Extreme weather events, such as heatwaves, cold snaps, and storms, frequently cause sudden and unpredictable shifts in electricity consumption, significantly complicating accurate load forecasting. Existing forecasting methods, predominantly offline-trained deep learning models, struggle to rapidly adapt to these abrupt changes due to limitations in real-time processing and the issue of catastrophic forgetting, and they rarely capture the uncertainties inherent in load predictions under extreme weather conditions. To overcome these challenges, this study proposes a novel complementary online learning network (COLNet) explicitly designed for probabilistic load forecasting during extreme weather events. The key innovations of COLNet include the following: first, a fast adaptation mechanism to rapidly assimilate new load patterns; second, an associative memory module to preserve historical load information and mitigate catastrophic forgetting; finally, a weather-aware gating mechanism that dynamically incorporates real-time meteorological variables, enhancing the model's sensitivity and forecasting robustness. Extensive comparative evaluations using real-world hourly datasets from the 2022 Australian floods, covering three affected regions over about 14 months, and from the 2021 Texas cold snap, comprising statewide load over about 24 months with a test set covering the mid-February event, are conducted. These evaluations confirm that COLNet substantially outperforms state-of-the-art methods, achieving mean absolute percentage error of 3.205%, 2.457%, and 4.882% during the flood period and 0.84% on the Texas event, corresponding to an average reduction of about 21% in point error relative to the strongest baselines and about 19% in probabilistic error, thereby improving both accuracy and uncertainty quantifications.

Topology changes in a distribution network are common due to planned reconfigurations and unintentional switching events during practical operations. Topology changes make it challenging for existing optimization- and learning-based distributed system state estimation methods to maintain accuracy. This difficulty arises from the lack of accurate structural information for the new topology and the absence of labeled data (recorded state variables) for model retraining. To this end, this article proposes an unsupervised-on-target learning-based state estimation method for the distribution network after topology changes without relying on the topology information and labeled data. In particular, a bipartite graph learning (BGL) method with rank constraints is first designed to learn the representation of each topology with a restricted set of measurements. Then, the Euclidean distance is employed to select the best-matched source domain historical topology according to the representation learned by the BGL. To extract invariant causal structures across the two topologies, a causality-inspired sparse structure learning for domain adaptation network is further designed. It relaxes the correlations between the selected historical and new topologies into an associative structure, represented by attention scores derived from the proposed inter- and intravariable attention networks. This allows the leverage of the causality to enhance the state estimation performance of the distribution network after topology changes without relying on accurate topology information and recorded labels used for training. The comparison results on two standard IEEE test systems validate the efficacy of the proposed method.

Islanded microgrid formation (IMF) enhances active distribution network resilience but is challenging for current mathematical optimization methods due to its mixed-integer nonlinear nature. Deep reinforcement learning (DRL) also struggles with complex IMF problems due to action space explosion and environmental nonstationarity. This article proposes a multiagent DRL (MADRL) method for IMF, incorporating probability space optimization and random sequential updating to address IMF problems with numerous switches. Specifically, a multiagent framework models each controllable switch as an independent agent, and a specific actor-critic network architecture is designed for discrete control problems. To achieve unbiased policy gradient estimation, a probability space optimization loss function is devised to replace the Gumbel-Softmax-based gradient estimation in existing action-aware discrete DRL algorithms. Combined with the random sequential updating mechanism, the environmental nonstationarity issue faced by each agent is effectively mitigated. This results in a discrete MADRL-based IMF strategy with high computational efficiency and stable convergence, even when numerous switches are involved. Case studies on a modified IEEE 123-node system demonstrate that this method achieves optimality ratios of 96.85% and 97.41% in 16-switch and 23-switch scenarios, respectively.

