Abstract:

Abstract:With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

Abstract:In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques.

Abstract:Transient stability assessment (TSA) based on security region is of great significance to the security of power systems. In this paper, we propose a novel methodology for the assessment of online transient stability margin. Combined with a geographic information system (GIS) and transformation rules, the topology information and pre-fault power flow characteristics can be extracted by 2D computer-vision-based power flow images (CVPFIs). Then, a convolutional neural network (CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set. The network consists of two components: the classification network classifies the input samples into the credibly stable/unstable and uncertain categories, and the prediction network is utilized to further predict the generator stability indices of the categorized samples, which improves the network ability to distinguish between the samples with similar characteristics. The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system, and the simulation results validate the effectiveness of the method.

Abstract:Lightning is one of the most common transient interferences on overhead transmission lines of high-voltage direct current (HVDC) systems. Accurate and effective recognition of faults and disturbances caused by lightning strokes is crucial in transient protections such as traveling wave protection. Traditional recognition methods which adopt feature extraction and classification models rely heavily on the performance of signal processing and practical operation experiences. Misjudgments occur due to the poor generalization performance of recognition models. To improve the recognition rates and reliability of transient protection, this paper proposes a transient recognition method based on the deep belief network. The normalized line-mode components of transient currents on HVDC transmission lines are analyzed by a deep belief network which is properly designed. The feature learning process of the deep belief network can discover the inherent characteristics and improve recognition accuracy. Simulations are carried out to verify the effectiveness of the proposed method. Results demonstrate that the proposed method performs well in various scenarios and shows higher potential in practical applications than traditional machine learning based ones.

Abstract:Transient stability assessment (TSA) is of great importance in power system operation and control. One of the usual tasks in TSA is to estimate the critical clearing time (CCT) of a given fault under the given network topology and pre-fault power flow. Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples. However, the influence of network topology on CCT is hard to be analyzed and is often ignored, which makes the models inaccurate and unpractical. In this paper, a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem. The model is a weighted sum of several sub-models. Each sub-model only uses the data of one topology to construct a kernel regressor. The weights are determined by both the topological similarity and numerical similarity between the samples. The similarities are decided by the parameters in Mahalanobis distance, and the parameters are to be trained. To reduce the model complexity, sub-models within the same topology category share the same parameters. When estimating CCT, the model uses not only the sub-model which the sample topology belongs to, but also other sub-models. Thus, it avoids the problem that there may be too few data under some topologies. It also efficiently utilizes information of data under all the topologies. Moreover, its decision-making process is clear and understandable, and an effective training algorithm is also designed. Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

Abstract:Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data. Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states, or even unstable state. However, in practice, the data of insecure or unstable state is very rare, as the power system should be guaranteed to operate far away from voltage collapse. Under this circumstance, this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system. The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data, while it fails to rebuild data of insecure states. Thus, the residual of reconstruction is effective in indicating VSA. Besides, to develop a more accurate and robust algorithm, long short-term memory (LSTM) networks combined with fully-connected (FC) layers are used to build the autoencoder, and a moving strategy is introduced to bias the features of testing data toward the secure feature domain. Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.

Abstract:The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.

Abstract:With the increasing complexity of power system structures and the increasing penetration of renewable energy, the number of possible power system operation modes increases dramatically. It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis. At present, problems of low efficiency and long time consumption are encountered in the formulation of operation modes, resulting in a very limited number of generated operation modes. In this paper, we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning. First, a discriminator is trained to judge the power flow convergence, and the output of this discriminator is used to construct a value function. Then, the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment. Finally, a large number of convergent power flow samples are generated using the learned adjustment strategy. Compared with the traditional flow adjustment method, the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model. Therefore, this strategy can be automatically learned without manual intervention, which allows a large number of different operation modes to be efficiently formulated. The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.

