With the rapid emergence of community choice energy agencies (CCAs) throughout the state, California schools, municipalities and other public agencies are well positioned to take advantage of the new choice in how their electricity is supplied. For most public agencies, transitioning to a CCA from their incumbent utility provider will result in electricity cost savings.
Many CCAs are expanding their electricity procurement plans to include deployment of distributed energy resources (DERs) like on-site solar, battery energy storage, electric vehicle supply equipment, etc. as a means to reduce their procurement costs and provide additional value to their customers. TerraVerde has partnered with CCAs to advance the deployment of DERs. In 2017, a California Energy Commission grant under the Local Government Challenge was awarded to MCE Clean Energy, TerraVerde, Center for Climate Protection and other partners to develop a software and a program, titled Building Efficiency Optimization (BEO) for CCA program and procurement managers to identify scalable and replicable programs for deploying DERs that optimize building electricity use and reduce greenhouse gas emissions on a community scale. In doing so, the program is designed to broaden the deployment of energy efficiency, electric vehicle charging infrastructure, solar and batteries deployment to facilitate local renewable energy integration and to increase benefits that are passed onto customers.
The BEO software is designed to perform multi variable analysis that can be framed in the following omni question format by the user:
“What is the Benefit or Cost to promote a Quantity of DERs to Customer Segment in a CCA’s Territory/Location over the span of Time Frame?”
Where the inputs are:
• A Quantity of DERs
• A Customer Segment of a CCA’s total customer base
• A CCA’s Territory/Location in California
• A Time Frame in the past or future
• A Benefit or Cost selection
And the output is:
• A Benefit or Cost calculation using an output function (ex. impact to GHG emissions, impact to CCA procurement cost, revenues collected from customers or payments avoided to customers, etc.).
For our first analysis using the software, we worked with two CCAs to address the cost of servicing solar NEM customers with net surplus generation. These are CCA customers with existing solar under the NEM tariff that produce more electricity than is used by their buildings (homes, schools, businesses, etc.). As a result of excess solar generation, these customers generate excess electricity bill credits which are required to be paid out by the CCAs through an annual true-up.
We studied the benefits of pairing energy storage systems to be used for load shaping while providing back-up power to the host customers. The load shaping strategy relied on charging the batteries with excess solar generation and discharging the batteries between 4 to 9 pm. The battery charge and discharge cycles can be selected as desired by each CCA to minimize electricity procurement beyond already contracted power when wholesale costs are high.
As a first step, we collected and ingested the 15 minute and hourly interval import and export data from all the customers in the study. We then grouped the customers by specific rate schedule and class to identify the top opportunity groups where a CCA can achieve the most benefit per deployed energy storage system. In this case, customers under the A-6 rate schedule were selected since just 90 customers provide 10.3% of all net electricity exports. The software then was used to perform a load clustering activity to identify the specific customers within the A-6 group of customers that had a consistent evening peak profile suitable for load flattening using the batteries. As shown in the presentation included below delivered at the 2019 Business of Local Energy Symposium conference CCA Cutting Edge Projects, the results of the simulations support the thesis that batteries could be used to provide economic benefits to CCAs by reducing the excess generation credit payments, reducing procurement of electricity during the specified 4 to 9 pm period, while still providing back up to the customers. Additionally, the greenhouse gas (GHG) emissions impact were studied using two methodologies. We will cover the GHG impact analysis in the next TerraBlog post!