Dynamic Economic Load Dispatch Research Articles

A Selection Hyper-Heuristic Algorithm for Multiobjective Dynamic Economic and Environmental Load Dispatch

Dynamic economic and environmental load dispatch (DEED) aims to determine the amount of electricity generated from power plants during the planning period to meet load demand while minimizing energy consumption costs and environmental pollution emission indicators subject to the operation requirements. This planning problem is usually expressed using a nonsmooth cost function, taking into account various equality and inequality constraints such as valve-point effects, operational limits, power balance, and ramp rate limits. This paper presents DEED models developed for a system consisting of thermal units, wind power generators, photovoltaic (PV) generators, and energy storage (ES). A selection hyper-heuristic algorithm is proposed to solve the problems. Three heuristic mutation operators formed a low-level operator pool to direct search the solution space of DEED. The high level of SHHA evaluates the performances of the low-level operators and dynamically adjusts the chosen probability of each operator. Simulation experiments were carried out on four systems of different types or sizes. The results verified the effectiveness of the proposed method.

Enhanced data communication models for real‐time power system monitoring in a distributed platform

ABSTRACTThe novelty behind this research is to develop enhanced models for real‐time power system monitoring in a platform‐independent and distributed platform. Distributed models for power system applications have been implemented using Remote Method Invocation (RMI), Common object Request Broker Architecture and. NET remoting technologies. The interoperability among the heterogeneous power system clients in a distributed platform has been achieved through XMLization of power system data which provides meaningful solution to the legacy issues associated with power systems. A RMI‐based model for real‐time power system monitoring in a distributed platform had been implemented and discussed in detail. A component model for real‐time power system monitoring has also been proposed and implemented to increase the reusability and scalability of the system. A location transparent model has been developed using Common object Request Broker Architecture with RMI‐Internet Inter‐Orb Protocol for load flow monitoring and the idea is extended to solve real‐time economic load dispatch and dynamic security monitoring. The detailed monitoring on performance of the proposed models has been done with load flow monitoring, dynamic security monitoring and economic load dispatch problems. A. NET remoting architectural model has been developed for real‐time economic load dispatch in a distributed platform. This research paper explores various issues in deploying power system applications in a distributed platform. It will serve the purpose of stimulating interest among power engineers and also will be extremely useful for electric power utilities.

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.

Real-time dynamic economic load dispatch integrated with renewable energy curtailment

Recent power system operations are faced with the difficulties due to large penetration levels of intermitted renewable energy sources (RESs), for example, photovoltaic power (PV) generations and w...

Robust Stochastic Dynamic Load Dispatch Against Uncertainties

At present, electric power systems face difficulties in system operations due to rapid increase in uncontrollable renewable energy sources (RESs), such as photovoltaic power generations (PVs). Reduction of controllable resources also yields concerns about system reliability issues. This paper focuses on a new dynamic load dispatch method for mitigating the irregularity associated with RES. The developed load dispatch method is to schedule the committed generating units outputs so as to meet required irregular load demand estimation which is frequently updated in real-time operation circumstance. A new algorithm is proposed for unit commitment and stochastic dynamic economic load dispatch as an efficient solution for a day-ahead and a real-time generation schedule (GS), respectively, that fully utilizes the limited resources of a power system under uncertainties. Day-ahead and real-time PV forecasts with the co-relations of PV forecasting errors are effectively treated in the proposed method. Special feature of the proposed method different from the existing works lies in a frequent real-time update of GS by using a fast algorithm to maximize robustness against uncertainties.

Dynamic Economic Load Dispatch using Levenberg Marquardt Algorithm

Economic Load Dispatch (ELD) is a very important feature of power system network. This work proposes the novel approach which considers the constraint of ramp rate limit (RRL) to solve the ELD problem. It build up the time varying dynamic economic load dispatch in which load dispatching is calculated for each specified time interval, first it is tested with conventional lambda iteration technique and then the outcomes are used to train artificial neural network (ANN) it is based on Levenberg Marquardt algorithm (LMA).As compared with any other ANN method, the Levenberg Marquardt algorithm based dynamic economic load dispatch is more swift and precise. The propose algorithm is tested on 9 generating unit and results are presented and discussed.

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.

Development of a Robust EMS against Large Amount of PV Installation

Abstract At present, electric power systems face difficulties in system operations due to rapid increase in uncontrollable renewable energy sources (RESs), such as photovoltaic power generations (PVs). Reduction of controllable resources also yields concerns about system reliability issues. This paper focuses on a new dynamic load dispatch method for mitigating the irregularity associated with RES. The developed load dispatch method is to schedule the committed generating unit’s outputs so as to meet required irregular load demand estimation which is frequently updated in real-time operation circumstance. A new algorithm is proposed for unit commitment and stochastic dynamic economic load dispatch as an efficient solution for a day-ahead and a real-time generation schedule (GS), respectively, that fully utilizes the limited resources of a power system under uncertainties. Day-ahead and real-time PV forecasts with the co-relations of PV forecasting errors are effectively treated in the proposed method. Special feature of the proposed method different from the existing works lies in a frequent real-time update of GS by using a fast algorithm to maximize robustness against uncertainties.

Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging

The climate change is addressing unprecedented pressures on conventional power system regarding the significant fossil fuel consumptions and carbon emissions, which largely challenges the conventional power system operation. This paper proposes a novel dynamic non-dominated sorting multi-objective biogeography-based optimization (Dy-NSBBO) to solve multi-objective dynamic economic emission load dispatch considering the mass integration of plug-in electric vehicles (PEVs), namely MO-DEELDP problem. First, a real-world economic emission load dispatch considering PEVs charging is first formulated as a constrained dynamic multi-objective optimization problem. Then a new multi-objective BBO is proposed adopting the non-dominated solution sorting technique, change detection and memory-based selection strategies in the multi-objective BBO method to strengthen the dynamic optimization performance. The proposed Dy-NSBBO is applied to solve three different dynamic economic emission load dispatch cases integrating four plug-in electric vehicle charging scenarios respectively. Comprehensive analysis shows that the novel algorithm is promising to bring considerable economic and environmental benefits to the power system operators and provides competitive charging strategies for policy makers and PEVs aggregators.

Development of the Algorithm for Optimal Operation of Connected System between the District Heating Energy Systems (Ⅰ)

Energy resources on the Earth may not be sufficient because more and more energy is needed for human beings as global economy grows further. Several alternatives being proposed to solve problems. Especially, CHP(Combined Heat and Power) is considered as the most realistic and efficient solution. This paper developed an optimal operation mechanism of connected system between the DHC(District Heating and Cooling). This operation system of DHC that is limited to power transaction can be expanded into the combined energy transaction including thermal transaction. This method can optimally operate connected systems and calculate the maximum profits by Dynamic Programing and Economic Heat Load Dispatch. Through the case studies, it is verified that the proposed algorithm is very realistic and effective.

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.

Efficient Dynamic Economic Load Dispatch Using Parallel Process of Enhanced Optimization Approach

In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution Strategy. In order to overcome the challenges like the clustering of PSO, optimization problems and maximum and minimum searching, a new approach is developed with the improvement of searching and efficient process. In this paper, an Enhanced Hybrid Differential Evolution and Particle Swarm Optimization (EHDE-PSO) is proposed with Dynamic Sigmoid Weight using parallel procedures. A hybrid form of the proposed approach combines the optimizing algorithm of Enhanced PSO with the Differential Evolution (DE) for the improvement of computation using parallel process. The implementation and the parallel process are analyzed and discussed to gather relevant data to show the performance enhancement which is better than the existing algorithm.

Enhanced Approach of EHDE-PSO based on SA for Dynamic Economic Load Dispatch

The evolution approach is a major key to solve the issues in Dynamic Economic Load Dispatch (DELD). The process of optimization and computation of generating unit is carried out according to the Dual Evolution Strategy. Here, to achieve a maximum limit of power and minimum cost function the Simulated Annealing (SA) is implemented by the proposed system. In order to solve the issues of Non Convex Economic Dispatch, the proposed Enhanced Hybrid Differential Evolution and Particle Swarm Optimization (EHDE-PSO) based on SA is implemented to have less cost and efficient generating unit. The SA approach is considered in the selection process of the values of EHDE-PSO. The proposed approach provides an efficient process of searching and feasibility in generating unit. The analysis and comparison of the proposed simulation results are illustrated to show the improvement and efficiency of the system. The simulation of hybrid approach is carried out using MATLAB and achieves improvement in computation, load demand, cost and robustness than the existing approaches.

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.

Opposition Based Differential Evolution Algorithm for Dynamic Economic Emission Load Dispatch (EELD) with Emission Constraints and Valve Point Effects

Optimal Power dispatch is the short-term decision of the optimal output of a number of power generation facilities, to meet the system demand, with the objective of Power dispatching at the lowest possible cost, subject to transmission lines power loss and operational constraints. The operational constraint includes power balance constraint, generator limit constraint, and emission dispatch constraint and valve point effects. In this paper, Opposition based Differential Evolution Algorithm (ODEA) has been proposed to handle the objective function and the operational constraints simultaneously. Furthermore, the valve point loading effects and transmission lines power loss are also considered for the efficient and effective Power dispatch. The ODEA has unique features such as self tuning of its control parameters, self acceleration and migration for searching. As a result, it requires very minimum executions compared with other searching strategies. The effectiveness of the algorithm has been validated through four standard test cases and compared with previous studies. The proposed method out performs the previous methods.