Energy Management of a Building Cooling System With Thermal Storage: An Approximate Dynamic Programming Solution

This paper concerns the design of an energy management system for a building cooling system that includes a chiller plant (with two or more chiller units), a thermal storage unit, and a cooling load. The latter is modeled in a probabilistic framework to account for the uncertainty in the building occupancy. The energy management task essentially consists in the minimization of the energy consumption of the cooling system, while preserving comfort in the building. This is achieved by a twofold strategy. The cooling power request is optimally distributed among the chillers and the thermal storage unit. At the same time, a slight modulation of the temperature set-point of the zone is allowed, trading energy saving for comfort. The problem can be decoupled into a static optimization problem (mainly addressing the chiller plant optimization) and a dynamic programming (DP) problem for a discrete time stochastic hybrid system (SHS) that takes care of the overall energy minimization. The DP problem is solved by abstracting the SHS to a (finite) controlled Markov chain, where costs associated with state transitions are computed by simulating the original model and determining the corresponding energy consumption. A numerical example shows the efficacy of the approach

Modeling, Verification, and Control of Complex Systems for Energy Networks (Dagstuhl Seminar 14441)

Power and energy networks) are systems of great societal and economic relevance and impact, particularly given the recent growing emphasis on environmental issues and on sustainable substitutes (renewables) to traditional energy sources (coal, oil, nuclear). Power networks also represent systems of considerable engineering interest. The aim of this Dagstuhl seminar has been to survey existing and explore novel formal frameworks for modeling, analysis and control of complex, large scale cyber-physical systems, with emphasis on applications in power networks. Stochastic hybrid systems (SHS) stand for a mathematical framework that allows capturing the complex interactions between continuous dynamics, discrete dynamics, and probabilistic uncertainty. In the context of power networks, stochastic hybrid dynamics arises naturally: (i) continuous dynamics models the evolution of voltages, frequencies, etc.; (ii) discrete dynamics models controller logic and changes in network topology (unit commitment); and (iii) probability models the uncertainty about power demand, power supply from renewables and power market price. The seminar has covered relevant approaches to modeling and analysis of stochastic hybrid dynamics, in the context of energy networks

Ružička days : International conference 18th Ružička Days “Today Science – Tomorrow Industry” : Proceedings

Proceedings contains articles presented at Conference divided into sections: chemical analysis and synthesis, chemical and biochemical engineering, food technology and biotechnology, medical chemistry and pharmacy, environmental protection and meeting of young chemists