The first swing rotor angle stability is still an important stability challenge for modern power systems integrated with a large number of renewable energy-based sources, such as wind farms and solar-photovoltaic farms. Therefore, innovative strategies must be developed to exploit the full potential of doubly fed induction generators (DFIGs) to improve the first swing rotor angle stability in the system. This study presents a new active power logic (APL) controller for DFIGs which can reduce the active power during the fault and slowly recover it after the fault to allow synchronous generators to increase the electrical power during and after the fault; thus, enabling synchronous generators to improve the first swing rotor angle stability. The feasibility of the proposed control scheme is investigated via theoretical analysis and simulation studies. The reliability and voltage stability test system is used to demonstrate the effectiveness of the proposed scheme for local and remote faults and increased DFIG penetration conditions. The comparative results show that the proposed APL controller of the DFIG improves the first swing rotor angle stability, specifically when the DFIGs are located near the synchronous generators.

Considering both environmental and commercial aspects, mixed three-terminal transmission lines are attractive. Mixed three-terminal lines consist of two sections of the overhead line and one section of underground cable. In this work, a selective auto-reclosing, fault location, and parameter estimation technique for mixed three-terminal lines using synchronised data from all three terminals are proposed. The proposed method uses the pre and during-fault data recorded at the three ends of the line. Using the recorded sampled data, pre-fault and during-fault phasors are estimated. Four functions are formulated from the pre-fault equivalent network of the system. Additionally, three sets of four functions each are formulated after assuming the fault to be on each of the three sections. Subsequently, the faulted section, fault location, and three sets of line parameters are simultaneously calculated by solving each set of equations. Finally, the correct faulted section, location, and parameters are identified based on the characteristics of obtained line parameters and fault location. Furthermore, the identified faulted section information is applied to enable or block the auto-reclosing function. The simulations and analysis are carried out using PSCAD/EMTDC and MATLAB scripts, respectively. The accuracy, capabilities, applications, and limitations are discussed for different fault scenarios.

The operation of a standalone microgrid is often optimised by the central controller that decides the set-points of the local controllers of the distributed generators. However, the optimised set-points may not remain optimal for a longer period due to the variability of loads and renewable generations. This study proposes a technique for readjusting the dispatch of the suitable generation units, between the optimisations carried out by the central controller, to support load changes. To this end, the potential field concept has been suggested to be adopted by the loads to select the appropriate generation units. The decision is made based on different criteria, such as their cost, reliability, emission, and power loss. This process requires low computational efforts, and thus, can act and make decisions immediately. This is valid as far as the change in the loads is not substantial. For a change above a predetermined level, the microgrid's central controller steps in to optimise the system. The central controller also performs the regular optimisation periodically to retune the whole system and reconfirm the optimal operation. The performance of the proposal has been evaluated by numerical analysis and validates the success and efficacy of the proposal.

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system.

The increasing permeation of the distributed generators in the power system brings great challenges for fault diagnosis, especially for the distribution networks with ungrounded neutral or grounded by Peterson coil as the fault current is limited and easily affected by the noises and interferences. A single phase-to-ground fault section identification method is proposed based on feature extraction of the synchronous waveforms and the calculation of the eigenvalues for the time-sequenced features. First, several fault features are defined and extracted from the synchronous current waveforms obtained by the fault recorders. Then, the topology related fault feature matrix is constructed using the time-series features obtained from different measurement sites, and the eigenvalues of the matrix are calculated based on the random matrix theory. Lastly, using the distribution characteristics of the eigenvalues, improved K-means clustering algorithm is utilised in classifying the fault cases and identifying the faulty sections. The effectiveness of the proposed scheme is verified by IEEE 34 nodes test system and a multi-feeder distribution network.

A control oriented approach is adopted for the implementation of a parametric fuzzy-predictive control on a compact model of a generalised energy storage device implanted in a multi-machine system. It is realised using a combination of a heuristic fuzzy logic control (HFLC) and a predictive step ahead control (PSAC) for energy storage reactive and real power modulation, respectively. While PSAC overcomes the computational burden innate to model predictive control, the HFLC with paramount simplicity lays down the design template for fuzzy control. Local bus measurements are translated into rate of change of frequency and voltage errors, while accommodating a second-order dynamics of a phase locked loop. To emphasise the computational simplicity and implementation speed of the proposed technique, all parameters of the problem are solved explicitly. These parameters account for the input–output mapping structure consisting of membership spreads and rule base in fuzzy domain. In predictive control, while approximating the storage-converter dynamics by a second-order system, relevant expressions are derived for the parameters involved in the loop. Umbrella power system stabilisation, viz., high oscillation damping with quicker settling times, frequency-nadir curtailment, power and voltage smoothing is assured with the parametric fuzzy-predictive controlled energy storage.

