Using Opal-RT Real-Time Simulation and HIL System in Power and Energy Systems Research

Using Opal-RT Real-Time Simulation and HIL System in Power and Energy Systems Research Shuhui Li Department of Electrical & Computer Engineering The University of Alabama Presented on February 15, 2017 Atlanta, GA

Contents 1. Opal-RT system at UA 2. Solar energy conversion, generation and grid integration 3. Charging stations and transportation integration 4. Microgrid control and management 5. IPM motor control: EV and automation 6. NSF I/UCRC Center for efficient vehicles and sustainable transportation systems (EV-STS)

Renewable Energy Systems Laboratory (RESyL) RESyL Science & Engineering Quad Science and Engineering Quad

Opal-RT HIL and compatible hardware facilities Hardware interface #1 Target computer #2 Target computer #3 Target computer #1 Target computer #4 Hardware Facilities

PCs connected to Opal-RT system High-performance PC Local area network Opal-RT system

Contents 1. Opal-RT system at UA 2. Solar energy conversion, generation and grid integration 3. Charging stations and transportation integration 4. Microgrid control and management 5. IPM motor control: EV and automation 6. NSF I/UCR Center for efficient vehicles and sustainable transportation systems (EV-STS)

Solar Photovoltaic Power Generation Systems Grid connected PV system

Problems- Uneven Solar Irradiation Conditions Clouds will cause a shading problem

Central dc/ac and dc/dc converters Power (kw) 20 15 10 5 None 50% 100% 0 0 100 200 300 400 500 Vs (V) Overall Power (W) 100 0-100 None -200 50% 100% -300 0 100 200 300 400 500 Vs (V) Shaded cell

String converter based PV system Central dc/ac inverter and string dc/dc converters String inverter configuration

Micro converter based PV system dc/dc optimizers per module and a central inverter Microinverter PV system

PV Module with Bypass Diode Power (kw) 20 15 10 5 n=18 n=36 0 0 100 200 300 400 500 Vs (V) I s full-sun n=1 Vs n=2 n=3 n=4 n=6 n=9 n=12

Computational and Hardware Experiments CPU 4 CPU 3 CPU 2 CPU 1 Output Power (kw) 20 15 10 5 Temperature (F) 90 80 70 60 Temp Max IC SF S-PI Irra 800 600 400 200 50 0 4 8 12 16 20 24 0 Time (Hour) 0 0.5 1 1.5 2 2.5 Time(s) 1000 Solar Irradiation(W/m2)

Grid-Connected PV and Energy Storage System Pref Qref

Artificial Neural Network for Control and Grid Integration of Residential PV Systems ANN Control for dc/ac inverter MPPT Control for dc/dc converter

320 (a) dc-link voltage (V) (b) grid current(a) 300 Vdc 280 260 240 220 200 0 0.5 1 1.5 2 2.5 Time (s) 40 20 0-20 I grid Vdc 0 0.5 1 1.5 2 2.5 3 Time (s) I grid 2.66 2.68 2.7 2.72 2.74 2.66 2.68 2.7 2.72 2.74 3k Time (s) Time (s) (e) PV power(w) 2k 1k P pv P pv (f) Mag(% of Fundamental) 0 0 0.5 1 1.5 2 2.5 10 5 Time (s) 0 0 5 10 15 Harmonic order THD=4.67% 0 0.5 1 1.5 2 2.5 3 Time (s) THD=12.98% 0 5 10 15 Harmonic order

Contents 1. Opal-RT system at UA 2. Solar energy conversion, generation and grid integration 3. Charging stations and transportation integration 4. Microgrid control and management 5. IPM motor control: EV and automation 6. NSF I/UCR Center for efficient vehicles and sustainable transportation systems (EV-STS)

Energy Storage for Grid Power Leveling Help make energy sources, whose power output cannot be controlled, smooth and dispatchable.

