• Energy Research

In the opening electricity market, distributed generation can be used as reserve capacity, which can help to improve the reliability of distributed network. A reliability price and compensation model for distribution network with distributed generation is proposed in this paper, where the interests of both the power supplier and the customer is fully considered, and the investment cost is apportioned according to the customer's reliability requirements to obtain the reliability price. In addition, this method considers the outage cost of unit electricity as a variable that varies with the duration of power outages, which breaks the traditional idea that the outage cost of unit electricity regarded as a fixed value. Furthermore, the value engineering theory is used to derive the formula of reliability compensation, and the reliability compensation mechanism for distribution network with distributed generation is constructed. Finally, an example on RBTS-BUS6 system is presented to show the validity and practicability of the proposed method.

IEC 61970 international standard which published by the International Electrotechnical Commission is abided by many power system software. Its application and implementation protects the Power Supply Companies' investments well. However, among the existing power system computation software, it is almost impossible to read the real-time data from other software's kernel. And this approach will cause some unexpected hazards to data security. Considering to the data security, the author will give out a solution to solve this problem in this paper. That's to be the semi-offline data interface. From the running software which follows IEC61970 international standard, data of the grid will be exported to a fixed path folder according to a certain time interval. And then parse the data from the folder when it is needed in the second auxiliary software of power system dispatching. The parsed data, containing necessary information of the grid's structure, will be used for the calculating in the second software's sub-module. The whole idea of the interface has got the relevant application of engineering practice, and achieved good results.

Power grid dispatching is a high-risk process, and its execution depends on an available cyber system. However, the effects of cyber systems have not caught enough attention in current research on risk assessments in dispatching processes, which may cause optimistic risk results. In order to solve this problem, this paper proposes a risk assessment model that considers the impact of a cyber system on power grid dispatching processes. Firstly, a cyber-physical switchgear state model that integrates the reliability states of both cyber system functions and switchgears is proposed, based on the transition of switchgear states in the dispatching process. Then, the potential effects of each operating step on power grid states are analyzed considering the failure model of cyber-physical system (CPS) components. The risk probabilities and consequences of the power grid states are calculated to quantify the risk index. Finally, the workings and effectiveness of this model are illustrated using the IEEE Reliability Test System-1979.

This paper presents a research on the integration and coordination control of distributed generation (DG) in a hybrid microgrid (MG). This approach integrates DGs by the direct current (DC) power lines added to the local power distri bution system, while the alternating current (AC) power lines take charge of providing the conversional power energy for the local loads, and an energy conversion station is designed to mix energy from the DC generation line with energy from the utility grid. The voltage stability logic controller makes it easier and more convenient for the coordinated control between the DGs and the utility grid, as well as avoids coping with AC integration problems, AC stability and line synchronization for instance. Matlab/Simulink simulation tool was used to show the robust operation performance and to validate the effectiveness of the proposed method above.

Aiming at the difficulty of determining the exact topology of distribution network during long-term reliability evaluation, a weighted cloud prediction model is proposed to predict the reliability of distribution network in long-term planning. Firstly, the digital features of cloud model of the influencing factors are obtained from the statistical historical data by an improved backward cloud generator. Secondly, based on the above digital features, the average weighted cloud prediction model are set up where the weight of each influencing factor is calculated by analytic hierarchy process according to the sensitivity of the influencing factors on reliability. Finally, the effectiveness and feasibility of the proposed method is demonstrated by comparison with the traditional cloud prediction method and the least square support vector machine method from the indices of maximum, minimum and average error.

With the development of power and information technology, the impact of network attacks on the power infrastructure has gradually become a hot issue at home and abroad. In order to measure the risk caused by network attack to power facilities, this paper proposes a risk assessment model for distribution network considering network attack. In this paper, firstly, the definition of attack object, network attack pattern and attack consequence are defined. Secondly, the risk assessment index of distribution network is put forward. And then, Monte Carlo method is used to simulate the network attack scene, that is, attack the elements in the target component set with a certain probability. Finally, the risk index of distribution network is calculated by the proposed evaluation model, and the risk assessment of distribution network is completed. The proposed model can deeply reveal the security level of power grid under network attack. The validity and feasibility of the proposed method are verified by an example of IEEE-33 node distribution system.

Due to the need for rural revitalization and renewable energy utilization, a large quantity of small hydropower stations is emerging, with weak-coupling flow-power features. However, a weak spatial coupling exists between the distribution of small hydropower station groups (SHSGs) and gaging stations since the small hydropower stations are usually located in remote areas lacking hydrographic facilities. That may cause weak or no coupling between the hydroregime and the power output of small hydropower plants in the target basin, thus hindering accurate power forecasting. To meet the need for short-term power generation prediction for SHSGs in intensive management areas, we propose a data-driven power-forecasting model which can mine the correlation information of weakly coupled basins while transferring hydrological knowledge to uncoupled basins. First, to make the task data domains before and after migration more similar, a similar watershed matching algorithm based on the nonlinear dimensionality reduction algorithm (Isomap) and the k -means++ algorithm is proposed; then a short-term interpretable runoff prediction model is pretrained, and features are extracted in the source basin using the temporal fusion transformer (TFT) network. After that, a heuristic ensemble fine-tuning model based on the k -fold cross-validation fine-tuning method and heuristic ensemble algorithm is proposed to transfer the public knowledge of the source basin to the uncoupled basin. Then, a TFT network is used to mine the weak-coupling relationship between the hydrological regime and the output power of an SHSG. Finally, the validity of the model is verified with an example from a European region. After considering the weakly coupled flow-power characteristics, the mean absolute percentage error (MAPE) of three SHSGs’ power prediction by the proposed method is on average 34.8% lower than that of the method without considering the hydrological information.