In distribution networks with arc suppression coil grounding, the fault line can be set as the energy leakage channel of the capacitive current of the entire system. During the period from fault occurrence to the whole action process of arc suppression coil, there is abundant fault information in the transient waves. However, the data of this period are usually ignored in previous methods. In this study, a novel fault section location method based on the leakage energy function is proposed. Only the fault phase voltage and fault phase current are used in the proposed method, to avoid the difficulty of the zero-sequence current measurement. Then, the action characteristics of two types of arc suppression devices are analysed. The influence of the capacitor current on the fault phase voltage and current during the entire process of arc suppression coil operation is deduced. The difference between the leakage energy on both sides of the fault section is used as the criterion for fault section location. Finally, the effectiveness of the method is verified by a case study. The simulation results indicate that the proposed method has high sensitivity and high impedance adaptability, that will have more advantages in the actual situations.

Modern societies these days are more dependent on electrical energy and they expect a continuous supply as per demand. In this regard, the complex power system is designed to supply electrical energy with a certain level of quality and continuity though it is still susceptible to vandalism, natural disasters, and extreme weather. The black sky event where the power grid goes down is more of a possibility nowadays than ever due to more frequent severe weather events. This in turn has increased the need to study resilience in the context of the power system. This study presents a comprehensive review of the literature on power system resilience from various perspectives. First, well-developed power system safety concepts are discussed and critically reviewed in view of large-scale power outages. Then, the various definitions and confounding features of resilience in the power system domain are presented. Subsequently, several frameworks, resilience curves, and quantitative metrics proposed in recent years for power system resilience are investigated, followed by a summary of hardening strategies. Next, a case study is presented to illustrate how the resilience of a 69-bus system is assessed against a hurricane. Finally, the study highlights challenges and proposes several future works to achieve a resilient power grid.

The present trend of inserting increasingly more solar photovoltaic (PV) sources into the electricity grid leads to a significant reduction in mechanical inertia. Inertia represents energy reserve in the grid, that inherently and instantaneously supports frequency stability. Therefore, recent studies have re-examined the frequency stability of the grid. Most of these consider scenarios where the participation from grid-following inverters remains relatively low. More importantly, these studies include an infinite bus in their analyses. The infinite bus acts as a very stiff voltage source which has a significantly stabilising influence, the more so as all control refers back to the infinite bus. Here, the frequency stability of a grid with significant generation from grid-tied PV inverters is considered without reference to an infinite bus. In the proposed simplified grid model, all the synchronous generators (SGs) are lumped as one large SG complete with classically operating controls. Similarly, all the PV generators are lumped as one large, quasi-instantaneous, non-linear power source. In this simplified network, the feasible operating region is identified using bifurcation techniques. It transpires that the stable operating region is bounded by a locus of Hopf bifurcations linked (inter-alia) to the fraction of power generated by grid-tied PV inverters.

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF-problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

For extra-high voltage transmission lines configured with a communication channel, the current differential protection is facing a dilemma between sensitivity and security. On the one hand, the resistive coverage will not be sufficiently high if security is guaranteed. Although the sequence-current-based differential elements are more sensitive, it may cause a longer time delay and deteriorate sensitivity under the open-phase operating condition. On the other hand, if high sensitivity is guaranteed at the cost of security, the main protection element will be blocked in the current transformer (CT) saturation scenario. To solve this problem, a novel type of impedance differential protection (IDP) is proposed. Under the open-phase condition awaiting an automatic reclosing, the proposed IDP can assist in accelerating the speed of fault-clearance for healthy phases, which is faster and more sensitive than using the zero-sequence current differential protection. For an external fault with heavy CT saturation, the proposed IDP can take over the fault identification capability before the recovery of CT, in which duration the current differential protection is blocked. Simulation results indicate that the proposed IDP coordinate perfectly with the current differential protection in the above two scenarios. The performance of the proposed IDP is also assessed using field data.

