25-06-2012, 12:15 PM
Application of computational intelligence in emerging power systems
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Abstract
Electric power systems, around the world, are changing in terms of structure, operation, management and ownership due to
technical, financial and ideological reasons. Power system keeps on expanding in terms of geographical areas, assets additions,
and penetration of new technologies in generation, transmission and distribution. This makes the electric power system complex,
heavily stressed and thereby vulnerable to cascade outages. The conventional methods in solving the power system design,
planning, operation and control problems have been very extensively used for different applications but these methods suffer
from several difficulties due to necessities of derivative existence, providing suboptimal solutions, etc. Computation intelligent
(CI) methods can give better solution in several conditions and are being widely applied in the electrical engineering
applications. This paper highlights the application of computational intelligence methods in power system problems. Various
types of CI methods, which are widely used in power system, are also discussed in the brief.
Keywords: Power systems, computational intelligence, artificial intelligence.
1. Introduction
Increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new
challenges for electric power system operation, control and automation. In liberalized electricity market, the operation and control
of power system become complex due to complexity in modeling and uncertainties. Power system models used for intelligent
operation and control are highly dependent on the task purpose. In competitive electricity market along with automation,
computational intelligent techniques are very useful. As electric utilities are trying to provide smart solutions with economical,
technical (secure, stable and good power quality) and environmental goals, there are several challenging issues in the smart grid
solutions such as, but not limited to, forecasting of load, price, ancillary services; penetration of new and renewable energy
sources; bidding strategies of participants; power system planning & control; operating decisions under missing information;
increased distributed generations and demand response in the electric market; tuning of controller parameters in varying operating
conditions, etc. Risk management and financial management in electric sector are concerned with finding an ideal trade-off
between maximizing the expected returns and minimizing the risks associated with these investments.
Computational intelligence (CI) is a new and modern tool for solving complex problems which are difficult to be solved by the
conventional techniques. Heuristic optimization techniques are general purpose methods that are very flexible and can be applied
to many types of objective functions and constraints. Recently, these new heuristic tools have been combined among themselves
and new methods have emerged that combine elements of nature-based methods or which have their foundation in stochastic and
simulation methods. Developing solutions with these tools offers two major advantages: development time is much shorter than
when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing
data/information known as uncertainty.
Due to environmental, right-of-way and cost problems, there is an increased interest in better utilization of available power
system capacities in both bundled and unbundled power systems. Patterns of generation that results in heavy flows, tend to incur
1 Corresponding author. Tel: +91-512-2597009
Saxena et al. / International Journal of Engineering, Science and Technology, Vol. 2 No. 3, 2010, pp. 1-7
2
greater losses, and to threaten stability and security, ultimately make certain generation patterns economically undesirable. Hence,
new devices and resrources such as flexible ac transmission systems (FACTS), distributed generations, smart grid technologies,
etc. are being utilized. In the emerging area of power systems, computation intelligence plays a vital role in providing better
solutions of the existing and new problems. This paper lists various potential areas of power systems and provides the roles of
computational intelligence in the emerging power systems. A brief review of computational techniques is also presented.
2. Potential Area of Research in Power System Using Computational Intelligence
There are several problems in the power systems which cannot be solved using the conventional approaches as these methods are
based on several requirements which may not be true all the time. In those situations, computational intelligence techniques are
only choice however these techniques are not limited to these applications. The following areas of power system utilize the
application of computational intelligence.
• Power system operation (including unit commitment, economic dispatch, hydro-thermal coordination, maintenance
scheduling, congestion management, load/power flow, state estimation, etc.)
• Power system planning (including generation expansion planning, transmission expansion planning, reactive power
planning, power system reliability, etc.)
• Power system control (such as voltage control, load frequency control, stability control, power flow control, dynamic
security assessment, etc.)
• Power plant control (including thermal power plant control, fuel cell power plant control, etc.)
• Network control (location and sizing of facts devices, control of facts devices, etc.)
• Electricity markets (including bidding strategies, market analysis and clearing, etc.)
• Power system automation (such as restoration and management, fault diagnosis and reliability, network security, etc.)
• Distribution system application (such as operation and planning of distribution system, demand side management &
demand response, network reconfiguration, operation and control of smart grid, etc.)
• Distributed generation application (such as distributed generation planning, operation with distributed generation, wind
turbine plant control, solar photovoltaic power plant control, renewable energy sources, etc.)
• Forecasting application (such as short term load forecasting, electricity market forecasting, long term load forecasting,
wind power forecasting, solar power forecasting, etc.)
