This article describes a novel power system architecture and operational concept which is founded on the idea of organising sets of distributed generators, loads, storages and multimodal interfaces at distribution level in supervised and controlled grid subsections called smart power cells, which are controllable entities intended to enforce the stability of the transmission network and participate in its operation. Further, the article addresses a control scheme which can be implemented within a smart power cell to control its active and reactive power exchange with the transmission network following set points provided by a superimposed control system, and thus participate in the operation of the transmission grid (e.g. participation in congestion management, system optimisation, balancing of generation and load). The dynamic behaviour of a future power system under consideration of the proposed system architecture including several smart power cells controlled with the developed control scheme is demonstrated through time-domain simulation using a combined transmission–distribution power system model.

A high voltage direct current system based on the modular multi-level converter (MMC-HVDC) is introduced as a suitable device for transmission of bulk powers over long distances. A pole-to-pole DC-side fault is a significant challenge for the MMC-HVDC system. Three main problems are the appearance of voltage spikes following a fast DC fault current break up, frequency instability due to insufficient inertia (networks such as renewable energy-based systems) and destruction of anti-parallel diodes within the MMC due to the high fault current. This study proposes a novel method for limiting DC fault current, improving isolation of the three-phase AC system from the DC-side. The suggested design employs high power resistors as virtual load such that continuity in active power flow will be kept on. Further, this prevents frequency deviation from exceeding the allowable limits. Also, transient over-voltages will be limited which are coming from stray inductances linked with electromagnetic effects of fault current level. Simulations and design practical issues have verified the validity of the proposal by using MATLAB/Simulink.

In this work, a comparative study is analysed between the fractional-order controllers such as fractional-order proportional–integral–derivative (FOPID), two-degree-of-freedom proportional–integral–derivative (DOF FOPID) and 3DOF FOPID, and conventional controllers such as proportional–integral–derivative (PID), 2DOF PID and 3DOF PID employed in automatic generation control (AGC) in power system. The proposed 3DOF FOPID controller is validated as superior one among all other controllers. The gains of all the controllers are optimally plucked by novel salp swarm algorithm (SSA). Further, hybrid salp swarm algorithm–simulated annealing (hSSA-SA) algorithm is introduced to enhance the proficiency of 3DOF FOPID controller by sensibly plucking the gain parameters. The proposed approaches are implemented in a two-area thermal-hydro-diesel system. Small hydro plants (SHPs) of similar characteristics are enforced in both areas with their dynamic responses by conceding frequency deviations of each area. Further, sensitivity and robustness analysis of the system with and without SHP are observed by varying some important parameters of the system. Finally, the supremacy of three novel approaches (fractional based controllers, hSSA-SA and SHP) is substantiated gracefully.

Security constrained unit commitment (SCUC), with renewable resources integrated into power grid, is one core function in the day-ahead market. However, it confronts with critical challenges as the heavy complicated security constraints are considered. Usually, it is observed that lots of security constraints are redundant, which can be identified and eliminated, so as to reduce the computational complexity. In this study, the authors first lift the uncertain feasible region of SCUC into a high dimensional space. Furthermore, a fast identification method is proposed to relax the original feasible region, which thus can be solved by the classical greedy algorithm. In order to prevent the over-relaxation and find more redundant constraints, an efficient feasible-based bound tightening strategy is utilised to provide a tighter bound. Numerical results on large-scale test systems verify the effectiveness of the proposed method.

Finding a global optimal solution to the distribution network reconfiguration (DNR) problem in a short time is a challenging task. This study proposes a real-time online data-driven DNR (3DNR) method. Power loss minimisation, lowest bus voltage maximisation and reliability maximisation are taken as objectives. First, in this study, a methodology combining heuristic algorithm and metaheuristic algorithm to solve DNR is proposed. Then a set of data that satisfies the data drive model requirements is obtained. Next, the improved convolution neural network is used to train the data set of DNR. Unlike the state-of-art methods, the proposed 3DNR can realise the real-time online reconfiguration without power flow calculation. The feasibility and effectiveness of the proposed method are demonstrated on IEEE-34, IEEE-123, and a practical distribution system in Taiwan.

