1. Introduction
Currently, in Denmark a large part of the power from Wind Power Plants (WPPs) (i.e., 3799 MW) is coming from onshore wind turbines [
1,
2]. These wind turbines are distributed either individually or in small-scale clusters. Conversely, the power from Photovoltaic (PV) production primarily consists of small dispersed residential units up to 6 kW [
3]. Additionally, the Danish Government targets achieving 50% of the total power production from renewable energy by the end of 2020 and 100% by 2050 [
4]. In the coming years, this anticipated trend will lead to the integration of huge concentration of not only offshore and onshore WPPs but also a large scale concentration of PV Plants (PVP) in transmission as well as distribution system, respectively [
5]. This foreseen huge penetration of ReGen plants into the Danish electricity supply may possibly create several challenges, as discussed in [
1,
2,
6]. A simple solution proposed in [
1,
6] is the provision of online reactive power support from the existing ReGen plants in the distribution grid. The provision of online voltage/reactive power support will allow down regulating the entire voltage profile in the distribution system, and also keep the voltage within the limits at the given nodes. Moreover, this capability is also required by today’s grid codes and has already been implemented in modern ReGen plants. Nevertheless, owing to the lack of technical infrastructure for communication and control, this capability is not yet utilized by the Distribution System Operators (DSOs). Considering the increasing number of dispersed units, an effective coordination between these units along with local control of voltage on a distribution grids is required to provide reactive power support. The DSOs in Denmark have already started to install a Supervisory Control and Data Acquisition (SCADA) system for this purpose that will be employed in the near future [
6]. On the other hand, in the absence of a regulatory framework, controlling the ReGen plants for reactive power support may not be reasonable in short term.
It is also pertinent to note that Networked Control Systems (NCSs) are known to be sensitive to the changing communication network properties, such as end-to-end delay and information loss rate, which can cause serious control performance degradations. Communication disruptions in control loops may in turn have serious consequences on the physical systems that are being controlled over communication networks. In SG, these disruptions can manifest themselves as over-voltages, physical damages in the power grid and in worst-case can lead to blackouts. Similarly, relying on the underlying communication networks to address voltage stability challenges (e.g., related to volatile voltage excursions in distribution systems) in power systems with large penetration of ReGen plants will put high responsibility on these networks. The authors have demonstrated the impact of using public network communication (i.e., cellular networks) for voltage control coordination from ReGen plants in [
7,
8], and also highlighted the challenges and risks associated to the use of Information and Communication Technologies (ICT) in the said scenario.
The integration of ICT into the electrical energy infrastructure is shifting from a phase of demonstration to large-scale deployment [
9]. This will not only have a strong impact on system architectures but it will also raise concerns about the issues related to cyber-security. The integration of ReGen plants (such as PVP, WPP, etc.) as well as the communication technologies in power grid has made it a cyber-physical energy system, also termed as Smart Grid (SG). A general framework for SG is therefore required for the validation that takes into account the mutual interactions and interdependencies between ICT and ReGen. However, today, lack of design and validation tools that are capable of analyzing power systems in combination with the ICT in a holistic manner is one of the main barriers.
Incorporating power system simulation tools with that of ICT requires collaboration among experts of both areas. This is because of a significant difference in both communication as well as electrical related equipment (including but not limited to messaging protocols, information models, reliability requirements, temporal consistency, hierarchy, etc.) [
9]. Here, a co-simulation platform for the integration analysis of both domains helps in understanding the impact of different ICT based solutions used for the operation of power systems. After solving the subsystems independently by their corresponding domain specific simulators [
10], co-simulation allows a joint and simultaneous investigation of models based on different tools, where intermediate results are exchanged during the execution of the simulations [
9,
10]. Therefore, co-simulation makes it possible to have a comprehensive view of the network behavior in connection with the physical energy system states. Further, before the implementation and roll out of the simulation studies, these should pass through a Real-Time (RT) simulation environment to test the controller design. This RT simulation shows how the designed controller responds, in real time, to realistic virtual stimuli. It can also be used to determine if the designed physical system model is valid or not.
