Abstract:The quantity and heterogeneity of intelligent energy generation and consumption terminals in the smart grid are increasing drastically over the years. These edge devices have created significant pressures on cloud computing (CC) system and centralised control for data storage and processing in real-time operation and control. The integration of edge computing (EC) can effectively alleviate the pressure and conduct real-time processing while ensuring data security. This paper conducts an extensive review of the EC-CC computing system and its application to the smart grid, which will integrate a vast number of dispersed devices. It first comprehensively describes the relationship among CC, fog computing (FC), and EC to provide a theoretical basis for the differentiation. It then introduces the architecture of the EC-CC computing system in the smart grid, where the architecture consists of both hardware structure and software platforms, and key technologies are introduced to support functionalities. Thereafter, the application to the smart grid is discussed across the whole supply chain, including energy generation, transportation (transmission and distribution networks), and consumption. Finally, future research opportunities and challenges of EC-CC while being applied to the smart grid are outlined. This paper can inform future research and industrial exploitations of these new technologies to enable a highly efficient smart grid under decarbonisation, digitalisation, and decentralisation transitions.

Abstract:The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems. In this paper, a novel conceptual framework for distributed monitoring of power system oscillations using multiblock principal component analysis (MB-PCA) and higher-order singular value decomposition (HOSVD) is proposed to understand, characterize, and visualize the global behavior of the power system. The proposed framework can be used to evaluate the influence of a given area or utility on the oscillatory behavior, uncover low-dimensional structures from high-dimensional data, and analyze the effects of heterogeneous data on the modal characteristics and interpretation of power system. The metrics are then investigated to examine the relationships between the dynamic patterns and participation of individual data blocks in the global behavior of the system. Practical application of these techniques is demonstrated by case studies of two systems: a 14-machine test system and a 5449-bus 635-generator equivalent model of a large power system.

Abstract:This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum. In each decision moment, the following tasks are executed in turn: ① locally linearizing the system model at the current operation point with the online model identification by using measurements; ② narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority; ③ minimizing the generation cost; ④ minimizing the security indicators within their security thresholds. Compared with the existing real-time dispatch strategies, the proposed method can adjust the deviations caused by unpredictable power flow fluctuations, avoid dispatch bias caused by model parameter errors, and reduce the constraint violations in the dispatch decision process. The effectiveness of the proposed method is verified with the IEEE 39-bus system.

Abstract:Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational adjustments. We examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time dispatch. Using a simple stylized example and a case study that is based on ISO New England, we compare system-operation costs with and without interim recommitment. We find an important tradeoff—later recommitment provides better wind-availability forecasts, but the system has less flexibility due to operating constraints. Of the time windows that we examine, hour-20 recommitment provides the greatest operational-cost reduction.

Abstract:The roll-out of a flexible ramping product provides independent system operators (ISOs) with the ability to address the issues of ramping capacity shortage. ISOs procure flexible ramping capability by committing more generating units or reserving a certain amount of headrooms of committed units. In this paper, we raise the concern of the possibility that the procured flexible ramping capability cannot be deployed in real-time operations due to the unit shut-down in a look-ahead commitment (LAC) procedure. As a solution to the issues of ramping capacity shortage, we provide a modified ramping product formulation designed to improve the reliability and reduce the expected operating cost. The trajectories of start-up and shut-down processes are also considered in determining the ramping capability. A new optimization problem is formulated using mixed integer linear programming (MILP) to be readily applied to the practical power system operation. The performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus system. The simulation results demonstrate that the proposed method can improve the generation scheduling by alleviating the ramping capacity shortages.

Abstract:False data injection attacks (FDIAs) against the load frequency control (LFC) system can lead to unstable operation of power systems. In this paper, the problems of detecting and estimating the FDIAs for the LFC system in the presence of external disturbances are investigated. First, the LFC system model with FDIAs against frequency and tie-line power measurements is established. Then, a design procedure for the unknown input observer (UIO) is presented and the residual signal is generated to detect the FDIAs. The UIO is designed to decouple the effect of the unknown external disturbance on the residual signal. After that, an attack estimation method based on a robust adaptive observer (RAO) is proposed to estimate the state and the FDIAs simultaneously. In order to improve the performance of attack estimation, the H∞ technique is employed to minimize the effect of external disturbance on estimation errors, and the uniform boundedness of the state and attack estimation errors is proven using Lyapunov stability theory. Finally, a two-area interconnected power system is simulated to demonstrate the effectiveness of the proposed attack detection and estimation algorithms.