With the increasing integration of renewable energy in power systems, accurate and rapid mode identification of the sub/super-synchronous oscillation (sub/super-SO) is essential for ensuring grid security. This article presents a deep-learning (DL)-based method for early prediction of the dominant frequency in sub/super-SO events. To address the challenge of limited sub/super-SO samples for DL training, we propose a domain-adversarial neural network-based transfer learning framework, which leverages easily obtainable forced oscillation data to capture sub/super-SO features. Our method introduces several enhancements over previous approaches in both model structure and optimization. First, an encoder-decoder module with a mask component is designed to reconstruct missing data from field sub/super-SO events, utilizing a specialized reconstruction loss function. Second, the proposed dual-predictor configuration applies supervised learning in both source and target domains, imposing stronger optimization constraints. Furthermore, the proposed method exhibits generality and robustness when the topologies of target and source wind farms differ, highlighting its potential for practical applications in power systems.

Finding a feasible solution is the primary concern in power system dispatch. This paper studies the feasibility condition of power system dispatch under a multistage robust optimization framework considering the non-anticipativity of dispatch policy, which is difficult to be expressed via explicit constraints. The multistage robust feasible regions (MRFRs) are defined as the sets in the state space containing all points that can maintain the robust feasibility in the next period against renewable and demand uncertainties; we give a polyhedral projection condition to characterize exact MRFRs in a recursive manner, which can be regarded as an analog of Bellman’s optimality condition. However, because the multistage dispatch problem of a bulk power system has a high-dimensional state space, the computation of exact MRFRs suffers from the curse of dimensionality. We propose an inner approximation method that identifies the maximal polyhedra that are embraced by the unknown exact MRFRs; we devise a computationally efficient algorithm to retrieve the hyperplane representation of the inner approximator. Finally, we discuss how MRFRs can be used in combination with existing approaches, such as dynamic programming and rolling horizon optimization. Numerical simulations on a modified IEEE 118-bus system verify the effectiveness and advantages of the proposed methodNote to Practitioners—This paper is motivated by the problem of maintaining the feasibility in power system multistage dispatch under renewable generation uncertainty. The proposed method regards the multistage dispatch as a sequential decision-making process and recursively defines the MRFR using polyhedral projection technique, which contains all the state points in each period that can guarantee the robust feasibility in the next period. To release the curses of dimensionality, the large-scale MRFR is approximated by an inner hyper-rectangle in the state space, which can be decomposed into independent intervals of state variables, such as the generation power range of coal-fired unit and the state-of-charge range of battery storage unit. These decoupled intervals are convenient for practical use and desired in the real-world power system. At the current stage, the calculation of MRFR requires a linear model; in the future research, integers will be addressed so that more facilities such as non-ideal energy storage units and fast-startup generators can be involved.

The control of heat–electricity-integrated multiple microgrid (MMG) systems is greatly challenged by anomalous measurements and inaccurate physical electricity and heat network models. Through the systematic integration of graph surrogate models, trajectory history information, and confederate image (CI) technology based distributed multiagent deep reinforcement learning (MADRL), we propose a robust coordinated control approach for the optimization of MMG systems. Each MG in the MMG system is first represented as a graph with tree topology that is processed by a graph neural network (GNN)-based module to produce robust representations of the measurements. Subsequently, the GNN-based module produces information that is fed into a fully connected layers module to model realistic power and thermal flow using historical data in a supervised manner, thereby forming the graph surrogate models. Before the MADRL training, the GNN-based module from trained surrogate models is embedded in the policy network of MADRL. With the support of CI, the state information and information from the GNN-based module are proceeded by the extracting trajectory history feature module. This process endows the MADRL-based controller with the ability to identify and correct anomalous measurements. The information from the GNN-based module further enhances the robustness against anomalous measurements. The trained surrogate models provide the reward signal to MADRL during MADRL training. It enables the proposed approach to be independent on accurate MMG parameter estimates. The effectiveness of the proposed approach is validated by the simulation results.