Abstract:With the increasing penetration of renewable energy, power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and accurate control actions derived in real time are vital to ensure system security and economics. To this end, solving alternating current (AC) optimal power flow (OPF) with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid. This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning (DRL) algorithm, which can greatly assist power grid operators in making rapid and effective decisions. The presented method adopts imitation learning to generate initial weights for the neural network (NN), and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence (AI) agents. Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems. The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

Abstract:With more data-driven applications introduced in wide-area monitoring systems (WAMS), data quality of phasor measurement units (PMUs) becomes one of the fundamental requirements for ensuring reliable WAMS applications. This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation, which can: ① identify bad data under both steady states and contingencies; ② achieve higher accuracy than conventional pre-filtering approaches; ③ reduce iteration burden for linear state estimation; ④ efficiently identify bad data in a parallelizable scheme. The proposed method consists of four key steps: ① preprocessing filter; ② online training of short-term deep neural network; ③ offline training of long-term deep neural network; ④ a decision merger. Through delicate design and comprehensive training, the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information. An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method. Multiple test scenarios are applied, which include steady states, three-phase-to-ground faults with (un)successful auto-reclosing, low-frequency oscillation, and low-frequency oscillation with simultaneous three-phase-to-ground faults. The proposed method demonstrates satisfactory performance during both the training session and the testing session.

Abstract:This paper proposes an energy management system (EMS) for the real-time operation of a pilot stochastic and dynamic microgrid on a university campus in Malta consisting of a diesel generator, photovoltaic panels, and batteries. The objective is to minimize the total daily operation costs, which include the degradation cost of batteries, the cost of energy bought from the main grid, the fuel cost of the diesel generator, and the emission cost. The optimization problem is modeled as a finite Markov decision process (MDP) by combining network and technical constraints, and Q-learning algorithm is adopted to solve the sequential decision subproblems. The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming (MINLP) problem into a series of single-stage problems so that each subproblem can be solved by using Bellman’s equation. To prove the effectiveness of the proposed algorithm, three case studies are taken into consideration: ① minimizing the daily energy cost; ② minimizing the emission cost; ③ minimizing the daily energy cost and emission cost simultaneously. Moreover, each case is operated under different battery operation conditions to investigate the battery lifetime. Finally, performance comparisons are carried out with a conventional Q-learning algorithm.

Abstract:As the penetration of renewable energy continues to increase, stochastic and intermittent generation resources gradually replace the conventional generators, bringing significant challenges in stabilizing power system frequency. Thus, aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry. However, in practice, conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals. The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating, ventilation, and air conditioning (HVAC) to provide reliable secondary frequency regulation. Compared with the conventional approach, the simulation results show that the risk-averse multi-armed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control. Besides, the proposed approach is more robust to random and changing behaviors of the users.

Abstract:The high-voltage direct current (HVDC) grid has been recognized as an effective solution for renewable energy integration. Currently, two main development trends for HVDC grids are being studied: a DC breaker based HVDC grid and fault-blocking converter based HVDC grid. Although the former has a perfect performance for fault clearance, its development is still highly constrained by the cost and maturity of DC breakers. The latter can extinguish DC faults by the fault-blocking converters. Without using DC breakers, there is no bottleneck in its technical feasibility. Nevertheless, in fault scenarios, such types of HVDC grids will be blocked at length for air-deionization, which is its main drawback. The aim of this paper is to minimize its power interruption time, by optimizing protection coordination strategies. To cover the most complex cases, the overhead line applications, in which the reclosure actions are required to be implemented, are considered. In this paper, the protection requirements of HVDC grids are first discussed, then the benefits of fault-blocking modular multilevel converters (MMCs) and their fault features are analyzed. Based on this, a control function is designed to reduce the air-deionization time. To minimize the influence of the DC faults, a separation methodology for restarting the system is proposed. The effectiveness of the proposed protection coordination schemes is validated by PSCAD/EMTDC simulations.

