Design Optimization and Demand Side Management of a Solar-Assisted Industrial Heating Using Agent-Based Modelling (ABM): Methodology and Case Study

Abstract

Heavy dependence on traditional fuels as well as connection to a significantly unreliable and inefficient grid (with about 20% energy loss) characterizes the Ethiopian industry. On the contrary, the country has many solar-suited industries for process heat augmentation in the low-to-medium temperature ranges. Thus, if properly designed and operated, solar thermal could give an opportunity for an efficient and cleaner alternative in these industries. This paper presents agent-based modeling (ABM) for an integrated optimization and demand side management (DSM) framework for solar-assisted industrial heating under varying load and weather condition. To demonstrate the validity and practicality of the proposed solution, a case study was conducted on Ethiopian textile industry. A payback period of 5.3 years and solar fraction of 66.7% was identified for an optimized system. Further with the implementation of DSM to guide the production policy of the industry, a 7.5% and 8.4% improvement in payback and solar fraction was achieved.

Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations

“…While industrial DR is typically implemented by large electricity consumers, flexibility in industrial processes can also be utilized by smaller consumers. The potential for DR in a textile factory in Ethiopia is assessed in [100], where a model of the factory's oil-powered boiler is used to simulate the integration of solar-assisted heating. By using DR strategies to align the factory's operation schedule with solar production, the utilization of solar heat for the boiler is shown to increase by up to 10% per month.…”

Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications

“…Iteration between the sizing and the operation module was done as such: sizing module gave the size of the system components, while the operation module returned the efficiency of the collectors for each hour, in order to simplify both MILP algorithms and allow for quicker convergence and optimal design of the system. Tilahun et al [7] and Fitsum et al [8] performed an agent-based optimization on design and control of a dyeing textile industry in Ethiopia. In [7], control optimization is performed in order to keep constant the outlet temperature of the storage, in opposition to a constant mass flow rate in the solar collector.…”