A high wind-power penetration level causes increased uncertainty in power system operation because of the variability and limited predictability of wind generation. This paper proposes a novel type of unit commitment (UC) considering spinning reserve and interruptible load (IL) as operating reserve facilities to increase system flexibility for reliable, economical operation. Two uncertainty sources, load and wind generation, were modeled via autoregressive moving averages (ARMA). The formulation of interruptible load was considered in the implementation of unit commitments. Lagrangian relaxation-dynamic programming (LR-DP) was used to solve the unit commitment problem efficiently. The expected energy not supplied (EENS) was regarded as a probabilistic reliability criterion. The effectiveness of the proposed unit commitment was evaluated using an IEEE 118-bus system. The simulation results clearly demonstrated that with demand-side participation, the operating cost was significantly reduced when handling the increased uncertainty due to wind power integration within the required reliability criteria.
Abstract:The use of appropriate hourly reserve margins can maintain power system security by balancing supply and demand in the presence of errors in the forecast demand, generation outages, or errors in the forecast of wind power generation. Because the cost of unit commitment increases with larger reserve margins, cost analysis to determine the most economical reserve margin is an important issue in power system operation. Here, we define the "short-term reliability of balance" and describe a method to determine the reserve margin based on the short-term reliability of balance. We describe a case study, in which we calculate the reserve margin using this method with various standards of short-term reliability of balance. A cost analysis is then performed to determine the most economic standard, and a comparison between our method and a conventional method is carried out. The results show that our method with an economic short-term reliability of balance enables more reliable and efficient operation of the power system. Moreover, with an hourly reserve margin, we show that an increase in wind power generation can result in a significant decrease in the operating cost, which makes wind power generation economically viable.
Abstract:The penetration level of renewable generation has increased significantly in recent years, which has led to operational concerns associated with the system ramping capability. Here, we propose the flexible ramping capacity (FRC) model, which considers the practical ramping capability of generation resources as well as the uncertainty in net load. The FRC model also incorporates the demand curve of the ramping capacity, which represents the hourly economic value of the ramping capacity. The model is formulated mathematically using ramp constraints, which are incorporated into unit commitment (UC) and economic dispatch (ED) processes. Simulations are carried out using a 10-unit system to compare the FRC model with conventional methods. We show that the FRC method can improve reliability and reduce expected operating costs. The simulation results also show that, by using the FRC model, system reliability can be satisfied at high wind power generation levels while achieving economic efficiency.
Abstract:A high penetration level of renewable energy in a power system increases variability and uncertainty, which can lead to ramping capability shortage. This makes the stable operation of a power system difficult. However, appropriate management of electric vehicles (EVs) can overcome such difficulties. In this study, EVs were applied as a flexible ramping product (FRP), and a method was developed to increase the system ramping capability. When increasing the FRP to the amount required for the system, the effect on transmission lines cannot be neglected. Thus, the required FRP considering transmission constraints is calculated separately for each zone to secure deliverability. To make adjustment possible, the zonal available capacity is calculated by considering the probabilities of the location and the plugged and charged states of EVs. The applicability of EVs as an FRP resource is examined, and the results showed that they can be used at a more significant level considering the transmission constraints.
-Renewable energy integration and increased system complexities make system operator maintain supply and demand balance harder than before. To keep the grid frequency in a stable range, an appropriate spinning reserve margin should be procured with consideration of ever-changing system situation, such as demand, wind power output and generator failure. This paper propose a novel concept of dynamic reserve, which arrange different spinning reserve margin depending on time. To investigate the effectiveness of the proposed dynamic reserve, we developed a new short-term reliability criterion that estimates the probability of a spinning reserve shortage events, thus indicating grid frequency stability. Uncertainties of demand forecast error, wind generation forecast error and generator failure have been modeled in probabilistic terms, and the proposed spinning reserve has been applied to generation scheduling. This approach has been tested on the modified IEEE 118-bus system with a wind farm. The results show that the required spinning reserve margin changes depending on the system situation of demand, wind generation and generator failure. Moreover the proposed approach could be utilized even in case of system configuration change, such as wind generation extension.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with đź’™ for researchers
Part of the Research Solutions Family.