China Southern Power Grid (China)

With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users' willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers' costs and improving the sharing of the PV energy.

Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.

Modular multilevel converters (MMC) are considered a top converter alternative for voltage-source converter (VSC) high-voltage, direct current (HVDC) applications. Main circuit design and converter performance evaluation are always important issues to consider before installing a VSC-HVDC system. Investigation into a steady-state analysis method for an MMC-based VSC-HVDC system is necessary. This paper finds a circular interaction among the electrical quantities in an MMC. Through this circular interaction, a key equation can be established to solve the unknown circulating current. A new steady-state model is developed to simply and accurately describe the explicit analytical expressions for various voltage and current quantities in an MMC. The accuracy of the expressions is improved by the consideration of the circulating current when deriving all the analytical expressions. The model's simplicity is demonstrated by having only one key equation to solve. Based on the analytical expressions for the arm voltages, the equivalent circuits for MMC are proposed to improve the current understanding of the operation of MMC. The feasibility and accuracy of the proposed method are verified by comparing its results with the simulation and experimental results.

A high-voltage direct current system using modular multilevel converter (MMC-HVDC) is a potential candidate for grid integration of renewable energy over long distances. The dc-link fault is an issue MMC-HVDC must deal with, especially for the nonpermanent faults when using overhead lines. This paper proposed a protection scheme to implement fast fault clearance and automatic recovery for nonpermanent faults on dc lines. By employing double thyristor switches, the freewheeling effect of diodes is eliminated and the dc-link fault current is allowed to freely decay to zero. Then, the dc arc can be naturally extinguished and the insulation on the short-circuit point can be restored. The thyristor switches convert the dc-link fault into an ac short circuit of the ac grid through MMC arms. The ac short-circuit current can be cleared simply by turning off all thyristor switches. Since circuit breakers are not tripped during fault clearance, MMC can immediately and automatically rebuild the dc-link voltage and restart power transmission. Simulation results using PSCAD/EMTDC have verified the validity of the proposed protection scheme.

This paper proposes a multi-timescale coordinated stochastic voltage/var control method for high renewable-penetrated distribution networks. It aims to utilize multiple devices to counteract uncertain voltage fluctuation and deviation. In the hourly timescale (first stage), capacitor banks and transformer tap changers are scheduled before stochastic renewable output and load variations are realized. In the 15-min timescale (second stage), inverters that interface the renewable energy resources provide var support to supplement the first-stage decision after uncertainty is observed. The coordination is model as a two-stage stochastic programming problem with scenario reduction. It is then converted to a deterministic mixed-integer quadratic programming equivalence model and solved by commercial solvers combined. Compared with existing methods, the proposed volt/var control can achieve lower expected energy loss and can sustain a secure voltage level under random load demand and renewable power injection. The proposed method is verified on the IEEE 33-bus distribution network and compared with existing practices.

A 1270 Hz resonance occurred between ±350 kV/ 1000 MW Luxi back-to-back voltage source converter based high-voltage dc transmission (VSC-HVDC) converter and the 525 kV ac grid after disconnection of several ac transmission lines. To understand the resonance and find a solution, the impedance-based stability analysis model considering different equipment is first established. Then, the resonance is analyzed and repeated in the simulation based on the established model. The system stability can be judged by the ratio of grid impedance to the equivalent impedance of all parallel-connected equipment with the converter. To evaluate the occurrence and risk of resonance, the frequency range where the impedance has a negative-real-part has been searched and studied. In order to narrow the negative-real-part region to avoid potential resonance, solutions such as control strategy improvement and passive or active impedance adapter may be applicable and are discussed. For a complex system containing various equipment, the equipment can be divided into several subsectors to avoid modeling all possible combinations of equipment, which can be exhausting. And analysis has shown sufficient but not necessary condition to stabilize the system is to avoid the negative-real-part region in each sector.

This brief studies dynamic characteristics of a permanent-magnet synchronous motor (PMSM). The mathematical model of the PMSM is first derived, which is fit for carrying out the bifurcation and chaos analysis. Then, the steady-state characteristics of the system, when subject to constant input voltage and constant external torque, are formulated. Three cases are discussed and, for each case, conditions are derived under which the dynamic characteristics of the system are either of steady-state type, limit cycles or chaotic, thus by properly adjusting some system parameters, the system can exhibit limit cycles (LCs) or chaotic behaviors at will. Finally, computer simulations are presented to verify the existence of strange attractors in the PMSM.

