Artificial bee colony optimization for multi objective economic load dispatch of a modern power system

An efficient and reliable evolutionary based meta-heuristic approach, termed as simulated annealing, is presented for the solution of optimal economic power dispatch. The generation of electricity from the fossil fuel releases several contaminants, such as sulphur oxides, nitrogen oxides and carbon dioxide, into the atmosphere. Simulated annealing algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The advantages of the approach in terms simulated annealing algorithmic problem reduction, flexibility and solution methodology are also discussed. In the Multi-objective problem formulation, four important non-commensurable objectives in an electrical thermal power system are considered. These are economy and environmental impacts because of NOx, SO2, CO2 emissions. The feasibility of the proposed method is compared with a simple genetic algorithm is described to multi-objective optimal economic dispatch of electrical power systems.

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

Optimal economic emission dispatch of hydrothermal power systems

To ensure minimization of power losses as well as economic feasibility of electrical power generation, economic load dispatch happens to be one of the most challenging optimization problems which is faced in electrical engineering. With the advent of distributed power systems, an interconnection of power systems generating from different sources have come into consideration. However, all sources do not operate in the same manner and hence the generation cost for different sources varies significantly. Economic Load Dispatch (ELD) can be defined as a technique to schedule the power generator outputs with respect to the load demands, and to operate the power system in the most economical way. This paper presents a neural network model for implementing economic load dispatch for a three as well as six generation system. The load is also varied for both the 3 and 6 generations systems. The results clearly indicate that the cost of generation increases with the increase in load, which is also intuitive and hence gets tested. Thus the proposed model can be used to implement an optimized economic load dispatch mechanism for multi-unit power systems which is a typical characteristic of distributed power systems.

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties of generators such as valve point effects (VPE), prohibited operating zones (POZ), ramp rate limits, multiple fuel options, and transmission loss. The WMA algorithm is a novel metaheuristic algorithm inspired by the mating behavior of woodpeckers and sound intensity (a physical quantity). The WMA is implemented on six test systems of different operational dimensions and characteristics to show its capacity for solving the ED problem. The results are compared with the latest and most efficient methods introduced in the literature. Proving the efficiency of the WMA to solve the ED problem, simulation results are promising and offer the optimal fuel cost of production.

International Journal of Computational Engineering ScienceVol. 05, No. 01, pp. 81-89 (2004) No AccessEVOLUTIONARY PROGRAMMING BASED ECONOMIC DISPATCH WITH LINE FLOW CONSTRAINTSP. SOMASUNDARAM, K. KUPPUSAMY, and R. P. KUMUDINI DEVIP. SOMASUNDARAMSchool of Electrical and Electronics Engineering, Anna University, Chennai, Tamil Nadu 600 025, India Search for more papers by this author , K. KUPPUSAMYSchool of Electrical and Electronics Engineering, Anna University, Chennai, Tamil Nadu 600 025, India Search for more papers by this author , and R. P. KUMUDINI DEVISchool of Electrical and Electronics Engineering, Anna University, Chennai, Tamil Nadu 600 025, India Search for more papers by this author https://doi.org/10.1142/S1465876304002277Cited by:1 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThis paper presents an efficient, simple and new method for solving Economic Dispatch (ED) problem with line flow constraints through the application of Evolutionary Programming (EP). The controllable system quantities in the base-case state are optimized to minimize some defined objective function subject to the base-case operating constraints. A 10-bus system is taken for investigation. The ED results obtained using EP are compared with those obtained using quadratic programming. The investigations reveal that the proposed algorithm is relatively simple, reliable, efficient and suitable for on-line applications.Keywords:Power System OptimizationEconomic DispatchEvolutionary ProgrammingLine Flow Constraints References B. H. Chowdhury and S. Rahman, IEEE Transaction on power systems 5(4), 1248 (1990). Crossref, Google Scholar Nanda, L. Hari and M. L. Kothari, IEE Proceedings-C 141(1), 1 (1994). Google ScholarJ. Nandaet al., IEE Proceedings-C 139(1), 39 (1992). Google ScholarJ. Y. Fan and L. Zhang, IEEE Transactions on Power Systems 13(2), 320 (1998). Crossref, Google ScholarP. K. Hota, R. Chakrabarti and P. K. Chattopadhyay, Institute of Engineers (India) Journal-EL 81, 21 (2000). Google ScholarJ. Nanda and R. Badri Narayanan, Electrical Power and Energy Systems 24, 723 (2002). Crossref, Google Scholar D. B. Fogel , Evoluationary Computation: Toward a New Philosophy of Machine Intelligence ( IEEE Press , USA , 1995 ) . Google ScholarL. L. Lai and J. T. Ma, IEEE Transactions on Power Systems 12(1), 198 (1997). Crossref, Google ScholarH. T. Yang, P. C. Yang and C. L. Huang, IEEE Transactions on Power Systems 11(1), 112 (1996). Crossref, Google ScholarK. P. Wong and J. Yuryevich, IEEE Transactions on Power Systems 13(2), 301 (1998). Google ScholarT. Jayabarathi, G. Sadasivam and V. Ramachandran, Electric Power Systems Research 52, 261 (1999). Crossref, Google ScholarT. Jayabarathi and G. Sadasivam, European Transaction on Electrical Power 10(3), 167 (2000). Crossref, Google ScholarP. Attaviriyanupapet al., IEEE Transactions on Power Systems 17(2), 411 (2002). Crossref, Google Scholar FiguresReferencesRelatedDetailsCited By 1FDR PSO-based Transient Stability Constrained Optimal Power Flow Solution for Deregulated Power IndustryM. Anitha, S. Subramanian and R. Gnanadass28 Aug 2007 | Electric Power Components and Systems, Vol. 35, No. 11 Recommended Vol. 05, No. 01 Metrics History KeywordsPower System OptimizationEconomic DispatchEvolutionary ProgrammingLine Flow ConstraintsPDF download

