An open-source model for optimal design and operation of industrial energy systems. (15th February 2017)

• Record Type: Journal Article
• Authors: Atabay, Dennis
• Is Part Of: Energy. Volume 121(2017)
• Page Start: 803
• Page End: 821
• Publication Date: 2017-02-15
• Subjects: Industrial energy systems -- Optimization -- Mixed-integer linear programming; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.01.030
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper presents a detailed mixed-integer linear optimization model for capacity-expansion planning and unit commitment of a factory's distributed energy supply system. The model identifies the cost-optimal configurations of specified energy-conversion processes and storage techniques to cover the factory's energy demand. The general formulation of the model allows it to deal with a large number of energy-system structures. The main constraint of the model is that it must cover given demand time series for different commodities such as electricity, heating and cooling. For each commodity, storage units with different technical and economic parameters as well as a possible external connection considering time-sensitive prices for import/export and peak demand charges can be defined. The model allows the users to specify processes, for converting between the different types of commodities. The conversion processes can handle multiple inputs and outputs and consider part-load performance as well as start-up behavior. The objective of the model is to find the optimal design and operation of the industrial energy system with minimal investment and operational costs. A case study based on the measured electric- and heat-demand time series of four different factories is presented, investigating the economic efficiency of combined heat and power (CHP) units and battery storage systems. The results show, that the decision as to whether CHP units are used, mainly depends on the relationship between gas and electricity costs, while the load profile of the factories and the applied pricing program only influence their size. Batteries are only considered in the results when their investment costs are reduced and they have little influence on the total cost. Highlights: Definition of a MILP optimization model for industrial energy systems. The general formulation allows to deal with a large number of energy system configurations. Time-sensitive prices for energy import and export and for peak demand charges are considered. Case study based on measured electric and heat demand time-series of four different factories.