Optimal Industrial Load Control in Smart Grid: A Case Study for Oil Refineries

CATEGORY: ENERGY MANAGEMENT
Optimal Industrial Load Control in Smart Grid: A Case Study for Oil Refineries
Armen Gholian, Hamed Mohsenian-Rad, Yingbo Hua
armen.gholian@email.ucr.edu
Department of Electrical Engineering, University of California, Riverside CA
Joe Qin
sqin@usc.edu
Department of Chemical Engineering & Material Sci., University of Southern California, Los Angeles, CA
Abstract
The current literature on optimal load control is mainly focused on residential and commercial load sectors. In this paper, we investigate optimal load control for industrial load which involves several new and distinct research challenges. For example, while most residential appliances operate independently, industrial units are highly inter-dependent and must follow a certain operational sequence. This is particularly the case in industries that involve process control: each unit can start operation only if its feeding units finish their operations. Considering an oil refinery industry as an example, we not only identify some of the most important operational sequences in this particular industry, but also develop mathematical models that can help us integrate the identified operational sequences in an optimization-based industrial load control framework. We assess the performance of our design through various simulations.
INTRODUCTION
The global demand for electricity has increased by 2.3% per year since 2008 and this trend is expected to continue [1]. To assure reliable service, generation capacity is designed to match the peak demand. Thus, it is desirable to reduce the peak-to-average ratio in load profile in each region to minimize the need to build new power plants. This can be achieved by a combination of smart pricing by utilities and optimal load control by consumers such that we can utilize any controllable load in each load sector to not only reduce the peak demand but also help users reduce their energy expenditure.
Most prior work on optimal load control has mainly focused on residential and commercial loads [2]–[4]. However, since over 40% of world’s generated electricity is consumed by industries [5], addressing industrial load control is also necessary to reduce the peak demand more effectively. Of course, the exact load profiles and potential for load control can vary among industries, for example, depending on whether the industry is a manufacturing unit (e.g., automotive, food, pulp-and-paper, chemicals, refining, and iron and steel) or nonmanufacturing unit (agriculture, mining, and construction).
Our focus in this paper is on manufacturing load control. A common characteristic that makes industrial load control different from residential load control is the typical interdependency among industrial units that belong to the same product chain, whether a car assembly line or an oil refinery: a unit cannot start its operation unless its feeding units produce its feed. Clearly, this is not the case in residential loads where appliances operate independently. For example, it is not necessary to start and finish the charging of an electric vehicle before a dishwasher or an air conditioner can start operation. Modeling the inter-dependencies among industrial units is challenging. There are several factors that must be considered. For example, in some cases, the operation of a unit that is being fed must start immediately after the feeding unit finishes its operation. This may be the case when the feeding unit’s output has to be used as soon as it reaches a certain pressure or temperature. Furthermore, the operation of some industrial units, e.g., in chemical industries, cannot be interrupted. Yet, there are units, e.g., in the automotive industry, that can be interrupted and later restored. While some of these aspects are briefly discussed, e.g., in [6] and [7], there is still a need to develop a more comprehensive framework for industrial load control that can support different types of industrial units. In this paper, we propose an optimization-based approach to industrial load control. To gain insight, we discuss a case study of oil refineries which are among the most energy intensive industries in the Unites States and around the world. In fact, it is estimated that the oil refineries in the United States purchased 46,195 MWh electricity in 2011 [8]. Our proposed optimization model is comprehensive and takes into consideration the day-ahead electricity price, operation completion constraints, sequential operation constraints, immediate start constraints, uninterruptable operation constraints, and maximum load constraints. The formulated problem is a tractable linear binary program and results in noticeably reducing the electricity cost of the oil refinery in the case study.
The rest of this paper is organized as follows: An overview of the oil refinery industry and its operational requirements are discussed in Section II. Optimization-based industrial load control is proposed in Section III. Numerical results are given in Section IV. The paper is concluded in Section V.
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