In recent years, the increasing penetration of non-programmable renewable energy technologies has made energy system flexibility essential for ensuring power grid stability, in terms of balance between power demand and supply. In particular, cogeneration plants can provide flexibility services by making a defined amount of power available at a given schedule, upon request. The grid operator can request it or not depending on the actual dispatch management. In the presence of this uncertain request, producers need smart controllers to meet thermal demand and, at the same time, to maximize profit while fulfilling the needs of the grid. This work presents a hierarchical predictive control architecture for optimal management of combined heat and power production sites equipped with a thermal energy storage tank supplying district heating networks. The feasibility of the proposed approach is investigated by simulating several scenarios with different degrees of uncertainty about the actual power request from the grid operator. It is shown that the controlled system is able to comply with the requirements to the maximum extent when the exact power dispatch is known a few hours in advance, also giving a profit increase as well as environmental benefits. While the grid operator request for flexibility is met up to 100%, the reduction in carbon emissions ranges from 3% to 7% and the operating margin is increased by 20%. This can be realized when the power availability is scheduled at periods of both maximum and minimum heat demand. Thus, the method is promising also in improving production planning of future integrated energy systems from the perspective of grid flexibility.

Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty / De Lorenzi, A.; Gambarotta, A.; Marzi, E.; Morini, M.; Saletti, C.. - In: APPLIED ENERGY. - ISSN 0306-2619. - 314:(2022), p. 118934.118934. [10.1016/j.apenergy.2022.118934]

Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty

De Lorenzi A.;Gambarotta A.;Marzi E.;Morini M.;Saletti C.
2022

Abstract

In recent years, the increasing penetration of non-programmable renewable energy technologies has made energy system flexibility essential for ensuring power grid stability, in terms of balance between power demand and supply. In particular, cogeneration plants can provide flexibility services by making a defined amount of power available at a given schedule, upon request. The grid operator can request it or not depending on the actual dispatch management. In the presence of this uncertain request, producers need smart controllers to meet thermal demand and, at the same time, to maximize profit while fulfilling the needs of the grid. This work presents a hierarchical predictive control architecture for optimal management of combined heat and power production sites equipped with a thermal energy storage tank supplying district heating networks. The feasibility of the proposed approach is investigated by simulating several scenarios with different degrees of uncertainty about the actual power request from the grid operator. It is shown that the controlled system is able to comply with the requirements to the maximum extent when the exact power dispatch is known a few hours in advance, also giving a profit increase as well as environmental benefits. While the grid operator request for flexibility is met up to 100%, the reduction in carbon emissions ranges from 3% to 7% and the operating margin is increased by 20%. This can be realized when the power availability is scheduled at periods of both maximum and minimum heat demand. Thus, the method is promising also in improving production planning of future integrated energy systems from the perspective of grid flexibility.
Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty / De Lorenzi, A.; Gambarotta, A.; Marzi, E.; Morini, M.; Saletti, C.. - In: APPLIED ENERGY. - ISSN 0306-2619. - 314:(2022), p. 118934.118934. [10.1016/j.apenergy.2022.118934]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2920130
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