Emission Level Functions Research Articles

Economic environmental dispatch of solar-wind-hydro-thermal power system

• EED is a significant chore in solar-wind-hydro-thermal power system. • It is an extremely restricted multi-objective optimization problem. • NSGA-II has been suggested for solving EED of solar-wind-hydro-thermal power system. Economic environmental dispatch (EED) is a significant chore in solar-wind-hydro-thermal power system comprising multi-reservoir cascaded hydro plant with time delay, thermal plants with non-smooth fuel cost, NO x emission level and SO 2 emission level functions, wind power generating units, solar PV plant and battery energy storage system. It is an extremely restricted multi-objective optimization problem linking conflicting objectives with both equality and inequality constraints. Here, non-dominated sorting genetic algorithm-II (NSGA-II) has been suggested for solving EED of solar-wind-hydro-thermal power system with battery energy storage system where generations are allocated among the on line units such that cost, NO x emission level and SO 2 emission level are optimized simultaneously while fulfilling all operational constraints. Test results of two test systems acquired from the suggested technique have been fit to that acquired from strength pareto evolutionary algorithm 2 (SPEA 2).

Multi-area dynamic economic emission dispatch of hydro-wind-thermal power system

Multi-area dynamic economic emission dispatch (MADEED) is a significant chore of hydro-wind-thermal power system. This system comprises multi-reservoir cascaded hydro plant with time delay, thermal plants with nonsmooth fuel cost and emission level functions, wind power generating units with the effect of reserve coefficient, penalty coefficient and wind power uncertainty. This paper suggests nondominated genetic algorithm–II (NSGA-II) for solving multi-area dynamic economic emission dispatch (MADEED) of hydro-wind-thermal power system where power generations are allocated among the on line units in such a manner that total cost and emission level are optimized simultaneously while fulfilling all operational constraints. Test results of a four-area system acquired from the suggested technique have been fit to that acquired from strength pareto evolutionary algorithm 2 (SPEA 2).

Extended Dynamic Economic Environmental Dispatch u sing Multi - Objective Particle Swarm Optimization

The purpose of this paper is to present the extended version of the conventional DEED to overcome the ramp rate violations when its optimal solutions for one period (normally one day) are implemented repeatedly and periodically over consequent dispatch periods to meet the periodic load demands. This dynamic dispatch problem, which is referred to as EDEED, is a multi-objective optimization problem which simultaneously minimizes both fuel cost and pollutants emission while satisfying a set of constraints. A multi-objective particle swarm optimization (MOPSO) method has been applied in this article for solving EDEED problem. The performance of the proposed method has been evaluated on the 10-unit test system with non-smooth fuel cost and emission level functions in comparison with those methods reported in the literature.

Economic Environmental Scheduling of Hydrothermal Power System

This paper presents an interactive fuzzy satisfying method based on simulated annealing technique to determine the economic environmental scheduling of hydrothermal power system consisting of multi-reservoir cascaded hydro plants with time delay and thermal plants with nonsmooth fuel cost and emission level functions. This paper treats cost and emission as competing objectives. Assuming that the decision maker (DM) has fuzzy goals for each of the objective functions, fuzzy satisfying method based on simulated annealing technique is applied for generating a corresponding optimal noninferior solution for the DM’s goals. Test results obtained from the proposed method are compared with those obtained by using evolutionary programming.

Immune-Based Algorithm for Power Economic Emission Dispatch

This paper proposes an artificial immune system for the economic environmental dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The artificial immune system (AIS) equipped with an accelerated operation and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the constraints of HPS. To show the advantages of the proposed algorithm, one example addressing the best compromise is applied to test the EED problem of the HPS. The proposed algorithm integrates the AIS, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits – 1)ease of implementation; 2)applicability to non-smooth fuel cost and emission level functions; 3)better effectiveness than the previous method, and 4)the requirement for only a small population in applying the optimal EED problem of the HPS.

Comprehensive Learning Particle Swarm Optimization (CLPSO) for Multi-objective Optimal Power Flow

The Optimal Power Flow (OPF) problem has been widely used in power system operation and planning for determining electricity prices and amount of emission. This paper presents a Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm to solve the highly constrained multi-objective OPF involving conflicting objectives, considering fuel cost and emission level functions. The proposed technique has been carried out on IEEE 30-bus test system. The results demonstrate the capability of the proposed CLPSO approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective OPF problem. The results show that the approaches developed are feasible and efficient.