Interconnected power grid exhibits oscillatory response after a disturbance in the system. One such type of oscillation, the inter-area oscillation has the oscillation frequency in the range of 0.1–1 Hz. The damping of inter-area oscillations is difficult with local controllers, but it can be achieved using a wide area damping controller (WADC). For effective control, the input to the WADC should be the most observable signal, and the WADC output should be sent to the most controllable generator. This study presents a measurement-based novel algorithm for multi-input-multi-output (MIMO) transfer function identification of the power system to estimate such oscillation frequencies. The wide-area control loop is estimated using the MIMO transfer function, and the WADC design is a combination of the discrete linear quadratic regulator and Kalman filtering for the damping of inter-area oscillations. Since the MIMO identification is performed using the actual measurements, the proposed method can accurately monitor changes in the power grid, whereas the conventional methods are derived from small-signal analysis of a linearized model that does not consider changing operating conditions. The overall algorithm is implemented and validated on a RTDS/RSCAD^® and MATLAB^® real-time co-simulation platform using two-area and IEEE 39-bus power system models.

This study provides a risk-based gas and electricity expansion planning model to coordinate the expansion of electricity and gas networks in a multi-carrier energy network. Generally, electricity and gas networks have separate owners having no mechanisms to share information. In this study, a distributed algorithm based on alternative direction method of multipliers is developed to preserve the privacy of electricity and gas networks while maintaining a coordination link. Probabilistic outage of components is implemented into the expansion planning model to investigate the interactions between electricity and gas networks and evaluate the risk of contingencies in generating units, transmission lines, and pipelines. Second fuel of gas consuming generating units is modelled to have a holistic approach while studying electricity and gas interactions in the case of contingencies. Moreover, conditional value at risk is used to adjust a balance between risk and investment where each of energy parties can decide on the risk level of their expansion plans. The proposed expansion planning approach is applied to a realistic case study to evaluate its performance.

This study presents the imbalance recovery performance analysis of modular multilevel converter high voltage direct current (MMC-HVDC) systems considering dc current dynamics, and proposes an enhanced control to improve the system's internal stability response. The numerical expressions of the imbalance recovery speed and overshoot amplitude response of dc current components in upper and lower arms of the MMC phase unit are firstly derived analytically, which reveal that the system can be prone to poor imbalance recovery performance after sudden disturbance impacts with no auxiliary control. According to the analysis results, an improved control based on the adaptive virtual resistance method is proposed for the MMC-HVDC systems to reduce the recovery time regarding different system operating states. The key control parameter design of the adaptive virtual resistance is also simplified since a wider adjustment range of the parameters is allowed in the proposed method. In addition, the dc current components rebalance controller consisting of a low-pass compensator is proposed to further reduce the overshoot amplitude of system internal response. With the implementation of the proposed control schemes, improved stability recovery performance of MMC-HVDC systems can be achieved. Simulation results verify the effectiveness of the proposed control.

This study proposes a new power flow formulation for islanded microgrids. The proposed power flow is based on the effect of the superposition principle and the solution of a small non-linear subproblem to determine the frequency of the microgrid and the voltage magnitude in the angular reference node in each iteration. These variables are determined by considering the following equations in the non-linear subproblem: active and reactive power balance equations and specified phase at the angular reference node. The application of the superposition principle allowed to obtain two versions of the proposed technique: one for radial networks − based on the current summation method − and another for meshed networks − based on the Gauss-Zbus method. Therefore, the iterative framework prosed in this study expands in a simple and integrated way the two most commonly used power flow methods in conventional distribution networks for islanded microgrids. The tests in microgrids with 33, 310 and 1438 nodes showed that the proposed approach has the same accuracy as Newton–Raphson algorithm, but with significantly lower computational cost in large scale microgrids. In addition, the proposed method for island microgrid showed good accuracy and convergence for the most common load models applied in power flow studies of islanded microgrids.

