Dynamic Economic Load Dispatch Problem Research Articles

Dynamic Economic Dispatch Problem in Hybrid Wind Based Power Systems Using Quasi Oppositional Based Chaotic Honey Badger Algorithm

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods.

A Cost Effective Solution to Dynamic Economic Load Dispatch Problem Using Improved Chimp Optimizer

The electricity sector has encountered several economic challenges in recent years. Increasing the expense of fossil fuels and environmental legislation such as the Kyoto Protocol and the Low Carbon Transition Plan have compelled governments to use renewable energy sources (RESs) more widely. In the proposed research, the dynamic economic load dispatch problem has been solved using improved chimp optimizer algorithm. The test systems consisting of 6, 7 and 10-unit generators has been taken into consideration along with significant contribution of renewable energy sources for effective research studies. The test systems has been evaluated for different cases considering renewable energy sources and electric vehicles using proposed algorithms. Experimentally, it has been observed that proposed optimizer yields better results as compared to other recently proposed optimizers.

AEFA Based Optimal Dynamic Economic Load Dispatch Problem Considering Penetration of Renewable Energy Systems

In power system, maintaining the load generation balance is critical. This paper proposes an artificial electric field algorithm (AEFA) for dynamic economic load dispatch (DELD) in a power system with security constraints, taking into account renewable energy sources (RES).. The RES penetration and total savings are examined considering variation of underestimation and overestimation costs associated with RES. Further, the impact of increased RES penetration on thermal fuel and overall costs has been studied. The AEFA algorithm performs better than PSO and DE, in terms of performance. The result reveals that when RES penetration rises savings as well as net cost of RES rises. Numerically, at 65% penetration of RES, a savings of 28.19% is achieved in the generator fuel cost and the RES penetration leads to a reduction in the thermal unit’s fuel cost.

Hybrid Mean Variance Mapping Optimization Algorithm for Solving Stochastic Based Dynamic Economic Dispatch Incorporating Wind Power Uncertainty

This paper presents a model for studying the dynamic economic load dispatch problem in power system including conventional thermal generators and wind turbines. The problem is solved using the hybrid variant Mean Variance Mapping Optimization technique. Due to the stochastic nature of wind speed, the Weibull distribution parameters of wind speed are obtained for different time intervals (24 hours). Consequently, the probability density function for the output wind power is achieved for each hour separately. The constraints for the thermal generators are considered including generation limits, ramp rate limits, valve-point loading and spinning reserve. The optimization problem is solved for different scenarios and total operating cost obtained shows the effect of wind power penetration as well as the overestimate and underestimate power probabilities.

Dynamic economic dispatch problem in hybrid wind based power systems using oppositional based chaotic grasshopper optimization algorithm

This article introduces a relatively current method named grasshopper optimization algorithm (GOA) for explaining power system based dynamic economic load dispatch (DELD) problem. However, like other optimization approaches, GOA suffers from premature convergence and a slow convergence rate. Thus, to boost the convergence mobility of GOA, oppositional based learning (OBL) is merged with the GOA approach. Moreover, another improvization, namely, chaotic concept, is also integrated with the GOA to improve its solution quality. The suggested oppositional based chaotic grasshopper optimization algorithm (OCGOA) method is applied to handle the DELD problem in the most efficient manner. Among the renewable energies, intermittent wind energy (WE) is the most sustainable one and remains technically and economically advantageous for electrical energy generation. The thermal and wind based power systems are hybridized, so that power generation is distributed among the operating units in an equivalent manner such that overall cost and loss part are optimized until all practical constraints are fulfilled. The production cost of the unpredictable wind generation power is further incorporated in the operational cost by employing a probability density function (PDF) formula. The precision and performance of the suggested GOA and OCGOA approaches are validated on 6-unit and 10-unit DELD systems for conventional and wind based energy systems. The efficacy and performance of the suggested OCGOA is judged by comparing it with conventional GOA and other presently developed meta-heuristic optimization techniques found in the literature.

