In recent years, new trends in electrification of the transport sector have been a major concern for distribution grid operators. New types of flexible, uncontrollable loads, such as EV, influence the reliability of distribution networks. This work is related to the distribution system planning framework, with a particular focus on uncoordinated flexible EV loads. The main focus is the enhancement of the hosting capacity of EVs on distribution networks, while maintaining power quality (especially voltage magnitude and voltage unbalance), which is ultimately a pre-requisite for increasing prosumer engagement. Several EV charging scenarios, in the context of UK/Irish distribution networks with increased penetration of EV prosumers are considered. The results show that reactive power compensation through STATCOM, in the context of EV integration, can provide continuous voltage support and thereby facilitate 90% penetration of network customer EV connections at a normal EV charging rate (3.68 kW). If fast charging (up to 11 kW) is employed, <30% of network EV customers can be accommodated due to bottlenecks presented by the substation transformer loading.

The results of an electromechanical transient simulation can be viewed as a time series data set. Storing these data requires a sizeable space, and the considerable data size renders comparative analysis difficult. For mitigating these problems, a method for analysing and describing a discrete-event chain in the dynamic process of a power system is proposed. Based on the symbolic description, the continuous dynamic process is represented as a discrete-event chain, resulting from the change in state variables beyond the given threshold. Therefore, the event-driven electromechanical transient simulation is developed, and the continuous dynamics and discrete-event chain simulation results are obtained. Subsequently, the event relation matrix is obtained, normalised, and then converted into a bitmap. Finally, the k-means method is used for the cluster analysis on the bitmap of the simulation result. Two test cases involving an IEEE 39-bus system and a practical system are considered. Case studies demonstrate the effectiveness of the proposed method.

This study proposes a distributed hierarchical coordination control paradigm for parallel inverters, which not only enhances the flexibility of plug and play architecture but also strengthens the accuracy of current sharing with low sensitivity to the different line impedances and with fast response to the transient current change in islanded microgrid. Two kinds of distributed coordinated controller are designed to remove the voltage and frequency deviation from primary control. One is proportional–integral–derivative-based control, which detects the error between the segmented reference current and the actual measured current of the local inverter to calculate the compensating control input of the primary controller. The other is consensus-based control, which uses distributed control actions to spread local information among neighbouring distributed units. To improve the robustness of consensus algorithm, the authors take a relative value instead of absolute value from the primary control as the consensus variable to achieve accurate current sharing among different nominal power rating inverters. Then, a detailed small-signal state-space model is developed with the proposed secondary cooperated controller to achieve the stability analysis and parameters design purpose. Finally, an islanded MG test system is built in MATLAB/Simulink and control performances of two strategies are verified and compared.

In this study, a new method for harmonic emission level assessment is proposed. The method is based on parameter identification analysis. The quantitative relationship between standard deviation values of equivalent parameters and harmonic emission levels is studied and discussed through theoretical derivations. The complete harmonic emission level assessment method is then proposed on the basis of quantitative analysis and validated by means of simulation studies and field tests. The method has the ability of distinguishing the active injection contents in load harmonic currents and determining the actual harmonic responsibilities of the customers in power systems. On-line power quality monitoring applications and incentive management schemes can be designed by the proposed method.

With the continuous expansion of distribution system, the structure of the power grid is becoming increasingly complex, and the limitations of traditional analysis methods are more and more obvious. In this study, a reliability prediction model of the distribution network based on back-propagation neural network and genetic algorithm is proposed. Strong correlation factors of reliability are extracted as the input of the neural network for training, and the trained model is used to predict the distribution system reliability level in the future. The neural network is improved by momentum and adaptive learning rate, and the initial weight and threshold are optimised by genetic algorithm to realise rapid and accurate prediction. The proposed prediction model is trained and validated by the actual data of Hubei power grid. The prediction results show that the method is effective. The sensitivity analysis of reliability-related factors is carried out by using the trained network to identify the key indicators that have a greater impact on the reliability of the distribution network. This research can provide the basis for reasonable decision making to improve the reliability of distribution system, and has certain practical significance for a cost–benefit analysis of distribution system reliability.

