scholarly journals Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Dakuo He ◽  
Le Yang ◽  
Zhengsong Wang
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Transmission Loss ◽  
Economic Dispatch ◽  
Evolutionary Process ◽  
Mutation Operators ◽  
Adaptive Differential Evolution ◽  
Dynamic Economic Dispatch

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

scholarly journals Solving Bid-Based Dynamic Economic Dispatch in Competitive Electricity Market Using Improved Simulated Annealing Algorithm

2020 ◽  
Vol 7 (4) ◽  
pp. 621-630
Author(s):  
Riyadh Bouddou ◽  
Farid Benhamida ◽  
Ismail Ziane ◽  
Amine Zeggai ◽  
Moussa Belgacem
Keyword(s):  
Simulated Annealing ◽  
Power Systems ◽  
Electricity Markets ◽  
Electricity Market ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Economic Dispatch ◽  
Unit System ◽  
Annealing Algorithm ◽  
Dynamic Economic Dispatch

Electricity markets are open after the deregulation of power systems due to competition. An optimization problem based on dynamic economic dispatch has recently come up in the new context of deregulated power systems known as bid-based dynamic economic dispatch (BBDED). It is one of the major operations and control functions in the electricity markets used to determine the optimal operations of market participants with scheduled load demands during a specified period. BBDED involves power generation companies (GENCOs) and customers to submit energy and price bids to the independent system operator (ISO) in a day-ahead market. The ISO clears the market with the objective of social profit maximization. In this paper, a BBDED problem is solved using an improved simulated annealing algorithm (ISA), including system constraints with different periods under bidding strategies. The proposed ISA technique is implemented in MATLAB and applied on a 3-unit system, a 6-unit system, and a 40-unit large-scale system. The proposed ISA is evaluated by comparison with relevant methods available in the literature, to demonstrate and confirm its potential in terms of convergence, robustness, and effectiveness for solving the BBDED problem.

scholarly journals Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Comparative Study ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Evolutionary Programming ◽  
Swarm Optimization ◽  
Dynamic Economic Dispatch

AbstractThis paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

Optimization Techniques for the Economic Dispatch Problem in Various Generation Plant

2013 ◽  
Vol 768 ◽  
pp. 323-328
Author(s):  
K. Thenmalar ◽  
A. Allirani
Keyword(s):  
Simulated Annealing ◽  
Firefly Algorithm ◽  
Artificial Bee Colony ◽  
Simulated Annealing Algorithm ◽  
Economic Dispatch ◽  
Bee Colony ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm ◽  
Adaptive Simulated Annealing Algorithm ◽  
Dynamic Economic Dispatch

The dynamic economic dispatch (DED) occupies important place in a power systems operation and control. It aims to determine the optimal power outputs of on-line generating units in order to meet the load demand and reducing the fuel cost. The nonlinear and non convex characteristics are more common in the DED problem. Therefore, obtaining a optimal solution presents a challenge. In the proposed system, firefly algorithm, Adaptive simulated annealing algorithm, artificial bee colony (ABC) algorithm a recently introduced population-based technique is utilized to solve the DED problem. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters. Keywords: ABC-Artificial Bee Colony Algorithm, DED-Dynamic Economic Dispatch, FA-firefly algorithm, ASA-Adaptive Simulated annealing algorithm

scholarly journals Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

2020 ◽  
Vol 25 (6) ◽  
pp. 719-727
Author(s):  
Riyadh Bouddou ◽  
Farid Benhamida ◽  
Mekki Haba ◽  
Moussa Belgacem ◽  
Mohammed Amine Meziane
Keyword(s):  
Simulated Annealing ◽  
Wind Energy ◽  
Electricity Market ◽  
Simulated Annealing Algorithm ◽  
Supply And Demand ◽  
Economic Dispatch ◽  
Test System ◽  
Energy Integration ◽  
Market Clearing ◽  
Dynamic Economic Dispatch

In this paper, a bid-based dynamic economic dispatch (BBDED) problem is solved in the electricity market system under bidding strategies, including wind energy penetration using simulated annealing (SA) algorithm. The multi-objective dispatch model allows generating companies (GENCOs) and their customers to submit supply and demand bids to a market controller known as the independent system operator (ISO) and follow a bidding strategy. ISO is responsible for the market clearing and settlement to maximize the social profit and benefit for GENCOs and customers during trading periods. To study the effect and advantages of wind energy integration in the BBDED problem, the wind energy generation is computed using the forecasted wind speeds and included in the dispatch model. In this regard, the ISO's dispatch model is formulated as a bilevel nonlinear optimization problem. The higher-level is solving the market-clearing with and without wind energy, and the lower level is maximizing GENCO's social profit. The proposed SA algorithm is evaluated for optimality, convergence, robustness, and computational efficiency tested on an IEEE 30-bus test system. The simulation results are compared with those found using different algorithm-based approaches, considering various constraints like power balancing, generator limits, ramp rate limits, and transmission losses.

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