An Effective Solution to Single-Area Dynamic Dispatch Using Improved Chimp Optimizer

This paper proposes the Improved Chimp Algorithm (ICHIMP) to solve single area dynamic economic load dispatch (ELD) problem of electric power system. Chimp is a biologically-stimulated heuristic optimization technique, which is embedded on impersonating the technique chimps hunt for food and remain existent by escaping from their adversary. The particularity of ICHIMP is that the chimps move in group for hunting but each chimp searches the prey independently. The single area dynamic dispatch problem is described as non-linear, complex and forced optimization problem with objective function to curtail the total generation price, whereas fulfilling the correspondence and dissimilarity constraints of the system. This proposed algorithm has been tested on five different test systems consisting of 3, 6, 13, 20 and 40- generating units.. The test results of ICHIMP determine its superiority over other existing algorithms addressed in literature and show that it outperforms for Single area dynamic dispatch problem of electric power system.

Ramp rate handling strategies in Dynamic Economic Load Dispatch (DELD) problem using Grey Wolf Optimizer (GWO)

ABSTRACTThis article presents multiple strategies to handle ramp rate in dynamic economic load dispatch (DELD) problem. DELD is a power system optimization problem that tackles the dispatch of thermal generators over a period of 24 h under numerous constraints like power balance, ramp rate, rippling effect and generator limits. All these constraints make DELD highly non-linear, complex, multi-dimensional and computationally intensive. In our research, we have devised unique ramp rate handling strategies and employed grey wolf optimizer (GWO) to implement them in MATLAB environment for the solution of IEEE standard 5-unit DELD system. GWO is a meta-heuristic optimization algorithm that has extensive global exploration and strong local exploitation capabilities. Addition of ramp rate handling strategies to the solution of DELD by GWO yielded diverse results. Each of these was statistically analyzed and the best results were compared with other techniques cited in the literature. Comparison indicated improvement in cost and solution time.

Flower pollination algorithm to solve dynamic economic loading of units with piecewise fuel options

This paper presents a Flower Pollination Algorithm (FPA) to solve Dynamic Economic Load Dispatch (DELD) problem with valve-point effects and piecewise fuel options. DELD aims to find out optimum generation schedule of the committed generating units over a certain timing period, sustaining practical constraints and power demands in each interval. Due to the valve-point effect and piecewise fuel options DELD becomes as complex problem, hence in order to achieve the cost reduction and satisfying the dynamic behaviour of the generating units proposed algorithm presented. The practicality of the proposed method is evaluated by performing simulations on standard 10-unit and 19-unit Indian utility systems for a 24 h time schedule at various load patterns. The simulation results attained by the FPA are related with other previous published techniques of the biography. These results clearly show that the proficiency and robustness of the proposed FPA method for resolving the non-linear constrained DELD problem.

Flower HPollination HAlgorithm Hto HSolve HDynamic HEconomic HLoading Hof HUnits Hwith HPractical HConstraints

Dynamic “economic load dispatch (DELD) aims to obtain optimum generation schedule of the committed generating units’ output hover ha certain timing horizon”, sustaining practical restrictions hand power demands in each period. ”Valve-point effect, the ramp hup/down limits, prohibited operation zones (POZs), hand power losses form the DELD has a complex, non-linear liable’ problem. The Flower Pollination Algorithm (FPA) his therefore anticipated in this paper to solve such a complex issue. The practicality of the proposed FPA method his assessed by conducting simulations hat different load patterns on standard h5-unit hand h10-unit systems for ha h24-hour schedule. The FPA’s simulation results hare related to other previously published biography techniques. These results clearly show that the skill hand robustness of the proposed FPA method to solve the non h- linear DELD problem has been restricted.

Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm

In electrical power system, economic load dispatch is a generic operation for optimal sharing of generation units to meet the system load. With the rapid development of the renewable infrastructure and wide encouragement for green energy have emerged hybrid generating systems in power systems. However, there continuous ever-increasing production is creating challenges as well as implicating economic factor also in operation. A collective cost function is considered with the conventional thermal generators along with the consideration of renewable energy sources to envisage the economic factor. For these renewable sources, like wind and solar, the proportional cost, their uncertainty and variability by overestimation and underestimation cost are considered. To achieve this economic day-ahead scheduling, dynamic operation in time scale of 1 h interval is performed. The stochastic nature of wind and solar is modelled by Weibull and Beta distributions, respectively. Moreover, economic optimisation is obtained by a newly developed algorithm called improved fireworks algorithm with non-uniform operator (IFWA-NMO). This introduces adaptive dimension strategy, limiting mapping operator and non-uniform operator. The effectiveness of proposed IFWA-NMO is investigated on standard dynamic economic load dispatch (DELD) system and also employed to solve conventional DELD with wind-solar system.

A Solution to Non-convex/Convex and Dynamic Economic Load Dispatch Problem Using Moth Flame Optimizer

Moth flame optimization algorithm is novel nature inspired heuristic paradigm inspired by navigation method of moths in nature and based on the concept that the moth eventually converges toward the light. This paper presents the application of MFO algorithm for the solution of non-convex and dynamic economic load dispatch problem of electric power system. The performance of MFO algorithm is tested for non-convex, convex and dynamic economic load dispatch problem of seven IEEE benchmarks and the results are verified by a comparative study with lambda iteration method, particle swarm optimization algorithm, genetic algorithm (GA), artificial bee colony, evolutionary programming (EP) and grey wolf optimizer (GWO). Also, in the proposed research, the impact of renewable energy sources (i.e. wind and solar) has been taken into consideration along with conventional thermal power generating units. Also, critical analysis has been made for percentage cost saving with due consideration of solar and wind power units and it has been experimentally observed that the addition of renewable energy sources to conventional thermal power system results in significant cost saving. Comparative results show that the performance of Moth Flame Optimizer Algorithm is better than recently developed GWO algorithm and other well known heuristics and meta-heuristics search algorithms.

밸브지점 균형과 교환 최적화 방법을 적용한 동적경제급전문제

This paper proposes a balance-swap method for the dynamic economic load dispatch problem. Based on the premise that all generators shall be operated at valve-points, the proposed algorithm initially sets the maximum generation power at . As for generator i with , which is the maximum operating cost $c_i

Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer

Ant lion optimizer (ALO) is a newly developed population-based search algorithm inspired by hunting mechanism of antlions and based on five steps of hunting the ants, i.e., the random walk of ants, building traps, entrapment of ants in traps, catching preys and re-building traps. This paper presents the application of ALO algorithm for the solution of non-convex and dynamic economic load dispatch problem of electric power system. The performance of ALO algorithm is tested for economic load dispatch problem of four IEEE benchmarks of small-scale power systems, and the results are verified by a comparative study with lambda iteration method, particle swarm optimization algorithm, genetic algorithm, artificial bee colony, evolutionary programming and Grey Wolf optimizer (GWO). Comparative results show that the performance of ant lion optimizer algorithm is better than recently developed GWO algorithm and other well-known heuristics and meta-heuristics search algorithms.

Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer

Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.

Novel Adaptive Evolutionary Computation Approaches to the Dynamic Economic Load Dispatch Problems with Non-Smooth Fuel Cost Function

paper presents novel approaches to solving the dynamic economic load dispatch (DELD) problem with valve-point loading effects. In dynamic environments, optimization problems change over time. They are also called time dependent or dynamic time-linkage problems, where decisions made at a given time may affect output obtained in a later time. It is therefore expected of algorithms solving dynamic optimization problems to both locate optimal solutions of the given problem and keep track of such solutions as they change with time. An investigation was made of three optimization methods in conjunction with three smart mutation variants, on benchmark problem cases involving 5 and 10 generating units, the major test cases in the literature with comparative results for other algorithms. The results suggest that the third approach which exploits the dynamic nature of the problem was capable of superior to the other two approaches. Comparisons with all approaches so far in the literature that have addressed these problems show that these evolutionary computation approaches are superior to other algorithms.

