Electricity pricing plans have increased as a result of the increased use of energy technologies like smart meters, solar panels, and battery storage. These plans ‘ prices change depending on the time of day or energy consumption. Simult software can be used to assess the effects of these pricing plans on the customer ( in terms of cost management ) and the electric grid ( as a result of peak energy usage reduction ). However, the majority of these simulations do n’t take into account the variations in energy usage between residential buildings or their occupants. For instance, more heating will be needed in an area with older, poorly insulated homes. Alternately, homes with various energy efficiencies can be installed in areas with varying median incomes. It is therefore challenging to assess how various residential customer types will be impacted by electricity pricing plans.
In collaboration with NIST, Santa Clara University researchers developed simulations that include a variety of residential building models that represent various residential customers. The size, insulation, number of windows, quantity of appliances, energy consumption, and other home characteristics are all taken into account by these models using data from the income level and climate zone. The building models and an electric distribution grid simulation were combined using a method known as co-simulation. To investigate the effects of various electricity tariffs on various types of residential customers, the research team used a variety of electricity rates in the simulation. According to the findings, residential customers ‘ reactions to electricity tariffs vary depending on their income levels. The fairness to customers at various income levels should be taken into account when comparing electricity tariffs.
In Applied Energy, this study was presented as a journal article. Hannah Covington, the lead author, was a former NIST Summer Undergraduate Research Fellowship ( SURF ) Program intern. She is from Santa Clara University.