System Theory, Modeling and Controls

Transcription

1 System Theory, Modeling and Controls Y7.FS2 Leader: Iqbal Husain, NC State University Co-PIs: Aranya Chakrabortty and Alex Huang (NCSU), Raja Ayannar (ASU), Chris Edrington (FSU) and Alex Stankovic (Tufts University) 1

2 Background/Motivation System Modeling Theory and Control (SMC) subthrust is responsible for developing and enabling validation of the control algorithms for IPM, IEM and IFM. A comprehensive FREEDM system computer simulation model built with physics based state space representation of the components is being developed for stability and performance analysis. This will be referred to as Synthesis Models. The synthesis will establish the requirements of each of FREEDM s subsystems and their interaction points, and is the key to designing the IPM, IEM and IFM controls. IPM and IEM control algorithms developed will be validated using tool based Analysis and Validation Models. 2

3 Technical Approach Each controller will have: 1. Local component (for speed of response): These local components based on localized state measurements are referred to as open loop since they don t involve communications between each other. 2. Contextual part: This part of the controller is enabled by communications, and will be referred to as closed-loop controls. Physics-based control design augmented with robustness tools for analysis the existing FREEDM controllers are anchored in physics (e.g., various frequency and voltage drooping laws) Controller structure corresponding to each design point is expected to handle all major modes of operation (normal, preventive, emergency, restorative). Means to achieve scalability in platform based control (PBC) design include: Motifs repeated basic structural control patterns Coordination via partial hierarchy (vertically) Global signals (e.g., frequency and MV voltage) 3

4 Efforts and Objectives in SMC Platform based controls framework with dynamic modeling of FREEDM layers Platform based Controls Team Analytical Models for, DESD, DRER (PV and Solar) Analytical Models Team Robust Controller Design for Analytical Models Team Implementation of Distributed and Autonomous Control for high bandwidth IPM in FREEDM System IPM Controls Team MPC based distributed control for IEM IEM Controls Team Developing Small Signal Stability Analysis Techniques/Metrics for FREEDM System IEM Controls Team State space based comprehensive system model for synthesis and design utilizing existing models Control oriented models (different from time domain simulation models): Leverage other ongoing projects with HIL and FREEDM architecture Controller incorporating communication, network configuration and impedance related uncertainties Instantaneous power balancing controllers with small-signal and large-disturbance stability analyses FREEDM system mode dependent model predictive controller utilizing the dynamic models for s, DESDs and DRERs Simulation based tool development for monitoring and control analysis of FREEDM 4 (extends on the DC side analysis to the AC side)

5 Analytical Models Model differential, algebraic, and rule-based equation arising from the physics behind, DRER and DESD Model type, format and salient characteristics to be determined based on the needs of fundamental controls group; iterative process Types of dynamic, analytical models: State space models for control study objectives IEM studies IPM studies Microgrid modes Small signal transfer functions from linearized models For example, for following P and Q commands from DGI, the transfer function from P * or Q * to actual P and Q; or for grid voltage control the transfer function from V * to actual terminal or remote V Dynamic phasor For analyzing large power systems with a large number of power converters leverage extensive work under high penetration PV project at ASU Capture the limits and saturation of internal and external variables dc link voltage limits, current ratings of different stages Models will accommodate ways to incorporate communication delays

6 Comprehensive FREEDM System Model Capable of operation with energy from the grid supply, or in islanded mode using only stored energy and distributed resources. Eight use cases to demonstrate functionality; and additional sub-cases to study failure modes and economic operation. The local controls are the charge / discharge of local storage and the power level control of controllable loads (e.g., on / off).

7 Comprehensive FREEDM System Model Graphics shown using three L2 FREEDM nodes Blue circles denote Energy storage devices (DESDs), red circles denote Renewable/local generation or local loads, brown circle is Physical topology inside first L 2 is denoted by a graph G 1, that in the other L 2 by G 2, and the inter-cluster graph between s by G E 7

8 Synthesis using Comprehensive Model Local Stability and Dynamic Performance Analysis In general, the dynamic model for the i th unit (which are DESDs and generation/loads) in the j th area can then be written as: j= 1, 2, 3,, p where p is the total number of L 2 systems, x ik denotes the state of the k th neighboring unit of the i th unit. Note that the state x ij (t) includes both the plant and the internal controller states of a unit. Closed Loop Stability and Dynamic Performance Analysis The closed-loop system, in comparison, is a cyber-physical system where outputs of each unit are measured in discrete-time, and fed back via communication links modeled by T j and T E. The closed-loop system, therefore, is an irregular sampled-data system. x ( t) = f ( x( t), G j, G E, T j, T E, z( t k )), 8 j= 1, 2, 3,, p, where z(t^k) denotes the k th time sample of the measured output vector z(t) communicated via the sensor network

9 Modes and Cases of FREEDM System

10 FREEDM System Distributed and Autonomous Control for IPM Autonomous Control: Intelligent Power Management (IPM) is to keep the sources and loads working normally under all grid conditions, such that the frequency of AC and the voltage of AC and DC distribution buses are controlled within the range of safe operation. Communication: The IPM control loops constitute the feedback loops at the local level that would deliver a stable and resilient system in all three modes. FREEDM system essentials: Plug-and-play: IPM works before DGI; large disturbance for IPM IEM: low priority compared with IPM; extra offset to IPM reference IFM: for fault isolation devices (FIDs); large disturbance for IPM

