Distributed Solutions for Grid Control

Transcription

1 Distributed Solutions for Grid Control GCEP Workshop Nov 1-2, 2007 Dr. Deepak Divan School of Electrical Engineering Georgia Institute of Technology, Atlanta, GA, USA 777 Atlantic Drive Atlanta, GA

2 Intelligent Power Infrastructure Consortium Research Thrusts UTILITY APPLNS Power line sensornets Wide area monitoring Distributed power line sensing Power line communications Neural nets for system I/D and control Sensors, Communications, Neural Nets, Monitoring INDUSTRIAL APPLNS Wireless sensor networks Sensorless motor fault detection Unscheduled down-time identification Wide area control Wide area protection Power system operation and state estimation Protective relaying Bulk power system reliability Congestion management System control and stability with FACTS Distributed energy sources Power Systems Engineering Power Electronics, Drives, Controls D-FACTS devices Current limiting conductors Fault current limiters Power quality and reliability Neural network controls for PE Component failure prediction Power and analog ICs Active filters Motor Drives Current surge limiters High power inverters & dc/dc converters

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4 Axioms for Smart-Grid Design Achieving a sustainable energy infrastructure will require an efficient, flexible, dynamic, fault tolerant and reliable electricity delivery network. Unlike other modern networks, the power grid lacks distributed intelligence and automation, resulting in poor reliability and asset utilization A complete replacement of the electricity infrastructure seems unjustified as there is no performance metric, e.g. bandwidth, that promises orders of magnitude improvement This leads to several fundamental tenets for Smart Grid design. Changes in infrastructure will have to be incrementally built to enhance and augment the legacy infrastructure Solutions cannot degrade the intrinsic reliability of the system high system reliability through redundancy and fail-safe modes. Resulting grid needs to be self-healing, exhibiting graceful degradation as communications and vital elements are lost

5 The Future is Massively Distributed! Transformation of communications and computing sectors has occurred because of the use of massively distributed solutions, e.g. Super-computers to Cluster-computers Satellite communications to Cellular communications Point to point communications to the Internet Multiple individual sensors to Sensor networks or Sensornets Standard, redundant, mass-manufactured parts in smart networks result in system performance and reliability gains while realizing lower cost. The new networks are robust and resilient and reorganize themselves as units fail. Systems continue to operate at basic sub-optimal levels as communication links fail. System exhibits graceful degradation and provides visibility to operator even as the main system fails. The sustainable energy infrastructure is predicated upon distributed sources and distributed smart loads, but the power delivery infrastructure is designed to operate with single point hierarchical control is this the weak link?

6 I-Grid A Distributed Grid Monitoring Solution $250 per sensor END-USER REPORTS REMOTE REMOTE MONITORS WIRELESS CONNECT ISP WAN Provider ISP INTERNET DATA INGESTOR I-GRID PLATFORM DYNAMIC WEB REAL-TIME ACCESS DATABASE FLEXIBLE STATIC NOTIFI- DATABASE CATION ENGINE USERS WEB BROWSER INTERNET OR INTRANET NOTIFICATION UTILITY STAFF & CUSTOMERS OTHER DATABASES DATA EXPORT Courtesy: Soft Switching Technologies

7 Wide Area Monitoring August 2003 Blackout Frequency data recorded by I-Grid on August 14, 2003 Data was available within minutes of the blackout. Cost was under $200 per monitored point.

