2. Attributes and Trends
2.1. Quantitative Results (Numerical Results Analysis)
Figure 2a shows the distribution of the “resolution techniques” (percentages) used in recent years. The results show that, in general, CI techniques have been used with a higher frequency. Regarding MP approaches, despite MP ability to guarantee global optimal solutions, these have been considered to have a lower frequency than CI techniques. Finally, HM appear as an option to solve the EMS optimization problem. Based on the increasing complexity and new requirements for MG operations (costeffectiveness, reliability, and resilience), HM may gain strength in the future.
Figure 2. (a) Resolution techniques used in recent years, (b) Objective function types used in recent years, (c) Operating model types used in recent years and (d) Consideration of SoC and SoH in storage.
On the other hand, Figure 2b shows the results of the objective function types researchers have used to formulate the mathematical optimization problem in EMS. The red line shows that, in general, SOBJ functions have been considered to have a higher frequency than MOBJ functions. This is an expected result for a technology that focuses on main objectives in early development phases. Nevertheless, a minimumcost operation is not the only objective of an EMS. In fact, authors have recently considered other objectives to improve MG overall performance, e.g., reduction of polluting gases, maximization of the use of renewable resources, maximization of the useful life of batteries, etc.
Upon the penetration of several DG technologies, electrical vehicles, and controllable loads, the need for a more detailed modeling of system components is a wellknown issue in the context of distribution systems. Thus, a proper management of reactive power requirements, minimization of losses, and unsymmetrical operations is required. However, this consideration introduces nonlinear equations that increase formulation complexity and computational burden ^{[29]}. This can explain that, in recent years, the authors have generally considered the use of DC load flow operating models. In fact, the results in Figure 2c show a predominant use of DC load flow approaches with nearly 85% of the references. In spite of the evidence, the authors expect an increase in AC approaches in the future.
Both SoC and SoH are important features to be considered in the operation of the EMS. Figure 2d shows the results on how these attributes are considered in the EMS mathematical optimization problem. In general, the large majority of the papers (nearly 70% on average) have considered the SoC to be part of the mathematical problem. In contrast, only about 15% of the papers integrate the SoH attribute into the optimization problem. No clear trends could be identified over the years for both attributes.
The high penetration of renewable generation into MGs increases multiple power injection variability. Furthermore, load forecasting uncertainty in MGs is higher compared with bulk power systems. This is explained by a high preponderance of individual loads in total MG loads and the integration of electromobility. The factors described above should be considered in EMS optimization approaches ^{[30]}^{[31]} to achieve more realistic and costeffective results. Figure 3 summarizes these results. The orange line (linear regression trend) shows that the consideration of uncertainties into the EMS mathematical problem has increased in recent years. Figure 3a shows that in the years 2017 and 2018, uncertainties were considered in approximately 53% and 41% of the papers, respectively. Additionally, Figure 3b shows the distribution of modeled uncertainties such as generation profile, load forecast, grid electricity prices, and storage SoC. Furthermore, Figure 3b shows that the authors have generally considered the uncertainty in generation and load while storage uncertainty seems to be at an early stage but increasing in importance. Electromobility can be a key driver in this direction ^{[32]}.
Figure 3. (a) Uncertainty trend in recent years, (b) Uncertainty consideration distribution in different MG agents.
The results of this section help to identify structural research issues from a comprehensive database of research works from past years. Thus, main resolution techniques, objective functions options, load flow types, storage attributes, and the integration of uncertainties were identified. Nevertheless, apart from the uncertainties (Figure 3), it was not possible to identify trends over the period of analysis (2012–2018). Moreover, a traditional quantitative analysis could not reveal how these research aspects are related. This situation motivated the following cluster analysis.
2.3. Cluster Analysis
The cluster analysis proposed is used in this section to identify additional trends in EMS developments. Only such features holding enough information (see Table 1) are considered for obtaining and analyzing these trends (e.g., detail level of components (A), objective function (B), resolution technique (C), operating model (F), and consideration of uncertainties (H)). More specifically, this identification is performed by using the Kohonen selforganizing maps (SOM) tool. The SOM is a clustering and data visualization technique based on a neural network approach. As with other types of centroidbased clustering, the objective of SOM is to find a group of centroids (reference vectors in SOM terminology) and assign each object in the data group to the centroid that provides the best approximation of that object ^{[33]}.
Table 1. Features and attributes of the distinctive papers selected.
Ref. 
A 
B 
C 
D 
E 
F 
G 
H 
I 
J 
MP 
CI 
HM 
^{[34]} 
STM 
SOBJ 
MILP 