Grid Integration: EV characteristics Three different kinds of vehicles make up the EV fleet: Plug-in Hybrid Electric Vehicles (PHEVs) Hybrid vehicles that run on an internal combustion engine with batteries that can be recharged by connecting a plug to an external power source. Larger batteries than traditional hybrid vehicles (e.g., 5-22 kwh). Unlimited driving range because of hybrid engines Extended Range Electric Vehicles (EREVs) Electric vehicles with relatively large batteries (e.g., 16-27 kwh) capable of relatively long all electric ranges (e.g., 40-60 miles). An on-board internal combustion engine provides an unlimited driving range by recharging the battery when needed. Battery Electric Vehicles (BEVs) Pure electric vehicles with no internal combustion engine Require recharging at the end of their designed driving range. Have the highest all-electric range (e.g., 60-300 miles) and the largest battery capacity (e.g., 25-35 kwh)

Grid Integration: Driving Characteristics Transportation data for U.S. driving patterns indicates 60% of domestic average daily driving is 30 miles or less Approximately 70% of driving is 40 miles or less. Upcoming EREVs: designed to drive 40 miles in all-electric mode. could accommodate 70% of driving in all-electric mode with a single over-night charge. daytime charging using public charging or at-work charging obviously extends vehicles effective all-electric driving ranges. BEVs have a limited driving range before extended charging is required (e.g., a 40-60 mile battery, or even a 100-mile battery), urban and close-in suburban areas are the ideal target market.

Grid Integration: Charging Characteristics 3 levels charging schemes Charge level Utility Service Charge Power (kw) Time to charge AC Level 1 120V, 20A 1.44 > 8 hours AC Level 2 240V, 15-30A 3.3 4 hours DC Level 3 480V, 167A 50-70 20-50 min The total energy required to charge a battery, and the average energy required per day, depend on the miles driven and the vehicle energy consumption per mile. Additional power may be required for accessories and air conditioning during summer months. EVs will have onboard communications, computing capabilities, and the other functionality in the near term that will enable them to be "smarter" than most end-use loads.

Charging Stations with Other Renewables

Charging Stations with Built-in Energy Storage Lower power loss caused by converters Lower cost Efficient energy management

Real time simulation implementation Real-time model structure of the EDV charging station

Simulation results (1) Iref/Ibatt (A) 50 30 10-10 -30-50 4 6 8 10 12 14 16 18 20 Time (s) Iref Ibatt Vref/Vbatt (V) 600 400 200 Vref Vbatt 4 6 8 10 12 14 16 18 20 State of Charge (%) 70.5 70 69.5 Time (s) 4 6 8 10 12 14 16 18 20 Time (s)

Smart Transportation Grid Integration Battery remaining capacity Charging station locations Price information How much energy charging station can provide.

Contents 1. Opal-RT system at UA 2. Solar energy conversion, generation and grid integration 3. Charging stations and transportation integration 4. Microgrid control and management 5. IPM motor control: EV and automation 6. NSF I/UCR Center for efficient vehicles and sustainable transportation systems (EV-STS)

Microgrid in Power Distribution System A typical microgrid: a low-voltage distribution network distributed generation (DG) units distributed storage (DS) units controllable loads. Grid-tied mode, islanded mode Control and management of a renewable-based microgrid: a renewable source level a microgrid central control (MGCC) level a utility distribution management system (DMS) level.

ANN-ADP vector controller at DG Level ADP: approximate dynamic programming ANN: artificial neural network trained to implement ADP-based optimal control ANN-ADP: has potential to integrate optimal, predictive, PI, and PR control advantages together

Types of DER (distributed energy resources) inverters Grid-following inverter: PQ inverter DER: operates by injecting active and reactive power into the microgrid PV inverter DER: operates by injecting active power into the microgrid while simultaneously maintaining the PCC bus voltage at a desired value Grid-forming inverter: V-f inverter DER: Operates based on the conventional droop control concept, which is a necessary requirement in the microgrid islanding operating condition. Droop control:, f f r P P V V r Q Q s s0 f ac ac0 ac ac0 V ac ac0

