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This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting. [ABSTRACT FROM AUTHOR]

This paper proposes a novel primary frequency regulation (PFR) scheme for multi‐infeed high voltage direct current (HVDC) (MIDC) system. First, a dynamic model for frequency response in the MIDC system is carried out to involve line‐commutated‐coveter (LCC)‐HVDCs in frequency regulation. Then, a disturbance observer (DO) is proposed to estimate both the power imbalance and the full order states of the system. By combining a Kalman filter, this DO gets an accurate estimation quickly with local frequency measurement. On the basis of the estimated disturbance and state variables, feedforward control schemes can be concluded to fully utilize the fast adjustability of LCC‐HVDCs. In this paper, a model predictive control based scheme is designed to coordinate LCC‐HVDCs under constraints of HVDC capacity and frequency boundaries. As a result, the optimal power‐sharing property of LCC‐HVDCs is achieved to enhance the PFR dynamic performance of the AC main system. Further, the stability of the proposed method is analysed using the Lyapunov method. Finally, numerical simulations are concluded to verify the effectiveness and the merits of the proposed method, by comparing it to typical droop controls. [ABSTRACT FROM AUTHOR]

With the rapid increase of wind power penetration, the frequency indicators of power system, encompassing frequency deviation and rate of change of frequency (RoCoF), are prone to exceed the pertinent relay thresholds, thus leading to serious power outages. Wind farms can participate in primary frequency regulation (PFR) to alleviate the above problem. However, wind farms reserve capacity (WFRC) presented by overspeed control and pitch angle control is the important factor for frequency safety especially when large disturbance occurs, and it varies obviously in different conditions. Thus, WFRC should be utilized optimally to avoid insufficient or excessive response of wind farms in PFR. To overcome the challenge, this paper proposes frequency‐trajectory‐oriented control (FTOC) for wind farms based on a two‐fold optimization framework, seeking to obtain the best frequency control performances in PFR through realizing the optimal compromise between frequency results and the consumption of WFRC. First, when the frequency indicators deviate significantly from the normal values, the optimal frequency trajectory is planned periodically according to the local standards and operation conditions, thereby minimizing the total consumption of WFRC on the premise of frequency safety. Second, based on model predictive control and a real‐time power system parameters estimator, the total reference power of wind farms, which helps the frequency track the latest planned trajectory, is optimally distributed among wind generators according to their reserve capacity. In this case, through the optimal frequency trajectory planning and tracking, the new method in this paper not only effectively prevents the frequency results from exceeding the local standards under diverse conditions, but also finds the optimal balance among frequency indicators safety, frequency trajectory smoothness, and the reserve capacity consumption of wind power generators. Finally, the simulation results prove the effectiveness and advancement of FTOC. [ABSTRACT FROM AUTHOR]

One of the main capabilities of grid‐forming converters is the ability to operate in both islanded and grid‐connected modes. The grid‐forming converters work autonomously and behave as an AC voltage source when they are isolated from the bulk power system. In a grid‐connected mode, it is necessary for them to offer a set of grid‐forming capabilities for enhancing system inertia and strength. A grid‐forming converter may experience significant fluctuations in voltage, current, and frequency when the transition occurs. This paper investigates an improved adaptive model predictive control (AMPC) method for grid‐forming converters to achieve a smooth transition from islanded to grid‐connected mode and balance the contradiction between frequency stability and response speed. The AMPC method uses the angular frequency and phase angle as the performance indicators and introduces the angular frequency variations to adaptively adjust its weight coefficient in the objective function. The implementations and effects of the proposed AMPC method on the grid‐forming converter are theoretically evaluated. Compared to the conventional control, the proposed control scheme can not only suppress voltage, current, and frequency fluctuations, but also achieve a smooth transition. Additionally, the frequency control strategy in the islanded mode is improved for better independent operation. Finally, experiment tests on a controller‐level hardware‐in‐the‐loop simulation platform are carried out to validate the proposed control strategy for achieving a smooth transition. [ABSTRACT FROM AUTHOR]