OIN 
DSET + OOU 
DC 
DY 
DM + GE 

MIN 
^{[35]} 
STM 
MOBJ 


MO + FL + ANN 
GFO + OIN 
DSET 
DC 
DY 
DM + GE 


^{[36]} 
STM 
SOBJ 
MINLP 


GFO + DFO + OIN 
OOU 
DC 
HR 



^{[37]} 
STM 
SOBJ 

LO 

OIN 
DSET + DSM 
AC 
DY 
GE 
To better visualize these trends, the SOM is integrated into RStudio ^{[38]}. It associates different colors to each attribute (e.g., red for SOBJ, yellow for MP; etc.) for a general view of the final results. The color code associated to each attribute is shown in Figure 4.
Figure 4. Attributes considered with their respective associated color.
Four distinctive groups are identified with the aid of the clustering technique. Figure 5 shows the number of papers contained by each group. Each of these groups has its respective features depending on the most common attributes present in the papers from a specific group (see Figure 6). The chart consists of a sequence of equiangular spaces, with each area representing one of the attributes. The data length of every pie is proportional to the magnitude of the attribute (weight) for the data point against the maximum magnitude of the variable across all data points. Furthermore, for every cluster there is a paper with the shortest distance to their representing attribute. This distinctive paper is known as the centroid of the cluster to which it belongs (see Figure 5). Table 2 shows the centroids (papers) for each cluster jointly with a detailed classification of their attributes.
Figure 5. Number of papers in each cluster.
Figure 6. Main attributes of each cluster.
The following modeling strategies, accordingly EMS trends, are identified based on this combination of attributes (see Figure 5 and Figure 6):
Cluster 1: Dealing with uncertainties: This cluster comprises 33% of the references; therefore, it is the main research trend. Its distinctive feature is the challenge of modeling the uncertainties present in various MG agents (DM, GE, GR) that will impact the performance of the EMS. This is in line with the general idea that uncertainties are becoming an increasingly important issue in MG developments due to DER integration, more active participation of loads, and electromobility. Basic modeling strategies involve static representation of components (STM) and a DC load flow approach. The optimization model corresponds to a SOBJ function, and it is solved by either MP or CI methods. This is a reflection of alternative solution techniques exploration in research proposals. Surprisingly, HMs are not part of this EMS development trend.
As previously mentioned, the centroid of Cluster 1 corresponds to Reference ^{[34]} (see Table 1). In this work, a strategy to deal with uncertainties associated with generation and demand is presented. The SOBJ objective function considered is total profit maximization. The optimization problem is formulated by using the MP technique MILP and solved by employing CPLEX. The focus is only on the interchange of active power among power generators, energy storage, energy consumption, and the grid. Thus, DC load flow is considered. Finally, MG components are formulated as STMs (discretetime formulation). By following step (8) from the proposed procedure, new researchspecific challenges in this trend are:

How to solve further problems entailing higher complexities by combining reactive and proactive techniques and considering the uncertainty of renewable resources and load consumption.

Guidelines to a simultaneous consideration of different factors (i.e., costs, environmental impact, social aspects, etc.) through the implementation of multiobjective optimization approaches.
Cluster 2: Multiobjective strategy: This cluster contains approximately 29% of the references. This second leading trend addresses a multiobjective approach. For this purpose, three resolution techniques are used for the resulting optimization problem, with a slight predominance of CI approaches. Uncertainties in the fields of DM and GE have been considered on a general basis. In this case, a DC load flow approach is also the basic network modeling approach. Nonetheless, as mentioned , a predominance of SOBJ approaches in the near future may be expected; a research community with a focus on MOBJ challenges exploring several solution techniques can be clearly identified.
The centroid of Cluster 2 corresponds to Reference ^{[35]}. The authors propose a MOBJ framework to minimize operational costs and the environmental impact of an MG. The mathematical problem is solved through FL and ANN, CI techniques used jointly with MP. The problem formulation includes optimum battery scheduling while considering the uncertainties of MG DERs and forecasted parameters. The objective functions are only subjected to active power constraints; therefore, a DC load flow is considered.
Researchspecific challenges are:

How a combination of CI and traditional techniques can achieve a clear and important minimization of operation costs, greenhouse gases emissions, and other technical and economic MGrelated issues.

