Experimental operation of a solar-driven climate system with thermal energy storages using mixed-integer nonlinear model predictive control

This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear model predictive control (MPC). The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed-integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed-integer nonlinear MPC application

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources

Experimental operation of a solar-driven climate system with thermal energy storages using mixed-integer nonlinear model predictive control

This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear model predictive control (MPC). The system is installed in a university building and consists of two solar thermal collector fields, an adsorption cooling machine with different operation modes, a stratified hot water storage with multiple inlets and outlets as well as a cold water storage. The system and the applied modeling approach is described and a parallelized algorithm for mixed-integer nonlinear MPC and a corresponding implementation for the system are presented. Finally, we show and discuss the results of experimental operation of the system and highlight the advantages of the mixed-integer nonlinear MPC application