4.1. Energy Management Systems for Distributed Energy Resources
The IEC 61970 standard defines the EMS as “a computer system comprising a software platform providing basic support services and a set of applications providing the functionality needed for the effective operation of electrical generation and transmission facilities so as to assure adequate security of energy supply at minimum cost” [
5,
99]. Beyond this, EMSs are able to monitor, analyze and even forecast the overall system power profile, accounting for electricity market prices as well. Consequently, based on this information, EMS can optimize the operation from different technical and economical points of view, by guaranteeing the compliance with all system constraints simultaneously. Once the best management strategy has been defined, EMS demands its implementation to suitable hardware and software interfaces, such as SCADA systems, human machines interfaces and local DER controllers [
5,
100]. EMS for the operation of DERs in SDN will be embedded in DMSs as advanced functions that rely on accurate input data and fast communication signals. For a proper DMS design, the impact of the state estimation uncertainties and of the communication system delays should be jointly evaluated [
101].
The EMS can be characterized by either centralized or decentralized architectures. The former class determines the reference signals for all DER local controllers by using suitable optimization criteria and solving procedures. The tracking of the optimal signals is then demanded to local controllers, which do not contribute to the overall system optimization. The drawbacks of centralized EMS are the high computational cost, especially when several DERs are involved, the weak scalability and poor robustness in case of failures [
5]. Some of these drawbacks can be overcome by decentralized EMSs, which propose optimal reference signals to local controllers; however, the latter are free to follow or not the suggested reference profiles in accordance with their inherent needs. Decentralized EMSs need bidirectional communications with all system components, which should be synchronized properly, thus leading to increased costs compared to centralized solutions. An alternative and interesting approach consists of distributed or hybrid architectures [
5,
102], namely a combination of centralized and decentralized structures in which local controllers can communicate to each other and with a centralized control unit in order to define the most suitable reference scenario. As a result, computational complexity is reduced compared to centralized architectures, but communications become more complex [
5,
102].
Regardless of EMS architecture, the synthesis of suitable reference signals can be achieved by setting appropriate objectives to optimize. A variety of optimization goals can be used, which can be classified in four main categories: economic, environmental, technical and user-driven [
103]. Although each of these optimization goals can be pursued individually, they may be also combined to each other by following, for example, a multi-objective optimization (see, for example, Chapter 11 of [
104]). In this regard, two main approaches can be followed, namely “single numeric” or “Pareto“ [
103]: the former consists of combining all the objectives into a single function, e.g., by a weighted sum. This approach has the advantage of simplifying the optimization process, but the results are affected significantly by the choice of the weighting coefficients, which is not a trivial task. Differently, the “Pareto” approach consists of optimizing multiple objectives simultaneously; thus, a priority criterion can be employed, which consists of optimizing all the objectives sequentially [
105]. Alternatively, other methods can be used, such as the minmax and the epsilon constraint methods [
103]; the former consists of setting a suitable reference signal for each variable to optimize and then minimizing the differences between actual and reference values. The epsilon constraint method consists instead of selecting a main objective and converting all the others into equivalent constraints by defining suitable thresholds. However, the most popular approach is identifying the Pareto frontier and selecting non-dominated solutions [
103,
106].
The EMS optimization criterion can be satisfied by using a number of optimization algorithms [
5,
103,
107], which can be carried out in advance and/or in real-time depending on the kind of services to provide [
108]. The most important optimization algorithms are summed up in
Table 3; each of these presents advantages and drawbacks, thus its employment is strictly related to the specific optimization criterion and inherent features of the system to optimize.
Generally speaking, classical methods are based on system modeling and parameters, whose accurate knowledge is thus fundamental for achieving suitable and effective solutions. However, these methods are generally high-demanding in terms of both computational resources and execution time [
103]. These drawbacks can be partially overcome by heuristic and meta-heuristic algorithms, but at the cost of achieving sub-optimal or local optimal solutions [
103]. AI methods may lead to improved results compared to meta-heuristic approaches, but they require proper training and increased computational effort. A specific and well-established approach is represented by multi-agent systems (MAS), which include multi autonomous entities (software or hardware). MAS are being used in an increasingly wide variety of optimization applications, as well as in DER optimization context [
109].
Regardless of the specific optimization procedure, suitable forecasting of both electricity production and consumption is generally required in order to enable a proper DER optimization over a given time horizon [
110,
111]. Some EMSs that rely on optimization approaches are actually being manufactured and marketed by several energy companies, such as Schneider Electric, ABB, General Electric and Siemens [
5].
4.2. Energy Storage Systems for Smart Distribution Networks
EMS optimization discussed in the previous paragraph is generally enabled by several kinds of ESSs [
112,
113,
114], which can be broadly classified in seven major categories: reservoir or pumped hydro energy storage (RHS or PHS), compressed air energy storage (CAES), hydrogen energy storage (HES), superconducting magnetic energy storage (SMES), flywheel energy storage (FES), electrochemical battery (EB) and ultra-capacitor (UC).