How to achieve a highly accurate forecasting of renewable energy sources by using CI techniques, in order to comply with an efficient MG energy management.
Cluster 3: Traditional paradigm: This third cluster contains approximately 21% of the references. By conceiving a traditional approach for EMS developments, the following options might be selected: SOBJ instead of MOBJ, a deterministic approach instead of focusing on uncertainty modeling, well proven MP approaches, a static representation of components (STM) instead of a DYM approach and finally a focus on active power simulation (DC) vs. an AC paradigm. This is exactly what can be observed in this modeling trend. This type of development can be expected to be very useful for improving current commercial EMS solutions that usually follow a more traditional modeling strategy. The only unexpected feature is a substantial use of HM for the solution techniques. This issue may indicate that most research proposals and innovations in this research group address the migration of traditional MP techniques. This makes use of the advantages of combining CI solutions that constitute HM strategies. In Figure 2a, a positive evolution of this strategy is identified.
The centroid of Cluster 3 is the paper in Reference ^{[36]}. In this work, a traditional SOBJ function is used to minimize the total cost of energy. The authors assumed that the voltage level is the same at all MG busses—a DC load flow operating model. MG components are formulated as STMs in a timediscrete formulation resulting in a number of algebraic constraints. Proposed energy management is based on local energy market and allows scheduling MG generation with minimum information shared by the generating units. For EMS optimization, the authors propose a novel algorithm based on a MINLP approach for an MG in islanding mode considering different scenarios. The optimization problem is solved in a GAMS/CONOPT environment.
The following specific challenges were identified:

How to enable the owners of the distributed generation units to establish their own strategies to participate in MG generation with minimum information shared between distributed generators. Additionally, comply with consumer’s requirements with a minimum energy cost.

How to use demand response programs either to avoid or to decrease penalty costs and the amount of unserved power, as well as to improve demand side management.
Cluster 4: The PQ challenge: This last cluster contains approximately 17% of the references. As mentioned in the previous section, proper management of reactive power requirements, voltage profile, ohmic losses minimization, and unsymmetrical operations are wellknown issues in MG developments. Nevertheless, Figure 2c does not identify a clear trend in this field. On the contrary, the cluster analysis was able to extract a trend in this field, where AC modeling is in the core of the solution approaches, together with a predominance of CIbased solution techniques. Additionally, a more deterministic focus in modeling approaches is identified, with the exception of uncertainties in generation (GE). This is the only trend that incorporates STM and some DYM approaches. Additionally, the use of either SOBJ or MOBJ proposals has been identified in this field.
The centroid of Cluster 4 corresponds to Reference ^{[37]}. In this study, an online EMS for realtime operation of MGs is presented that considers the power flow and system operational constraints. The SOBJ formulation is to minimize the longterm operational cost, while delivering reliable and highquality power to customers. Online energy management is modeled as a stochastic optimal power flow (AC load flow model) capable of capturing uncertainties in generation (GE). The EMS problem is solved by the Lyapunov optimization approach (CI).
Finally, the following researchspecific challenges are:

How to properly consider the underlying power distribution network and its associated power flow and system operational constraints in order to achieve control decisions that do not transgress realworld constraints.