It can be ascertained from the above discussion that there are several stages in the design and implementation of a SGs related concepts, which should be carried out in a sequential and hierarchical fashion. Thus, as a first contribution, this paper introduces a Model-Based Design (MBD) approach in SGs that proves to be an important methodology in the design, implementation and roll out of SG technologies, solutions and corresponding products. In addition to the several benefits, this approach will offer collaboration among different laboratories at international level, which, in turn, has a positive impact on interoperability and confidence in the applicability of the research under different grid conditions. Additionally, as a part of MBD, this paper demonstrates the provision of online voltage control coordination support from ReGen plants via a Real-Time Hardware-In-the-Loop (RT-HIL) framework. This RT-HIL framework is available in Smart Energy Systems Laboratory (SES Lab) [
11] at the Department of Energy Technology, Aalborg University, Denmark. It uses hierarchical industrial controller platforms, e.g., Bachmann’s M1 controller [
12], that are used nowadays in power plants. This testbed has been used for several projects in the past, such as [
13,
14,
15].
The concept and design of online voltage support from ReGen plants has already been published [
1,
6,
16], while the authors in [
7,
8] have illustrated the impact and associated risks (such as cyberattacks) of using existing public network communication infrastructure on the online voltage control coordination from ReGen plants in distributed grids, respectively. Since the work presented in [
1,
6,
7,
8,
16] is mainly based on offline or non-Real-Time (non-RT) co-simulation framework, the authors in this paper prove through real-time set of recordings that the control concepts developed in [
1,
6,
16] are valid. The validation is done by first verifying and validating the proposed ICT model for non-RT studies in [
7,
8] through different test cases against the complete network model and related data traffic implemented in the SES Lab. Based on the results, the validation of coordinated voltage control for ReGen plants in Medium Voltage (MV) grids is achieved.
Hence, the scope of this paper is two-fold: (1) its introduces an MBD approach in SGs as an important methodology in design, implementation and roll out of SG technologies; and (2) as a part of MBD approach, it validates the already presented concepts and results related to the provision of online voltage control coordination support from ReGen plants via RT-HIL framework. The remainder of this paper is organized as follows:
Section 2 introduces MBD and explains the different stages involved in this approach. A brief description related to the concept of distributed online voltage control concept that was presented in previous publications along with its RT-HIL setup implementation description is presented in
Section 3. The details and challenges related to ICT simulation model in providing the online voltage control support from ReGen plants are outlined in
Section 4.
Section 5 provides the assessment and validation of both ICT simulation model as well as the RT voltage control coordination concept. Finally, the study is concluded with future research directions in
Section 6.
2. Model Based Design in Smart Grids
Today’s control capabilities for power systems and SGs are more and more demanding, especially with high share of power generation from renewable sources. Design and tuning of control algorithms is typically based on dedicated simulation tools, e.g., MATLAB/Simulink, while control verification may require other power system tools. Translation of control schemes between tools may be time consuming and the typical iterations in control development may increase the time even more. Once the control schemes are verified in a power system tool under various operating conditions and test cases, an implementation phase on the actual target hardware is required. Another series of tests are then required to validate the implementation. Finally, the controller is tested in a real environment. Some of the measurements are later used to validate the designed control performance but also the models used in the design process. It is worth mentioning that site testing may not involve specific tests that require measurements of real events in the power grid. Thus, typically, open loop testing is performed. Some of these events, e.g., large frequency excursion, may not occur during the limited testing period of controllers.
To overcome some of the drawbacks encountered during the design and testing of control schemes for power systems and SGs, an MBD approach is employed. The MBD approach was initially used by automotive industry and then for motor drives [
17]. A brief history of MBD can be found in [
18]. The different stages used in designing and testing of control schemes for power systems while employing the MBD approach are shown in
Figure 1. An overview of these stages from development to testing is presented in the following subsections.
2.1. Desktop Simulations
Initially, the control development and tuning process uses simplified/reduced order models of power system and assets. Either continuous or discrete time models can be used according to the tuning methodology [
17]. However, a continuous time domain tuning is preferred at this stage [
17] because the dynamics in the power system applications involve a wide range of time constants as well as various sampling times for the related subsystems. Further, considerations about translation of models in discrete models are made in respect to the actual expected sampling time of various control platform used.
