Abstract:The variable nature of solar power has hitherto been regarded as a major barrier preventing large-scale high-penetration solar energy into the power grid. Based on decades of research, particularly those advances made over the recent few years, it is now believed that dispatchable solar power is no longer a conception but will soon become techno-economically feasible. The policy-driven information exchange among the weather centers, grid operators, and photovoltaic plant owners is the key to realizing dispatchable solar power. In this paper, a five-step forecasting framework for enabling dispatchable solar power is introduced. Among the five steps, the first three, namely numerical weather prediction (NWP), forecast post-processing, and irradiance-to-power conversion, have long been familiar to most. The last two steps, namely hierarchical reconciliation and firm forecasting, are quite recent conceptions, which have yet to raise widespread awareness. The proposed framework is demonstrated through a case study on achieving effectively dispatchable solar power generation at plant and substation levels.

Abstract:This paper develops a physics-guided graph network to enhance the robustness of distribution system state estimation (DSSE) against anomalous real-time measurements, as well as a deep auto-encoder (DAE)-based detector and a Gaussian process-aided residual learning (GARL) to deal with challenges arising from topology changes. A global-scanning jumping knowledge network (GSJKN) is first designed to establish the regression rule between the measurement data and state variables. The structural information of distribution system (DS) and a global-scanning module are incorporated to guide the propagation of scarce measurements in the graph topology, contributing to valid estimation precision in sparsely measured DSs. To monitor the topology changes of the network, a DAE network is employed to learn an efficient representation of the measurements of the system under a certain topology, which can achieve online monitoring of the network structure by observing the variation tendency of the reconstruction error. When the topology change occurs, a Gaussian process with a composite kernel is applied to the modeling of the pre-trained GSJKN residual to adapt to the new topology. The embedding of the physical structural knowledge enables the proposed GSJKN method to restore the missing/noisy values utilizing the adjacent measurements, which enhances the robustness to typical data acquisition errors. The adopted DAE network and special GARL-based transfer method further allow the DSSE method to rapidly detect and adapt to the topology change, as well as achieve effective quantification of the estimation uncertainties. Comparative tests on balanced and unbalanced systems demonstrate the accuracy, robustness, and adaptability of the proposed DSSE method.

Abstract:The development of low-carbon energy systems and renewable energy sources (RESs) are critical to solving the energy crisis around the world. However, renewable energy generation control strategies lead to fault characteristics such as fault current amplitude limitation and phase angle distortion. Focusing on large-scale renewable energy transmission lines, the sensitivity of traditional current differential protection and distance protection may be reduced, and there is even the risk of maloperation. Therefore, a suitable transmission line model is established, which considers the distributed capacitance. Afterward, a novel dynamic state estimation based protection (DSEBP) for large-scale renewable energy transmission lines is proposed. The proposed DSEBP adopts instantaneous measurements and additional protection criteria to ensure the quick action and reliability. Finally, faults are identified by checking the matching degree between the actual measurements and the established transmission line model. The performance of the proposed DSEBP is verified through PSCAD/EMTDC and real-time digital simulator (RTDS) hardware-in-loop tests. The results demonstrate that the proposed DSEBP can identify various types of faults quickly and reliably. Meanwhile, the proposed DSEBP has a better capability to withstand fault resistance and disturbance.

Abstract:Meter encoding, as a side-effect-free scheme, has been proposed to detect false data injection (FDI) attacks without significantly affecting the operation of power systems. However, existing meter encoding schemes either require encoding lots of measurements from different buses to protect a substantial proportion of a power system or are unhidden from alert attackers. To address these issues, this paper proposes a smart inverter enabled meter encoding scheme for detecting FDI attacks in distribution system state estimation. The proposed scheme only encodes the measurements from the existing programmable smart inverters. Meanwhile, this scheme can protect all the downstream buses from the encoded inverter bus. Compared with existing schemes, the proposed scheme encodes fewer meters when protecting the same number of buses, which decreases the encoding cost. In addition, by following the physical power flow laws, the proposed scheme is hidden from alert attackers who can implement the state estimation-based bad data detection (BDD). Simulation results from the IEEE 69-bus distribution system demonstrate that the proposed scheme can mislead the attacker’s state estimation on all the downstream buses from the encoded bus without arousing the attacker’s suspicion. FDI attacks that are constructed based on the misled estimated state are very likely to trigger the defender’s BDD alarm.

Abstract:This work presents a new topological observability algorithm to strengthen the performance of the hybrid power system static state estimation, assuming that the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements are recorded at the same time intervals. The observability of each estimated state variable is assessed by the value assigned to its least local redundancy index. The algorithm has been specifically developed to enhance the observability of an existing wide-area monitored system and exempts its expansion from critical sets and critical measurements. These objectives are achieved by building the incidence matrices of the measurements for the nodes and branches. The performance of the proposed algorithm is evaluated using the IEEE test systems and the SIN test systems of Brazilian equivalent systems.