Abstract:This paper proposes an L_p (0<p<1) quasi norm state estimator for power system static state estimation. Compared with the existing L₁ and L₂ norm estimators, the proposed estimator can suppress the bad data more effectively. The robustness of the proposed estimator is discussed, and an analysis shows that its ability to suppress bad data increases as p decreases. Moreover, an algorithm is suggested to solve the non-convex state estimation problem. By introducing a relaxation factor in the mathematical model of the proposed estimator, the algorithm can prevent the solution from converging to a local optimum as much as possible. Finally, simulations on a 3-bus DC system, the IEEE 14-bus and IEEE 300-bus systems as well as a 1204-bus provincial system verify the high computation efficiency and robustness of the proposed estimator.

Abstract:With the increasing penetration of renewable energy sources, transmission maintenance scheduling (TMS) will have a larger impact on the accommodation of wind power. Meanwhile, the more flexible transmission network topology owing to the network topology optimization (NTO) technique can ensure the secure and economic operation of power systems. This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices. The problem is formulated as a two-stage stochastic mixed-integer programming model. The first stage arranges the maintenance periods of transmission lines. The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios. The proposed model cannot be solved efficiently with off-the-shelf solvers due to the binary variables in both stages. Therefore, the progressive hedging algorithm is applied. The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%, which proves its effectiveness.

Abstract:The outage of power system equipment is one of the most important factors that affect the reliability and economy of power system. It is crucial to consider the influence of contingencies elaborately in planning problem. In this paper, a distributionally robust transmission expansion planning model is proposed in which the uncertainty of contingency probability is considered. The uncertainty of contingency probability is described by uncertainty interval based on the outage rate of single equipment. An epigraph reformulation and Benders decomposition are applied to solve the proposed model. Finally, the feasibility and effectiveness of the proposed model are illustrated on the IEEE RTS system and the IEEE 118-bus system.

Abstract:Relieving congestion significantly influences the operation and security of the transmission network. Consequently, the congestion alleviation of transmission network in all power systems is imperative. Moreover, it could prevent price spikes and/or involuntary load shedding and impose high expenses on the transimission network, especially in case of contingency. Traditionally, the increasing or decreasing generation rescheduling has been used as one of the most imperative approaches for correctional congestion management when a contingency occurs. However, demand response programs (DRPs) could also be a vital tool for managing the congestion. Therefore, the simultaneous employment of generation rescheduling and DRPs is proposed for congestion management in case of contingency. The objective is to reschedule the generation of power plants and to employ DRPs in such a way so as to lessen the cost of congestion. The crow search algorithm is employed to determine the solution. The accuracy and efficiency of the proposed approach are assessed through the tests conducted on IEEE 30-bus and 57-bus test systems. The results of various case studies indicate the better performance of the proposed approach in comparison with different approaches presented in the literature.

Abstract:Modern power systems are incorporated with distributed energy sources to be environmental-friendly and cost-effective. However, due to the uncertainties of the system integrated with renewable energy sources, effective strategies need to be adopted to stabilize the entire power systems. Hence, the system operators need accurate prediction tools to forecast the dynamic system states effectively. In this paper, we propose a Bayesian deep learning approach to predict the dynamic system state in a general power system. First, the input system dataset with multiple system features requires the data pre-processing stage. Second, we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow model. Third, by incorporating the state matrix with the system features, we propose a Bayesian long short-term memory (BLSTM) network to predict the dynamic system state variables accurately. Simulation results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

Abstract:This paper presents a novel power supply system based on the use of fuzzy inference logic to improve the power control of renewable energy sources. The system comprises renewable solar and wind sources, and an accumulator battery is used as an additional power source. The procedure for the parallel connection of multiple energy sources provides a stable power supply and optimal charging of the accumulative element. Renewable energy sources are connected in parallel using two serial converters and controlled by the controller based on the fuzzy logic. The reference voltage control of the serial converter enables an optimal use of available energy sources. The accumulative element is connected in parallel to compensate for the shortage of solar and wind energies, whereas if the available renewable energy exceeds the needs of the consumers, the surplus energy is accumulated in the battery. All measurements are conducted on the prototype of the hybrid power system under real conditions and compared with the applied systems of this type. This novel system is mainly used in remote telecom locations where there is no power distribution network.

Abstract:As photovoltaic energy increasingly penetrates in power systems, transmission system operators have started to request its participation in providing ancillary services. One of the demanded services is the power ramp-rate control (PRRC), which attempts to limit the power ramps produced by intermittent irradiance conditions. In order to achieve the desired objective, solutions based on storage systems or modifying the maximum power point tracking (MPPT) in perturb and observe (P&O) algorithms are commonly adopted. The starting point in PRRC is the determination of the instantaneous power ramp-rate, and different methods have been proposed in the literature for its calculation. However, the accuracy and computational speed of existing procedures can be improved, which may be critical in situations with rapid irradiance fluctuations. In this paper, a decoupled photovoltaic power ramp-rate calculation method is presented, in which the effect of variable irradiance and the P&O algorithm are computed separately. The proposed method has been theoretically demonstrated and tested through simulation and experimental tests. Simulation results show that it can improve the previous methods in terms of accuracy and computation time. Experimental validation with hardware-in-the-loop demonstrates the suitability of the proposed method for real-time applications, even in presence of noisy measurements.

