Uncertainty of the wind power output is the main influence factor for wind power utilization in bulk power system. Because of the large capacity and the load characteristic, energy-intensive load holds great potentials to share the burden of wind power fluctuation smoothing. In this paper, a control scheme of energy-intensive load power is proposed to smooth wind power fluctuation. Based on the characteristic of the energy-intensive load production, the feasibility of energy-intensive load control and its influence on the production is discussed. Feedback control is designed to regulate the saturable reactor and transformer tap changer when power support from bulk power system is limited. Regulation capacity is analyzed quantitatively based on energy-intensive load equivalent circuit model. A grid-connected energy-intensive enterprise driven by self-generation wind power plants and thermal power plants is studied as an example case. By the proposed energy-intensive load control scheme, power fluctuation of the wind farms can be smoothed with the minimum control cost. Besides, the regulation requirement of the bulk power system is eased correspondingly so that the utilization capacity of the power system can be improved significantly. Simulation is done on the real-time digital simulator (RTDS) to verify the effectiveness of the proposed control method.

With electrical performance similar to that of energy storage systems, electric vehicles (EVs) show great potential for demand response to facilitate higher penetration of distributed renewable energy. Therefore, this study presents a look-ahead scheduling model for EVs to offer a load curtailment service in a distribution network, with the aim of smoothing out the power fluctuations caused by distributed renewable energy sources. An equivalent aggregation method that is not sensitive to heterogeneous private parameters is used to extract the overall dynamic power regulation characteristics of the EV population, while a decomposition model ensures the completion of the regulation task. Besides, in the scheduling model, the variable baselines are dynamically refreshed to capture the load rebound effects caused by load curtailment, allowing more accurate regulation plans to be obtained. A two-stage iterative solution strategy is also adopted to obtain the approximate optimal solution. Simulations demonstrate both the accuracy of the aggregation model and the effectiveness of the scheduling model.

A method for evaluating the charging and discharging scheduling potential of electric vehicles in case of the occasional operation of a power grid is proposed, which describes the variations in the users’ responses. Considering the social relationships and mutual learning among users, an evolutionary game model for the optimal response in a complex network is established to study the response rule of the users for the scheduling measures. Wardrop's first traffic equilibrium model is applied to calculate the shortest time for the vehicles to arrive at the charging station from their response sites. Finally, the scheduling potential at a charging station is effectively evaluated considering the influence of the type of activities that the users participate in and their credits. Taking a test traffic network and the traffic network of Beijing third ring road as examples, the responses of the electric vehicle users are studied, and the scheduling potential of electric vehicles in different scenarios is verified. Although an increased incentive can improve the response level of the electric vehicles, it may lead to a reduction in the scheduling capacity, delays in the charging and discharging starting time, and a shortened scheduling duration.

Frequency control of isolated power systems with high penetration of wind power is a challenging task for system operators. The difficulty results from the limited frequency response capability of thermal power units to keep up with the fluctuations of wind power. This paper develops a field verification architecture to leverage aluminum smelting loads (ASLs) to participate in primary frequency control. A new method is proposed to coordinate ASLs with thermal power units to quickly stabilize the isolated power system with significant wind power fluctuations or curtailments. Moreover, a closed-loop control system for the ASLs controller is developed and tested on a real-world isolated power system in Inner Mongolia, China. Hardware-in-the-loop simulations, as well as field tests, are conducted under various scenarios of wind power fluctuations or curtailments to verify the proposed architecture for frequency control. The highly consistent simulation results and field tests demonstrate the good frequency control capability of the ASL. The regulated power from the ASL is demonstrated to be able to reach 6% of its rated power within 4 s. Working jointly with thermal power units, the ASL helps maintain the stable operation of the isolated power system, significantly improving the frequency response characteristic.

Due to the rapid development of phasor measurement units (PMUs) and the wide area of interconnection of modern power systems, the security of power systems is confronted with severe challenges. A novel framework based on data for static voltage stability margin (VSM) assessment of power systems is presented. The proposed framework can select the key operation variables as input features for the assessment based on partial mutual information (PMI). Before the feature selection procedure is completed by PMI, a feature preprocessing approach is applied to remove redundant and irrelevant features to improve computational efficiency. Using the selected key variables, a voltage stability assessment (VSA) model based on iterated random forest (IRF) can rapidly provide the relative VSM results. The proposed framework is examined on the IEEE 30-bus system and a practical 1648-bus system, and a desirable assessment performance is demonstrated. In addition, the robustness and computational speed of the proposed framework are also verified. Some impact factors for power system operation are studied in a robustness examination, such as topology change, variation of peak/minimum load, and variation of generator/load power distribution.

Developing an appropriate bidding strategy for the day-ahead market is key for load aggregators. Demand-side distributed renewable energy sources (DRESs) are increasingly connected to the grid, and each is a source of uncertainty. These uncertainties together with the coupling relationship between load and electricity price uncertainty caused by demand response finally result in the complex uncertainty situation. With the goal of obtaining robust profit, the optimal bidding decision in this complex uncertainty situation for a load aggregator when considering time-shiftable loads and DRESs is investigated in this article. The bidding process is decomposed into two stages: an energy consumption model and a bidding model. Scenarios and information gap decision theory (IGDT) are used in the two models, respectively, to handle the uncertainties in electricity prices and DRESs. To solve the established bilevel nonlinear model, a non-KKT method and backward solving algorithm are creatively proposed; they transform the bilevel model into a single-level model and greatly increase the solving speed.