Integrated energy carriers in the framework of energy hub system (EHS) have an undeniable role in reducing operating costs and increasing energy efficiency as well as the system's reliability. Nowadays, power-to-gas (P2G), as a novel technology, is a great choice to intensify the interdependency between electricity and natural gas networks. The proposed strategy of this study is divided into two parts: (i) a conditional value-at-risk-based stochastic model is presented to determine the optimal day-ahead scheduling of the EHS with the coordinated operating of P2G storage and tri-state compressed air energy storage (CAES) system. The main objective of the proposed strategy is to indicate the positive impact of P2G storage and tri-state CAES on lessening the system uncertainties including electricity market price, power generation of the wind turbine, and even electrical, gas, and thermal demands. (ii) A demand response program focusing on day-ahead load shifting is applied to the multiple electrical loads according to the load's activity schedule. The proposed strategy is successfully applied to an illustrative example and is solved by general algebraic modeling system software. The obtained results validate the proposed strategy by demonstrating the considerable diminution in the operating cost of the EHS by almost 4.5%.

Many utilities are still reluctant in adopting optimal power flow (OPF) tools for decision-making in operation. This paper scrutinizes this issue from the perspective of whether all control actions proposed by an OPF are truly effective to an operator. To this end, the paper focuses on suppressing ineffective control actions in OPF problems. This goal is aligned with the meaning of optimization in practice, that is improvement of operation performance of slightly noisy or imperfectly known real world models. The paper proposes a conceptually different new approach, which computes automatically the number of effective control actions that do not worsen the ideal model OPF objective by more than an operator-specified tolerance. The proposed approach relies on a three-step methodology that solves different OPF problems, in which smooth continuous approximation functions are used to convert the benchmark mixed integer nonlinear programming (MINLP) problems into nonlinear programming (NLP) problems. The proposed approach is compared with two other alternatives for the OPF problem of thermal congestion management using three test systems of 60, 118, and 2746 buses, respectively. The results show that, among the competing approaches, the solutions of the proposed continuous approximation lead to the best trade-off between sub-optimality and computation speed.

The desire to modernise electrical infrastructure, and the increasing number of extreme weather events, has led to an increased interest in deploying microgrids as resiliency resources. While there are times when a microgrid is operated to increase the resiliency of critical end-use loads, there are also other operational goals, such as efficiency, that may be considered. However, it can be challenging for microgrid operators to coordinate assets in real time to balance operational objectives. This study presents a slider-based multi-objective control that enables microgrid operators to change the operational set points of microgrid assets to respond to rapidly changing system conditions.

In the near future power systems, efficient management of uncertainties with considering the system constraints without any simplification will be a challenge for system operators. Considering AC constraints leads to providing more accurate schedule of generating units, which can have a significant impact on the reduction of operating costs. Although numerous studies have been done to convexify AC optimal power flow constraints, most of the models are non-linear, which can be intractable for large-scale systems. In this study, a novel linear robust AC model is introduced using a combination of the quadratic convex relaxation (QCR) and the Frank–Wolfe algorithm for linearising the AC constraints. The uncertainties are modelled by applying the robust optimisation with recourse to obtain an optimal schedule for the conventional units in multi-period real-time markets. The Benders-dual algorithm is implemented to solve the optimisation problem. The proposed model was applied to the IEEE 3-bus, 118-bus, and 300-bus systems. The results indicate that the proposed algorithm obtains more precise approximation than the QCR method. In addition, the costs and losses of the proposed model are less than those of the conventional robust DC and stochastic models. Furthermore, because the proposed model is linear, its runtime is rational.

