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This article presents an innovative method to address the economic load dispatch (ELD) problem in power systems incorporating renewable energy sources within thermal units. Employing the 2-m point estimation technique for determining renewable energy output power, the proposed approach effectively addresses challenges associated with renewable-based ELD by utilizing the artificial electric field method. Inspired by the electrostatic force principle among charged particles, this approach guides particles toward optimal solutions within the search space. Validation on power systems featuring 3, 5, 6, 15, and 40 units demonstrates superior performance compared to established algorithms, confirmed by the Wilcoxon signed-rank test. The research contributes to the advancement of sustainable and efficient power systems. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

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Economic Load Dispatch (ELD) is an effective dispatch strategy to improve economic efficiency while ensuring the safety and stability of the power system. It minimizes production costs by rationally allocating the power generated by each power system unit. In this paper, an Manta Ray Foraging Optimization (MRFO) algorithm based on the mathematical spiral foraging strategy is proposed to solve the ELD problem in power systems considering transmission losses. Eight different mathematical spirals were introduced into the MRFO algorithm's foraging strategy, including the Rose spiral, Archimedes spiral, Fermat spiral, Cycloid spiral, Hypotrochoid spiral, Epitrochoid spiral, Inverse spiral and Lituus spiral. The mathematical spiral foraging strategy can enhance the global search ability of the MRFO algorithm and improves its convergence velocity. To verify the performance of the proposed improved MRFO algorithm, 30 benchmark functions are tested and the optimization performances are compared with BOA, AOA, SCA, HHO, WOA, RSA, and GWO. The simulation experimental results show that the performance and application of the manta ray foraging optimization algorithm based on the mathematical spiral foraging strategy outperform other intelligent optimization algorithms tested on 30 benchmark functions. Finally, two ELD cases with total demands of 2500 MW and 10500 MW are selected and solved using the improved manta ray foraging optimization algorithm. Comparing the simulation results with other optimization algorithms, it is shown that the proposed improved algorithm obtains the best fuel cost and smaller transmission loss in almost every test case, which canl help to improve the economic efficiency of the power system and achieve the goal of economic load dispatch.

Economic Load Dispatch (ELD) is an effective dispatch strategy to improve economic efficiency while ensuring the safety and stability of the power system. It minimizes production costs by rationally allocating the power generated by each power system unit. In this paper, an Manta Ray Foraging Optimization (MRFO) algorithm based on the mathematical spiral foraging strategy is proposed to solve the ELD problem in power systems considering transmission losses. Eight different mathematical spirals were introduced into the MRFO algorithm's foraging strategy, including the Rose spiral, Archimedes spiral, Fermat spiral, Cycloid spiral, Hypotrochoid spiral, Epitrochoid spiral, Inverse spiral and Lituus spiral. The mathematical spiral foraging strategy can enhance the global search ability of the MRFO algorithm and improves its convergence velocity. To verify the performance of the proposed improved MRFO algorithm, 30 benchmark functions are tested and the optimization performances are compared with BOA, AOA, SCA, HHO, WOA, RSA, and GWO. The simulation experimental results show that the performance and application of the manta ray foraging optimization algorithm based on the mathematical spiral foraging strategy outperform other intelligent optimization algorithms tested on 30 benchmark functions. Finally, two ELD cases with total demands of 2500 MW and 10500 MW are selected and solved using the improved manta ray foraging optimization algorithm. Comparing the simulation results with other optimization algorithms, it is shown that the proposed improved algorithm obtains the best fuel cost and smaller transmission loss in almost every test case, which canl help to improve the economic efficiency of the power system and achieve the goal of economic load dispatch. [ABSTRACT FROM AUTHOR]

