## 3 Maintenance and Lifespan Management
### Advanced Decision-Making Systems
#### 3.3.3 Multiple Criteria Decision Making  
Decision making is a totally interactive task, so the current data is critical. Bidirectionality in information is fundamental, and in this sense, fuzzy logic can help decision-making, as [PERSON] and [PERSON] [217] have shown.  
The criteria that affect electricity supply are basically social, environmental, technical and economic, as reported by [PERSON] et al. [218]. Numerous techniques are used under the umbrella of multiple criteria decision making (MCDM), but blurred logic always appears.  
Complex scenarios can give optimal results if MCDM is used. In this sense, MCDM is highly employed in the electrical system, since the results are very positive. [PERSON] et al. [219] consider that MCDM can support the integration of renewable generation sources. The classification of MCDM is: multi-objective decision making (MODM), multi-attribute decision making (MADM) and combinations of MODM and MADM. Further, the multi-criteria decision models are: elimination and choice translating reality (ELECTRE) group, preference ranking organization method for enrichment evaluation (PROMETHEE), simple additive weighting (SAW), analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), novel approach to imprecise assessment and decision environment (NAIADE), and stochastic multi-objective acceptability analysis (SMAA).  
[PERSON] et al. [220] design an architecture of interactions between customers and the utility. The defined programming is based on finding a solution to two problems, centralized sequential decision making with hidden model Markov decision process (HM-MDP) at the controller to maximize an accumulated reward for the whole microgrid.  
The decision making must be based on the monitoring of the system. [PERSON] et al. [221] present a novel approach to the decentralized optimal reactive power control (DORPC) problem based on a holonic architecture. The approach is approached with MAS.  
Some authors optimize the installation from a techno-economic perspective, as in the case of [PERSON] and [PERSON] [222]. The presented model is based on a multi-objective genetic algorithm (MOGA).  
[PERSON] et al. [223] use a methodology based on fuzzy logic, and the authors take into account qualitative and quantitative criteria, as well as varied performance indicators in the SG.  
It is interesting to obtain new island detection algorithms, for example, [PERSON] [224] implement a new multi-criteria based island detection algorithm.  
In photovoltaic systems, decision-making criteria have always been economic. In this sense, [PERSON] et al. [225] propose a new multi-criteria framework to evaluate the financial support of small-power photovoltaic plants.  
Related to intelligent decision-making, [PERSON] et al. [226] present a new fast Gauss-Newton algorithm for the detection of islanding condition in DGs systems.  
[PERSON] et al. [227] develop a novel technique for flexible interconnection of DG resources to distribution grids. The technique is successful and augurs great application success.  
A new decision-making model based on stochastic programming is implemented by [PERSON] and [PERSON] [228].  
The frequency estimation of signals in the electrical system can be obtained by means of a genetic algorithm, as developed by [PERSON] et al. [229].  
To summarize, Table 9 shows all the work described in this section. In the rows are shown different areas (infrastructure, DER and similar and intelligent decision-making) while in the columns is shown the application rate on advanced decision-making (AI and other techniques). As can be seen in the previous table, advanced decision-making is essential for the energy infrastructure (generation), DER, DR and advanced decision-making systems. For this, these mechanisms may be based on AI and other simpler techniques.