E3S Web Conf.
Volume 238, 2021100RES 2020 – Applied Energy Symposium (ICAE), 100% RENEWABLE: Strategies, Technologies and Challenges for a Fossil Free Future
|Number of page(s)||6|
|Section||Energy Grids and Flexibility|
|Published online||16 February 2021|
Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility
Center for Energy and Environment (CIDEA), University of Parma, 43124 Parma, Italy
2 Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy
* Corresponding author: email@example.com
In recent years, the flexibility of energy systems has become essential due to the growing penetration of renewable energy sources. The producers and consumers can enhance this flexibility by enabling a given amount of power that they can produce or consume in every condition. This is made available to the grid operator to globally optimize the dispatch management and to stabilize the grid. However, this can interfere with the operation of production units such as cogeneration plants, which also have to meet thermal demand. Therefore, producers and consumers require smart controllers to comply with grid operator requests at any time. This paper proposes a robust control strategy based on Model Predictive Control, which manages distribution networks and production plants by considering the uncertainty of the requirements for flexibility from the grid operator. The simulation case study is the district heating network of a school complex supplied by a Combined Heat and Power plant and a Thermal Energy Storage tank. The robustness of the proposed optimization is investigated by simulating several scenarios with different degrees of uncertainty about the request for electricity from the grid operator. The results show that the plant operator is able to comply with the electricity requirements to different extents depending on the degree of uncertainty and on system design choices. These considerations make it possible to improve the plant design and production planning from the perspective of grid flexibility.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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