Optimal Planning of Power Distribution Networks in Rural Areas - - PowerPoint PPT Presentation

Optimal Planning of Power Distribution Networks in Rural Areas

PACE Seminar 2017

Tyrone Fernando Herbert Iu Mark Reynolds Shervin Fani 2nd March 2017 James Fletcher

Outline

Research area – Power distribution network planning

• Core goal
• Complexity of the problems
• Approaches and constraints when solving the problems

Traditional network planning + Microgrid planning

• Motivation
• Objectives
• Methodologies
• Constraints
• Results
• Ongoing and future work

Power Distribution Network Planning

(Western Power, 2016)

To minimize the costs of constructing and

• perating the traditional power network

according to technical and operational constraints.

(SpidaView 2016)

Problem Complexity

Base problem:

• Nodes (load points and source)
• Edges (poles and wires)
• Radiality constraint

1 10 100 1000 10000 100000 1000000 10000000 100000000 1E+09 1E+10 1E+11 5 10 15

Steps Nodes

Complexity:

• NP-hard
• Exponential (or worse) steps

E.g.

• Brute-force for NP-hard

problem may be factorial

• NP-hard problems still solvable by

deterministic algorithms given the problem size is small enough

Optimisation Methodologies

Deterministic Methods

• MILP
• MINLP
• Dynamic Programming

Heuristic Methods

• Simulated Annealing
• Ant Colony System
• Swarm Intelligence
• Genetic Algorithm

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 x 10

5

6.385 6.39 6.395 6.4 6.405 6.41 6.415 6.42 6.425 6.43 x 10

6

X Location (m) Y Location (m)

Single Phase Node Three Phase Node

Complexity ~ NP-hard

• Deterministic methods for small-medium problems
• Heuristics for small-large problems

The Western Power Network

• 100,000+ kms of network
• Infrastructure aging
• Continual organic addition
• f customers over lifetime of

the network

• Is rebuilding the existing

network the best solution?

(Western Power 2016)

Network Optimisation Methods

• Re-route the network connections to reduce costs while meeting

constraints.

• Use genetic algorithm (GA)

Method 1

• Introduce stand-alone power systems (SPS) to supply electricity to

isolated loads by generating power at the point of demand.

• Develop network tracing algorithm to find candidates

Method 2

Stand-alone Power Systems (SPS)

• An off-grid power system used to supply electricity to isolated loads by

generating power at the point of demand.

• Renewable (Solar, Wind etc.) and Non-renewable (Diesel)
• Batteries banks are often used as backup to store energy.

(Western Power 2016)

What is an SPS?

Network Routing - GA

Genetic Algorithm

• Optimisation metaheuristic which

mimics the evolutionary process through the use of genetic operators What is the GA?

• Ability to optimise complex

combinatorial problems

• Application in literature

Why did we choose the GA? New Population Initial Population Selection How does the GA work?

Network Routing - GA

Objective Function Genetic Algorithm Procedure

Network Routing – IP + Selection

• Minimum Spanning Tree
• Random reformations

Initial Population

• Stochastic Tournament

Selection

.

Population Gr 1 Gr 2

.

Best Best

.

Network Routing – Operations

• a. Direct Steiner point
• b. Indirect Steiner point
• c. Steiner point removal
• d. SPS addition
• e. SPS Removal

f. Reconfiguration Genetic Operations

Network Routing – Constraints

• Voltage deviation limit
• Radiality
• Geographical weights

• Improvement over the existing network architecture

Network Routing – Case Study

Reduction ~11%

Network Routing – Case Study

• Stand-alone power systems provided optimisation beyond grid connections
• Stand-alone power systems provided optimisation beyond grid connections

Reduction ~11% Reduction ~11% ~20%

Network Routing - Results

Locked 3 phase Single phase network doesn’t need to be next to roads Fixed discount rate Outage costs are not yet incorporated No decommissioning cost

Network Routing - Assumptions

SPS Motivation

6.7 6.75 6.8 6.85 6.9 6.95 7 x 10

5

6.318 6.32 6.322 6.324 6.326 6.328 6.33 6.332 6.334 6.336 6.338 x 10

6

X Location (m) Y Location (m)

KDNZ340-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 6.77 6.78 6.79 6.8 6.81 6.82 6.83 6.84 x 10

5

6.3325 6.333 6.3335 6.334 6.3345 6.335 6.3355 6.336 6.3365 x 10

6

X Location (m) Y Location (m)

KDNZ340-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 6.82 6.84 6.86 6.88 6.9 6.92 x 10

5

6.32 6.322 6.324 6.326 6.328 6.33 x 10

6

X Location (m) Y Location (m)