A benchmark LV network with microgrid Grid connected Islanding

A benchmark LV network with microgrid Grid connected Islanding

Tracking variable reference commends (T s =1ms) 12 Wind Speed (m/s) 10 8 6 4 4 6 8 10 12 14 16 18 20 Time (s) 200 Id Iq Id* Iq* Currents (A) 0-200 -400 0 2 4 6 8 10 12 14 16 Time (sec)

Connecting to the grid without synchronization control abc currents (A) abc currents (A) 300 200 100 0-100 -200-300 0.95 0.975 1 1.025 1.05 Time (sec) 200 100 0-100 -200 1.95 1.975 2 2.025 2.05 Time (sec)

Hardware Experiment System

Hardware Experiment Results d-axis current (A) 1.5 1 0.5 0 Id Id-ref -0.5 0 20 40 60 80 100 120 140 160 180 200 Time (sec) Grid d-axis current waveform Voltage (V) 70 60 50 40 30 0 20 40 60 80 100 120 140 160 180 200 Time (sec) dc link voltage q-axis current (A) 1 Iq 0 Iq-ref -1-2 0 20 40 60 80 100 120 140 160 180 200 Time (sec) Grid q-axis current waveform Voltage (V) 20 10 0-10 -20 100.3 100.32 100.34 100.36 100.38 100.4 Time (sec) Three-phase PCC voltage

Contents 1. Opal-RT system at UA 2. Solar energy conversion, generation and grid integration 3. Charging stations and transportation integration 4. Microgrid control and management 5. IPM motor control: EV and automation 6. NSF I/UCR Center for efficient vehicles and sustainable transportation systems (EV-STS)

PM motor control with standard three-leg inverter in EVs Speed or torque commend

Motor Controller Issues: Conventional Standard PMSM Control d Ld 0 vsd isd dt isd L i q sd 0 Rs e e PM v sq i sq d i sq L i d sq 1 0 L q dt * r * i d P I * i q P I P I * * v sq v a, b, c * v sd i q i a, b, c r e i d

How to address the issues: ANN-ADP Solution mech Outer rotor flux control * rd Outer speed-loop control mech * rd * mech rd - + + + + - mech PI PI i sd _ ref i sq _ ref P + + + - + - i sq i sd Conventional standard current-loop control PI PI v sd _ comp v sq _ comp + + * v sd * v sq d / dt j e e j e e e mech * v 1 * v 1 2/3 i, 2/3 2/3 * v a 1, b 1, c 1 Encoder PWM i sa i sb i sc Vdc + - v sc v sb v sa Motor o o Replace the conventional controller by a Neural Network Motor Controller (NNMC) Our NNMC uses Artificial Intelligence techniques that adapt quickly and efficiently in real-time i sq _ ref NN controller i sd _ ref - - + - i sq NN structure + + i sd s sd Input s sq e sd e sq NN structure Hidden layer Hidden Output * v sd * v sq tanh tanh Input Preprocess s sd 1/Gain2 tanh tanh tanh Output layer s sq tanh tanh tanh tanh V*sd e sd tanh tanh tanh tanh V*sq e sq 1/Gain tanh tanh tanh tanh tanh

Simulation for IPM Operating in Linear Over modulation Conditions (d- and q-axis currents)

HIL Dyno System for Motor Control Evaluation

Operation of IPM Motors in Linear and Over Modulation Regions Current (A) 20 15 10 5 0 Ref ADP Conv -5 (a) -10 0 10 20 30 40 50 60 70 80 90 100 10 Time (A) Current (A) Modulation Index 5 0-5 (b) Ref ADP Conv -10 0 10 20 30 40 50 60 70 80 90 100 Time (Sec) (c) 2 ADP Conv 1.5 1 0.5 0 10 20 30 40 50 60 70 80 90 100 Time (Sec)

Investigate Applying Real-Time Simulation in Robotics and Automation From Solidworks to Simulink to Opal-RT

Contents 1. Opal-RT system at UA 2. Solar energy conversion, generation and grid integration 3. Charging stations and transportation integration 4. Microgrid control and management 5. IPM motor control: EV and automation 6. NSF I/UCRC Center for efficient vehicles and sustainable transportation systems (EV-STS)

Focusing Areas of NSF I/UCRC Center The Grid