Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey
Abstract
:1. Introduction
2. High PV Penetration Challenges
Challenges with Present Level of PV Penetration
3. Envisaged Future Challenges of Very High PV Penetration
3.1. Future Impacts of PHEVs
3.2. Smart Cities
3.3. Future Impact of Solar Eclipse with High PV Penetration
3.4. Transactive Energy Concept
3.5. Big Data, Communication and Cybersecurity Issues
3.5.1. Big Data
- Volume: There is an increase in the size of the data (smart inverter data, PV generation data, whether data, temperature data, data for forecasting, etc.) due to increasing PV penetration.
- Velocity: The speed at which these data need to be acquired and transmitted increases. There is a need for real time control and data acquisition.
- Variety: There is an influx of data from different sources. There is heterogeneity in the data sources including data from weather stations, PMUs, inverters, meters, power quality meters and other sensors. They usually have different format and structures which need to be processed.
- Veracity: The accuracy of the data being transmitted and introduction of noise from the devices and other external sources.
- Volatility: The length of time to store the acquired data.
- Validity: This refers to the timeliness of the data, i.e., the value of the data is bound by time, after which it becomes irrelevant or invalid for processing.
- Value: This refers to the end contribution of the smart grid big data in terms improving grid reliability, efficiency and resiliency.
3.5.2. Communication
3.5.3. Cybersecurity
3.6. Environmental Impacts with Increased PV Penetration
3.6.1. Land Use
3.6.2. Water Usage
3.6.3. Hazardous Materials
3.6.4. The Use of Natural Resources
3.6.5. Life-Cycle Emissions
3.6.6. Other Impacts
4. Existing Solutions with Future Directions
4.1. The Use of MIR and RPFR
4.2. DCI and Smart Inverter Functionalities
4.3. Dynamic and Composite Energy Storage Systems
4.4. Solid State Transformers
- Management of fault scenarios
- Limiting of currents especially during fault scenarios
- The ease of connecting and disconnecting of circuits attached to it
- Power Management
- Ease and possibility of controlling the power flow in the system and the distribution feeder’s power factor
- The ease and flexibility of changing and controlling the customer’s and/or the feeder’s voltage.
- It can provide DC power when needed
- SSTs can be used to mitigate system harmonic which is one of the drawbacks of inverter-based DERs
- The capacity of ride through during abnormal situations
- It can provide support for the DERs when on the islanding mode
- Energy Management Support
- Capability to for real time energy storage monitoring
- Capability for power control and dispatch
- SST can be integrated into the mix of demand side management
4.5. Optimal Energy Routing
4.6. Distribution Supervisory Control and Data Acquisition (D-SCADA) with Advanced Distribution Management System (ADMS).
4.7. Advanced Relay Communication and Protection Coordination (ARCPC)
4.8. Geographic Smoothing and Optimal Location of PV Systems
4.9. Optimal Mix and Dispatch of Renewable Energy Sources
4.10. Demand Response Management
- The use of large time constant loads to create a virtual energy storage which is used to smoothen the intermittent output of PVs through DRMs [208].
- An IoT-based, real time smart-direct load control (S-DLC) was proposed by [209]. The algorithm creates a schedule for the customer loads, then controls and optimize the loads (which already has an intelligent electronic devices (IED) embedded) through a load shedding and forecasting algorithm.
- Karapetyan et al. proposed an event-based DRM using the greedy approach for customer load curtailment. An integer programing problem was formulated which estimated the amount of loads to be curtailed while using the maximum available generated power [210].
- Sivaneasan et al. [211] proposed a DRM algorithm that controls the air-conditioning and ventilation systems in a building. Whenever there is a drop in PV power generation, the developed systems adjust the air conditioning system by putting into consideration the well-being of the occupants of the building. This system incorporates a battery storage management system and a load shedding algorithms that is based on the level of priority of the loads.