Comprehensive Learning Particle Swarm Optimization (CLPSO) for Multi-objective Optimal Power Flow

The Optimal Power Flow (OPF) problem has been widely used in power system operation and planning for determining electricity prices and amount of emission. This paper presents a Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm to solve the highly constrained multi-objective OPF involving conflicting objectives, considering fuel cost and emission level functions. The proposed technique has been carried out on IEEE 30-bus test system. The results demonstrate the capability of the proposed CLPSO approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective OPF problem. The results show that the approaches developed are feasible and efficient.

Hybrid Differential Evolution Based Multi-Objective Approach for Hydrothermal Power Systems

This paper develops a hybrid differential evolution based multi-objective approach for the optimal economic emission dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The hybrid differential evolution (HDE) equipped with an accelerated operation and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the multi-objective problem. To show the advantages of the proposed algorithm, one example addressing the best compromise is applied to test the EED problem of the HPS. The proposed approach integrates the HDE, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits - ease of implementation; applicability to non-smooth fuel cost and emission level functions; better effectiveness than the previous method; better efficiency than differential evolution (DE) with the MU (DE-MU), and the requirement for only a small population in applying the optimal EED problem of the HPS.

Economic environmental dispatch of hydrothermal power system

This paper presents multi-objective differential evolution for economic environmental dispatch of hydrothermal power system, consisting of multi-reservoir cascaded hydro plant with time delay and thermal plants with nonsmooth fuel cost and emission level functions. The problem is formulated as a nonlinear constrained multi-objective optimization problem. Multi-objective differential evolution is proposed to handle economic environmental dispatch of hydrothermal power system as a true multi-objective optimization problem with competing and noncommensurable objectives. Numerical results for a sample test system has been presented to demonstrate the capabilities of the proposed method. The results obtained from the proposed method have been compared to those obtained using nondominated sorting genetic algorithm-II.

Particle swarm optimisation-based fuzzy satisfying method for economic environmental dispatch of hydrothermal power systems

A Particle Swarm Optimisation (PSO) based fuzzy satisfying method has been proposed to determine the optimal non-inferior hourly generation schedule of economic environmental dispatch of hydrothermal power system, consisting of multi-reservoir cascaded hydro plant with time delay and thermal plants with non smooth fuel cost and emission-level functions. This paper treats economy and emission as competing objectives for optimal dispatch, which requires some form of conflict resolution to arrive at a solution. The multiobjective problem is configured as a minimax problem under the assumption that the Decision-Maker (DM) has fuzzy goals for each of the objective functions. The present algorithm shows the suitability of employing the PSO method, a recently proposed combinatorial optimisation technique, in solving this problem. This random search based technique has been recently effectively employed in several application domains. The solution methodology can offer a global or near-global non-inferior solution for the DM. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method.

Distortion product otoacoustic emission contralateral suppression functions obtained with ramped stimuli

The purpose of this research was to investigate the changes that occur in human distortion product otoacoustic emission (DPOAE) level functions over continuous frequency bands in response to activation of the medial olivocochlear (MOC) efferent system by contralateral broadband noise. DPOAEs were obtained using continuous upward ramps of the lower frequency tone (f(1)) while the higher frequency tone (f(2)) was fixed. These ramps were designed to change the stimulus frequency ratio f(2)/f(1) over a fixed range for each fixed f(2) value of 2, 3, and 4 kHz. Contralateral noise was presented on alternating ramps and the DPOAEs with and without contralateral noise were averaged separately. Stimulus frequency ratios of 1.10 and 1.22, and noise levels of 60 and 50 dB sound pressure level (SPL) were employed. Changes in DPOAE level were generally suppression (a reduction in DPOAE magnitude), but enhancement was also observed. For most participants, changes were evident for much of the frequency ranges tested. Average absolute changes for 60 dB SPL noise were 0.95, 0.81, and 0.42 dB for the wider stimulus frequency ratios and f(2) of 2, 3, and 4 kHz, respectively. For the narrower ratio and 60 dB SPL noise, the changes were larger with average absolute changes of 1.33, 1.09, and 0.87 dB. For the narrower ratio and 50 dB SPL noise, the changes were 1.08, 0.78, and 0.55 dB with f(2) of 2, 3, and 4 kHz, respectively. DPOAE nulls were observed and a common response pattern was a shift of emission morphology to higher frequencies with contralateral acoustic stimulation. The method appears promising for relatively rapid evaluation of the MOC efferent system in humans and offers information complementary to measurement strategies that explore the effects of stimulus level.

Optimal economic emission dispatch of hydrothermal power systems

This study develops an improved genetic algorithm-based multi-objective approach for the optimal economic emission dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The improved genetic algorithm (IGA) equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the multi-objective problem. To show the advantages of the proposed algorithm, which is applied to test EED problems of the HPS with one example considering the best cost, one example considering the best emission, and one example addressing the best compromise. The proposed approach integrates the IGA, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits – ease of implementation; applicability to non-smooth fuel cost and emission level functions; better effectiveness than the previous method; better efficiency than genetic algorithm with the MU (GA-MU), and the requirement for only a small population in applying the optimal EED problem of the HPS.

