Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty

Sirus MOHAMMADI; Babak MOZAFARI; Soodabeh SOLEYMANI

Makale detayları

Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty

Yazarlar:

Sirus MOHAMMADI | Babak MOZAFARI | Soodabeh SOLEYMANI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 3, pp.735-753

Belge Tipi:

Makale

Yayın Tarihi:

5/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000743013

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

This paper analyzes the behavior of Hong's point estimate  method to account for uncertainties in probabilistic energy management  systems to optimize the operation of a microgrid (MG). These uncertainties  may arise from different sources, such as the market prices, load demands,  and electric power generation of wind farms and photovoltaic systems. Point  estimate methods constitute a remarkable tool to handle stochastic power  system problems because good results can be achieved using the same routines  as those corresponding to deterministic problems, while keeping the  computational burden low. The problem is formulated as a nonlinear  constraint optimization problem to minimize the total operating cost.  Weibull, beta, and normal distributions are used to model the uncertain  input variables in this study. Moreover, the firefly algorithm is applied to  achieve optimal operational planning with regard to cost minimization. The  efficiency of Hong's point estimate method is validated on a typical MG.  Results for the case study are presented and compared against those obtained  from the Monte Carlo simulation. Specifically, this paper shows that the use  of the 2m + 1 scheme provides the best performance when a high number of random  variables, both continuous and discrete, are considered.

 

This paper analyzes the behavior of Hong's point estimate  method to account for uncertainties in probabilistic energy management  systems to optimize the operation of a microgrid (MG). These uncertainties  may arise from different sources, such as the market prices, load demands,  and electric power generation of wind farms and photovoltaic systems. Point  estimate methods constitute a remarkable tool to handle stochastic power  system problems because good results can be achieved using the same routines  as those corresponding to deterministic problems, while keeping the  computational burden low. The problem is formulated as a nonlinear  constraint optimization problem to minimize the total operating cost.  Weibull, beta, and normal distributions are used to model the uncertain  input variables in this study. Moreover, the firefly algorithm is applied to  achieve optimal operational planning with regard to cost minimization. The  efficiency of Hong's point estimate method is validated on a typical MG.  Results for the case study are presented and compared against those obtained  from the Monte Carlo simulation. Specifically, this paper shows that the use  of the 2m + 1 scheme provides the best performance when a high number of random  variables, both continuous and discrete, are considered.

Makale detayları

Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty

Yazarlar: Sirus MOHAMMADI | Babak MOZAFARI | Soodabeh SOLEYMANI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 3, pp.735-753

Belge Tipi: Makale

Yayın Tarihi: 5/2014

Belge Dili: Türkçe

Document ID (Org No.): 2000743013

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Sirus MOHAMMADI | Babak MOZAFARI | Soodabeh SOLEYMANI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 3, pp.735-753

Belge Tipi:

Makale

Yayın Tarihi:

5/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000743013

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632