Summary: The paper presents a novel methodology based on a BARON solver in general algebraic modeling system (GAMS) computational environment to solve the stochastic profit‐based unit commitment (SPBUC) problem in the deregulated energy market. The profit is considered as the difference between the total revenue collected by selling energy in the competitive energy market and the production cost of energy. The concepts of the deregulated energy market like market clearing price and market reserve price are employed for the determination of the expected revenue. Energy storage systems and electric vehicles have the benefit to act as load as well as generating sources during the uncertain and intermittent nature of renewable energy sources (RES). Thus, the RES in combination with battery energy storage systems and plug‐in hybrid electric vehicles (PHEVs) are modelled and included in the present study. The Monte Carlo Simulation technique is employed to deal with the uncertain nature of wind speed, solar irradiance and PHEVs. First, the proposed solution methodology is tested on a 10‐unit thermal system with and without valve point loading wherein it has provided an enhanced profit of 1.28% when compared with existing results reported in published literature. Secondly, the comparative results obtained by solving the SPBUC problem integrated with RES with different solvers available in GAMS computational environment demonstrate that the BARON solver outperformed the other solvers in terms of computation time and expected profit attained. [ABSTRACT FROM AUTHOR]

The objective of economic load dispatch (ELD) is to minimize the overall cost of generation under the premises of meeting the load demand and satisfying different operational constraints. A new improved Arithmetic Optimization Algorithm (AOA) based on elementary function disturbance was proposed to solve ELD problems. Math optimizer accelerated (MOA) and math optimizer probability (MOP) are two important parameters in the original AOA. Ten six elementary functions were adopted, which are sine function, tangent function, hyperbolic secant function, power function, hyperbolic cosecant function and arc-cosine function. Adding the disturbance generated by six elementary functions respectively on the original MOA and MOP parameters can effectively balance the exploration and exploitation ability of AOA, enhance the global search ability, avoid falling into local optimization, and improve the convergence speed of AOA. Then the original AOA was compared with 6 variants by using 23 benchmark functions in CEC-2005, and selected the best improved method. Next, some intelligent optimization algorithms with better performance in optimizing CEC-2005 benchmark functions are adopted to carry out the simulation experiments with the improved AOA to verify its superiority. Finally, three ELD problem cases including valve point effect, prohibited operation area, transmission loss and other factors are selected with a total demand of 1263 MW, 2500 MW and 10,500 MW respectively. The AOA based on elementary function disturbance was used to solve these three ELD problems. Experimental results show that the proposed AOA has the best performance in solving ELD problems. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) is one of the vital prospects in the energy management system for determining the optimal power generation distribution among several committed power generating units. The objective of ED is to generate the electric power to meet the load demand by minimizing the total operating cost subjected to various equality and inequality constraints. This ED problem is generally solved using conventional techniques such as a gradient method by assuming the objective function as linear and convex. However, in general, the ED problem is constrained, non-convex, large scale, multimodal and highly nonlinear optimization problem. In order to overcome this drawback, various heuristic techniques have been introduced in the literature. Symbiotic organism search (SOS) is one of the emerging nature-inspired meta-heuristic algorithms. Due to simple in implementation and free from algorithm-specific control parameters, the SOS algorithm has gained a reputation among the researchers. The SOS technique mimics the biological interaction between two distinct species to survive and proliferate in the ecosystem. To evade the suboptimal solution and to enhance the exploration and exploitation of the SOS algorithm, the disruption operator is integrated with the basic SOS to form a novel disruption based SOS (DSOS) algorithm. The disruption strategy is originated from astrophysics and has the ability to shift between exploration and exploitation during the process of finding an optimum solution. To illustrate the effectiveness of the proposed method, it is applied on various test systems such as 3-unit, 5-unit, 6-unit, 13-unit, 20-unit, 38-unit, and 110-unit systems to solve different ED problems, namely, without transmission losses (TL), with transmission losses, by simultaneously considering valve point loading (VPL) effect along with TL, by simultaneously considering the effect of ramp rate limits (RRLs), prohibited operating zones (POZs) and TLs, and dynamic economic load dispatch by considering RRLs, POZs, VPL, and TL. The comparison of DSOS based simulation results with other methods reveal the efficiency and robustness of the proposed method to provide minimum total generation cost in all the cases with better convergence rate and less computational time. [ABSTRACT FROM AUTHOR]

