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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

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Front. Energy    2014, Vol. 8 Issue (4) : 412-425    https://doi.org/10.1007/s11708-014-0315-9
RESEARCH ARTICLE
Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization
Iraj AHMADIAN1,Oveis ABEDINIA2,Noradin GHADIMI2,*()
1. West Regional Electric Company-Electrical Transmission Department of Ilam, Ilam 693, Iran
2. Department of Electrical Engineering, Ardabil Branch, Islamic Azad University, Ardabil 045, Iran
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Abstract

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.
Keywords component      distributed energy resources      fuzzy optimization      loss reduction      interactive honey bee mating optimization (IHBMO)      voltage deviation reduction      stochastic programming     
Corresponding Author(s): Noradin GHADIMI   
Online First Date: 06 November 2014    Issue Date: 09 January 2015
 Cite this article:   
Noradin GHADIMI,Iraj AHMADIAN,Oveis ABEDINIA. Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization[J]. Front. Energy, 2014, 8(4): 412-425.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-014-0315-9
https://academic.hep.com.cn/fie/EN/Y2014/V8/I4/412
Fig.1  Load duration curve (LDC) of RTS
Fig.2  Probability density function of system demand
Fig.3  Membership function of saving
Fig.4  Membership function of node voltage deviation
Fig.5  IEEE 30-bus distribution system
From bus i To bus j Active load at j/MW Reactive load at j/Mvar rij/pu xij/pu
Main feeder 1 0 0 0.0964 0.3253
1 2 0.5220 0.1740 0.0414 0.0022
2 3 0 0 0.0659 0.0651
3 4 0.9360 0.3120 0.2255 0.1931
4 5 0 0 0.1046 0.0953
5 6 0 0 0.3143 0.1772
6 7 0 0 0.2553 0.1444
7 8 0 0 0.2553 0.1458
8 9 0.1890 0.0647 0.2345 0.1416
9 10 0 0 0.2506 0.1412
10 11 0.3360 0.1120 0.7506 0.4229
11 12 0.6570 0.2190 0.3506 0.1975
12 13 0.7830 0.2610 0.1429 0.0805
13 14 0.7290 0.2430 0.2909 0.1639
8 15 0.4770 0.1590 0.0898 0.0781
15 16 0.5490 0.1830 0.1377 0.0775
16 17 0.4770 0.1590 0.2467 0.1390
6 18 0.4320 0.1440 0.0915 0.0795
18 19 0.6720 0.2240 0.3005 0.2612
19 20 0.4950 0.1650 0.2909 0.1639
6 21 0.2070 0.0690 0.1143 0.0994
3 22 0.5220 0.1740 0.1066 0.1054
22 23 1.9170 0.0630 0.0649 0.0641
23 24 0 0 0.1083 0.0941
24 25 1.1160 0.3720 0.2760 0.2399
25 26 0.5490 0.1830 0.2009 0.1746
26 27 0.7920 0.2640 0.2857 0.1609
1 28 0.8820 0.2940 0.0881 0.0047
28 29 0.8820 0.2940 0.3091 0.1741
29 30 0.8820 0.2940 0.2106 0.1187
Tab.1  IEEE 30-bus distribution system data
Load Probability
1 0.626 0.120
2 0.693 0.141
3 0.759 0.140
4 0.826 0.121
5 0.892 0.085
6 0.958 0.085
7 1.025 0.092
8 1.091 0.079
9 1.157 0.077
10 1.224 0.060
Tab.2  Quantized load levels and their respecting probability
Drone β δ α Child Itermax
Deterministic problem 30 0.7 0.7 0.8 30 150
Probabilistic problem 50 0.7 0.7 0.8 30 200
Tab.3  IHBMO algorithms’ parameters
Load level Duration in 3-year/h Duration in 3-year/%
1 0.500 2000 0.076
2 0.540 2000 0.076
3 0.583 2000 0.076
4 0.700 5260 0.200
5 0.756 5260 0.200
6 0.816 5260 0.200
7 1.000 1500 0.057
8 1.080 1500 0.057
9 1.166 1500 0.057
Tab.4  Load levels and durations for deterministic case
Bus Size of DER/kW
6 600
11 1500
14 300
15 300
22 900
25 1700
Tab.5  Optimal solution for the case–deterministic optimization problem considering several objectives for BCR>1.3
Fig.6  Voltage profile at peak load level for compensated and uncompensated systems at peak load level of the present year, deterministic case
Bus Size of DER/kW
13 1900
17 1700
19 1000
21 1000
24 2800
3-year profit/$ BCR LR/MWh LRpeak/kW PPR/MWh
6658313.1 1.3004 2585.36 1042.9 220752
Tab.6  Optimal solution for the case–stochastic single objective problem, for BCR>1.3
Bus Size of DER/kW
2 1900
14 1400
15 1800
17 400
24 500
26 500
27 1400
3-year profit/$ BCR LR/MWh LRpeak/kW PPR/MWh
6255752.6 1.3001 3671.32 942.2 197015
Tab.7  Optimal solution for the case–stochastic optimization problem considering several objectives, for BCR>1.3
Fig.7  Optimization procedure by IHBMO for stochastic problem
Best solution Worst solution Mean value Standard deviation/%
3-year profit/$ 6255752.6 6256712.1 6256236.6 0.0076
LR/MWh 3671.32 3653.01 3660.3 0.249
LRpeak/kW 942.2 938.0 941.1 0.235
PPR/MWh 197015 195101 196107 0.487
Tab.8  Statistical analysis of results
σ = 2 % σ = 3 % σ = 4 % σ = 5 %
3-year profit/$ 6255752.6 6255402.5 6254610.1 6254222
Maximum voltage deviation/pu 0.0412 0.0435 0.0431 0.0442
Tab.9  Effects of uncertainties on optimal solution
First year Second year Third year
Without DER 0.0350 0.0378 0.0381
With DER 0.028502 0.026649 0.025138
Tab.10  Mean value of voltage deviation at peak load level
Fig.8  Voltage profile at peak load level of each year for compensated and uncompensated systems, stochastic case
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