2.2. Rapid Control Prototyping
In this stage, the entire model is implemented in discrete domain with the proper sampling time for various subsystems. This model is implemented in a RT discrete simulator, e.g., Opal-RT. The control algorithms are tested intensively under various operating conditions to identify the robustness of the design. Based on the executed tests and their results, iterations of the control design and tuning may be necessary. It is worth mentioning that information exchange between different subsystems, i.e., plant controller, aggregator, assets, etc., is also captured at this stage with a dedicated model that is able to simulate the communication networks and their traffic (see
Section 4). By using dedicated communication models for information exchange, it can be identified if specific ICT can meet the requirements imposed by control response. Moreover, this helps to specify minimum requirements (or boundaries) for ICT to provide a specific control function. This will allow the selection of the ICT for the deployment. Finally, the control is ready for implementation on the target hardware when the control design verification is finalized.
2.3. Automatic Coding Implementation
Typically, target hardware requires coding of control algorithms in different software, specific to manufacturers of the platforms e.g., structured text, C/C++, etc. This process can be shortened if the simulation tools used for control design are capable of generating the code automatically, such as MATLAB/Simulink. Since, SES Lab already uses the MATLAB/Simulink platform to design and simulate controllers as well as to translate to the required code, this step can be skipped when using the facilities in this lab.
2.4. RT-HIL Co-Simulation
Verifying control algorithms on large-scale power systems requires testing of the controller platform connected to a RT model of the power system including assets i.e., WPP, PVP, energy storage devices, etc. Moreover, for a realistic testing, RT model of the communication networks is used. Thus, the controller platform including the developed algorithms in the first phase is tested in realistic conditions close to daily operation. Moreover, power system events that cannot be measured in real life can also be replicated in a controlled environment, for instance, during extreme weather conditions for both WPP and PVP. Similarly, the normal data traffic associated to specific network technologies as well as with failure conditions (e.g., due to cyber-attacks) is captured without actually involving the real technologies. The advantage of this approach can be summarized as:
Extensive testing and verification of control algorithms under operating conditions which cannot be encountered during the field-test trials.
Accounting for impact of communication technologies, protocols and traffic on the developed control algorithms in a unified and consistent without being constraint by the physical systems.
The developed control algorithms including physical implementation on target hardware is then ready for site testing. This stage corresponds to a Technology Readiness Level (TRL) of 6. According to HORIZON 2020—Work Programme 2014–2015 [
19], TRL-6 stands for the demonstration of technology in a relevant environment, i.e., industrially relevant environment in the case of key enabling technologies [
19].
2.5. Validation
Once the designed controller is passed through all aforementioned stages, it has to be validated using actual plant hardware in real-life situations. The main goal for this process is to ensure that no physical equipment will be damaged during the tests. For this, the actual controller platform should be tested on-site under operating conditions allowed by the physical power grid and assets. Typically, the targeted tests have to be reported and documented to power system operator (DSO or Transmission System Operator) as well as to the plant operator or owner before execution. However, this process becomes complicated in terms of coordination, etc. when different owners/operators are involved. Moreover, the testing campaign is typically limited in time and power system events in scope for the developed algorithms and, therefore, large voltage and frequency excursions may not occur in the system during the testing period. Thus, an open loop approach is used. This means that the controller is fed with pseudo-measurements and the output of the plants is recorded. However, the actual impact on the power grid cannot be evaluated as well as possible control interactions between assets. These recordings may be used to validate some of the developed models including modeling assumptions used in the previous stages. This is where a RT-HIL again plays its role and allows tests and validation without even involving the physical power grid and assets. By allowing to use the designed model to represent the real-life scenario, RT-HIL platform offers benefits in terms of cost and practicality [
20].
The existing facilities in SES Lab [
11] allow all of the above design and verification procedure being a powerful environment for achieving high TRLs. Therefore, in the following section, we first present a brief overview of the distributed online voltage coordination concept from ReGen plants, established in [
16], and subsequently the implementation of this concept in the RT-HIL setup in SES Lab is described.
