یک روش برنامه ریزی خطی برای مشکل ظرفیت قرارداد برق

Determination of electricity contract capacity is a problem faced by all industrial customers in Taiwan. In the literature, the problem has been solved using metaheuristics, such as genetic algorithm and particle swarm optimization, which require substantial computation time to solve. In this paper we formulate the problem as a linear program, which requires only polynomial time. Our proposed linear program is better than any metaheuristic approach because a globally optimal solution can be guaranteed while using much less computation time. Two real-world cases, one from a university and the other from a paper mill, are used to demonstrate that the model can minimize the electricity bill for industrial customers.

Soaring energy prices are making it harder for organizations, even non-profit organizations such as universities, to survive. Electricity comprises an important part of operating costs for many organizations. In Taiwan, all electricity is supplied by the Taiwan Power Company (hereafter referred to as Taipower). Taipower has different electricity tariffs for residential, commercial and industrial customers [1] and [2]. The service types applicable to industrial customers are further classified into low-tension and high-tension. The rate schedules available for high-tension service are based on time of use (TOU) and maximum demand. This paper focuses on the electricity contract decisions of high-tension industrial customers. Many larger industrial customers opt to sign a maximum contracted demand contract with Taipower. Such an electricity bill consists of an energy charge and a capacity charge. The energy charge is based on kilowatt hours, while the capacity charge is based on maximum kilowatts consumed (averaged over 15 min) during each TOU period. If the peak demand does not exceed the contract capacity, a fixed capacity charge is levied. On the other hand, if the peak demand exceeds the contract capacity, a penalty charge from two to three times the basic rate is levied. Hence, choosing an excessively low contract capacity will impose high capacity charges, while choosing an excessively high contract capacity may result in an unnecessary basic capacity charge. Therefore, optimal contract capacity decisions have received significant attention from customers with high electricity usage [3] and [4]. Several metaheuristics, including genetic algorithm and particle swarm optimization, have been proposed for contract capacity problems with different variations [5], [6] and [7]. However, a careful examination of existing literature reveals that the considered problem has not been proved to be NP-hard. This indicates that the problem can probably be solved in polynomial time. In this paper, we successfully formulate the problem as a polynomial time linear programming (LP) model. The proposed LP model not only can yield a globally optimal solution [8] and [9], but takes much less computation time than the existing metaheuristics [5] or heuristic search method [6]. The remainder of this paper is organized as follows. Section 2 provides a detailed description of the Taipower tariff structure and gives a formal definition of the problem. Section 3 proposes two LP models: (1) only peak contract capacity needs to be determined and (2) both peak and off-peak contract capacity need to be determined. Section 4 applies the LP models to two real-world cases, one from a university and the other from a paper mill. Section 5 concludes with some comments and directions for future research.