Multi-stage stochastic programming for demand response optimization
Embargo Lift Date: 2023-02-19
Computers and Operations Research
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The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule different kinds of electrical appliances under uncertain weather conditions and availability of renewable energy. We incorporate appliances with chargeable and dischargeable batteries to better utilize the renewable energy sources. Our aim is to minimize the electricity cost and the residents’ dissatisfaction. We use a scenario groupwise decomposition (group subproblem) approach to compute lower and upper bounds for instances with a large number of scenarios. The results of our computational experiments show that the approach is very effective in finding high quality solutions in small computation times. We provide insights about how optimization and renewable energy combined with batteries for storage result in peak demand reduction, savings in electricity cost and more pleasant schedules for residents with different levels of price sensitivity.
Multi-stage stochastic programming
Scenario groupwise decomposition