Considering vehicle-to-grid (V2G) coordination schemes, plug-in electric vehicles (PEVs) are expected to be an energy storage system (ESS) to smooth out the fluctuation of the intermittent renewable energy source (RES) and the power load profile. In this study, firstly the optimal coordination of PEVs minimising total costs is shown to possess the valley-fill and peak-shift property for any pair of adjacent instants. Due to the decoupling relationship among the admissible sets of coordination behaviours of all the PEVs in the modelling issue, a decentralised algorithm is proposed by applying the gradient projection method, such that the coordination behaviours of individual PEVs can be updated locally and simultaneously. It is proved that the convergence and optimality of the proposed method are guaranteed in case the step-size parameter of the update procedure is in a certain region, and its performance does not rely on the shape of the net demand trajectory. Furthermore, a receding horizon-based algorithm is presented to account for the forecast errors occurred in the predictions of PEV populations, RES productions and inelastic load profiles. The results developed in this study are demonstrated with numerical simulations and comparisons with other decentralised methods are also provided.

Microgrid (MG) with optimal operating (OC) and capital cost (CC) is highly required. This study presents dual-mode energy management system (DM-EMS) operation with the objective of minimising the overall OC of the MG. DM-EMS determines the best mode to operate battery and fuel cell at a particular time and its duration to operate MG with minimum cost. The CC of an MG is mainly depending on battery size and battery life. In this study, novel battery sizing method by using the proposed life cycle cost (LCC) function is introduced. Considering the impact of battery size and life in the OC and CC of an MG, the proposed DM energy management strategy and novel battery sizing are concurrently optimised by using the proposed LCC function. The proposed strategy is validated using an MG system consisting of wind, battery storage and fuel cell. The results show that better optimal OC and CC with an accurate optimal battery capacity of an MG are achieved. In addition, analysis on the optimal battery size variations for different battery level [state of charges (SOCs)] is conducted. Changes in OCs and CC for the different range of initial battery SOC are also assessed.

Policy, technology, and economic uncertainties affect the net benefits of grid reinforcements, and should be considered in planning. Stochastic optimisation can improve the robustness and expected performance of transmission plans, but is computationally intensive because model size grows as more scenarios are considered. Therefore, the ability to find a small number of scenarios while still capturing the benefits of stochastic programming is crucial. In this study, the authors evaluate the performance of several promising scenario sampling methods. Criteria for comparison include an index of the economic consequences of simplifying scenarios (the expected cost of naïve solution), changes in first-stage investment decisions, and maximum regret. The results of an application to multidecadal planning of the Western Electricity Coordinating Council system show that solutions perform well when based on scenarios chosen by either a distance-based method or the stratified scenario section method with moment-matched probabilities. In particular, for this application, these methods’ results closely resemble solutions obtained from a much larger model using the full scenario set, and surprisingly have a lower worst case regret. Thus, careful scenario reduction can result in useful models that are more easily solved or, alternatively, can be expanded to accommodate other important features of power systems and markets.

Unprecedented mismatch occur frequently in the photovoltaic (PV) array systems, challenging the classical monitoring systems. However, the study of artificial intelligence methods lacks some well-designed experiments for a systematic verification because mismatch are influenced by many aspects such as the PV array, the DC–DC converter, the DC–AC inverter, the loads as well as the environmental variables. The objective has two folds. One is to design some identical apparatuses to emulate the ‘same’ real-world solar power station that is operated under multiple mismatch for a controllable, repeatable and comparable experimental study. Another is to propose a novel condition monitoring strategy based on the backward propagation neural network and a decision-making formula. Some controllable indoor experiments and uncontrollable outdoor experiments are carried out to verify the ideas. Such work has never been studied before. The experimental results show that the derived monitoring systems identify and classify accurately different mismatch in both indoor tests and outdoor experiments. Moreover, real-time experimental results infer that the data-driven approach with the self-learning capabilities is adaptive to the environmental changes. Therefore, the supervised learning-based condition monitoring strategy is promising in the solar power industry in terms of operation management and performance enhancement.

This study aims to present an architecture for the planning of microgrids (MGs) in order to support system operator decision. In short, the proposed strategy is an iterative procedure that tries to find the optimal size of distributed energy resource (DER), which attends the necessities of stakeholders. The architecture has five distributed and correlated stages named MG coordination, MG operation optimisation, reliability assessment, contingency assessment, and searching mechanism. Since the DER selection involves multiple criteria and interests of different parts, it requires a multi-attribute decision system providing a list of possible configurations based on their relative importance as denoted by the stakeholders. Owing to that, the particle swarm optimisation is used to create the multidimensional space of search in which the optimal solution will be selected by means of Pareto front decision criteria. As a result, the architecture provides a candidate solution to optimal size (optimal rated power) of each DER, that must be installed in the MG in order to have an optimal balance between technical, economical, social, and environmental aspects. To have realistic results, such a strategy is performed on a case study of a potential campus MG program.