4.11. Big Data Solutions
4.11.1. Data Processing Frameworks
4.11.2. Cloud Computing Frameworks
- Software as a Service (SaaS)
- Infrastructure as a Service (IaaS)
- Platform as a Service (PaaS)
- Data as a Service (DaaS)
- Communication as a service (CaaS)
- Monitoring as a Service (MaaS)
4.11.3. Post-Cloud Computing Networks
4.12. The Use of Artificial Intelligence
- Random Forest
- Deep Learning
- Generalized Linear Models (GLM)
- Decision Trees
- Gradient Boosting Machine (GBM)
- Principal Components Analysis (or Dimension Reduction)
- Anomaly Detection
- Clustering
5. Summary of Present and Future Challenges with Suggested Combinatorial Solutions and Future Direction
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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MIR | Minimum Import Relay |
RPFR | Reverse Power Flow Relay |
SI | Smart Inverter |
Smart Inverter with Load Control | |
SI+DLHC | Smart Inverter with Dynamic Load Harmonic Control |
D-SCADA | Distribution Supervisory Control and Data Acquisition |
AMI | Advanced Metering Infrastructure |
ADMS | Advanced Distribution Management System |
ST | Smart Transformer |
ARCPC | Advanced Relay Communication and Protection Coordination |
FRT | Fault Ride Through |
OER | Optimal Energy Routing |
IED | Intelligent Electronic Device |
DCESS | Dynamic and Composite Energy Storage Systems |
GS | Geographic Smoothing |
VVWO | Volt-VAR/Watt Optimization |
DERMS | Distributed Energy Resource Management System |
GIS | Geographic Information System |
CIS | Customer Information System |
STLF | Short Term Load Forecasting |
SE | State Estimation |
MDMS | Meter Data Management System |
FLISR | Fault Location, Isolation and Service Restoration |
OMS | Outage Management Systems |
CRM | Customer Relationship Management |
Functionalities | Sub-Functionalities | Specific Settings |
---|---|---|
Voltage Ride Through (VRT) | Low/High VRT | Voltage, Duration (time) |
Frequency Ride-Through (FRT) | Low/High FRT | Frequency, Duration (time) |
Dynamic Volt-VAR/Watt Control | Volt-VAR, Volt-Watt | Volt-VAR/Watt Curves |
Ramping | Ramp rates | |
Power Factor setting/control | Values | |
Soft start | Ramp rate, Time delay | |
Limit Real and Reactive Power | Enable/Disable | |
Frequency-Watt | Frequency-Watt Curve | |
Dynamic Current Support | ||
Output Scheduling | Time of start, Time to end, Real and Reactive power value, operational schedule | |
Frequency Deviation Support | ||
Control of Reactive Power Dynamically. | ||
Dynamic Load Control | ||
Dynamic Harmonic Control |
Challenges | Existing (with Present Penetration Levels) | Future (with Smart Cities, PHEVs, Solar Eclipse, Transactive Energy, Big Data, Cybersecurity etc.) | Suggested Future Solutions |
---|---|---|---|
Reverse Power flow | incipient problem depending on the point of interconnection with the feeder. | Increase expected. Reduced the choice of point of interconnection. | Minimum load ensured on feeders. MIR, RPFR. . SI+D-SCADA, AMI |
Voltage instability issues | OLTC and DVRs has been effective. | Increase expected. | STs. DCESS, OLTC. STATCOMs. DVRs, SI+D-SCADA with FRT. GS with PV fleet management. |
Complexity in protection coordination | No major issues with Coordination in relays, sectionalizers, fuses, reclosers. | Increased bidirectional flow of current and fault current levels, line to ground voltage increase due to more single phase prosumers, possible desensitization the substation relays, unwanted blowing of fuses, maloperation of reclosers and sectionalizers. | Advance short circuit analysis with high PV penetration. SI with fault current monitoring and control capabilities. ARCPC |
Power factor problems | No major concerns. | Increase expected. | Use of SI with dynamic reactive power control for both utilities and prosumers. SI+D-SCADA, OER. |
Harmonics | No major concerns. | Increase expected. | All SI compliance with UL 1741. SI+DLHC capabilities. Use of STATCOMs |
Frequency Instability | No major concerns. Germany’s ’50.2 Hz’ problem . | Increase expected. | GS with PV aggregation for utility-scale PV systems.DESS. SI+FRT, OER |
Feeder losses | Slight increase depending on POI | Possible future increase. | Robust optimal PV placement algorithms, OER on the distribution feeders. |
Thermal limits of the grid | No significant effects | Increase expected. | UL 1741 compliance for all SI. Optimal placement of utility-scale and small scale aggregated PV system, OER |
Security of supply | No major issue. | Threatened. | Accurate estimation methods of prediction (of security of supply) should include future market analysis consideration of the intermittent nature of PV system as well as the development of other dispatchable energy sources. |
Communication within Distributed Energy Resources (DER) and substation, Cybersecurity | No communication and control link. IEEE 2030 standard has not been fully developed. | Reliable and well defined communication and control protocols needed. Interoperability of DERs in a TE environment. | IEDs .Robust IEEE 2030 standards and adoption by all PV systems. Fast computing and communication architecture. |
Dynamic modeling of the high penetration PV | GIS-based Distribution Management Systems (DMS) models PV systems as a negative load. | System modeling with PHEVs, and proliferation of prosumers would be required. Energy routing modeling for IoT enabled TE would be required. More detailed studies solar eclipse impacts would be needed. | Dynamic models PV systems should be developed for GIS-based DMS and GIS-based Energy Management Systems (EMS) for remote monitoring and control. |
Forecasting | Forecasting always have some level of uncertainty. The level of accuracy is still low | Accuracy will be key to adequate planning, unit commitment and dispatch. | Hybrid-forecasting (nowcasting+forecasting). More accurate prediction models using multiple forecasting methods. |
Dispatch and Scheduling problem | No major issues reported | Increase on PV penetration in transactive environment will require the implementation of optimal power flow and optimal dispatch with high PV penetration mandatory | Optimal Smart Inverter Dispatch (OSID). Optimal set point for storage systems. Mitigation techniques for forecast and communication errors in (OSID) |
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Share and Cite
Olowu, T.O.; Sundararajan, A.; Moghaddami, M.; Sarwat, A.I. Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey. Energies 2018, 11, 1782. https://doi.org/10.3390/en11071782
Olowu TO, Sundararajan A, Moghaddami M, Sarwat AI. Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey. Energies. 2018; 11(7):1782. https://doi.org/10.3390/en11071782
Chicago/Turabian StyleOlowu, Temitayo O., Aditya Sundararajan, Masood Moghaddami, and Arif I. Sarwat. 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey" Energies 11, no. 7: 1782. https://doi.org/10.3390/en11071782