Optimal Scheduling of Manufacturing and Onsite Generation Systems in Over-Generation Mitigation Oriented Electricity Demand Response Program


Manufacturing system is considered a valuable source that can provide electricity load adjustment in electricity demand response program to balance the supply and demand of the electricity throughout the grid. In this paper, we propose a mathematical model to identify the optimal participation strategy for manufacturing end use customers with onsite energy generation system in the demand response program designed for mitigating electricity over-generation due to high penetration of renewable sources in electricity grid. The background of over-generation mitigation oriented demand response program is described first. Then, the manufacturer's decision making procedure for identifying the optimal participation strategy is modeled as a mixed nonlinear integer programming. In particular, the manufacturers€™ participation strategies including the decision of participating or not, and corresponding production schedule of manufacturing system as well as utilization schedule of onsite generation system, are modeled as decision variables in the objective function to minimize the overall cost considering the benefits due to the participation, energy billing cost, onsite generation cost, and production loss penalty cost. Particle swarm optimization is used to find a near optimal solution for the formulated problem. A numerical case study with sensitivity analysis is then conducted to demonstrate the effectiveness and robustness of the proposed model.


Computer Science

Second Department

Engineering Management and Systems Engineering

Research Center/Lab(s)

Intelligent Systems Center


This work is supported by Energy Research and Development Center at Missouri University of Science and Technology

Keywords and Phrases

Manufacturing system; Optimal scheduling; Over-generation mitigation oriented demand response; Particle swarm optimization; Sensitivity analysis

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Article - Journal

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© 2018 Elsevier, All rights reserved.

Publication Date

01 Jan 2018