3. Distributed Online Voltage Coordination Concept and RT-HIL Framework
According to the Grid Code requirements, the voltage profile in a power system should remain within the desired tolerance band. For instance, in MV distribution grids, the voltage should remain within
of its nominal value [
16]. This challenge has to be fulfilled by each generation unit connected to the power system. The violation of this limit at certain points within the grid may possibly lead to severe stability problems and damage to the entire power system [
6]. Although each ReGen plant can contribute to voltage regulation, as ascertained in [
7,
16], there can be additional control objectives required by the DSO, for example to reduce the grid power losses caused by reactive power provision. This objective can be accomplished by optimizing the control settings in the distributed online coordination scheme, as defined in [
16], which constitutes an NCS of the SG developed on top of a communication network. The basic NCS architecture for distributed online voltage coordination is given in
Figure 2.
This control architecture requires a grid layout and parameters provided by the DSOs, measurements from secondary side of primary substations as well as measurements from each of the controlled ReGen plants. It provides voltage setpoints and droop values for each ReGen plant considered in the asset’s portfolio. Since the power output of ReGen plants continuously varies and thus the voltages in the distribution grid, it is foreseen that an aggregator of grid support services may take over the task in future. This aggregator will be responsible for continuously updating the controller settings of the ReGen plants in real-time according to the actual operating point. The detailed NCS architecture for distributed online voltage coordination is given in
Figure 3. The aggregator (indicated as Distributed Online Coordination block in
Figure 3) receives measurement signals i.e., voltage, active power, reactive power (
,
,
) and the available reactive power (
) from all ReGen plants (
) and in return dispatches the droop settings (
,
) for the voltage controllers.
Further, a Benchmark Distribution Grid (BDG) was developed in [
16] to identify voltage stability challenges in distribution systems with large penetration of ReGen and to assess the voltage control functionalities shown in
Figure 3. The BDG is based on a real MV grid operated by Himmerland Elforsyning (HEF) near Aalborg City in North Jutland, Denmark, and is considered as starting point for the definition of the BDG. To account for realistic scenarios regarding the current and future penetration of renewable power plants in Danish distribution grids, the BDG has been supplemented by the following ReGen plants providing voltage control functionality, i.e., one WPP with type-4 (full-scale converter connected) wind turbines and three PVPs:
WPP (18 MW) representing 6 WTs of 3 MW each
PVP 1 (10 MW) representing remotely located ground-mounted system
PVP 2 and PVP 3 (2.5 MW each) representing typical rooftop systems mounted on top of large industrial plants and shopping centers
The BDG represents a typical radial feeder topology with primary substation (
kV) as shown in
Figure 4, where the ReGen plants are shown as WPP, PVP 1, PVP 2 and PVP 3 (the BDG model is presented in detail in [
16]). Based on the control architecture shown in
Figure 3, this BDG is used to analyze voltage control in time domain for a volatile power profile of the ReGen plants, used as a benchmark test scenario that covers the crucial operating points with high solar irradiation and high wind speed (for details, see [
16]).
The authors have demonstrated in [
16] that, using the NCS architecture shown in
Figure 3, the overall performance of online coordination for voltage profile management, control stability and the present voltage fluctuations remained satisfactory. The power losses within the distribution grid have also been shown to reduce to a measurable extent. Concerning the intervals at which the voltage set-points are to be updated, the authors demonstrate it via several test scenarios in [
16] that the aggregator should dispatch set-point signals to ReGen plants in time intervals of 10 s to few minutes. However, it has also been recommended that power losses need to be evaluated for longer time periods (such as months or years) to provide meaningful recommendations for the controller specifications, taking into account the economic benefits for a DSO.
The following subsection elaborates on the implementation of the described distributed voltage control coordination concept along with the BDG in the RT-HIL setup.
3.1. RT-HIL Setup
SES Lab was used to implement the control architecture for distributed online voltage coordination in the RT-HIL framework.
Figure 5 shows the front view of this lab with various subsystems and workstations involved in the RT-HIL setup.
