A Model-in-the-Loop application of a Predictive Controller to a District Heating system

Cadau, Nora; Lorenzi, Andrea; Gambarotta, Agostino; Morini, Mirko; Saletti, Costanza

doi:10.1016/j.egypro.2018.08.088

Cited by 15 publications

(14 citation statements)

References 13 publications

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“…The algorithm requires a statespace representation of the system to be controlled with one state. Hence, it has been firstly demonstrated on the heat distribution network of an individual building application, in which the system state is the building internal temperature and the system manipulated variables are the mass flow rate and temperature of the water to the building substation heat exchanger [11]. Nonetheless, the extension to larger networks is straightforward and can be achieved by means of applying the multi-agent approach outlined below [12].…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…The case study simulation is implemented in the MATLAB ® /Simulink ® environment. The detailed model, which emulates the real system and constitutes the benchmark for the robust controller testing, is built by means of a library of energy system components presented in previous works [11]. A brief description of the main components is given below:…”

Section: Detailed System Modelmentioning

confidence: 99%

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Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

et al. 2021

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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.

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Section: Model Predictive Controlmentioning

confidence: 99%

Section: Detailed System Modelmentioning

confidence: 99%

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

et al. 2021

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“…The coefficients UV and CV calculated in [13] With this validation, the procedure and the aggregated models of the six regions of the Vä sterå s network is assessed. In order to perform a preliminary test of its feasibility, a simple application in the Simulink environment is developed [18]. The supply of the thermal poweravailable from the historical datafrom the substation heat exchanger to the model of the aggregated consumer is simulated and the equivalent indoor temperature of the aggregated consumer is then visualized.…”

Section: Model Validationmentioning

confidence: 99%

A Scale-Free Dynamic Model for District Heating Aggregated Regions

Saletti¹,

Zimmerman²,

Morini³

et al. 2020

Preprint

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District heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. The characteristics of these networks vary remarkably depending on the urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. Design, optimization and control of these systems are enabled by the availability of fast and scalable models of district heating networks. This work addresses this issue by proposing a novel method to develop a scale-free model of large-scale district heating networks. Starting from coarse data available at the main substations, a physics-based model of the system aggregated regions is developed by identifying the heat capacity and heat loss coefficients. The model validation on the network of Västerås, Sweden, shows compatibility with literature data and can therefore be exploited for system design, optimization and control-oriented applications. In particular, the possibility to estimate the heat storage potential of network regions allows new smart management strategies to be investigated.

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“…Their resulting optimization problem is modeled as a mixed-integer linear program with logical and continuous variables. A method for MPC, which incorporates the variance of the outdoor temperature, is presented by Cadau et al [22] for a large municipal building not connected to the DHN. In their approach, they use a dynamic model and MPC in Matlab/Simulink to control the internal building temperature by using dynamic programming.…”

Section: State Of the Art-backgroundmentioning

confidence: 99%

Achieving Lower District Heating Network Temperatures Using Feed-Forward MPC

2019

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The focus of this work is to present the feasibility of lowering the supply and return temperatures of district heating networks in order to achieve energy savings through the implementation of feed-forward model predictive control. The current level of district heating technology dictates a need for higher supply temperatures, which is not the case when considering the future outlook. In part, this can be attributed to the fact that current networks are being controlled by operator experience and outdoor temperatures. The prospects of reducing network temperatures can be evaluated by developing a dynamic model of the process which can then be used for control purposes. Two scenarios are presented in this work, to not only evaluate a controller’s performance in supplying lower network temperatures, but to also assess the boundaries of the return temperature. In Scenario 1, the historical load is used as a feed-forward signal to the controller, and in Scenario 2, a load prediction model is used as the feed-forward signal. The findings for both scenarios suggest that the new control approach can lead to a load reduction of 12.5% and 13.7% respectively for the heat being supplied to the network. With the inclusion of predictions with increased accuracy on end-user demand and feed-back, the return temperature values can be better sustained, and can lead to a decrease in supply temperatures and an increase in energy savings on the production side.

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A Model-in-the-Loop application of a Predictive Controller to a District Heating system

Cited by 15 publications

References 13 publications

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility

A Scale-Free Dynamic Model for District Heating Aggregated Regions

Achieving Lower District Heating Network Temperatures Using Feed-Forward MPC

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