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Center for Nonlinear Studies |
Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this talk, after a briefly review of the motivation and the main issues in demand response design, we will first discuss an abstract market model for designing demand response to match power supply. We characterize the resulting equilibria in competitive as well as oligopolistic markets, and propose distributed demand response algorithms to achieve the equilibria. We then discuss another market model for designing demand response to shape power demand. We consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute these optimal prices and demand schedules. Numerical experiments show that it is effective in reducing the peak load, smoothing the entire demand profile, and saving significant generation costs.
Host: Misha Chertkov, chertkov@lanl.gov, 665-8119