The distribution networks have more frequent and fast voltage fluctuations with the large‐scale access of distributed energy resources and electric vehicles. Based on remote terminal unit (RTU) and distribution terminal unit (DTU), traditional hierarchical control methods are difficult to achieve rapid global coordinated voltage regulation due to static modeling. For this purpose, this paper proposes an integrated dynamic voltage control strategy which integrates the objectives of local voltage control and secondary voltage control. First, simplified dynamic models of controllable equipment in active distribution network are established instead of the static models. On this basis, the state‐space prediction model of the control system is established. Then, based on the improved model predictive control (IMPC), the integrated dynamic voltage control strategy realizes rapid and global coordinated control through state prediction, rolling optimization and real‐time feedback correction. Numerical results show that the proposed strategy can achieve fast global coordinated voltage control in the time scale of seconds. [ABSTRACT FROM AUTHOR]

Conventional power systems function under the assumption that loads are uncontrollable, and that the generation control is the primary means of preserving system voltage, frequency, and stability. Thanks to recent advancements of power electronics technology and communication schemes, demand‐side resources are now capable of providing fast frequency regulation. Controllable loads can provide upward/downward reserve during frequency excursions. In this paper, the authors consider collective contribution of large clusters of controllable loads which modulate their aggregate demand power to regulate the primary frequency. Koopman model predictive control is designed to handle local frequency variations caused by various disturbances at each load bus, considering uncertain load models. The efficacy of the proposed method has been validated using the New‐England power system considering two scenarios, namely, load variation, and generation outage. [ABSTRACT FROM AUTHOR]

As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive nonlinear extended state observer‐based model predictive controller (ANLESO‐MPC) for residual current compensation (RCC) inverters to compensate fault characteristics in resonant grounded power systems (RGPSs). In the proposed scheme, the adaptation feature is used to adapt the adjustable inductor appearing in the dynamical model of RGPSs by satisfying the desired fault current compensation requirement which in turn will ensure the resonance condition. Since the adapted parameter is used in the control signal, the ANLESO‐MPC offers robustness against parameter sensitivity. The neutral voltage is considered as a disturbance and the change in this voltage as an extended state to estimate this disturbance while incorporating the adapted parameter so that the adaptive nonlinear extended state observer (ANLESO) provides robustness against disturbances. Finally, the model predictive controller (MPC) is designed for the RCC inverter utilizing estimated disturbances and adapted parameters to fully achieve the fault current compensation. The performance of the controller is evaluated using simulations in MATLAB/Simulink and OPAL‐RT real‐time platforms under varying fault impedances as the fault characteristics change with variations in the fault impedance. Results are also compared with a conventional MPC where the proposed ANLESO‐MPC seems more effective to compensate fault characteristics. [ABSTRACT FROM AUTHOR]

System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally. [ABSTRACT FROM AUTHOR]

With the rapid development of carbon trading market, the volatility trend of carbon emission trading price (CETP) becomes one of the factors that cannot be ignored in energy system planning. Based on this, this paper proposes a multi‐objective expansion planning model for park‐level integrated energy system (PIES) that takes into account the volatility trend of CETP. First, the influencing factors of CETP prediction are filtered and downscaled, and a kernel extreme learning machine (KELM) model based on the improved multi‐objective grey wolf algorithm optimizer (IMOGWO) is used for probabilistic interval prediction of CETP. Next, the operational characteristics of each carbon emission device are analysed and a model for calculating the cost of carbon trading is proposed. Then, a two‐layer PIES planning model is developed with the objective of minimizing the annualized system cost and carbon emissions during the expansion planning cycle, the upper‐layer model is a planning model for solving equipment expansion scenarios, and the lower‐layer model is an operation model for calculating typical operation schemes. Finally, the simulation effect of the prediction model is verified by the European carbon trading data, and the planning schemes are compared and analysed to prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To ensure the safe and stable operation of 5G base stations, it is essential to accurately predict their power load. However, current short‐term prediction methods are rarely applied rationally in pertinent circumstances to the features of base station power load over time. For high accuracy and generalization capabilities, this work proposes a hybrid approach that combines gated recurrent unit (GRU) with particle swarm optimization (PSO) and completes ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The complex load is decomposed into multiple components to improve the fit of the neural network. To deal with the nonlinearity that constrains the modal aliasing and data noise, CEEMDAN is used to reconstruct the trend and noise sequences. The GRU network is utilized to improve the neural network fitness and obtain long‐term features. In addition, PSO is used to optimize the number of neurons and the learning rate of the GRU. Compared with existing neural network GRUs, the RMSE is reduced by 16.6, the mean absolute error is reduced by 11.97, and the coefficient of R2 is improved by 0.13, indicating that the model has a better fitting effect. The comparisons prove that the proposed model has better accuracy than the existing methods. [ABSTRACT FROM AUTHOR]

