Frankfurt (Germany), 6-9 June 2011

AN OPTIMISATION MODEL TO INTEGRATE ACTIVE NETWORK

MANAGEMENT INTO THE DISTRIBUTION NETWORK INVESTMENT PLANNING TASK

Robert MacDonaldGraham Ault

University of Strathclyde

Robert MacDonald, Graham Ault – UK – RIF Session ….. – 1025

Frankfurt (Germany), 6-9 June 2011

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

Frankfurt (Germany), 6-9 June 2011

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

Frankfurt (Germany), 6-9 June 2011

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)

Feasibility Sub-Problem (Linear Programming)

Network Operation Sub-Problem (Customised Load

Flow)

Investment decision variables are fixed and sent to next sub-model

If investment results in infeasible operation – infeasibility cuts generated and sent back to Master Problem

If no optimality, optimality cuts sent back to Master Problem

If feasible – Master decision variables fixed and sent to Operation Sub-Problem

Solution

Solved once optimality criterion met

Frankfurt (Germany), 6-9 June 2011

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)

100

302303305

306

307

308309

1103

1104 1105

1102

Thermal Overload

Frankfurt (Germany), 6-9 June 2011

Basic test-case results

DG Units Connected

Investment Decision

DG Output (MWh)

Curtailed Energy (MWh)

% Energy curtaile

d

1: 20MW Wind

ANM 151987 2725 1.8%

1+2: 30MW Wind

ANM 194002 38066 19.6%

1: 20MW Non-wind (Rated Output)

Line Upgrade 350400 (300560)

0 (49840) 0 (16%)

1+2: 30MW Non-Wind (Rated Output)

Line Upgrade 525600(300560)

0 (225040)

0 (42.8%)

100

302303305

306

307

308

309

1103

1104 1105

1102

DG1

DG2

----- 132kV

----- 33kV

----- 11kv

Frankfurt (Germany), 6-9 June 2011

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