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PhD in automatic control from EPFL university, Swittzerland. Specialist of theory and practice of Model Predictive Control, in particular to energy systems and buildings. Senior R&D researcher in CSEM, Switzerland.
We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary features to provide the benchmarking and control capabilities, i.e., standardized evaluation scenarios, key performance indicators (KPIs) and forecasts of uncertain variables. We go on to characterize the evaluation scenarios for each of the models and give formal definitions of the KPIs. We describe the calibration methodologies used for constructing the models and illustrate their usage through examples.
Paul Scharnhorst; Baptiste Schubnel; Carlos Fernández Bandera; Jaume Salom; Paolo Taddeo; Max Boegli; Tomasz Gorecki; Yves Stauffer; Antonis Peppas; Chrysa Politi. Energym: A Building Model Library for Controller Benchmarking. Applied Sciences 2021, 11, 3518 .
AMA StylePaul Scharnhorst, Baptiste Schubnel, Carlos Fernández Bandera, Jaume Salom, Paolo Taddeo, Max Boegli, Tomasz Gorecki, Yves Stauffer, Antonis Peppas, Chrysa Politi. Energym: A Building Model Library for Controller Benchmarking. Applied Sciences. 2021; 11 (8):3518.
Chicago/Turabian StylePaul Scharnhorst; Baptiste Schubnel; Carlos Fernández Bandera; Jaume Salom; Paolo Taddeo; Max Boegli; Tomasz Gorecki; Yves Stauffer; Antonis Peppas; Chrysa Politi. 2021. "Energym: A Building Model Library for Controller Benchmarking." Applied Sciences 11, no. 8: 3518.
This paper introduces the Maestro library. This library for Python focuses on the design of predictive controllers for small to medium-scale energy networks. It allows non-expert users to describe multi-carrier (electricity, heat, gas) energy networks with a range of energy production, conversion, and storage component classes; together with consumption patterns. Based on this description a predictive controller can be synthesized and tested in simulation. This controller manages the dispatch of energy in the network, making sure that the demands are met while minimizing the total energy cost. Alternative objectives can be specified. The library uses a mixed-integer linear modeling framework to describe the network and can be used in stand-alone based on standardized input files or as part of the larger energy network control platform PENTAGON.
Tomasz T. Gorecki; William Martin. Maestro: A Python library for multi-carrier energy district optimal control design. IFAC-PapersOnLine 2020, 53, 13293 -13298.
AMA StyleTomasz T. Gorecki, William Martin. Maestro: A Python library for multi-carrier energy district optimal control design. IFAC-PapersOnLine. 2020; 53 (2):13293-13298.
Chicago/Turabian StyleTomasz T. Gorecki; William Martin. 2020. "Maestro: A Python library for multi-carrier energy district optimal control design." IFAC-PapersOnLine 53, no. 2: 13293-13298.
Luca Fabietti; Faran A. Qureshi; Tomasz T. Gorecki; Christophe Salzmann; Colin N. Jones. Multi-time scale coordination of complementary resources for the provision of ancillary services. Applied Energy 2018, 229, 1164 -1180.
AMA StyleLuca Fabietti, Faran A. Qureshi, Tomasz T. Gorecki, Christophe Salzmann, Colin N. Jones. Multi-time scale coordination of complementary resources for the provision of ancillary services. Applied Energy. 2018; 229 ():1164-1180.
Chicago/Turabian StyleLuca Fabietti; Faran A. Qureshi; Tomasz T. Gorecki; Christophe Salzmann; Colin N. Jones. 2018. "Multi-time scale coordination of complementary resources for the provision of ancillary services." Applied Energy 229, no. : 1164-1180.
In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. The main objective is to track a power trajectory, called the dispatch plan, which is computed the day before the beginning of operation. In this paper, we propose a method to optimally compute the dispatch plan so as to optimize the operation of the feeder while making sure to allocate enough local reserves to absorb deviations of the realizations. Indeed, during real-time operation, due to the stochasticity of part of the resources in the feeder portfolio, tracking errors need to be absorbed in order to track the committed dispatch plan. This is achieved by modulating the power consumption of a utility-scale battery energy storage system and of the heating, ventilation and air conditioning system of a commercial controllable building. To this end, a hierarchical controller is designed to coordinate these two controllable entities while requiring a minimal communication infrastructure. Due to the inherent different response times of these systems, the power injection of the electrical battery is controlled at a sub-minute time-scale so as to absorb high-frequency tracking errors and, therefore, deliver the dispatch service. At a slower time-scale, the controllable building is controlled to maintain the state of charge for the electrical battery at a scheduled level by means of a model predictive controller. The model predictive controller is designed in order to account for both comfort and operational constraints of the controllable building, as well as power limits for the electrical storage. The effectiveness of the proposed control framework is demonstrated by means of both an extensive simulation analysis, as well as a set of 12 full day experimental results on the 20kV distribution feeder in the campus of the Swiss institute of technology in Lausanne, that is comprised of: 1) a set of uncontrollable resources represented by five office buildings (350kWp) and a roof-top photovoltaic installation (90kWp), 2) a set of controllable resources, namely, a grid-connected electrical storage (720kVA-500kWh), and a fully-occupied multi-zone office building (45 kWp).
Luca Fabietti; Tomasz T. Gorecki; Emil Namor; Fabrizio Sossan; Mario Paolone; Colin N. Jones. Enhancing the dispatchability of distribution networks through utility-scale batteries and flexible demand. Energy and Buildings 2018, 172, 125 -138.
