Design of Real-Time Demand Bidding Algorithm for Incentive-based Demand Response in Discrete Manufacturing Facilities

Abstract

Demand bidding (DB) is a type of demand response (DR) that may be considered as the most efficient mechanism to flatten the demand curve in the smart grid. DR is designed to induce changes in demand-side consumption to avoid the grid instabilities and large voltage fluctuations caused by significant imbalances in electricity generation and consumption. DR can be classified into incentive-based DR and price-based DR. Incentive-based DR provides incentive payments to consumers if they cut down the load during periods of power system stress. Price-based DR (also known as time-based DR) motivates end-users to change their energy usage patterns to respond to time-varying electricity prices. DB belongs to the incentive-based DR. During periods of power system stress, DB programs encourage large electricity consumers to submit curtailment capacity bids and carry out load reduction, in return for financial rewards. Thousands of previous papers have focused on the price-based DR, however, there are very few reports studying on incentive-based DR mainly because incentive-based DR as an implementation is relatively new. Thus, DB, the most important and promising form of the incentive-based DR, is worth exploring. There are only two existing articles on DB: one focused on DB energy management in hotels; the other focused on DB energy management in residential sectors. Both of them are day-ahead DB (DA-DB). Compared to DA-DB, real-time DB (RT-DB) has greater potential for maintaining power balance in the grid. But, there are no existing works on RT-DB and there are no studies on DB energy management in industrial sectors as well. In this thesis, a RT-DB program with applications in discrete manufacturing facilities is considered. A discrete manufacturing production model is constructed and an automated RT-DB algorithm is designed. An optimization problem with the objective of maximizing the profits for manufacturers is formulated. Solving this problem enables the RT-DB algorithm to automatically generate optimal load-reduction bids with adjusted production and energy plans. A case study is described, which shows that the proposed algorithm reduced the load demand during an RT-DB event, increasing the manufacturer’s profits. Furthermore, the relationship between the incentive rate and the demand elasticity of the consumer, as well as the production volume and profits is described.