The combined heat and power (CHP) generator not only generates electrical power but also generates heat energy in a single process, which decreases the emission level significantly. The integration of wind power into the power system will lead to an impact on the economic operation of the system as well as the bus voltage and transmission losses. In this paper, a formulation of optimal power flow (OPF) problem of a power system incorporating stochastic wind power is presented. To solve this problem, a modified version of the moth swarm algorithm (MMSA) is proposed. Three objective functions, which are the minimization of operating cost, the minimization of transmission power loss, and the voltage profile improvement, are considered in this paper. To minimize the operating cost, the direct, overestimation, and underestimation costs of wind power units are considered. Two test systems are considered to prove the effectiveness and the superiority of the proposed MMSA in comparison with other methods. The comparison with other methods proves the efficiency and the superiority of the proposed MMSA.

The solution of Combined Economic and Emission dispatch (CEED) problems aims to minimize the operating fuel cost and emission levels simultaneously, while satisfying load demand and operational constraints. The CEED problem can be formulated as a nonlinear optimization problem with equality and inequality operating constraints. Moth-flame optimization algorithm (MFO) is one of the newest bio inspired optimization techniques. However, this paper proposes a modified moth-flame optimization algorithm (MMFO) to find the optimal solution of CEED problems. MMFO is mainly based on the concept of MFO with modification the path of moths in new spirals around the flame. The developed MMFO algorithm can be used to improve the convergence characteristics of CEED optimal solutions compared to other optimization algorithms. The performance of MMFO is compared with different optimization techniques. Valve point effect, environmental emissions, and transmission line losses are included in the CEED analysis to provide more practical results. MMFO is validated using different standard test systems. The simulated results show the effectiveness and the superiority of the developed MMFO against other well-known optimization techniques. [ABSTRACT FROM AUTHOR]

Power flow in energy systems is one of the major problems. Several classical analysis methods are utilized for solving this problem. However, power generation limits, valve loading effects of units also makes the power flow problem become much harder to solve in the system. In this case, it is possible to achieve the most appropriate solutions with evolutionary algorithms. In this study, optimal power flow problems are solved under same beginning conditions, comprehensively performed with evolutionary algorithms which are recently used and associated algorithm performance is analyzed in IEEE 30-bus test system for two cases. Energy gains of algorithms are obtained; the best, worst and mean values found from optimization are evaluated; convergence analyses are performed comparatively. Thus the effectiveness and efficiency of evolutionary algorithms are clearly demonstrated on solution of optimal power flow problems.

Enerji sistemlerinde güç akışı, önemli problemlerden biridir. Bu problemin çözümü için farklı klasik çözümleme yöntemlerinden faydalanılmaktadır. Ancak sistemdeki jeneratörlerin güç üretme limitleri, valf yükleme etkileri gibi parametreler güç akışı probleminin ilgili yollarla çözümünü zorlaştırmaktadır. Bu durumda evrimsel algoritmalarla en uygun çözümleri gerçekleştirmek mümkün olabilmektedir. Gerçekleştirilen çalışmada optimal güç akışı problemlerinin çözümü, iki farklı durumda 30 baralı IEEE test sisteminde güncel evrimsel algoritmalar ile eşit başlangıç şartlarıyla karşılaştırmalı olarak gerçekleştirilmiş ve ilgili algoritmaların performans değerlendirmeleri yapılmıştır. Algoritmaların enerji kazanımları elde edilmiş; optimizasyon sonucunda elde edilen en iyi, en kötü ve ortalama değerleri hesaplanmış; yakınsama analizleri karşılaştırmalı olarak gerçekleştirilmiştir. Böylece optimal güç akışı probleminin çözümünde evrimsel algoritmaların etkinlik ve verimlilikleri açıkça ortaya konulmuştur.