Selective harmonic elimination (SHE) is a suitable pulse‐width modulation (PWM) technique for some applications of modular multilevel converters (MMC) with lower number of semiconductor submodules (SM). However, SHE can make it difficult to control circulating currents in the MMC. Moreover, the MMC is vulnerable to SM switch failures that reduce its output power magnitude and quality. This paper proposes a fault‐tolerant model predictive control strategy for MMCs operating with SHE (FT SHE‐MPC). The effects of switch failure on phase voltage waveforms are studied first. Then, a method is proposed to design the FT SHE waveform to preserve its harmonic performance. Next, the proposed FT SHE‐MPC is designed to increase the capacitor voltages during an SM failure to maintain the fundamental component of the faulty phase voltage and control the circulating currents, maintaining balance among line voltages and the output power of the MMC. Additionally, the proposed method offers as much as 60% faster FT transition time and as much as 36% lower capacitor voltage ripple. The advantages of the proposed method are verified through offline and real‐time simulation studies on a three‐phase nine‐level MMC in PSCAD/EMTDC software and RTDS. [ABSTRACT FROM AUTHOR]

Along with the increased installation of distributed energy resources (DER), long‐term voltage stability can be improved if proper coordination is developed between DERs and grid controllers, for example, Load Tap Changer (LTC). In most of the proposed methods, the steady‐state voltage‐sensitivity analysis has been implemented to predict the voltage state, which is the state's dynamic evolution in abnormal operations of the grid with high shares of DERs. This paper presents a system identification‐based model predictive control (MPC), which can coordinate DERs and LTC to restore the voltage to the pre‐fault condition after emergencies. First, the online voltage evolution is predicted based on the sparse identification of the nonlinear dynamics (SINDY) technique. Then, the SINDY‐based voltage prediction is combined with an adaptive MPC in the centralized controller. In addition, the nonlinear of DERs have been modeled to avoid the MPC‐based coordination jeopardizing the local constraints of DERs. The proposed method has been tested in a modified CIGRE benchmark network. Simulation results show that the dynamic voltage is effectively estimated by the SINDY method. Furthermore, the developed MPC model smoothly supports faster voltage recovering time with the least number of control actions of LTCs. [ABSTRACT FROM AUTHOR]

Microgrid clusters (MGC) can improve the consumption of renewable energy and the system reliability. The control of microgrid cluster with large‐scale microgrids is the focus of microgrid cluster research. In order to better realize the coordination control and energy scheduling among microgrids, this paper proposes a distributed optimal strategy for complex MGC based on model predictive control (MPC) and block‐wised alternating direction multiplier method (BADMM). Firstly, an optimization model is established based on a honeycomb active distribution network (HADN); then the network is decomposed into several regions according to BADMM. The global optimization goal of the cluster is achieved by iterating the optimization results of each region in a time domain. Finally, the case study results verify the effectiveness and convergence of the proposed distributed optimization strategy, and provide a reference for energy management of MGC. [ABSTRACT FROM AUTHOR]

