The proliferation of distributed generation and the electrification of heat and transport pose significant challenges to distribution system operators (DSOs) and transmission system operators (TSOs). These challenges include the choice between network upgrades or operating increasingly constrained networks, with a reliance on the flexibility of distributed energy resources (DERs). This study presents a novel market-based coordination scheme, which allows both the DSO and TSO to access DER flexibility, while respecting distribution system limits. The DSO's objective in this work is to minimise the cost incurred by DSO adjustments to DERs, required to ensure stable distribution network operation. The methodology presented has the advantages of being compatible with existing TSO balancing market operation, and scalable enough to include multiple DSO markets coordinating with the TSO. The approach is demonstrated on a section of Great Britain distribution network, using high DER growth scenario data for the year 2030. The case studies demonstrate the proposed DSO market mechanism to maintain thermal and voltage limits during periods of peak demand and DER output. The DSO is given priority in using DERs to solve distribution network constraints, however, significant flexibility remains for the TSO even during periods of peak demand and maximum export.

The conventional distribution network is becoming an active grid due to widespread integration of distributed energy resources (DERs). This integration raises great concerns about security of the power system considering the growing interactions between the transmission and active distribution systems. One way to ensure stability of the integrated power system is prediction of critical contingencies which can jeopardize the power system security. Therefore, this paper aims to investigate transient stability contingency ranking in power grids considering the uncertainties raised by DERs of active distribution networks. To this end, probabilistic transient stability prediction framework is provided, in which the probability density function of transient stability condition is calculated based on the normalized stability indicators. The normalized stability indicators are based on the rotor angle and rotor speed. The normalized stability indicators are only utilized in the case of using fault data for prediction. Based on the proposed scheme, the instability of generators is anticipated through the proposed probabilistic transient stability prediction framework. Also, the effects of the probable topology changes on the line overload and also the voltage profile are investigated. Finally, the performance of the proposed method is validated and compared with the time domain simulation under various operating conditions.

Introducing the concept of distributed generations (DGs) and local demand supply have led to significant changes in distribution networks. The idea of aggregating loads and DGs in an appropriate way have resulted in microgrids' (MGs) appearance. Considering the economic and environmental concerns, MGs will play important roles in future distribution systems. However, it is necessary to implement an efficient procedure for the optimal design of MGs. This study proposes optimised methods in order to construct a set of independent, self-sufficient MGs in distribution networks. Increasing self-adequacy of constructed MGs is obtained by minimising the power imbalance within them or minimising transferred power between them. The stochastic nature of DGs and consumed loads are also considered. In this regard, optimal allocating and sizing of distributed resources are also studied. In this study, the optimal DG placement is considered with the aim of improving system load factor and decreasing system losses. The presented strategy facilitates controlling strategies of smart distribution systems, such as self-healing, by using virtual MGs in the future distribution systems. The related formulation and solution algorithms are presented in this study. Moreover, the proposed strategy is studied in the PG&E 69-bus distribution system.

Grid operation becomes a more challenging task due to the increasing amount of distributed generators in the system. Consequently, critical situations with violations of operational limits occur more frequently. Hence, grid operators have to take corrective countermeasures to ensure the security of supply. This study presents a discrete optimisation approach to determine the optimal amount of distributed generator curtailment regarding multiple objectives. The proposed method is based on non-linear AC load flow analysis of the network. The method is implemented into a modified version of the IEEE 14-bus test system and the necessary amount of generation curtailment to retain predefined operational limits is identified. Simulations are carried out for different contingency scenarios and several possible increments of curtailment levels are compared. Results reveal the potential to reduce the total amount of power adjustments distinctly by using curtailment levels with smaller step sizes compared to todays' applied standards in German grid operation. Furthermore, the developed methodology enables distributed generators to be used for managing congestions of transformers connecting different voltage levels.

