The energy Internet operation platform provides market entities such as energy users, energy enterprises,suppliers, and governments with the ability to interact, transact, and manage various operations.Owing to the large number of platform users, complex businesses, and large amounts of data-mining tasks, it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users.This study examines the two core technologies of platform task scheduling and multiuser concurrent processing, proposing a distributed taskscheduling method and a technical implementation scheme based on the particle swarm optimization algorithm, and presents a systematic solution in concurrent processing for massive user numbers.Based on the results of this study, the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.

more

As the intermittency and uncertainty of photovoltaic (PV) power generation poses considerable challenges to the power system operation, accurate PV generation estimates are critical for the distribution operation, maintenance, and demand response program implementation because of the increasing usage of distributed PVs.Currently, most residential PVs are installed behind the meter, with only the net load available to the utilities.Therefore, a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study, an unsupervised PV capacity estimation method based on net metering data is proposed, for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then, the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation, the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally, the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the stateof-the-art PV disaggregation algorithm, the proposed method exhibits a reduction of over 15% for the mean absolute percentage error and over 20% for the root mean square error.

more

The coordinated optimization problem of the electricity-gas-heat integrated energy system (IES) has the characteristics of strong coupling, non-convexity, and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile, the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency, which is difficult to apply online.For the coordinated optimization problem of the electricity-gasheat IES in this study, we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization, dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization, the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method, it can better cope with uncertainty and realize realtime decision making of the system, which is conducive to the online application.Finally, we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.

more

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method, the original image sequence data is first separated from the foreground and background.Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

more

Photovoltaic (PV) systems are adversely affected by partial shading and non-uniform conditions.Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots.It also produces numerous peaks in the PV array’s power-voltage characteristics, thereby trapping conventional maximum power point tracking (MPPT) methods in local peaks.Swarm optimization approaches can be used to address this issue.However, these strategies have an unreasonably long convergence time.The Grey Wolf Optimizer (GWO) is a fast and more dependable optimization algorithm.This renders it a good option for MPPT of PV systems operating in varying partial shading.The conventional GWO method involves a long conversion time, large steady-state oscillations, and a high failure rate.This work attempts to address these issues by combining Cuckoo Search (CS) with the GWO algorithm to improve the MPPT performance.The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe (P&O).A comparative analysis reveals that under non-uniform operating conditions, the hybrid GWO CS (GWOCS) approach presented in this article outperforms the GWO and P&O approaches.

more

With development of distributed generation (DG), configuration of optimization equipment is crucial for absorbing excess electricity and stabilizing fluctuations.This study proposes a two-layer configuration strategy coordinates active cyber control and the physical energy storage (ES) system.First, an upper economic model is developed.Based on chanceconstrained programming, an operation model accounts for inherent uncertainty are then developed.Under constraint of voltage risk level, a lower operation model is developed.Finally, a solution based on differential evolution is provided.An IEEE 33 bus system simulation was used to validate efficacy of model.The effects of risk level, equipment price, and chance-constrained probability were analyzed, providing a foundation for power consumption and expansion of cyberphysical systems.

more

The accurate identification of the oil-paper insulation state of a transformer is crucial for most maintenance strategies.This paper presents a multi-feature comprehensive evaluation model based on combination weighting and an improved technique for order of preference by similarity to ideal solution (TOPSIS) method to perform an objective and scientific evaluation of the transformer oil-paper insulation state.Firstly, multiple aging features are extracted from the recovery voltage polarization spectrum and the extended Debye equivalent circuit owing to the limitations of using a single feature for evaluation.A standard evaluation index system is then established by using the collected time-domain dielectric spectrum data.Secondly, this study implements the per-unit value concept to integrate the dimension of the index matrix and calculates the objective weight by using the random forest algorithm.Furthermore, it combines the weighting model to overcome the drawbacks of the single weighting method by using the indicators and considering the subjective experience of experts and the random forest algorithm.Lastly, the enhanced TOPSIS approach is used to determine the insulation quality of an oil-paper transformer.A verification example demonstrates that the evaluation model developed in this study can efficiently and accurately diagnose the insulation status of transformers.Essentially, this study presents a novel approach for the assessment of transformer oil-paper insulation.

more

The BeiDou-3 navigation satellite system (BDS-3) provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation.However, BDS-3 power system positioning faces certain challenges,such as complex electromagnetic interference and incomplete error elimination.Herein, a back propagation neural networkimproved least mean square (BP-LMS) adaptive filtering method is proposed for the BDS-3 full-domain and high-precision power system positioning, which utilizes the loss function to update the weight of the BP hidden layer, computes the pseudo compensation range, and eliminates the impact of electromagnetic interference to enhance the accuracy of power system positioning.Simulation results confirm the superior performance of BP-LMS in positioning accuracy and error elimination.Compared with LMS and normalized least mean square (NLMS), the filtering error of the proposed BP-LMS adaptive filtering method is decreased by 57.14% and 51.38%, respectively.

more

With the large-scale application of 5G technology in smart distribution networks, the operation effects of distribution networks are not clear.Herein, we propose a comprehensive evaluation model of a 5G+ smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method (FAHPEWM).First, we establish comprehensive evaluation indexes of a 5G+ smart distribution network from five dimensions:reliable operation, economic operation, efficient interaction, technological intelligence, and green emission reduction.Second, by introducing the principle of variance minimization, we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight, so as to reduce the defects of subjective arbitrariness and promote objectivity.Finally, a comprehensive evaluation model of 5G+ smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information.The example analysis indicates that the overall operation of the 5G+ smart distribution network project is decent, and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method, which verifies the effectiveness and rationality of the proposed evaluation method.Moreover, the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.

more

During the hot summer season, using electricity systems increases the local anthropogenic heat emission, further increasing the temperature.Regarding anthropogenic heat sources, electric energy consumption, heat generation, indoor and outdoor heat transfer, and exchange in buildings play a critical role in the change in the urban thermal environment.Therefore, the Weather Research and Forecasting (WRF) Model was applied in this study to investigate the heat generation from an indoor electricity system and its influence on the outdoor thermal environment.Through the building effect parameterization (BEP) of a multistorey urban canopy scheme, a building energy model (BEM) to increase the influence of indoor air conditioning on the electricity consumption system was proposed.In other words, the BEP+BEM urban canopy parameterization scheme was set.High temperatures and a summer heat wave were simulated as the background weather.The results show that using the BEP+BEM parameterization scheme of indoor and outdoor energy exchange in the WRF model can better simulate the air temperature near the surface layer on a sunny summer.During the day, the turning on the air conditioning and other electrical systems have no obvious effect on the air temperature near the surface layer in the city, whereas at night, the air temperature generally increases by 0.6 ℃, especially in densely populated areas, with a maximum temperature rise of approximately 1.2 ℃ from 22:00 to 23:00.When the indoor air conditioning target temperature is adjusted to 25–27 ℃, the total energy release of the air conditioning system is reduced by 12.66%, and the temperature drops the most from 13:00 to 16:00, with an average of approximately 1 ℃.Further, the denser the building is, the greater the temperature drop.

more