Impact of Information and Communication Technology Limitations on Microgrid Operation
- the aging of the centralized energy infrastructure, which can be more vulnerable with the increasing power demand;
- the governmental carbon pollution standards around the world to reduce carbon dioxide (CO2) emissions along with federal and state regulatory actions to reduce greenhouse gas (GHG) emissions from new and existing power plants. This encouraged the implementation of more renewable energy sources (RESs) into the electric grid [1,2], especially, since 29% of the U.S. GHG emissions are only produced from electricity generation . However, RESs are intermittent by nature imposing a challenge on load forecasting and maintaining generation/demand balance; and,
- the rise in the frequency and magnitude of major events, as well as the increasing salience of threats, such as cyber and physical attacks against the grid, make it necessary to not only think about the reliability, but also about the resilience of the grid and its ability to withstand and recover from major events, and be a flexible platform for higher levels of integration of RES.
- Section 2 presents a literature review about smart grids and microgrids and their conceived capabilities, followed by a literature survey and the research gaps regarding MG’s types and control techniques. Finally, introduces a review of the conducted research and the research opportunities regarding the impact of ICT on MGs control and performance.
- Section 2 also discusses the communication architecture and its functional requirements (e.g., allowable delays, bandwidth ranges) for smart grids and MGs applications. Moreover, it references the available software that could be utilized to simulate communication networks, and it discusses the sources of delays in these networks.
- Section 3 introduces a DC MG model that will be used as a case study to show the impact of communication delays on MG performance.
- Section 4 shows the control layer of the aforementioned DC MG.
- Section 5 demonstrates the impact of one of the ICT’s degradation, which is the delay, on the performance of the DC MG case study.
- Finally, Section 6 introduces the conclusion of this paper.
2. Literature Survey
2.1. Smart Grid Vision
- enabling massive deployment and efficient usage of MGs and distributed energy resources (DERs) with integration abilities to communication-based control platforms. This mandate expanding existing standards and data models to accommodate new technologies; such efforts are seen in new revisions of the IEC 61850 standard;
- improving resilience against disruption providing stable and continuous electricity supplies, thus averting wide area accidental blackouts. In other words, smart grid shall guarantee the secure and normal operation of the grid even during emergency issues (e.g., extreme weather, natural disasters, and intentional breakage), enhances sustainability and provides self-healing capabilities;
- securing the information transmitted all over the grid; facilitating the interaction of consumers with energy management systems (EMS) to support load shaping (e.g., peak shaving) and demand-response functionalities;
- allowing for scalable real-time monitoring of grid operations and status. This effort can be seen in the deployment of advanced metering infrastructures (AMIs) and monitoring systems (e.g., phasor measurement units (PMUs));
- implementing an optimized configuration of resources and reducing grid losses; and,
- at the customer level, MGs can exploit time-of-use pricing to lower consumer costs through energy arbitrage, or provide needed energy/capacity to the grid;
- at the distribution level, MGs can participate in demand side management and demand response programs to help the utilities shave their peak demand, while also offering economic benefits to MGs owners. Besides, MGs can relieve congestion on transmission lines (TLs) by satisfying a portion of the loads locally, eliminating some of the high fees that utilities would normally be charged by their respective ISO/RTOs when using congested TLs (i.e., heavily loaded); and,
- at the transmission level, if large-scale or aggregated MGs can provide frequency regulation, and potentially help defer investments in generation and transmission infrastructures. MGs can also provide spin/non-spin reserve by using their backup generator (e.g., diesel engines) or installed energy storage system (ESS) (e.g., battery). Voltage support and black-start can also be provided by MGs.
2.3. Microgrid Types and Control Techniques
2.3.1. Centralized Communication-Based Control (CCC)
2.3.2. Distributed Communication-Based Control (DCC)
2.3.3. Voltage Droop Control (VDC)
2.3.4. Hybrid Control Techniques
2.3.5. Future Recommendations
2.4. Impact of ICT Degradation on Microgrids Control and Performance
2.4.1. Impact of ICT Degradation on Distributed Communication-Based Controlled MGs
2.4.2. Impact of ICT Degradation on Hybrid Controlled MGs
2.4.3. Impact of ICT Degradation on Centralized Controlled MGs
2.4.4. Future Recommendations
- conducting detailed analysis regarding the impact of ICT degradation on microgrid communities’ operations and resilience;
- defining clear communication-based control architectures and minimum requirements for various MGs’ types and applications;
- studying the impact of latency on the event of synchronizing a single MG with the electric grid or within a MG community;
- suggesting and analyzing non-hardware solutions to mitigate the severe impacts of latency on MGs. Since the impact of delay varies with the operational conditions, which are unpredictable factors and function of time. Therefore, more adaptive techniques have to be developed. (e.g., utilizing AI tools, such as machine learning to sense and mitigate communication delays);
- conducting a risk assessment of ICT degradation’s impact on the physical structure (i.e., hardware) of different types of MGs (i.e., DC, AC, hybrid);
- investigating the impact of ICT degradation on Fault Diagnosis and Prognosis processes (FDP). As the name implies FDP performs two tasks, fault diagnosis and fault prognosis. How would the delay impact the FDP processes and consequently the health of the MGs;
- examining how the ICT delay impact-severity varies statistically with the scalability of a single MG and MG communities;
- analyzing how MG community dispatch-capabilities’ delay might impact the utility peak demand in demand response programs;
- evaluating the impact of delaying the control signals to the dispatchable resources such as batteries within renewable based MGs during renewable resources intermittency events.
