Global Dynamic Optimization Of The Electric Power Grid


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Global Dynamic Optimization Of The Electric Power Grid

  1. 1. Summary of EPRI-NSF Workshop held in Playacar, Mexico, April 2002, on GLOBAL DYNAMIC OPTIMIZATION OF THE ELECTRIC POWER GRID By Ronald G. Harley Duke Power Company Distinguished Professor November 3-4, 2003
  2. 2. Playacar, Mexico <ul><li>Over the next ten years, demand for electric power in the USA is expected to increase by about 20% while under the current plans the electric transmission capacity will increase only by 4%. </li></ul><ul><li>Three-day workshop, sponsored by EPRI and NSF, held in Playacar, Mexico, from April 11 to 13, 2002. </li></ul><ul><li>Theme centered around optimizing the dynamic behavior of the electric power grid on a global scale in order to produce and transmit more electric power with existing generators and transmission networks, without jeopardizing various operating constraints. </li></ul><ul><li>26 persons from academia, industry, EPRI and NSF attended selected from the power engineering, control and computational science communities. </li></ul><ul><li>Six Grand Challenges were considered. </li></ul><ul><li>Conclusions from the workshop are that some technology already exists, but some power engineers seem to be unaware of this, and in other cases new technology will have to be developed. </li></ul><ul><li>Some research groups have already developed optimization algorithms, which should now be demonstrated and validated on a small pilot electric network. </li></ul>
  3. 3. The Grand Challenges <ul><li>1: How to select the type of control hardware, size it and choose its location. </li></ul><ul><li>2: Integrated network control </li></ul><ul><li>3: Should we have centralized or decentralized control; how to coordinate? </li></ul><ul><li>4: What infrastructure hardware will the various implementation strategies require? </li></ul><ul><li>5: A benchmark network model is needed for testing theories </li></ul><ul><li>6: Pilot schemes will be needed to prove validity of concepts after simulation </li></ul>
  4. 4. Grand Challenge 1: How to select the type of control hardware, size it and choose its location. <ul><li>Placement of control devices, such as FACTS devices, phase shifters, tap changers, switched capacitors. </li></ul><ul><li>Objectives: </li></ul><ul><ul><li>Transient stability improvement. </li></ul></ul><ul><ul><li>Inter-area oscillation damping. </li></ul></ul><ul><ul><li>Voltage collapse avoidance. </li></ul></ul><ul><ul><li>Subsynchronous resonance mitigation. </li></ul></ul><ul><li>Tools to select, size, locate and control one or more of these control devices in a network according to some optimization criteria and avoid dynamic interaction between these devices and the rest of the network. </li></ul><ul><li>Global coordination suitable for dynamic control requires knowledge of states of control devices at speeds much higher than currently in use. </li></ul>
  5. 5. Critical Capability Gaps <ul><li>Most research work on control devices has been on single machine and two machine power systems and do not address the unsolved problems mentioned above. </li></ul><ul><li>Global dynamic coordination and control requires data transmission at speeds higher than existing systems can handle. </li></ul><ul><li>A suitable variety of proven mathematical tools for optimal sizing and placing of appropriate control hardware. </li></ul><ul><li>Power engineers are mostly unaware of the tools available in other disciplines. </li></ul><ul><li>How to control the generators, the FACTS devices, the switched capacitors and the transformer tap changers, etc. in a global fashion over a wide area geographically. </li></ul>
  6. 6. Grand Challenge 2: Integrated network control <ul><li>Electric grid is a sprawling network with many operational levels involving a range of energy sources with many interaction points. </li></ul><ul><li>Modern power system problems are becoming increasingly complex, diverse and heterogeneous. </li></ul><ul><li>The need for seamless interaction of numerous heterogeneous power network components represents a formidable challenge, especially for networks that have traditionally used simple methods of system optimization and control. </li></ul><ul><li>Mathematical models of such systems are typically derived based on linear techniques, and wide margins of safety are allowed in order to ensure stable operation. </li></ul><ul><li>Optimization would have to be subject to boundary conditions such as dynamic and voltage stability, security, reliability, thermal overloads and market forces. </li></ul>
