ABSTRACT. With the rapid development of the power generation technology of distributed renewable energy, great quantities of prosumers emerged and joined to the regional energy internet (REI), which could lead to challenges for conventional energy transaction matching mechanisms. At the present stage, electricity transaction match mostly adopts traditional mechanisms such as double auction, which may exist problems in practical utilization such as the match of clean energy is relatively difficult and the waste of electricity transmission is hard to be indicated, etc. In addition, the conventional centralized matching platform could face challenges considering the widely deployment of building and even house level prosumers among regions, as well as the instantaneous variable characteristic of clean energy generation such as photovoltaic and wind. Aiming at the challenges, in this paper a blockchain-based multi-factor electricity transaction matching mechanism is proposed. The suitability of multiple factors such as electricity price, transaction volume, transmission distance, and energy type are calculated and compared based on the calculation formula of dissimilarity degree in the K-prototypes clustering algorithm. The seller and the purchaser with the smallest dissimilarity value between electricity sale demands and electricity purchase demands are selected as the matching objects. In addition, corresponding automatic match, signing matching records by private key and settlement of the transactions were designed based on smart contract. The experimental results illustrated that comparing with conventional solutions, by adopting the proposed mechanism, the supply proportion of clean energy in the entire REI increased to 80.40%; the actual unit price gap between clean energy and non-clean energy decreased by 63.00%; the total electricity sale volume in the entire REI increased by 83.37%.

ABSTRACT. Renewable energy sources is becoming the main form of energy supply side in the energy internet. To improve the absorption capacity and operation analysis level of large-scale distributed renewable energy, it is important to guarantee the accuracy of renewable energy operation data. Based on multi-scenario application analysis, this paper proposed a data quality analysis, abnormal data detection and repair method for renewable energy operation data. Firstly, the renewable energy data types are analyzed, the K-means clustering analysis method is used step by step to form data characteristic curve for data evaluation, and a diagnosis method for abnormal data. Then rough set theory is used to reduce the associated attributes of the operation data value, and establish the importance between data attribute types and data values. Finally, a predictive decision-making attributes forecasting tree is constructed to repair the abnormal data. A numerical load case verifies the effectiveness of the method.

ABSTRACT. The stable and efficient management and dispatching of power system depend on the accurate short term load forecasting of the following few minutes to a week. With the rapid development of the power Internet of Things, the number of networks edge devices and data volume have increased exponentially. However, the traditional centralized method cannot accurately grasp load variation patterns of all area, which entails storage pressure and delays of data calculation and transmission. In addition, the centralized method has potential data security risk for its transmitting and storing all data in the data center. The present research proposes an ultra-short term load forecasting method for the power Internet of Things based on federated learning, which learns the model parameters from the data distributed in multiple edge nodes. Simulation results show that the method effectively generates accurate load forecasting and reduces the data security risk under the condition that the data of each edge node does not come out of its location.

ABSTRACT. Phasor measurement unit (PMU), which could provide high-frequency phasors including the amplitude and phase angle synchronized with GPS/ BDS signal, plays vital role in dynamic monitoring of power system. However, if the GPS/BDS signal could not be received, the time of PMU, which is depending on the crystal oscillator, would have a measurement deviation provides that its crystal frequency shifts. This paper analyzes and deduces the impact of constant crystal frequency deviation on PMU measurement in steady state. First, the phasor measured by the PMU in case of constant the frequency deviation of the crystal oscillator in the steady state is derived. Then, the detailed analysis of the impact of the frequency deviation of the crystal oscillator on the PMU measurement, according the value of deviation is carried out. Finally, with simulation, the impact of crystal frequency deviation on angle and amplitude data of PMU measurements are verified, according to small and large deviation, respectively.

ABSTRACT. With the large-scale integration of clean energy into smart grid, how to use the flexible regulation of demandside load to improve the efficiency of new energy has become an issue in recent years. However, facing more and more demandside services, the pressure of communication network will multiply, which will easily cause communication delay and affect real-time control services. Therefore, this paper proposes an improved electric water heater model, which introduces the error of load running state change caused by communication delay, which can simulate the real-time consumption in real environment more accurately, and then feedback and optimize the collaborative loads control strategy to improve the consumption rate of new energy. Finally, the effectiveness and advantages of the model and strategy are verified by simulation.

