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Robert MacDonald Graham Ault University of Strathclyde

AN OPTIMISATION MODEL TO INTEGRATE ACTIVE NETWORK MANAGEMENT INTO THE DISTRIBUTION NETWORK INVESTMENT PLANNING TASK. Robert MacDonald Graham Ault University of Strathclyde. Robert MacDonald, Graham Ault – UK – RIF Session ….. – 1025. Active Network Deployment.

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Robert MacDonald Graham Ault University of Strathclyde

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  1. AN OPTIMISATION MODEL TO INTEGRATE ACTIVE NETWORK MANAGEMENT INTO THE DISTRIBUTION NETWORK INVESTMENT PLANNING TASK Robert MacDonald Graham Ault University of Strathclyde Robert MacDonald, Graham Ault – UK – RIF Session ….. – 1025

  2. Active Network Deployment • ANM schemes emerging as alternative to network reinforcement • Power-Flow Management via DG curtailment can eliminate thermal constraints • Requirement to integrate ANM Deployment into planning stage of Network development • Requirement to model dynamic operational characteristics of ANM schemes • Must model dynamic changes in operational states • Consider uncertainty in demand, intermittent DG output

  3. Network Planning Optimisation Model • Objective is to find lowest-cost investment decisions over planning period • Deployment of ANM may add operational cost as compensation for curtailed energy • Stochastic Programming used to incorporate uncertainty into optimisation model • Find optimal investment solution which hedges against future uncertainty • Estimated operational cost over planning period calculated using Monte-Carlo method

  4. Problem Decomposition • 3 quasi-independent sub-problems • Master: Make investment decisions • Feasibility: Check investment decisions meet security criteria • Operation: Calculate expected operational actions and cost over planning period • Sub-problems coupled by Benders cuts • Cuts share optimality information between sub-problems in form of constraints Master Investment Problem (Binary Programming) If investment results in infeasible operation – infeasibility cuts generated and sent back to Master Problem Investment decision variables are fixed and sent to next sub-model Feasibility Sub-Problem (Linear Programming) If no optimality, optimality cuts sent back to Master Problem If feasible – Master decision variables fixed and sent to Operation Sub-Problem Network Operation Sub-Problem (Customised Load Flow) Solved once optimality criterion met Solution

  5. 100 1103 306 305 303 302 307 1102 308 309 1104 1105 Basic test-case • Section of rural network • 4 Scenarios for new DG connections: • 20MW – Wind • 20MW – Non-Wind with Full Rated output • 30MW – Wind • 30MW – Non-Wind with Full Rated output • 2-year planning period • Investment decisions: • Deploy ANM at DG (CAPEX:100, OPEX:1) • Upgrade weak line capacity (CAPEX:500/1000) Thermal Overload

  6. 100 1103 306 305 303 302 307 1102 308 309 1104 1105 Basic test-case results ----- 132kV ----- 33kV ----- 11kv DG1 DG2

  7. Conclusions • Incorporated deployment of ANM scheme into network planning optimisation model • Stochastic Programming structure considers probabilistic nature of intermittent DG and demand • Basic test cases validate decomposition approach

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