How to develop an intelligent algorithm on the customer side to generate demand requests that allow the EMS to make more accurate operating decisions in the MG.
These papers’ analyses on cluster centroids validate the rationality of cluster descriptions and their main attributes.
2.3. Key Lessons Learned, Key Challenges, and Future Research Directions
The key challenges and the way to cope with them are identified based on the analysis of each trend, the results of previous reviews, and the previous experience of the authors.
Uncertainties in renewables is a topic with an increasing interest to researchers (see Figure 3a) but that is only partially integrated by the research community with a third of research results. It can be envisaged that uncertainties in renewables, demand, and grids will become a common modeling aspect in EMS proposals in the near future. Decentralized solutions will increasingly integrate local renewable sources required by small productive processes (e.g., agriculture, farming, tourism, etc.). Consequently, more complex customer/prosumer behaviors are expected. The modeling of this type of load involves new uncertainties and the need of new simulation strategies that exceeds the traditional ZIP approach (load model that considers constant impedance “Z”, constant current “I”, and constant power “P” components) or a time series analysis.
Some obstacles for the researchers to cope with additional modeling challenges that occur on a simultaneous basis have been identified, e.g., multiple objective functions, full AC network representations, and the application of hybrid solution methods. Each of these combinations constitute new challenges in this research area. In fact, researchers have recently considered introducing more than one objective into the objective functions to enhance overall MG performance (e.g., minimization of operational cost, reduction of polluting gases, maximization of the service life of batteries, etc.). An emerging topic in this field is the integration of dynamic socioenvironmental preferences from the local community. The work on the relative weights of the different objective functions is limited, and new consensus strategies resulting from community participation are being developed. Socioenvironmental preferences may also be linked to present and future economic costs, which are not only related to the cost of electricity. The integration of all the foregoing is optimal for developing an EMS that considers all realworld constraints while satisfying consumer requirements at the same time. This increase in complexity will impact not only the computational burden of the resulting optimization problems but also the convexity properties of mathematical models, the availability and quality of input data, communication network requirements (bandwidth), cost reduction needs, among others. Based on the specific MG features (size, type and number of technologies, environmental conditions, etc.) a balance and tradeoff analysis should be conducted by the researchers in order to develop adequate and practical proposals. Internet of Things (IoT) may play a key role in this area for both the access to a wide range of information sources and types and the costeffective data acquisition approach. In fact, an important benefit of IoT to MGs is the ability to control noncritical loads (demand side management), which is the ability to run the MG at the lowest possible cost whilst providing the highest possible reliability to provide power to critical loads and improve its flexibility. Moreover, IoT allows the application of DSM in a context of diverse commercial solutions of appliances, a common issue related to MG globally. In this context, more accurate and computationally efficient dynamic models for critical MG components are needed. The centralized approach needs a representation of the critical stability aspects related to operational decisions. The transition between the operation modes is a clear example in the case of isolated operations. The feasibility and impact of each transition should be considered as part of the EMS optimization strategy. Nowadays, this aspect is only considered for specific MG configurations (i.e., failure of the master unit and replacement of a specific predefined backup unit). This constitutes a barrier for a massive integration of these solutions in everchanging local conditions.
On the other hand, storage systems are one key component for EMS operation. Thus, both SoC and SoH should be fully considered in the EMS mathematical optimization problem. However, according to the findings from this work, SoH is still not considered on a frequent basis. Consequently, research efforts should focus on developing valid, general, and suitable SoH models to be included in the EMS mathematical problem. Moreover, to consider multiple storage systems in a single, MG.; equivalent storage system approaches or clustering should be considered.
The results have shown that most of these papers hold a high consideration of DC load flow. However, reactive power is also an important part of MG and should be considered in energy management to achieve a reliable and secure system. However, this consideration introduces nonlinear equations that increase formulation complexity and computational burden. Therefore, research efforts should focus on developing low complexity and computationally efficient AC load flow models.
To solve the EMS mathematical optimization problem, MP, CI, and HM have been used. CI approaches have advantages in convergence speed and largescale problem solving compared to MP methods. However, CI techniques may fall into local optima as MG complexity increases. HM approaches that combine the benefits of the above methods have attracted researchers’ attention. Thus, it could be a great opportunity to focus on developing new HM approaches.
The results of this research trend analysis show that there is no evidence of a research cluster where all EMS development challenges were dealt with on a simultaneous basis. In fact, research proposals in every cluster are mainly focused on the improvement of specific areas, while making some simplifications in others. Thus, these proposals can be addressed with the mathematical and computational resources currently available. A clear understanding of each of the four trends identified can be considered a good starting point and guidance for future research contributions. In spite of current proposals imposing some simplifications, they have been accepted for the deployment of EMSs around the world.
Finally, innovative DSM and storage applications and concepts may play a key role to overcome the current and future challenges faced in the design and solutions of MGs. Thus, more costeffective, reliable, social, and environmentally compatible solutions are feasible. These opportunities, i.e., key enablers for massive MG integration, have not been properly addressed in the literature.