The low power wide area network (LPWAN) technologies, which is now embracing a booming era with the development in the Internet of Things (IoT), may offer a brand new solution for current smart grid communications due to their excellent features of low power, long range, and high capacity. The mission-critical smart grid communications require secure and reliable connections between the utilities and the devices with high quality of service (QoS). This is difficult to achieve for unlicensed LPWAN technologies due to the crowded license free band. Narrowband IoT (NB-IoT), as a licensed LPWAN technology, is developed based on the existing long-term evolution specifications and facilities. Thus, it is able to provide cellular-level QoS, and henceforth can be viewed as a promising candidate for smart grid communications. In this paper, we introduce NB-IoT to the smart grid and compare it with the existing representative communication technologies in the context of smart grid communications in terms of data rate, latency, range, etc. The overall requirements of communications in the smart grid from both quantitative and qualitative perspectives are comprehensively investigated and each of them is carefully examined for NB-IoT. We further explore the representative applications in the smart grid and analyze the corresponding feasibility of NB-IoT. Moreover, the performance of NB-IoT in typical scenarios of the smart grid communication environments, such as urban and rural areas, is carefully evaluated via Monte Carlo simulations.

In this paper, an edge computing system for IoT-based (Internet of Things) smart grids is proposed to overcome the drawbacks in the current cloud computing paradigm in power systems, where many problems have yet to be addressed such as fully realizing the requirements of high bandwidth with low latency. The new system mainly introduces edge computing in the traditional cloud-based power system and establishes a new hardware and software architecture. Therefore, a considerable amount of data generated in the electrical grid will be analyzed, processed, and stored at the edge of the network. Aided with edge computing paradigm, the IoT-based smart grids will realize the connection and management of substantial terminals, provide the real-time analysis and processing of massive data, and foster the digitalization of smart grids. In addition, we propose a privacy protection strategy via edge computing, data prediction strategy, and preprocessing strategy of hierarchical decision-making based on task grading (HDTG) for the IoT-based smart girds. The effectiveness of our proposed approaches has been demonstrated via the numerical simulations.

According to the energy policy, which encourages local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers is proved to be a more effective way compared with independent operations of each prosumer. In this paper, an energy storage (ES)-equipped energy-sharing provider (ESP) is proposed to facilitate the energy sharing of multiple PV prosumers. With the help of the ESP, the autonomous PV prosumers can be formed as an energy-sharing network, and the energy-sharing activities can be categorized as direct sharing and buffered sharing. First, with the assistance of the ES, a day-ahead scheduling model of the ESP is built to increase the operation profit and improve the net power profile of the energy-sharing network, which considers the uncertainty of PV energy, electricity prices, and prosumers' load via stochastic programming. Moreover, to further increase the energy sharing, a real-time demand response model based on a Stackelberg game is presented to coordinate the energy consumption behavior of prosumers by using internal prices. Finally, through a practical case study, the effectiveness of the method is verified in terms of improving the economic benefits and PV energy sharing.

This paper proposes a robust optimization approach for optimal operation of microgrids. The uncertain output variation of renewable energy sources (RESs) is addressed by collaboratively scheduling of energy storage (ES) and direct load control (DLC) through a two-stage complementary framework: an hour-ahead charging/discharging of ES and a quarter-hour-ahead activation of DLC. The objective is to maximize the total profit of the microgrid considering operation and maintenance costs of ES units, wind turbines and photovoltaics, and transaction with main grid and customer loads. Assuming the power output of RES randomly varies within a bounded uncertainty set, the problem is modeled to a two-stage robust optimization model and solved by a column-and-constraint generation algorithm. Compared with conventional operation methods, the ES and DLC are coordinated in different time-scales, and RES uncertainties are fully addressed during operation decision-making, ensuring the solutions to be optimal and robust for any realization of uncertainty. The proposed methodology is verified on the IEEE 33-bus distribution system through a wide range of different tests.

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.

The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.

Proactive preparedness to cope with extreme weather events is significantly helpful in reducing the restoration cost and enhancing the resilience of distribution systems. This paper is focused on the resource allocation problem in distribution systems ahead of a coming hurricane. Generation resources such as diesel oil and batteries are considered for allocation, which can be used to serve outage critical load in the post-hurricane restoration. Electric buses are also considered as a kind of resource. Considering the uncertainties of system faults, the allocation problem is formulated into a mixed-integer stochastic nonlinear program. A heuristic method is then proposed, which obtains the allocation plan by solving a mixed-integer linear program. Numerical simulations are performed on the IEEE 123-node feeder system under several scenarios to demonstrate the effectiveness of the proposed method. The impacts of resources transportation cost, initial distribution of electric buses, and hurricane severity on the allocation plan are discussed.