11 FREEDM System Local Controls Local controls for every device (, DESD, DRER) Robust Controller main control loops Grid current control / terminal voltage control Droop control for example, in microgrid mode HVDC link and LVDC link control Load voltage control/ inner current control Protections

12 Distributed Control Objectives and Approaches Objective I: Prove FREEDM system with the IPM control is stable in three modes when the loads could be supplied by the available sources. Small signal stability analysis of FREEDM system Tools: State-space models Impedance method: transfer function / characteristics of every device Deliverable: Design rules/protocols for the s, DESDs and DRERs in FREEDM

13 Objective II: Prove FREEDM system with the IPM control is stable when system structure changes Large signal stability analysis of FREEDM system Tools: Software simulation Deliverable: Simulation demonstration of a simplified FREEDM system Objective III (Long term goal): Hardware demonstration of 3 nodes system in a low voltage testbed Tools: 3 s built up based on IPM Controls 6 converters with storage from DESD group Deliverable: Experimental verification in the GEH Testbed

14 FREEDM System Intelligent Energy Management (IEM) System constraints (e.g. generation limits, charge/discharge rate limits, line capacities,etc) System performance measures (e.g. generation cost, system losses) Decision Making Optimization Generation dispatch commands Demand response commands Storage device commands Import/Export Commands Approaches: Gossip/Consensus based Model Predictive Control Scheduling Conventional Dynamic Programming Evolutionary methods Hybrid approaches. Forecasting Trade-offs: Computational complexity and quality of result Available data and quality of result Forecasting horizon and quality of result Forecasting horizon and computational complexity. Historical data Data Collection Physical system data *Courtesy of Dr. Mo-Yuen Chow - NCSU Weather forecast Market Information 14

15 IEM Modeling Approach Identify the characteristics and operation of: generators, energy storage, loads and s Dynamic models (linear, non-linear), identify the inputs/ outputs Operational profiles monthly and annually in use cases State constraints in term of power and energy limits Implement system identification in order to ascertain input-output behavior Apply models to the power system architecture 15

16 FREEDM Architecture with IEM System model IEM Constraints MPC Commands Historical data Market info Legend 12 kv-ac Communication 380 V-DC 120/240 V-AC MV Distribution Bus 1 MVA Electricity market IEM Loads, sources profiles Power commands IEM IEM IEM IEM Load AC/DC Battery AC/AC AC Gen DC/DC Battery DC/DC PV Load DESD DRER DESD DRER L1 Energy Cell IEM IEM IEM IEM IEM L2 IEM 16

17 Developing Stability Analysis Techniques & Metrics to Monitor and Control FREEDM System Sub-component Analytical Models and Robust Controller Design Outcome to assess the small-signal stability of the from both AC and DC interfaces under various loading conditions Dynamic Modeling of FREEDM Layers with Distributed IPM control Utilize existing device (e.g., FID, DRER, DESD) and FREEDM Distribution System models to investigate the stability Expand real-time stability analysis criterion Assess small-signal stability in AC side of PEC s interface in conjunction with the newly structured SMC Develop small-signal stability metrics For both DC and AC sections of FREEDM system Algorithm synergy with DGI/RSC sub-thrust Long term goal: Utilize HIL-TB and FSU/CAPS resources to test technique at appreciable power levels 17

18 Stability Analysis in RTDS IEEE34-Bus Test System 1 with Load at Bus IEEE 34-Bus Test System AC AC Interface kv DC 400 V DC Series Voltage Injection Bus 802 RL Load Shunt Current Injection Source Impedance AC-DC Rectifier Isolated Bidirectional DC-DC Converter DC-AC Inverter Series Voltage Injection Load Admittance 18

19 Inputs for SMC Teams Input Date From To Purpose Format for the analytical models of sub-components Gen II hardware results Open Loop FREEDM System Synthesis Model System Performance Analysis Data Closed Loop FREEDM System Synthesis Model Q Analytical Models SMC team Platform based Controls team Q Gen II team Analytical Models SMC team Q Q Q Platform based Controls team Platform based Controls team Platform based Controls team IPM Control Team IPM and IEM Control Teams IPM and IEM Control Teams To make the model of subcomponents directly suitable for system level stability studies To validate the analytical models with experimental results (in addition to validation with high fidelity simulation models) Verification of state-space model/transfer function of single device with IPM controls Establish controller design guidelines, and facilitate controller design for IPM, IEM and IFM Verification of Synthesis models 19

20 Outputs and Deliverables Output Date From To Purpose Validated analytical models Small-signal statespace models and transfer functions in IPM control specifications and models Robust controller design MPC based IEM Control Algorithm Stability Analysis in a FREEDM relevant environment Q Q Q Q Q Q Analytical Models SMC team SMC IPM team Analytical Models SMC team Analytical Models SMC team IEM Control Team IEM Control Team Platform based SMC team Platform based SMC team FAWG HIL testbed HIL Testbed FAWG For use in system level stability studies Open-loop model of, DRER, DESD after their local IPM control is closed-loop For inclusion in the FREEDM architecture document For controller implementation in controllerhardware-in-loop simulations MPC implementation on a candidate cyberphysical system representation Small-signal stability analysis results applied to a relevant FREEDM environment adhering to FAWG guidelines and SMC objectives 20