Research on wind power capacity credit at the operational level plays an important role in power system dispatching.With the popularity of energy storage devices, it is increasingly necessary to study the impact of energy storage devices on wind power operational capacity credit.The definition of wind power operational capacity credit is given.The available capacity model of different generators and the charging and discharging model of the energy storage are established.Based on the above model, the evaluation method of wind power operation credible capacity considering energy storage devices is proposed.The influence of energy storage on the wind power operation credible capacity is obtained by case study, which is of great help for the power system dispatching operation and wind power accommodation.

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Wind power, solar power, and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load, the uncertainty on the production and load sides are both increased, bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object (wind, solar, or load), a few involve the above two or three objects, but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However, there is no corresponding review at present.Hence, our study provides a comprehensive review of wind, solar, and electrical load forecasting methods.Furthermore, the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

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With the establishment of the carbon-peak target by 2030, the direction of carbon emission reduction in China’s energy system has been further clarified.As the industry with the largest proportion of carbon emissions in China, the lowcarbon transformation of the electric power industry is critical to realize the carbon-peak target.Current research mostly focuses on technical analysis or system cost accounting of the carbon-peak realization path at the national level.There is a lack of targeted research on regional power systems with complex inter-regional power flow exchange and limited energy resource development.Simultaneously, the calculation of the system cost lacks the perspective of the life cycle and ignores the inertia of the stock and change inertia of incremental disturbance.From the perspective of the life cycle, this study proposes a calculation model of power supply cost for regional power systems according to the carbon-peak target,analyzes the realization path of the carbon target from an economic perspective, and provides references for the path selection and policy formulation of system transformation.

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The simulation of wind power time series is a key process in renewable power allocation planning, operation mode calculation, and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment; however, they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem, a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First, a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second, considering the typical daily output processes as status variables, a wind power time series simulation model based on Markov algorithm is constructed.Finally, a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics, probability distributions, and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover, modeling efficiency considerably improves.

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In this paper, a novel robust fault-tolerant control scheme based on event-triggered communication mechanism for a variable-speed wind energy conversion system (WECS) with sensor and actuator failures is proposed.The nonlinear WECS with event-triggered mechanism is modeled based on the Takagi-Sugeno (T-S) fuzzy model.By Lyapunov stability theory, the parameter expression of the proposed robust fault-tolerant controller with event-triggered mechanisms is proposed based on a feasible solution of linear matrix inequalities.Compared with the existing WECS fault-tolerant control methods, the proposed scheme significantly reduces the pressure of network packet transmission and improves the robustness and reliability of the WECS.Considering a doubly-fed variable speed constant frequency wind turbine, the eventtriggered mechanism based fault-tolerant control for WECS is analyzed considering system model uncertainty.Numerical simulation results demonstrate that the proposed scheme is feasible and effective.

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The high-energy consumption and high construction density of 5G base stations have greatly increased the demand for backup energy storage batteries.To maximize overall benefits for the investors and operators of base station energy storage, we proposed a bi-level optimization model for the operation of the energy storage, and the planning of 5G base stations considering the sleep mechanism.A multi-base station cooperative system composed of 5G acer stations was considered as the research object, and the outer goal was to maximize the net profit over the complete life cycle of the energy storage.Furthermore, the power and capacity of the energy storage configuration were optimized.The inner goal included the sleep mechanism of the base station, and the optimization of the energy storage charging and discharging strategy, for minimizing the daily electricity expenditure of the 5G base station system.Additionally, genetic algorithm and mixed integer programming were used to solve the bi-level optimization model, analyze the numerical example test comparison of the three types of batteries and the net income of the configuration, and finally verify the validity of the model.Furthermore, the sleep mechanism, the charging and discharging strategy for energy consumption, and the economic benefits for the operators were investigated to provide reference for the 5G base station energy storage configuration.

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With the ever-increased installed capacity of renewable energy generation units in a power system, the so-called shared energy storage (SES), a novel business model under the umbrella of the shared economy principle, has the potential to play an essential role in the accommodation of renewable energy generation.However, unified evaluation standards and methods, which can help decision-makers analyze the performance of the SES market, are still not available.In this paper,an evaluation index system of the SES market is designed based on the trading rules of China’s Qinghai province and the structure-conduct-performance (SCP) analytical model.Moreover, the definition and characteristics of the indices, which can show the performance of the SES market from different perspectives, are given.Furthermore, the ideal cases are presented as the evaluation benchmark based on the development expectation of the SES market, and the analytic hierarchy process(AHP) and the technique for order preference by similarity to an ideal solution (TOPSIS) are applied to evaluate the SES market comprehensively.Finally, a case study based on actual data of the SES trading pilot project in Qinghai shows that the evaluation index system can reflect the operation status, existing problems and influencing factors of the SES market.

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Owing to the expansion of the grid interconnection scale, the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However, traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore, this study presents a rolling frequency-prediction model based on a graph convolutional network (GCN) and a long short-term memory (LSTM) spatiotemporal network and named as STGCN-LSTM.In the proposed method, the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features, while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained, and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.

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With the application of the advanced measurement infrastructure in power grids, data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However, owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users, the performance of the existing usage-pattern-based method could be affected.In addition, the detection results of some unsupervised learning algorithm models are abnormal degrees rather than “0-1” to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues, this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft, and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.

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The pulse current method, acoustic and ultrasonic partial discharge (PD) detection, and voiceprint PD detection are commonly used detection methods for the PD detection of power equipment.To study the characteristics of PD signals of typical discharge models based on the principles of the above three detection methods, an acoustic detection experimental system consisting of a needle-tip model and a surface model was built.Acoustic tests were carried out on needle-tip models with different curvature radii and surface discharge models with different lengths of conductive paste.The experimental results showed that acoustic and ultrasonic PD detection and voiceprint PD detection exhibited different sensitivities to the needle-tip discharge models, and the combination of acoustic and ultrasonic PD and voiceprint PD detection was more beneficial for the comprehensive detection of cable PD signals.Based on voiceprint recognition technology, this study drew FFT (Fast Fourier Transformation) diagrams of different types of PD acoustic signals and analyzed the differences in the ultrasonic signal frequency distribution.The frequency band of the voiceprint PD signal of the needle-tip discharge models was concentrated in the range 17-27 kHz, and the frequency band of the voiceprint PD signal of the conductive paste discharge models was concentrated in the range 20-25 kHz.The measurement of voiceprint PD signals in these frequency bands were strengthened when the PD of a cable was detected on-site, which provides the basis for the use of the cable model for on-site PD detection.

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