Due to advantages such as being clean and safe, photovoltaic (PV) generation has become an effective way to solve the energy crisis and environmental pollution problems. However, the PV inverter does not have inertia and damping characteristics. With the increased penetration of the PV generation into the power system, large-scale PV grid-connection has posed a great challenge to grid frequency stability. The Cascaded H-bridge (CHB) inverter has become a promising candidate for the PV generation system by virtue of advantages such as modular structure and direct grid-connection without power frequency transformers. An inverter control strategy with grid frequency support function is proposed in this paper for the three phase CHB based PV generation system. With the PV string active power reserve, grid frequency dynamic characteristics are improved without energy storage devices. In order to solve the common module power unbalance problem for the CHB topology, the PV string output power is controlled to be balanced with the reserved power distribution control between PV strings proposed in this paper. The H-bridge inverter input power balance is maximized, and balanced three phase grid-connected current output is achieved. The over-modulation risk of H-bridge modules is reduced. At last, simulation models are built in Matlab/Simulink, and a 10kW experimental platform is also built. The effectiveness of the control strategy proposed is validated by simulation and experimental results.

In a medium voltage wind turbine with multiple rotors, multiple rotors are used for the wind energy capture, and directly drive permanent magnet synchronous generators. The medium voltage grid is coupled with a cascaded H-bridge inverter. Advantages such as more wind energy capture, higher reliability and easy extension to higher power and grid connection voltage ratings can be achieved. In this paper, a novel frequency response control strategy for the wind turbine is proposed. Under the normal operation condition, all rotors are controlled individually to extract maximum wind energy. During the deloading operation, the coordinated control on all rotors is executed to obtain the required power reserve. In case the grid frequency sudden dip occurs, the power reserve is released quickly to provide the wind turbine frequency response. A novel control scheme to maintain the utmost transmitted power equilibrium between three phases, and between modules in one phase, and three phase grid connection current equilibrium at both deloading and frequency response modes is proposed, leading to the H-bridge inverter over-modulation risk reduction and grid connection current quality improvement. Simulation and experimental results have validated the proposed frequency response control strategy for the medium voltage wind turbine with multiple rotors.

This paper adopts a hierarchical multi-time scale coordinated scheduling framework to jointly optimize occupant thermal comfort and operating cost in building microgrids. Operating cost measures system economic performance, while thermal discomfort is quantified by the time-weighted $L_2$ norm of predicted mean vote (PMV). With other thermal comfort parameters held constant, PMV is evaluated using the indoor temperature obtained from a first-order RC building thermal model. In the day-ahead stage, the optimization is carried out based on day-ahead forecasts, with the operating cost and the thermal discomfort index minimized simultaneously. In the intraday stage, short-term forecasting information is used to update the day-ahead baseline schedule through rolling optimization. To ensure continuous scheduling, a power constraint based on a state-of-charge (SOC) trajectory corridor is further proposed for the battery energy storage system, thereby guaranteeing consistency between the initial and terminal SOC. The resulting bi-objective optimization problem is solved via a reinforcement learning-assisted multi-objective particle swarm optimization (MOPSO) algorithm. Compared with fixed-parameter MOPSO under the same runtime budget, the proposed algorithm increases hypervolume by 34.64% and reduces inverted generational distance by 87.14% on average. In a tested scenario, intraday rolling optimization reduces operating cost and thermal discomfort by 6.24% and 15.91%, respectively, relative to day-ahead scheduling alone.