Precise short-term residential load forecasting (STRLF) is essential for maintaining stable and cost-effective operations on the demand side. Both spatial and temporal information are important for the STRLF tasks, but effectively extracting them remains a significant challenge due to the highly volatile and stochastic nature of residential consumption patterns. To this end, this article proposes pyramidal attention (PATNet), a low-complexity transformer network with spatiotemporal PATNet, to explore multiresolution spatiotemporal representations of residential load series and forecast multiple residential loads several steps ahead. Specifically, the temporal and spatial patterns of residential load series are, first, formulated as a temporal pyramidal graph and a spatial pyramidal graph according to the periodic characteristics of load time series and the spatial correlations of different residential units, respectively. Two types of low-complexity attention mechanisms—temporal and spatial PATNet—are, then, specifically designed for the temporal and spatial pyramidal graphs such that the short- and long-range temporal dependencies and dynamic spatial correlations among various groups of residents can be captured. Moreover, to enhance multistep forecast performance, we design a gated fusion unit that is capable of adaptively fusing extracted spatiotemporal information and a transform attention block that can translate historical loads into future forecasts. Numerical simulations using several real-world residential load datasets demonstrate that the proposed framework outperforms state-of-the-art load prediction methods by 7.98% at least in single-step forecasting and 11.38% at least in multistep forecasting.

Power systems are well-engineered systems designed to supply power generation and services to end users. They are considered as the most crucial infrastructures in modern societies as numerous facilities such as telecommunication, transportation, public health, and emergency services heavily rely on a continuous power supply for their normal operation. The complex structure and interconnection with other facilities make power systems vulnerable to external threats such as natural disasters, extreme weather, and vandalism. On the other hand, with the advancements of artificial intelligence in the recent years, various cutting-edge control and optimization methods are emerged. Among them, deep reinforcement learning (DRL) is the most promoted for enhancing power system resilience. In this context, this article performs an in-depth review of various DRL methods and their applications in resilience optimization and enhancement of power systems. The review offers a structured examination of DRL methodology, along with a systematic categorization of existing literature into three themes (proactive actions, emergency response, and restoration and recovery) according to their contingency stages. The strengths and limitations of DRL-based resilience enhancement strategies are discussed across robustness, scalability, interpretability, and safety. A research roadmap is provided to highlight possible avenues for further exploration.

This article proposes a robust multiarea distribution system state estimation method for interval estimation of state variables based on a physics-informed decentralized graphical representation network and Gaussian process (GP)-aided multiarea state estimators. The real-time and pseudomeasurements are first cast to a graph with tree topology and a graph attention-based representation network is employed to capture the structural information between measurements from the historical data. A centralized pretraining and distributed inference framework is developed to extract essential global information from historical data and extend it to various subregions. Then, the robust nodal features extracted by the graphical network are fed into the GP with a multitask kernel for multiarea state estimation. The adopted kernel can find relevance between tasks for different subregions that are useful for the multiarea state estimation. The embedding of structural information in the representation network enables the proposed method to achieve robustness in the presence of outliers. The adopted kernel further allows us to reduce the reliance on network communication and achieve accurate multiarea state estimation. It also offers the ability to quantify the uncertainty of state variables, yielding more valuable estimation outcomes. Experimental results demonstrate the effectiveness of the proposed method in handling abnormal data and accurately quantifying the uncertainty of state variables.

Precise multienergy load forecasting (MELF) significantly contributes to the stable and economic operation of integrated energy systems (IES). However, existing MELF approaches exhibit three primary limitations: (i) naively aggregate all input features without explicit mechanisms to capture complex coupling relationships between multiple energy loads; (ii) incapable of fully exploiting the local load characteristics of each individual task; (iii) provide only deterministic forecasting results. To address these limitations, in this article, we propose a global–local probabilistic multi-energy load forecasting framework based on hybrid attention mechanism-enabled Transformer (HAT) network and sparse variational Gaussian process (SVGP)-aided residual learning method. Specifically, HAT is first utilized to capture the consumption behavior of the multi-energy loads. It employs a temporal attention module to extract the load patterns of each task and a task attention module to explicitly capture the coupling relationships between different tasks. The multiple pieces of information are fused through a gated fusion unit for the joint predictions of multiple loads. Then, an SVGP with a composite kernel is adopted to learn the local load characteristics specific to each individual task by modeling the residual of the forecasting outcomes. This further enhances the performance of the proposed method and allows us to achieve effective quantification of the forecasting uncertainties. Numerical simulations using real IES load data reveal that the proposed framework outperforms state-of-the-art deterministic load forecasting by 11% at least in mean absolute percentage error (MAPE) and probabilistic load forecasting by 5% at least in both pinball loss and Winkler score metrics.

