Power system flexibility is becoming increasingly critical in modern power systems due to the quick switch from fossil fuel-based power generation to renewables, old-fashioned infrastructures, and a sharp rise in demand. If a power system complies with financial restrictions and responds quickly to unforeseen shifts in supply and demand, it can be considered flexible. It can ramp up production during periods of high demand or increase it during unanticipated or scheduled events. The broad use of renewable energy in the power grid can provide environmental and economic benefits; nevertheless, renewables are highly stochastic in nature, with variability and uncertainty. New management with adequate planning and operation in the power system is necessary to address the challenges incorporated with the penetration of renewable energy. The primary aim of this review is to provide a comprehensive overview of power system flexibility, including appropriate definitions, parameters, requirements, resources, and future planning, in a compact way. Moreover, this paper potentially addresses the effects of various renewable penetrations on power system flexibility and how to overcome them. It also presents an emerging assessment and planning of influential flexibility solutions in modern power systems. This review’s scientific and engineering insights provide a clear vision of a smart, flexible power system with promised research direction and advancement.

Wireless power transfer network (WPTN) is composed of nodes and power transfer routes, which can supply power for group devices. In the WPTN, it is difficult to build a definite power transfer route under the random node distribution condition. In this article, a dynamical construction and optimization strategy of power transfer route is proposed to adapt to random node distribution. In order to establish a definite route among grouping nodes, each node is designed to switch its role flexibly through a topology switching method. Furthermore, in the switching process of power transfer route, the switching method of node role shows that the transition is smooth. Then, an actual WPTN with five randomly distributed nodes verifies the effectiveness of the proposed method.

Hourly wind power ramps in ERCOT are studied by applying extreme value theory. Mean excess plot reveals that the tail behavior of large hourly wind power ramps indeed follows a generalized Pareto distribution. The location, shape, and scale parameters of generalized Pareto distribution are then determined by using mean excess plot and the least square technique, from which risk measures including α quantile value at risk and conditional value at risk are calculated.

Real-time actuation of scheduled reserve capacity in power system operations with high penetration of wind power is prone to failure on account of unexpected shortcomings in network transfer capability. In this paper, a graph partitioning-based reserve zoning method is incorporated into the security-constrained unit commitment to improve the deliverability of operating reserves in a reserve zone and mitigate possible congestions caused by uncertain wind power. A graph representation of power system is proposed in which the edge weights are quantified by the likelihood of secure transmission utilization for each line. The probability distribution of line flows are characterized by the uncertainty of multiple correlated wind farm output forecasts as well as credible line outage contingencies reflected on the line flows using distribution factors. The minimum k-cut problem using the Gomory-Hu equivalent tree is addressed as a simple and efficient method to solve the NP-complete partitioning problem. The resultant zones can assure reduced risk of congested operating conditions and thus, provide a new approach to efficient management of intra-zonal congestions.

Numerical weather predictions used for wind power forecasting might not be updated in a timely manner in practice, due to its high computational complexity and complicated postprocessing. Thus, the accuracy of wind power forecasts could be significantly compromised especially during wind ramp events. This paper presents an innovative method for improving regional wind power ramp forecasting through ensemble learning of numerical weather prediction models, by using real-time weather measurements as the supervisory data. The numerical weather prediction models are combined to minimize the discrepancy between the forecast values and the real-time measurements in the trend of wind ramps, and the weights of the linear combination are calculated through gradient boosting. The proposed method is non-intrusive and could be efficiently carried out online. The proposed method is evaluated on historical ERCOT wind power ramp events, and compared with existing ensemble aggregation method using simple averaging. The results reveal the effectiveness of the proposed method for improving wind power forecasting during wind ramp events.