## 2 Operation and Control
### Distributed Energy Resources
#### 2.4.2 Applied Methodologies  
Relating to DER management, [PERSON] et al. [86] present a distribution layer planning model, and this model uses a self-adaptive global-based harmony search algorithm (SGHSA) model and fuzzy logic.  
[PERSON] et al. [87] present a demand-side management strategy based on load shifting technique of future SGs with an evolutionary algorithm (EA).  
Some models focus their solution on centralization, while others are based on decentralization. [PERSON] et al. [88] study this problem in depth, and the authors give different solutions to each scenario.  
The penetration of DER and EV can be a problem, the increase of DG and storage will make the power flows are changing, in addition, the increase of EV will require a more robust electrical infrastructure and a greater contribution of energy from renewable. For this reason, [PERSON] and [PERSON] [89] present a complete analysis and a solution to the automation of these scenarios.  
[PERSON] et al. [90] propose a smart energy distribution and management system (SEDMS); they use dynamic patterns (autoregressive integrated moving average-ARIMA-model) as controls appliances. The model is valid in a real environment, and the results are very positive.  
As [PERSON] and [PERSON] [91] show, one of the enabling instruments is the real-time pricing of energy with dynamic and flexible tariffs, helping the power grid to stay in balance; the new services are implemented on service-oriented architecture (SOA).  
[PERSON] [92] proposes a socio-ecological energy system (SEES) framework to improve the functionalities of the system, and the author uses innovative concepts such as: just-in-time (JIT), just-in-place (JIP) and just-in-context (JIC). These concepts come from the industrial world, but the work shows that it is possible to use them optimally in the SG.  
Regarding to microgrids and aggregated DER, [PERSON] et al. [93] present a mathematical model for optimizing aggregate of DER (residential) and micro-CHP. The combination of microgenerators and an optimal management system are fundamental for the deployment of the microgrid.  
[PERSON] and [PERSON] [94] present a new algorithm (mixed linear programming), which optimizes your electrical and thermal demand in a VPP. This type of programming allows to communicate with the distributed generators that make up the VPP, and in an optimal way.  
[PERSON] et al. [95] present a practical example of microgrid by means of INGENIAS, for which the system is based on multi-agent systems (MAS). The system is fully developed and the results show that it is possible to use MAS for the management and control of a microgrid.  
[PERSON] et al. [96] design a management system for a microgrid, where DR elements are used for emergencies. The model is based on a hardware-in-the-loop simulation (HILS) system, and the authors show that one application of DR is the use of energy for emergencies.  
The key to improving building energy efficiency (smart buildings) in operation is to coordinate and optimize the operation of DERs and loads ([PERSON] et al. [97]). [PERSON] et al. [98] present a high-resolution model (based on fuzzy logic system) of domestic electricity use with photovoltaics systems in smart buildings, and they demonstrate the potential of these techniques in these scenarios.  
To summarize, Table 4 shows all the work described in this section. In the rows are shown different areas (problems and challenges and applied methodology) while in the columns is shown the application rate on DER (management, microgrids, new energy services and aggregating DER). As can be seen in the previous table, DER will integrate numerous actors, such as: demand and microgeneration management, microgrids, new energy services and aggregating DER (such as VPP). In all cases, new problems should be addressed, as well as new methodologies should be applied.