Abstract:Once an asymmetrical fault occurs on the AC side of the receiving-end of a high-voltage direct current (HVDC) transmission system, the current reference will be affected by the control regulation on the DC inverter side and the commutation voltage asymmetry. In this case, the advance firing angle will fluctuate periodically, causing security threats to the system. If the fault cannot be cleared in time, the effect may be even more serious. However, the traditional proportional-integral (PI) controller cannot effectively suppress the periodic components in the input error signal, which is an important cause of continuous commutation failure. Thus, the system requires more time to recover from the fault. Motivated by this, a self-adaptive auto-disturbance rejection PI controller is proposed in this study. The controller has the advantages of fast response speed and strong anti-interference ability of the auto-disturbance rejection controller. On one hand, it can automatically adjust PI, and the parameters can maintain the system’s adaptive ability. On the other hand, the discretization process satisfies the computer simulation requirements. By applying the proposed controller to a system under constant current control and extinction angle control, the dynamic response speed can be improved and the robust performance of the system can be ensured when dealing with a wide range of perturbations. Finally, simulation results show that the proposed algorithm can effectively suppress the continuous commutation failure of DC transmission systems.

Abstract:An intelligent control method is proposed to improve the performance of power supply for tundish electromagnetic induction heating, which can adequately regulate the tundish temperature. The topology structure of power supply for tundish electromagnetic induction heating is presented, and its working principle is analyzed. The power supply consists of six power units, and each of them consists of a fore-stage three-phase rectifier and back-stage single-phase inverter. The feed-forward control DC voltage is used by three-phase rectifier to obtain the stable DC voltage supplied to the inverter. The cloud controller based intelligent temperature control algorithm is combined with the power feed-forward algorithm to obtain accurate tracking of the output current and constant temperature control of the tundish steel in the back-stage inverter. The simulation and experiment are performed to verify the accuracy and effectiveness of the proposed method.

Abstract:For optimal operation of microgrids, energy management is indispensable to reduce the operation cost and the emission of conventional units. The goals can be impeded by several factors including uncertainties of market price, renewable generation, and loads. Real-time energy management system (EMS) can effectively address uncertainties due to the online information of market price, renewable generation, and loads. However, some issues arise in real-time EMS as battery-limited energy levels. In this paper, Lyapunov optimization is used to minimize the operation cost of the microgrid and the emission of conventional units. Therefore, the problem is multi-objective and a Pareto front is derived to compromise between the operation cost and the emission. With a modified IEEE 33-bus distribution system, general algebraic modeling system (GAMS) is utilized for implementing the proposed EMS on two case studies to verify its applicability.

Abstract:Complex phenomena such as prolongedly undamped ultra-low frequency oscillation (ULFO) and eigenmode re-excitation are observed in the simulations of hydroelectric power systems. Emphases are put on nonlinearities and mode interactions, which cannot be analyzed by traditional eigen-analysis methods. In order to investigate the mechanism of the evolvement of the nonlinear dynamic process in ULFO, this paper proposes a method to analyze the mode interactions quantificationally. First, a disturbed trajectory is decoupled into a set of time-varying components. Second, transfer matrices of eigenmodes are extracted along the trajectory. Third, consecutive sequences of eigenvalues and trajectories of components are formed by a proposed technique. Based on the decoupled components and transfer matrices, the mechanisms of mode interactions and inheritance relationships between eigenmodes are analyzed. The causes and developments of the above complex phenomena are revealed by the proposed method in a test two-machine system. Meanwhile, the accuracy of the eigenmode matching technique is verified in the New England system.

Abstract:In this paper, the hybridization of standard particle swarm optimisation (PSO) with the analytical method ( rule) is proposed, which is called as analytical hybrid PSO (AHPSO) algorithm used for the optimal siting and sizing of distribution generation. The proposed AHPSO algorithm is implemented to cater for uniformly distributed, increasingly distributed, centrally distributed, and randomly distributed loads in conventional power systems. To demonstrate the effectiveness of the proposed algorithm, the convergence speed and optimization performances of standard PSO and the proposed AHPSO algorithms are compared for two cases. In the first case, the performances of both the algorithms are compared for four different load distributions via an IEEE 10-bus system. In the second case, the performances of both the algorithms are compared for IEEE 10-bus, IEEE 33-bus, IEEE 69-bus systems, and a real distribution system of Korea. Simulation results show that the proposed AHPSO algorithm converges significantly faster than the standard PSO. The results of the proposed algorithm are compared with those of an analytical algorithm, and the results of them are similar.

