Optimization of Power System Problems Methods, Algorithms
and MATLAB Codes by Mahmoud Pesaran Hajiabbas and Behnam Mohammadi-Ivatloo | PDF Free Download.
Providing a reliable and secure power and energy system is one of the main challenges of the new era. The efficient operation of power systems contributes to a decrease in fuel consumption and gas emission, conservation of natural resources, ensuring sustainability with better planning, and providing cleaner energy.
The evolving modern optimization methods lead to more effective solutions and are promising for the continuously changing power system management, planning, and operation.
One of the most favored tools of researchers and electric system developers for power system optimization is MATLAB software.
Therefore, there has been an increased call for sharing the properly developed codes for power system optimization.
The book is suitable for dedicated and general audiences that include power system professionals, as well as researchers and developers from electrical power engineering and power system planning communities.
It is expected that readers to be graduates of energy and power engineering degree programs having a basic mathematical background.
The book is organized under two main sub-topics, comprising of power system optimal planning and configuration and power system optimal operation. A brief description of the chapters’ content is presented in the following paragraphs.
The chapter “Modelling for Composite Load Model Including Participation of Static and Dynamic Load” focuses on modeling for composite load model including the participation of static and dynamic load.
It is well recognized that voltage problems in the power system are much affected through the connected loads. Different types of load can be modeled on their characteristic basis for computation of power system problems effectively.
For different power system studies, especially in the area of power system optimization problems that includes voltage control with reactive power compensation, transfer function ΔQ ⁄ΔV of composite load is required.
This chapter gives detailed mathematical modeling to compute the reactive power response with small voltage perturbation for the composite load.
The composite load is defined as a combination of static and dynamic load models. To develop this composite load model, the exponential load is used as a static load model and induction motors are used as a dynamic load model in this chapter.
To analyze the dynamics of induction motor load, fifth-, third-, and first-order models of induction motor are formulated and compared using differential equation solver in MATLAB coding.
Since the decentralized areas have many small consumers which may consist of large numbers of induction motors of small rating, it is not realistic to model either a single large rating unit or all small rating induction motors together that are placed in the system.
In place of using a single large rating induction motor, a group of motors is being considered, then the aggregate model of induction motor is developed using the law of energy conservation, and this aggregate model is used as a dynamic load model.
The transfer function of composite load is derived in this chapter by successive derivation for the exponential model of static load and for fifth- and third-order induction motor dynamic load models using the state-space model.
The chapter “A Novel Forward-Backward Sweep Based Optimal DG Placement Approach in Radial Distribution Systems” presents a novel forward-backward sweep-based optimal DG placement approach in radial distribution systems.
This chapter proposes a novel backward–forward sweep (BFS)-based methodology for optimal allocation of DG micro-plants in radial distribution systems aiming to minimize total real power losses. The voltage-permitted range limit and feeder capacity criterion are considered as optimization constraints.
The simulation of the BFS-based DG placement method is conducted on the 33-bus distribution network to demonstrate its robustness and effectiveness in comparison with other procedures.
The chapter “Optimal Capacitor Placement in Distribution Systems Using a Backward-Forward Sweep Based Load Flow Method” investigates optimal capacitor placement in distribution systems using backward–forward sweep-based load flow method.
This chapter aims to present a backward–forward sweep (BFS)- based algorithm for optimal allocation of shunt capacitors in distribution networks.
The minimum value of real power losses is selected as an objective function. Moreover, feeder current capacity and bus voltage magnitude limits are considered as optimization constraints.
In addition, it is assumed that the sizes of capacitors are known parameters. The first capacitor is considered to be located on the first bus of the test system.
Then, BFS load flow is run and the objective function is saved as the first row and first column component of a loss matrix.
Secondly, the first capacitor is assumed to be installed at bus 2 and BFS load flow is run to obtain objective function as the second row and first column component of loss matrix.
When all buses are assessed for installation of capacitor 1 and losses are calculated in each scenario, similar analyses are carried out and objectives are saved as the second column of loss matrix.
The same strategy is applied to other capacitors. Finally, a loss matrix is formed with a number of rows and columns equal to a number of buses and shunt capacitors, respectively. Best places for the installation of capacitors are determined based on components of the loss matrix.
Simulation of the BFS-based capacitor placement problem is conducted on the 33-bus distribution network to demonstrate its robustness and effectiveness in comparison with other procedures.
Chapter “Optimal Capacitor Placement and Sizing in Distribution Networks” discusses optimal capacitor placement and sizing in distribution networks.
Utilizing capacitor banks in order for local compensation of load reactive power is common in distribution networks.
Using capacitors has positive effects on networks such as power and energy loss reduction, voltage deviation, and network harmonic reduction as well as improvement in network power factor.
Capacitor placement is applied to the network in the form of single or multi-objective problems. Decreasing the total network loss is often the main reason for using capacitors in distribution networks.
The capacitor placement approach involves the identification of location for capacitor placement and the size of the capacitor to be installed at the identified location.
An optimization algorithm decides the location of the nodes where the capacitors should be placed. As we know, the capacitors are categorized into two main types of fixed and switchable capacitors.
Selecting an appropriate type of capacitor is related to the topology of the network, load value, and economic situation. They are also different from a coding point of view.
In this section, the model of coding is presented at first, and then, the approach of applying is described based on the optimization algorithm. The capacitors are often used for peak loads, but they may be present in the network in off-peak due to the switching issues.
The network voltage may be increased in off-peak with the presence of capacitors. Therefore, it is very important to consider both peak and off-peak in the capacitor sizing and placement problem.