Frequent and sizable voltage fluctuation, a common issue faced by the modern distribution system (DS), could lead to potential equipment failures and power interruption. This brings huge negative impact on the power supply reliability of the DS. Existing voltage regulation methods typically rely on the precise physical parameters, complete measurements, and perfect communication, all of these premises are difficult to meet in practice. To this end, a decentralized control method that is robust to measurement acquisition errors is developed for DS in this article. Specifically, a graph learning-based surrogate network is first built to simulate the power flow computing procedure and capture the structural characteristics of the DS. The centralized surrogate model is, then, divided into several decentralized representation networks according to the network partition results to obtain the robust embedding of the regional information of each subnetwork. Subsequently, the representation networks are embedded in the front of the actor networks of the multi-agent soft actor-critic algorithm, the agents of which are learned in a centralized fashion according to the reward value estimated by the centralized surrogate model. The systematic integration of the three components allows us to achieve cooperation between different subregions and robustness against anomalous measurements without the reliance on precise circuit parameters. Comparative studies on IEEE test system illustrate the robustness of the proposed approach.

This paper proposes a modeling method for the input series and output parallel (ISOP) dual active bridge (DAB) converter under triple phase shift (TPS) modulation. Moreover, the reduced-order state-space average (RSSA) model is established to further obtain its equivalent small signal circuit model both for DAB and ISOP DAB converter. The model shows that the ISOP-DAB converter is a multi-input and multi-output (MIMO) system, the decoupling algorithm is adopted to convert the MIMO system to single-input and single-output (SISO) system, which is convenient for the control parameter design of the controller. Besides, the decoupled transfer function of ISOP system is approximately first order system. Finally, the correctness of the modeling method is validated by simulation in time and frequency domain.

Effective detection of fault in rolling bearings with a limited amount of data is essential for the safe operation of electric machines. This article proposes a novel meta-learning-enabled method for the detection of fault in rolling bearings of electric machines under varying working conditions with limited data. The fault diagnosis under various working conditions is cast as a few-shot classification problem, which is solved using a model-agnostic meta-learning-based model. Specifically, a meta-learner is first trained using a series of interrelated fault-diagnosis tasks under various working conditions. During this stage, the gradient-by-gradient rule is utilized for parameter optimization to achieve an effective representation of these tasks. Then, the parameters of the meta-learner are refined on a new task. This technique can achieve fast adaptation to new tasks by utilizing only few-shot samples. The proposed method can obtain high fault-detection accuracy under various working conditions when only a limited amount of data is available. Comparative tests among various methods were carried out on the Case Western Reserve University Bearing Dataset and the Paderborn University Rolling Bearing Dataset. The results show that the proposed model performs better than other state-of-the-art methods under various working conditions; our method has stronger generalization ability and faster adaptation ability. The fault diagnosis accuracy for both datasets was at least 99%, which proves that the proposed strategy can be flexibly applied to various scenarios.

The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This article proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining the excellent performance of capturing the forecasting uncertainties.

The uncertainties of charging behavior of electric vehicle (EV) owners have a negative impact on the loss of life (LOL) of distribution transformer. This article proposes a decentralized EV charging framework for optimization of the LOL of distribution transformer considering the dissatisfactions of EV owners. Specifically, long-short-term memory (LSTM) neural network is first utilized to capture the uncertainties caused by the load demand and electricity price. After that, each EV is modeled as an intelligent agent and a multiagent deep reinforcement learning approach is applied to solve the coordinated charging problem based on the forecasting information by the LSTM network. All the agents are trained in a centralized manner to develop coordinated control strategies while informing decisions based on local information when finishing the training process. The proposed approach can achieve coordinated charging management of EVs based on local information, which helps preserve the privacy of EV owners, reduce the cost induced by the deployment of communication devices, and avoid single-point failure. In addition, the parameter space noise and deep dense architecture in reinforcement learning are introduced to overcome premature convergence, training instability, and inefficiency due to the large action space of multiagent scenario. Comparative tests are carried out among several benchmarks utilizing real-world data to illustrate the effectiveness of the proposed approach.