The quality of electric power equipment directly affects and decides the securable and stable operation of the power grid. Revealing the quality problem causes and influencing factors are considered as the key point to improve and guarantee the quality of power apparatus. However, the data of power supplies quality problems have the features of diversity and complexity. It is of great value to take the full advantages of the multi-source heterogeneous data, especially the possess flow information tracing time and space, to locate vulnerable processes, problem causes and influencing factors. This study put forward a data source system, which includes general information, quality problem information, process flow information and other supplementary information. Furthermore, an improved neural network model utilising knowledge–data fusion method is proposed. In this way, the efficiency and accuracy of analysis for quality problems is available and enhanced. To verify the validity of the knowledge–data fusion model, a case study with 2084 sample data of gas-insulated switchgear is carried out, proving help to strengthen the management and control measures of power equipment quality problems.

As the coupling between transmission systems and distribution systems is significantly enhanced, transmission–distribution coordinated optimisation becomes more and more necessary. Considering that the objectives of the transmission system operator (TSO) and the distribution system operator (DSO) are usually significantly different in the type and dimension, a synthesised-objective collaborative model is proposed in this study, by normalising different objectives, introducing a weight factor to reflect the importance degree difference of multiple objectives and constructing a common objective. Then, a modified heterogeneous decomposition (M-HGD) algorithm is proposed to solve the model. In the M-HGD, two modifications on the update of iterative variables and the stop criteria are proposed, which effectively relieves the fluctuation of the iterative variables and restrains the tail effect of the convergence curve. The proposed model and algorithm can avoid privacy issues between the TSO and the DSO, achieve Pareto optimality solutions, and have powerful flexibility. Numerical experiments validate the effectiveness of the proposed model and demonstrate that the M-HGD has high accuracy and has better convergence and efficiency than traditional distributed optimisation methods.

An adaptive technique for out-of-step (OOS) protection by modifying the impedance-based method is proposed in this study. Impedance-based methods are powerful and are operational in the power system as the main protection of OOS conditions. The setting of these relays depends on some parameters of the generator and power system that should be updated for the setting. The presence or absence of generator units in a power plant and transient conditions directly affects the machine dynamic parameters. Service or not in service of power lines and power system configurations can change the impedance of the power system at the generator terminals. Furthermore, to use the methods in a multi-machine power system, an equivalent two-area system is needed that is a complicated and time-consuming procedure. It should be made clear that these complexities limit the application of the impedance-based methods. Therefore, to remove these complexities, all factors that influence the relay setting are considered together in this research. The simulation results show that the proposed adaptive technique overcomes some of the problems and difficulties associated with impedance-based methods as well.

This paper investigates stability of a DC microgrid with integrated constant power loads (CPLs). The investigation is based on an interconnected model of the DC microgrid with two subsystems. One subsystem consists of a CPL under examination and the other subsystem is constituted by remainder of DC microgrid (ROM). Analysis is carried out to indicate when an oscillation mode of CPL subsystem is in the proximity of an oscillation mode of ROM subsystem on complex plane, stability of the DC microgrid decreases as caused by the CPL, leading to growing oscillations in the DC microgrid in the worst case. Hence, the investigation reveals the mechanism about why the CPL may cause instability of DC microgrid from a new perspective of modal proximity. An index is proposed to detect the instability risk by applying the modal analysis to subsystems. The proposed application extends the merit of widely used frequency-domain analysis to the modal analysis, because instability danger can be detected by the proposed modal analysis in practice without having to establish parametric model of the DC microgrid. Two example DC microgrids with CPLs are presented to demonstrate and evaluate the analysis and conclusions made in the paper.

This study proposes a new methodology for a probabilistic power system reliability evaluation using a Monte Carlo simulation in case of multi-energy storage system (ESS) installed at wind farms. A large-scale wind turbine generator (WTG) creates significant power fluctuations and effect the stability, frequency control, and then reliability of the power system. A high penetration of wind farms can result in unacceptable variations in the frequency and voltage in the power system. The significant power fluctuation impact of the WTG can, however, be reduced by installing an ESS. The proposed model can facilitate the reliability analysis and evaluation in a viewpoint of the contribution of each ESS installed at multiple wind farms integrated to a power system. The proposed method can also be used to assess the reasonable capacity of an ESS in the power system from a sensitivity analysis. A case study is demonstrated for the proposed model and methodology using a power system with similar size to the one in Jeju Island, South Korea.