Abstract:The electrical array reconfiguration (EAR) method has become a promising solution to enhance photovoltaic (PV) system performance under partial shading conditions. Existing studies focus on maximizing single-period PV generation but neglect the impact of power fluctuation on grid stability. To address this, we propose a multi-period EAR method for multi-PV systems considering net power fluctuation mitigation. First, we design a multi-period EAR model to maximize total revenue by balancing electricity sales and net power fluctuation penalties, formulated as a stochastic mixed-integer quadratic programming problem. The model incorporates constraints on the average number of switching actions per unit time to ensure practical implementation. Then, to handle the unpredictability of partial shading conditions, we develop a Lyapunov optimization-based online algorithm to decouple the time-coupling constraints involving state transitions. Additionally, we propose a reduced set of EAR strategies to improve the computational efficiency. Numerical studies demonstrate that the proposed method significantly reduces net power fluctuations in distribution networks with high PV penetration rate and enhances total revenue compared with conventional methods.

Abstract:This paper proposes a method for obtaining nonlinear models of distribution grid based on available measurements from the power grid. We formulate a maximum likelihood estimation (MLE) problem that estimates unknown line parameters—specifically, the impedance between nodes—using measured voltage magnitudes and net active and reactive power injections at each node. The nonlinear model for the distribution grid uses a nonlinear approximation of the DistFlow model, which includes line losses and is parameterized by the unknown line impedances. We solve the resulting MLE problem using an expectation maximization (EM) algorithm, tailored for the nonlinear model, and provide a numerically robust implementation. The proposed method is demonstrated on the IEEE 37-node test network, and we compare it with the state-of-the-art methods. The proposed method achieves a 70% reduction in voltage error and an error for state variables that is more than 10000 times smaller. A final comparison uses data from a real network, and the proposed method achieves parameter estimates with errors 100 times smaller than competing methods.

Abstract:Wind power prediction interval (WPPI) models in the literature have predominantly been developed for and tested on specific case studies. However, wind behavior and characteristics can vary significantly across regions. Thus, a prediction model that performs well in one case might underperform in another. To address this shortcoming, this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness. Another important and often overlooked factor is the role of probabilistic wind power prediction (WPP) in quantifying wind power uncertainty, which should be handled by operating reserve. Operating reserve in WPPI frameworks enhances the efficacy of WPP. In this regard, the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account. Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.

Abstract:This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic (PV) power prediction that arises due to insufficient data samples for new PV plants. First, a time-series generative adversarial network (TimeGAN) is used to learn the distribution law of the original PV data samples and the temporal correlations between their features, and these are then used to generate new samples to enhance the training set. Subsequently, a hybrid network model that fuses bi-directional long-short term memory (BiLSTM) network with attention mechanism (AM) in the framework of deep & cross network (DCN) is constructed to effectively extract deep information from the original features while enhancing the impact of important information on the prediction results. Finally, the hyperparameters in the hybrid network model are optimized using the whale optimization algorithm (WOA), which prevents the network model from falling into a local optimum and gives the best prediction results. The simulation results show that after data enhancement by TimeGAN, the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

Abstract:With the increasing proportion of renewable energy sources (RESs) in power grid, the reserve resource (RR) scarcity for correcting power deviation of RESs has become a potential issue. Consequently, the power curve of RES needs to be more rigorously assessed. The RR scarcity varies during different time periods, so the values of assessment indicators should be dynamically adjusted. The assessment indicators in this paper include two aspects, i.e., deviation exemption ratio and penalty price. Firstly, this paper proposes a method for dynamically calculating the supply capacity and RR cost, primarily taking into account the operating status of thermal units, forecast information of RES, and load curve. Secondly, after clarifying the logical relationship between the degree of RR scarcity and the values of assessment indicators, this paper establishes a mapping function between them. Based on this mapping function, a dynamic setting method for assessment indicators is proposed. In the future, RES will generally be equipped with battery energy storage systems (BESSs). Reasonably utilizing BESSs to reduce the power deviation of RESs can increase the expected income of RESs. Therefore, this paper proposes a power curve optimization strategy for RESs considering self-owned BESSs. The case study demonstrates that the dynamic setting method of assessment indicators can increase the revenue of RESs while ensuring that the penalty fees paid by RESs to the grid are sufficient to cover the RR costs. Additionally, the power curve optimization strategy can help RESs further increase income and fully utilize BESSs to reduce power deviation.

Abstract:Interval state estimation (ISE) can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements, thereby analyzing the impact of uncertain pseudo-measurements on states. However, predicted pseudo-measurements have prediction errors, and their confidence intervals do not necessarily contain the truth values, leading to estimation biases of the ISE. To solve this problem, this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression (GPR) model, thereby improving the prediction accuracy of pseudo-measurement confidence intervals. Besides, a weight assignment strategy for improving the robustness of weighted least squares (WLS) ISE is proposed. This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudo-measurement intervals with larger deviations, thereby improving the estimation accuracy and robustness of the ISE. This paper adopts the data from the supervisory control and data acquisition (SCADA) system of the New York Independent System Operator (NYISO). It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods. In addition, this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case. Finally, the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.