8 Power Line Sensornets Low-cost couplers that perform sensing, power scavenging and communications functions Operate as a self-organizing sensor network that performs distributed condition monitoring of the entire length of the power line Sensing of dynamic line capacity, imminent vegetation contact, failing insulators, security risks Improve system reliability and asset utilization Sensors that perform sensing, power scavenging and communications functions exist today the cost is still too high Moving towards self-organizing sensor networks that perform distributed condition monitoring of the entire length of the power line Target functions include sensing of dynamic line capacity, power theft, imminent vegetation contact, failing insulators, security risks Objective: Improved system reliability and asset utilization Courtesy: Protura

9 Distributed Monitoring Distributed monitoring of the grid is rapidly becoming technically feasible. Self-organizing ad-hoc sensor networks offer a new approach for realizing a resilient and cost-effective distributed monitoring system Distributed monitoring can enable several value streams improved system reliability, lower power theft, reduced downtime, improved asset utilization, and improved visibility/response to security threats Sensor and communication costs (including installation and operating costs) need to be extremely low Data latency, compaction and visualization are critical issues that need to be addressed. Having data on parameters such as the dynamic thermal capacity of a line can improve system utilization, but do we have the muscle to realize the desired control for all lines in the system?

10 Major Challenges for Power Delivery Existing infrastructure is aging and underutilized New lines are expensive and difficult to get approved (ROW & NIMBY) As new generation and load is added, portions of the grid are getting overloaded Power flow in individual lines cannot be controlled. This results in sub-optimal grid operation and poses significant operational challenges for utilities Danger of load shedding and poor reliability under high-load and under contingency conditions Fault currents that may exceed safe levels Lines that exceed thermal ratings, even as neighboring lines are under-utilized Conservative operation because of lack of visibility, leading to significant under-utilization of assets Inability to cope with dynamics and intermittencies that are characteristic of a sustainable system. Solutions are needed for improving the controllability and utilization of the power grid

11 Typical Power Delivery Systems Radial System Easy to control power flow and node voltage with conventional solutions such as tap changers, capacitors and phase shifting transformers Suffers from low system reliability under contingency conditions Radial system Meshed System or Network Highest reliability due to redundancy of transmission lines Poor line utilization because current flow in parallel lines cannot be controlled Difficult to satisfy voltage and line loading constraints simultaneously, even when grid state data is available. Meshed system

12 Meshed Distribution Grid NYC

13 Improving Grid Reliability and Utilization Reliable power is the paramount mission for a utility. Cost and system-utilization can be more effectively controlled in radial systems, whereas reliability of meshed networks is substantially higher. However, it is difficult to control where current flows in a networked (meshed) power system - results in loop flows, congestion and poor system utilization. 138kV 0 j16 Ω 675A 138kV 7.75 j24 Ω 450A In such networks, the first line that gets thermally limited constrains the power transfer capacity of the entire network. The real situation is worse, as (N-1) and (N-2) contingency conditions can result in overloaded lines and a cascading failure. Networks are often used in dense urban areas, such as New York, where reliability needs override asset utilization issues.

14 Other Issues Associated With Lack of Grid Control Static and dynamic balance of line currents under dynamic load, source and network conditions is not possible as there aren t sufficient control handles. Even if controllers could be deployed at every node, finding the optimal operating points would be difficult for a meshed network. Insufficient transmission capacity creates islanded networks that require higher level of generation reserves to ensure system reliability. Transmission constraints can limit ability of end-users to access low-cost generation, leading to higher costs. Building a new line can eliminate congestion, but will reduce line utilization and cost of electricity, making it harder to recover the investment! Utility operations are based on the assumption that it is not cost effective to control power flow along individual lines. Realizing dynamic control of power flows on the grid can have a profound impact on grid reliability, asset utilization and sustainability.

15 Loading Levels for the IEEE 39 Bus System Poor line utilization, average of 59% (N-1) contingency reduces system capacity from 1904 MW to 1450 MW, utilization to 47%.

