Explore the vast range of titles available.

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks.

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs.

Electric motors play a crucial role in modern industrial and domestic applications. With the trend of more and more electric drives, such as electric vehicles (EVs), the requirements for electric motors become higher and higher, e.g., high power density with good thermal dissipation and high reliability in harsh environments. Many efforts have been made to develop high performance electric motors, such as the application of advanced novel electromagnetic materials, modern control algorithms, advanced mathematical modeling, numerical computation, and artificial intelligence based optimization design techniques. Among many advanced magnetic materials, soft magnetic composite (SMC) appears very promising for developing novel electric motors, thanks to its many unique properties, such as magnetic and thermal isotropies, very low eddy current loss, and the prospect of low-cost mass production. This paper aims to present a comprehensive review about the application of SMC for developing various electric motors for electric drives, with emphasis on those with three-dimensional (3D) magnetic flux paths. The major techniques developed for designing the 3D flux SMC motors are also summarized, such as vectorial magnetic property characterization and system-level multi-discipline robust design optimization. Major challenges and possible future work in this area are also discussed.

One of the keys to the success of the fourth industrial revolution (Industry 4.0) is to empower machinery with cyber–physical systems connectivity. The digital twin (DT) offers a promising solution to tackle the challenges for realizing digital and smart manufacturing which has been successfully projected in many scenes. Electrical machines and drive systems, as the core power providers in many appliances and industrial equipment, are supposed to be reinforced on the verge of Industry 4.0 in the fields of design optimization, fault prognostic and coordinated control. Therefore, this paper aims to investigate the DT modelling method and the applications in electrical drive systems. Firstly, taking the high-speed permanent-magnet machine drive system as an example, multi-disciplinary design fundamentals and technologies, aiming at building initial mechanism and simulation models, are reviewed. The state-of-the-art of DT technologies is figured out to serve for high-precision and multi-scale dynamic modelling, by which a framework for DT models of electrical drive systems is presented. More importantly, fault diagnosis and optimization strategies of electrical drive systems in the decision and application layer are also discussed for the DT models, followed by the conclusions presenting open questions and possible directions.

As we face issues of fossil fuel depletion and environmental pollution, it is becoming increasingly important to transition towards clean renewable energies and electric vehicles (EVs). However, designing electric motors with high power density for EVs can be challenging due to space and weight constraints, as well as issues related to power loss and temperature rise. In order to overcome these challenges, a significant amount of research has been conducted on designing high-power-density electric motors with advanced materials, improved physical and mathematical modeling of materials and the motor system, and system-level multidisciplinary optimization of the entire drive system. These technologies aim to achieve high reliability and optimal performance at the system level. This paper provides an overview of the key technologies for designing high-power-density electric motors for EVs with high reliability and system-level optimal performance, with the focus on advanced magnetic materials and the proper modeling of core losses under two-dimensional or three-dimensional vectorial magnetizations. This paper will also discuss the major challenges associated with designing these motors and the possible future research directions in the field.

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Under a two-part tariff, the user-side installation of photovoltaic and energy storage systems can simultaneously lower the electricity charge and demand charge. How to plan the energy storage capacity and location against the backdrop of a fully installed photovoltaic system is a critical element in determining the economic benefits of users. In view of this, we propose an optimal configuration of user-side energy storage for a multi-transformer-integrated industrial park microgrid. First, the objective function of user-side energy storage planning is built with the income and cost of energy storage in the whole life cycle as the core elements. This is conducted by taking into consideration the time-of-use electricity price, demand price, on-grid electricity price, and energy storage operation and maintenance costs. Then, considering the load characteristics and bidirectional energy interaction of different nodes, a user-side decentralized energy storage configuration model is developed for a multi-transformer-integrated industrial park microgrid. Finally, combined with the engineering practice constraints, the configuration model is solved by mixed integer linear programming. The simulation test demonstrates how the proposed model can successfully increase the economic benefits of an industrial park. Electricity and demand costs are reduced by 11.90% and 19.35%, respectively, and the photovoltaic accommodation level is increased by 4.2%, compared to those without the installation of energy storage system.

The magnetic properties of magnetic cores are essential for the design of electrical machines, and consequently appropriate mathematical modeling is needed. Usually, the design and analysis of electrical machines consider only the one-dimensional (1D) magnetic properties of core materials, i.e., the relationship of magnetic flux density (B) versus magnetic field strength (H), and their associated power loss under 1D magnetization, in which the B and H are constrained in the same orientation. Some studies have also been performed with the two-dimensional (2D) magnetizations in which the B and H are vectorial, rotating on a plane, and they may not be in the same direction. It has been discovered that the 2D rotational property is very different from its 1D alternating counterpart. However, the magnetic fields in an electrical machine, in particular claw pole and transverse flux machines, are naturally three-dimensional (3D), and the B and H vectors are rotational and may not lie on the same plane. It can be expected that the 3D vectorial property might be different from its 2D or 1D counterpart, and hence it should be investigated for the interests of both academic research and engineering application. This paper targets at a general summary about the magnetic material characterization with 3D vectorial magnetization, and their application prospect in electrical machine design and analysis.

With the increasing demand for smaller, lighter, and more affordable electromagnetic devices, there is a growing trend toward developing high-power-density transformers and electrical machines. While increasing the operating frequency is a straightforward approach to achieving high power density, it can lead to significant power loss within a limited volume, resulting in excessive temperature rise and device degradation. Therefore, it is crucial to design high-power-density electromagnetic devices that exhibit low power loss and efficient thermal dissipation to address these challenges. Advanced techniques, such as the utilization of novel and advanced electromagnetic materials, hold great promise for overcoming these issues. Specifically, nanocrystalline and amorphous magnetic materials have emerged as highly effective solutions for reducing power loss and increasing efficiency in electromagnetic devices. This paper aims to provide an overview of the application of nanocrystalline and amorphous magnetic materials in transformers and electrical machines, along with key technologies and the major challenges involved.

In this paper, to study the impact of immobilized microorganisms on runoff wastewater treatment, the immobilized microbial activity pellets have been prepared in the paper by using polyvinyl alcohol as the embedding agent, the sodium alginate, silicon dioxide, calcium carbonate, attapulgite or activated carbon as the additive and the Limon microbial bacteria as a microbial agent. Through the orthogonal experiment, the optimal embedding condition of microbial immobilization is determined by taking the removal rate of overlying waterbody COD and NH super(+) sub(4)-N as the assessment index, reaching up to 71.87% and 92.65%, respectively. The correlation analysis shows that the simultaneous nitrification and denitrification reactions are carried out in the removal process of overlying waterbody NH super(+) sub(4)-N.