Combined Economic Emission Dispatch Research Articles

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Combined Economic Emission Dispatch Including Variable Energy Resources

Sustainable energy solutions are becoming more and more necessary as the world's energy needs increase as a result of population expansion and industrialization. Using renewable energy sources has become essential to meeting these needs and reducing the negative effects on the environment. The increasing incorporation of renewable energy sources (RES) like wind and solar into microgrid systems poses a notable obstacle to attaining optimum power dispatch because of their intrinsic unpredictability. The combined economic emission dispatch (CEED) issue may become inefficient as a result of this fluctuation, especially in islanded microgrid systems. In particular, in areas with significant RES potential, resolving this problem is essential to improving the sustainability and dependability of the energy supply. In this work, the optimization of the CEED issue in an islanded microgrid system with wind, solar, and thermal energy sources is the main emphasis. By employing a weighted sum approach and a Butterfly Optimization Algorithm (BOA), the research aims to provide an efficient solution to the multi-objective CEED dilemma. The proposed method outperforms traditional optimization techniques, offering a more robust framework for integrating RES into microgrids. This research reveals a number of limitations that affect the effectiveness of energy dispatch systems, such as thermal unit ramp rates and operating restrictions. Subsequent investigations have to concentrate on delving deeper into these limitations and devising tactics to augment the flexibility of optimization algorithms such as the Butterfly Optimization Algorithm (BOA) approach.

Comparative analysis of optimization approaches for combined economic emission dispatch- a comprehensive review

This comprehensive review explores a range of optimization approaches for Combined Economic Emission Dispatch (CEED), covering conventional, non-conventional, and hybrid techniques. CEED is critical in minimizing economic costs and emissions while ensuring power system reliability. Traditional methods focus on cost minimization but overlook environmental considerations. Optimization techniques address this gap by simultaneously optimizing economic and environmental objectives. Hybrid techniques, combining multiple algorithms or integrating renewable energy, further enhance CEED performance. The review evaluates these approaches’ strengths and limitations, considering factors like computational efficiency and solution accuracy. Over the past few decades, a great deal of study has been done on the use of renewable energy (RE) as an alternative source in power generation systems. As a result, the power dispatch problem currently uses the Combined Economic Emission Dispatch (CEED) of thermal and renewable energy resources. It discusses the potential of hybrid techniques and take in consideration renewable energy integration in achieving cost savings and emission reductions, highlighting areas for future research.

A new model of electrical cyber–physical systems considering stochastic communication link failures

This article establishes a model for electrical cyber–physical systems (ECPS), integrating the power grid, power information network and control network. The power grid is modelled based on DC power flow. The power information network enables the exchange of information between the power grid and control network. Stochastic communication link failure is a common problem in real control networks. To address this issue, this article models the control network as a Bernoulli network. The incorporation of an asynchronous gossip algorithm with price penalty factors enables the model to analyse multi-target combined economic emission dispatch problems (CEED) involving line losses in an imperfect control network. The effectiveness and validity of the model are illustrated through simulations.

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

A metaheuristic approach to compare different combined economic emission dispatch methods involving load shifting policy

AbstractDistributed generators (DGs), which can be traditional fossil fuel generators or renewable energy sources (RES), must be appropriately planned in order to reduce a power network’s overall generating cost. Renewable energy sources (RES) should be prioritized because they provide a clean and sustainable energy supply and are abundant in nature. Demand side management (DSM) optimizes the scheduling of flexible loads to reduce peak demand and improve the load factor, while keeping daily demand unchanged. The test system in this research employs a dependable and effective hybrid optimization tool to plan the DGs of a dynamic system in a way that matches low active power production costs with low pollutant emissions. The fitness functions used in the test system were non-linear due to the presence of the valve point effect (VPE). The costs and emissions were evaluated for various fitness functions which included involvement of wind, DSM, and different types of combined economic emission dispatch (CEED) methods. The test system’s peak demand was cut by 12% and the load factor was raised from 0.7528 to 0.85 when DSM technique was used. The generation cost has been reduced from $1,014,996 to $1,012,182 using CSAJAYA algorithm which was further reduced to $1,007,441 after incorporating DSM. Likewise, the CEEDppf was also observed to be reduced to $1,231,435 and $1,216,885 with and without DSM compared to $1,232,001 from reported literature. Numerical results show that both the cost and emission were reduced significantly using the proposed CSAJAYA compared to a long-sighted list of algorithms published in literature. Graphical Abstract