Figure 6 illustrates the setup used for the implementation of control architecture for distributed online voltage coordination in the RT-HIL framework seen in the lab. Before presenting the overview of each subsystem shown in
Figure 6, it should be noted that in the near future an ancillary market is also expected for the provision of grid support services [
21]. It is foreseen that in close collaboration with local DSOs, the aggregators of ReGen plants will take the charge for hosting voltage control capabilities besides energy trading [
6]. Therefore, at this stage, it is assumed that the aggregator control unit is in charge of providing reactive power support along with controlling voltage locally on the distribution grid. In the following, a brief overview of each subsystem shown in
Figure 6 is presented (for detailed description, see [
17]):
Aggregator Hardware Platform is based on the M1 controller hardware provided by Bachmann Electronics Gmbh [
12]. One reason for choosing this platform is that it supports the MBD approach using MATLAB/Simulink. Moreover, it is widely used in the renewable energy industry and hence considered as a benchmark system. In the context of research applications of SG scenarios, this controller platform offers the possibility of utilizing open communication protocols (e.g., User Datagram Protocol (UDP) /Internet Protocol (IP)) for the data exchange. In this way, the obtained results for RT-HIL validation are not affected by any manufacturer specific protocols, which may introduce additional delays, etc. In the current setup, the M1 controller receives measurements from primary substation and ReGen plants (implemented in Opal-RT System), and provide voltage set-points and the droop values, respectively, through the RT ICT Emulator. It is important to note that the distributed online voltage coordination scheme presented in [
16] is used as such. The only modifications are related to Transmission Control Protocol/Internet Protocol i.e., TCP/IP interfaces between different hardware platforms.
Host PC—Aggregator Hardware is a dedicated Professional Computer (PC) used for developing the initial controller schemes in MATLAB/Simulink. It is also hosting the dedicated software to communicate with the controller for setting up the configuration of the controller.
Opal-RT System [
22] hosts the BDG. It sends measurements to and receives set-points from the Aggregator Hardware through RT ICT Emulator. The grid layout and ReGen plants RT models are developed using ePHASORsim tool from RT-Lab [
23,
24]. The wind power plant and solar PV plant models defined in [
19] are directly implemented in the Opal-RT System.
Host PC—Opal RT is a PC dedicated for the development, control and monitoring of the RT model. Automatic code generation of the RT Model is included in the RT-Lab suite [
24] provided with the Opal-RT system. The Monitoring and Control Module is sending the following signals to the Opal-RT System:
- (a)
Meteorological data: wind speeds, solar irradiation
- (b)
External grid voltage setpoint
- (c)
Load profiles in terms of active and reactive values
This module also receives selected signal from the Opal-RT System for monitoring the model performance during simulations.
RT ICT Emulator. The RT network emulator is based on KauNet [
25] that provides pattern based network emulation by enabling ingoing as well as outgoing data packets to pass a queue configured with a buffer length and service time according to a given stochastic model of a network. KauNet provides control on bit errors, packet losses, bandwidth, and delay changes. Using KauNet, reproducible behavior of network along with an exact control on network traffic over Internet can be provided [
26]. The traffic using KauNet is routed through a set of buffers, where each buffer emulates specific network characteristics in terms of delay, packet losses, etc. [
26]. The network capacity can also be emulated by shaping the buffer sizes, such that packet losses can be efficiently emulated. The idea behind this setup is to have flexibility to assess any types of networks as well as the power control systems networks that generally are more deterministic in nature. Further, it is meant to illustrate the strength of being able to assess control over third party stochastic-networks which is highly relevant for operators to assess their possibility to support connectivity services to DSOs.
All the data exchange between aggregator hardware and Opal-RT system is executed through the RT ICT Emulator. This means that only these signals are prone to delays, packet drop and cyber security threats. The data exchange between host PCs and corresponding hardware is not affected.
3.2. Considerations on Real Time Implementation
The various subsystems presented in
Figure 6 are running with different sampling times to capture the realistic behavior of such a system used in real applications. The following considerations are made regarding specific sampling time.
- (i)
Host PC-Opal RT: The Monitoring and Control Module uses a 200 ms sampling time for sending the meteorological data as well as for monitoring of the internal variables from Opal-RT System. The reason for this fast sampling is the available time resolution of meteorological data (i.e., wind speed and solar irradiation).