Several research papers are published in various journals and conferences. Some conferences in the power system areas are
completely dedicated to intelligent system applications and organized regularly such as intelligent system application to power
systems (ISAP) held alternate years in different locations of the world. Power system computing conference is another very
important conference held once in three years. Similarly, several reputed journals are dedicated to CI applications in the field of
engineering and science. There are several books which address of CI application in power systems (Fogel et al, 1966; Sobajic,
1993; Song, 1996; Warvick, 1997; El-Hawary, 1998; Lai, 1998; Wehnekel, 1998; Momoh, 2000).
3. Various Computational Intelligence Techniques
Computational intelligence (CI) methods, which promise a global optimum or nearly so, such as expert system (ES), artificial
neural network (ANN), genetic algorithm (GA), evolutionary computation (EC), fuzzy logic, etc. have been emerged in recent
years in power systems applications as effective tools. These methods are also known as artificial intelligence (AI) in several
works. In a practical power system, it is very important to have the human knowledge and experiences over a period of time due to
various uncertainties, load variations, topology changes, etc. This section presents the overview of CI/AI methods (ANN, GA,
fuzzy systems, EC, ES, ant colony search, Tabu search, etc.) used in power system applications.
3.1 Artificial Neural Networks
An artificial neural network (ANN) is an information-processing paradigm that is inspired by the biological nervous systems,
such as the brain, process information (Bishop, 1995). The key element of this paradigm is the novel structure of the information
processing system composed of a large number of highly interconnected processing elements (neurons) working in unison to solve
the specific problems. ANNs, like people, learn by example. The starting point of ANN application was the training algorithm
proposed and demonstrated, by Hebb in 1949, how a network of neurons could exhibit learning behaviour. During the training
phase, the neurons are subjected to a set of finite examples called training sets, and the neurons then adjust their weights according
to certain learning rule. ANNs are not programmed in the conventional sense, rather they learn to solve the problem through
interconnections with environment. Very little computation is carried out at the site of individual node (neuron). There is no
explicit memory or processing locations in neural network but are implicit in the connections between nodes. Not all sources of
Saxena et al. / International Journal of Engineering, Science and Technology, Vol. 2 No. 3, 2010, pp. 1-7
3
input feeding a node are of equal importance. It all depends on weight which can be negative or positive. Inputs arriving at a node
are transformed according to the activation function of node.
The main advantages of ANNs are as follows:
[attachment=25380]
Abstract
Electric power systems, around the world, are changing in terms of structure, operation, management and ownership due to
technical, financial and ideological reasons. Power system keeps on expanding in terms of geographical areas, assets additions,
and penetration of new technologies in generation, transmission and distribution. This makes the electric power system complex,
heavily stressed and thereby vulnerable to cascade outages. The conventional methods in solving the power system design,
planning, operation and control problems have been very extensively used for different applications but these methods suffer
from several difficulties due to necessities of derivative existence, providing suboptimal solutions, etc. Computation intelligent
(CI) methods can give better solution in several conditions and are being widely applied in the electrical engineering
applications. This paper highlights the application of computational intelligence methods in power system problems. Various
types of CI methods, which are widely used in power system, are also discussed in the brief.
Keywords: Power systems, computational intelligence, artificial intelligence.
1. Introduction
Increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new
challenges for electric power system operation, control and automation. In liberalized electricity market, the operation and control
of power system become complex due to complexity in modeling and uncertainties. Power system models used for intelligent
operation and control are highly dependent on the task purpose. In competitive electricity market along with automation,
computational intelligent techniques are very useful. As electric utilities are trying to provide smart solutions with economical,
technical (secure, stable and good power quality) and environmental goals, there are several challenging issues in the smart grid
solutions such as, but not limited to, forecasting of load, price, ancillary services; penetration of new and renewable energy
sources; bidding strategies of participants; power system planning & control; operating decisions under missing information;
increased distributed generations and demand response in the electric market; tuning of controller parameters in varying operating
conditions, etc. Risk management and financial management in electric sector are concerned with finding an ideal trade-off
between maximizing the expected returns and minimizing the risks associated with these investments.
Computational intelligence (CI) is a new and modern tool for solving complex problems which are difficult to be solved by the
conventional techniques. Heuristic optimization techniques are general purpose methods that are very flexible and can be applied
to many types of objective functions and constraints. Recently, these new heuristic tools have been combined among themselves
and new methods have emerged that combine elements of nature-based methods or which have their foundation in stochastic and
simulation methods. Developing solutions with these tools offers two major advantages: development time is much shorter than
when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing
data/information known as uncertainty.