Application of Krill Herd and Water Cycle Algorithms on Dynamic Economic Load Dispatch Problem

Dynamic economic dispatch (DED) is a complicated nonlinear constrained optimization problem and one of the most important problems in operation of power systems. In this paper two novel optimization algorithms have been proposed to be applied on DED problem. The first method, Krill herd (KHA) is a novel meta heuristic algorithm for solving optimization problems which is based on the simulation of the herding of the krill swarms as a biological and environmental inspired method and is applied on DED problem with two configurations named KHA1 and KHA2. The second algorithm is based on how the streams and rivers flow downhill toward the sea and change back in nature, named Water Cycle (WCA) method. Two common case studies considering various constraints have been used to show the effectiveness of these methods. The results and convergence characteristics show that the proposed methods are capable of giving high quality results which are better than many other previously applied algorithms.

Optimum Load Dispatching Model in Intelligent Distribution Network

The increase of the intensive power proportion in distribution network brings new challenges to load dispatch of power plant, and intelligent distribution would be a key issue in the future power integrated system. The paper combined the strength Pareto evolutionary algorithm with the parallel genetic algorithm (PGA) ,namely the Pseudo-parallel SPEA2 Algorithm for solving the dynamic economic load dispatch problem. This problem is formulated as a nonlinear constrained multi-objective optimization problem, meanwhile, synthetically considers dynamic constraints handling, the minimum of fuel cost and the pollution emission. Besides, the paper analyzed the feasibility and validity of this algorithm in the load dispatch of power system.

Gravitational Search Algorithm Optimization for Dynamic Economic Load Dispatch with Valve-Point Effects

The dynamic economic load dispatch problem (DELDP), with the consideration of valve-point effects, is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. The main goal of DELDP is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. In this paper, a new swarm based optimization algorithm, so called gravitational search algorithm (GSA) has been proposed to solve the DELDP with considering valve-point effects and transmission power loss constraints. The performance of the proposed method is rigorously tested for optimality, convergence, robustness and computational efficiency on a 10-unit test system with a 24-h load demand at each 1-h time intervals which is so well-known and common in literature. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits. The simulation results reveal that, compared with other methods reported in literatures recently, the proposed GSA method are capable of obtaining better quality solutions with higher efficiency. In addition, sensitivity analysis has been performed on the controlling parameters of GSA to reach the best arrangement for them.

Feasible solution for dynamic economic load dispatch using feasible operation region

AbstractHigh penetration of renewable energy in the future power system will pose a big problem to the load dispatch operation. The large disturbance and high forecast error must be considered when scheduling a limited number of controllable generators to follow rapid change in load. This paper proposes a dynamic economic load dispatch (DELD) problem approach based on the concept of a feasible operation region (FOR). FOR is defined as the region that committed generators may operate in to match the load profile without violating the ramp‐rate constraints. The DELD problem is solved in two stages. In the first stage, FOR of each generator is computed using recent real‐time forecasted load as well as renewable energy generation. In the second stage, a generation schedule is determined by solving the DELD problem interval by interval while considering ramp‐rate constraints and FOR constraints. The method can gives feasible solution for feasible load and specify the amount of compensation required for feasible solution for infeasible load. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Solution of Dynamic Economic Load Dispatch (DELD) Problem With Valve Point Loading effects and Ramp rate limits Using PSO

Dynamic economic load dispatch (DELD) is one of the major operational decisions in power system operation and control. It is a Dynamic problem due to dynamic nature of Power system and the large variation of load demand. This absolute problem is normally solved by discretisation of the entire dispatch period into a number of small time intervals over which the load is assumed to be constant and the system is considered to be in temporal steady state. This paper presents particle swarm optimization technique to solve the DELD problem for the determination of the global or near global optimum dispatch solution. To illustrate the effectiveness of the proposed approach, three test systems consisting of 5,10 and 15 generating units, with incorporation of load balance constraints, operating limits, valve point loading, ramp constraints and network lossesare considered and tested. The comparison of numerical results demonstrate the performance and applicability of the proposed method. Keywords: Dynamic economic load dispatch (DELD), Particle Swarm Optimization, Valve - point loading effect, Ramp Rate Limits. DOI: http://dx.doi.org/10.11591/ijece.v1i1.60