Mid-long term voltage stability refers to voltage stability concerning the dynamics of the slow dynamic elements and protection devices in power systems. To improve the mid-long term voltage stability, this paper proposes a scheme of model predictive control (MPC) based on Galerkin method. The basic principle of MPC is to solve the optimization problem regarding voltage stability control based on the prediction model periodically. By applying the optimal control scheme to power systems, the mid-long term voltage stability can be enhanced. The Galerkin method-based polynomial approximation approximates the relationship between mid-long term trajectories and control parameters by a polynomial function. The polynomial function acts as a prediction model to predict future system trajectories under different values of control parameters in MPC. The advantage of the proposed method is that its prediction accuracy is relatively high and thus the control efficiency of MPC is improved, as the polynomial function can embody the non-linear characteristics of the power system. In the case study, the 3-machine 10-bus system and the New England 39-bus system are taken as examples to compare the MPC based on the proposed method and the trajectory sensitivity method in terms of prediction accuracy, computation time and control results. [ABSTRACT FROM AUTHOR]

This paper presents a novel, sequentially executed supervised machine learning‐based electric theft detection framework using a Jaya‐optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre‐processing phase. An oversampling algorithm based on the Robust‐SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning‐based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re‐trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is the basis of power system operation and analysis and is of great significance for the stable operation of power systems. To solve the problems of insufficient mining of the intrinsic information of load data, randomness, and non‐linearity, and to improve the accuracy of load prediction, the authors propose a short‐term power load prediction model based on empirical wavelet transform (EWT) decomposition and the temporal convolutional network (TCN). First, the Pearson coefficient is used to eliminate the less relevant influencing factors to reduce the complexity of the model. Then, the EWT algorithm is used to decompose the original load signal to fully exploit the load data time domain and frequency domain information, while solving the problems of non‐linearity and randomness. Finally, the decomposed subsequences are input to the TCN network for prediction superposition to obtain the final results. The experimental results show that, compared with the single models TCN, gated recurrent units (GRU), and long short‐term memory (LSTM), and the hybrid models EWT‐GRU, EWT‐LSTM, and VMD‐TCN, the R2 of the short‐term power load forecasting model based on EWT‐TCN is improved by 0.2099%, 0.2519%, 0.3453%, 0.0334%, 0.1766%, and 0.1228%, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pumped-storage unit (PSU) often acts as the peak and frequency modulation sources in modern power systems. The frequent operation condition conversions and tight hydraulic couplings with conduit system make its governing control a tough work in practise. To overcome the difficulty in the proportional--integral--derivative (PID) parameter tuning during transient processes, a hierarchical model predictive control (MPC) scheme, in which constrained generalised predictive control (CGPC) cooperates with classic PID is proposed. In the upper hierarchy of the MPC controller, CGPC acts as an auxiliary optimiser for the optimal PID parameters through online optimisation, whereas in the lower hierarchy, PID directly steers the movements of servomotor to control the guide vane opening; thus, to match the existing governing equipment in engineering practise. Also, typical constraints on the input/output signals and value ranges of PID parameters are discussed. Simulation experiments are conducted on a non-linear distributed-parameter simulation platform. It can precisely reflect hydraulic and mechanical dynamic behaviours of PSU and its conduit system. The results indicate that the proposed hierarchical MPC scheme not only outperforms in speed tracking and oscillation damping, but also attenuate instability phenomena caused by the negative 'S' character in pump turbine. [ABSTRACT FROM AUTHOR]

In the present study, the economic model predictive control (EMPC) scheme is presented for the time of use tariff application of photovoltaic and diesel generator backup system, which is connected to the grid‐connected microgrid. Through the use of the closed‐loop control system, the prior open‐loop optimal control is enhanced. Minimizing the grid energy and fuel costs by evaluating the fuel level restrictions in the diesel tank is the main goal of this study. To accommodate the restrictions among controllable variables, this control method fulfils the load requirements. To gain the benefits of feedback and predictions, optimal power scheduling is modelled as a control problem. Furthermore, Blockchain technology is applied to secure interchanged information in the grid to avoid manipulating data by attackers. Specifically, the analysis is divided into two scenarios. The first one takes place in the alternative style when a blackout happens between [7 am and 6 pm] h and the other occurs in a grid energy mode when the network is available over 24 h. Energy performance, cost savings, and daily revenue have all been improved by EMPC. As a result, the daily energy savings are up to 52%, whereas diesel energy does not go over 85%. [ABSTRACT FROM AUTHOR]