The short-term load forecasting is crucial in the power system operation and control. However, due to its nonstationary and complicated random features, an accurate forecast of the load behavior is challenging. An improved short-term load forecasting method is proposed in this article. At first, the load is decomposed into different frequency components varying from the low to high levels realized by the ensemble empirical-mode decomposition algorithm. Then, the smooth and periodic low-frequency components are predicted by the multivariable linear regression method while maintaining the efficient computation capacity, while the high-frequency components with strong randomness are forecasted by the long short-term memory neural network algorithms. Thus, the actual load behavior is obtained by combining these two methods. Finally, the proposed method is validated by experiments, in which the tested data from the west area of China, Uzbekistan, and PJM Interconnection (USA) are used. The prediction of the load behavior is accurate globally along with the local details, as presented in the experiments, which verify the effectiveness of the proposed method.

A probabilistic model for the predicted power sources is typically assumed for the existing uncertain optimal power flow (UOPF). However, obtaining accurate information of that is very challenging in practice due to the limited available data of renewable energy and loads with complex correlations. To address that, this article proposes a belief function based framework for UOPF with a large number of uncertain power sources. Multiple imperfect models with q-least committed joint basic belief density are developed to characterize the knowledge deficient power sources. This yields the integration of the generalized Bayesian theorem with the traditional evidence theory into a unified manner. The former allows estimating the uncertain model of the predicted power sources, whereas the latter is to obtain the probability box of the UOPF variables. Comparison results with the Monte Carlo simulations and the three-point estimation approach show that the proposed method is able to get accurate UOPF results while achieving high computational efficiency for large-scale systems with a large number of knowledge deficient power sources.

This article presents a hardware/software implementation for the vibration reduction control of a switched reluctance machine (SRM). A mixed partitioning-based architecture is proposed to make full use of both microprocessor and field-programmable gate array (FPGA) resources. The aim of the proposed controller is to control directly the radial forces in the stator teeth allowing a significant vibration reduction compared to regular controller applied to SRM. However, if the control is software-only implemented, it suffers from performance degradation due to the high execution time required by this implementation solution. For this reason, the control system implementation is partitioned. The speed control is implemented using a microprocessor, while the current control and direct force control are implemented in FPGA. This solution provides high dynamic response especially needed to control the radial force and the currents. The effectiveness of the hybrid implementation is verified by experimental results.

This paper proposes a heuristic planning energy management controller, based on a Dyna agent of reinforcement learning (RL) approach, for real-time fuel saving optimization of a plug-in hybrid electric vehicle (PHEV). The presented method is referred to as the Dyna-H algorithm, which is a model-free online RL algorithm. First, as a case study, a detailed vehicle powertrain modeling of the Chevrolet Volt is built, where all the control components have been experimentally validated. Four traction operation modes are allowed by managing the states of two clutches and one brake. Furthermore, the Dyna-H algorithm is introduced via incorporating a heuristic planning strategy into a Dyna agent. This is the first time to apply the Dyna-H algorithm in the energy management field of PHEVs. Finally, a comparative analysis of the one-step Q-learning, Dyna, and Dyna-H algorithms is conducted in simulations. Numerous testing results indicate that the proposed algorithm leads to definite improvements in equivalent fuel economy and computational speed.

