Power System Optimization Problems Research Articles

Kriging Based Surrogate Modeling for Fractional Order Control of Microgrids

This paper investigates the use of fractional order (FO) controllers for a microgrid. The microgrid employs various autonomous generation systems like wind turbine generator, solar photovoltaic, diesel energy generator, and fuel-cells. Other storage devices like the battery energy storage system and the flywheel energy storage system are also present in the power network. An FO control strategy is employed and the FO-proportional integral derivative (PID) controller parameters are tuned with a global optimization algorithm to meet system performance specifications. A kriging based surrogate modeling technique is employed to alleviate the issue of expensive objective function evaluation for the optimization based controller tuning. Numerical simulations are reported to prove the validity of the proposed methods. The results for both the FO and the integer order controllers are compared with standard evolutionary optimization techniques, and the relative merits and demerits of the kriging based surrogate modeling are discussed. This kind of optimization technique is not only limited to this specific case of microgrid control, but also can be ported to other computationally expensive power system optimization problems.

Evolution of the passive harmonic filters optimization problem in industrial power systems

Several authors have treated the optimization of passive filters in electric distribution systems. Optimization methods like: sequential quadratic programming (SQP), simulated annealing (SA), differential evolution (DE), artificial neural networks (ANN), particle swarm optimization (PSO), genetic algorithm (GA), etc., have been employed for optimizing certain configurations of passive filters. These optimization methods have been employed to solve several formulations of the problem of the project of filters. These formulations can be classified in: formulations of one or several objectives. The objective of the present work is to show the evolution of the formulation of this problem in the lasts years respect to the objective functions and constraints used. This analysis shows how the formulations employed have been upgraded from single-objective to multi-objective formulations to achieve a better representation of this complex problem.

An Approximate Power Flow for Distribution Systems

Power flow calculation is the most used procedure in electrical power systems. Generally, most of optimization problems in electric power systems need a steady state analysis to evaluate the objective function. Especially, those optimization problems that make use of artificial intelligence techniques, where, due to the high number of iterations, the power flow calculation has a crucial role. In these cases, the method used for solving the power flow must be fast and accurate at the same time. This paper presents an approximate power flow solution method to solve distribution systems. The proposed method is based on current mismatch equations. The system of obtained equations cannot be presented as a set of linear complex equations. So, a simplification is introduced in this paper in order to express the system by a set of linear complex equations instead of a double set of linear real equations. Regarding the topology of the network, the proposed method is valid for both radial and mesh distribution systems. Finally, so as to demonstrate the validity and the effectiveness of the proposed method, a set of test distribution systems obtained from the technical literature have been used to test it.

Environmental/economic power dispatch with wind power

Economic environmental dispatch (EED) is a significant optimization problem in electric power system. With more wide spread use of wind power, it is necessary to include wind energy conversion system (WECS) in the EED problem. This paper presents a model to solve the EED problem incorporating wind power. In addition to the classic EED factors, the factors accounting for overestimation and underestimation of available wind power in both economic and environmental aspects are also considered. In order to obtain some quantitative results, the uncertain characteristic of available wind power and the performance of WECS are determined on the basis of the statistical characteristic of wind speed. The optimization problem is numerically solved by a scenario involving two conventional generators and two wind-powered generators. The results demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the cost for overestimation and underestimation of available wind power, and by the multiplier related to the emissions for underestimation of available wind power. Nevertheless, the multiplier related to the emissions for overestimation of available wind power has little impact on the allocation. Taking account of economic factors, environmental factors and impacts of wind power penetration, the proposed EED model is beneficial to finding the right balance between radical and conservative strategy for wind power development.

ANALYSIS OF ECONOMIC LOAD DISPATCH USING FUZZIFIED PSO

With large interconnection of the electric networks, the energy crisis in the world and continuous rise in prices, it is very essential to reduce the running charges of the electric energy i.e., reduce the fuel consumption for meeting a particular load demand. The main aim in the economic dispatch is to minimize the total cost of generating real power at various stations while satisfying the loads and the losses in transmission links. A novel method is introduced in the planning and operation of power systems to provide reliable and quality power to consumers at economical cost. The primary objective of this paper is to develop efficient and fast Fuzzified Particle Swarm Optimization (FPSO) algorithm that can be applied to obtain the optimal solutions of multi-constrained dynamic ED, OPF. In this paper, I considered Fuzzified particle swam optimization to solve multi-constrained dynamic economic dispatch problem. The FPSO Method is inferred to have superior features than conventional methods and gives faster and stable convergence characteristics. PSO is popular due to its significant property of dealing with optimization problem and ease of computation. But the critical drawback of PSO is more number of iterations required and hence the computation time is required to obtain the optimal solution is more and also the premature convergence Hence to overcome the premature convergence and to speed up the process, a classical PSO technique is incorporated with fuzzy logic to get a faster convergence This improved technique is termed as Fuzzified Particle Swarm Optimization (FPSO). The proposed method can provide an accurate solution with fast convergence and has the potential to be applied to other power system optimization problems.

Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study

One of the major optimization problems in power systems is the optimal reactive power dispatch (ORPD) problem. ORPD problem is a multi-variable problem with non-linear characteristics and has established significant emphasize on secure and economical operation of power systems. Therefore, finding an effective algorithm to solve this problem is absorbing a growing attention among the academics. In this paper, a reliable and effective algorithm is introduced by combining modified teaching learning algorithm (MTLA) and double differential evolution (DDE) algorithm in order to handle the ORPD problem. Moreover, the proposed algorithm is applied to ORPD problem on IEEE 14-bus, IEEE 30-bus and IEEE 118-bus power systems for performance assessment and validation purposes. The obtained results demonstrate the efficiency of the proposed hybrid algorithm and show faster convergence and better solutions in comparison with previously reported algorithms.

Future Wind Power Scenario Synthesis Through Power Spectral Density Analysis

Scenarios of near future wind power are synthesized by considering the power spectral density (PSD), statistical characteristics, and the future capacity. The PSD of the wind power follows different power laws over different frequency ranges and is approximated by a piecewise function. A scaling exponent of the power law for a particular piece can be approximated by the slope of an affine function fitted to a logarithmic plot of the PSD. Each piece of the function has a different trend as the total capacity increases. Slope trends, the first PSD value, and the last PSD value are trained to forecast the PSD. Then, future wind power scenarios are synthesized from the forecasted PSD. In this process, phase angles are searched using a genetic algorithm while satisfying forecasted statistical characteristics for the given capacity. Our approach is simulated and validated for wind power for seven years in ERCOT and is used to synthesize a future wind power scenario at 10,000 MW capacity. Our approach could also be used to generate wind power scenarios at present capacity for many stochastic optimization problems in power systems.

Essential issues on solving optimal power flow problems using soft-computing

Optimal power flow (OPF) problems are important optimization problems in power systems which aim to minimize the operation cost of generators so that the load demand can be met and the loadings are within the feasible operating regions of the generators. This brief paper emphasizes two essential issues related to solving the OPF problems and which are rarely addressed in recent research into power systems: 1) the necessity to validate operational constraints on OPF, which determine the feasibility of power systems designed for the OPF problems; and 2) and the necessity to develop conventional methods for solving OPF problems which can be more effective than the commonly-used heuristic methods.

Distributed cooperative regulation for multiagent systems and its applications to power systems: a survey.

Cooperative regulation of multiagent systems has become an active research area in the past decade. This paper reviews some recent progress in distributed coordination control for leader-following multiagent systems and its applications in power system and mainly focuses on the cooperative tracking control in terms of consensus tracking control and containment tracking control. Next, methods on how to rank the network nodes are summarized for undirected/directed network, based on which one can determine which follower should be connected to leaders such that partial followers can perceive leaders' information. Furthermore, we present a survey of the most relevant scientific studies investigating the regulation and optimization problems in power systems based on distributed strategies. Finally, some potential applications in the frequency tracking regulation of smart grids are discussed at the end of the paper.

Evolutionary Algorithm Based Optimal Location of Series FACTS Device to Reduce the Losses in a Power System

In recent years, continuous and reliable electric energy supply is the objective of any power system operation. FACTS devices are promising devices from the past ten years and are becoming popular. Also FACTS devices are very effective solution for many power system transmission problems. FACTS controllers can be used for steady state voltage regulation and control, steady state power flow can be controlled on a transmission line, transient stability enhancement, along with this it also reduces the problem of sub synchronous resonance. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices play a key role in enhancing controllability and to increase power transfer capability of the network. Thyristor Controlled Series Compensator (TCSC) is an upcoming FACTS device which can be used to meet this objective. The conventional methods in solving optimization problems in power systems suffer from several limitations due to necessities of derivative existence, providing suboptimal solutions, etc. computational Intelligence plays an important role in finding the optimal solutions for multi-objective functions. A combinatorial analysis problem in power systems can be solved by modern heuristic methods in finding optimal solution. Thus, in this paper, Genetic Algorithm, a type of evolutionary computation encapsulated with heuristic approach, is proposed for finding optimal location of TCSC. IEEE standard 30bus system is considered to test the credibility of the proposed algorithm and the results obtained are subjected to analysis to determine the optimal location.

