# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 2 State of the Art Control Strategies for District Heating (DH) and District Cooling (DC) Systems
### Advanced Control Strategies in DHC Systems  
Given the fact that smart DHC grids are becoming more complicated than before due to the exploitation of non-programmable renewable thermal energy sources and sector-coupling with the power grid, traditional strategies, even if they are robust, are not sufficient to cope with these challenges. Smart control strategies at centralized and decentralized levels are of paramount importance and need to be applied on top of the basic control strategies presented in Sections 2.1 and 2.2. These strategies against challenges such as weather uncertainty, user behavior and customer-sited substation operation are trying to achieve good performance of the system satisfying capacity constraints at some points of the DH network. In recent years, model predictive control (MPC) has become a high-tech solution to several complicated control problems. It attracted research communities' attention in different fields and in particular in the DHC sector. MPC is based on a predictive process that exploits a dynamic model of the system and allows finding the vector of the manipulate inputs over a time horizon by solving an on-line constrained optimization problem that takes into account the system details [52]. For instance, an MPC controller based on a dynamic programming (DP) optimization algorithm has been presented in [PERSON] et al. (2020) [53] to supply the thermal energy to a school complex located in NorthernItaly. Results show a decrease in natural gas consumption of the boiler up to more than 7% in comparison with a standard PID controller. In [PERSON] et al. (2020) [54], the same tool and approach are applied in different case studies. The comprehensive smart control strategy consists of pledges efficient energy distribution, flexibility and low-carbon energy integration. Achievements are a 6% reduction in operating cost and up to 34% in energy consumption, whereas meeting consumers' needs. Moreover, MPC has been applied in [PERSON] et al. (2017) [55] by scheduling boilers, TES units and flexible loads. Results show that the MPC succeeded in reducing operating and maintenance costs in a DH power plant. This methodology integrated projection on fluctuating demand within the optimization problem and answered it in a receding horizon fashion. [PERSON] et al. (2014) [56] developed an MPC to improve the thermal storage tank management in a multi-energy district boiler. The results revealed that the controller reduced fossil energy consumption and CO\({}_{2}\) emissions, while the economic profit increased. A model predictive control (MPC) introduced in [PERSON] et al. (2019) [57] has been applied to decentralized solar thermal collectors (STC) fields connected to a DH network in the return-supply configuration. The authors showed that the MPC implementation in the real SDH system in Lerum (Sweden) operated in a robust way solving the problems of the previous PID controller that was not capable of limiting severe supply temperature and flow rate cycling variations that can be a cause of fatigue problems in buried steel pipes. Sometimes for covering the disadvantages of MPC and obtaining the best performance and result, MPC has been merged with other methods. For example, to control the supply temperature in the DH network, authors in [PERSON] et al. (2013) [58] used the combination of the MPC and fuzzy direct matrix control (FDMC). Findings confirm that FDMC can command the inherent nonlinearity in the acknowledgment characteristics of DH systems by considering the volume flow rate at the plant as a fuzzy variable. Additionally, the trade-off between pumping and heat loss costs can significantly impact minimizing operational costs. A tool named XEMS13 using MPC strategy has been introduced in [PERSON] et al. (2019) [59] in order to optimize operational costs by considering technical and operational constraints. This tool is developed by the Energy Department of the Politecnico di Torino and LINKS.