Economic dispatch in wind-integrated power systems is a critical challenge, yet many recent metaheuristics suffer from premature convergence, heavy parameter tuning, and limited ability to escape local optima in non-smooth valve-point landscapes. This study proposes a new hybrid optimization framework, the Fractional Grasshopper Optimization algorithm (FGOA), which integrates fractional-order calculus into the standard Grasshopper Optimization algorithm (GOA) to enhance its search efficiency. The FGOA method is applied to the economic load dispatch (ELD) problem, a nonlinear and nonconvex task that aims to minimize fuel and wind-generation costs while satisfying practical constraints such as valve-point loading effects (VPLEs), generator operating limits, and the stochastic behavior of renewable energy sources. Owing to the increasing role of wind energy, stochastic wind power is modeled through the incomplete gamma function (IGF). To further improve computational accuracy, FGOA is hybridized with Sequential Quadratic Programming (SQP), where FGOA provides global exploration and SQP performs local refinement. The proposed FGOA-SQP approach is validated on systems with 3, 13, and 40 generating units, including mixed thermal and wind sources. Comparative evaluations against recent metaheuristic algorithms demonstrate that FGOA-SQP achieves more accurate and reliable dispatch outcomes. Specifically, the proposed approach achieves fuel cost reductions ranging from 0.047% to 0.71% for the 3-unit system, 0.31% to 27.25% for the 13-unit system, and 0.69% to 12.55% for the 40-unit system when compared with state-of-the-art methods. Statistical results, particularly minimum fitness values, further confirm the superior performance of the FGOA-SQP framework in addressing the ELD problem under wind power uncertainty.

Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms

By integrating simulated annealing and the interactive best-compromise approach, a method to determine economic dispatch in power systems, while accounting for carbon emissions, is proposed in this study. To satisfy load demands and operational constraints, traditional economic dispatches are determined by minimizing total power generation costs based on the power output of the generating units in a system. In the proposed method, carbon emissions are taken into consideration. A carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) equivalent model that accounted for the various fuel types (e.g., coal, oil, and natural gas) used in power generation was created. The correlation between power generation costs and carbon emission was determined according to CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission equivalent tradeoff and incremental cost CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> reduction curves. The proposed method was applied to the Taiwan Power Company system. The results indicated that the method was effective at determining the influence of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions on power generation costs during off-peak, semi-peak, and peak hours, as well as daily load demands. The system allows economic and environmental benefits to be considered simultaneously.

Economic Dispatch is an important optimization problem in power system planning. This article presents an overview of the economic dispatch problem, its formulation, and a comparison of addressing the problem between the vertically integrated market and the liberalized market environments.

Renewable energy resources such as wind power generators are important alternatives in electric power systems considering their congenial environmental effects. Short-term wind speed forecasting have a huge impact on the load variation decisions and economic load dispatch in the wind-integrated power systems. Wind power is intermittent and is sometimes non-dispatchable because of its dependency on the atmospheric conditions, therefore accurate forecasting becomes necessary. In this paper different ANN algorithms i.e Levenberg-Marquardt back propagation, Bayesian Regularization & Scaled Conjugate Gradient algorithms has been applied in short-term wind speed forecasting that is one hour-ahead hourly forecast of the wind speed of Oak Park Weather Station, Ireland using MATLAB R14a. The data used in the forecasting are hourly historical data of the wind speed, temperature and wind direction. The simulation results have shown accurate one hour ahead forecasts with small error in wind speed forecasting.

The optimization of economic dispatch in hybrid diesel photovoltaic systems within Non-Interconnected Zones (NIZ) is essential to enhance energy sustainability and reduce operating costs. The variability of renewable generation and the uncertainty of electricity demand hinder efficient planning, underscoring the need for advanced optimization models. The purpose of this research was to develop an economic dispatch model for diesel generators integrated with photovoltaic generation, incorporating electricity demand forecasting. The methodology was based on formulating a quadratic programming problem and applying vector autoregressive models supported by socioeconomic variables. Simulations were carried out in Python using the IPOPT (Interior Point Optimizer) solver. The proposed model aimed to optimize operational efficiency by reducing CO₂ emissions and production costs. The analysis was applied to a modified version of the IEEE 33-bus distribution system. The results showed that the optimal dispatch reduced generation costs by 32.1%, decreasing from USD 15 853.83 in the base scenario to USD 10 769.82 with the inclusion of photovoltaic generation. Likewise, daily fuel consumption decreased by 4 227.4 gallons, while CO₂ emissions were reduced by 41 926.1 kg. In addition, solar generation contributed 4 249.2 kWh per day, equivalent to 5.09% of total demand, directly reducing technical losses from 292 kW to 243 kW. In conclusion, the results demonstrate that the integration of predictive models and optimization techniques improves operational performance and supports sustainable energy planning in isolated communities.

In this research paper a recently developed meta-heuristic technique whale algorithm with fuzzy decision making is proposed to solve the Economic emission load dispatch which focuses on optimizing the generation cost and emission function. Economic and emission both objectives are conflicting in nature so this is a multi-objective optimization problem known as multi-objective economic load dispatch (MELD). In this work fuzzy decision making is used to decide optimum point between these objectives. Transmission losses and valve pointing loading are taken into account with MELD to provide practical application to the proposed method. Penalty membership function is used to handle the equality constraints. Proposed approach of MELD is applied to six and ten unit generating systems.

Optimal economic dispatch for multi heat-electric energy source power system