- (ii)
Opal-RT System: The power grid and the ReGen plants are running with 10 ms sampling time as a standard value for Root Mean Square (RMS) simulations (half cycle of 50 Hz system). The feedback signals from ReGen plants and primary substation to Aggregator Hardware are updated every second. This sampling rate is chosen to exchange steady-state signals, as the typical time response value for the active power control loop of Wind Turbine Generators (WTGs) is below 1 s [
16].
- (iii)
Aggregator Hardware: The sampling time for distributed online voltage control coordination algorithm depends on the sending of set-point values as per the update rate [
16].
3.3. Summary of Involved Hardware/Software Platforms
Given a variety of hardware/software environments used in this work,
Table 1 summarizes the involved suites, indicating their role to facilitate the understanding of interconnected functions and to provide the whole perspective about the presented approach.
4. Information and Communication Technologies (ICT) Model
An essential feature of the distributed online voltage coordination is the use of ICT for gathering and acting on information collected from various ReGen plants in an automated fashion. To enable bi-direction information flow for this purpose, various communication technologies can be used. These technologies include wired (such as copper cable and fiber optics) and wireless (such as WiMAX and cellular networks) public/private networks. Utilizing existing Internet access networks is a viable and cost-effective solution offering a good coverage in most European countries [
27]. However, networks providing Internet access have time-varying network properties. This particularly creates a problem for NCS, where the main issue that causes degradation of control performance are network induced time-varying delays and packet losses [
27]. Furthermore, as the communication systems (specifically wireless Internet access communication networks) are becoming more and more complex with decreasing time-to-market, accurate simulations models of upcoming standards are essential. The accuracy of these models becomes even more significant when used in combination with critical systems such electrical power systems. It requires to have more detailed view of the communication system and considering extra details when designing the controllers. For instance, when designing a communication system model, the several features to be taken into account are the offered load, traffic patterns, information loss, end-to-end delay, etc. Thus, communication models that reflect real life scenarios (in terms of end-to-end quality of service) should be considered even during the designing phase to depict true impact on the system’s performance.
The simulations related to power system controllers (such as, the voltage controller in this paper) are often done using the Simulink toolbox for MATLAB, where MATLAB/Simulink is based on vectors of bits/symbols/samples. The majority of such MATLAB implementations usually deal with equivalent symbol timing and bypass network aspects beside interference within physical frames. According to [
20], network protocols (TCP, IP), network topology and queuing are rarely considered due to the complexity issues, because each time a new random parameter (such as queue length, etc.) is introduced, the computational complexity increases (at least) linearly (
). Similarly, the computational complexity of the simulations increases as a polynomial function (
) or even exponentially (
) [
20] with respect to the number of assets. This requires a higher level of abstraction as in the network simulators (such as ns-3, OPNET, OMNET++, etc.). Since there are no built-in libraries that allow networking considerations, MATLAB/Simulink (or similar tools) is not suitable for complex network simulations. In such a situation, indirect methods [
20] can be used, i.e., creating interface between a network simulators and simulators like MATLAB/Simulink. Although MATLAB can be used via an interface (e.g., from C code) with the network simulators, the interfacing is not particularly easy to handle nor is it quite fast [
20]. Moreover, since such interfacing methods require very detailed modeling, for larger and complex deployments, the methodology does not scale with regard to complexity. The simulation run time becomes so large that even for moderately complex topologies there is no (or very little) advantage left from the detailed simulation model. Likewise, evaluating simulation results becomes much more difficult because of a large number of parameters influencing the performance. Therefore, implementing a network simulation tool that supports control simulations with ease of handling should be highly preferred, especially for testing complex and critical systems.
Further, the tools used to simulate communication networks usually do not reflect the real life scenario, i.e., those are based on very low and deterministic latency without considering the geographical distances between communicating entities. However, in real scenarios the long geographic distance between different networked devices as well as the amount of traffic on the link may cause unexpected delays in transmission and even signal drops that result in unexpected and faulty control behavior. Specifically, while considering public network infrastructures for implementation, constant delays or packet loss probabilities do not depict the actual scenario. Therefore, dedicated communication network simulation tools are required that not only reflect real networks but also, in combination with the power system tools, assist in analyzing the effects of realistic latencies, packet losses or failures in the communication. Such communication simulators should also facilitate investigations related to cyber-security, such as Denial-Of-Service (DOS) protection, confidentiality and integrity testing.