A Novel Optimal Power Flow Formulation Based on the Lyapunov Theory

This paper proposes a novel computing paradigm aimed at solving constrained optimization problems in electric power systems (a.k.a. optimal power flow analysis). The insight is to formulate the OPF problem by a proper set of ordinary differential equations, whose equilibrium points correspond to the optimization problem solutions. Starting from the Lyapunov theory, it will be shown that this artificial dynamic system could be designed to be stable with an exponential asymptotic convergence to equilibrium points. This important feature allows the analyst to overcome some of the inherent limitations of the traditional iterative minimization algorithms that can fail to converge due to the highly nonlinearities of the first-order conditions. Extensive simulation studies aimed at assessing the effectiveness of the proposed computing paradigm are presented and discussed.

Fuzzy Based Multi-Objective Evolutionary Algorithm for Optimal Location of FACTS Devices in a Power System

This paper presents a Multi Objective Evolutionary Algorithm (MOEA) to solve nonlinear power system optimization problem. The optimization is done with three variables; choice, location and rating of FACTS devices. The targeted technical objectives are; to control the power flow, increase the transmission line capability to its maximum thermal limits, to keep the voltages within limits while minimizing losses. The parameters of objectives are optimized independently and then the formulation of a nonlinear constrained multi-objective optimization problem is done while carrying out concurrent optimization taking two and three objectives. A Fuzzy min max approach is incorporated to reduce the best solution out of non dominated Pareto optimal set. Assessments have been done on IEEE 14 and IEEE 30 bus system for different loading conditions with two devices SVC and TCSC modeled in steady state and the results affirm the potency of the propound approach.

Performance comparison of heuristic optimization methods for optimal dynamic transmission expansion planning

Modern heuristic optimization algorithms have gained considerable attention in different research fields due to their conceptual simplicity and their ability to deal with different complex optimization problems. Motivated by promising results from the application of these emerging tools on other power system optimization problems, this paper presents a comparative study on the capabilities of three heuristic optimization algorithms for solving the problem of optimal dynamic centralized transmission expansion planning (CTEP). Among the studied algorithms are: Evolutionary particle swarm optimization, differential evolution, and a newly developed tool named mean–variance mapping optimization. Considering a predefined indicative generation expansion plan, which is usually contemplated in regulations for the power system planning in most Latin American countries, modified versions of the well-known Garver's system and the IEEE-118 bus multi-area system are used to evaluate the performance of each algorithm for solving the dynamic CTEP with a medium-term planning horizon of 10 years. Copyright © 2013 John Wiley & Sons, Ltd.

Reserve Capacity Optimization of the Regional Power System with Wind Farms

For the reserve capacity optimization problem of regional power system with wind farms, a joint optimization model was establishes which can effectively reduce the reserve capacity requirements of the regional power system with wind farms. This model was based on chance constrained programming method ,and quantified the processes that subsystems without wind farms providing reserve capacity support to subsystem with wind farms, each subsystem was conditioned by the level of security and stability, and the security constraints of regional system were also comprehensively considered. Monte-Carlo stochastic simulation model of genetic algorithm was used to solve the model. The model was tested by an example, and the results achieved the expected, which shows the model has a high reference value for the reserve capacity optimization of the power system with wind farms.

Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch

The reactive power dispatch (RPD) problem is a very critical optimization problem of power system which minimizes the real power loss of the transmission system. While solving the said problem, generator bus voltages and transformer tap settings are kept within a stable operating limit. In connection with the RPD problem, solving reactive power is compensated by incorporating shunt capacitors. The particle swarm optimization (PSO) technique is a swarm intelligence based fast working optimization method which is chosen in this paper as an optimization tool. Additionally, the constriction factor is included with the conventional PSO technique to accelerate the convergence property of the applied optimization tool. In this paper, the RPD problem is solved in the case of the two higher bus systems, i.e., the IEEE 57-bus system and the IEEE 118-bus system. Furthermore, the result of the present paper is compared with a few optimization technique based results to substantiate the effectiveness of the proposed study.