This study presents an automatic network balancing technique to limit the capacitive unbalance in resonant grounded power distribution systems (RGPDSs). The aim of this capacitive balancing technique is to minimise the unbalance current through the line to ground (which is the current through the neutral of the system) in order to automatically limit the neutral voltage. The proposed technique is designed by combining the weighted-sum technique and genetic algorithm (GA), where distributed switched capacitor banks (SCBs) are used for balancing RGPDSs. The proposed technique is employed to optimise available SCBs for limiting the network unbalance at the substation under a pre-defined threshold considering all possible network configurations. The unbalances at different locations of the network are also minimised to limit the system unbalance within the threshold due to minor changes in network parameters. Since the lifetime of capacitor banks relies on the switching, the proposed technique is designed in such a way that the system balance is achieved with the minimum switching. The performance of the proposed technique is evaluated through simulation studies in MATLAB/SimpowerSystems environment. Simulation results show that the proposed technique works well and capable to maintain the capacitive balance of the system with changes in network configurations.

Underground cables such as pipe-type cables are widely used in urban power industry. In this study, an advanced thin-wire model of the pipe-type cables is 3D FDTD simulations. In this model, the multi-conductor cables are represented with two-level transmission line equations. A stabilising technique with a 1D spatial low-pass filter is proposed to maintain computational stability. Frequency-dependent losses are fully considered by using a vector-fitting technique. The proposed thin-wire model is validated with the multi-conductor transmission line theory analytically and the traditional FDTD method numerically. Good agreements are observed. It is found that the simulation maintains stability for 360,000-time steps. Compared to the traditional FDTD method, the memory space and computation time of the proposed model can be reduced by 73% and 98%, respectively. Induced lightning currents in a cable connection station are analysed. It is found that, without considering soil ionisation and soil stratification, the peak current in the metallic armour is 1.54 times as much as the one with considering these non-linear effects. It can be reduced by 9.04% and 18.6% if the cable is buried at depths of 1 m and 1.5 m, compared with the case of a 0.5 m buried depth.

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

This study presents a novel framework for investment prioritisation in a distribution network by performing a flexibility needs assessment with regards to power quality parameters. Power range and time instances of activation are identified with a view of maintaining normal operation. A step forward is made by incorporating the importance of the flexibility needs assessment in investment prioritisation as part of the network expansion planning. The proposed framework consists of three procedures: input data preparation, operational calculations and the post-processing of results, which are used to quantify the flexibility needs and propose an investment prioritisation list. A bus node, which forms a part of the existing distribution network in Slovenia, is used to demonstrate the general applicability of the framework. As a result, an investment prioritisation list was compiled by assessing the flexibility needs. The secure power supply buffer of the node is determined, while the quantification of the flexibility power range and time instances of activation are also provided in order to further mitigate network constraints. Apart from providing valuable information to network operators, the newly proposed framework lays the foundation for aggregators and market players to plan and self-balance their portfolio and position when providing flexibility market services.

The authors report on a step towards the development of direct methods for transient stability assessment of power systems with integrated wind turbine generators subject to low voltage ride-through constraints. In this study, they propose an approach to assess transient stability of a single wind turbine generator against an infinite bus using an energy function along with an extension of the potential energy boundary surface method. The proposed approach exploits the theory of constrained dynamical systems and the concept of constrained stability region to efficiently estimate critical clearing times taking into account not only the issue of stability due to rotor acceleration but also the issue of operational limit violations, such as low-voltage ride-through curve violations. Very often, wind turbine generators are disconnected due to violation of operational limits, such as under-voltage limits, instead of rotor acceleration. The proposed approach correctly estimates the critical clearing times to avoid operational limit violations, such as the low-voltage ride-through constraint violation, while traditional direct methods would fail to appropriately estimate such clearing times. They illustrate the effectiveness of the proposed approach by means of case studies considering two types of wind turbine generators, the fixed- and limited-variable-speed wind turbine generators.

With the real-time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

Three-phase electric vehicle (EV) battery chargers are expected to not impact the unbalance of distribution networks; moreover, using active power factor correction topologies they are expected to behave like resistive loads not affecting the system harmonic distortion. In this study, unexpected unbalance characteristics of fundamental and harmonic currents of three-phase EV battery chargers are analysed by means of experimental tests performed on two EV cars of different technology. In order to quantify unbalance and harmonic distortion, proper comprehensive indices, in part developed in this study with specific reference to currents, are utilised. The unbalanced current absorbed even under ideal voltage balanced supply conditions by one of the two EV tested is shown and quantified constituting a real-life case study of endogenous unbalance. Then, the influence of voltage unbalance supply conditions on fundamental and harmonic currents is evaluated by means of a proposed testing procedure, obtaining case studies of exogenous unbalance. The experimental results obtained for the two EVs should be extended to a larger set of cars; anyway, they show the need, before moving forward to full grid EV connection, for utilities and for designers to improve the current standard certification process and the control performances of three-phase battery chargers.