Both RHS and PHS systems have been widely employed for several decades, as proved by the fact that they account for almost the 99% of the energy storage capacity worldwide, 34% of which in Europe [
112]. This ESS technology is thus well established and conventionally used to cope with the peak power demand [
112]. However, the rapid diffusion of RESs and their related issues makes these systems useful for primary and secondary frequency regulation [
113]. Currently, research and development activities on RHS/PHS are focused on improving their dynamic performances and increasing their power operating range by means of variable-speed motor-generator systems [
113]. It is worth noting that the use of either RHS or PHS requires an appropriate geographical location, which may increase investment costs significantly [
112]. Regarding CAES systems, they represent a relatively well-established technology, especially if large systems are considered [
113]. However, CAES is not as widespread as other similar ESS that share the same level of technological development; this is due mainly to siting constraints and high investment costs, which prevent CAES from being economically viable [
112]. This issue could be partially addressed by enabling CAES to provide multiple grid services, such as supporting off-shore wind power plants, black start and secondary/tertiary frequency regulation [
113]. Further research and development activities are also needed in order to improve the energy conversion process, especially in terms of efficiency [
113]. HES systems are very flexible because the stored energy, once converted into hydrogen, can be employed in several different forms (electrical, chemical, thermal, etc.) [
112]. HES systems are well-suited to large-scale energy services over long time horizons, such as primary reserve, energy arbitrage and seasonal storage [
112,
113]. Main drawbacks of HES technology arise from high installation costs, low efficiency, safety issues and the lack of proper infrastructures able to exploit HES potentiality to the maximum extent [
112,
113].
The SMES systems seem particularly suitable for providing power services to the electric grid, such as voltage/frequency regulations and power quality applications [
113]. This is due to several advantages, among which high power density, high efficiency and robustness, high cyclability (in terms of number of charging/discharging cycles) and long lifetime. However, the main SMES drawback consists of very high investment costs, which are related mainly to the cooling system needed for guaranteeing the superconductivity of SMES coils; in this regard, research and development efforts are actually focused on novel superconducting materials able to operate at higher temperature, which would reduce cooling system requirements and costs [
113]. Another popular ESS is represented by FES, which is made up of a rotating mass accelerated and decelerated by an electrical machine; this operates in motoring and generating mode alternatively, thus ensuring a bidirectional power flow between the FES and the electric grid. Several FES systems are on the market for different applications (automotive, road and rail vehicles, UPS, etc.) [
113]; focusing on power system applications, FES systems are suitable for voltage/frequency regulation due to their weak energy density [
113]. In addition, FES systems are characterized by some safety and control issues, especially as far as high rotating speed is concerned (tens of krpm). EB will become the most used ESS all over the world, although they currently present limitations relating to their relatively short lifespan due to cycling as well as the round trip efficiency [
112]. There are several kinds of EB, among which conventional EBs represent a well-established and low-cost technology, but suffer from long charging time, high temperature sensitivity, periodic maintenance, low cyclability and reliability [
113]. High-temperature EBs, especially Ni-NaCl, are instead characterized by high reliability and long lifespan, as well as high energy and power densities. Nevertheless, there is the need for keeping high operating temperatures, which require appropriate thermal insulation and heating systems [
113]. Better performance in terms of both energy and power densities can be achieved by Li-Ion EBs, which are widespread in spite of their early development stage and high costs [
113]. Regardless of the specific technology, EBs are valid solutions for providing energy and, to a less extent, power services. Differently from EBs, UCs present very high power density, high efficiency, fast dynamic responses, high cyclability and long lifetime; these features make them suitable for power services such as frequency regulation and power quality improvement [
113]. Main UC weaknesses consist of very low energy density and high costs, which prevent their employment for energy-intensive applications [
112].
Based on the all the above considerations, a degree of suitability of each ESS technology for the different SDN infrastructures discussed in the previous sections (MG, VPP and EH-MES) can be identified, as resumed in
Table 4. In particular, this classification has been carried out by assuming VPP geographical areas wider than EH-MES and, especially, than MG. Consequently, RHS, PHS and CAES seem more suitable for VPP because the geographical constraints characterizing these ESS technologies are less strict as far as wide geographical areas are concerned. HES technology is instead more suitable for EH-MES due to its inherent flexibility of converting the stored energy into different forms; this ESS technology is also suited for both VPP and MG, but to a less extent due to low efficiency, installation and infrastructure issues. SMES and FES present similar degrees of suitably, although FES seems more suited than SMES for VPP because of lower costs and higher maturity. EB is generally suited for all kinds of SDN infrastructure, but especially for MG due to its limited geographical extension, as proved by several pilot projects and installations carried out worldwide. The same does not go for UC, especially due to their weak energy content, which make them weakly suitable for large-scale SDN infrastructure (VPP and EH-MES).
Apart from the ESS technology classification just introduced, which highlights different degrees of suitability in accordance with MG, VPP or EH-MES applications, it seems clear that the main issue that prevents ESS diffusion consists mainly of high investment costs, which may not be completely counterbalanced by subsequent economic benefits [
103]. This is mainly due to the limited number of energy and/or power services that a single ESS technology can provide, especially for FES and UC. In this regard, a viable and very promising solution is represented by hybrid energy storage systems (HESSs); these combine two or more ESS technologies characterized by complementary features, in order to increase the number of grid services they can provide and, thus, their economic viability [
103]. Among all the possible ESS combinations, those made up of EB and UC seem the most promising [
115,
116]; this is mainly due to their high level of modularity, which enables them to fit different energy and/or power requirements and, thus, to provide multiple grid services simultaneously [
117,
118,
119].