ABSTRACT. In order to improve the quality of power supply to the park and conduct differentiated service and management, the influence of the power quality (PQ) level of the park needs to be considered. At the same time, the traditional steady-power quality anomaly identification method only compares the value with the limit value and does not consider the change trend of the data, which has some limitations. The characteristics of time-series trend changes of steady-state power quality data is focused on this paper and a steady-state power quality anomaly identification method based on time series trend analysis is proposed. Firstly, data preprocessing is carried out through piecewise linearization to filter out data fluctuations and retain the main trend change characteristics of data. Secondly, the trend change of data is represented by the trend pattern, and the similarity between different trend sequences is calculated by the pattern distance. Finally, combined with the amplitude anomaly index, the comprehensive anomaly index of the data to be identified relative to the normal data segment is calculated to identify whether there are anomalies in the steady-state power quality of the corresponding measurement point. Through simulation examples and case analysis, it is proved that the proposed method is accurate, applicable and easy to implement, and can be easily integrated into the existing power quality monitoring system.

ABSTRACT. Energy Internet needs a comprehensive grasp of all power generation equipment. In order to simulate the behavior of human experts in real-time diagnosis of equipment operating status and fault types, a research on the fault diagnosis expert system of rotating machinery in thermal power plants is carried out, and a recommendation and election expert system based on the integration of rules and examples is proposed. The expert system combines traditional rule-based fault tree inference with case-based inference, and proposes a stepped inference strategy through online elections, which can perform online real-time fault diagnosis based on signals such as vibration and speed to improve the accuracy of fault diagnosis.

ABSTRACT. Voltage sag can cause abnormal interruption of production process of industrial users, resulting in huge economic losses. In view of the single governance angle and poor governance effect of existing voltage sag mitigation schemes, according to the electrical characteristics and physical attributes of industrial processes, this paper proposes electrical means focusing on voltage control and physical means focusing on process quantity maintenance. From the point of view of process parameters, the effects of various mitigation measures are measured in a unified way. A voltage sag mitigation method is proposed, which combines the installation of customized power equipment with the renovation of plant facilities, in order to maintain the process parameters during the period of disturbance and avoid the serious consequences of process interruption. Considering the investment cost and income of various mitigation measures, the allocation of mitigation measures is determined with the goal of maximizing the net present value of the scheme. The mitigation strategy proposed in this paper is applied to a new energy material enterprise. The results show that the proposed method can effectively mitigate the consequences of voltage sag and is more economical than the traditional voltage compensation scheme.

ABSTRACT. In view of the problem that it is impossible to deploy voltage sag monitoring devices in the whole network, resulting in the lack of basis for industrial users to enter the network, a scheme for evaluating the severity of voltage sag at the grid side in the provincial power grid is proposed. Firstly, by analyzing the characteristics of voltage sag transmission, the simulation scope of provincial power grid is determined, and the differences of fault rate and fault type proportion of different voltage grades highlighted due to the expansion of simulation scope are considered, which makes the short circuit fault simulation model based on Monte Carlo method closer to reality. Secondly, from the engineering point of view, improve the BPA data maintained by the power grid dispatching department, call the BPA parallel simulation calculation through the development interface, and analyze the calculation results to obtain the grid side voltage sag severity evaluation index.

ABSTRACT. A large number of individual power prosumers are emerging and participating in the regional energy internet (REI) with the rapid development of the power generation technology of distributed renewable energy. The increasing scale of the REI may bring challenges to the traditional centralized electricity trading and match platform in the REI. However, at the present stage, distributed electricity transaction platform based on blockchain mostly adopts traditional mechanisms such as double auctions, which may exist problems in practical utilization such as the match of clean energy is relatively difficult, the waste of electricity transmission is hard to be indicated, and the electricity seller cannot choose the electricity purchaser, etc. In this paper, a blockchain based distributed controllable electricity transaction matching system is designed and implemented in order to propose a feasible system to overcome the existing challenges. The automatic match of a single transaction on the blockchain is implemented based on smart contract technology in the system, which avoids the interference of the number of transactions and the matching cycle on the matching speed of single transaction in the matching process. Experimental results indicated that compared with traditional solutions, the supply proportion of clean energy in the entire REI increased from 4.69% to 78.15% by adopting the proposed system, and the electricity transmission loss in the entire REI is reduced by 1.08%.