# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 4 Discussion
### Advanced Control Strategies
Figure 5: Receding horizon principle: inputs (**u**) and outputs (**y**) trajectories over the prediction horizon at the current control timestep \(t\) (**a**) and at the next control timestep \(t\)+1 (**b**). decentralized heat pumps are present at the user substations such as in [64], in the problem formulation, there is also included the electricity costs for their consumption. In [55], since controllable loads are present, an additional cost is considered in the optimization problem for the curtailment of the flexible loads together with start-up and shut down costs of the boilers and TES charging. In a few cases, the effect of the DH supply temperature on the thermal energy loss through the pipes is taken into account such as in [55, 132]. In some cases, energy consumption minimization is the goal of the problem formulation such as in [53, 132], while application of minimization of CO\({}_{2}\) emission or non-renewable primary energy consumption has not been encountered. Moreover, formulations that may be relevant in real applications to also take into account the constraints on the electricity production allocated in the day-ahead electricity market the day before for the following one have not been encountered. As a synthesis of the material examined in this study, the following strengths (+) and weaknesses have been identified for the application of MPC:  
(+) Instead of corrective operations, it employs a proactive approach with anticipatory control actions. (+) Load forecast and stochastic disturbances can be handled. (+) Systems with delays and operational constraints are taken into account. (+) Multiple objectives can be formulated in a cost function and achieved by exploiting advanced optimization algorithms. (+) It can be formulated in both centralized and distributed fashion but also at the master or slave level. (-) Time-consuming for the implementation and model identification phase. (-) Non-technical users require specific background knowledge of the method. (-) Require significant higher expenditures, which may not be repaid by additional savings in a short period. Among the different MPC formulation in DHC application, mixed-integer linear programming (MILP) resulted in one of the most used, since it is able to handle both continuous and binary variables that are needed for set-points definition and on-off planning [60, 61, 62]. This hard-implementation sometimes called also hybrid MPC, which is characterized by [133, 134, 135]:  
(+) Good and exact optimal solution. (+) Rigorousness, flexibility and extensive modelling capability. (-) Time-consuming particularly for large problems. (-) It is known to often have weak linear programming relaxations. (-) Loss of original discrete structure. (-) Introduction of auxiliary binary variables. With the growth of artificial intelligence (AI) research field, the development of MPC with soft and hybrid control technique are also appearing in DHC applications. In particular, black-box machine learning (ML) models are mainly used to cope with system non-linearities, and meta-heuristic optimization algorithms are used to solve the constrained optimization problem. The latter are usually population-based biological-inspired algorithms and can be mainly distinguished by [136]:  
(+) Straightforward coding in any programming languages. (+) Very quick convergence. (+) No need to do complicated mathematical operations, since they are gradient-free. (-) Easy to drop into local optimum. (-) Possible stagnation after some initial stages. (-) Efficiency reduction with an increase in problem dimension. Adaptive control is a technique that is also applied in some cases in DH. In particular, their application in controlling the supply [137] or the return [138] temperature from substation heat exchangers emerges. It is a methodology that tries to overcome the problem of low performance of conventional feedback controllers during changes in the process dynamic.