KDNZ340-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network

SPS – Bushfire Response Area 1

6.9 6.95 7 7.05 7.1 7.15 7.2 x 10

5

6.29 6.295 6.3 6.305 6.31 6.315 6.32 6.325 6.33 x 10

6

X Location (m) Y Location (m)

KDNZ341-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 6.995 7 7.005 7.01 7.015 7.02 7.025 7.03 7.035 7.04 x 10

5

6.317 6.318 6.319 6.32 6.321 6.322 x 10

6

X Location (m) Y Location (m)

KDNZ341-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 7.01 7.02 7.03 7.04 7.05 7.06 x 10

5

6.3075 6.308 6.3085 6.309 6.3095 6.31 6.3105 6.311 6.3115 6.312 x 10

6

X Location (m) Y Location (m)

KDNZ341-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 7.155 7.16 7.165 7.17 7.175 7.18 7.185 7.19 7.195 7.2 x 10

5

6.298 6.2985 6.299 6.2995 6.3 6.3005 6.301 6.3015 6.302 6.3025 6.303 x 10

6

X Location (m) Y Location (m)

KDNZ341-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network

SPS – Bushfire Response Area 2

5.25 5.3 5.35 5.4 5.45 5.5 5.55 5.6 x 10

5

6.22 6.222 6.224 6.226 6.228 6.23 6.232 6.234 6.236 6.238 x 10

6

X Location (m) Y Location (m)

KATZ44-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 5.4 5.41 5.42 5.43 5.44 5.45 5.46 x 10

5

6.223 6.224 6.225 6.226 6.227 6.228 x 10

6

X Location (m) Y Location (m)

KATZ44-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network 5.48 5.485 5.49 5.495 5.5 5.505 5.51 5.515 5.52 5.525 5.53 x 10

5

6.2205 6.221 6.2215 6.222 6.2225 6.223 6.2235 6.224 6.2245 x 10

6

X Location (m) Y Location (m)

KATZ44-SPS Network Correlated poles Correlated DTs Uncorrelated Poles Uncorrelated DTs 3ph DT 1ph DT 3ph Removed Network 1ph Removed Network 3ph Network 1ph Network

SPS – Bushfire Response Area 3

Scenario Current Network (km) Cost Reduction (per unit) Number of SPS Simulation Time (seconds) KDN/Z340 68.5 12.70 9 11.6 KDN/Z341 71.0 4.56 3 12.4 KAT/Z44 76.0 2.90 2 17.8

SPS – Bushfire Outcomes

SPS Selection

• Operational costs

Planned Works

• Visual Relationship to a Financial Relationship
• SPS solutions do exist and are quickly found

SPS Identification – Modelling

For SPS identification it is necessary to know:

Equipment Mapping Trans ID Meter ID

123 10 123 11 123 12

• Poles
• Transformers
• Meters
• Switches
• Protection

Devices

• Power Quality

Devices Etc… Database

DISTRIBUTION SUBSTATION FEEDER BACKBONE SPURS

• Pole IDs &

Locations

• Transformer IDs

& Locations

• Bay IDs &

Connections Network Formation (Grid) Customer Load (SPS)

• Equipment

Mapping

• Historical Meter

Consumption Data

• 1. Network Formation
• 2. Customer Load

Meter Usage (kWh) SPS Size (kWh)

12.0 20 35.3 40 1.2 2

SPS Identification - Path Tracing

SPS Identification - Path Tracing

• Finds all individual edge of network opportunities
• Can miss groups of SPS candidates

Application of SPS Algorithm

• 191 customers
• Loads: 0 – 50 kWh/day
• 580 km of network
• 60 km x 80 km

Results: Network Details:

Base SPS SPS with Constraints Number of SPS

99 (52%) 61 (32%)

Pole Reduction

39 % 28 %

Conclusions and Future Work

• SPS identification model has identified numerous SPS opportunities
• Some SPS candidates provide significant financial benefits

Conclusions:

• Data quality issues
• Consideration of risk and reliability
• Customer clustering mechanic
• Shift focus to GA

Future Work:

Investment period is 50 years Fixed discount rate Cost parameters are correct Outage costs are not yet incorporated No decommissioning cost Geographical constraint not taken into account

SPS Assumptions

Network Optimisation - Summary

Optimal routing of distribution networks in rural areas using GA Method 1: SPS as a network alternative using a pathing algorithm Method 2:

https://www.westernpower.com.au/community/blog/cost-saving-innovation-for-electricity-networks/

News

Acknowledgements

Patrick Doran-Wu

Network Planning, WP

Brad Smith

Network Planning, (former) WP

Jai Thomas

Regulation and Investment Management, WP

Matthew Webb

Network Planning, WP

Questions?