Abstract
In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. In addition to this, lack of CM can impose a hindrance in electricity trading. This paper presents a novel, growing radial basis function neural network (GRBFNN)-based approach for CM. For achieving CM, Nodal congestion price (NCP) forecasting is performed in real time competitive power market. NCP forecasting is an effective way of price-based preventive CM as it directly indicates the presence as well as the severity of the congestion in the system. In present paper, GRBFNN has been developed for NCP forecasting dividing the whole power system into various congestion zones. An unsupervised learning vector quantization (VQ) clustering algorithm is applied as feature selection technique for the developed GRBFNN and for partitioning the power system into different congestion zones. For each congestion zone a separate neural network has been developed to ensure faster training and accurate forecasting results. The proposed approach of CM is implemented on an RTS 24-bus system. The results obtained are compared with a different constructive algorithm-based RBF network called as general regression neural network (GRNN) and two back-propagation algorithms based ANNs. Comparison results show that proposed GRBFNN is more computationally efficient with better predictive ability.
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Abbreviations
- \( C_{{{\text{S}}_{{_{i} }} }} \) :
-
supply bid of unit i in $/MWh
- \( C_{{{\text{D}}_{j} }} \) :
-
demand bid of unit j in $/MWh
- \( P_{{{\text{S}}_{i} }} \) :
-
supply bid volume of unit i in MW
- \( P_{{{\text{D}}_{j} }} \) :
-
demand bid volume of unit j in MW
- \( P_{{{\text{S}}_{{\max_{i} }} }} \) :
-
upper limit of supply bid volume of unit i in MW
- \( P_{{{\text{S}}_{{\min_{i} }} }} \) :
-
lower limit of supply bid volume of unit i in MW
- \( P_{{{\text{D}}_{{\max_{j} }} }} \) :
-
upper limit of demand bid volume of unit j in MW
- \( P_{{{\text{D}}_{{\min_{j} }} }} \) :
-
lower limit of demand bid volume of unit j in MW
- \( Q_{{{\text{G}}_{i} }} \) :
-
reactive power output of unit i in MVAr
- \( Q_{{{\text{G}}_{{\min_{i} }} }} \) :
-
upper limit of reactive power at unit i in MVAr
- \( Q_{{{\text{G}}_{{\max_{i} }} }} \) :
-
lower limit of reactive power at unit i in MVAr
- V k :
-
voltage magnitude at node k in pu
- \( V_{{\min_{k} }} \) :
-
lower limit of voltage magnitude at node k in pu
- \( V_{{\max_{k} }} \) :
-
upper limit of voltage magnitude at node k in pu
- I mk :
-
line currents between nodes m and k in pu
- \( I_{{mk_{\max } }} \) :
-
upper limit of line current between nodes m and k
- P mk :
-
real power flow between nodes m and k
- \( P_{{mk_{\max } }} \) :
-
upper limit of real power flow through line between nodes m and k
- P k :
-
net real power injected at bus k in MW
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Acknowledgments
The authors sincerely acknowledge the financial support provided by AICTE, New Delhi, India, under Research grant no. 8023/BOR/RPS-60/2006-07, dated 26-2-07 and Director, ABV-IIITM, Gwalior, India for providing facilities to carry out this research work. Seema N. Pandey thanks the Ministry of Technical Education, Madhya Pradesh, Bhopal, for sponsoring her to carry out her doctoral work under QIP (Poly), AICTE, Govt. of India.
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Pandey, S.N., Tapaswi, S. & Srivastava, L. Growing RBFNN-based soft computing approach for congestion management. Neural Comput & Applic 18, 945–955 (2009). https://doi.org/10.1007/s00521-008-0205-3
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DOI: https://doi.org/10.1007/s00521-008-0205-3