A Comparative Smart Computation Algorithm for Economic And Emission Dispatch Optimization of Tanjung Jati Power Plant

This research delves into optimization strategies for enhancing power generation efficiency and reducing costs in PLTU Tanjung Jati. The study explores three computational algorithms for optimization: Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), focusing on optimizing power generation costs while considering penalty costs and emissions. Through extensive simulations and evaluations, the results indicate that the GWO algorithm achieves the most optimal outcomes in terms of cost optimization, demonstrating fast convergence and lesser simulation time compared to the other algorithms. The research provides valuable insights into selecting optimal algorithms for complex optimization problems like Combined Economic Emission Dispatch (CEED) in power generation systems, ultimately contributing to more efficient and cost-effective power generation.

An improved gossip-based distributed control strategy of multi-objective combined economic emission dispatch in smart grids with stochastic communication link failures

This paper proposes a distributed control strategy, leveraging gossip-based communication, for combined economic emission dispatch (CEED) in smart grids. It optimizes economic dispatch while considering separate objectives for SO2, NOx, CO2 emissions. The network is modeled as a Bernoulli process to describe stochastic communication link failures. Utilizing the Analytic Hierarchy Process to determine the weights between multiple objectives in the CEED to aid decision-makers at thermal power plants in selecting appropriate solutions based on the actual conditions of power grid. Simulation results demonstrate the strategy effectiveness.

Combined emission economic dispatch using quantum-inspired particle swarm optimization and its variants

The ever-increasing electricity demand, its dependency on fossil fuels, and the consequent environmental degradation are major concerns of this era. The worldwide domination of fossil fuels in bulk electricity generation is rapidly increasing the emissions of CO2 and other environmentally dangerous gases that are contributing to climate change. The economic and emission dispatch are two important problems in thermal power generation whose combination produces a complex highly constrained nonlinear optimization problem known as combined economic and emission dispatch. The optimization of combined economic and emission dispatch aims to allocate the generation of committed units to minimize fuel cost and emissions, simultaneously while honoring all equality and inequality constraints. Therefore, in this article, we investigate a solution of the combined economic and emission dispatch problem using quantum particle swarm optimization and its two modified versions, that is, enhanced quantum particle swarm optimization and quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor. The enhanced quantum particle swarm optimization algorithm achieves particles’ diversification at early stages and shows good performance in local search at later stages. The quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor boosts search performance of quantum particle swarm optimization and attains better global optimality. The suggested methods are employed to achieve solution for the combined economic and emission dispatch in four distinct systems, encompassing two scenarios with 6 units each, one with a 10-unit configuration, and another with an 11-unit setup. A comparative analysis with methodologies documented in existing literature reveals that the proposed approach outperforms others, demonstrating superior computational performance and robust efficiency.

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.

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system.

Effective demand response program addresing carbon constrained economic dispatch problem of a microgrid system

Electricity is produced, transferred, and delivered within a small geographic area using a constrained power distribution system called a microgrid. Such microgrids employed to guarantee that renewable energy sources (RES) are utilised to their fullest extent. Additional advantages of microgrids include reduced losses during transmission and related costs. The objectives of economic dispatch, emission dispatch, combined economic emission dispatch (CEED) founded on fractional programming (FP), and environmental constrained economic dispatch (ECED) are compared and contrasted in this study. The feasibility of a low-voltage microgrid system is examined for three situations. A novel, robust, and hybrid swarm-intelligence optimisation algorithm, based on merging the characteristics of the conventional grey-wolf optimizer (GWO), sine-cosine algorithm (SCA), and crow search algorithm (CSA), is used as a research optimisation tool. Results show that switching from a fixed pricing plan to a time-of-use (TOU) model resulted in a 15% less cost in production during the period of the research. When a portion of the microgrid customers engaged in the incentive-based demand response (IBDR) programme, further reductions in total generation costs and emissions were achieved. The peak demand was reduced by 3.5% as a result of participation in the IBDR programme, from 90 kW to 86.863 kW. and provided participants with incentives, according to numerical results.