This manuscript presents a decentralised control scheme for coordinated SVC (CSVC) of large-scale power networks. In this way, for each area of the power grid, a model predictive controller (MPC) which modifies the set points of reactive power compensators participating in CSVC algorithm is designed. The proposed controller takes into account reactive power limits of these compensation devices. The novelty of the method lies in the consideration of measured reactive power deviation on tie-lines between neighbouring areas as measured disturbance and compensation of the disturbance by regional MPC controllers. As another contribution of this work, the validation of the proposed algorithm is done in real-time simulation environment in which the decentralised MPC controllers are run in parallel on separate computational cores. The stability and robustness of the presented algorithm is validated for a large-scale realistic transmission network with 5000 buses considering standard communication protocols to send and receive the data. Simulation results show that the proposed method can regulate the voltages on the pilot buses at the desired values in the presence of load variations and communication delays. Finally, the computational burden of the proposed method is evaluated in real time. [ABSTRACT FROM AUTHOR]

The battery energy stored quasi‐Z‐source (BES‐qZS) based photovoltaic (PV) power generation system combines advantages of the qZS inverter and the battery energy storage (BES) system. To realize multi‐objective cooperative control, a model predictive control (MPC) strategy for the PV grid‐connected system based on an energy‐storage quasi‐Z source inverter (ES‐qZSI) is proposed. The energy storage battery is added to the traditional quasi‐Z source inverter (qZSI). The MPC strategy is adopted to realize maximum power point tracking (MPPT), battery, and point of common coupling (PCC) tripartite energy control in a single‐stage conversion system. The discrete‐time model of ES‐qZSI is derived, and the cost function was constructed by using the system state variables. The PV power MPPT module, the battery power management module, and the PCC power module are designed to generate the reference signal of the prediction function. Finally, the proposed algorithm was validated by simulation. The theoretical analysis and simulation results were consistent. [ABSTRACT FROM AUTHOR]

At different times, the load correlation of the integrated energy system (IES) is different and its changes over time are regular. However, the regularity of such changes is rarely considered in the current IES prediction research. In view of the above reasons, an IES short‐term load forecasting method based on load‐correlation peaks and valleys is proposed. Inspired by the concept of load peaks and valleys, the concept of load‐related peaks and valleys is put forward. Furthermore, based on the above concept, a method of establishing different prediction models within a day based on the correlation threshold is proposed. In order to implement the initial correlation threshold reasonably, a marine predator algorithm with an integrated gray wolf optimizer (MPAIGWO) is proposed. After the initial parameters are selected, use the self‐learning ideas in situational awareness to change the parameters. In the case study, the power, thermal, and cold loads of IES were predicted. The comparison results show that the prediction results of this method have high accuracy. In addition, this method can also overcome the influence of irrelevant input variables on prediction. The algorithm comparison proves that the MPAIGWO has the highest optimization performance under the same conditions. [ABSTRACT FROM AUTHOR]

This study proposes a power management strategy (PMS) that can efficiently and accurately address the nonlinear dynamics of ship power systems for all‐electric ships (AESs). The design of a PMS can be considered naturally in a model predictive control framework. However, the control error caused by a model mismatch is a challenge of the PMS for physical system implementation. The proposed PMS is based on a complex system‐level nonlinear model that ensures a minimum mismatch. In addition, an efficient optimization algorithm is proposed to efficiently apply the PMS. The proposed method is verified using real‐time simulations with physical systems in various scenarios. The experimental results demonstrate the superior efficiency of the proposed optimization algorithm and the accuracy of the developed PMS for physical system implementation. [ABSTRACT FROM AUTHOR]

The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra‐short‐term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground‐based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground‐based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra‐short‐term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground‐based cloud images can reach 96%, compared with the model without the feature information of ground‐based cloud images, the accuracy is improved by 5%. The proposed ultra‐short‐term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather. [ABSTRACT FROM AUTHOR]