There is a growth of distributed generation (DG) impacting the network. There are several studies which focus on defining the location and size of DG to optimise network performance. However, this approach is only possible in a market regulation where utilities can control the DG. Otherwise, this information depends on the consumer and not on the utilities. In this case, the optimisation studies do not make sense anymore. This work aims to identify if the distribution network expansion planning (DNEP) will be changed by the DG integration in a scenario where DG is not controlled by the utilities. The method consists of performing load flows in the feeder considering the load growth during the planning horizon to calculate voltage levels, currents and technical losses. Then, the procedures are repeated considering DG integration in many penetration levels, after that, the results are compared. The results showed that, for the feeder under study, the critical hours do not necessarily occur at the period of peak load, but in the moments with greater generation and smaller load. Therefore, the focus of the DNEP should be changed when considering DG integration.

Building-integrated photovoltaics (PVs) is one of the most promising and elegant ways of producing on-site electricity. The compact and cost-effective PV integration system needs to be developed to convert dc voltage into ac compatible with the grid. A single-stage PV inverter is proposed, and its design and control system implementation are focused on. The proposed system is able to transfer active power to the grid with maximum power point tracking. At the same time, the PV inverter can control reactive power and suppress harmonics due to non-linear loads. By coupling to grid via a series LC branch, the proposed system operates on a dc bus voltage much lower than that of the conventional PV inverter, so that less PV panels need to be connected in series. Therefore, the proposed system is a low-cost and multi-functional alternative to PV inverters in building-integrated applications. Simulation and experimental results are provided to validate its effectiveness.

To achieve an adequate level of integration, microgrids are facing challenges in three fronts; technical, financial and regulatory. In this study, a comprehensive study was carried out to investigate the main financial deficiencies and shortcomings faced by microgrids in their way to reach a grid-scale penetration. Accordingly, a systematic approach is proposed to explore the financial and policy factors affecting microgrid integration in distribution networks and evaluate possible conceptual microgrid business models. Economic analysis was performed to assess the proposed business models as investment options using the net present value method. Using the proposed approach, a platform to investigate the capacity of amending financial structure in helping achieve grid-scale microgrids is discussed. The proposed structure is implemented on the sample network and the results are evaluated.

In this study, improved predictive model control (PMC) strategy with prediction horizon of one sampling time for standalone solar photovoltaic (PV) system is discussed. Two control strategies based on the improved PMC approach are designed and implemented to ensure a fast and accurate stability of the system. Enhanced PMC-based maximum power point tracking is developed and implemented to extract maximum power and achieve high performance from solar PV under the presence of severe conditions by estimating the equivalent system resistance and output PV voltage. Furthermore, to achieve high performance without saturation issue, adaptive proportional–integral controller with anti-windup is employed. In addition, modelling as well as the parameters design method are offered. An efficient dc–dc boost converter is employed. To regulate the output ac voltage and frequency, as well as, supply a clean power to the connected load, improved PMC controller is developed and implemented. To predict the attitude of the output ac voltage at each sampling prediction interval for all possible switching states, the cost function is defined and evaluated as a standard to select the position function of the switches. The performances of the proposed system and their enhanced control strategies are verified using MATLAB/Simulink, and validated in real time using laboratory prototype.

The AC optimal power flow (OPF) problem is essential for the schedule and operation of power systems. Convex relaxation methods have been studied and used extensively to obtain an optimal solution to the OPF problem. When the exactness of convex relaxations is not guaranteed, it is important to recover a feasible solution for the convex relaxation methods. This paper presents an alternative convex optimisation (ACP) approach that can efficiently recover a feasible solution from the result of second-order cone programming (SOCP) relaxed OPF in mesh networks. The OPF problem is first formulated as a difference-of-convex programming (DCP) problem, then efficiently solved by a penalty convex concave procedure (CCP). By using the solution of a tightened SOCP OPF as an initial point, the proposed algorithm is able to find a global or near-global optimal solution to the AC OPF problem. Numerical tests show that the proposed method outperforms those semi-definite programming (SDP) and quadratically constrained quadratic programming (QCQP)–based algorithms.

