Big wind farms must be integrated to power system. Wind power from big wind farms, with randomness, volatility and intermittent, will bring adverse impacts on the connected power system. High precision wind power forecasting is helpful to reduce above adverse impacts. There are two kinds of wind power forecasting. One is to forecast wind power only based on its time series data. The other is to forecast wind power based on wind speeds from weather forecast. For a big wind farm, due to its spatial scale and dynamics of wind, wind speeds at different wind turbines are obviously different, that is called wind speed spatial dispersion. Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper. An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion. A case study of several wind farms has demonstrated that even if the forecasting average wind speed is accurate, the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.

Integrating a battery energy storage system (ESS) with a large wind farm can smooth the intermittent power obtained from the wind farm, but the smoothing function will not be achieved if multiple ESSs are used to smooth the fluctuations in individual wind power plants in a distributed pattern. Therefore, this study focuses on the development of a control strategy to optimize the use of multiple ESSs to accelerate the adoption of wind energy resources. This paper proposes a quasi-automated generation control (QAGC) strategy to coordinate multiple ESSs, which responds to the grid dispatch demand rather than smoothing out the intermittent power from individual wind farms. The aims of QAGC are to ensure that multiple ESSs provide a service that is as balanced as possible, so more wind power systems at various scales can be accepted by the grid, as well maximizing the low-carbon benefits of ESSs. The effectiveness of QAGC is demonstrated by using data from an actual gigawatt scale cluster of wind plants.

For heavily curtailed wind power caused by the coexistence of large-scale wind power and high-penetration combined heat and power (CHP) units, especially during the winter heating period in northern China, the flexible operation of CHP units with the introduction of thermal energy storage (TES) and heating load flexibility is very helpful for reducing wind power curtailment in northern China. Therefore, based on the construction of a two-stage trading mode in the day-ahead market, which consists of the conventional energy market and peak regulation market, a three-level optimization method is proposed for integrating heating loads and storage control through the coordinated operation of CHP units and TES devices and heating load management. Thus, the operation mode of CHP units can be optimized to supply peak regulation capacity for curtailed wind power under a certain agreed peak regulation price. Finally, the typical influence factors, such as the capacity of TES devices, the agreed peak regulation price, and the different operation modes, will be discussed to demonstrate the resulting economic benefit and curtailed wind power consumption.

Allowing a significant reduction of capacitor volumes as well as scalable in terms of voltage levels and control flexibility, stacked flying capacitor inverter has gained great industrial attention recently. However, it also needs to ensure balancing flying capacitor voltages under all operation conditions. A novel PWM method is proposed for stacked flying capacitor multilevel inverter to control the balance of flying capacitor voltages, which can be readily implemented on a digital signal processor and also guarantee the switch frequency of each switching device almost same. Extensive simulation results demonstrated its validity in a 5-level stacked flying capacitor inverter