The short-circuit current characteristics fed by the renewable energy source are quite different from those of the synchronous generator, which may lead to the phase selection error or failure of the traditional fault phase selection component, and then affect the operating reliability of the distance protection and the automatic reclosing. This study analyses the existing issues in the application of traditional phase selection components to the tie-line of renewable energy power, according to the fault characteristics of different renewable energy sources such as photovoltaic power source, direct-drive wind turbine and doubly-fed wind turbine. Furthermore, the sequence voltage expressions in the case of different types of faults, which takes account of the influence of the renewable energy type and the transition resistance, are theoretically derived. Hereby, a novel phase selection method by using sequence-voltage phase comparison and phase-voltage amplitude comparison is proposed. This method is applicable to the tie line of different types of renewable energy power station, and possesses the superiorities of high reliability and strong tolerance towards large fault transition resistance. Digital simulation results verify the favourable performance of the proposed method.

An asynchronous heterogeneous decomposition (async-HGD) algorithm for integrated transmission and distribution networks coordinated optimisation considering communication conditions is proposed. Compared with traditional synchronous HGD (sync-HGD), a partial synchronisation mechanism is introduced instead of the full synchronisation to alleviate the influence of communication delays. Under this mechanism, the transmission system operator does not need to wait for the slowest distribution system operator to complete its subproblem and update boundary parameters before the next iteration can proceed. Also, a bounded delay is introduced under this mechanism to ensure sufficient freshness of all the updates. Further, the previous value strategy (PVS) and linear regression strategy (LVS) are applied to handle non-instantaneous interruptions in the communication networks as an improvement of the async-HGD. Numerical experiments show that considering communication delays in the actual operation; the async-HGD takes less time to converge compared with the sync-HGD under appropriate settings. In addition, PVS and LVS can both enhance the robustness of the overall algorithm by achieving approximate solutions, and the LVS can achieve more accurate results compared with the PVS.

This study presents a stochastic bidding strategy for electrical vehicle charging stations (EVCSs) to participate in frequency containment reserves (FCRs) markets. To achieve this, the study starts by developing deterministic models to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle. These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation and frequency containment reserve for disturbances. These deterministic models will be combined with historical data of charging records in EVCS to develop a methodology to calculate the probability density functions of the FCR profiles. Finally, the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, are estimated by performing a stochastic optimisation. The proposed methodology is evaluated by using empirical charging data of public EVCS in the Helsinki area.

The accurate estimation of harmonic impedance on the utility side of a point of common coupling (PCC) is much important to harmonic control, harmonic emission level, and harmonic responsibility analysis. In this study, a novel estimation method for the utility harmonic impedance is proposed, which employs the Gaussian mixed model (GMM) to fit the measured data at a PCC, because the authors find that the statistical distribution of the measured data at a PCC is close to the Gaussian distribution. Also, in theory, GMM can approach the probability distribution of any data. In this method, the Norton equivalent circuit model at a PCC is first expressed as a GMM, and the utility harmonic impedance in the GMM is estimated by expectation maximisation iterative method. Compared to the existing estimation methods, an outstanding advantage of this method is that it is less affected by background harmonics; therefore, higher accuracy can be acquired in this method. The effectiveness and accuracy of this method are verified by simulation experiments and field data.

The objective of this study is to propose a dynamic generator maintenance scheduling (GMS) algorithm for long-term power sector forecasting and planning studies in which electricity price and the resulting supply composition are determined with merit-order dispatch. Compatible with the GMS algorithm, a reasonable strategy for the utilisation of storage hydropower plants along with clear definitions for each stage including must-run renewable electricity generation modelling, calculation of reserve capacity, derivation of scenarios for storage hydropower plants and problem formulation is presented. Generation from storage hydropower plants are modelled such as must-run and price-dependent parts, to better approximate reality. The proposed structure is tested with real data of the Turkish system, with a demand and capacity projection in the long term. The results are compared with the actual maintenance plan of the base year and the general profile is evaluated as satisfactory. The results show that the GMS plan and profile may significantly change based on hydro and renewable generation expectation, future capacity evolution, and storage hydropower plant utilisation. Therefore, the proposed GMS algorithm can be utilised especially in long-term price forecasting and supply modelling studies, instead of using a fixed factor to represent the maintenance effect on available generation capacity.