In order to reduce the levelised cost of energy (LCOE) of the offshore wind farm, many optimization works should be done to reduce the investment and increase the energy production. As one of the main expenses, the electrical system can take up more than 15% of the total investment while cable costs take a large proportion. In order to make a cost-effective wind farm, the cable connection layout should be optimized. This paper proposes a novel way for offshore wind farm cable connection layout design. The LCOE, which concerns three aspects: electrical power losses, power captured by wind turbines (WT) and investment, is selected to set up the objective function. Since all the optimization variables are integers, a heuristic algorithm, integer particle swarm optimization algorithm (IPSO), is adopted to find a near optimal solution. To improve the performance of the IPSO, an adaptive method for parameter control is used to help to find a better solution. Comparisons are made with results obtained by the Norwegian center for offshore wind energy (NORCOWE) reference wind farm and the presented method. From the simulation, it can be noticed that the presented approach can help to find a cable connection scheme which can reduce the LCOE by 1.75%.

Traditional power systems have become outdated, unable to provide the clean energy enforced by current regulations. In addition, the intermittent renewable energy and the distributed generation make it difficult for the current electricity grid to reliably respond to increased energy demands. For that, a heat and power integrated energy system (IES) can provide better results compared to individually operating the two sub-systems. In this paper, a detailed sequential modeling procedure is presented. In addition, an optimal power flow solution (OPF) algorithm, suitable for islanded IES is also presented. The proposed method is utilized in an IES consisted of 11 electrical buses and 11 heating nodes. The study demonstrates that during the islanded mode of operation a more economical and valid solution, compared to the standard OPF that only considers the electric part, can be achieved.

Due to the increasing penetration of wind power, operating characteristic of wind turbines may cause great concerns to frequency stability of the power system with large-scale wind power integration. Wind power plant participation in frequency regulation and the coordination with stored energy of the power system will be a trend in the future. However, the traditional wind turbines may not have the capability of frequency regulation. The issue of how wind power plants can participate in frequency regulation becomes research hotspot in recent years. Currently, most of frequency control methods of wind turbines are designed to have a new control loop from the existing control modules, such as speed control, droop control and pitch angle control. Theses novel control methods could realize the frequency support in some cases. However, these single-functional control loops have some drawbacks and could not solve the issues of frequency control effectively. For instance, the speed control could only work under the rated wind speed and the pitch control may not have fast-speed response ability to support the frequency control when the system frequency starts to fall. In this paper, a comprehensive review is presented regarding the latest studies about the multi-controller method of variable speed wind turbines in order to solve the problems of primary and secondary frequency control.

Due to the increasing size of offshore wind farm, the impact of the wake effect on energy yields become more and more evident. The seafloor topography would limit the layout of the wind farm so that irregular layout is usually adopted in large scale offshore wind farm. However, the calculation for the energy yields in irregular wind farm considering wake effect would be difficult. In this paper, a mathematical model which includes the impacts of the variation of both wind direction and velocity on wake effect is established. Based on the wake model, a binary matrix method is proposed for the energy yields calculation for irregular wind farms. The results show that the proposed wake model is effective in calculating the wind speed deficit. The calculation framework is applicable for energy yields calculation in irregular wind farms.

The study of wind power system strongly relies on simulations in all kinds of methods. In industry, the feasibility and efficiency of wind power projects also will be verified by simulations at first. However, taking time cost and economy into consideration, simulations in large scales often sacrifice model details or computing precision in order to gain acceptable results in higher simulating speed and lower hardware costs. To balance the contradiction of costs and performance, in this paper, a novel distributed simulation platform based on PC network and Matlab is proposed. Compared with other simulation approaches, this platform can improve the speed of simulations in large scales without sacrificing details or precision largely. By means of connected computers and paralleled models, it becomes easier to study further about harmonics, control strategies in current experiment conditions. Through the data interfaces, the platform can import practical data to simulate environment situations, faults and devices, which makes the simulation much more close to reality and forms a test-bed for wind farms and power systems as well. Thus, The platform can connect to certain Supervisory Control and Data Acquisition (SCADA) systems and Energy Management System (EMS), etc. to realize non-real-time semi-physical simulation for wind farm and power system control researches.