The system equivalent inertia of the power grid is gradually decreased with the increasing penetration of renewable energy resources, which leads to a higher risk of frequency fluctuation after a major fault. For the frequency emergency control in the scenario of a high-voltage direct current (HVDC) line block fault, a novel criterion for enabling the fast response of user-end distributed loads is proposed in this study based on voltage feature identification and frequency drop detection. The reactive power to voltage sensitivity coefficient is combined with the Hausdorff algorithm to increase the accuracy of the calculation. The proposed criterion can realise the HVDC block fault identification at the end of the distribution grid, so as to achieve fast decentralised decision-making for distributed loads. This method may greatly reduce the need for high-speed communication and take actions faster than the primary and secondary frequency controls of the traditional units. The criterion makes it possible for low voltage small load in the user-end to be controlled precisely and selectively to fill the large power imbalanced caused by an HVDC block fault. The validity and superiority of the criterion are verified by simulation experiments using multiple test systems.

With the rapid development of voltage source converter based high-voltage direct current, power flow analysis becomes increasingly important for AC/DC hybrid systems. This study extends the holomorphic embedding (HE) method to the VSC-based AC/DC power flow problem. With an appropriate embedding technique, a general embedded model is established for AC/DC power flow with VSCs, which can not only preserve the deterministic property of HE that enables it to derive the desired solution in an explicit manner but also flexibly accommodate various bus types, VSC configurations, control strategies and operating conditions. Moreover, the sparsity of the recursive matrix in power series calculations and the introduction of the iterative calculation of the inverse matrix (ICIM) to rational approximants contribute to the high computational efficiency of the model. The proposed method is shown to be feasible and efficient by illustrative cases and is further extended to multi-terminal (VSC-MTDC) configurations to verify its flexibility.

In this study, a normal form based excitation controller for oscillatory dynamics in power systems is investigated. Afterwards, instead of using a conventional linear control technique for the secondary controller, a novel non-linear objective for the rotor mode is considered to emulate ideal power system stabiliser (PSS) characteristics. With the aid of eigenvalue analysis, it is shown that the proposed method exhibit ideal PSS characteristics. Moreover, it is demonstrated the proposed technique is robust when compared with the conventional controllers. Time-domain simulation is performed on the third-, fourth-, and sixth-order model (i.e. IEEE model 1.0, 1.1, and 2.2) of the synchronous machines. Simulation results are shown on a single machine infinite bus power system model.

Electricity price forecasting is very important for market participants in a deregulated market. However, only a few papers investigated the impact of forecasting errors on the market participants' behaviours and revenues. In this study, a general formulation of bidding in the electricity market is considered and the participant is assumed to be a price-taker which is general for most of the participants in power markets. A numerical method for quantifying the impact of forecasting errors on the bidding curves and revenues based on multiparametric linear programming is proposed. The forecasted prices are regarded as exogenous parameters for both deterministic and stochastic bidding models. Compared with the existing method, the proposed method can calculate how much improvement will be achieved in the cost or revenue of the bidder if he reduces the price forecasting error level, and such calculation does not require any predefined forecasting results. Numerical results and discussions based on real-market price data are conducted to show the application of the proposed method.

The authors propose a novel decentralised mixed algebraic and dynamic state observation method for multi-machine power systems with unknown inputs and equipped with phasor measurement units (PMUs). More specifically, they prove that for the third-order flux-decay model of a synchronous generator, the local PMU measurements provide enough information to reconstruct algebraically the load angle and quadrature-axis internal voltage. Due to the algebraic structure, a high numerical efficiency is achieved, which makes the method applicable to large-scale power systems. Also, they prove that the relative shaft speed can be globally estimated combining a classical immersion and invariance observer with – the recently introduced – dynamic regressor extension and mixing parameter estimator. This adaptive observer ensures global convergence under weak excitation assumptions that are verified in applications. The proposed method neither requires the measurement of exogenous input signals such as the field voltage and the mechanical torque nor the knowledge of mechanical subsystem parameters.

To solve the problem of manpower and time consumption caused by power flow state adjustment in a large-scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision-making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi-sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39-bus standard system and actual power grid are used to verify the effectiveness of the method.

