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Objectives:
Objectives:
To develop a methodology to determine the optimal control strategy for a distributed generation plant.
The model enforces mass end energy balances and accounts for the nonlinear and the basic dynamic behavior of each energy converter, for the time varying energy prices and environmental conditions, for maintenance and cold start costs, and for the possibility to store energy.
Activity Description
Activity Description
Distributed generation plants include different subsystems: fuel boilers, electrical power generators, heat recovery boilers, mechanical and/or absorption chillers, energy storage, and possibly other. The objective function and the constraints deriving from energy and mass balances are non-linear, being the system efficiency a function of the set-point. The energy storage and the costs or constraints related to turning on and off the equipments establish a mathematical connection between the time-steps. Thus, a proper optimization procedure requires to determine the minimum of a non-linear function that depends on a huge number of variables that are the set-points of each subsystem of the plant at each time-step.
We utilize a lumped parameter approach, where all the subsystems are modeled as black-boxes. The model accounts for: (i) the design performance of all the subsystems; (ii) the derating of the performance at part load; (iii) the effects of environmental conditions on the efficiency and on the maximum power of the machinery; (iv) energy demand and costs as functions of time; (v) maintenance, and cold start (ignition) costs; (vi) constraints related to the dynamic behavior of the equipment, such as the minimum time interval between two consecutive starts or shutdowns (minimum stay constraints); (vii) the possibility to store energy.
The objective function is discretized with respect to the plant state and in time, and the problem is represented as a weighted and oriented graph. The costs that are functions of the subsystems set-point at the local time, such as fuel costs, are associated to the graph nodes. Conversely, costs that depend on the set-point variation, such as cold-start costs, are associated to arcs.The optimal control strategy is determined by seeking for the shortest path across the graph through dynamic programming. An heuristic procedure that drastically reduces the number of nodes of the graph.The idea underlying the proposed heuristic is that only the points that are, to some extent, optimal are retained in the graph.
We utilize a lumped parameter approach, where all the subsystems are modeled as black-boxes. The model accounts for: (i) the design performance of all the subsystems; (ii) the derating of the performance at part load; (iii) the effects of environmental conditions on the efficiency and on the maximum power of the machinery; (iv) energy demand and costs as functions of time; (v) maintenance, and cold start (ignition) costs; (vi) constraints related to the dynamic behavior of the equipment, such as the minimum time interval between two consecutive starts or shutdowns (minimum stay constraints); (vii) the possibility to store energy.
The objective function is discretized with respect to the plant state and in time, and the problem is represented as a weighted and oriented graph. The costs that are functions of the subsystems set-point at the local time, such as fuel costs, are associated to the graph nodes. Conversely, costs that depend on the set-point variation, such as cold-start costs, are associated to arcs.The optimal control strategy is determined by seeking for the shortest path across the graph through dynamic programming. An heuristic procedure that drastically reduces the number of nodes of the graph.The idea underlying the proposed heuristic is that only the points that are, to some extent, optimal are retained in the graph.
Main Results
Main Results
Dynamic programming is effective for the optimization of distributed generation plants;
Optimization increments the performance of DG plants with results comparable to technology improvements;
Set-point optimization should be used also for design and sizing of DG plants;
Optimized set-points should be considered to evaluate PBT instead of LCOE.
Work in progress
Work in progress
Implementation of an object oriented platform for the simulation and optimization of DG plants. The model will be distributed with GPL license for the implementation and testing of different optimization algorithms.
Application of Artificial Intelligence for the optimization fo DG plants.
Relevant Publications
Relevant Publications
- Andrea L. Facci, Vesselin K. Krastev, Giacomo Falcucci, Stefano Ubertini, Smart integration of photovoltaic production, heat pump and thermal energy storage in residential applications, Solar Energy, Volume 192, 2019, Pages 133-143, ISSN 0038-092X, https://doi.org/10.1016/j.solener.2018.06.017.
Downolad Green Open Access pdf - Andrea L. Facci, Stefano Ubertini, Analysis of a fuel cell combined heat and power plant under realistic smart management scenarios, Applied Energy, Volume 216, 2018, Pages 60-72, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2018.02.054.
Download Green Open Access Pdf - Andrea L. Facci, Stefano Ubertini,Meta-heuristic optimization for a high-detail smart management of complex energy systems, Energy Conversion and Management, Volume 160, 2018, Pages 341-353, ISSN 0196-8904, https://doi.org/10.1016/j.enconman.2018.01.035.
Download Green Open Access Pdf - Andrea L. Facci, Viviana Cigolotti, Elio Jannelli, Stefano Ubertini, Technical and economic assessment of a SOFC-based energy system for combined cooling, heating and power, Applied Energy, Volume 192, 2017, Pages 563-574, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2016.06.105
Download Green Open Access Pdf - Francesco Cappa, Andrea Luigi Facci, Stefano Ubertini, Proton exchange membrane fuel cell for cooperating households: A convenient combined heat and power solution for residential applications, Energy, Volume 90, Part 2, 2015, Pages 1229-1238, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2015.06.092
Download Green Open Access Pdf - Facci, A. L., Andreassi, L., Martini, F., and Ubertini, S. (April 17, 2014). "Comparing Energy and Cost Optimization in Distributed Energy Systems Management." ASME. J. Energy Resour. Technol. September 2014; 136(3): 032001. https://doi.org/10.1115/1.4027155
Download Green Open Access Pdf - Andrea L. Facci, Luca Andreassi, Stefano Ubertini, Enrico Sciubba, Analysis of the Influence of Thermal Energy Storage on the Optimal Management of a Trigeneration Plant, Energy Procedia, Volume 45, 2014, Pages 1295-1304, ISSN 1876-6102, https://doi.org/10.1016/j.egypro.2014.01.135
- Andrea Luigi Facci, Luca Andreassi, Stefano Ubertini, Optimization of CHCP (combined heat power and cooling) systems operation strategy using dynamic programming, Energy, Volume 66, 2014, Pages 387-400, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2013.12.069
Download Green Open Access Pdf - Chiappini, D., Facci, A. L., Tribioli, L., and Ubertini, S. (March 1, 2011). "SOFC Management in Distributed Energy Systems." ASME. J. Fuel Cell Sci. Technol. June 2011; 8(3): 031015. https://doi.org/10.1115/1.4002907
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