Economic Emission Load Dispatch Research Articles

Stochastic Search and Rescue Method for Day Ahead Economic Emission Load Dispatch Under Wind Uncertainty

Stochastic Search and Rescue Method for Day Ahead Economic Emission Load Dispatch Under Wind Uncertainty

Improved Pelican optimization algorithm for solving load dispatch problems

The Pelican Optimization Algorithm (POA) is a newly developed algorithm inspired by the hunting behavior of pelicans. Despite its fast convergence rate, it suffers from premature convergence, the imbalance between exploration and exploitation, and lack of population diversity. In this work, an improved POA is proposed to attenuate these shortcomings. IPOA benefits from three motion strategies and predefined knowledge-sharing factors that better describe the stochastic hunting behavior of pelicans, as well as a modified dimension-learning-based hunting (DHL) behavior to retain diversity. To test the effectiveness of these improvements, it was used to solve 23 benchmark functions, including unimodal, multimodal, and 6 composite functions (CEC 2017). To evaluate performance, it was applied to solve economic and combined economic emission load dispatch problems that play a critical role in real-world power system planning and operation while considering environmental impacts. This experiment includes 6, 10, 11, 40, 140, 160, and 320 generating units with nonconvex and non-smooth objective functions. The comparison is performed for benchmark functions and the optimal dispatch problems. The results confirm the competitive and almost superior performance of the IPOA in several cases, which proves the applicability and efficiency of the proposed approach in solving real-world problems.

Optimal economic-emission load dispatch in microgrid incorporating renewable energy sources by golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO)

This article proposes the use of hybrid technique to achieve balanced and compromised solution among the generation cost and the emission of pollutants. Microgrids (MGs) are the restricted power energy system that transmitted, generated, and distributed. The renewable energy sources (RESs) are used in their fullest extent. Advantages of MG include reducing cost and transmission losses. Operated in different modes like wind turbine (WT), microturbine (MT) and fuel cell (FC). The proposed technique used to execute the golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO) named as GJO–MAO technique. The objective of the technique is to solve dissimilar optimization issues in MG reduces the computational cost and maximize performance. The objectives of economic dispatch are based on fractional scheduling and restricted environment. Three different scenarios, low-voltage MG system are investigated. GJO–MAO techniques used to optimize various issues on MG by using renewable energy. The proposed technique performance is done in the MATLAB. When the time-of-use (TOU) energy market price strategy with the fixed pricing approach, the economic dispatch is calculated by time-of-use electricity market pricing method, generating cost decreases by 18.5%, 13.5% if the FP-related combined economic emission dispatch (CEED) is examined, and 15% after evaluating the environmental-constrained-economic-dispatch (ECED). The MG producing cost targets for ECED and ECD are according to renewable energy sources. The best and most system is used for finding a fair compromise between the cost of generating and emission. The smallest values of implementation time and standard deviation of superiority and robustness are achieved.

Multi-objective optimization of economic emission load dispatch incorporating load forecasting and solar photovoltaic sources for carbon neutrality

In this paper, the future load condition is predicted by proposing an effective single-layer Black Widow Optimization based Functional Link Artificial Neural Network (BWO-FLANN) model. Also, a novel Non-dominated Sorting Multi-Objective Teaching Learning Based Optimization (NS-MOTLBO) has been proposed for obtaining the solution to Economic Emission Load Dispatch (EELD) in a combined operation of the grid with solar photovoltaic source by considering the future predicted load. The EELD is an inherently multi-objective problem with nonlinear constraints, such as the generator power limits, power balance criteria, transmission line capacity, prohibited operating zones, etc., that make it more complex, causing extra computational burden and convergence issues. An effective constraint-handling methodology is also used in this paper to achieve the lowest possible constraint violation. The proposed approach has been implemented in two test situations, each with distinct solar radiation. The Pareto optimal fronts for various hours were obtained, indicating possible scheduling of the corresponding periods. Non-parametric statistical testing is carried out to investigate the dominance of our proposed approach compared to the other state-of-the-art prediction models.

A New PSO Technique Used for the Optimization of Multiobjective Economic Emission Dispatch

Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.

Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Constrained Static/Dynamic Economic Emission Load Dispatch Using Elephant Herd Optimization

The rapid growth in greenhouse gases (GHGs), the lack of electricity production, and an ever-increasing demand for electrical energy requires an optimal reduction in coal-fired thermal generating units (CFTGU) with the aim of minimizing fuel costs and emissions. Previous approaches have been unable to deal with such problems due to the non-convexity of realistic scenarios and confined optimum convergence. Instead, meta-heuristic techniques have gained more attention in order to deal with such constrained static/dynamic economic emission load dispatch (ELD/DEELD) problems, due to their flexibility and derivative-free structures. Hence, in this work, the elephant herd optimization (EHO) technique is proposed in order to solve constrained non-convex static and dynamic ELD problems in the power system. The proposed EHO algorithm is a nature-inspired technique that utilizes a new separation method and elitism strategy in order to retain the diversity of the population and to ensure that the fittest individuals are retained in the next generation. The current approach can be implemented to minimize both the fuel and emission cost functions of the CFTGUs subject to power balance constraints, active power generation limits, and ramp rate limits in the system. Three test systems involving 6, 10, and 40 units were utilized to demonstrate the effectiveness and practical feasibility of the proposed algorithm. Numerical results indicate that the proposed EHO algorithm exhibits better performance in most of the test cases as compared to recent existing algorithms when applied to the static and dynamic ELD issue, demonstrating its superiority and practicability.