On the inter-area optimal voltage and reactive power control

Scheduling voltage and reactive power is one of the major problems and concerns of the Transmission System Operators (TSOs) especially after deregulation of power market. Due to locally provision of the reactive power resources, each TSO has developed its own specific method. Hence, the voltage and reactive power coordination in the interconnected system has got less attention. In this respect, current practices of different TSOs in Europe and North America, as examples of interconnected power systems, are investigated in this paper. Afterwards, it focuses on the inter-area optimization of the voltage and reactive power. Instead of performing the studies around one particular optimization, different aspects of the mathematical formulation of the optimization problem in the interconnected power system, including objective function, constraints and appropriate modeling of neighboring areas, are revisited more in depth. Various possible implementations of coordinated approaches, including centralized and decentralized structures, as well as non-coordinated approach in collaborative and non-collaborative environments are studied. In this respect, new contributions are proposed in this paper by using the distributed slack bus model and the limitation of the voltage and reactive power in the interconnections links. The comparative analysis between the available and proposed methods are discussed in terms of sub-optimality and time to convergence. The discussions are based on New England 39 bus system and the presented results in the literatures.

Evolutionary Algorithm Based Optimal Location of Series FACTS Device to Reduce the Losses in a Power System

In recent years, continuous and reliable electric energy supply is the objective of any power system operation. FACTS devices are promising devices from the past ten years and are becoming popular. Also FACTS devices are very effective solution for many power system transmission problems. FACTS controllers can be used for steady state voltage regulation and control, steady state power flow can be controlled on a transmission line, transient stability enhancement, along with this it also reduces the problem of sub synchronous resonance. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices play a key role in enhancing controllability and to increase power transfer capability of the network. Thyristor Controlled Series Compensator (TCSC) is an upcoming FACTS device which can be used to meet this objective. The conventional methods in solving optimization problems in power systems suffer from several limitations due to necessities of derivative existence, providing suboptimal solutions, etc. computational Intelligence plays an important role in finding the optimal solutions for multi-objective functions. A combinatorial analysis problem in power systems can be solved by modern heuristic methods in finding optimal solution. Thus, in this paper, Genetic Algorithm, a type of evolutionary computation encapsulated with heuristic approach, is proposed for finding optimal location of TCSC. IEEE standard 30bus system is considered to test the credibility of the proposed algorithm and the results obtained are subjected to analysis to determine the optimal location.

Solution of transient stability-constrained optimal power flow using artificial bee colony algorithm

The transient stability constraint should be taken into consideration for the solution of the optimization problems in power systems. This paper presents a solution for the transient stability-constrained optimal power flow (TSCOPF) by a novel approach based on the artificial bee colony algorithm. The formulas of TSCOPF are derived through the addition of rotor angle inequality constraints into optimal power flow relationships. In this nonlinear optimization problem, the objective function is taken into consideration as the fuel cost of the system. The proposed approach is tested on both a WSCC 3-generator, 9-bus system and a IEEE 30-bus system, and the test results obtained are compared to prove the efficiency and effectiveness of the approach with other approaches in the literature.

The Study on Electric Power System Based on Swarm Intelligence

In this paper, we introduce the swarm intelligence computation and its applications in power system. Because swarm intelligence does not need any precondition of centralized control and global model, it is very suitable to solve large scale power system nonlinear optimization problems which are hard to establish effective formalized models and difficult to be solved by traditional methods. In order to apply swarm intelligence better in power system, we propose two central research directions in the future: (1) The mathematical basis of swarm intelligence is unsubstantial and it lacks profound and pervasive theoretical analysis, so we must analysis its convergence and selection of parameters, especially the parameter selection of large scale power system optimization problems. (2) Because swarm intelligence is internally parallel, we should realize it based on the parallel computation theory. This work will also be helpful for the real-time need of power system.

Fuzzified Particle Swarm Optimization Algorithm for Multi-area Security Constrained Economic Dispatch

This article presents a new computationally efficient fuzzified particle swarm optimization algorithm for solving the security-constrained multi-area economic dispatch of an interconnected power system. The core objective of the security-constrained multi-area economic dispatch is to determine the generation allocation of each committed unit in the system and the power exchange between areas so as to minimize the total generation cost without violating the tie-line constraint. The proposed fuzzified particle swarm optimization algorithm is based on the combined application of fuzzy logic strategy incorporated in the particle swarm optimization algorithm. The proposed method was tested on a system of three interconnected areas. The investigation reveals that the proposed method can provide an accurate solution with fast convergence and has the potential to be applied to other power system optimization problems.