This study investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in smart distribution system integrated with electric vehicles (EVs) and wind power generation. A two-stage framework is proposed for real and reactive power management and DFR. In the first stage, demand dispatch of EVs is coordinated to utilise available wind power and minimise the total load variations. In the second stage, a comprehensive problem formulation is developed to minimise the losses in the system with simultaneous EVs reactive power management and DFR. A genetic algorithm is used to solve this two-stage optimisation problem. A case study of 33-bus radial distribution system is presented to highlight the efficacy of proposed approach. The obtained results are quantitatively assessed in terms of losses and node voltages. A comparative benchmarking analysis is presented to substantiate the role of DFR and reactive power capability of EVs in improving the system performance.

This study introduces a techno-economic planning strategy of reactive power (VAr) in power transmission systems. This planning strategy primarily has been focused on reactive power planning (RPP) through operating cost minimisation ensuring cost-oriented system security. The objective function (operating cost) is blended with four different components viz. cost due to real power loss, VAr generation cost, additional VAr compensation devices cost and line charging cost. FACTS (flexible AC transmission system) devices are installed at weak positions through different echelons to improve the system voltage stability. The authors also implemented the probabilistic hybridisation of crow search algorithm and JAYA to find out the optimal set of controlling parameters related to VAr. Finally, a sharp and minute analysis of the results has been done to validate the proposed strategy under multi-loading conditions. To understand the efficiency and efficacy the code profiling and simulation has been rendered on IEEE 30 bus and UPSEB 75 bus test system.

It is known that marine cable is responsible for the transmission of electric energy and electrical signals of the ship. In order to predict the residual life of cable under discontinuous operation, a non-destructive online life prediction model is established based on retention rate of hardness (RRH). This study takes the marine ethylene propylene rubber (EPR) cable as the research object. Using time temperature superposition principle, the optimal time temperature shift factor is calculated using particle swarm optimisation and steepest descent method. After that the main curve was fitted exponentially to acquire the relationship between RRH and ageing time. Compared with the existing model, the results indicate that the proposed model has higher prediction accuracy. On this basis, the residual life prediction model based on RRH of marine EPR under discontinuous operation is presented by adding time parameters. The effectiveness of the method is verified by simulation. Thus, it provides theoretical guidance for solving the problem of marine cable replacement time.

This work focuses on calculating the amount of control reserve, which can be provided by a pool of renewable power plants on the next day. The power forecast of wind and solar power plants depends on the weather forecast, which always contains errors. A merger of individual plants at different locations is advantageous in order to reduce the overall forecast error. Still, the amount of control reserve needs to be determined with a high level of reliability. For the calculation, a probabilistic approach based on historical and current weather data is chosen. In order to model the spatial dependencies of the forecast errors between individual plants, R-vine copulas are used. In the copula theory, R-vine copulas provide high accuracy in modelling the dependency of stochastic variables. The methodology is validated and compared to three alternative approaches with a use case of 32 wind and solar plants. The calculated amount of control reserve provided and the achieved reliability proves to be superior to alternative approaches. Additionally, the required reliability level is varied to investigate the impact on the amount of control reserve, which can be offered with the pool.

In this study, new technologies such as dynamic thermal rating (DTR) technology and energy storage system (ESS) are simultaneously used to optimise the integration of renewable energy sources (RESs) and minimise the load shedding. For achieving the mentioned aims, a new method is proposed to select the candidate lines for the implementation of DTR technologies. The DTR technology is responsible for increasing lines limited capacity. Moreover, optimally placed and sized ESSs save RESs generated power in non-peak-hours and inject it to the network in peak hours. For validating the performance of the proposed solution, comprehensive simulations are performed in several stages on IEEE RTS-24- and 30-bus test systems networks. To meet the increased power demand, RESs (wind and solar) are optimally allocated by using the genetic algorithm (GA) in the test systems. Then, ESS devices are optimally sized and placed by using GA. Finally, candidate lines are selected based on the proposed method and DTR devices are added to the systems. Comprehensive comparisons are presented for comparing the previously presented solutions and the proposed one. It is proved that using DTR technology and ESSs along with the proposed line selection method is the superior solution for system operators.