Today, coordinated expansion planning is one of the key challenges for electricity systems including active distribution networks (ADNs) and transmission networks (TNs) hosting distributed renewable generation as well as large-scale wind energy generation. Accordingly, this study presents a decentralised hybrid robust and stochastic (HR&S) expansion planning optimisation method to determine a robust generation and transmission planning for a TN and stochastic expansion planning for ADNs. The proposed HR&S planning model is formulated with the objective of achieving an effective expansion of both TN&ADN while minimises the investment and operation costs of TN&ADN planning considering wind uncertainty in TNs and load uncertainty in ADNs. Finally, the IEEE 30-bus test system has been analysed to show the effectiveness of the proposed TN&ADN expansion planning framework and decentralised solution strategy.

In this new era of communication, the advent of the smart grid has revolutionised the power system network. The goal of smart grids is to provide a more reliable, environment-friendly and economically efficient power system. Demand side management or demand side response is one of the key components of the smart grid which accomplishes the smart grid that would provide intelligence to the traditional grid. Here, a new approach has been proposed for the demand side management, which is based on shifting a load from peak to off-peak time. The main objective of the work is to reduce the peak hour demand and the utility bill of the consumers. To achieve these objectives, the proposed strategy is modelled as a minimised optimisation problem and it tries to find out the optimal solution. For that, two optimisation algorithms, the first one is particle swarm optimisation algorithm and the second one is grasshopper optimisation algorithm, are proposed and applied in three area loads of the smart grid, i.e. residential, commercial and industrial. The obtained simulation results show a significant reduction in peak hour demand and utility bills.

While the concept of microgrids and renewable energy systems is not entirely new, these integrated technologies have become a special topic of interest for researchers, utility providers and governments. Many challenges must be overcome to achieve better integration of renewable sources into the energy framework. This study presents some viable possibilities for the utilisation of a hybridised microgrid system. The hybridisation is achieved by an efficient design approach for the enhancement of both load and system reliability indices through the intelligent placement and sizing of hybrid distributed generation (DG) systems. Real-time models of solar photovoltaics, wind turbines, batteries and thermal DGs are presented and implemented. Also, network component failures are stochastically modelled via Monte Carlo simulations, and a general tie-set algorithm using an adapted breadth-first search is proposed. Moreover, mixed-integer multi-objective particle swarm optimisation is employed, giving a four-dimensional Pareto solution that is attained by optimising four reliability-related objectives, namely system average interruption frequency index, System Average Interruption Duration Index (SAIDI), Energy Not Supplied (ENS) and total cost.

This study proposes a novel robust transmission-constrained unit commitment model with adjustable conservatism (denoted as RAC-TCUC). Ellipsoidal uncertainty set (EUS) is adopted in this model to well fit the spatial–temporal correlated wind power. The affine policy is utilised in the generation dispatch process of the model for the sake of computational tractability. To reduce the conservatism, a novel criterion for budget value selection of the EUS is presented. Moreover, this study discusses a crucial, yet barely addressed issue in the literature: the feasibility of the solution against the realisation of uncertainties beyond the prescribed EUS. To prove the validity of the criterion, an analytical relationship between the budget value of the EUS and the actual probability of solution's feasibility against all possible scenarios of uncertainties is presented. Finally, the testing results demonstrate the effectiveness and economic benefits of the proposed method.

The electricity price plays an important role in stimulating electric vehicles (EVs) to participate in the power grid's scheduling. It is necessary to formulate a reasonable discharging price for the power grid and electric vehicles. Here, a negotiation strategy of electric vehicles participating in optimal scheduling under the multi-agent situation which aims to formulate reasonable discharging price is proposed, and then a two-stage negotiation model considering multiple agents is established. First, a charging and discharging optimisation scheduling model considering EV travel characteristics is proposed, based on which the bidding limits of the power grid and EV agents are calculated. Then, the negotiation process is divided into two stages. In the first stage, all negotiators offer tentative bidding; in the second stage, negotiators will adjust their bidding based on learning other negotiators and the discharging price is obtained finally. In numerical cases, the proposed negotiation model is proved to be effective in balancing benefits of power grid and electric vehicles as well as peak load shifting.

