Environmental Economic Dispatch Problem Research Articles

A Review on Economic Dispatch of Power System Considering Atmospheric Pollutant Emissions

The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods.

A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-area Emission (Environmental) Economic Dispatch Problem

This paper presents a semi-definite programming (SDP) optimisation approach for solving multi area, emission/economic dispatch (MAEED) problems in thermal power systems. The multi-objective problem was transformed into matrix form as an SDP relaxation problem and subsequently solved with a MATLAB programming suite. The system inequality and equality constraints were entered into a Self-Dual Minimisation (SeDuMi) parser. Simulations were tested on four (4) IEEE benchmark models to validate the efficiency of the proposed SDP method. However, in the generation of the Pareto-fr ont solutions, ideal minimum points were used in the determination of the maximum spread out of the Pareto solutions by the algorithm. This involves the use of a standard weighted sum method in generating the Pareto-optimal solution between two objective functions. Twenty-one (21) runs were carried out for the parameter value to explore the effect of control weight selection k1. A comparative study demonstrated the effectiveness of tie-line constraint and without tie-line constraint, respectively on the optimi sation model for the power dispatch problems solved by using the proposed SDP approach. The optimisation results for the minimum fuel cost using the SDP approach for the case when a tie-line constraint is included was US$ 2,151.7 /hr with minimum emission of 3.4687 Ton/hr. This is lower than US$ 2,162.2 /hr with minimum emission of 3.6662 Ton/hr obtained when the tie-line constraint is not included in the optimisation model on IEEE four-area, sixteen network. Additionally, the comparative results with all other methods reported in the literature for different test cases were also presented in this paper. Lower operational costs were obtained for both tie-lines and no tie-lines power transfer cases, showing that the SDP method performed well in accuracy compared to other methods. The SDP method was employed in this research to deal with the multi-area emission economic power dispatch problem due to its inherent advantages in achieving a global optimal solution and, this approach would also assist the power administrator to come up with an improved decision making in attaining an optimum power dispatch of power generation.

Application of the Slime Mould Algorithm on the Bi-Objective Environmental Economic Dispatch Problem

This paper investigates the performance of one of the latest metaheuristic swarm-based approaches called Slime Mould Algorithm (SMA). SMA is used here to solve the static bi-objective constrained Economic Emission Dispatch (EED) problem in the presence of renewable energy sources while considering the Valve-Point Effects (VPE). The SMA approach is applied to indicate the adequate optimal solutions for operating the committed thermal units under different operational constraints. The sought optimal solutions are the midpoint between cost saving and pollutant gas emission reduction. This study also examines the influence of integrating renewable energy sources (wind and solar) into the conventional power production network. SMA technique is applied on 3- and 6-unit IEEE test systems in several case studies. The simulation numerical results indicate that SMA has a high efficiency and a better performance compared to known state-of-the-art algorithms. The proposed approach was programmed and simulated in MATLAB.

Multi-Agent Distributed Reinforcement Learning Algorithm for Free-Model Economic-Environmental Power and CHP Dispatch Problems

In conventional methods for Economic Dispatch Problem (EDP) and Economic-Environmental Dispatch Problem (EEDP), a full connection between the units and the control center is considered, while some faults in the communication system are possible in practical conditions. Hence, an intelligent and high-speed distributed method that is robust against disconnection is needed. In this paper, a Multi-Agent Distributed Reinforcement Learning (MADRL) algorithm based on consensus control for EDP and EEDP is presented. In this method, the incremental cost of units is optimized based on Lagrange method and the reinforcement learning algorithm. Thus, a performance index is defined for each agent in proposed algorithm to be independent of the unit model. The performance index of each agent is the sum of two terms, including i) the difference between the previous performance index value and local power and heat mismatch values and ii) the sum of the difference between the incremental cost of the unit with other neighboring units. In other words, optimizing the defined performance indexes of the agents eliminates the need for the system model. The MADRL method is tested on several grids and compared with other methods. The numerical results show an improvement in the algorithm speed and optimal point.

A multi-objective multi-verse optimizer algorithm to solve environmental and economic dispatch

The combustion and emission of coal have always been a concern. A class of multi-objective Environmental Economic Dispatch (EED) problems has been widely studied to reduce the pollution problem of fossil fuel power plants. In this study, a multi-objective Multi-Verse Optimization algorithm based on Gridded Knee Points and Plane Measurement technique (GKPPM-MVO) is proposed for the multi-objective EED problems. Knee points are usually considered as the most critical points in unbiased decision-making, while plane measurement can find the largest distant points in the population neighborhood. We apply the knee and maximum plane distance points in the local search phase. The original mechanism of parameter control in local search is replaced by using the two above points to exploit the pretty information and inherit it to the next generation. The algorithm is applied to various EED problems. The four algorithms, including MOMVO, NSGA-ii, MOABC, and MOEGO are also used to compare the performance of the algorithms thoroughly. Results show that the GKPPM-MVO algorithm has good convergence performance, high stability, and high uniformity of the Pareto Front.

