Potential capability estimation for real time electricity demand response of sustainable manufacturing systems using Markov Decision Process

Electricity demand response has been considered as a critical methodology to realize the strategy of sustainable development for manufacturing enterprises by effectively reducing the increasing electricity demand and Greenhouse Gas emissions. Most existing studies about the electricity demand response implementation focus on either the supply side management, e.g., policy making, price setting, or the customer side applications for the end-users in residential and commercial building sectors. As for the industrial sector, only a few papers utilizing the long term scheduling methodology to reduce the electricity consumption during peak periods are available. Little work has been implemented on the decision-making for the real time electricity demand response in industrial manufacturing systems considering system throughput constraint. In this paper, an analytical model is established to identify the optimal energy control actions and estimate the potential capacity of power demand reduction of typical manufacturing systems during the period of demand response event without compromising system production. Markov Decision Process is used to model the complex interaction between the adopted demand control actions and the system state evolutions. A numerical case study on a section of an automotive assembly line is used to illustrate the effectiveness of the proposed approach.