Editorial for the Special Issue on Sustainable Power Systems and Optimization

In recent years, the installed capacity of renewable energy in power systems has increased rapidly to resolve global climate warming issues and promote sustainable development. Large numbers of renewable energy resources with intermittent characteristics, such as wind turbines, solar PVs, and electric vehicles, have been equipped on both the power generation and demand sides, bringing significant challenges to the optimal planning, operation, and control of sustainable power systems. This Special Issue in Sustainability, titled “Sustainable Power Systems and Optimization,” compiles some recent research accomplishments in the field of sustainable power systems. It comprises eleven recent papers, including the latest modeling techniques, algorithm improvements, and practical applications.

The power prediction problems were studied to cope with the impact on the power system brought by the gradually increasing uncertainty of power generation and demand [1,2]. Chen et al. [1] tackled the power prediction issue of prosumers with the dual attributes of load and power supply. Based on dynamic curve segmentation and trend feature perception, the proposed short-term power prediction model enhanced the ability to explore the long-term macroscopic trend, short-term local variations, and time-sharing consumption features. The proposed model could achieve higher accuracy under different renewable energy permeability scenarios. Zhang et al. [2] dealt with the multi-energy load forecasting process on the demand side. They used the CNN-Attention-LSTM model based on federated learning, which can increase data diversity and improve model generalization while protecting data privacy. The study concluded that federated models could achieve an accuracy comparable to the central model while having a higher precision than individual models.

The papers [3,4,5,6] researched the integrated energy microgrid model to realize flexible resource allocation. From the perspective of exploring the potential value of reliable resources in the integrated energy microgrid, Chen et al. [3] established the reliability principal–agent mechanism. They proposed a cooperative gaming model of Integrated Energy Operator (IEO) and Integrated Energy User (IEU) based on the optimal dispatching model. They conducted economic, system reliability, and energy structure analyses through the case study. Wen et al. [4] discussed the microgrid with electric vehicles. They constructed the mathematical model of the microgrid with EVs and proposed a microgrid optimization scheduling strategy based on Deep Q-learning. Compared with the traditional optimization algorithms, the proposed strategy had the ability of online learning and could cope with the randomness of renewable resources better.

Behera et al. [5] described a multi-area (two and three) renewable-energy-source-integrated thermal-hydro-wind power generation structure along with fleets of plug-in electric vehicles (PEVs) in each control area. The study concluded that the proposed controller provided comparable results with other fractional-order and conventional controllers under varying load conditions. The coordinated control of frequency and natural gas pressure for the electric–gas system was the topic that was covered by Fan et al. [6]. They established a multi-microgrid load frequency control model, including MT, P2G equipment, electric vehicles (EVs), distributed power sources, and loads. Then, the Muti-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm was proposed to design the frequency controller for the multi-microgrid. The results showed that frequency deviation caused by wind power, load disturbance, and pressure fluctuation caused by load fluctuation of the natural gas network was significantly suppressed.

The papers [7,8] were devoted to improving the algorithm tackling the operation problem of the sustainable power system. Yang et al. [7] leveraged the BP neural network algorithm to make an approximate sensitivity calculation method in the SC-OPF model. The BP neural network was introduced into the SC-OPF to analyze the mapping relationship between the generation, bus injection power, line power, and the minimum damping ratio of the system. The combination of AI and SC-OPF is instructive in improving sustainable power systems’ power quality and stability. Zhou et al. [8] realized the distributed computation by combining the logarithmic barrier function and virtual agent. They investigated economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP). In addition, a source-load-storage collaborative optimization scheme was formed, which can implement the beneficial interaction between power generators and power users. The study concluded that a distributed improved ADMM algorithm could reduce the complex computation and simplify the solution process.

The research on the device’s performance improvement in practical applications was carried out deeply [9,10,11]. Faizan et al. [9] studied a long-life LED driver with the ability of power factor correction based on integrating a half-bridge LLC resonant converter and two boundary-conducted boost converters. Compared with conventional switch-mode LED drivers, the proposed driver can reduce switching losses, improve harmonic distortion, achieve power factor correction, etc. Industrial manipulators have been widely applied to improve the efficiency of renewable energy systems.

Shi et al. [10] proposed an error-tracking adaptive learning control method for the constrained flexible-joint manipulator with initial errors. The output tracking accuracy of the manipulator was guaranteed under arbitrary initial values and iteration-varying tasks. Moreover, the presented method solved the repeated positioning problem, which can be appropriate for a realistically sustainable energy system. Zhou et al. [11] studied the influence of temperature distribution during the curing process of the capacitor core on the margin design for bushing construction. This paper guided the comprehensive evaluation and manufacturing of the low-defect capacitor cores of large-size high-voltage direct current (HVDC) bushings.

We thank all the authors for submitting their papers to this Special Issue. We also wish to thank all the reviewers for their careful and timely reviews, which helped improve the quality of this Special Issue.