Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization
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
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.
. [J]. Frontiers in Energy, 2014, 8(4): 412-425.
Iraj AHMADIAN,Oveis ABEDINIA,Noradin GHADIMI. Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front. Energy, 2014, 8(4): 412-425.
Pai P F, Yang S L, Chang P T. Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms. Expert Systems with Applications, 2009, 36(7): 10746–10751
https://doi.org/10.1016/j.eswa.2009.02.035
2
Ghasemi A. A fuzzified multi objective interactive honey bee mating optimization for environmental/economic power dispatch with valve point effect. International Journal of Electrical Power and Energy Systems, 2013, 49: 308–321
https://doi.org/10.1016/j.ijepes.2013.01.012
3
Shayeghi H, Ghasemi A.Multiple PSS design using an improved honey bee mating optimization algorithm to enhance low frequency oscillations. International Review of Electrical Engineering (I.R.E.E.), 2011, 6(7): 3122–3133
4
Jain N, Singh S N, Srivastava S C. A generalized approach for DG planning and viability analysis under market scenario. IEEE Transactions on Industrial Electronics, 2013, 60(11): 5075–5085
https://doi.org/10.1109/TIE.2012.2219840
5
Gil H A, Joos G. Models for quantifying the economic benefits of distributed generation. IEEE Transactions on Power Systems, 2008, 23(2): 327–335
https://doi.org/10.1109/TPWRS.2008.920718
6
Chiradeja P, Ramakumar R. An approach to quantify the technical benefits of distributed generation. IEEE Transactions on Power Systems, 2004, 19(4): 764–773
7
Prenc R, ?krlec D, Komen V. Distributed generation allocation based on average daily load and power production curves. International Journal of Electrical Power & Energy Systems, 2013, 53, 612–622
8
Wang C, Nehrir M H. Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE Transactions on Power Systems, 2004, 19(4): 2068–2076
https://doi.org/10.1109/TPWRS.2004.836189
9
Georgilakis P S, Hatziargyriou N D. Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Transactions on Power Systems, 2013, 28(3): 3420–3428
https://doi.org/10.1109/TPWRS.2012.2237043
10
El-Khattan W, Bhattacharya K, Hegazy Y, Salama M M A. Optimal investment planning for distributed generation in a competitive electricity market. IEEE Transactions on Power Systems, 2005, 20(4): 1718–1727
11
Ahmadigorji M, Abbaspour A, Rajabi-Ghahnavieh A, Fotuhi-Firuzabad M. Optimal DG placement in distribution systems using cost/worth analysis. World Academy of Science, Engineering and Technology, 2009, 25: 746–753
12
Wang S, Watada J, Pedrycz W. Recourse-based facility location problems in hybrid uncertain environment. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 2010, 40(4): 1176–1187
https://doi.org/10.1109/TSMCB.2009.2035630
13
Wang S, Watada J, Pedrycz W. Value-at-Risk-based two-stage fuzzy facility location problems. IEEE Transactions on Industrial Informatics, 2009, 5(4): 465–482
https://doi.org/10.1109/TII.2009.2022542
14
Wang S, Watada J. A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty. Information Sciences, 2012, 192(1): 3–18
https://doi.org/10.1016/j.ins.2010.02.014
15
Celli G, Ghiani E, Mocci S, Pilo F. A multiobjective evolutionary algorithm for the sizing and sitting of distributed generation. IEEE Transactions on Power Systems, 2005, 20(2): 750–757
https://doi.org/10.1109/TPWRS.2005.846219
16
Das D. Optimal placement of capacitors in radial distribution system using a Fuzzy-GA method. International Journal of Electrical Power and Energy Systems, 2008, 30(6–7): 361–367
https://doi.org/10.1016/j.ijepes.2007.08.004
17
Neimane V. Distribution network planning based on statistical load modeling applying genetic algorithms and Monte-Carlo simulations. In: IEEE Power Tech Proceedings. Porto, Portugal, 2001
18
Roh J H, Shahidehpour M, Fu Y. Market-based coordination of transmission and generation capacity planning. IEEE Transactions on Power Systems, 2007, 22(4): 1406–1419
https://doi.org/10.1109/TPWRS.2007.907894
19
Hashemzadeh H, Ehsan M. Locating and parameters setting of unified power flow controller for congestion management and improving the voltage profile. Asia-Pacific Power and Energy Engineering Conference. Chengdu, China, 2010
20
Santoso N I, Tan O T. Neural-net based real-time control of capacitors installed on distribution system. IEEE Transactions on Power Delivery, 1990, 5(1): 266–272
https://doi.org/10.1109/61.107283
21
Basu A K, Chowdhury S, Chowdhury S P. Impact of strategic deployment of CHP-based DERs on microgrid reliability. IEEE Transactions on Power Delivery, 2010, 25(3): 1697–1705
https://doi.org/10.1109/TPWRD.2010.2047121