2.5. Communication Network Requirements in MGs and Smart Grids
- Home Area Network (HAN), which is suitable for short coverage ranges (i.e., up to hundreds of meters), and low communication bandwidths (i.e., up to hundreds of Kbps). HAN is mostly used in smart grid applications at the prosumer level (e.g., communication between MG assets, between smart meters and home appliances, in home automation applications). The common communication protocols used in HAN are mostly WiFi, Zigbee, and Bluetooth ;
- Field Area Network (FAN) is considered to be a portal to transmit information between the HAN and WAN layers. This layer is most suitable for MG communities’ applications, since the coverage ranges could be in Kilometers and the communication bandwidth could reach up to tens of Mbps. It could be realized using WiMax or LTE [66,67]. Utilities uses power line communication (PLC) for long distance communications, for real-time energy management and monitoring, which could also be utilized in FANs. PLC is preferable, since it relies on an existing infrastructure, however, it is prone to noise that is imposed on power lines, especially as the geographic foot print increases. Additionally, RF-mesh is considered to be one of the promising solutions for FAN applications (e.g., AMI applications). Its deployment is relatively cheap, and the utility does not have to rely on telecommunication providers, which introduces more confidentiality. However, these advantages could also be considered as drawbacks, since the absence of unified standard between vendors could degrade the interaction between systems, and RF-mesh has low data rate. Moreover, all radio-based communications are prone to interference, as PLC is vulnerable to noise. However, the remedy in RF-signals could be relatively easy with a mesh, as compared to PLC deploying PLC-gateways at the points showing problems could be challenging and costly [68,69,70];
- Wide Area Network (WAN) is the layer where all the aggregated data are being processed and command signals are being sent/received to and from the portal (i.e., FAN) and then to the HAN layer. WAN’s applications are on the power transmission/generation scales since it has wide coverage ranges and considerable communication bandwidths. Fiber optics infrastructure remains to be the first choice for this layer, since it is dealing with high transmission power, but this option is costly when compared to wireless technologies. Table 1 shows some applications in the three different layers of HAN, FAN, and WAN [71,72,73].
- Processing and queueing
- Congestion of communication links due to high volume of traffic.
- Exhausting computational powers of communicating nodes also due to high volume of traffic.
- Distance between transceivers.
- Obstacles between transceivers, such as trees, buildings.
- Noise and interference from other devices/radio networks.
- Delays due to malicious activity
- Network flooding.
- Complete denial of service.
- Co-simulation. In this approach, software for network simulation is coupled with the power grid simulator to model the behavior of the communication network, which includes the introduction of delays. An example of these software that are used in the engineering literature are Network Simulator (NS) 2 and 3, OPNET, and OMNET++. The clocks of the network simulator and the grid simulators have to be synchronized to achieve realistic operation. This is mostly achieved through a third-party event synchronization tool.
- Network-in-the-loop simulation. In this approach, the control logic is decoupled from the simulation software, and it is implemented on hardware controllers that are interfaced with the power grid simulator through an interface which this simulator allows. There are several works in the literature that are implemented this approach, for purposes other than delay studies, where commercial Intelligent Electronic Devices were interfaced with real-time digital simulators.
3. DC Microgrid Case Study
4. Control Scheme of the Studied DC Microgrid
5. Results and Analysis
Conflicts of Interest
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|Home Area Network|
|Example of Applications||Average Allowable Latency Ranges||Communication Technologies|
|Field Area Network|
|Demand response||<1 min|
|Electric transportation (e.g., pricing info, charge status)||<15 s|
|Wide Area Network|
|Adaptive islanding||<0.1 s|
|Voltage stability monitoring||<5 s|
|Bidirectional converter||LBD||4.5 mH, 0.25 Ohm|
|Smoothing coil||8 mH, 1 Ohm|
|Boost converter||LB||4.5 mH, 0.25 Ohm|
|Inverter||LDC||19 m H, 1.4 Ohm|
|LAC||3-phase each (19 mH, 1 Ohm)|
|Converter||Control Technique||Outer Loop||Inner Loop|
|Bidirectional converter||Current control||N/A||N/A||0.02||110||0.02||3|
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Saleh, M.; Esa, Y.; Hariri, M.E.; Mohamed, A. Impact of Information and Communication Technology Limitations on Microgrid Operation. Energies 2019, 12, 2926. https://doi.org/10.3390/en12152926
Saleh M, Esa Y, Hariri ME, Mohamed A. Impact of Information and Communication Technology Limitations on Microgrid Operation. Energies. 2019; 12(15):2926. https://doi.org/10.3390/en12152926Chicago/Turabian Style
Saleh, Mahmoud, Yusef Esa, Mohamed El Hariri, and Ahmed Mohamed. 2019. "Impact of Information and Communication Technology Limitations on Microgrid Operation" Energies 12, no. 15: 2926. https://doi.org/10.3390/en12152926