Short-Term Hydrothermal Scheduling (STHS) is a nonlinear, multi-constrained and timevarying optimization problem. When the valve point effect is considered, the problem becomes nonconvex and more complicated. In order to improve the search ability of the Krill Herd Algorithm (KHA) in the STHS problem, the hybrid chaotic map is introduced to improve the global convergence speed of KHA. In order to avoid premature convergence of the algorithm, by recording the number of times that the fuel cost values of the best individual in each generation remain unchanged and making the decision that a positional mutation in the non-positionally dominant individual within its feasible domain, a hybrid chaotic krill herd algorithm (HCKHA) is proposed. HCKHA and KHA，CKHA were applied to the standard STHS test systems such as "four hydro and three thermal plants" and "four hydro and ten thermal plants"，independently. The simulation results show that HCKHA has better optimization ability，fuel cost values and transmission loss values than KHA, CKHA and the optimization methods in other related literatures. [ABSTRACT FROM AUTHOR]

In this paper, a new meta-heuristic algorithm, called Squirrel Search Optimizer (SSO) is applied to solve various types of economic load dispatch (ELD) problems. The SSO mimics the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. In SSO algorithm, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The key idea of the suggested approach is to determine the optimal generation scheduling by minimizing total generation cost of units while satisfying various constraints such as power balance constraint, prohibited operating zones (POZ), ramp rate constraints and operating limits of generators. The different generating unit’s characteristics, quadratic fuel cost function, non-convex fuel cost function and multiple fuel options (MFO) are also considered. The feasibility of the proposed algorithm is tested on four different power test systems having different sizes and intricacies. The results are examined in terms of both solution quality and the computational efficiency, and compared with the other approaches in the literature. The comparisons prove the robustness and effectiveness of the proposed algorithm and show that it could be used as a consistent optimizer for solving various ELD problems. [ABSTRACT FROM AUTHOR]

This paper bestows a new swarm intelligence approach, Squirrel Search Algorithm (SSA) to solve Economic Load Dispatch (ELD) of the thermal unit by addressing the valve point loading effects and multiple fuel options. SSA inspires the foraging behavior of squirrels which is based on dynamic jumping and gliding strategies. The main intention of the ELD problem is to minimize the total generation cost of units while assuring various system constraints. Renovate strategy and selection rules are used in the SSA algorithm to handle the constraints appropriately. The practicability of the proposed algorithm is tested on six different power test systems having different sizes and intricacies. Simulation results ascertain that the proposed SSA approach outperforms the other existing heuristic optimization techniques in terms of solution quality, robustness, and computational efficiency. Consequently, the proposed SSA can be an efficient approach for solving the ELD problems with valve point loading impacts and multi-fuel options. [ABSTRACT FROM AUTHOR]

This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are introduced in the paper by employing two different random walk generators for diffusion process in which SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from three test system. These systems with 6, 10, and 20 units with different objective function forms and different constraints are inspected. Numerical result comparison can confirm that the applied approach has better solution quality and fast convergence time when compared with some recently published standard, modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving the ELD problem. [ABSTRACT FROM AUTHOR]

This study suggests chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving intricate actual world combined heat and power dynamic economic dispatch (CHPDED) problem with demand side management (DSM) incorporating renewable energy sources and pumped‐storage‐hydraulic unit. The valve point effect and proscribed workable area of thermal generators and solar and wind power uncertainty have been pondered. DSM programmes decrease cost and boost up power system security. To investigate the upshot of DSM, the CHPDED problem is solved with and without DSM. In the recommended CFCEP technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). Introduction of chaotic sequences helps FCEP to avoid premature convergence. The efficiency of the recommended technique is revealed in a test system. Simulation outcomes of the recommended technique have been evaluated with those attained by FCEP and differential evolution. It has been examined from the assessment that the recommended CFCEP has the capability to bestow with a better‐quality solution. [ABSTRACT FROM AUTHOR]

