Quantifying the capacity credit as a reliability-based index for a large-scale wind power generation planning using Stochastic Programming
As the deployment of renewable power generation in large-scale utilization remains to grow, it will become increasingly important to evaluate the reliability and economic impact of these intermittent resources in the power grid. Therefore, capacity credit (CC) is proposed to measure the effective load carrying capacity of the installed wind power generation (WPG) units at designated reliability level. Furthermore, the CC values assist the decision makers to better manage the operation and planning of the electrical power system and to ensure the resiliency when bulk renewable power generations are integrated to the grid with the minimum capacity of Energy storage system (ESS) required to resolve the uncertainty of renewable resources availability. The penetration factor and the availability of the renewable power generation are significant factors influencing the capacity credit value, besides the overall power system reliability level. The capacity credit is probabilistically determined using Monte Carlo simulation to evaluate the loss of load expectation (LOLE) at different peak loads and analytically determined the capacity credit of the power generation for several installed capacities. This research project addresses a unique approach of WPG planning problem that determines its capacity and location by maximizing the expected capacity credit of planned WPG units. This approach comprehensively evaluates the existed power system generation and transmission capabilities and utilize it to run the planning process to fulfill the highest generation adequacy to serve the demand with minimum operational cost. The stochastic programming optimization is used to model the planning scheme with considering all possible scenarios associated with the wind resources and load demand. Case studies based on the IEEE RTS-96 system are conducted to demonstrate the effectiveness of the proposed method, followed by a study on a selected area within the Saudi Arabia national grid system, to adopt the proposed planning strategy supported by an operational sensitivity analysis using Monte Carlo simulation. The proposed project is aligned with the enormous contributions that are taking place along with the ambitious plan in renewable energy deployment in the Kingdom of Saudi Arabia, to satisfy the 2030 vision of reducing the fossil fuel as the primary source of electric power generation.