Capacitor-current-feedback active damping is an effective method to suppress the LCL-filter resonance in grid-connected inverters. However, due to the variation of grid impedance, the LCL-filter resonance frequency will vary in a wide range, which challenges the design of the capacitor-current-feedback coefficient. Moreover, if the resonance frequency is equal to one-sixth of the sampling frequency (f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> /6), the digitally controlled LCL-type grid-connected inverter can be hardly stable no matter how much the capacitor-current-feedback coefficient is. In this paper, the optimal design of the capacitor-current-feedback coefficient is presented to deal with the wide-range variation of grid impedance. First, the gain margin requirements for system stability are derived under various resonance frequencies. By evaluating the effect of grid impedance on gain margins, an optimal capacitor-current-feedback coefficient is obtained. With this feedback coefficient, stable operations will be retained for all resonance frequencies except (f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> /6). Second, in order to improve system stability for a resonance frequency of (f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> /6), a phase-lag compensation for the loop gain is proposed. Finally, a 6-kW prototype is tested to verify the proposed design procedure.

Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate.

Microgrid is an effective means to integrate distributed generation (DG) resource. However, uncertain renewable DG such as wind turbine and photovoltaic outputs and load demands can introduce tremendous difficulties for energy management in microgrids. To mitigate such difficulties, price-based demand response (PBDR) can adjust the loads to adapt to the renewables. On the other hand, dispatchable DG such as microturbines can coordinate with the PBDR to further manage the power balance and achieve economic benefits. In this paper, a two-stage robust microgrid coordination strategy is proposed: a PBDR is scheduled a day ahead and microturbine outputs are modified hourly. A two-stage robust optimization model is proposed to address the coordination problem with guaranteed robustness against the uncertainties of renewable DG and load demands. Simulation results show PBDR and multiple DG units can coordinate effectively to accommodate the renewable and demand uncertainties while maximizing the microgrid benefits.

Traditional grid-connected inverters (TGCI) could suffer from small-signal instability owing to the dynamic interactions among inverters and a weak grid. In this letter, the small-signal sequence impedance model of the virtual synchronous generator (VSG) is built, and the sequence impedance characteristics of the VSG and the TGCI are compared and analyzed. The sequence impedance of the TGCI is mainly capacitive in the middle-frequency area, and the impedance amplitude is quite high. By contrast, the sequence impedance of the VSG, being consistent with the grid impedance characteristics, is generally inductive, and the impedance amplitude is quite low. Based on the sequence impedance model and the Nyquist stability criterion, the influence of the grid stiffness, the number of paralleled inverters, and the phase-locked loop (PLL) bandwidth on the stability of the VSG and the TGCI systems is analyzed. The stability analysis results show that the TGCI loses stability easily, whereas the VSG still works well without PLL restrictions under an ultraweak grid or with a large number of inverters connected to the grid. Therefore, the VSG is more suitable than the TGCI for achieving high penetration of renewable energy generation in an ultraweak grid from a system stability viewpoint. Finally, the experimental results validate the sequence impedance model and the stability analysis.

An algorithm based on a nonlinear interior-point method and discretization penalties is proposed in this paper for the solution of the mixed-integer nonlinear programming (MINLP) problem associated with reactive power and voltage control in distribution systems to minimize daily energy losses, with time-related constraints being considered. Some of these constraints represent limits on the number of switching operations of transformer load tap changers (LTCs) and capacitors, which are modeled as discrete control variables. The discrete variables are treated here as continuous variables during the solution process, thus transforming the MINLP problem into an NLP problem that can be more efficiently solved exploiting its highly sparse matrix structure; a strategy is developed to round these variables off to their nearest discrete values, so that daily switching operation limits are properly met. The proposed method is compared with respect to other well-known MINLP solution methods, namely, a genetic algorithm and the popular GAMS MINLP solvers BARON and DICOPT. The effectiveness of the proposed method is demonstrated in the well-known PG&E 69-bus distribution network and a real distribution system in the city of Guangzhou, China, where the proposed technique has been in operation since 2003.