This manuscript applied fractal firefly optimization technique to the integrated load dispatch (ILD) problem involving loads allocation to power generating units with the minimum total fuel cost and emissions while handling the constraints for large-scale multi-fueled power plants and integration of stochastic wind to provide sustainable energy. The penetration of wind energy into conventional grids has resolved power system scheduling issues at the cost of rise in the stiffness of nonconvex ILD optimization problem. In order to enhance the dispatch performance with significant penetration of wind energy, an effective framework of fractal heritage is introduced inspired by Firefly algorithm. In order to validate the efficacy and flexibility of the proposed algorithm, sundry non-convex ILD optimization scenarios with valve-point effects, capacity constraints, multi-fuels power plants and Weibull's distributed stochastic wind uncertainty is implemented. The results reveal stupendous performance in term of the minimum fuel cost with higher efficiency and finer convergence rate than that of conventional algorithms. • A novel fractal firefly algorithm for large-scale ILD problem with stochastic wind. • Near-optimal solutions of constrained ILD problems are consistently attained. • The performance is validated for several historic data terms and fractional orders. • The efficacy is substantiated for sundry scenarios of renewable energy resources. [ABSTRACT FROM AUTHOR]

This study is centred around the economic dispatch (ED) problem of thermal generating units over a multi‐time interval, to have generators collectively meet the load demand and network losses while minimising the system fuel costs. The underlying optimisation problem displays non‐convexity due to the consideration of the valve point effect and ramping rates limit of generators. Additionally, the system operator by itself cannot determine the behaviours of generators since they are not willing to share their own cost functions and wish to make decisions by themselves. In view of these reasons, the authors provide a decentralised method based on gradient projection and piecewise approximation for the ED problem. It is shown that the proposed method converges to the global optimum when applying appropriate step‐size parameters. The performance of the proposed method is demonstrated on the IEEE standard 5 and 10 units test systems, and via comparisons with the existed methods in the literature. [ABSTRACT FROM AUTHOR]

This article develops and recommends fast convergence real-coded genetic algorithm (FCRCGA) for solving solar-wind-hydro-thermal power generation scheduling with battery energy storage system (BESS). Genetic algorithm (GA) is based on inbred operation of human chromosomes. GA has the ability to establish the global or very close to the global optima. In this article, FCRCGA has been suggested to heighten convergence speed and solution quality. The efficacy of the suggested technique has been confirmed on two test systems and 15 benchmark functions. Simulation outcomes of the suggested technique have been matched up to those acquired by real-coded genetic algorithm (RCGA) technique. It has been observed from the comparison that the suggested FCRCGA technique has the ability to endow with superior solution. [ABSTRACT FROM AUTHOR]

This paper presents symbiotic organisms search (SOS) algorithm to solve economic emission load dispatch (EELD) problem for thermal generators in power systems. The basic objective of the EELD is to minimize both minimum operating costs and emission levels, while satisfying the load demand and all equality–inequality constraints. In other research direction, this multi-objective problem is converted into single-objective function by using price penalty factor approach in order to solve it with SOS. The proposed algorithm has been implemented on various test cases, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. The results of the algorithms indicate that SOS gives good results in both systems and very competitive with the state of the art for the solution of the EELD problems. [ABSTRACT FROM AUTHOR]

Power flow in energy systems is one of the major problems. Several classical analysis methods are utilized for solving this problem. However, power generation limits, valve loading effects of units also makes the power flow problem become much harder to solve in the system. In this case, it is possible to achieve the most appropriate solutions with evolutionary algorithms. In this study, optimal power flow problems are solved under same beginning conditions, comprehensively performed with evolutionary algorithms which are recently used and associated algorithm performance is analyzed in IEEE 30-bus test system for two cases. Energy gains of algorithms are obtained; the best, worst and mean values found from optimization are evaluated; convergence analyses are performed comparatively. Thus the effectiveness and efficiency of evolutionary algorithms are clearly demonstrated on solution of optimal power flow problems. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem considers as an optimization problem, in which the utility strives to reduce its global costs while pleasing all of its constraints. Therefore, Interior Search Algorithm (ISA) is applied to treat this problem. Where, ISA is a specific type of artificial intelligence and a mathematical programming, not a meta- heuristic algorithm. The effectiveness of this method in solving the OPF problem is evaluated on two test power systems, the IEEE 30-bus and the IEEE 57-bus test systems. For the first example, the ISA-OPF algorithm finds an answer that agrees with published results. For the 57-bus system, the ISA-OPF demonstrates its ability to transact with larger systems. Thus, the ISA-OPF algorithm is shown to be a robust tool to treat this optimization compared with other methods. Moreover, the advantage of ISA is that it has only one parameter of control which makes the simplicity in the main algorithm.