With the increasing distribution network accidents caused by typhoon disasters, risk management in typhoon scenarios is necessary. A risk management framework for power distribution networks (PDNs) undergoing a typhoon disaster is proposed. First, a risk prediction model of the PDN under typhoon conditions based on the Batts model is developed to predict the loss of load risk (LLR) index and economic loss risk (ELR) index of the PDN before the typhoon lands. Then, a vulnerability index of overhead lines, considering the failure frequency of specific overhead lines and their load importance, is introduced for use in a weak link identification model to identify weak links in the PDN. Finally, a two‐time‐step risk relief model based on typhoon forecast information and the identified weak links is developed to allocate PDN relief materials to reduce the LLR and ELR during a typhoon disaster. An IEEE RBTS BUS6 system is employed as a test system to verify the risk management strategy. The vulnerability index proposed by the strategy considers both the importance of load and line failure. The risk management strategy adopts a two‐time‐step model, which reduces the impact of meteorological forecast errors and enables more efficient use of materials. The results show that the proposed framework can take more effective use of relief materials to reduce the loss of PDNs compared with the traditional strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents a model predictive control (MPC)-based centralised control approach for maintaining the voltages of the nodes within permissible limits in the presence of high photovoltaic (PV) penetration. The proposed control scheme coordinates the actions of on-load tap changer (OLTC) and PV inverters optimally to fulfil the desired objectives. The objectives are minimisation of change in control variables, slack variables, energy loss, and voltage error. These objectives are weighted to form the overall objective function. Three rules are formulated based on the severity of voltage magnitudes. The weights of the objectives are adjusted according to these pre-defined rules. Simulations are performed in an active distribution network (ADN) integrated with and without microgrids, where both demands and generations vary hourly over the day. As power is injected by the microgrids during most of the time of the day, the excursions of node voltages are slightly higher in the microgrids integrated ADN. Moreover, the incorporation of the proposed rule-based MPC drastically reduces the energy loss due to active power loss in distribution networks, as is evident from the simulation results obtained by comparing the proposed approach with an existing MPC-based approach. [ABSTRACT FROM AUTHOR]

The low and variable inertia due to the integration of large-scale wind generation poses severe challenges to the frequency control of the power grid. In this study, a novel model predictive control (MPC)-based load frequency controller (LFC), which can adapt to the changes in system inertia, is proposed to improve the frequency control effect. First, the inertia estimation scheme based on measurements is designed. Then, the state-space model, which is the key that determines the control effect of MPC, is corrected in time based on the estimated inertia. The proposed method in this study can avoid the problem of MPC-based LFC failure caused by inaccurate model parameters. The performance of the proposed strategy is demonstrated in two scenarios, i.e. wind power fluctuation and load multi-step, using a two-area interconnected power system with time-variant inertia. [ABSTRACT FROM AUTHOR]

The finite control set model predictive control (FCS–MPC) has drawn considerable attention in the integrated control of the unified power quality conditioner (UPQC) due to the characteristics of high dynamic response, internal decoupling and multiple constraints. In this study, a new direct prediction compensation strategy (DPCS) based on FCS–MPC is proposed, which eliminates the harmonic detection algorithm, simplifies the UPQC controller structure and reduces the steady-state errors. By compensating for the time delay of FCS–MPC, the UPQC can mitigate the power quality problems more accurately and timely. The FCS–MPC value function can be with various constraints, which makes the UPQC suitable for more situations. Compared with the indirect prediction compensation strategy, the designed DPCS has better time-delay compensation effects, which can reduce the harmonic components of the user-side under transient disturbances. The simulation results show that the proposed compensation strategy can achieve better dynamic and steady-state performance on power quality problems such as voltage sags, swells, interruptions and harmonics. [ABSTRACT FROM AUTHOR]