Abstract:Recently, we demonstrated the success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems. In this paper, we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements. It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations. As such, we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming (MILP) problems. The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted. The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system, both of which are incompletely observed by micro-phasor measurement units.

Abstract:Networked microgrids (NMGs) are critical in the accommodation of distributed renewable energy. However, the existing centralized state estimation (SE) cannot meet the demands of NMGs in distributed energy management. The current estimator is also not robust against bad data. This study introduces the concepts of relative error to construct an improved robust SE (IRSE) optimization model with mixed-integer nonlinear programming (MINLP) that overcomes the disadvantage of inaccurate results derived from different measurements when the same tolerance range is considered in the robust SE (RSE). To improve the computation efficiency of the IRSE optimization model, the number of binary variables is reduced based on the projection statistics and normalized residual methods, which effectively avoid the problem of slow convergence or divergence of the algorithm caused by too many integer variables. Finally, an embedded consensus alternating direction of multiplier method (ADMM) distribution algorithm based on outer approximation (OA) is proposed to solve the IRSE optimization model. This algorithm can accurately detect bad data and obtain SE results that communicate only the boundary coupling information with neighbors. Numerical tests show that the proposed algorithm effectively detects bad data, obtains more accurate SE results, and ensures the protection of private information in all microgrids.

Abstract:In recent years, the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network. However, due to the scarcity of historical data for these new consumers, it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods. This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering, deep learning, and transfer learning technologies to address this issue. To begin, this paper leverages the domain adversarial transfer network. It employs limited data as target domain data and more abundant data as source domain data, thus enabling the utilization of source domain insights for the forecasting task of the target domain. Moreover, a K-shape clustering method is proposed, which effectively identifies source domain data that align optimally with the target domain, and enhances the forecasting accuracy. Subsequently, a composite architecture is devised, amalgamating attention mechanism, long short-term memory network, and seq2seq network. This composite structure is integrated into the domain adversarial transfer network, bolstering the performance of feature extractor and refining the forecasting capabilities. An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically. In the case study, the relative mean square error of the proposed method is within 30 MW, and the mean absolute percentage error is within 2%. A significant improvement in accuracy, compared with other comparative experimental results, underscores the reliability of the proposed method. The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecasting methods.

Abstract:This paper develops an adaptive two-stage unscented Kalman filter (ATSUKF) to accurately track operation states of the synchronous generator (SG) under cyber attacks. To achieve high fidelity, considering the excitation system of SGs, a detailed 9 ^th-order SG model for dynamic state estimation is established. Then, for several common cyber attacks against measurements, a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel. Subsequently, to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions, a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix. Finally, a large number of simulation experiments are carried out on the IEEE 39-bus system, which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.

Abstract:The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper establishes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical dataset, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT-empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long short-term memory model) for capturing the hidden spatiotemporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real dataset is conducted to assess the effectiveness of the proposed method.

Abstract:Lately, the power demand of consumers is increasing in distribution networks, while renewable power generation keeps penetrating into the distribution networks. Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude. Hence, this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques. First, we formulate the short-term probabilistic residential load forecasting problem. Then, we propose a sequence-to-sequence (Seq2Seq) adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain (with massive consumption records of regular loads) to the target domain (with limited observations of new residential loads) and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem. For implementation, the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network, including the Seq2Seq recurrent neural networks (RNNs) composed of a long short-term memory (LSTM) encoder and an LSTM decoder, and quantile loss. Finally, this study conducts the case studies via multiple evaluation indices, comparative methods of classic machine learning and advanced deep learning, and various available data of the new residentical loads and other regular loads. The experimental results validate the effectiveness and stability of the proposed scheme.

Abstract:As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.

Abstract:The output power variability of photovoltaic (PV) power plants （PVPPs） is one of the major challenges for the operation and control of power systems. The short-term power variations, mainly caused by cloud movements, affect voltage magnitude and frequency, which may degrade power quality and power system reliability. Comprehensive analyses of these power variations are crucial to formulate novel control approaches and assist power system operators in the operation and control of power systems. Thus, this paper proposes a simulation-based approach to assessing short-term power variations caused by clouds in PV power plants. A comprehensive assessment of the short-term power variations in a PV power plant operating under cloud conditions is another contribution of this paper. The performed analysis evaluates the individual impact of multiple weather condition parameters on the magnitude and ramp rate of the power variations. The simulation-based approach synthesizes the solar irradiance time series using three-dimensional fractal surfaces. The proposed assessment approach has shown that the PVPP nominal power, timescale, cloud coverage level, wind speed, period of the day, and shadow intensity level significantly affect the characteristics of the power variations.