# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 4 Discussion  
### Advanced Control Strategies  
The implementation of advanced control strategies in DHC systems is challenging, since they are affected by time-varying nonlinear dynamics, time-varying set-points and disturbances. Likewise, the implementation in HVAC systems, advanced control strategies applied in DHC can be classified as hard, soft and hybrid control [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2020); [PERSON] et al. (2020). In the following, some more details are provided about hard and soft implementation of model predictive and agent-based control strategies highlighting their strengths and weaknesses. The focus by the authors on these two main approaches is motivated by a bibliographic study that show the growing number of publications related to the application of these control approaches in DHC systems between 2010 and 2020. The bibliographic analysis, whose results are plotted in Figure 4, has been performed through the database Scopus considering both peer-reviewed and conference papers. The results are also listed in Appendix A with the string used to query the database. It is also evident the growing number of works related to applications of neural networks, whereas in very few cases, robust control and reinforcement learning have been studied. MPC can be considered a cutting-edge technology for improving the operation of DHC systems. It requires a surrogate model of the plant that it is used to simulate the behavior of the system in a dynamic optimization problem. The latter is implemented to minimize a cost function in a receding horizon fashion. This methodology consists of a time window that moves forward by one time-step at each control timestep \(t\) according to the schematic shown in Figure 5. The length \(N\) of this time window is constant and called prediction horizon. It represents the future time interval in which the outputs of the model (\(y(t)\)) are forecasted according to the trajectory of inputs received (\(u(t)\)). In order to speed up the optimization algorithm, some \"blocking\" methodologies can be applied. For instance, according to Figure 5, one can adopt a control horizon (\(M\)) that is shorter than the prediction horizon (\(N\)). In this way, the last value of the control variable in the control horizon is kept constant and used for the rest of the simulation. Only the first element (\({u_{0}}^{*}\)) of the optimal input trajectory provided by the optimization algorithm is sent to the plant,whereas the rest is discarded. The optimization process restarts again at the next control timestep after updating the state of the surrogate model with monitored data. According to the scientific literature reviewed, in several cases, the control problem is implemented in the form of economic MPC with the objective of minimization an operating cost function that can be formulated according to the following general expression for a generation system that supplies a DH network with CHP and boiler units:  
\[\min\left\{ \begin{array}{c}\text{cost of heat production by fossil fired CHPs}\\ -\text{revenues from electricity production by CHPs}\\ +\text{cost of heat production by fossil fired boiler units}\\ +\text{cost for buying excess heat}\\ +\text{cost for electricity consumtion by auxiliaries}\end{array} \right\} \tag{1}\]  
where constraints are foreseen for the minimum and maximum capacity of the units as well as their ramp-up rates. Auxiliaries can be, for instance, hydraulic pumps, and the revenues from the electricity production by CHPs can include subsidies such as in [65]. However, some variants of the above general formulation are possible according to the problem and the system analyzed. For example, when low-temperature networks with  
Figure 4: Number of publications related to different advanced control methodologies for DHC systems between 2010–2020.