16 Achieving Power Flow Control P 12 = V V 1 sinδ 2 X L Active power flow can be controlled by changing the impedance of the line or the angle of bus voltages Conventional Solutions Phase Shifting transformers: Expensive, dynamic control is difficult Shunt compensation: Provides voltage support but does not have the ability to control real power flow Alternative Solution Series compensation: Offers the strongest control capability by dynamically changing the reactive line impedance Kalyan Sen Xian Tutorial

17 Shunt vs. Series VARs for Power Flow Control Simple two bus example with fixed voltage sources compares the power flow control using shunt or series VAR compensation Series VARs have a much stronger influence on the real power flow For series VARs, 8 MVAR increases power by 25 MW For shunt VARs, 80 MVAR increases power by 5 MW Decoupled control is needed Shunt VARs to control voltage Series VARs to control power flow MWS Through Line Series Compensation Shunt Compensation MWARS

18 Existing Solutions for Increasing Grid Capacity Traditional solutions, such as new lines, are expensive and subject to siting and ROW delays. New lines also make system utilization worse. Shunt VAR compensation provides voltage support but has weak impact on controlling power flows in the system. Power flow control requires Series VAR Compensation not widely used. Technology solutions such as Flexible AC Transmission Systems (FACTS) are well known and have been commercially available for >15 years. Type Parameter FACTS Device Type A Series/Shunt P and Q UPFC Type B - Series P TCSC, SSSC Type C - Shunt Q SVC, Statcon

19 Typical FACTS Configurations Shunt Shunt Series Series - Shunt

20 Typical FACTS System Series Series - Shunt Established in 1998, 160 MVA unit. This was the first UPFC installation in the World

21 Experience With FACTS Devices STATCOM High total cost of ownership. (The 200 MVA Marcy Convertible Static Compensator (CSC) cost $54 million.) High fault currents (60,000 Amps) and high voltage levels (1000 kv) make implementation of series FACTS systems difficult and expensive. Utilities are claiming reliability levels of ~96-98% for FACTS devices, significantly lower than the % reliability that is typical for the utility system itself. Highly customized and complex design of FACTS devices requires skilled work force in the field, normally not within a utility s core competency. As a result, even though the technology is mature, significant market penetration has not occurred. There is an opportunity for a new paradigm in grid power flow control

22 Distributed Solution for Series VARs Smart Wires Distributed control of line impedances offers a new approach for controlling power flow in networked systems, allowing higher reliability & utilization Line impedance control is accomplished using a large number of clamp-on couplers (modules) that float on the line, electrically and mechanically. The modules use magnetic induction to increase or decrease line impedance, to push current away from, or to pull current into a line. Modules are standardized and made in high volume using easily available lowcost components. Reliability is obtained through redundancy, where failure of individual modules has no impact on system operation. Smart Wires can be implemented using passive or active modules. couplers

23 Distributed Series Impedance Passive Smart Wires Power Line Transformer X M S S 1 R 2 X L X c Injected Impedance Inductive Capacitive Controller Power flow control achieved through change of line impedance. Injects series capacitance or inductance to change line impedance as needed. A large number of modules realize gradual change in line impedance and increase reliability. A coupling device in the form of a Single Turn Transformer (STT) is used to inject the required impedance. System control and communications used to set level of injection.

24 Distributed Series Reactance Passive Smart Wires Power Line Transformer X M N X m Power Supply S M S 1 Control I 0 Line Current (I Line ) I thermal Simplest implementation of DSI, with inductive impedance injection (Current Limiting Conductor or CLiC) functions as a current limiting system As current in a line approaches the thermal limit, CLiC modules incrementally turn on, diverting current to other under-utilized lines Increase in line impedance can be realized by injecting a pre-tuned value of magnetizing inductance of the STT Each module is triggered at a predefined set point to reflect a gradual increase in line impedance No communication required and the devices operate autonomously

25 Can Smart Wires Work in Practical Systems? 138 kv 345 kv 765 kv Thermal Line Capacity 184 MVA 1195 MVA 6625 MVA Current carrying capacity 770 A 2000 A 5000 A # of conductors/ diameter (inches) 1/1.0 2/1.2 4/1.45 Reactance ohms/mile Reactive voltage drop/mile 608 V 1200 V 2700 V 1% Compensation/mile 6.1 V 12 V 27 V Smart Wire kva- 1% Comp/mile 14 kva 72 kva 400 kva Total 10 kva modules/mile/1% Number of modules for 10% control on a 30 mile 138 kv line at 770A: 420 modules or 4.6 modules/phase/mile.