Abstract:This paper studies the coordination of heterogeneous thermostatically controlled loads (TCLs) to provide the real-time ancillary services. A market-based control framework is adopted for its advantages. The first advantage is that the demand curve-oriented approach makes it possible to form a unified control scheme for heterogeneous loads without identifying their different characteristics. The second one is that the broadcast price signal helps simplify the downlink control and reduce the implementation cost. Then, the separate demand curve construction strategies based on a virtual price for different types of TCLs are presented. The flexibility of each TCL is reflected through the curve, and its practical constraints, i.e., comfort requirements of users and operation constraints of devices, are satisfied explicitly. To ensure the control fairness and full utilization for the regulation ability of TCL cluster, a comfort-level-equality principle is applied in demand curve construction. Simulations are carried out to verify the effectiveness of the proposed method in providing frequency regulation services, for which a regulation capacity estimation method is developed. Finally, a series of case studies are conducted considering the practical situations, e.g., model errors, imperfect communication and sudden load change after the end of services.

Abstract:Smart grids are increasingly dependent on data with the rapid development of communication and measurement. As one of the important data sources of smart grids, phasor measurement unit (PMU) is facing the high risk from attacks. Compared with cyber attacks, global position system (GPS) spoofing attacks (GSAs) are easier to implement because they can be exploited by portable devices, without the need to access the physical system. Therefore, this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference (PAD) deviation. Specifically, this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA, i.e., the step attack and the ramp attack. Then, the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed, which does not require the line parameters. After obtaining the estimated PAD deviations, the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations. Finally, with the case studies, the effectiveness of the proposed method is demonstrated, and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.

Abstract:Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency. However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids. Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA. Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system. A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software. From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

Abstract:Continuous increase of wind power penetration brings high randomness to power system, and also leads to serious shortage of primary frequency regulation (PFR) reserve for power system whose reserve capacity is typically provided by conventional units. Considering large-scale wind power participating in PFR, this paper proposes a unit commitment optimization model with respect to coordination of steady state and transient state. In addition to traditional operation costs, losses of wind farm de-loaded operation, environmental benefits and transient frequency safety costs in high-risk stochastic scenarios are also considered in the model. Besides, the model makes full use of interruptible loads on demand side as one of the PFR reserve sources. A selection method for high-risk scenarios is also proposed to improve the calculation efficiency. Finally, this paper proposes an inner-outer iterative optimization method for the model solution. The method is validated by the New England 10-machine system, and the results show that the optimization model can guarantee both the safety of transient frequency and the economy of system operation.

Abstract:High-precision day-ahead short-term photovoltaic (PV) output forecasting is essential in PV integration to the smart distribution networks and multi-energy system, and provides the foundation for the security, stability, and economic operation of PV systems. This paper proposes a hybrid model based on principal component analysis, grey wolf optimization and generalized regression neural network (PCA-GWO-GRNN) for day-ahead short-term PV output forecasting, considering the features of multiple influencing factors and strong uncertainty. This paper first uses the PCA to reduce the dimension of meteorological features. Then, the high-precision day-ahead short-term PV output forecasting based on GWO-GRNN model is realized. GRNN is used to regressively analyze the input features after dimension reduction, and the parameter of GRNN is optimized by using GWO, which has strong global searching ability and fast convergence. The proposed PCA-GWO-GRNN model effectively achieves a high precision in day-ahead short-term PV output forecasting, which is demonstrated in a case study on a real PV plant in Jiangsu province, China. The results have validated the accuracy and applicability of the proposed model in real scenarios.