Security Restricted Dispatch Optimization Using Improved LDOA Technique: In an Islanded Microgrid System

ABSTRACT Microgrids are a single entity that manages several distributed generators and linked networks. This is the most recent study field in which traditional and renewable technologies may be combined to address the difficulties of transmission losses and CO2 emissions. Making microgrids smarter and more efficient requires cost-effective scheduling. As a result, a lot of new technologies are moving in the same direction. The study presented in this paper relates to the optimum scheduling of an islanded microgrid with three conventional DGs, one wind farm, and one solar power plant. A new improved method Levy Dingo Optimization algorithm (LDOA) of already existing technique named as Dingo Optimization algorithm (DOA) is designed and successfully tested on 23 bench-mark functions. Further, this hybrid technique is implemented on Economic load and Emission dispatch, Combined Eco-nomic Emission Dispatch (CEED) by considering various integration of distributed generators which is going to share the load for 24 h. The efficacy of the proposed technique is tested and compared with some current techniques like GWO, PSO, SOS, DE, and WOA as well as with newly developed approaches like DOA. In all four instances, i.e., without taking into account solar energy, without taking into account wind energy, without taking into account renewable energy sources and considering all five sources, the suggested solution outperforms the existing strategies, indicating that it has a lot of potential in this field.

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.

A fast data-driven optimization method of multi-area combined economic emission dispatch

To provide more feasible schemes for power system optimization management and operation, the interconnection of power grids in different areas is inevitable. Naturally, the multi-area combined economic/emission dispatch (MACEED) problem becomes a more important decision problem. However, as the dimensions of MACEED problems increase, existing studies may not obtain feasible scheduling decisions in a suitable time. On this background, MACEED problems are transferred into computational expensive problems. In order to solve high-dimensional, large-scale MACEED problems, a data-driven surrogate-assisted approach is proposed. First, a feature engineering-based support vector regression surrogate model is proposed to replace the traditional objective functions in high-dimensional MACEED problems. Second, knowledge distillation is applied as a freezing and fine-tuning mechanism for the improved support vector regression surrogate models, which significantly reduces the time required to build surrogate models. Third, an improved non-dominated sorting genetic algorithm-III is proposed to obtain feasible solutions to high-dimensional MACEED problem. The proposed algorithm enhances the convergence and diversity of optimal solutions. The effectiveness of the proposed data-driven approach is demonstrated through simulations of a four-area 40-unit test system under different constraints.

Carbon constrained economic dispatch of a microgrid system based on different grid pricing strategies considering uncertainty

AbstractMicrogrids can be considered a reduced power system network wherein the generation, transmission, and distribution of power occurs within a limited geographical area. They are employed to use the maximum penetration of renewable energy sources (RES). Microgrid also has advantages like reduction of transmission losses and costs pertaining to such losses. The objectives of economic dispatch, emission dispatch, penalty‐factor (PPF) based combined economic emission dispatch (CEED) and environmental constrained economic dispatch (ECED) are evaluated in this paper. A low‐voltage microgrid system for three different scenarios is studied. A novel, robust and powerful hybrid swarm‐intelligence optimization algorithm is used, which is based on amalgamating the features of conventional gray‐wolf optimizer, sine‐cosine and crow search algorithm. Due to the use of RES, the active power production cost and carbon emission of the microgrid system were reduced to approximately 3% for all the objectives. More than 15% reductions in generation costs were obtained throughout the study when the time‐of‐usage electricity market pricing strategy was used instead of the fixed pricing strategy. Also, results show that the ECED methods of economic emission dispatch provided better‐compromised solutions than the PPF‐based CEED method. Finally, non‐parametric statistical analysis is used to analyze and validate the quality of the projected novel technique, which is measured against the outcomes found with algorithms used in the state‐of‐art literature.

Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment

The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions.

Real‐time emission and cost estimation based on unit‐level dynamic carbon emission factor

AbstractThe real‐time carbon emission estimation of generators helps quantify the carbon emission costs and reduce power system emissions of power generation. Accurate estimation relies on the accuracy of the carbon emission factors (EFs) and power generation measurements. The dynamic carbon emission factor (DEF) of generators was proposed recently as a linear function of the output power. However, there is a significant deviation between the modelled DEFs and the actual measurement EFs, especially when the output power is low. This paper first presents the general definition of the DEF to characterize the emissions and focuses on the unit‐level DEF (UDEF). The piecewise non‐linear UDEF (P‐UDEF) model is then proposed, which can better represent the unit emission characteristics. Then an accurate piecewise linear cost approximation method is proposed considering the segment points and extreme points of both P‐UDEF and generation costs function. Last, the system carbon emissions and costs estimation are estimated by combined economic emission dispatch (CEED), and the reduction potential is evaluated. Case studies on an IEEE 30‐bus system with piecewise linear cost functions show that the proposed P‐UDEF can realize real‐time emission and cost estimation as well as reduce the total system emissions by considering the incomplete combustion cost of generating units.

SMO Algorithm to Unravel CEED Problem using Wind and Solar

This research proposes a more advanced way to address Combined Economic Emission Dispatch (CEED) concerns. Economic Load Dispatch (ELD) and Economic Emission Dispatch (EED) have been implemented to reduce generating unit fuel costs and emissions. When both economics and emission targets are taken into account, the dispatch of an aggregate cost-effective emission challenge emerges. This research affords a mathematical modeling-based analytical technique for solving economic, emission, and collaborative economic and emission dispatch problems with only one goal. This study takes into account both the fuel cost target and the environmental impact of emissions. This bi-intention CEED problem is converted to a solitary goal function using a price penalty factor technique. In this case, a metaheuristic and an environment-inspired, intelligent Spider Monkey Optimization technique (SMO) are used to address the CEED dilemma. By following the generator’s scheduling process, the SMO method is used to regulate the output from the power generation system in terms of pollution and fuel cost. The Fission-Fusion social (FFS) structure of spider monkeys promotes them to utilize a global optimization method known as SMO during foraging behaviour. The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and handle dispatch problems with constraints. The economic dispatch has been remedied, and the improved result demonstrates that the system’s performance is stable and flexible in real time. Finally, the system’s output demonstrates that the system has improved in resolving CEED difficulties. When compared to earlier investigations, the proposed model’s findings have improved. As the generating units, wind and solar are used to explore the CEED crisis in the IEEE 30 bus system.

Combined Economic and Emission Power Dispatch Control Using Substantial Augmented Transformative Algorithm

The purpose of the Combined Economic Emission Dispatch (CEED) of electric power is to offer the most exceptional schedule for production units, which must run with both low fuel costs and emission levels concurrently, thereby meeting the lack of system equality and inequality constraints. Economic and emissions dispatching has become a primary and significant concern in power system networks. Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations. The optimal power allocation to generators serves as a solution to this problem. Emission dispatch reduces emissions while ignoring economic considerations. A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions. Consequently, this work manages the Substantial Augmented Transformative Algorithm (SATA) to take care of the Combined Economic Emission Dispatch Problem (CEEDP) of warm units while fulfilling imperatives, for example, confines on generator limit, diminish the fuel cost, lessen the emission and decrease the force misfortune. SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms. The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution. The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search. The simulation of the proposed system is developed using MATLAB Simulink software. Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues. The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Simulated Annealing (SA), and Differential Evolution (DE) methods. The fuel cost and gas emission of the proposed system are 128904 $/hr and 138094.4652$/hr.