An improved particle swarm optimization (IPSO) is used to solve economic dispatch problem (EDP). The IPSO has two position updating strategies. In the early stage of iteration, the individual in the population updates the position according to its own best experience with a large probability. In the later stage of iteration, the individual updates the position according to the best experience in the population with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can increase the diversity of population and prevent the premature convergence. IPSO has been used to solve EDP with valve point effect. The experimental results show that IPSO is an effective algorithm to solve EDP.

This paper develops and suggests fast convergence evolutionary programming (FCEP) for solving multi-area economic dispatch problem with tie-line constraints, transmission losses, multiple fuels, valve point effect, and disallowed operating region. Evolutionary programming (EP), a class of evolutionary algorithm, is based on the basic genetic operation of human chromosomes. EP has the ability to seek out the global or close to the global optima. FCEP has been developed to boost convergence speed and solution quality. The efficacy of the developed technique has been tested on three different types of test systems. Test results have been compared with those acquired from improved fast evolutionary programming, EP and other stated evolutionary techniques. It has been observed from the comparison that the developed FCEP has the ability to offer superior solution. [ABSTRACT FROM AUTHOR]

Economic Load Dispatch (ELD) is an imperative assignment in contemporary aggressive power demand market. Dearth of power generation in all dimensions of energy resources will result escalating in generation cost wants the optimal power dispatch at minimum fuel cost. Owing to the confined optimum convergence, the predictable optimization methods are not proficient to crack such problems. Evolutionary optimization techniques are proved to be superior to the conventional techniques to solve ELD problems. Differential Evolution Algorithm (DEA) is one of the foremost and recent evolutionary techniques in modern optimization state of affairs. The application of DEA in multi directional ELD problem has been technologically summarized in this paper. [ABSTRACT FROM AUTHOR]

The economic emission dispatch (EED) problem aims to control generation cost and reduce the impact of waste gas on the environment. It has multiple constraints and nonconvex objectives. To solve it, the collective neurodynamic optimization (CNO) method, which combines heuristic approach and projection neural network (PNN), is attempted to optimize scheduling of an electrical microgrid with ten thermal generators and minimize the plus of generation and emission cost. As the objective function has non-derivative points considering valve point effect (VPE), differential inclusion approach is employed in the PNN model introduced to deal with them. Under certain conditions, the local optimality and convergence of the dynamic model for the optimization problem is analyzed. The capability of the algorithm is verified in a complicated situation, where transmission loss and prohibited operating zones are considered. In addition, the dynamic variation of load power at demand side is considered and the optimal scheduling of generators within 24 h is described. [ABSTRACT FROM AUTHOR]

The dynamic economic emission dispatch (DEED) of power system occupies a decisive position in the process of energy conservation and emissions reduction. DEED incorporating valve point effect of generator units is a dynamic multi-objective non-convex optimization problem with the characteristic of nonlinearity, strong-constraint, and multi-peak. This paper proposed a new modified non-dominated sorting genetic Algorithm-II with selection based on dynamic crowding distance and new controlled elitism to solve DEED, which can guarantee the diversity of population under the premise of elitism of population. A deep analysis about blocked evolution produced by heuristic operation which is used to handle the complicate constraints is executed, and a modified heuristic operation based on forward search operator has been firstly implemented to solve the problem. A new membership function has been defined to describe the superiority-inferiority of the individuals in the Pareto set, from which the best compromise solution is selected. Finally, case studies were carried out with the classic 10-units test system. The simulation results demonstrate the better global search and better results acquirement capability of the proposed modified non-dominated sorting genetic Algorithm-II comparing with existing non-dominated sorting genetic Algorithm-II. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]