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.

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.

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 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.

Simulated Annealing Technique for Dynamic Economic Dispatch

2006 ◽  
Vol 34 (5) ◽  
pp. 577-586 ◽  
Author(s):  
C. K. Panigrahi ◽  
P. K. Chattopadhyay ◽  
R. N. Chakrabarti ◽  
M. Basu
Keyword(s):  
Simulated Annealing ◽  
Economic Dispatch ◽  
Dynamic Economic Dispatch

Decline of Real Power Loss by the Combination of Ant Colony Optimization and Simulated Annealing Algorithm

2016 ◽  
Vol 23 ◽  
pp. 113-119 ◽  
Author(s):  
K. Lenin ◽  
B. Ravindhranath Reddy ◽  
M. Suryakalavathi
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Power Loss ◽  
Simulated Annealing Algorithm ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Local Optimum ◽  
Real Power ◽  
Combined Algorithm

Combination of ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm has been done to solve the reactive power problem.In this proposed combined algorithm (CA), the leads of parallel, collaborative and positive feedback of the ACO algorithm has been used to apply the global exploration in the current temperature. An adaptive modification threshold approach is used to progress the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the inactivity, immediately SA algorithm is used to get a local optimal solution. Obtained finest solution of the ACO algorithm is considered as primary solution for SA algorithm, and then a fine exploration is executed in the neighborhood. Very importantly the probabilistic jumping property of the SA algorithm is used effectively to avoid solution falling into local optimum. The proposed combined algorithm (CA) approach has been tested in standard IEEE 30 bus test system and simulation results show obviously about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.

A Modified ABC Algorithm for Solving Non-Convex Dynamic Economic Dispatch Problems

2016 ◽  
Vol 6 (6) ◽  
pp. 2621
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Optimization Method ◽  
Population Based ◽  
Test System ◽  
Time Interval ◽  
Ramp Rate ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Dynamic Economic Dispatch

<p>In this paper, a modified artificial bee colony (MABC) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature. It is shown that the optimum results can be obtained more economically and quickly using the proposed method in comparison with the earlier methods.</p>

scholarly journals Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zhenyu Zhao ◽  
Shuguang Yuan ◽  
Qingyun Nie ◽  
Weishang Guo
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Gradient Descent ◽  
Economic Dispatch ◽  
Test System ◽  
Modeling Approach ◽  
Continuous State ◽  
Market Operation ◽  
System Operator ◽  
Action Spaces

In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov’s decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers’ bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach.

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.

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