LAUSR

In this paper, a bi-level optimization model is proposed for determining the optimal dynamic retail electricity price, which balances the monetary benefits between utility companies and large industrial customers. That is, the utility company at the upper level devotes to maximize the profit of electricity sale by tuning dynamic retail electricity prices, while large industrial customers at the lower level minimize their power consumption costs by optimally scheduling tasks and generation outputs of self-provided power plants. Specifically, the task scheduling problem of large industrial customers is formulated as a modified continuous-time mixed-integer linear programing (MILP) problem, for effectively handling the exact start time and cancellation of tasks as well as optimally deploying and executing tasks of large industrial customers. A hybrid optimization algorithm by integrating genetic algorithm (GA) and MILP is proposed for addressing computational complexity of the proposed bi-level optimization problem. That is, GA is used to solve the upper level problem, while the lower level scheduling problem is solved by a commercial MILP optimizer. Numerical case results show that the proposed method can effectively increase the profit of the utility company, while improving electricity consumption patterns and reducing average power consumption costs of large industrial customers.