1. Smart Virtual Power Plants for the Future Electric Grid – Realization and Benefits
Authors: Somasundaram Essakiappan, Martin Koerner, Jens-Fredrik Rees, Ehab Shoubaki, Johan Enslin
Presenter: Dr. Johan H. Enslin, FIEEE, FSAIEE, PrEng
Director, Energy Production and Infrastructure Center (EPIC)
Duke Energy Distinguished Chaired Professor
UNC Charlotte, NC, USA
EPIC.UNCC.EDU
The authors wish to acknowledge and thank Duke Energy for their financial support to this project.

2. The Vision: An Integrated Grid
Power System that is Highly Flexible, Resilient and
Connected and Optimizes Energy Resources
Source: EPRI

3. The Vision: Virtual Power Plants
Power System that is Highly Flexible, Resilient and
Connected and Optimizes Energy Resources
Virtual Power Plants (VPP) are the building blocks of the Grid of the Future
Small, DERs located throughout a region work together to provide same functionality as a large central power station
Achieved through centralized and distributed controls, monitoring, diagnostics, prediction and forecasting
Perform control at local-level, minimize communication infrastructure

4. Microgrids and Virtual Power Plants (VPP)
Microgrids are typically confined to smaller geographical area; VPPs may not be – components may be far away from each other and may consist of several microgrids
Microgrids have a local controller for close control of the energy balance and voltage profile; VPPs have centralized dispatch control (ISO based) for dispatch, ancillary services, spin, demand response, frequency and voltage regulation.
VPPs emulate a large power generating plant by responding to market demand, pricing, ADR/ancillary service pricing, load and resource forecasting
Source: DOE

5. Structure of a Virtual Power Plant (VPP) – forecast and analytics
Precise models of DER plants and weather forecasts are inputs
Accurate forecasts are assumed available
ADR resources, LMP, auxiliary service requirements from an economic perspective
System agnostic approach using historical generation data

6. Structure of a Virtual Power Plant (VPP) – electrical systems control
DER-level controllers calibrate power references based on dispatch schedule
Power to/from grid is excess/deficit power in VPP area
Local loads are serviced and various bus voltages are supported
BESS support bus frequency by switching to charging/discharging mode

7. An Example of the Emerging Virtual Power Plant
Source: Petra Solar, NJ USA

8. Forecasting & Simulation Analysis using RTDS
VPP analyzed three PV plants for load servicing and two BESS for bus V & f support
Forecasting using analytical method for resources (PV) and loads
PV power dispatch determined using results of the forecast
BESS provide voltage and frequency support at the buses
Droop characteristics and machine inertia of large synchronous generators implemented using power electronics control
For resiliency purposes, VPP can be microgrid

9. Forecasting using statistical methods:
Load forecasting is non-deterministic – hence affected by stochastic fluctuations
Linear regression model1 is used to predict load for defined time intervals - based on the correlation between load, temperature and time
In a ‘training phase’ historical data is used to calculate coefficients β0 to β9
‘Trend’ captures the data trend for the entirety of historical data; ‘Day’, ‘Month’ and ‘Hour’ indicate time instant in future
1T. Hong, “Short-term electric load forecasting”, PhD dissertation, September 2010

10. Forecasting using statistical methods:
Forecast and Actual load for one week (Data from publicly available databases for a city in Pennsylvania2).
Discrepancies in forecasting are to be managed and mitigated by control algorithm
2ISO-PJM, “Hourly load data from ISO-PJM operating area”, Available online

11. VPP Electrical System Control & Simulation:
Hz/W and Volt/VAr characteristics implemented in Smart Inverters
During over-frequency, PV is curtailed, characteristics reprogrammed based on dispatch schedule
During nominal frequency, PV injects all available real power
Voltage support provided outside nominal range (0.98 to 1.02 in this example)
During under- frequency, BESS discharge providing frequency support at PCC (f range determined by SoC
During over-frequency, BESS charge before PV curtailment

12. VPP Electrical System Control & Simulation:
Time
11:00
12:00
13:00
Loadfore kW kW kW
PV1fore
63 kW
67 kW
70 kW
PV2fore
55 kW
59 kW
62 kW
PV3fore
57 kW
61 kW
64 kW
Bat1(SoC)
0 kW (SoC = 90%)
Bat2(SoC)
Psold
68.4 kW
79.43 kW
87.24kW
Load forecast using regression function
PV forecast from insolation and temperature forecasts
VPP generation – VPP load = Psold

13. RTDS Simulation Results – Power Generation
1 hr simulation time interval scaled to 20s of real-time
Pref values for DGs change according to dispatch schedule & forecast
BESS stay in frequency support mode

14. RTDS Simulation Results – Frequency Support
Grid demand surge causing a dip in system frequency
BESS injects watts, providing frequency support at the node

15. RTDS Simulation Results – Voltage Support
Sags / Swells in bus voltage
Controller responds by injecting or consuming VARs, regulating the voltage within limits

16. Resiliency to Forecasting Errors – 24 hr. simulation
24 hours
Psold is the power sold on the day-ahead market (or similar) using the best available forecasts for load and resources
Load forecast is superimposed with a 5% stochastic error, for analysis purposes (5% further variations than anticipated)
Insolation forecast superimposed with a 30% stochastic error
The electrical system controllers should provide robust voltage and frequency support, and continue to generate “Psold”

17. Impact to Forecasting Errors
Controller robust performance verified – errors in forecast are mitigated using BESS and power injected to grid closely follows Psold
Profile of loads serviced closely follows demand
Power generation by PV plants (all plants rated the same), fluctuations reflect insolation swings
BESS compensating for errors and providing V&f support
Power injected to grid (red) closely follows day-ahead power sold on the market (black)
Results shown till 12:00 noon due to results file size limits

18. Summary and Conclusions:
VPP’s need accurate forecast and real-time analytics
VPP analytics provide forecasting of loads, DERs and generate a dispatch schedule
VPP distributed control provides droop-based power control and bus voltage/frequency support
Communications are for supervisory and energy market participation
Future Work:
Robustness of VPP to communications delays and breakdown
Improvement of forecasting resolution to 15 minutes or better
Development of the ability to operate DGs in a plug-and-play manner – truly system agnostic VPP
Develop scaled VPP in laboratory as demonstration.

19. Effect of Forecasting Errors on BESS Sizing
Results show direct correlation between %error and BESS sizing
Better load and resource forecasting leads to reduction BESS size requirements (energy rating of BESS)
As error% goes up, mitigation requires higher energy throughput through BESS
Type I and Type II errors
Amplitude errors (stochastic)
Baseline scenario (no forecasting errors)
Image source: ABB