Browsing by Author "Silvente, Javier"

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    Efficient planning of energy production and maintenance of large-scale combined heat and power plants
    (Elsevier, 2018-05-31) Kopanos, Georgios M.; Murele, Oluwatosin C.; Silvente, Javier; Zhakiyev, Nurkhat; Akhmetbekov, Yerbol; Tutkushev, Damir
    In this study, an efficient optimization framework is presented for the simultaneous planning of energy production and maintenance in combined heat and power plants, and applied in the largest coal-fired cogeneration plant of Kazakhstan. In brief, the proposed optimization model considers: (i) unit commitment constraints for boilers and turbines; (ii) minimum and maximum runtimes as well as minimum idle times for boilers and turbines; (iii) bounds on the operating levels for boilers and turbines within desired operating regions; (iv) extreme operating regions for turbines; (v) energy balances for turbines; (vi) total electricity and heat balances for satisfying the corresponding demands for electricity and heat (for each heat network); and (vii) maintenance tasks for units that must occur within given flexible time-windows. The minimization of the annual total cost of the cogeneration plant constitutes the optimization goal here, and consists of startup and shutdown costs, fixed operating and fuel costs, maintenance costs, and penalties for deviation from heat and electricity demands, and penalties for turbines for operating outside the desired operating regions. An extensive data analysis of historical data has been performed to extract the necessary input data. In comparison to the implemented industrial solution that follows a predefined maintenance policy, the solutions derived by the proposed approach achieve reductions in annual total cost more than 21% and completely avoid turbines operation outside their desired operating regions. Our solutions report substantial reductions in startup/shutdown, fuel and fixed operating costs (about 85%, 15%, and 13%, respectively). The comparative case study clearly demonstrates that the proposed approach is an effective means for generating optimal energy production and maintenance plans, enhancing significantly the resource and energy efficiency of the plant. Importantly, the proposed optimization framework could be readily applied to other cogeneration plants that have a similar plant structure.
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    A rolling horizon approach for optimal management of microgrids under stochastic uncertainty
    (Elsevier, 2017-09-22) Silvente, Javier; Kopanos, Georgios M.; Dua, Vivek; Papageorgiou, Lazaros G.
    This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information.
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    ItemOpen Access
    A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids
    (Elsevier, 2015-06-25) Silvente, Javier; Kopanos, Georgios M.; Pistikopoulos, Efstratios N.; Espuña, Antonio
    This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information.