Martin Strelec

Modeling and simulation of a microgrid as a Stochastic Hybrid System

Microgrids (MGs) are small-scale local energy grids. While dedicated to cover local power needs, their structure and operation is usually quite complex. Complexity arises due to a number of factors: in the first instance, a variety of operational modes - among them, MGs can be considered to be operated autonomously whenever the main distribution grid is not available; furthermore, the heterogeneity of energy types in a MG - not exclusively electrical energy, but also thermal for instance; also, the different functions that a MG energy management system has to fulfill - like coordination and dispatching of multiple generation, transfer, transformation and storage devices; finally, the external and internal random factors that affect operations. All these aspects make control and scheduling of a MG quite a challenging task. On the other hand, this widespread complexity leaves much room for improvement on the current state of the art. An advancement on the state of the art requires the development of a realistic model of the system at hand. This work puts forward a model of a MG that is based on the framework of Stochastic Hybrid Systems (SHS). SHS models can capture the interaction between probabilistic elements and discrete and continuous dynamics, and thus promise to be able to tame the complexity of the systems discussed above. This work displays the outcomes of model simulations and discusses potential development of general analysis and synthesis approaches over SHS models (e.g., based on model checking and on approximate dynamic programming) for typical challenges in MGs.

Approximate dynamic programming-based control of a building cooling system with thermal storage

The paper addresses the energy management of a building cooling system comprising a chiller plant with two chillers, a thermal storage unit, and a cooling load representing a building. Uncertainty affects the system since the cooling load depends on the building occupancy. The goal is to minimize the energy consumption of the cooling system, while preserving comfort in the building. This is achieved by optimally distributing the cooling load demand among the chillers and the thermal storage unit, and modulating the building temperature set-point to some (limited) extent. The problem can be decomposed into a static optimization problem, and a dynamic programming problem, which is solved based on the abstraction to a Markov chain of the stochastic hybrid system modeling the cooling system.

Micro-grid energy management: A computational approach based on simulation and approximate discrete abstraction

We address the problem of designing an energy management system for a small scale micro-grid comprising a chiller plant and a cooling load representing some zone. The energy management task involves distributing the cooling power request among the chillers constituting the chiller plant, and modulating the temperature set-point of the zone so as to save energy while preserving comfort. The problem can be decoupled into a static optimization problem and a dynamic programming problem for a discrete time stochastic hybrid system. The latter one is here addressed by abstracting the stochastic hybrid model to a (finite) controlled Markov chain, where costs associated to 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.