Abstract:Active power control of the photovoltaic (PV) power generation system is a promising solution to regulate frequency fluctuation in a power system with high penetration of renewable energy. This paper proposes an autonomous active power control of a small-scale PV system for supporting the inertial response of synchronous generators and power-frequency control. In the proposed control approach, an effective grid frequency regulation scheme is realized using slow- and fast-frequency responses. A low-pass filter based frequency measurement is used for slow-frequency response, while direct frequency measurement is used for fast-frequency response. The designed dual droop characteristic-based control is shaped to achieve a smooth transition between slow- and fast-frequency responses. The performance of the proposed control approach is demonstrated for serious disturbance scenarios, i.e., considerable power-load imbalance and generation trip. In the power-load imbalance test scenario, the proposed control approach works properly within the normal frequency deviation region even when the frequency deviation exceeds that region occasionally. In the generation trip test, the frequency deviation is mitigated quickly, and the employed droop control is smoothly transferred from the slow- to fast-frequency responses.

Abstract:The penetration of renewable energy sources (RESs) in the distribution system becomes a challenge for the reliable and safe operation of the existing power system. The sporadic characteristics of sustainable energy sources along with the random load variations greatly affect the power quality and stability of the system. Hence, it requires storage systems with both high energy and high power handling capacity to coexist in microgrids. An efficient energy management structure is designed in this paper for a grid-connected PV system combined with hybrid storage of supercapacitor and battery. The combined supercapacitor and battery storage system grips the average and transient power changes, which provides a quick control for the DC-link voltage, i.e., it stabilizes the system and helps achieve the PV power smoothing. The average power distribution between the power grid and battery is done by checking the state of charge (SOC) of a battery, and an effective and efficient energy management scheme is proposed. Additionally, the use of a supercapacitor lessens the current stress on the battery system during unexpected disparity in the generated power and load requirement. The performance and efficacy of the proposed energy management scheme are justified by simulation studies.

Abstract:High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.

Abstract:Battery energy storage systems (BESSs) need to comply with grid code and fault ride through (FRT) requirements during disturbances whether they are in charging or discharging mode. Previous literature has shown that constant charging current control of BESSs in charging mode can prevent BESSs from complying with emerging grid codes such as the German grid code under stringent unbalanced fault conditions. To address this challenge, this paper proposes a new FRT-activated dual control strategy that consists of switching from constant battery current control to constant DC-link voltage control through a positive droop structure. The results show that the strategy ensures proper DC-link voltage and current management as well as adequate control of the positive- and negative-sequence active and reactive currents according to the grid code priority. It is also shown that the proposed FRT control strategy is tolerant to initial operating conditions of BESS plant, grid code requirements, and fault severity.

Abstract:High penetration of renewable energies enlarge the peak-valley difference of the net load of the distribution system, which puts forward higher requirements for the operation scheduling of the distribution system. From the perspective of leveraging demand-side adjustment capabilities, an optimal scheduling method of the distribution system with edge computing and data-driven modeling of price-based demand response (PBDR) is proposed. By introducing the edge computing paradigm, a collaborative interaction framework between the control center and the edge nodes is designed for the optimization of the distribution system. At the edge nodes, a classified XGBoost-based PBDR modeling method is proposed for large-scale differentiated users. At the control center, a two-stage optimization method integrating pre-scheduling and re-scheduling is proposed based on demand response results from all edge nodes. Through the information interaction between the control center and edge nodes, the optimized scheduling of the distribution system with large-scale users is realized. Finally, a case study is implemented on the modified IEEE 33-node system, which verifies that the proposed classified modeling method has lower errors, and it is beneficial to improve the economics of the system operation. Moreover, the simulation results show that the application of edge computing can significantly reduce the calculation time of the optimal scheduling problem with PBDR modeling of large-scale users.