The proposed model is applied to IEEE 10 and 33-bus standard test cases in order to demonstrate the efficiency of the proposed model.
Chapter “Binary Group Search Optimization for Distribution Network Reconfiguration” studies binary group search optimization for distribution network reconfiguration.
Total loss minimization is considered as the objective which is solved subject to system radial operation and power flow constraints.
Here, the basics of the GSO algorithm are presented first and then necessary modification for developing BGSO is discussed.
The main part of this chapter deals with a source code, which expresses the step-by-step implementation of the BGSO method to optimal network reconfiguration problem.
Needless to emphasize that the BGSO and associated source code presented in this chapter is a general engine that can be easily adjusted to any optimization problem with binary variables. In addition, the source code associated with the developed forward-backward sweep-based load flow study is also provided.
The simulation studies are performed on different distribution networks to examine the scheme at various conditions and problem complexities.
Comprehensive simulation studies conducted in this chapter verify the effectiveness of the BGSO and developed source code for solving optimal distribution network reconfiguration problem.
Chapters “Combined Heat and Power Economic Dispatch Using Particle Swarm Optimization,” “Combined Heat and Power Stochastic Dynamic Economic Dispatch Using Particle Swarm Optimization Considering Load and Wind Power Uncertainties,”
and “Economic Dispatch of Multiple-Chiller Plants Using Wild Goats Algorithm” exercises the combined heat and power economic dispatch using particle swarm optimization,
the combined heat and power stochastic dynamic economic dispatch using particle swarm optimization considering load and wind power uncertainties, and the economic dispatch of multiple-chiller plants using wild goats algorithm, respectively.
Chapter “Optimization of Tilt Angle for Intercepting Maximum Solar Radiation for Power Generation” investigates the optimization of the tilt angle for intercepting maximum solar radiation for power generation.
The novelty is the determination of optimum tilt angles (b_opt) for the photovoltaic system at 11 different sites for Gujarat in India.
The b_opt is searched for maximum incident solar radiation (SR). For calculation, SR values given by the National Aeronautics and Space Administration (NASA) are utilized. It was found that the optimum tilt angle varies between 1° and 57° throughout the year in Gujarat, India.
The monthly optimum tilt angle is maximum in December for different sites in Gujarat, India. This study is useful for industry and researchers to install a PV system in India to generate maximum power.
Chapter “Probabilistic Power Flow Analysis of Distribution Systems Using Monte Carlo Simulations” analyzes the probabilistic power flow analysis of distribution systems using Monte Carlo simulations.
This chapter aims to present a Monte Carlo simulation-based probabilistic power flow method for finding all critical buses against variations of active and reactive loads. In this approach, backward–forward sweep-based load flow is used to find the optimal operating point of the benchmark distribution grid in each scenario.
The number of scenarios with bus voltage magnitude violation probability is used to cluster nodes into two critical and non-critical categories. Robustness and effectiveness of the Monte Carlo-based probabilistic power flow algorithm are revealed by simulations on the 33-bus radial distribution system.
Chapter “Long-Term Load Forecasting Approach Using Dynamic Feed-Forward Back-Propagation Artificial Neural Network” implements the long-term load forecasting approach using dynamic feed-forward back-propagation artificial neural network.
This chapter presents a novel approach based on a dynamic feed-forward back-propagation artificial neural network (FBP-ANN) for long-term forecasting of total electricity demand.
A feed-forward back-propagation time series neural network consists of an input layer, hidden layers, and an output layer and is trained in three steps: a) Forward the input load data, b) compute and propagate the error backward, and c) update the weights.
First, all examples of the training set are entered into the input nodes. The activation values of the input nodes are weighted and accumulated at each node in the hidden layer and transformed by an activation function into the node’s activation value.
It becomes an input into the nodes in the next layer until eventually the output activation values are found.
The training algorithm is used to find the weights that minimize mean squared error. The main characteristics of FBP-TSNN are the self-learning and self-organizing.
The proposed algorithm is implemented on Iran’s power network to prove its accuracy and effectiveness and compare it with real historical data.
The chapter “Multi-objective Economic and Emission Dispatch Using MONICA: A Competitive Study” applies MOICA on multi-objective economic and emission dispatch using.
The application of the multi-objective imperialist competitive algorithm is investigated for solving economic and emission dispatch problems.
It is aimed to minimize two conflicting objectives, economic and environmental while satisfying the problem constraints.
In addition, nonlinear characteristics of generators such as prohibited zone and ramp up/down limits are considered. To check the applicability of the MOICA, it is applied to 12 h of the IEEE 30-bus test system.
Then, results of MOICA are compared with those derived by non-dominated sorting genetic algorithm and multi-objective particle swarm optimizer. The finding indicates that MOICA exhibits better performance.
The chapter “Voltage Control by Optimized Participation of Reactive Power Compensation Using Fixed Capacitor and STATCOM” integrates fixed capacitor and STATCOM to control voltage by optimized participation of reactive power compensation.
Finally, the chapter “Backward-Forward Sweep Based Power Flow Algorithm in Distribution Systems” employs backward–forward sweep-based power flow algorithm in distribution systems. To solve this problem, a backward– forward sweep (BFS) load flow algorithm is presented by scholars.
This chapter aims to present MATLAB codes of the BFS power flow method in a benchmark distribution grid.
Feeder capacity and voltage magnitude limits are considered in finding a good operating point for a test grid. Input data such as bus and line information matrices are presented in MATLAB codes.
Simulations are conducted on the IEEE 33-bus radial distribution system. Feeder current, bus voltage magnitude, active and reactive power flowing in or out of buses, and total real power loss system are found as outputs of the BFS load flow approach.
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