Electricity load forecasting has been developed as an important issue in the deregulated power system in recent years. Many researchers have been working on the prediction of daily peak load for next month as an important type of mid-term load forecasting (MTLF). Nowadays, MTLF provides useful information for assessing environmental impacts, maintenance scheduling, adequacy assessment, scheduling of fuel supplies and limited energy resources etc. The characteristics of mid-term load signal, such as its non-stationary, volatile and non-linear behaviour, present serious challenges for this forecasting. On the other hand, many input variables and relative parameters can affect the load pattern. In this study, a new composite method based on a multi-layer perceptron neural network and optimisation techniques has been proposed to solve the MTLF problem. The proposed method has an optimal training algorithm composed of two search algorithms (particle swarm optimisation and improved ant lion optimiser) and a multi-layer perceptron neural network. The accuracy of the proposed forecast method is extensively evaluated based on several benchmark datasets.

Nowadays, the continuous growth of energy demand micro-grid (MG) networks that use as distributed energy source. MGs are usually installed at locations, where access to the power grid is not economical due to the far distance and losses. To cost reduction in MG networks, it is necessary to optimize placement of distributed generation (DG) units in MG networks. In this study, the optimal allocation of DG units using craziness-based particle swarm optimization (CRPSO) algorithm based on the game-theoretic formulation strategy was proposed to decrease power supply costs. The objective function of optimization comprises of the cost of buying power, power loss, communication and load shedding. Load shedding is considered so that the cost of operation of MGs declined. The optimization includes the size, site and the optimal order of joined DG units (wind turbine and photovoltaic) in test system. The novelty of this research is that it determines the order of joined DG units in the coalition when the arrangement of seller MG after DG unit's placement is reshaped. The simulation results in the modified IEEE 33 buses system validated through the MATLAB. The simulation results show the reduction in the costs of operation and rise in the sellers' profit.

Voltage unbalance is an important power quality issue, which occurs in electrical power systems (EPSs) and causes severe problems for them. In this work, a general mathematical model for EPS including its long transmission line is developed using the generalised circuit parameters method. Then, a hybrid Particle Swarm Optimisation–Artificial Neural Network (PSO–ANN) algorithm is proposed to overcome the voltage unbalance problem by controlling the firing angles of thyristor-controlled reactor (TCR) which varies the amount of reactive power at the load side. PSO algorithm is responsible for determining the optimal set of TCR firing angles required to retrieve the balance conditions in offline mode for different load changes, employing the developed mathematical model of the EPS. Then, a dataset is taken as training samples for the ANN to be used in online mode. Aqaba Qatranah South-Amman (AQSA) EPS is considered as a real case study and simulated in MATLAB environment to validate the proposed algorithm. The simulation results are compared with other ANN algorithms available in the literature. Finally, a laboratory prototype is built for AQSA EPS including its long transmission line to test the proposed hybrid PSO–ANN algorithm for real unbalance conditions acquired from the laboratory prototype.

As more than 80% of single line to ground faults in power transmission lines (PTLs) are transient, single pole auto-reclosure (SPAR) is of considerable importance for increasing the successful SPAR rate, improving power system stability and reducing shocks to the power systems. In this study, a new two-step strategy for ASPAR is presented. In the first step, the fault type (permanent or transient) is determined using the derivative of the first harmonic of the faulty phase voltage. Then in the second step, the extinguishing moment of the secondary arc is detected in the case of transient occurrence, in which the reclosing signal is sent to the side circuit breakers of the PTL after the secondary arc is completely extinguished. The major features of the proposed scheme are: its capability to detect the permanent fault from the transient one, and determine the extinguishing moment of the secondary arc without the need to define a specific threshold level, its independence from the PTL type and its appropriate structure that facilitates its online hardware implementation and makes it more comprehensive. Extensive simulations in the ElectroMagnetic Transient Program-RV, as well as practical data testing, demonstrate the accuracy and precision of the proposed method.

