Reducing The Monthly Electricity Bill Using Real-time Pricing Optimization

Open Access
Chen, Kewei
Area of Honors:
Computer Science
Bachelor of Science
Document Type:
Thesis Supervisors:
  • Bhuvan Urgaonkar, Thesis Supervisor
  • John Joseph Hannan, Honors Advisor
  • electricity
  • bill
  • optimization
  • RRTP
  • real-time pricing
  • renewable energy
  • peak power
  • scheduling
  • solar energy
  • wind energy
The electricity bill is increasing every year. Traditional methods of reducing the cost all involve reducing electricity consumption, which is not always desirable. In this thesis, we develop an optimization model that proposes an alternate solution to rising costs. There are some appliances, such as a dishwasher, that can be run at a different time period. Consumers do not care when these types of appliances are run as long as the tasks are finished before they are needed. The model takes advantage of residential real- time pricing (RRTP) to schedule certain appliances during low-peak periods. But, there are other appliance tasks that cannot be moved. For that type of energy usage, another solution was devised. During high-peak periods, instead of using energy from the grid, energy is drawn from onsite and offsite renewable resources, which can sometimes be cheaper than RRTP. I incorporated all of these ideas into the optimization model. The objective is to minimize the electricity bill. I coded scripts in Python to modify datasets. Other scripts were coded to write the optimization model to a .lp file, which then was read in by CPLEX, a linear program optimizer. The three main components of the optimization model include the objective function, constraints, and bounds.