1 Distribution System Expansion Planning Using a GA-Based Algorithm Shiqiong Tong, Yiming Mao, Karen Miu Center for Electric Power Engineer Drexel University

2 Introduction Problem Formulation Solution Algorithm Simulations Conclusions Outline

3 Careful DG placement is an option to - expand generation capacity - release transmission and distribution system capacity - delay equipment upgrade - enhance system reliability New strategies and methods for distribution system expansion planning need to be developed Introduction

4 Previous work about DG placement Grffin et. al. [6] provided a method based on loss sensitivity or load distribution to reduce losses. (2000) Nara et. al. [7] applied tabu search to minimize interruption cost. (2001) Kim et. al. [8] used fuzzy-GA method to minimize distribution loss cost. (2002) Teng et. al. [9] proposed a GA to maximize the benefit/cost ratio of DG placement. (2002)

5 We proposed a cost-based problem formulation including: - feeder upgrade costs - DG installation costs - DG operating costs - wheeling costs A GA-based algorithm is designed to solve this problem Introduction

6 Problem Formulation st. aggregate objective function where: x : continuous state variables u : discrete control variables Constrained Optimization Problem: voltage magnitude, current magnitude, feeder capacity, DG penetration constraints 3 power flow equations

7 Problem Formulation DG location: DG type: DG output: Feeder upgrade:

8 Objective Function: Problem Formulation where: C 1 (x,u) : total feeder upgrade cost C 2 (x,u) : total DG installation cost C 3 (x,u) : total DG operating cost C 4 (x,u) : total wheeling cost

9 DG model - P|V| bus - same type DG can be installed on one bus Balanced three-phase transformer and branch upgrades Keep original configuration of radial distribution power system Modeling Issues

10 GA-based algorithm Design - Genetic Algorithm  Determine DG control variables  DG initial locations are bias using available feeder capacities - Heuristic Algorithm  Determine feeder upgrade control variables - using three-power flow studies with DG information to decide upgrade options Solution Algorithm

11 GA-based Algorithm Design (Continued) –Coding Solution Algorithm

12 A 20-bus distribution system Two 4MVA transformers 17 line feeders ( total length about 7.18 miles) 12 loads Simulations

13 Loads - The total three-phase base load  MW and Mvar - Three load levels  Low: 0.7 times of the base load  Medium: the base load  High: 1.1 times of the base load - Each load level lasts one year Simulations

14 Cost data - Wheeling cost  $0.065 per KWh - Transformer upgrade cost  $400,000 for each - Line upgrade cost:  $30,000 per 1000 feet - DG cost:  see Table 2 on the paper Simulations

15 Transformer between Bus 2 and Bus 4 is over- loaded - Transformer capacity : 4 MVA - Medium-load level: = MVA > 4 MVA - High-load level: = MVA > 4 MVA Simulations Case 1. Original system

16 Upgrade the transformer between Bus 2 and Bus 4 Total cost: $11,320,006 Total cost = Wheeling costs + Upgrade costs = $10,920,006 + $ 400,000 = $11,320,006 Simulations Case 2. Feeder upgrade without DG placement

17 Use proposed GA-based algorithm Solution: - No feeder upgrade - One DG on bus 8:  0.5 MW reciprocating  operating outputs: 0,0.5 and 0.5 MW - Total cost: $11,043,429 Total cost = Wheeling costs + DG installation costs + DG operating costs = $10,182,569 + $ 217,000 + $643,860 = $11,043,429 Simulations Case 3. Feeder upgrade with DG placement

18 Simulations

19 The proposed GA-based algorithm successfully generate high quality solutions DG placement with feeder upgrade can provide more diverse expansion solutions DG placement can avoid or delay equipment upgrades Considering DGs’ impacts to outage cost may further decrease cost for utilities. Simulations

20 In this paper, a problem formulation of distribution expansion planning with DG placement was proposed. A cost-based objective function considering feeder upgrade costs, DG installation costs, DG operating costs, and wheeling costs were discussed. A GA-based algorithm was discussed The simulation results using different methods were compared Conclusions