In order to improve the high torque density of coaxial magnetic gear, a novel high temperature superconducting (HTS) bulks CMG with an eccentric pole of inner rotor and Halbach arrays of outer rotor is proposed in this paper. According to the Meissner effect, the HTS bulks replace the traditional epoxy resin to improve the flux-modulation effect. Moreover, the HTS bulks CMG has an eccentric structure of permanent magnets (PMs) on inner rotor and has Halbach PMs arrays on the outer rotor. The proposed HTS bulks CMG topology with 4 inner and 23 outer pole pairs is established. The topology performance is assessed and verified by finite element analysis (FEA).

Modern power systems are confronted with widespread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load-frequency-control (LFC) scheme based on a parameter tuning strategy for fractional-order proportional–integral–derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, and gas power generator and a hydrogen-based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme is tested through load disturbance and renewable energy fluctuations test scenarios and provides a comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of the IPS and has excellent robustness.

To achieve automatic generation control coordination in the islanded smart grid environment resulted from the increasing penetration of renewable energy, a novel ecological population cooperative control (EPCC) strategy is proposed in this paper. The proposed EPCC, based on the new win‐loss criterion and the time tunnel idea, can compute the win‐loss criterion accurately and converge to Nash equilibrium rapidly. Moreover, based on a multiagent system stochastic consensus game (MAS‐SCG) framework, a frequent information exchange between agents (AGC units) is implemented to rapidly calculate optimal power command, which achieves the optimal cooperative control of the islanded smart grid. The PDWoLF‐PHC(λ), WPH strategy (wolf pack hunting), DWoLF‐PHC(λ), Q(λ)‐learning, and Q‐learning are implemented into the islanded smart grid model for the control performance analysis. Two case studies have been done, including the modified IEEE standard two‐area load frequency control power system model and the islanded smart grid model with distributed energy and microgrids. The effectiveness, stronger robustness, and better adaptability in the islanded smart grid of the proposed method are verified. Compared with five other smart ones, EPCC can improve convergence speed than that of others by nearly 33.9%–50.1% and the qualification rate of frequency assessment effectively by 2%–64% and can reduce power generation cost.

Traditional anti-quantum methods and multi-signature technologies to secure the blockchain against quantum attacks will quickly reduce the efficiency and scalability of the industrial blockchain, where the computational resources will experience a polynomial rise with the increasing number of traders. Here, a quantum blind multi-signature method is proposed for the multi-party transaction to provide anti-quantum security. First, the proposed multi-party transaction frame and quantum key distribution in the industrial blockchain are introduced. It integrates a novel quantum blind multi-signature algorithm that is based on the quantum entanglement mechanism, and it is absolutely secure in theory. Second, the anti-quantum multi-signature algorithm is illustrated, where there are four phases, i.e., initialization, signing, verification, and implementation. Third, the security and complexity of the proposed framework are analyzed and compared with related methods in references, and our proposed method is verified to be able to offer good computational performance and blockchain scalability for multi-party transaction. Last, the paper is summarized and future research directions are proposed.

Studying the faster and more efficient method of solving the uncertain security-constrained unit commitment (SCUC) problem is an urgent need for the development of power systems under the background of large-scale wind power access and power dispatching. This study proposes an improved constrained order optimization (COO) algorithm to solve the uncertain SCUC problem. First, the data-driven discrete variable identification strategy is incorporated into the COO rough model, and then, the invalid security constraints identification strategy is incorporated into the COO accurate model. Finally, the improved COO algorithm combines the discrete variable identification with the invalid security constraint identification to make the uncertain SCUC decision. The results of the IEEE 118-bus test system showed that, compared with the traditional COO algorithm, the improved COO algorithm proposed has higher accuracy and better efficiency.

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.