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Modeling in building-to-grid integration
Published in Jan L.M. Hensen, Roberto Lamberts, Building Performance Simulation for Design and Operation, 2019
Sen Huang, Thomas Sevilla, Wangda Zuo
The duck curve describes the power demand on the power grid (named power draw) under a high PV power penetration. As shown in Figure 17.12, the power draw begins to drop slowly in the morning when the power generation from the PV becomes available. It then reaches its valley in the afternoon as the PV power generation reaches its peak. After that, the power draw starts to increase since the PV power generation decreases. In the early evening, the power draw ramps up quickly when the power usage peaks and the contribution from PV power falls due to the sunset. Since the power draw curve resembles a duck, it is named duck curve.
Advanced Protection and Control for the Smart Grid
Published in Stuart Borlase, Smart Grids, 2018
Jens Schoene, Muhammad Humayun, Stuart Borlase, Marco C. Janssen, Michael Pesin
Load Masking and Changing Load Shape: PV deployed in large numbers can mask load, which is illustrated in Figure 6.17 where PV generation reduces the power consumption on the feeder observed at midday. The resulting shape is often referred to as a “duck curve”. Conventional power plants on the transmission system need to ramp up generation quickly to supply the sharply changing load demand (between 3 pm and 6 pm in the example illustrated in the figure); this is one of the DER integration challenges that migrate from the distribution system to the transmission system.
Model Predictive Control Based Ramp Minimization in Active Distribution Network Using Energy Storage Systems
Published in Electric Power Components and Systems, 2019
Jiayong Li, Zhao Xu, Jian Zhao, Songjian Chai, Yi Yu, Xu Xu
Net load, defined as the difference between the actual load and the renewable generation, has been widely used to investigate the impact of renewable energy sources (RESs) integration. Over the years, the California independent system operator (CAISO) has observed a sharp decline during the sunrise and a steep rise during the sunset of the daily net load curve, which is known as the “duck curve” [5]. As a result, the duck curve of net load imposes a hard ramp-down and ramp-up requirement on power systems. In order to address this issue, several approaches have been proposed. For instance, Crăciun et al. [6] proposes a ramp limitation oriented control strategy for large scale PV power plants considering the PV curtailment. Though it is effective in controlling the PV ramp, it also leads to the reduction of economic and environmental benefit of RES. Another method is to design new products called flexible ramping products (FRPs) and procure them from the market, which has been extensively studied before recent implementation by CAISO and Midcontinent ISO (MISO) [7]. Wu et al.[4] evaluates the influence of FRPs on the optimal economic dispatch and shows that FRPs can reduce dispatch cost. Wang and Hodge [8] presents a comprehensive review on the modeling and utilization of FRPs. To sum up, much effort has been made to alleviate the ramping effect in transmission networks. Nevertheless, few works have studied the ramping problem in DNs even though it can aggravate the shortage of ramping capacity in transmission networks.
A simulation study for residential electricity user behavior under dynamic variable pricing with demand charge
Published in IISE Transactions, 2018
As the solar energy production reaches its highest value around noon and remains high during the mid-afternoon, we expect the additional solar power to directly fulfil the demand during this period. When the solar production starts to decline toward the end of the day, we observe a sudden ramping of power required from the grid, since the residential load demand does not reduce until after around 9 pm (Fig. 10). This ramping nature of the required power from the grid results in a typical “duck curve” (Denholm et al., 2015). Practically, this suggests that when a utility company decides on an appropriate capacity for its solar plant, it needs to acknowledge that an increased capacity of the solar plant will cause the average load, but not necessarily the peak load, to reduce. The latter is true especially when the peak has already been shifted toward the evening. Our simulation results in Figure 11 confirm this phenomenon by plotting the reduction in average monthly peak against various capacities of the solar plant. For the city of study with an estimated 5500 households, we observed that any capacity above 30 MW will not help in further peak reduction and thus we selected 30 MW as the solar plant capacity for our study.