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Voltage Control of Distribution Network Using an Artificial Intelligence Planning Method

Voltage Control of Distribution Network Using an Artificial Intelligence Planning Method. Jianing Cao 1 Keith Bell 1 Amanda Coles 2 Andrew Coles 2 Department of Electronic and Electrical Engineering Department of Computer and Information Sciences University of Strathclyde, UK.

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Voltage Control of Distribution Network Using an Artificial Intelligence Planning Method

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  1. Voltage Control of Distribution Network Using an Artificial Intelligence Planning Method Jianing Cao1 Keith Bell1 Amanda Coles2 Andrew Coles2 Department of Electronic and Electrical Engineering Department of Computer and Information Sciences University of Strathclyde, UK Jianing Cao – UK – Session 5 – 1112

  2. Background • Distribution Network active control Figure 1. Example of a substation with active management facilities Source: R.A.F. Currie, G.W. Ault, C.E.T. Foote, G.M. Burt, J.R. McDonald

  3. Objectives • Improve settings for controllers in a Distribution Network • E.g. mechanically switched capacitors (MSC) & tap changing transformers • Minimise control actions & wear-and-tear on equipment • Plan control targets to minimise human intervention • Respect the voltage limits • E.g. ± 6% for 33kV/11kV [1] (Case study: ± 5%) [1]: D.A. Roberts, SP Power Systems LTD, 2004, “Network management systems for active distribution networks – a feasibility study”

  4. Methodology • Multi-objective Artificial Intelligence planning method [2] • forecast demand and generation for a given period, e.g. a day (re-planning might be needed) • Load flow simulation • Linear sensitivity factors reflecting voltage changes with respect to control actions [2]:K. Bell, A.I. Coles, M. Fox, D. Long, A.J. Smith, 2009, "The Role of AI Planning as a Decision Support Tool in Power Substation Management", AI Communications, IOS Press, vol.22, 37-57.

  5. Details • Planner objective function [2] • PM: plan metric T: transformer steps • M: MSC switches LV/HV: low voltage/high voltage • α/β: cost of control/switch action from transformer/MSC • γ/δ: relative “cost” of voltage below 0.95 p.u / above 1.05 p.u [2]:K. Bell, A.I. Coles, M. Fox, D. Long, A.J. Smith, 2009, "The Role of AI Planning as a Decision Support Tool in Power Substation Management", AI Communications, IOS Press, vol.22, 37-57.

  6. Existing Planner Figure 2. Overview of the VOLTS system Source: Keith Bell, Andrew Coles, Maria Fox, Derek Long and Amanda Smith • Using PDDL (planning domain definition language) • 1. A domain file for predicates and actions • 2. A problem file for objects, initial states & goal

  7. Software integration Network Parameters Derive sensitivity factors Planner parameters Metric-FF (planner) Run sequence of Load Flow Control actions/ Control targets Voltages outside limits? No Final plan Yes Update sensitivity factors

  8. Distribution Network Model Source: AuRA-NMS project

  9. Demand data in case study • Assumption of load • Constant power factor for each load throughout the day • Profiles follow National Grid’s half-hourly metered data • E.g. 30-Oct-2010

  10. Generation data in case study • Combined Heat and Power (CHP) • Capacity of 4MW • Output maximum power when space & water heating needed • Power factor of unity or 0.8

  11. Simulation Process 1: setting base cases • Base case 1: • voltage target of transformer set to 1.0 per unit • Run load flows to get tap settings & voltages • Base case 2: • tap position of transformers set to nominal (0) • Run load flows to get voltages to compare

  12. Simulation Process 2: optimisation • Feed the planner with sensitivity factors & initial conditions from load flow results • Generate new transformer tap settings • In set of load flows, set tap positions according to the planner’s control output • Compare against the base case.

  13. Simulation results • Tap settings • Minimum voltage on the network PF1 mode: Base Case 1: transformer tap varied from -3% to -2% Base Case 2: minimum voltage 0.947 per unit at 18:00 Planner’s result: 0.96 per unit PFC/VC mode: Planner suggested no change from base case 2 since voltage is not beyond limits

  14. Summary • Conclusion • Successful integration between the planner and a load-flow simulator • A sequence of control settings were found • Achieved required voltage profile with fewer tap changes in planned mode • Hence, less wear-and-tear on the equipment

  15. Summary • Future development • Larger distribution network with more controllers/loads/distributed generators • Test another ‘worst case’ scenario – low demand & high generation • The planner’s robustness to forecast errors to be tested Thankyouforyourattention. • Acknowledgement: the work described has been funded by • EPSRC under research grant EP/D062721 • Supergen ‘HiDEF’ programme

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