26 Simulation Results Passive Smart Wires Simplified Four Bus System Increase in ATC by as much as 75% Increase in Transfer Capacity 160 Line 2,4 Overload Line 2,5 Overload Load 1 (MW) Line 2 Overload Line 1,2,5 Overload 40 Line 5 Overload 20 Contingency Condition: Generator Outage Profile of Line 2 Current Load 2 (MW) A Network Performance Index: PI = MW MVAR Line Current (KA) A Generator Taken Off 624.2A Performance Factor Injected MVARs DSR Active Time (sec)

27 Prototype Passive Smart Wires Module Specifications Electrical Operating : 161 KV / 1,000 A Injected impedance= 10 mω per module ACSR Conductor: Drake (795 Kcmil) R= 0.128Ω, X= 0.4Ω per mile Fault level: 50,000 A Mechanical Target weight per module: 120 lb (critical design parameter) Packaging to avoid corona discharge, and other mechanical, thermal and environmental issues

28 CLiC Prototype

29 Active Smart Wires Distributed Static Series Compensator (DSSC) Active solution employing a synchronous voltage source inverter Each module rated for 5 KVA (capable of injecting ± A) Communication interface is required to realize the bi-directional control Main transformer Line Current Current feedback Power supply V Filter PWM Inverter Controls Communication Module DC Capacitor

30 Active Smart Wires Prototype Experimental Waveforms Current (A) or Voltage (V) Current (A) or Voltage (V) I_total/2 I_1 (DSSC) I_2 (uncontrolled) V_DSSC* Time (s) I_total/2 I_1 (DSSC) I_2 (uncontrolled) V_DSSC*100 Line2 Injected Voltage x Time (s) Line1-DSSC Min. Current (A) or Voltage (V) I_total/2 I_1 (DSSC) I_2 (uncontrolled) V_DSSC*100 Line2 Line Time (s) (b) (c) (a) Module Rating: volts

31 Smart Wires Operational Benefits Ability to increase or decrease steady state line current autonomously or under system controller command Ability to respond autonomously in case of fast transients or communications channel failure Ability to monitor actual conductor temperature and manually or automatically limit current as a function of conductor temperature High system reliability due to massive redundancy, single unit failure has negligible impact on system performance Zero footprint solution Robust and rugged under typical fault conditions Can be used with conventional or advanced conductors Mass produced modules can be stocked on the shelf and repaired in factory does not require skilled staff on site Easy and rapid installation (may be possible on live line)

32 Reliability/Economic Benefits System is resilient and operates as a self-organizing and self-healing power grid even in the face of unplanned contingencies Enhance network reliability and operation under (N-X) contingencies by automatically routing current from overloaded lines to lines with available capacity reduces possibility of cascading failures Distributed scalable solution allows strategic, targeted and incremental deployment of modules for maximum budget flexibility. Relieve network congestion at specific points as required. Defer investments in new power lines while enhancing system capacity Enable energy contracts between low-cost generators and interested end-users using existing lines that have available capacity (merchant transmission) Zero footprint solution. Reduce or defer access to new ROW. Mass produced modules. High system reliability and availability. Rapid and incremental deployment. Reprogrammable to meet changing needs

33 Increase in Network Utilization G8 Current Profile With CLiC Modules 39 G1 G MVAR MVAR MVAR G G MVAR 9.15 MVAR MVAR G MVAR 19 G4 24 G MVAR MVAR 36 G6 23 G7 Line Current (KA) Generator Taken Off 940 A Current Without CLiC Modules 940 A Current With CLiC Modules 643 A CLiC Active Time (s) Current in Line drops from 940A to 643 A with CLiCs following outage of generator 7 Increase in Transfer Capacity from 1904 MWs to 2542 MWs (59% to 93.3%) (congested corridors and the required MVARs are shown by red lines) System simulations are used to establish worst case loading under normal and contingency conditions to compute number of DSR modules. Line Currents (%Thermal Limit) Line2_3 Line6_5 Line6_7 Line9_39 Network Performance With CLiC Line10_13 Line12_11 Line13_14 Line19_16 Line22_21 Line23_24 Line currents with CLiC Line currents without CLiC Line25_26 Line26_27 Line29_26 Line29_28 Power Lines

