Martin Strelec

High penetration of the renewable energy sources (RES) into the transmission power systems poses a number of new challenges for power network operators (e.g. TSO). Impact assessment of RES integration to power transmission system can constitute one of these challenges. In this paper, we present a method for impact assessment of the RES integration to determination of the volume of ancillary services reserves. The method updates the statistical properties (mean value, standard deviation) of local RES power prediction (namely prediction error), aggregates these properties and evaluates the impact of the prediction error of RES production to determination of volume of ancillary services reserves. Here the method is theoretically described and afterwards the application of the method is demonstrated in various energy market environments which are described in this paper as well.

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.

Microgrid energy management stands for challenging optimization problem where continuous (economic dispatch) and discrete optimization (unit commitment) tasks are solved. Often Microgrid optimization leads to complex problem where optimization methods usually meet curse of dimensionality. We adopt approximate dynamic programming (ADP) as the promising optimization technique which can overcome curse of dimensionality. In this paper, energy management system based on ADP is introduced and its behavior is demonstrated on small scale Microgrid which is connected to distribution network and includes wind turbine, chiller plant, thermal storage and cooling load. The paper describes policy search approach to ADP and selected approximation architectures in the context of energy optimization. The ADP results are compared with the results of the solution based on dynamic programming approach.

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.

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.