Abstract:Soft open points (SOPs) are power electronic devices that may replace conventional normally-open points in distribution networks. They can be used for active power flow control, reactive power compensation, fault isolation, and service restoration through network reconfiguration with enhanced operation flexibility and grid resiliency. Due to unbalanced loading conditions, the voltage unbalance issue, as a common problem in distribution networks, has negative impacts on distribution network operation. In this paper, a control strategy of voltage unbalance compensation for feeders using SOPs is proposed. With the power flow control, three-phase current is regulated simultaneously to mitigate the unbalanced voltage between neighboring feeders where SOPs are installed. Feeder voltage unbalance and current unbalance among three phases are compensated with the injection of negative-sequence and zero-sequence current from SOPs. Especially in response to power outages, three-phase voltage of isolated loads is regulated to be balanced by the control of SOPs connected to the feeders under faults, even if the loads are unbalanced. A MATLAB/Simulink model of the IEEE 13-bus test feeder with an SOP across feeder ends is implemented, and experimental tests on a hardware-in-the-loop platform are implemented to validate the effectiveness of the proposed control strategy.

Abstract:Micro-energy grids have shown superiorities over traditional electricity and heating management systems. This paper presents a hybrid optimization strategy for micro-energy grid dispatch with three salient features. First, to enhance the ability to support new storage equipment, an energy hub model is proposed using the non-supplementary fired compressed air energy storage (NSF-CAES). This provides flexible dispatch for cooling, heating and electricity. Second, considering the unique characteristics of the NSF-CAES, a sliding time window (STW) method is designed for simple but effective energy dispatch. Third, for the optimization of energy dispatch, we blend the differential evolution (DE) with the hyper-spherical search (HSS) to formulate a hybrid DE-HSS algorithm, which enhances the global search ability and accuracy. Comparative case studies are performed using real data of scenarios to demonstrate the superiorities of the proposed scheme.

Abstract:The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the operation mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

Abstract:The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors. Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy. However, this assumption may not be true in reality, particularly when a power market is newly launched. To help power suppliers bid with the limited information, a modified continuous action reinforcement learning automata algorithm is proposed. This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game. Simulation results verify the effectiveness of the proposed learning algorithm.

Abstract:The problem of reclosing current limiting in voltage source converter based high-voltage direct current (VSC-HVDC) systems is becoming more and more serious. A soft reclosing scheme for DC permanent faults is presented in this paper. Because the converter voltages of stations at both terminals of the disconnected faulty line may be different, the choice of which terminal to reclose first will affect the reclosing overcurrent. A method for selecting the terminal to reclose first is investigated to achieve a minimum peak overcurrent during the reclosing process. In order to ensure that the hybrid DC circuit breaker (HDCCB) adapts to the needs of the reclosing process better, the traditional HDCCB is improved by adding a soft reclosing module (SRM). The energy dissipated in the arresters is significantly reduced when using the improved HDCCB. The improved HDCCB will be able to reclose multiple times safely and thus increase the possibility of successful reclosing. Moreover, the recovery time after the HDCCB is successfully reclosed is very short with the improved HDCCB and its control principles. Simulation results show that this proposed scheme is capable of limiting the reclosing overcurrent when the fault still exists.

Abstract:This paper provides a comprehensive analysis of local and concurrent commutation failure (CF) of multi-infeed high-voltage direct current (HVDC) system considering multi-infeed interaction factor (MIIF). The literature indicates that the local CF is not influenced by MIIF, whereas this paper concludes that both the local CF and concurrent CF are influenced by MIIF. The ability of remote converter to work under reduced reactive power enables its feature to support local converter via inter-connection link. The MIIF measures the strength of electrical connectivity between converters. Higher MIIF gives a clearer path to remote converter to support local converter, but at the same time, it provides an easy path to local converter to disturb remote converter under local fault. The presence of nearby converter increases the local commutation failure immunity index (CFII) while reducing concurrent CFII. Higher MIIF causes reactive power support to flow from remote converter to local converter, which reduces the chances of CF. A mathematical approximation to calculate the increase in local CFII for multi-infeed HVDC configurations is also proposed. A power flow approach is used to model the relation between MIIF and reactive power support from remote end. The local and concurrent CFIIs are found to be inverse to each other over MIIF; therefore, it is recommended that there is an optimal value of MIIF for all converters in close electric proximity to maintain CFII at a certain level. The numerical results of established model are compared with PSCAD/EMTDC simulations. The simulation results show the details of the influence of MIIF on local CF and concurrent CF of multi-infeed HVDC, which validates the analysis presented.

Abstract:Constraints on each node and line in power systems generally have upper and lower bounds, denoted as two-sided constraints. Most existing power system optimization methods with the distributionally robust (DR) chance-constrained program treat the two-sided DR chance constraint separately, which is an inexact approximation. This letter derives an equivalent reformulation for the generic two-sided DR chance constraint under the interval moment based ambiguity set, which does not require the exact moment information. The derived reformulation is a second-order cone program (SOCP) formulation and is then applied to the optimal power flow (OPF) problem under uncertainty. Numerical results on several IEEE systems demonstrate the effectiveness of the proposed SOCP formulation and show the differences with other DR chance-constrained OPF approaches.