4.1. Non-RT Communication Network Model
To comply with the rapid control prototyping stage described in MBD (see
Section 2), a network emulator was developed in Simulink that provides pattern-based network emulation. This network emulator, being implemented directly in Simulink, removes many complications such as time synchronization and interfacing between two different simulation tools. The patterns that describe the desired changes in the traffic can be created from analytical expressions or traces collected through a real network and are matched with traffic packets to required behavior in time driven mode. Thus, providing a user with a reasonable estimate of what end-to-end performance can be expected from a communication network. In this work, the patterns (based on end-to-end delays and packet loss) are obtained using NetMap [
28,
29] (for details, see
Section 4.2). In addition to the pattern based network emulation, other relevant features of this network emulator are as follows:
Selection of transport layer protocols
Control over the sampling time (update rate) of information
Ability to introduce packet loss probabilities
Selection of information access schemes (reactive or proactive [
30]) with control over the related parameters
4.2. RT Communication Network Model
The online coordination between ReGen plants and the aggregator highly relies on the underlying ICT infrastructure. This imposes huge responsibility on ICT to make sure that the connection is reliable and meet other requirements for the said purpose. At this stage, implementing a dedicated fiber optics connection to all ReGen plants in the grid seems the best possible option. However, using fiber optic communication in this case will be a highly expensive option considering the huge penetration of ReGen plants in the distribution grids. There are a number of other options too, as detailed in [
31,
32,
33], but the authors propose to use an existing ICT infrastructure that could offer low operating costs, faster deployment, high speeds and flexibility along with the provision of full expertise and manning to operate the network. For instance, cellular networks (with technologies such as Universal Mobile Telecommunications System (UMTS), Third-Generation Cell-Phone Technology (3G), Fourth-Generation Cell-Phone Technology (4G), Long Term Evolution (LTE), etc.) are already extensively deployed by the telecom operators throughout Europe with high coverage [
34]. Therefore, in their previous work [
7], the authors demonstrated (via offline simulations) on the use of the existing cellular network communication infrastructure and showed how it effects the provision of online voltage control and coordination functionalities from ReGen plants in distributed grids. Through non-RT simulation results, it is shown in [
7] that, under normal network conditions, the cellular networks support the proposed online voltage control and coordination functionalities for ReGen plants in distribution grids.
To validate the results obtained in [
7] via RT-HIL setup, it is essential to get a realistic and accurate model of the cellular networks that reflect the true network behavior especially within the area having BDG (hereafter referred to as Benchmark Grid Area (BGA)). As KauNet has been opted in the RT-HIL setup as a RT network emulator that provides patterns based network emulation, the pattern files were captured from BGA using NetMap [
28,
29]. The NetMap patterns are useful as they provide realistic performance under realistic conditions from networks that are normally hard to obtain information from/about, and are relevant, e.g., for operators to assess their possibility to provide connectivity service to control systems.
Figure 7 shows the map of BGA where the pattern files were collected. The map also shows Google map based locations of all ReGen plants in BDG along with the location of communication masts surrounding the area. Thus, the NetMap pattern files reflect the current nature of the access network in the relevant area.
It is pertinent to mention here that NetMap is a performance measurement system for mobile-networks that is based on crowd sourcing. It employs end user smart devices to automatically measure and gather network performance metrics from mobile networks. The obtained metrics comprise of throughput, round trip times, packet loss rates, connectivity, and signal strength, supplemented by a wide range of context information about the device state [
28]. NetMap also offers a Network Performance Map (NPM) based on actual measurements on existing networks using actual end user devices in real end user scenarios. The measurements obtained via NPM are beneficial in the sense that these provide a more realistic image of what the end system can expect if the measurements are performed with similar devices [
28]. According to the obtained measurements [
28], the existing public network infrastructure can sufficiently fulfill throughput requirements to support the amount of data in said scenario. Therefore, the analysis in this paper is based on the end-to-end delay along with the packet losses a signal might experience while travelling between ReGen plants and aggregator controller to understand the impact on the performance of voltage controller.
