A genetic algorithm – differential evolution based hybrid framework: Case study on unit commitment scheduling problem

Trivedi, Anupam; Srinivasan, Dipti; Biswas, Subhodip; Reindl, Thomas

doi:10.1016/j.ins.2016.03.023

Cited by 90 publications

(20 citation statements)

References 73 publications

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“…In such cases, metaheuristic optimization methods such as genetic algorithms, which are easy to implement and do not require derivative information, are often employed and have been widely used in energy system modelling studies [6,25,34,41]. In addition, the intrinsic parallelism of genetic algorithms could be exploited in our future investigations e.g.…”

Section: Introductionmentioning

confidence: 99%

An integrated optimization + learning approach to optimal dynamic pricing for the retailer with multi-type customers in smart grids

Meng

Zeng

Zhang

et al. 2018

Information Sciences

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In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE). The main objective of this paper is to support the retailer to make optimal day-ahead dynamic pricing decisions in such a mixed customer pool. To this end, we propose a two-level decision-making framework where the retailer acting as upper-level agent firstly announces its electricity prices of next 24 hours and customers acting as lower-level agents subsequently schedule their energy usages accordingly. For the lower level problem, we model the price responsiveness of different customers according to their unique characteristics. For the upper level problem, we optimize the dynamic prices for the retailer to maximize its profit subject to realistic market constraints. The above two-level model is tackled by genetic algorithms (GA) based distributed optimization methods while its feasibility and effectiveness are con-✩

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Section: Introductionmentioning

confidence: 99%

An integrated optimization + learning approach to optimal dynamic pricing for the retailer with multi-type customers in smart grids

Meng

Zeng

Zhang

et al. 2018

Information Sciences

View full text Add to dashboard Cite

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“…Hybrid algorithms normally give better optimal results. Some of the efficiently deployed hybrid metaheuristic algorithms in existing literature are the neural-network-based tabu search (NBTS) [89], GA and differential evolution (DE) [90], simulated annealing-based (EP) [91], PSO and EP [92], binary successive approach (BSA) and civilized swarm optimization (CSO) [93], and binary particle swarm optimization (BPSO) and PSO [94].…”

Section: Overview Of Algorithms For Solving Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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“…GA is considered as one of the modern optimization algorithms to solve the combinatorial optimization problem and is used to determine the parameters of model BN-KRR. Based on the survival and reproduction of the fitness, GA is continually applied to get new and better solutions without any pre-assumptions, such as continuity and unimodality [26][27][28]. The proposed model BN-KRR has been implemented in Matlab 7.8 programming language.…”

Section: N) (2)mentioning

confidence: 99%

Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

et al. 2019

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The Kernel ridge regression ( K R R) model aims to find the hidden nonlinear structure in raw data. It makes an assumption that the noise in data satisfies the Gaussian model. However, it was pointed out that the noise in wind speed/power forecasting obeys the Beta distribution. The classic regression techniques are not applicable to this case. Hence, we derive the empirical risk loss about the Beta distribution and propose a technique of the kernel ridge regression model based on the Beta-noise ( B N-K R R). The numerical experiments are carried out on real-world data. The results indicate that the proposed technique obtains good performance on short-term wind speed forecasting.

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A genetic algorithm – differential evolution based hybrid framework: Case study on unit commitment scheduling problem

Cited by 90 publications

References 73 publications

An integrated optimization + learning approach to optimal dynamic pricing for the retailer with multi-type customers in smart grids

An integrated optimization + learning approach to optimal dynamic pricing for the retailer with multi-type customers in smart grids

Energy Storage System Analysis Review for Optimal Unit Commitment

Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

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