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 Regents of the University of California, San Diego


La Jolla, CA

Recipient Location


Senate District


Assembly District



Amount Spent



Project Status

Project Result

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.

The Issue

The variable nature of solar power is of concern to electric grid operators in California. If short-term solar variability cannot be predicted or reduced, the integration cost of solar power increases through investment in energy storage or regulation capacity by the grid operator. Especially at the microgrid and distribution feeder level, the geographic diversity is less available and solar generation is the primary contributor to net load variability, causing voltage issues affecting service quality and reliability.

Project Innovation

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.

Project Benefits

The uncontrollable generation of renewable energy sources, such as solar photovoltaics poses numerous challenges to the electric grid. The large growth of electric vehicles (EV) has potential to exacerbate those challenges due to increases in load, especially at inopportune times. However, the flexibility of scheduling EV charging around forecasted PV production provides a solution to this problem. Furthermore, the project improved solar energy forecast accuracy by 10% over the existing persistence forecast method for 10 minute ahead to optimize the operation of distributed energy resources. It will mitigate the concerns of electric operations over the variable nature of solar power that contributes to net load variability, causing voltage issues affecting service quality and reliability.

Lower Costs


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.

Economic Development

Economic Development

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

Environmental & Public Health

Environmental Sustainability

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

Greater Reliability


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

Key Project Members

Project Member

Jan Kleissl



San Diego Gas & Electric Company


South Coast Air Quality Management District


University of California, Los Angeles


Mason Willrich


Olivine, Inc.




Match Partners


San Diego Gas &amp


Electric Company


Itron, Inc. dba IBS


University of California, San Diego, San Diego Supercomputer Center




Contact the Team