Research on the environment & economic indicators for electricity trading

In this paper, the environmental and economic indicators based on the reactive power pricing are proposed so as to formulate the novel environmental economic power dispatch (NEED) problems for electricity trading. Here, a new way of calculating total cost is proposed based on the reactive power pricing instead of the pure fuel cost, which leads to the NEED problems. In addition, an improved tunicate swarm algorithm (ITSA) is proposed to deal with the NEED problems. Different from the original tunicate swarm algorithm (TSA), adaptive weight parameter and mutation process are used to improve the disadvantages of TSA which converges too fast and easily falls into local optimum. In combination with the Pareto dominance rule, the ITSA can be more effective in dealing with the NEED problems. In order to examine the performance of the ITSA, simulation tests of ITSA, TSA, and particle swarm optimization (PSO) are conducted in IEEE30 node system. During the experimental process, optimized objective functions include total cost considering reactive cost, pollution emission and power loss. The final comparison results show that the ITSA is able to get better distributed Pareto front (PF) and superior best compromise solution (BCS), which proves the high efficiency of the ITSA in dealing with the NEED problems.

Application of NSGA-II for environmental constraint economic dispatch of thermal-wind-solar power system

Economic environmental dispatch (EED) of a thermal-wind-solar power system with battery energy storage system is a significant chore in electric power plant operation that involves allocation of generation among the online units so that the price, NOx extraction level and SO2 extraction level are enhanced concurrently whilst gratifying each and every experimental constraint. In the current research, Nondominated Sorting Genetic Algorithm-II (NSGA-II) has been suggested for solving EED problem. The experimental results of the two test systems obtained from the proposed technique for EED have been fit to that acquired from Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

Economic-environmental dispatch for the integrated energy system considering off-design conditions

The integrated energy system (IES) is recognized as a promising energy utilization approach enabled to both improve the energy efficiency and reduce pollutant emissions. Although the economic-environmental dispatch (EED) problem of the IES has been widely studied, the fact that the IES is operated under off-design conditions, having a significant influence on the efficiency of energy devices, is neglected usually, resulting in the scheduled operations, for the IES could not be accurate enough in many actual situations. This study investigates the EED problem of the IES under off-design conditions. Technically, by integrating an efficiency correction process into the traditional energy hub (EH) model, a dynamic energy hub (DEH) model is first formulated for adapting itself to variable energy conversion efficiencies. Then, a deep neural network (DNN)-based efficiency correction method is proposed to predict and correct the time-varying efficiency of energy devices based on three main off-design conditions including the load rate, air temperature, and pressure. A multi-objective EED model is finally formulated for the IES, with the framework of the DEH model, aiming at establishing a trade-off between operational cost and emitted pollutants. Case studies show that the proposed approach helps in enhancing the accuracy of IES dispatch while having a good performance in both the operational cost and pollutant emission reduction.

Ladybug Beetle Optimization algorithm: application for real-world problems.

In this paper, a novel optimization algorithm is proposed, called the Ladybug Beetle Optimization (LBO) algorithm, which is inspired by the behavior of ladybugs in nature when they search for a warm place in winter. The new proposed algorithm consists of three main parts: (1) determine the heat value in the position of each ladybug, (2) update the position of ladybugs, and (3) ignore the annihilated ladybug(s). The main innovations of LBO are related to both updating the position of the population, which is done in two separate ways, and ignoring the worst members, which leads to an increase in the search speed. Also, LBO algorithm is performed to optimize 78 well-known benchmark functions. The proposed algorithm has reached the optimal values of 73.3% of the benchmark functions and is the only algorithm that achieved the best solution of 20.5% of them. These results prove that LBO is substantially the best algorithm among other well-known optimization methods. In addition, two fundamentally different real-world optimization problems include the Economic-Environmental Dispatch Problem (EEDP) as an engineering problem and the Covid-19 pandemic modeling problem as an estimation and forecasting problem. The EEDP results illustrate that the proposed algorithm has obtained the best values in either the cost of production or the emission or even both, and the use of LBO for Covid-19 pandemic modeling problem leads to the least error compared to others.