The pattern files obtained from BGA via NetMap include delay traces (in terms of Round Trip Times (RTT)) as well as packet losses measured using many end devices located at different distances from the aggregator control unit (which is assumed to be located in the primary substation, as shown in
Figure 7). The measurements in pattern files are based on around 3500 TCP-RTT measurement sequences at different distances/locations of the end devices from the aggregator control unit using three different Internet Service Providers (ISPs) available in Denmark. The three ISPs are referred as A, B and C in
Figure 8. Moreover, these measurements are based on 2G, 3G and 4G technologies. (Note: Since the data were collected via cell phones, they are not what a non-mobile electrical unit would accurately achieve in terms of network performance. However, it gives a reasonable estimate of what an asset can expect in terms of end-to-end performance from a communication network.)
Figure 8a shows the combined histogram of RT-RTT measurements, while
Figure 8b shows the packet loss probabilities captured from BGA using several devices. It is important to note that the measurements shown in
Figure 8 have been obtained over a period of 1.5 years with varying number of end devices. In
Figure 8a, it can be observed that, for majority of the cases, RTT lies around 30 ms approximately. This means that for a transfer of information update, a minimum of 15 ms delay (half of RTT—assuming the same route for request and reply to/from the server) can be expected for the maximum times in daily operations. However, as this network is heterogeneous (and shared by many users), the delay continuously varies depending on the network conditions and number of users using the network. The worst case for end-to-end delay is observed as high as 500 ms (RTT) (see
Figure 8a). Further,
Figure 8b shows a varying number of packets sent along with the number of drop packets via different ISPs based on 2G, 3G and 4G technologies. Here, the number of packets sent through a particular technology depends on its available service within the BGA. 2G has the minimum number of sent packets because of its limited service available around BGA. However, according to
Figure 8b, the packet losses observed while capturing these measurements were observed as high as 20% for 2G and as low as 0% in 4G technology.
6. Conclusions
The main goal of this paper is to validate coordinated online voltage control algorithms via RT-HIL framework that were proposed in previous publications. A model based design approach in SGs has also been introduced as an important methodology in the design and implementation of SG technologies, solutions and corresponding products. Based on this approach, the paper addressed the validation of the proposed ICT model for off-line studies. The proposed ICT model is verified and validated through different test cases against the complete network model and related data traffic implemented in the SES Lab. It has been shown that the performance and characteristics of the non-RT ICT model matches with the detailed RT-HIL model. However, the non-RT model should be used for preliminary control studies, while, for validation purposes, the detailed RT-HIL model should be used to achieve a high TRL. Further, the validation of coordinated online voltage control for ReGen plants in MV grids is achieved. Two test cases, based on the results from validation of the ICT model as well as the main findings in previous publication were implemented in the RT-HIL setup. Deviations below 1% were obtained for the main variables involved from both off-line and RT studies. These results confirm that the main assumptions regarding ICT behavior considered in previous studies are valid.
The proposed methodology offers a systematic approach in tuning and design of voltage control coordination applicable in distribution grids with high penetration of ReGen plants. Classical control design and reliable operation of distribution grids are combined with ICT aspects that are of crucial importance in these applications. The presented MBD approach not only simplifies but also shortens the time-to-market for SG applications. Thus, confidence in new control approaches along with high TRLs (up to 6) can be obtained in early stages of the development with realistic emulation of real power grid and communication networks including the respective data traffic. The proposed method will ultimately support the DSOs in stability and security of the power supply.
As part of future activities, the network parameters such as packet loss rates, delay components and various update rates along with the laboratory hardware will be considered for sensitivity analysis. Moreover, validation of the following ancillary services from ReGen plants will be considered: (1) frequency restoration reserve (FRR, also known as secondary control) using a detailed model of the power grid e.g., modified 12-bus systems; and (2) frequency containment reserve (FCR, also known as primary control).