With the large-scale wind power, centralised access to high-voltage direct current (HVDC) asynchronous interconnected sending system, the random fluctuation of wind energy increases the uncertainty of frequency regulation. A frequency coordinated control (FCC) strategy based on situation awareness is proposed to make full use of the frequency regulation potential of wind turbines and HVDC transmission. Firstly, the influence of wind power permeability and HVDC power ratio on frequency regulation capability is analysed, and the mathematical model of FCC is established. Secondly, based on the information of FCC obtained by wide-area measurement system and data acquisition system, a non-linear mathematical prediction model is constructed, and the frequency response in finite time domain is predicted according to the frequency deviation and its change rate. According to the results of the prediction, the parameters of FCC are dynamically adjusted to realise the whole process variable objective optimal control on the time scale of primary frequency regulation. Finally, the simulation results show that the maximum frequency deviation of the proposed control method is reduced by about 46.6% and the frequency callback time is reduced by about 11.6% compared with the traditional control method with fixed parameters under the same disturbance. [ABSTRACT FROM AUTHOR]

Missing values are a common occurrence in condition monitoring datasets. To effectively improve the integrity of data, many data imputation methods have been developed to replace the missing values with the estimated values. However, these methods do not always perform well in datasets containing different types of missing values. Three types of missing data are defined, namely isolated missing value, continuous missing variable, and continuous missing sample. A three-step data imputation method is proposed to sequentially impute these missing values following the principle from easy to difficult. The original time series data is first to split into different segments according to the positions of continuous missing samples. Then, interpolation and space-based methods are applied to sequentially estimate isolated missing values and continuous missing variables in each segment. Finally, a stepwise extrapolation prediction model based on the long short-term memory network is established to repair continuous missing samples between each segment. Two application examples are implemented on different dissolved gas analysis datasets and load datasets. Compared with state-of-the-art techniques, the proposed three-step data imputation method is general and can be applied to many scenarios because it establishes a rational data recovery sequence to accurately repair both stationary and non-stationary condition monitoring data. [ABSTRACT FROM AUTHOR]

A novel smart impedance (SI) is introduced for load harmonics (distortion power) freewheeling in microgrids which are called predictive SI (PSI). 'PSI' provides solutions to the load harmonics conditioning, reactive power compensation, and parallel resonance suppression. The 'PSI' allows for harmonics, sub-harmonics, and inter-harmonics compensation with using only one control loop. The control model, which is used for the 'PSI' enables its impedance phasor variation in a wide and continuous range, including inductive and capacitive ones. The 'PSI' consumes small active power and can provide reactive power compensation without the need for a separate capacitor bank. The introduced 'PSI' does not generate parasitic harmonics and so does not need an additional passive filter in its output. The 'PSI' operates such as a harmonic isolator and only compensates for load harmonics and thereby prevents from the source side harmonics flowing into the load. It does not need the phased locked loop for synchronisation to the grid and can be designed for selective harmonic compensation if needed. A droop-boost control strategy is proposed for optimum and prioritised utilising of the SI capacity. At the end of the study, by using simulations in Matlab/Simulink, the figure of merits of the PSI are proved. [ABSTRACT FROM AUTHOR]

This study addresses the problem of voltage variations in active low‐voltage distribution networks caused by distributed photovoltaic (PV) generation. Three strategies based on model predictive control (MPC) are introduced to flatten the voltage profile in a cost‐optimal way. The compared strategies are the business as usual approach that manipulates a controllable on‐load tap changer at the primary substation, the problematic feeder control strategy (CS) that adds an additional degree of freedom by controlling the critical secondary substations (SSs), and finally the compensation strategy, which controls the primary substation and compensates the non‐critical SSs. A sensitivity analysis on the CSs has been conducted comparing the voltage variation reduction and the asset utilization with regard to the accuracy of the prediction models and the forecasted disturbance data. The results show that better (and more costly) characterisation of these parameters only provide a marginal improvement in the reduction of the voltage variations due to the restriction caused by the heavy tap change penalisation. Moreover, the tested case‐study shows that the problematic feeder CS outperforms the compensation strategy in terms of larger voltage variation reduction for similar asset utilisation. [ABSTRACT FROM AUTHOR]