In this study, a technique for developing a distribution management system (DMS), which possesses the flexibility to take both preventive and corrective actions against thermal overloading of branches in active distribution networks (ADNs), has been demonstrated. An ADN comprises microgrids that consist of photovoltaic and battery energy storage systems (BESSs). The DMS primarily minimizes the hourly cumulative cost incurred by loads due to energy pricing of utility, by effectively dispatching the BESSs. Besides, the DMS regulates BESS state of charge and bus voltages within their limits. It also controls loading of branches by taking corrective measures during overloading or preventive measures during critical loading conditions. This DMS has been designed using a reinforcement learning based technique, namely, adaptive critic design (ACD). This study elaborates the formulation of ACD algorithm so that an effective performance of the controller can be achieved. As case study, a modified IEEE 5-bus system along with a microgrid and its controllers have been modelled in detail and simulated in real-time by developing a simulation-in-the-loop testbed using OPAL-RT and DSpace. This testbed facilitates simulation of the detailed model along with its power electronic components, such that both transient and steady-state performance of the system can be observed.

The grounding electrode buried in soil is prone to corrosion affected by soil and leakage current. The surface of corroded grounding electrodes is wrapped with corrosion product, which affects the normal current dissipation process and weakens the grounding performance. The accurate analysis of the grounding performance with corrosion layer can lay a solid theoretical basis for the safety of the power system. In this study, the corrosion layer is regarded as a layer of conductive medium between the electrode and soil. The leakage current along the electrode and the charge on the interface are analysed by coupling point matching method with boundary element method. Finally, the leakage current, grounding resistance, ground potential rise (GPR) and the step voltage of the two typical electrodes with corrosion layer are investigated. The results show that the corrosion layer causes the reduction of the leakage current at the end of the electrode and the enhancement of the leakage current in the middle location. Furthermore, the corrosion layer causes the increase of the grounding resistance and GPR, and the decrease of the step voltage. In addition, the influence of the reduced radius on the grounding performance is much smaller than that of the corrosion layer.

The grid-tied solar photovoltaic system requires a multiobjective control approach for performing the dual operation of active power transfer at unity power factor and harmonic filtering. In this context, this study proposes an improved robust-mixed-norm (RMN) filter-based multiobjective control strategy for integrating the solar photovoltaic system with the utility grid. The standard RMN filtering control exhibits slow convergence, requires large sampling time and involves high computational cost. Therefore, a switching parameter is designed to toggle between least mean square and least absolute deviation for extracting the benefits of both filtering techniques as per requirement. The switching parameter will also help in minimising the computational intensiveness during hardware implementation and successfully confronts the conflict between convergence speed and steady-state misadjustment in the presence of outliers. The proposed control is implemented with the help of multiple delayed input vectors to extract the fundamental weight component from the non-sinusoidal load current. In addition, the effects of grid voltage distortion and imbalance have been detached by using complex vector filter-based synchronisation technique. The practicality of the proposed multiobjective approach has been verified under different operating conditions of supply and load through both MATLAB/Simulink tool and low-cost microcontroller-STM32F407VGT6-based laboratory prototype.

With the increasing penetration of plug-in electric vehicles (EVs), it has become important for utilities to identify how EV charging will affect their low-voltage (LV) systems. In this context, EV hosting capacity can be useful to assist utility engineers. However, the appropriate strategy to estimate this index and determine its practical application for utilities is still unclear. In response, this study provides a framework to obtain and apply the hosting capacity information for EVs. Results of analyses are obtained considering the whole universe of a utility. Firstly, a method is developed for estimating this index based only on information readily available to utility engineers. The method is then used in a wide-scale assessment of EV hosting capacity on 75,550 real LV systems. Quantitative results of this study provide insights into how to manage a system with high EV penetration. It is seen, e.g. that EV charging location is the most important variable to consider when stochastically assessing EV impacts, reducing difficulties to apply this type of solution for practical cases. Practical applications that employ EV hosting capacity statistics are also presented and discussed. Results are shown to be useful not only for utilities but also for regulatory agencies.