The virtual synchronous generator (VSG) improves the robustness of the inverter-interfaced distributed generator (IIDG) against instability by introducing a virtual inertia. However, the transient response of the active power and the angular frequency conflict with each other for the IIDG with fixed inertia control. It is necessary to adopt adaptive control to improve overall performances of power and frequency as the operating condition changes. This paper analyzes the impact of the inertia on power and angular frequency. A dual-adaptivity inertia control strategy is proposed to offer a responsive and stable frequency support and also achieve the balance between power regulation and frequency regulation according to different operating conditions. The principle of parameter design is given to obtain the range of adaptivity. Quantitative assessment considering the cumulative effect of the output deviation and its duration is also presented to evaluate the proposed strategy intuitively. The strategy is further verified based on PSCAD/EMTDC and a hardware-in-loop experiment platform based on RTDS. Results confirm that the proposed strategy not only achieves rapid frequency response with slight dynamic deviations under disturbances but also strikes a balance between the frequency and power and leads to improved overall control.

Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

Large-scale wind power integrated into power grids brings serious uncertainties and risks for power system safe operation, and it is imperative to evaluate power system security risk pertinent to high-level of uncertainties. In this paper, a comprehensive source–network–load probabilistic model, representing the typical uncertainties penetrated in power generation transmission consumption portion, is firstly set for power system operation. Afterwards an integrated LHS–CD approach based on the Latin hypercube sampling (LHS) and Cholesky decomposition (CD) is tailored to effectively conduct the security risk assessment, in which the LHS is utilized to stratified sample the uncertainties of wind power and thermal power, transmission line outage, and load demands, while the CD part is adopted to address the correlations of uncertainties by rearranging the sampled matrix generated by LHS. Moreover, static voltage risk and transmission line overloaded risk index are properly defined for quantitatively evaluating power system operational security risk. Simulation results of a modified New England 39-bus system confirm that the proposed integrated LHS–CD approach is effective and efficient for power system security risk assessment with consideration of source–network–load demand uncertainties.

To meet the challenge of large-scale renewable energy penetration and take full advantage of existing AC infrastructure, the bipolar DC distribution system is of interest. In this article, the system structure and characteristics of the bipolar DC distribution network are proposed. The three-level Neural Point Clamped Converter (NPC) is used in the proposed system to construct the bipolar DC system. To optimize the DC system performance, an improved cooperative control and energy management strategy is proposed mainly to mitigate DC voltage fluctuation and balance the positive and negative phase voltage. The improved strategy consists of (1) 2-degree of freedom (2DOF) PID controller in traditional voltage control loop; (2) cooperative controller to take full advantage of storage system; (3) voltage equalization controller to balance two-phase voltages; and (4) the energy management system to dispatch the response job to batteries and supercapacitors. Experiments and simulations are performed to validate the effectiveness of the proposed strategy.