Solar Forecast Based Optimization of Distributed Energy Resources in the LA Basin and UC San Diego Microgrid
Solar forecast based optimization of distributed renewable energy resources
The utility customers who use solar forecasting and smart EV charging could achieve a 67% reduction in energy costs over the year, reducing monthly peak demand by 63%. This study reveals that using aggregated vehicle load large enough to absorb the solar output on the studied circuit is years in the future. The studied circuit showed that connected PV output created an energy valley of 64.5 MWh. Using a typical commuter PEV requiring 7 kWh means that roughly 9,200 vehicles must be connected during the solar output period to create an adequately sized energy sink to absorb the full amount of this oversupply. The executive order B-48-18 will improve the perspectives for EV charging and grid net load balancing in California. But at 929,000 commercial buildings in California, Oregon, and Washington, even 250,000 chargers will fall short of the amounts required in this example.
This project aimed to integrate high-accuracy solar forecasting to optimize the operation of distributed energy resources, and utilize the value of solar forecasting in utility grid operations to improve grid reliability, reduce ratepayer costs and increase safety. The objectives were to apply forecasts to inform control and scheduling decisions for distributed energy resources with emphasis on energy storage and electric vehicle charging control at warehouse photovoltaic clusters in the LA-Orange-Riverside-San Bernardino-San Diego Counties as well as the UCSD microgrid.
The project showed that utility customers who use solar forecasting and smart electric vehicle charging could achieve a 67 percent reduction in energy costs over the year. Monthly peak demand was reduced by 63 percent on average.
The tool and strategies developed in the project have the potential of boosting the economic activities associated with the optimized use of distributed solar energy resources and the reduction of grid net load variability. The e
Optimized use of distributed solar energy technologies will lead to reduced water consumption and greenhouse gas (GHG) emissions in the energy generation sector. Furthermore, adoption of electric vehicles (EV) as alternative to f
The project integrates high-accuracy solar forecasts to distributed energy resources (DERs) and provide the grid operators and balancing authorities the information needed to optimize operations leading to a more responsive and r