A modified artificial bee colony algorithm based on a non-dominated sorting genetic approach for combined economic-emission load dispatch problem

The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations.

Hybrid kernel search and particle swarm optimization with Cauchy perturbation for economic emission load dispatch with valve point effect

Due to growing concerns over environmental protection, economic and environmentally responsible power dispatching has become a hot topic in the field of power system control. Multi-objective optimization minimizes fuel costs and pollution emissions without violating operational constraints. To solve this problem, the MOP is decomposed into individual objects via the weighted sum method, and Newton’s method is used to tackle equality constraints iteratively. To this end, a hybrid algorithm (HKSOPSO-CP) based on kernel search optimization (KSO) and particle swarm optimization (PSO) with Cauchy perturbation is proposed in this paper. An experiment with 23 CEC benchmark functions shows that HKSOPSO-CP offers better performance compared with various popular algorithms proposed in recent years. When employed to solve the IEEE standard economic emission dispatch (EED) problems with 6, 10, 40, and 110 units, the proposed HKSOPSO-CP algorithm produces results indicating a better trade-off between the objectives relating to fuel costs and emissions compared to other algorithms that have recently been reported on in the literature.

Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques.

Chaos-enhanced multi-objective tunicate swarm algorithm for economic-emission load dispatch problem

Climate change and environmental protection have a significant impact on thermal plants. So, the main principles of combined economic-emission dispatch (CEED) problem are indeed to reduce greenhouse gas emissions and fuel costs. Many approaches have demonstrated their efficacy in addressing CEED problem. However, designing a robust algorithm capable of achieving the Pareto optimal solutions under its multimodality and non-convexity natures caused by valve ripple effects is a true challenge. In this paper, chaos-enhanced multi-objective tunicate swarm algorithm (CMOTSA) for CEED problem. To promote the exploration and exploitation abilities of the basic tunicate swarm algorithm (TSA), an exponential strategy based on chaotic logistic map (ESCL) is incorporated. Based on ESCL in CMOTSA, it can improve the possibility of diversification feature to search different areas within the solution space, and then, gradually with the progress of iterative process it converts to emphasize the intensification ability. The efficacy of CMOTSA is approved by applying it to some of multi-objective benchmarking functions which have different Pareto front characteristics including convex, discrete, and non-convex. The inverted generational distance (IGD) and generational distance (GD) are employed to assess the robustness and the good quality of CMOTSA against some successful algorithms. Additionally, the computational time is evaluated, the CMOTSA consumes less time for most functions. The CMOTSA is applied to one of the practical engineering problems such as combined economic and emission dispatch (CEED) with including the valve ripples. By using three different systems (IEEE 30-bus with 6 generators system, 10 units system and IEEE 118-bus with 14 generating units), the methodology validation is made. It can be stated for the large-scale case of 118-bus systems that the results of the CMOTSA are equal to 8741.3 $/h for the minimum cost and 2747.6 ton/h for the minimum emission which are very viable to others. It can be pointed out that the cropped results of the proposed CMOTSA based methodology as an efficient tool for CEED is proven.

Economic-emission load dispatch for power system operation using enhanced sunflower optimization

Conventional thermal power plant uses limited sources of gas, fuel or coal which contributes to the rise of air pollution. Thus, it is crucial to efficiently use the natural sources and minimize the emissions of greenhouse gases and other pollutants. This paper presents an optimal economic dispatch considering three factors which are cost of generation, loss of power transmission and amount of emission for an efficient operation of power generation. Enhanced sunflower optimization (ESFO) algorithm is applied to determine the solution for three different cases: economic load dispatch, emission load dispatch and economic-emission load dispatch. The optimal solution based on the minimum generation cost and emission is obtained for the IEEE 6-unit test system using MATLAB software

A Novel Oppositional Approach for Solving Different Economic Emission Dispatch Problems

An oppositional-based learning approach with a real coded chemical reaction algorithm (ORCCRO) has been considered in this manuscript. The ORCCRO algorithm has been used here to find the near-global optimum solution for multi-objective economic-emission load dispatch problem with having nonlinear constraints. Emission extract from thermal power plants like NO x , inequality constraints like power generation operating limit and equality constraints like power balance consideration are considered here. ORCCRO follows the process to reach a stable state in lower energy with different molecular chemical collision. Oppositional-based learning mechanism has also hybrid here with RCCRO to find out more effective solution. Three different test systems are considered for simulation studies revealed that the ORCCRO method is much superior in comparison with other effective algorithms. The results obtained in these three test systems prove the robustness and the efficiency of ORCCRO.