  7. 7. Critical Capability Gaps <ul><li>Current corrective measures for emergency recovery depend on preprogrammed actions based on local information, and are executed independently in many control rooms. These should rather be dynamic actions based on both local and global information, executed in a coordinated fashion by local or global intelligent agents. </li></ul><ul><li>End-use technologies with adaptability and robustness. </li></ul><ul><li>Real time techniques to detect dynamic stability margins, and predict dynamic voltage collapse. </li></ul><ul><li>Multilayered intelligent agents capable of carrying out the following tasks during dynamically changing local and global conditions: </li></ul><ul><ul><li>Control, Adapt, Optimize, Communicate, Negotiate, Advise humans. </li></ul></ul><ul><li>Power engineers are currently not using multi-objective optimization techniques for both continuous and hybrid systems, and which have become available in the last decade. Revise education. </li></ul>
  8. 8. Grand Challenge 3: Should we have centralized or decentralized control; how to coordinate? <ul><li>Completely Decentralized Control contributes to fragmented decisions and catastrophes. </li></ul><ul><li>Centralized Global Control could include some decentralized control, and execute through three layers: </li></ul><ul><ul><li>Reactive Layer: This layer of agents in every local subsystem performs preprogrammed self-healing actions that require an immediate response. </li></ul></ul><ul><ul><li>Coordination Layer: The agents in this layer include heuristic knowledge to identify which triggering event from the reactive layer is urgent, important, or resource consuming. </li></ul></ul><ul><ul><li>Deliberative Layer: This layer consists of cognitive agents that have goals. The goals of agents in this layer are for example, reliability, economics, robustness, and self-healing. </li></ul></ul><ul><li>With system information available to a local intelligent controller, it could take appropriate actions to optimize local and even some global variables. </li></ul>
  9. 9. Critical Capability Gaps <ul><li>Centralized control requires system wide variables and currently no infrastructure exists for this to happen at fast enough speeds of data transmission. </li></ul><ul><li>The intelligent agents should be embedded with tools like game theory for example, with computational and resource bounds that can allow them to carry out automated negotiation and risk management. </li></ul><ul><li>Self-healing techniques inherent in local and global agents need to be available. </li></ul>
  10. 10. Grand Challenge 4: What infrastructure hardware will various implementation strategies require? <ul><li>Centralized control depends on global information of the power system. </li></ul><ul><li>Power grid dynamics are changing continually. </li></ul><ul><li>Speed and reliability of communication networks, sensor networks and intelligent system identification. </li></ul><ul><li>Power grid is vulnerable to physical and cyber disruption which further underlines the need for fast reliable communication and sensor networks. </li></ul>
  11. 11. Critical Capability Gaps <ul><li>Present SCADA systems are usually refreshed at a rate of about once every second. This is sufficient for slow steady state controls, but is inadequate for dynamic control especially when higher bandwidths are needed (10 – 100 Hz). </li></ul><ul><li>Intelligent sensors and actuators, and techniques for verification and validation are required for system identification. Some existing techniques can be used in automatically providing updates and relayed via wireless or other networks to control centers to carry out global optimal control of the electric power grid. </li></ul><ul><li>Tools for optimal selection and placement of sensors. </li></ul><ul><li>Real time wide-area sensing will open issues on who accesses such information while maintaining security. </li></ul><ul><li>Some information and encryption technology agents are required. </li></ul>
  12. 12. Grand Challenge 5: A benchmark network model is needed for testing theories <ul><li>Currently, no suitable large benchmark networks exist for studies involving controllers. </li></ul><ul><li>In order to compare and evaluate the potentials of different control and optimization approaches and devices to solving similar problems, a benchmark network with standard traditional controllers is needed. </li></ul><ul><li>These benchmark systems need to be accepted and widely used by researchers and industries. </li></ul>
  13. 13. Grand Challenge 6: Pilot schemes will be needed to prove validity of concepts after simulation <ul><li>There is a wide gap between simulation studies and real time practical implementations. Some laboratory scale tests have been conducted, but a pilot scheme on a practical plant is needed. </li></ul><ul><li>Most US universities no longer have power programs, and most of the power programs do not have a power systems lab containing a number of synchronous generators, transmission line simulators, etc. </li></ul>
  14. 14. Summary <ul><li>Some form of automated coordinated wide area control could possibly have averted the recent black out. </li></ul><ul><li>Wide area control could also be used to provide Global Dynamic Optimization of the Electric Power Grid, by optimizing selected system variables during steady state conditions. </li></ul><ul><li>FINISH </li></ul>