# 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
Different configurations with increasing cost-saving, installation costs, renewable energy source generation and primary energy saving are presented, and results demonstrated the success of the method. Linear programming (LP) and mixed-integer linear programming (MILP) are commonly used to individuate complex energy systems' best size or scheduling. A recent application of LP related to this scope for a 5 GDHC system supplying several buildings of a university campus can be found in [PERSON] et al. (2020) [60], where the problem has been set up in Python and solved by means of the solver Gurobi. A MILP model for the design and operation of an urban energy system in the center of England has been presented by [PERSON] and [PERSON] (2018) [61]. This model is based on a flexible value web framework for representing integrated networks of resources and technologies. It can be used for different temporal and spatial scales. Researchers in [62] have applied MILP to minimize the total annual cost for an industrial area located in the northeast of Italy with the integration of solar thermal production. Their results showed that the yearly total cost and primary energy consumption reduce 5 and 15%, respectively. In the framework of the H2020 FLEXYNETS project [63], to reduce the overall heat generation cost of a DH supplied by a large share of low-grade excess heat, [PERSON] et al. (2017) [64] indicate that the suggested MILP-based controller managed to reduce the system operational costs by 11% with respect to a standard rule-based control. Similarly, in [PERSON] et al. (2015) [65], the authors applied a MILP method to minimize the total costs. To this end, four different strategies for calculating temperature stratification inside thermal storage are performed. Procedures are tested with an operation scheduling use case that aims at minimizing operational costs by achieving a reduction of 6-7%. MILP has been applied in the work of [PERSON] et al. (2017) [66] using the commercial solver CPLEX in a receding horizon fashion with a timestep of 15 min and a prediction horizon of 24 h. Differently from other works,here, the optimal scheduling of the generation units assessed takes into consideration the effects on the DH network with the integration of a dynamic model in Modelica. The simultaneous optimization of DH production and distribution resulted in the use of the DH network as storage by increasing the supply temperature prior to a peak of the demand, reducing the differential pressure and avoiding the start-up of expensive peak units. In [67], a more complete overview of the results is provided by the authors showing that with respect to an empirical piling method, the production optimization only was capable of achieving 6.4% of savings on the production costs and no energy savings, whereas with both production and distribution optimization, it was possible to obtain 8.3% of savings in production costs and 1.7% reduction in the energy consumption. Adequate optimization algorithm depends on the problem formulation; likewise, how to formulate the problem depends on the optimization algorithm to use. In some cases, the execution time increases exponentially according to the problem dimensions and deterministic optimization algorithms cannot solve challenging and complicated issues. Meta-heuristic algorithms are a kind of random algorithm that are used to find an optimal response to this problem. Genetic algorithm (GA), particle swarm optimization (PSO) and ants colony optimization (ACO) are some of the most used population-based meta-heuristic algorithms. Although these methods' performance and ability have been proved in many references, they have rarely been used directly in DH application. For instance, a two-level optimization algorithm is proposed in [PERSON] et al. (2019) [68]. MILP formulation has plenty of pros for optimal operation problems while overcoming its disadvantages, GA is applied.