Transmission system operators prepare against threats to system security through preventive actions and the planning of corrective actions. Required actions are determined by performing security analyses according to some reliability management approach and criterion. Probabilistic approaches have been proposed as an alternative to the deterministic N − 1 approach, by using a probabilistically filtered contingency list. This study begins by formally defining system trajectories, as a means of connecting the initiating contingency to the system response, corrective control, and restoration activities. A system trajectory considers the realisation of exogenous variables, as well as a sequence of contingencies and the subsequent system response and control behaviours. Using this definition, it is argued that the system trajectory should be modelled until service and component outages are resolved, rather than only modelling its reaction to the initiating contingency. The authors then propose some simplifying assumptions to achieve a scalable approach to probabilistic real-time security assessment. To support this argument, contingency-based and trajectory-based probabilistic security assessments are compared in terms of their residual risk (the risk associated with future system trajectories that are not modelled). This comparison shows that security assessments based on trajectories allow for previously implicit assumptions on modelling accuracy to be made explicit.

The objective of this paper is to suggest a suitable design procedure that may be useful for the practical realisation of the bi-layer wide area control (WAC) architecture. The bi-layer WAC architecture indicates the deployment a communication-free supplementary WAC loop in parallel to the conventional WAC loop. The bi-layer WAC system was found to have the potential to counterbalance the communication-related problems that arise in the conventional WAC loop. However, until now, no specific work has been reported in literature for the practical design of the supplementary wide area damping controller (WADC). It is essential to design the supplementary WADC in a way so that it can be practically implemented without the support of any communication network. The particular problem is resolved here by defining a suitable structure of the feedback gain matrix in the supplementary WAC loop. A novel structurally constrained norm optimisation technique is developed to design the particular feedback gain matrix with the proper structure. Here, the structurally constrained norm optimisation is performed for an output feedback loop instead of a state feedback loop. The usefulness of the supplementary WADC design produced is thoroughly verified through real-time (RT) experimentations.

The conventional capacitor voltage balancing strategy of the modular multi-level converter (MMC) requires real-time sorting in each control cycle, which brings huge computational load to the controller. Therefore, a fast capacitor voltage balancing method for MMC is proposed. Based on the conventional strategy, this method combines the operating rules of the sub-module (SM), considering the voltages of SMs and historical information on charging and discharging of the capacitor. It selects the appropriate insert object and insertion process according to the relationship between the number of the switched-on and switched-off SMs in the last control cycle, and quickly forms the ordered sequence of SMs in the control cycle. The simulation results show that the proposed strategy can achieve voltage balancing with low computational complexity, significantly improve the system simulation speed, and effectively reduce the controller resources occupied by the balancing strategy.

This study describes a maiden application of salp swarm algorithm (SSA) optimised cascade tilt–integral–derivative controller (CC-TID) for frequency and tie-line power control of interconnected power systems (IPSs). The proposed controller includes both the merit of the cascade control algorithm and fractional-order calculus. In the proposed CC-TID controller, TID controller is used as a slave controller and a proportional–integral (PI) controller served the role of master controller. To elucidate the effectiveness, the designed controller has employed to, initially, two-area IPS followed by four-area IPS. SSA is used to pursue the optimum settings of the CC-TID controller via the minimisation of area control error. Performances of CC-TID controller is obtained and compared with I-, PI-, PI–derivative-, cascade PI–PD, and TID controllers. Simulation results, apparently, show that SSA: CC-TID controller exhibits superior performance compared with other controllers described above. Moreover, the performance of SSA is evaluated and compared with differential evolution and flower pollination algorithm in terms of convergence profile, power density, and statistical results. Finally, time-varying step load perturbation and random load perturbation are applied to confirm the robust behaviour of SSA: CC-TID controller.

Single-phase 25 kV AC traction power supply systems (TPSSs) are widely used in high-speed railways, leading to an increasingly prominent problem of voltage unbalance (VU) in the power grid. To fairly regulate the system VU level, implementing VU limit pre-assessment is an urgent need. A grid connection scheme including traction transformer selection and exchange phase connection design is a vital part of TPSS design that obviously affects the VU factor of power systems. A uniform mathematical model is established using Thevenin's theorem, which reveals the impact of multiple grid connection schemes on the VU propagation behaviour of a three-phase grid. Afterwards, under the requirements of IEC/TR 61000-3-13, an improved VU limit pre-assessment process is proposed, which correlates mapping between the grid connection scheme and VU limit. The unbalance compensation calculation is given, adopting the allocation limit; furthermore, the optimal design of the grid connection scheme with minimised compensation capacity as an object is presented. The correctness of the model and the rationality of the pre-assessment process are verified by a case study.