Search and rescue optimization algorithm for combined economic and emission load dispatch


 
 
 Economic-emission load dispatch minimizes fuel cost factors and gas emissions to provide optimal performance in generation units in a power plant, ensuring demand supply. The first variable is definitive to maintain business continuity, and the second to assure compliance with environmental legislation and no environmental harm. This work investigates the application of a new computational optimization technique known as the Search and Rescue Optimization Algorithm (SAR), which improves searching capability by employing specified parameters. This algorithm has three effective parameters and works with all generators to minimize the cost and emission while satisfying the defined demand. Simulations were run on multiple IEEE test systems with varying demand values. The obtained findings were compared to each other as well as to the results of other procedures mentioned in the literature. The acquired results appear to be superior to those achieved by other metaheuristics algorithms.
 
 

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

This paper deals with the combined economic emission load dispatch (CEELD) problem with and without the integration of renewable energy sources (RESs), in some more rational test scenarios of single CEELD and multi-objective CEELD (MO-CEELD) optimization. Hence, an efficient and coherent approach is presented to minimize the generation and emission cost using one of the bio-inspired metaheuristic algorithms named flower pollination algorithm (FPA). The evolution of a power system along with the integration of RESs demands equal advancement in the operation and control algorithms of the power grid. Therefore, the proposed approach in this paper offers an evolutionary single and multi-objective optimization process based on a bio-inspired FPA. Further, it has been validated by achieving the best compromise solution (BCS) using the Pareto categorizing process and fuzzy membership function. Moreover, different study cases comprising eleven and fifteen thermal units with and without considering RESs are tested with the proposed technique. Finally, the effectiveness of the proposed approach is tested by comparing the simulation results with some already existing techniques in terms of overall fuel and emission cost. Significantly, it has been noticed from the results that it outperforms all the previously presented approaches like PSO, DE, GSA, AEO, BA, and dBA, thus justifying its applicability.

A Modified Artificial Bee Colony Algorithm Based on a Non-Dominated Sorting Genetic Approach for Combined Economic-Emission Load Dispatch Problem

A Modified Artificial Bee Colony Algorithm Based on a Non-Dominated Sorting Genetic Approach for Combined Economic-Emission Load Dispatch Problem

A Modified Artificial Bee Colony Algorithm Based on a Non-Dominated Sorting Genetic Approach for Combined Economic-Emission Load Dispatch Problem

A Modified Artificial Bee Colony Algorithm Based on a Non-Dominated Sorting Genetic Approach for Combined Economic-Emission Load Dispatch Problem

An integrated optimal operating strategy for a grid-connected AC microgrid under load and renewable generation uncertainty considering demand response

The challenge of a modern energy system is to be reliable, robust, easily accessible, economically viable, and environment-friendly. This paper addresses the above issue by proposing a novel integrated optimal energy management scheme for a grid-connected AC microgrid. The objectives of the energy management scheme are to minimize the cost of operation and reduce the emission. A group of measures like incentive-based demand response (DR) program economic-emission load dispatch, optimal battery scheduling, and network reconfiguration is simultaneously implemented in the energy management scheme to enhance the steady-state performance of the microgrid. Uncertainties are modeled using a stochastic approach in this work. The bi-objective optimization is carried out in the fuzzy domain employing the “fuzzy max–min” satisfaction criterion. Further, the incentive value of the DR program is optimized, and a sensitivity analysis is carried out to investigate the impact of the incentive value on the operating cost. Simulation studies on a thirty-three node test system demonstrate that the operating cost can be reduced by 18.90%, and emission by 45.44% for the given test system. If both cost and emission are to be reduced simultaneously, then the operating cost and emission are reduced by 9.08% and 34.85%, respectively.

Economic power dispatch for an interconnected power system based on reliability indices

Reliability indices are always one of the most important factors in the power systems. In this paper, the problem of the economic load dispatch (ELD) and the problem of economic emission load dispatch (CEELD) have been improved taking into account reliability indices. That is, the problem and reliability of ELD are proposed as combined economic load dispatch reliability (CELDR) and the problem CEELD is suggested as (CEELDR). In solving CELDR and CEELDR problems, tried to use power generators in a very reliable way to save system load, as well as minimum fuel and emission costs. In this effort, the ELD of power plants is successfully implemented in a single system containing 6 generating units, taking into account the reliability and emissions of the system with and without system power loss, inequality and inequality constraints, and valve point effects using the exchange market algorithm(EMA). The results suggest that reliability indicators in ELD can be used to create greater reliability in providing consumers with uninterrupted power.

A Hybrid Optimization Technique for Solving Economic Emission Load Dispatch Problem

A Hybrid Optimization Technique for Solving Economic Emission Load Dispatch Problem