AOL Energy recently discussed how electric system operators in areas such as Texas are facing potential capacity shortages this summer. These capacity constraints can leave power system operators between the proverbial rock and a hard place. They need to either find some way to reduce demand—which can mean rolling blackouts if businesses and consumers don’t voluntarily reduce consumption—or face the prospect of cascading, uncontrolled outages.
Smart grid technology promises to help alleviate the problem, without resuscitating inefficient generation resources or rebuilding grid infrastructure to support these infrequent summer demand peaks. Demand response is one approach that will help reduce peak demand by encouraging consumers and businesses to reduce electricity consumption in exchange for lower electric bills. This is not an entirely new approach. So-called interruptible rate plans for large power users—or plans that offer lower rates for reduced electricity usage at peak times—have existed for years. The latest demand response applications use smart meters to make this an option for smaller electric power users, and consumers in particular. However, such technology is still evolving. And even as the technology matures, it remains to be seen whether consumers will want to set their thermostats a few degrees higher to save a bit of money when electricity demand is high. The price savings would need to be significant enough to warrant a change in their electricity usage patterns.
Another approach to peak shaving puts control squarely in the hands of utilities. It’s volt/var optimization with conservation voltage reduction. These applications allow utilities to effectively reduce demand by lowering voltage levels within accepted power quality norms. Businesses and consumers don’t need to take any action, and they won’t see an impact in their electric service. Further, volt/var optimization reduces the amount of electricity lost during distribution, effectively freeing additional capacity to meet demand.
Volt/var optimization may not entirely solve the problem of how to reduce peak demand in the summer, but it’s a step in the right direction.
What approach would you use to reduce peak summer demand? Please use the comment form to let us know.