In low-voltage power distribution systems with high penetration of photovoltaics (PVs) generation and electric vehicles (EVs), the over-voltage problem arises at times because of large PV generation, and under-voltage problem also arises sometimes because of simultaneous charging of massive EVs. Over- and under-voltage problems lead to more difficulties in achieving voltage regulation. Demand response (DR) is expected to be promising and cost-effective in promoting smart grids, and hence, the utilisation of flexible resources (FRs) through DR can be helpful for distribution system voltage regulation. This study introduces a hierarchical control structure of a community energy management system (CEMS) and multiple sub-CEMSs to apply an FR-based two-stage voltage regulation technique. In the first stage, i.e. the day-ahead scheduling stage, each sub-CEMS optimises the FRs’ schedules for minimising customers’ electricity cost and network voltage violation times. In the second stage, i.e. the real-time operation stage, the voltage sensitivity-based FRs’ shifting method is proposed to eliminate network voltage violations caused by errors of estimated day-ahead data. The proposed models and methods are verified based on a realistic distribution system in Japan, where voltage violations, customer electricity cost and a number of on-load tap changer tap operations are proved to be reduced.

In this study, a D-vine copulas modelling based probabilistic load flow (PLF) computation method is proposed, which considers the dependence among multiple wind generators. Furthermore, this method is not restricted by the type of wind speed distribution, i.e. allow random variables to comply with any types of distribution model. Copula theory plays an important role on dependency modelling. However, when high-dimensional correlation is taken into account, standard multivariate copula suffers from the problems of inflexible structure. Vine copula is flexible to build high-dimensional dependence and able to construct complicated dependence structure by applying bivariate copulas. For marginal distributions of wind speed, non-parametric model can provide a better estimation than those parametric models. An improved Latin hypercube sampling based Monte Carlo simulation method is utilised to solve PLF problems. A modified IEEE 33-node distribution system is used to conduct the numerical experiments for the accuracy and efficiency verification of the proposed PLF method, under the MatlabR2016a platform. The simulation results verify the outstanding accuracy, efficiency and robustness of the proposed PLF method.

In this study, the authors propose a multi-task learning with deconvolution network (MTL-DN) method for the multi-label classification of multiple power quality disturbances (MPQDs). First, the labels of MPQDs are assigned to three groups corresponding to three learning tasks and the label correlations among various PQDs are utilised in the joint learning of interrelated tasks. A weighted joint loss function is adopted to balance multiple tasks and to ensure that all of the tasks achieve the global optimum. Second, considering the effect of the pooling operation on transient disturbances, a deconvolution network is employed to reconstruct the erased feature and to merge them into high-level feature for the final classification. Finally, the authors employed two sets of evaluation metrics to verify the validity of the MTL-DN method and compared it with three state-of-the-art multi-label classification methods. Extensive experiments based on simulated and real-world datasets demonstrated that their method performed better and it greatly improved the accuracy rate for identifying MPQDs under different signal-to-noise ratio conditions.

In this study, a new method for utility harmonic impedance measurement based on robust independent component analysis (RICA) and bootstrap check is proposed. The RICA approach which adopts exact line search to seek the optimal step-size in iteration has better performance for short data records. At the same time, a bootstrap check technique to remove the singular solutions of this sort of independent component analyses methods is presented as well. This verification technique is performed through judging the dispersion degree of the estimates of the bootstrap samples which is quantified by the coefficient of variation. Based on the results of computer simulation and filed test, it is proved that the combination of the RICA approach with the bootstrap check technique can reduce the adverse impact of the background harmonic fluctuation and the singular solutions on impedance estimation effectively, and satisfactory measurement results can be acquired.

A high level of renewable resource penetration could lead to displacement of conventional synchronous generators from dispatch, and consequently, reduce the system inertia. The decline in the system inertia will increase the primary frequency response (PFR) need requiring to maintain grid frequency stability. This study proposes a new market design to co-optimise energy, inertia and PFR while considering uncertainties in renewable energy productions. A full dynamic model of Electric Reliability Council of Texas (ERCOT) interconnection is used to quantify the PFR requirement for the system inertia. The proposed method explicitly incorporates frequency dynamics and uncertainties in energy production from renewable resources in the scheduling process, which is formulated as a stochastic unit commitment problem. A case study of ERCOT grid demonstrated that the proposed stochastic scheduling of energy, inertia and PFR could yield a more cost-effective solution than the traditional deterministic formulation for the grids with a high penetration of renewable resources.