34 Increase in System Reliability and Capacity Baseline transfer capacity 1904 MWs Under (N-1) contingency, capacity is limited to 1469 MWs With (N-1) contingency, DSR s can increase system capacity to 2300 MW High PI until capacity reaches ~2000 MW (most cost effective regime) 220 KW of increased capacity per module Congestion relief with 2400 modules rated at 24 MVARs increases capacity by 530 MW G8 Performance Factor Injected MVARS 39 G1 G MVAR MVAR MVAR G MVAR MVAR G G4 24 G MVAR MVAR G6 23 G7 G3

35 Investment Cost on IEEE 39 Bus Cost of laying additional lines: $500,000/mile Target cost of DSR unit: $100/KVA Cost of redeployment: $30/KVA ATC of the system is limited to 1904 MWs DSR modules can relieve network congestion at a lower cost for the first 400 MWs Initial performance index is ~7.4 DSR continues to be attractive for the next 300 MWs Building additional lines can be suspended for the first 700 MWs A combination of the two schemes can provide a much lower investment cost As a line is built DSR modules can be redeployed to realize high performance index Investment Cost ($) 2.5 x Investment Cost with only DSI technology Investment Cost with a combination of DSI technology and building additional lines Line Line 6-5 Line29-28 Line 2-3 Line A Load (MWs) A' X B'

36 Solution Cost Under Contingencies (N-1) Line contingency is simulated here. (Line 19_16 is taken out. Worst case) Network congestion occurs at a load of 1469 MW, as opposed to 1904 MW with no contingencies CLiC modules provide a very attractive investment for the first 530 MW of load growth. If 2.5% load growth is assumed, the investment in the new line can be deferred for years, even when contingencies are factored in. Reduces uncertainty and risk in building a new line. Interest on deferred cost of new line will pay for CLiC modules! Time for line permitting and building, and time value of money, indicate that the total cost of ownership for the DSR module solution may be significantly lower cost than for new line construction, even when the first cost is the same. Investment Cost ($) 2.5 x Line Line Line Line 2-3 Line 6-5 Line Load (MWs)

37 Economic Impact of CLiC Modules In an open market, if Gen A had lower cost, he would have direct incentive to build new transmission capacity, to be paid for by windfall profits and increased sales. Under Locational Margin Pricing, neither Gen A nor the Transmission Operator get share of windfall profits, eliminating incentive for investment, and reducing competitive pressure on Gen B. If increasing marginal costs are factored in, Consumer A actually pays a higher price if new transmission capacity is built. Further, Consumer B gets no relief as he still pays the Gen B pricing. As a result, consumers do not benefit directly from transmission investment. Congestion costs are returned in the form of FTRs, insuring against pricing spikes. Societal benefit from building new transmission capacity is clear, but the existing market rules reduce the ROI and the benefit that can be derived from new capacity.

38 Smart Wires in a Smart Grid to Enable Sustainability It is proposed that the use of distributed solutions based on low-power power electronics can allow utilities to move towards dynamically controllable meshed grids, significantly enhancing grid reliability, capacity and utilization. This enables- Improved coordination between sources and loads Improved dynamic coordination between regions Reduction in dynamic capacity reserve for generators Can be applied at the transmission, sub-transmission and distribution levels. Can be layered onto the existing infrastructure as desired, and will not degrade the inherent reliability of the existing system. Makes the grid self-healing, automatically maintaining safe operating levels even in the face of contingencies. Can significantly enhance grid capacity and utilization without building new lines. Redundancy and ability to operate with local data provide high system reliability and availability Provides solutions that are low-cost and can be implemented in a gradual manner as resources and budgets permit