A Framework of Customizing Electricity Retail Prices

The problem of designing customized pricing strategies for different residential users is investigated based on the identification results of residential electric appliances and classifications of end-users according to their consumption behaviors. This study is based on the following assumptions: 1) Each retailer purchases electricity from the forward contract market, day-Ahead spot market, and real-Time market; 2) the competition among retailers is modeled by a market share function; 3) each retailer adopts fixed time-of-use prices for end-users; 4) the price fluctuations in day-Ahead and real-Time spot markets as well as uncertainty of electricity consumption behaviors are considered as main sources of risk. Under these assumptions, a pricing framework for retailers is established based on the bilevel programming framework and the optimal clustering in a time sequence. Meanwhile, profit risk is considered by taking conditional value at risk as the risk measure. The proposed bilevel optimization model is finally reformulated into a mixed-integer nonlinear programming problem by solving Karush-Kuhn-Tucker conditions. The online optimization solvers provided by the network-enabled optimization system server and the commercial solver AMPL/GUROBI are used to solve the developed models, respectively. Finally, a case study is employed to demonstrate the feasibility and efficiency of the developed models and algorithms. © 1969-2012 IEEE.