Due to environmental, right-of-way and cost problems, there is an increased interest in better utilization of available power
system capacities in both bundled and unbundled power systems. Patterns of generation that results in heavy flows, tend to incur
1 Corresponding author. Tel: +91-512-2597009
Saxena et al. / International Journal of Engineering, Science and Technology, Vol. 2 No. 3, 2010, pp. 1-7
2
greater losses, and to threaten stability and security, ultimately make certain generation patterns economically undesirable. Hence,
new devices and resrources such as flexible ac transmission systems (FACTS), distributed generations, smart grid technologies,
etc. are being utilized. In the emerging area of power systems, computation intelligence plays a vital role in providing better
solutions of the existing and new problems. This paper lists various potential areas of power systems and provides the roles of
computational intelligence in the emerging power systems. A brief review of computational techniques is also presented.
2. Potential Area of Research in Power System Using Computational Intelligence
There are several problems in the power systems which cannot be solved using the conventional approaches as these methods are
based on several requirements which may not be true all the time. In those situations, computational intelligence techniques are
only choice however these techniques are not limited to these applications. The following areas of power system utilize the
application of computational intelligence.
• Power system operation (including unit commitment, economic dispatch, hydro-thermal coordination, maintenance
scheduling, congestion management, load/power flow, state estimation, etc.)
• Power system planning (including generation expansion planning, transmission expansion planning, reactive power
planning, power system reliability, etc.)
• Power system control (such as voltage control, load frequency control, stability control, power flow control, dynamic
security assessment, etc.)
• Power plant control (including thermal power plant control, fuel cell power plant control, etc.)
• Network control (location and sizing of facts devices, control of facts devices, etc.)
• Electricity markets (including bidding strategies, market analysis and clearing, etc.)
• Power system automation (such as restoration and management, fault diagnosis and reliability, network security, etc.)
• Distribution system application (such as operation and planning of distribution system, demand side management &
demand response, network reconfiguration, operation and control of smart grid, etc.)
• Distributed generation application (such as distributed generation planning, operation with distributed generation, wind
turbine plant control, solar photovoltaic power plant control, renewable energy sources, etc.)
• Forecasting application (such as short term load forecasting, electricity market forecasting, long term load forecasting,
wind power forecasting, solar power forecasting, etc.)
Several research papers are published in various journals and conferences. Some conferences in the power system areas are
completely dedicated to intelligent system applications and organized regularly such as intelligent system application to power
systems (ISAP) held alternate years in different locations of the world. Power system computing conference is another very
important conference held once in three years. Similarly, several reputed journals are dedicated to CI applications in the field of
engineering and science. There are several books which address of CI application in power systems (Fogel et al, 1966; Sobajic,
1993; Song, 1996; Warvick, 1997; El-Hawary, 1998; Lai, 1998; Wehnekel, 1998; Momoh, 2000).
3. Various Computational Intelligence Techniques
Computational intelligence (CI) methods, which promise a global optimum or nearly so, such as expert system (ES), artificial
neural network (ANN), genetic algorithm (GA), evolutionary computation (EC), fuzzy logic, etc. have been emerged in recent
years in power systems applications as effective tools. These methods are also known as artificial intelligence (AI) in several
works. In a practical power system, it is very important to have the human knowledge and experiences over a period of time due to
various uncertainties, load variations, topology changes, etc. This section presents the overview of CI/AI methods (ANN, GA,
fuzzy systems, EC, ES, ant colony search, Tabu search, etc.) used in power system applications.
3.1 Artificial Neural Networks
An artificial neural network (ANN) is an information-processing paradigm that is inspired by the biological nervous systems,
such as the brain, process information (Bishop, 1995). The key element of this paradigm is the novel structure of the information
processing system composed of a large number of highly interconnected processing elements (neurons) working in unison to solve
the specific problems. ANNs, like people, learn by example. The starting point of ANN application was the training algorithm
proposed and demonstrated, by Hebb in 1949, how a network of neurons could exhibit learning behaviour. During the training
phase, the neurons are subjected to a set of finite examples called training sets, and the neurons then adjust their weights according
to certain learning rule. ANNs are not programmed in the conventional sense, rather they learn to solve the problem through
interconnections with environment. Very little computation is carried out at the site of individual node (neuron). There is no
explicit memory or processing locations in neural network but are implicit in the connections between nodes. Not all sources of
Saxena et al. / International Journal of Engineering, Science and Technology, Vol. 2 No. 3, 2010, pp. 1-7
3
input feeding a node are of equal importance. It all depends on weight which can be negative or positive. Inputs arriving at a node
are transformed according to the activation function of node.
The main advantages of ANNs are as follows: