Session: 02-03 Building Management and Control

Paper Number: 107421

107421 - Stochastic Method for Generating Residential Household Energy Models of Varying Income Level and Climate Zone for Testing Energy Fairness of Dynamic Electricity Pricing for Residential Buildings by Income Level 

In an effort to encourage energy conservation and peak load shaving across the grid, research communities and utility providers suggest electricity pricing plans where rates are based on supply and demand, either on average or following real time data. Consumers can benefit from these pricing plans if they can shift their energy consumption away from peak price hours, but they may be financially penalized if they are unable to reduce consumption during these times. A prevalent concern is that lower-income households may be disproportionately burdened by the transition to demand-based pricing because if they have high energy consumption during peak times, monetary penalties will consume a higher percentage of household budget. Lower-income households may have less flexibility to alter their energy consumption schedules, and they often own older and less efficient appliances, thereby increasing base load and decreasing potential for benefits from load shifting. They may have less time to research and have fewer monetary resources to install cost and energy saving upgrades to their homes. These factors present an issue of fairness in accessibility to energy with regards to income level. The goal of this work is to determine the extent of this fairness concern. First, a model generation method is proposed to create realistic and diverse household models based on income level and climate zone. The method utilizes national-level surveys as a basis of information and a stochastic method to select individual characteristics for each household. These models served as a testbed for observing the effects of utility pricing plans on 5 income levels in different U.S. climate zones. Electric tariff systems vary by geographic region, but they generally fit into three categories: tiered, time-of-use, and wholesale-based. Simulations were performed using EnergyPlus building energy modeling with co-simulation for advanced HVAC control.  Simulation results showed evidence of unfairness between households of differing income level, with a bias against households of lower-income. This unfairness was observed in annual utility expenses, and it increased when households switched from tiered to time-of-use or wholesale-based pricing. One potential solution evaluated was installing a smart thermostat, which can reduce costs by automatically shifting load away from peak times and adjusting setpoints to improve thermal comfort over fixed settings. The results of this work encourage actions to reduce disparity in energy affordability, such as implementing fairness considerations into the utility pricing model, or creating programs that make cost-saving home upgrades more accessible to lower-income households.  

Presenting Author: Hannah Covington Santa Clara University, Department of Mechanical Engineering

Presenting Author Biography: Hannah is a graduate student in the mechanical engineering department at Santa Clara University. She is planning to graduate in '23 with an M.S.M.E. emphasizing in design & manufacturing. She recieved her B.S. in mechanical engineering at Santa Clara University in '22 with Magna Cum Laude. Her interests include sustainability and renewable energy.