California Independent System Operator Market Research Articles

Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach With Clustering Technique

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate cluster-specific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long- and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.

Active-salinity-control reverse osmosis desalination as a flexible load resource

The proposed active-salinity-control reverse osmosis (ASCRO) system dynamically controls energy consumption by operating across a range of feed salinity, allowing it to shift over a wide range of pump feed flows and pressures. ASCRO utilizes feedwater from both low- and high-salinity sources. Enabling a dynamic power consumption profile can enhance demand-response capabilities, compensating for stressors on the grid. Moreover, ASCRO can improve the integration of renewable energy (RE) by responding to power fluctuations without compromising permeate production. This system can include on-site RE and energy storage to power the ASCRO plant and provide services to the grid. We considered the following grid-connected scenarios: 1) ASCRO, 2) ASCRO and battery storage, 3) ASCRO and photovoltaics (PV), and 4) ASCRO, battery storage, and PV. The levelized cost of water (LCOW) was minimized by providing load-shifting and regulation capacity services in the California Independent System Operator (CAISO) market. We quantified that the ASCRO plant can ramp from minimum to maximum load within 84 s, which is adequate for participation in fast-timescale markets. The LCOW for these scenarios ranged from 49 to 59 cents/m3. We also present sensitivity analyses showing the effects of capital cost, CAISO market prices, and PV size on LCOW.