Predicting the environmental economic dispatch problem for reducing waste nonrenewable materials via an innovative constraint multi-objective Chimp Optimization Algorithm

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called Chimp Optimization Algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result of ChOA is compared with Individual Best Memory Penalty Factor Grey Wolf Optimizer (IBMPF-GWO), Improved Whale Trainer (IWT), Chaotic Biogeography-Based Optimizer (CBBO), Non-Linear Migration BBO (NLBBO), Hybrid Gravitational Search Algorithm Particle Swarm Optimization (GSAPSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), Salp Swarm Algorithm (SSA), Dragonfly Algorithm (DA), and Fuzzy Grasshopper Optimization Algorithm (FGOA) to confirm its efficiency. For both single- and multi-objective optimization, ChOA's assessment index and convergence rate are superior to other benchmark algorithms, regardless of whether the goal is to reduce emissions or to reduce fuel costs. The efficacy and robustness of the ChOA in handling environmental economic dispatch problems have been shown by discovering a good compromise value.

Environmental economic dispatch optimization using niching penalized chimp algorithm

The use of traditional fossil fuels has contributed to rapid economic expansion while simultaneously having negative implications, such as increasing global warming and the devastation of the biosphere. Niching Penalized Chimp Optimization (NPChOA) is presented in this research in order to address the Environmental Economic Dispatch (EED) problem. In terms of global search capability, robustness, convergence rate, and durability, the proposed NPChOA outperforms the current ChOA. Following that, a novel constrained handling operator addresses the multi-objective optimization difficulty. The performance of NPChOA is evaluated using an IEEE 30 bus with six generators and a ten-unit system. The result of NPChOA is compared with Grey Wolf Optimizer (GWO), Space Reduction Strategy Particle Swarm Optimization (SRSPSO), Chaotic Biogeography-Based Optimizer (CBBO), Dynamic Population Artificial Bee Colony (DPABC), Modified Bacterial Foraging Algorithm (MBFA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), standard ChOA, Dynamic Levy Flight ChOA (DLF-ChOA), and Weighted ChOA (WChOA) as the most recent modified version of ChOA to confirm its efficiency. NPChOA's evaluation score and convergence rate are outstanding compared to other benchmarks for single and multi-objective optimizations. The NPChOA's efficacy and robustness in dealing with environmental economic dispatch challenges have been demonstrated by discovering a good compromise value.

Multi-objective whale optimization approach for cost and emissions scheduling of thermal plants in energy hubs

To a great extent, most of the power generation systems are dependent on fossil fuels, and their rapid usage is not only declining the reserves but also producing the greenhouse gasses that pollutes the ecology of planet and causes significant long haul harms to the earth habitats. The continuous dependency of hydrocarbon deposit will also cause shortage of fuel, causing excessively costly generation in future. In this article, a soft computing framework named as whale optimization algorithm (WOA) is proposed for the solution of multi-objective optimization problem known as economic/environmental dispatch problem (EEDP), which is among the most vibrant problem in the power system control area. Our designed architecture provides fast convergence rate, optimal generation cost, and reduction in environmental pollutants while handling the complex system constraints such as valve-point loading effect (VPLE) and prohibited zones. To demonstrate feasibility and effectiveness of the provided scheme, three highly nonlinear case studies are computed by while considering all system contingencies. The statistical results demonstrate significant reduction in fuel emission costs, and the superiority and performance of suggested technique is compared with the recently published approaches in the literature.

Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions

The promotion of carbon neutrality targets will significantly influence the energy management of multi micro energy grid (MMEG) systems that can supply flexible local loads using various distributed energy resources. So far, few works incorporate the carbon emissions management with the fully-distributed coordination of MMEG systems to protect the environment and individual privacy and improve the energy efficiency. To fill this gap, in this paper, a hierarchical optimization framework is proposed for the MMEG system to solve an environmental economic dispatch problem, such that the energy coordination strategy could be obtained in a fully-distributed way. At the upper level, under a local and time-varying communication network, we develop an aggregate model based on a real-time price policy for the MMEG system. On this basis, a fully distributed consensus-based gradient projection method is proposed to minimize the total system generation cost with the coupled emission constraint satisfied, wherein a piecewise linearization technique is applied to handle the non-convexity and non-smoothness of the cost function caused by valve-point loading effects (VPLE) of thermal units. At the lower level, we apply an accelerated distributed augmented Lagrangian (ADAL) method to deal with a cost minimization optimization problem for users, such that each user could implement its own optimal strategy simultaneously based on the obtained aggregated strategy from the upper level. The effectiveness of the proposed approach is demonstrated by numerical simulations on an MMEG test system.