AMA StyleLuca Fabietti, Tomasz T. Gorecki, Emil Namor, Fabrizio Sossan, Mario Paolone, Colin N. Jones. Enhancing the dispatchability of distribution networks through utility-scale batteries and flexible demand. Energy and Buildings. 2018; 172 ():125-138.
Chicago/Turabian StyleLuca Fabietti; Tomasz T. Gorecki; Emil Namor; Fabrizio Sossan; Mario Paolone; Colin N. Jones. 2018. "Enhancing the dispatchability of distribution networks through utility-scale batteries and flexible demand." Energy and Buildings 172, no. : 125-138.
Tomasz T. Gorecki; Colin N. Jones. Constrained bundle methods with inexact minimization applied to the energy regulation provision problem * *This work has received support from the Swiss National Science Foundation under the GEMS project (grant number 200021 137985) and the European Research Council under the European Unions Seventh Framework Programme (FP/2007-2013)/ ERC Grant Agreement n. 307608 (BuildNet). IFAC-PapersOnLine 2017, 50, 12471 -12476.
AMA StyleTomasz T. Gorecki, Colin N. Jones. Constrained bundle methods with inexact minimization applied to the energy regulation provision problem * *This work has received support from the Swiss National Science Foundation under the GEMS project (grant number 200021 137985) and the European Research Council under the European Unions Seventh Framework Programme (FP/2007-2013)/ ERC Grant Agreement n. 307608 (BuildNet). IFAC-PapersOnLine. 2017; 50 (1):12471-12476.
Chicago/Turabian StyleTomasz T. Gorecki; Colin N. Jones. 2017. "Constrained bundle methods with inexact minimization applied to the energy regulation provision problem * *This work has received support from the Swiss National Science Foundation under the GEMS project (grant number 200021 137985) and the European Research Council under the European Unions Seventh Framework Programme (FP/2007-2013)/ ERC Grant Agreement n. 307608 (BuildNet)." IFAC-PapersOnLine 50, no. 1: 12471-12476.
Tomasz T. Gorecki; Luca Fabietti; Faran A. Qureshi; Colin Jones. Experimental demonstration of buildings providing frequency regulation services in the Swiss market. Energy and Buildings 2017, 144, 229 -240.
AMA StyleTomasz T. Gorecki, Luca Fabietti, Faran A. Qureshi, Colin Jones. Experimental demonstration of buildings providing frequency regulation services in the Swiss market. Energy and Buildings. 2017; 144 ():229-240.
Chicago/Turabian StyleTomasz T. Gorecki; Luca Fabietti; Faran A. Qureshi; Colin Jones. 2017. "Experimental demonstration of buildings providing frequency regulation services in the Swiss market." Energy and Buildings 144, no. : 229-240.
Many engineering problems that involve hierarchical control applications, such as demand-side ancillary service provision to the power grid, can be posed as a robust tracking commitment problem. In this setting, the lower level controller commits a set of possible reference trajectories over a finite horizon to an external entity in exchange for a reward corresponding to the size of the reference set and the allowed margin of tracking error. If the commitment is accepted, the lower level system is required to track any reference trajectory that can be sampled from the committed set. This paper presents the framework of robust tracking commitment and a method to solve the optimal commitment problem for constrained linear systems subject to uncertain disturbance and reference signals. The proposed method allows tractable computations via convex optimization for conic representable uncertainty sets and lends itself to distributed solution methods. We demonstrate the proposed method in a simulation based case study with a commercial building that offers frequency regulation service to the power grid.
Altug Bitlislioglu; Tomasz T. Gorecki; Colin Jones. Robust Tracking Commitment. IEEE Transactions on Automatic Control 2017, 62, 4451 -4466.
AMA StyleAltug Bitlislioglu, Tomasz T. Gorecki, Colin Jones. Robust Tracking Commitment. IEEE Transactions on Automatic Control. 2017; 62 (9):4451-4466.
Chicago/Turabian StyleAltug Bitlislioglu; Tomasz T. Gorecki; Colin Jones. 2017. "Robust Tracking Commitment." IEEE Transactions on Automatic Control 62, no. 9: 4451-4466.
A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. Using methods inspired from robust model predictive control, the proposed approach certifies the ability of a system to track any reference drawn from a polytopic set on a finite time horizon by solving a linear program. Optimization over a parameterization of the set of reference signals is discussed, and particular instances of parameterization of this set that result in a convex program are identified, allowing one to find the largest set of trackable signals of some class. Infinite horizon feasibility of the methods proposed is obtained through use of invariant sets, and an implicit description of such an invariant set is proposed. These results are tailored for the application of power consumption tracking for loads, where the operator of the load needs to certify in advance his ability to fulfill some requirement set by the network operator. An example of a building heating system illustrates the results.
Tomasz T. Gorecki; Altug Bitlislioglu; Giorgos Stathopoulos; Colin N. Jones. Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response. 2014, 1 .
AMA StyleTomasz T. Gorecki, Altug Bitlislioglu, Giorgos Stathopoulos, Colin N. Jones. Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response. . 2014; ():1.
Chicago/Turabian StyleTomasz T. Gorecki; Altug Bitlislioglu; Giorgos Stathopoulos; Colin N. Jones. 2014. "Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response." , no. : 1.