In the context of flexible operation and bidirectional line flows in modern transmission systems, suitable line flows are essential for safe, secure and stable power systems. Improper line flows may deteriorate stability and longevity of transmission lines. To avoid line outage and faults, improvement in thermal margin of conductors by regulating line flows is a practical consideration. To maintain and control the line flows, the unified power flow controller (UPFC) is the most appropriate compensating device. Thus, in this study, line flows are considered as state variables to optimise several functions, like, voltage deviation index, active and reactive power losses, temperature of conductors and cost of UPFC. The UPFC is modelled with practical losses like switching, coupling and internal transmission losses. Suitable line flows are obtained for the 30-bus and 57-bus test systems by applying an adaptive-version of the wind-driven optimisation (AWDO) algorithm. The AWDO is designed using two innovative tuning parameters: a decaying friction coefficient to reduce the probability of stagnation, and an adaptive velocity function to maintain diversity. The results are compared among different cases of line flows, validating the necessity of the technical challenge. The performance of AWDO is compared with other techniques, suggesting its superiority over other methods.

For renewable energy penetration, a frequency control strategy is proposed to assure the multi-area hybrid power system stable operation. The load frequency control (LFC) and the battery energy storage system (BESS) are used together to design the frequency control strategy. Furthermore, in order to improve the control accuracy and reduce the battery capacity, the area control error and active power disturbance are divided into low-frequency components and high-frequency components. Therefore, LFC is designed by using sliding mode algorithm for the low-frequency deviation and BESS is adjusted by using the designed disturbance observer for the high-frequency disturbance. Finally, the effectiveness of the proposed frequency control strategy is proved by the real-time digital simulator which can optimise the frequency quality and reduces the capacity of the BESS.

This study scrutinises the impacts of demand-side resources (DSRs) on the power system reliability via a novel multi-target generation maintenance (GM) model. The nominated model uses the lexicographic preferences to hierarchically consider the environmental issues, economics, and reliability of power systems. In this regard, the produced emission, which reflects the per unit produced pollutant values in different locations, is minimised. Taking into account the environmental constraints, the total incurred expenditures, including operating and maintenance costs, reserves costs, and total incentives are also minimised. Subsequently, the GM problem considering the correlation constraints is handled while the reliability index, the average net reserve value, is maximised over the scheduling horizon. The DSRs improve the system reliability such that the total costs, and emission level, do not exceed the situation in which DSRs are not available. The GM scheduling is a highly complicated problem and considering DSRs makes it even more complicated. To handle this problem more efficiently, appropriate linearisation technique is used, while the proposed model is formulated in GAMS modelling language. To evaluate the capability of DSRs in system reliability improvement, the modified 24-bus IEEE-RTS is conducted. Results indicate that by selecting proper location and incentives, significant improvement is obtained.

An AC–DC hybrid microgrid is gradually becoming popular. For economic viability and environmental sustainability, an AC–DC microgrid should be operated optimally. This study introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC–DC hybrid microgrid under load and generation uncertainties. The operational attributes of an AC–DC hybrid microgrid, and load and renewable generation uncertainties are incorporated in the optimal scheduling problem by using a customised power-flow technique, and by modelling uncertainties by Hong's two-point estimate method, respectively. The economic and environmental objectives are modelled in the fuzzy domain by fuzzy membership functions. A combination of particle swarm optimisation and fuzzy max–min technique is then employed for obtaining the optimal solution. The static active power droop constants of the dispatchable units are the control variables. Simulation results on a 6-bus AC–DC hybrid microgrid system demonstrate that optimal scheduling results in 4.26% reduction of operating cost and 13.91% reduction of emission in comparison to capacity based droop settings. Further, a comparison between the proposed method and the elitist multi-objective GA indicates that the optimised solution lies on the Pareto-front, thereby validating the proposed technique.

In growing economic energy systems, the interdependency of various energy infrastructures has led to a change in countries' policies in their expansion planning of energy networks. In this study, a mixed-integer non-linear programming (MINLP) model is proposed for expansion planning of the multi-carrier systems including electricity and gas distribution networks. The optimal planning determines the best location, time, and alternative for network assets in order to minimise investment costs and reduce losses. Also, as another distinctive feature of this study, given the integration of electricity and gas distribution networks and the complexity of the problem, a new MILP model using linearisation methods is presented. In this planning model, several types of alternative plans and a set of candidates for the new placements or increase of the capacity of transformers, feeders, distributed generation, gas pipelines, and city gate stations are considered. The proposed MILP model provides the convergence of the problem to a global optimum response using the powerful commercial software. In addition, a solution based on Benders decomposition algorithm is finally proposed to reduce the solution time of the problem. Finally, the efficiency of the proposed model is evaluated by means of some simulation results.