australia. A range of generation and storage technologies, such as diesel generators, wind turbines, solar photovoltaic arrays, battery banks, and flywheels are considered. A minimum system operating reserve is maintained to ensure a certain degree of reliability. Four different configurations of hybrid power systems are analysed in hourly time-steps, over a year, using real data for system load, solar insolation, ambient temperature, and wind speed. Particle swarm optimisation approach is used to optimise the capacity of various components of each hybrid system to obtain the minimum net present cost (NPC) over a 20-year system lifespan. Suitable capital, operating, and maintenance costs in Australian context are considered in evaluating the system. Various simulation results, such as annual energy balances, NPCs, levelised cost of energy and cash flow analysis obtained for each optimised system are carefully analysed and discussed. The modelling and results confirms the feasibility and economical replacement of the diesel power system with those of hybrid power systems which use renewable energy supply.

The complexity and volatility of power system operation increase when larger parts of the power production is based on distributed and non-controllable renewable energy sources. Ensuring stable and secure operation becomes more difficult in these modern power systems. For security assessment, the results of traditional offline simulations may become obsolete prior to the completion of the assessment. In contrast, real-time stability and security assessment aims at online computation, and it is therefore dependent on very fast computation of properties of the grid operating state. The study develops the reduce–factor–solve approach to real-time computation of two key components in real-time assessment methods, network reduction, and calculation of Thevenin impedances. The aim is to allow online stability assessment for very complex networks. The theoretical foundation behind the reduce–factor–solve approach is described together with the ability to handle both algorithms in a common framework. By exploiting parallelisation of the reduce and solve steps in combination with fast matrix factorisation, Thevenin impedances and reduced networks are computed much faster than previous approaches. The reduce–factor–solve algorithm is evaluated on power grids of varying complexity to show that Thevenin impedance computation and network reduction for complex power systems can be performed on a milliseconds time scale.

The ladder network parameter identification for transformer winding is crucial for the interpretation of the frequency response function data. The traditional identification method, mainly based on intelligent optimisation algorithm, is generally very time-consuming due to a large amount of computation. This study proposes to combine the intelligent algorithm and Gauss–Newton iteration algorithm (GNIA) to improve the optimisation efficiency notably with a sharply dropped calculation workload. These two methods are well-complementary since the intelligent algorithm holds excellent global search ability while the search of the GNIA is directional and quantitative. This study solves three key problems for the combined algorithms. The first problem is the calculation of the least-square correction solution to the network parameters in the iteration algorithm. The treatment of the ill-conditioned Jacobian matrix in the iteration algorithm is the second challenge. Another issue is the determination of the network parameter with zero sensitivity. The identification results on an isolated winding show that the combined algorithms can obtain a more precise solution with far less amount of computation.

The envisaged decarbonisation of electricity systems has attracted significant interest around the role and value of energy storage systems (ESSs). In the deregulated electricity market, there is a need to investigate the complex impacts of ESSs, considering the potential exercise of market power by strategic players. This study aims at comprehensively analysing the impacts of both price-taking and price-making storage behaviours on energy market efficiency, corresponding to potential settings with small and large storage players, respectively. In order to achieve this and in contrast to previous papers, this work develops a multi-period equilibrium programming market model to determine market equilibrium stemming from the interactions of independent strategic producers and ESSs, while capturing the time-coupling operational constraints of ESSs as well as network constraints. The results of case studies on a test market capturing the general conditions of the GB electricity system demonstrate that the introduction of ESSs mitigates market power exercise and improves market efficiency, with this beneficial impact being higher when ESSs act as price takers. When the electricity network is congested, the location of ESSs also affects the market outcome, with their beneficial impact on market efficiency being higher when they are located in higher-priced areas.

It is vital of practical engineering significance to connect the isolated station into the DC grid smoothly without affecting other stations or triggering protective systems. To this end, an active charging method with damping control of the isolated station is proposed and will be used in a 500 kV-level DC grid in China. First, the analytical expression of starting current is calculated. The key factors influencing the starting current are analysed to provide theoretic evidence for simplifying the calculation model. Second, the selecting expression of start-up resistor is derived by simplifying the main circuit parameters, DC line parameters, control processes and so on. Third, the principle of active charging method is clarified, the selecting principle of decreasing slope and parameters of damping controller are presented, and the required time of charging process is calculated. This method does not need the start-up resistor any more but still ensure no oscillation and impulse during the active charging process. Meanwhile, it is also faster than the traditional method with a better economy. Finally, electromagnetic transient simulation models are built to verify the correctness of the resistor selecting expression and the effectiveness of the active charging strategy with damping control.