The penetration of solar energy into the distribution network is affected by the seasonal and day-to-day variability of the solar power generation. In underdeveloped and developing countries, the power quality (PQ) deterioration issues are primarily observed due to the presence of the weak utility grid. Therefore, smart grid operability is achieved through power exchange along with the improvement in PQ indices by the application of the biquad filter, thereby exhibiting multifunctional control capability. The biquad filter is less affected by quantization errors and involves no additional usage of sensors or control loops, which are the significant advantages observed in its implementation. The biquad filter utilization in this work, includes the estimation of fundamental load current along with mitigation of harmonics, improving PQ, reactive power compensation, and satisfactory performance during voltage unbalance, sag, distortion, swell, and unbalanced loading conditions, which are observed during weak grid conditions. The stochastic inputs of the solar PV array interfaced utility grid system are agitated due to the erratic availability of solar power, and are overcome through an adaptive perturb and observe technique, which uses a variable perturbation step size. Test cases considered here, validate the performance in accordance to the IEEE-519 standard.

It is an effective way to mitigate voltage sag by equipping custom power devices in premium power park (PPP), and the key lies in the balance between technology and economy. This study proposes a method to mitigate voltage sags to meet the needs of different sensitive users in PPP to ensure the mitigation effect and reduce mitigation costs. Firstly, a strategy of shared mitigation equipment (ME) and its operation mode is presented, including the multiple stakeholders, the structure of shared ME, and the scheme of the switch on/off to improve the utilisation rate of ME. Secondly, a sensitive equipment (SE) grouping method is proposed, which is suitable for shared ME strategy, considering the minimum compensation voltage magnitude and the economic loss caused by voltage sag. Meanwhile, this study proposes an assessment method of SE failure probability based on the three-parameter Weibull function to design the capacity of ME reasonably. Thirdly, a multi-objective collaborative optimisation model is established based on economic loss assessment to maximise the net income of investors and users. Finally, the effectiveness of the proposed method has been proved through the case study of two sensitive users in PPP.

This study proposes an operation task-aware energy management strategy for ship power systems that consist of main engines, diesel–electric engines, and energy storage systems. The proposed strategy aims to meet the fuel consumption and task-dependent objectives of the vessel by optimally dispatching the generation and storage units. Firstly, rule-based decisions are made based on the operational task requirements and specifications by the classification societies. Then, in the optimisation stage, these decision variables are used as inputs to formulate and update the operational constraints and the objectives of the optimisation. The optimisation problem is formulated as a mixed-integer linear programming model, and with the help of rule-based decision variables, the problem can be efficiently solved by the exhaustive search algorithm. Four case studies with different operation task sequences and ship topologies are performed to demonstrate the effectiveness of the dispatching scheme. Furthermore, the results indicate the simplicity and practicality for the actual implementation, as well as flexibility and applicability of the proposed optimal energy dispatch for different types of vessels.

To cater unceasing supply to the modern AC and DC loads, renewable energy resources integrated hybrid DC/AC microgrid (HMG) are considered as a viable technological solution. In this study, a multiple parallel-connected bidirectional converter (MPBC) topology has been proposed to enhance the solid-state transformer (SST) power handling capability with flexible control of the HMG. To improve the high-frequency AC-link power quality and to control the power transfer through the SST in each dual active bridge a split-step phase shift (SSPS) control technique has been used. A power management control strategy has also been proposed for efficient power transfer between the sources and loads of the HMG. The proposed MPBC-based SST is suitable for interconnecting multiple utility feeders. Multiple redundant utility feeders ensure stiff bus voltages and omit the power supply reliability issues. The operation of the proposed MPBC-SST converter system with corresponding control algorithm is verified through the simulation results in both grid feeding and grid fetching operating modes. Furthermore, experimental results are presented to validate the performance of the proposed MPBC-SST converter along with its proposed controller strategy.

As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low-voltage ride-through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF-infinite bus power system and the IEEE 39-bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs.

Due to deforestation, greenhouse gases and increasing air conditioner systems, the temperatures are changing drastically and the effects are prominent in summer and winter seasons. These ambient temperature variations are affecting the transmission system parameters and also the voltages at various buses. Changes in the transmission line parameters have non-negligible impact on system performance and present Energy Management applications are not considering these temperature variations in the power system state estimation. In this paper, impact of temperature variations on the power system Voltage Stability assessment is considered. Voltage stability assessment methods make use of the load flow results. Load flow results without considering the temperature variations are inaccurate in the real-time and leading to the erroneous results in the voltage stability assessment. In view of this, studies have been carried out on IEEE 30-bus, 118-bus and 300-bus systems, incorporating the seasonal temperature variations on various types of conductors, and also demonstrated the impact with conventional and ZIP type load models. In this paper, four voltage stability indices NLVSI, , FVSI and VCPI(power) are used for identifying the proximity of voltage collapse point, critical line, critical bus voltage, critical bus angle, total system active power loss, total system reactive power loss etc.