Bi-Objective Generation Scheduling of Fixed Head Hydrothermal Power Systems through an Interactive Fuzzy Satisfying Method and Particle Swarm Optimization

This paper presents particle swarm optimization based interactive fuzzy satisfying method for bi-objective generation scheduling of fixed head hydro plants and thermal plants with nonsmooth fuel cost and emission level functions. The bi-objective problem is formulated considering two objectives (i) economy and (ii) emission. These two objectives are mutually conflicting and equally important. Assuming that the decision maker has imprecise or fuzzy goals for each of the objective functions, the bi-objective problem is transformed into a minimax problem, which is then handled by the particle swarm optimization technique. The solution methodology can offer a global or near-global noninferior solution for the decision maker. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared to those found by interactive fuzzy satisfying method based on evolutionary programming technique.

A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems

A novel multiobjective optimization method for economic emission load dispatch of fixed head hydro plants and thermal plants with nonsmooth fuel cost and emission level functions is presented. In this paper, the problem treats economic and emission as competing objectives. Assuming that the decision-maker has goals for each of the objective functions, the multiobjective problem is converted into a single-objective optimization by the goal-attainment method, which is then handled by the simulated annealing technique. The solution methodology can offer a global or near-global noninferior solution in a sense close to the decision-maker's requirements. Numerical results for a sample test system have been presented to demonstrate the applicability and validity of the proposed method.

Goal-Attainment Method Based on Simulated Annealing Technique for Economic-Environmental-Dispatch of Hydrothermal Power Systems with Cascaded Reservoirs

A goal-attainment method based on simulated annealing technique is applied to determine the optimal noninferior hourly generation schedule of economic-environmental-dispatch problem in a hydrothermal power system consisting of multi-reservoir cascaded hydro plants with time delay and thermal plants with nonsmooth fuel cost and emission level functions. In this article, cost and emission are treated as competing objectives. Assuming that the decision maker has goals for each of the objective functions, the multiobjective problem is converted into a single-objective optimization by a goal-attainment method which is then handled by a simulated annealing technique. The solution methodology can offer an optimal noninferior solution for the decision maker. Results of the application of the proposed method are presented.

Multiobjective Generation Scheduling of Fixed Head Hydrothermal Power Systems Through an Interactive Fuzzy Satisfying Method and Evolutionary Programming Technique

This article presents evolutionary programming based on interactive fuzzy satisfying method for multiobjective generation scheduling of fixed head hydro plants and thermal plants with nonsmooth fuel cost and emission level functions. The multiobjective problem is formulated considering two objectives; (1) economy, and (2) emission. These two objectives are mutually conflicting and equally important. Assuming that the decision maker (DM) has imprecise or fuzzy goals for each of the objective functions, the multiobjective problem is transformed into a minimax problem, which is then handled by the evolutionary programming technique. The solution methodology can offer a global or near-global noninferior solution for the DM. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared to those found by interactive fuzzy satisfying method based on simulated annealing technique.

An Interactive Fuzzy Satisfying-Based Simulated Annealing Technique for Economic Emission Load Dispatch with Nonsmooth Fuel Cost and Emission Level Functions

The economic emission load dispatch problem treats economic and emission as competing objectives for optimal dispatch, which requires some form of conflict resolution to arrive at a solution. Assuming that the decision maker (DM) has imprecise or fuzzy goals for each of the objective functions, the multiobjective problem is transformed into a minimax problem, which is then handled by the simulated annealing (SA) technique. The solution methodology can offer noninferior optimal solution for the DM. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method.

A Simulated Annealing-Based Goal-Attainment Method for Economic Emission Load Dispatch with Nonsmooth Fuel Cost and Emission Level Functions

A novel multiobjective optimization method for economic emission load dispatch (EELD) with nonsmooth fuel cost and emission level functions in power system operation andscheduling phases is presented. In this paper, the problem treats economy, emission, and transmission line security as competing objectives. The load constraints and operating constraints are taken into account. Assuming that the decision maker (DM) has goals for each of the objective functions, the multiobjective problem is converted into a single-objective optimization by the goal-attainment method, which is then handled by the simulated annealing (SA) technique. The solution methodology can offer a global or near-global noninferior (best compromising) solution in a sense close to the DM's requirements. Results for a sample test system have been presented to demonstrate the applicability and validity of the proposed method.