To reduce the convergence time and improve the anti-disturbance capability of the permanent magnet synchronous motor (PMSM) drivers, a fast sliding mode speed controller based on a new compound reaching law (NCRL) and disturbance observer compensation technique is proposed. Firstly, to solve the conflict between fast convergence rate and chattering reduction in the existing solutions, the NCRL is proposed by introducing the exponential function and the power term piecewise function. The convergence of NCRL is proved and the reaching time is certified to be independent of the system’s initial state. The proposed NCRL can effectively reduce the reaching time, and smooth the output signal while maintaining the high tracking performance of the controller under different conditions. However, external load disturbances may degrade the performance of the proposed NCRL-based speed controller. The proposed controller is then combined with an improved sliding mode observer (ISMO), named the NCRL+ISMO method, to improve the estimation accuracy of the lumped disturbance and the robustness of the system. The stability of the proposed method is analyzed by the Lyapunov function. Experimental results show that the proposed method can reduce speed fluctuation and convergence time, and enhance anti-disturbance capability.

This paper proposes a frequency regulation strategy applied to wind turbine generators (WTGs) in an isolated grid. In order to complement active power shortage caused by load or wind speed change, an improved deloading method is proposed to improve the regulation capabilities in different speed sections and to provide WTG power reserves. Considering torque compensation may cause power fluctuation, speed reference of conventional pitch control system should be reset. Moreover, to suppress disturbances caused by load and wind speed as well as overcome dependence on system parameters, a model predictive controller (MPC) is presented to generate torque compensation for each deloaded WTG, which allows each WTG to react to the disturbance differently, depending on its generator speed and the frequency deviation. Hardware-in-the-loop simulation and experimental results show that the proposed strategy can enhance frequency response ability during load changes and smoothen power fluctuations resulting from wind speed variations.

In the power sector, microgrids play a supportive role in bridging the adequacy gap in the conventional electricity supply. Trading of the generated energy has recently been improved by blockchain technology which offers a new cheap, secure, and decentralized transaction approach. Its operation is however associated with an undesired inherent delay during energy transactions initiated by the prosumers, thus, failure to timely attend to incidences of urgent demand could end up in catastrophe at the consumer’s side. This paper thus proposes a cyber-enhanced transactive microgrid model using blockchain technology with optimized participants’ permission protocol to ameliorate this challenge. It is demonstrated that the optimized blockchain participants’ permission model leads to improved transaction speed and greater convenience. The transaction speed simulation is thereafter performed and it was also demonstrated that the node population has a greater effect than the transaction block size on the transaction speed improvement.

In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO₂, SO₂, NO_x, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO₂, SO₂, NO_x, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.