This paper presents an overview of researches on power system stability with high wind power penetration including analyzing methods and improvement approaches. Power system stability issues can be classified diversely according to different considerations. Each classified issue has special analyzing methods and stability improvement approaches. With increasing wind power penetration, system balancing and the reduced inertia may cause a big threaten for stable operation of power systems. To mitigate or eliminate the wind impacts for high wind penetration systems, although the practical and reliable choices currently are the strong outside connections or sufficient reserve capacity constructions, many novel theories and approaches are invented to investigate the stability issues, looking forward to an extra-high penetration or totally renewable resource based power systems. These analyzing methods and stabilization techniques are presented and discussed in this paper.

The investment of large scale offshore wind farms is high in which the electrical system has a significant contribution to the total cost. As one of the key components, the cost of the connection cables affects the initial investment a lot. The development of cable manufacturing provides a vast choice space and a great opportunity to optimize the system cost while meets the operational requirements of the offshore wind farms and the connected power systems. In this paper, a new cost model for AC-cable is proposed and the optimal cable selection framework is established using the optimization platform in Matlab. A real offshore wind farm is chosen as the study case to demonstrate the proposed method. Furthermore, the optimization is also applied to an offshore wind farm under development. It can be observed from the results that the proposed optimal cable selection framework is an efficient and systematical way for the optimal selection of cables in large scale offshore wind farms.

The paper deals with control techniques for minimizing the operating loss of doubly fed induction generator based wind generation systems when providing reactive power. The proposed method achieves its goal through controlling the rotor side q-axis current in the synchronous reference frame. The formula for the control reference is explicitly deduced in this paper considering the losses of the generator, the power electronic devices and the filter. Three control strategies are compared with the proposed method under different wind speeds and different reactive power references. The simulation results validate the effectiveness of the proposed method.

A static synchronous series compensator (SSSC) has the ability to emulate a reactance in series with the connected transmission line. A static synchronous compensator (STATCOM) is able to provide the reactive power to an electricity network. When fed with some supplementary signals from the connected power system, both SSSC and STATCOM are able to participate in the power system inter-area oscillation damping by changing the compensated reactance or the provided reactive power. This paper analyses the influence of SSSC and STATCOM on power system small signal stability. The damping controller schemes for SSSC and STATCOM are presented and discussed. The IEEE 39-bus New England system model as the test system is built in DIgSIELNT PowerFactory, in which the damping control strategies for both SSSC and STATCOM are validated by time domain simulations and modal analysis. Furthermore, comparison studies show that the SSSC is a better solution in term of equipment capabilities and costs.

In some networks Distributed Generators (DGs) are phasing out conventional power plants in terms of power production but still large efforts are required for providing ancillary services. In this paper the usage of fast response units like a Micro Turbines (MT) and a stationary Electric Vehicle Battery (EVB) is proposed for providing primary regulation in grid connected mode and for hierarchically manage an islanded LV distribution feeder. The unit models are described and a novel EVB model directly based on manufacturer's data is proposed and evaluated comparing its performances with SimPowerSystems library block. Moreover a voltage dependant power term is applied to the Voltage-Source Converter (VSC) control scheme of the EVB for improving the performances of the islanded feeder. The control is tested in case of under frequency and consequent load shedding occurring at the residential feeder of CIGRE C6.04.02 benchmark network.

Residue method is a commonly used approach to design the parameters of a power system stabilizer (PSS). In this paper, a residue identification method is adopted to obtain the system residues for different input-output pairs, using the system measurements data from time domain simulations. Then a coordinated approach for multiple PSS selection and parameter design based on residue method is proposed and formatted as an optimization problem. Particle swarm optimization (PSO) is adopted in this coordination process to find suitable parameters for PSSs so that the dominant oscillation modes can be well damped; while locations and input signals of PSSs are selected to keep PSS outputs small. The IEEE 39-bus New England system model as the test system is built in DIgSIELNT PowerFactory 14.0, in which the proposed coordination method is validated by time domain simulations and modal analysis.