Multi-objective Dynamic Environmental Economic Dispatch Problem Considering Plug in Electric Vehicles by Using the Improved Exchange Market Algorithm

Global Warming and progression of modern power networks have profoundly changed traditional power grids in terms of fossil fuel consumption and emission of toxic gases. Therefore, auxiliary power plants and ancillary services have been introduced as an effective alternative, to overcome these new challenges in power systems. In this work, the dynamic environmental economic dispatch (DEED) problem, is investigated by considering the plug-in electric vehicles (PEVs), minimizing the fuel cost and greenhouse gas emissions from fossil fuel units. In the mentioned problem, to make it more practical, various operational constraints, including valve-point loading effect (VPLE), ramp rate limits (RRLs) and generation capacity limits are considered. This paper proposes a new multi-objective exchange market algorithm (EMA) based on the non-dominated sorting theory to find the Pareto front. In addition, the impacts of PEVs, as an uncertainty source, on the mentioned problem are analysed in four different charging scenarios. The efficiency of the proposed method has been detailed on three experimental systems and the obtained results are compared with other algorithms in this field. The results show that the maximum percentage reduction in costs for test cases 1 to 3, are about 2.13, 2.69, and 39.48, respectively, and bout 45.96, 48.20 and 44.07, for emission, respectively. The comparative analysis verify the proposed method efficiency, and accuracy in solving the suggested problem.

A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-Area Emission (Environmental) Economic Dispatch Problem

A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-Area Emission (Environmental) Economic Dispatch Problem

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Distributed Optimal Economic Environmental Dispatch for Microgrids Over Time-Varying Directed Communication Graph

This paper investigates the combined economic environmental dispatch problem of Microgrids in a distributed manner. In order to optimize the competing agminated operation cost and environmental impact objectives simultaneously, a distributed method which combines the distributed consensus based algorithm with dynamic weights is proposed to assign the energy among generation units, energy storage units, and load units. By this method, each unit only needs to share its local information among neighbours, thus it offers better flexibility, robustness and privacy. Besides, different from existing distributed optimization algorithms, the proposed algorithm can solve the multi-objective problem over time-varying directed communication graph with a fixed step-size. Furthermore, it is proved that the proposed algorithm can converge to the optimal solution if the step-size does not exceed some upper bound. The simulation results show that the proposed method can simultaneously coordinate the two conflicting goals of the economic and environmental aspects of Microgrids, and obtain the entire Pareto front, effectively reducing operating costs and pollutant emissions. The advantage of convergence speed is also shown compared with some distributed algorithms.

Multi-Objective Evolutionary Algorithm for Economic Energy and Emission Dispatch Consolidating Global Environmental Regulations

To a great extent, most of the power generation systems are dependant on fossil fuels, and their rapid usage is not only declining the reserves but also producing the greenhouse gasses that pollutes the ecology of planet and causes significant long haul harms to the earth habitats. The continuous dependency of hydrocarbon deposit will also cause shortage of fuel, causing excessively costly generation in future. In this article, a soft computing framework named as whale optimization algorithm (WOA) is proposed for the solution of multi-objective optimization problem known as economic/environmental dispatch problem (EEDP) among the most vibrant problem in the power system control area. Our designed architecture provides fast convergence rate, optimal generation cost, and reduction in environmental pollutants while handling complex system constraints such as valve-point loading effect (VPLE) and prohibited zones. To demonstrate feasibility and effectiveness of provided scheme, three highly nonlinear case studies are computed on MATLAB while considering all system contingencies. The statistical results demonstrate significant reduction in fuel emission costs, and the superiority and performance of suggested technique is compared with the recently published approaches in the literature.

Multi-objective optimization of space adaptive division for environmental economic dispatch

With the increasingly severe global energy and environmental issues, the traditional economic load dispatching with the single goal of power generation cost has been unable to meet the national macro strategic requirements for energy conservation and emission reduction. This research mainly discusses the multi-objective optimization of spatial adaptive partitioning based on environmental economic dispatch problems. This study uses the method of calculating power generation costs and pollution control costs to select a compromise solution. When initializing the individual optimal position memory bank and the global optimal position memory bank, put the first-generation particle position into the individual optimal position memory bank, and put the optimal position obtained by comparing the first-generation particle into the global optimal position. In the location memory. Then calculate the fitness value corresponding to each objective function of each particle, compare the calculated result with the corresponding results of each Pareto solution in the internal memory bank and the external memory bank, and replace the particle position with the best. In the multi-objective processing strategy, in order to better obtain the Pareto optimal solution set and the Pareto optimal frontier, an external archive is set up to save and update the new elite Pareto optimal solution generated after each iteration. Since the quality of the particles in the random simulation particle swarm algorithm will directly affect the convergence and diversity of the final Pareto optimal solution set, this study selects the historical optimal particles according to the Pareto dominance relationship. When the multi-objective optimized power system economic dispatch research model with wind farms was adopted, the cost of power generation increased by 6.21 million yuan, the cost of polluting gas treatment was reduced by 8.1 million yuan, and the total cost was reduced by 1.89 million yuan. The research results show that the multi-objective optimization model proposed in this research has achieved a relatively satisfactory balance between power generation costs and environmental advantages.