Review of stochastic optimization methods for smart grid

Reddy, S. Surender; Vuddanti, Sandeep; Jung, Chan-Mook

doi:10.1007/s11708-017-0457-7

Cited by 72 publications

(39 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The optimization problem arising from the search for a cost-effective control strategy has been extensively studied. Three recent survey papers Olivares et al (2014); Reddy et al (2017); Liang and Zhuang (2014) summarize different methods used for optimal usage, expansion and voltage control for the microgrids. Heymann et.…”

Section: Introductionmentioning

confidence: 99%

Regression Monte Carlo for microgrid management

et al. 2019

View full text Add to dashboard Cite

We study an islanded microgrid system designed to supply a small village with the power produced by photovoltaic panels, wind turbines and a diesel generator. A battery storage system device is used to shift power from times of high renewable production to times of high demand.We build on the mathematical model introduced in Heymann et al. (2017) and optimize the diesel consumption under a "no-blackout" constraint. We introduce a methodology to solve microgrid management problem using different variants of Regression Monte Carlo algorithms and use numerical simulations to infer results about the optimal design of the grid. *

show abstract

Section: Introductionmentioning

confidence: 99%

Regression Monte Carlo for microgrid management

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Less power deviation not only makes the system robust, but also affects the overall operational cost of the system. Table 4 shows the simulation results for various cost components which are described by Equations (1) to (7) in Section 2. Table 4 also shows the results of cost components for case 2.…”

Section: Study With Less Wind Power Generationmentioning

confidence: 99%

A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty

Bhattacharya

Chakraborty

Mandal

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary The trend of today's power system is to incorporate more green power in the conventional grid to mitigate the power demand and to minimize environmental pollution. This paper proposes a multi‐energy system with wind thermal–pump storage plant aiming to minimize the total cost of operation. In this paper, a forecasted data of wind speed is considered. Because of the variable nature of wind power, real‐time generation scheduling may lead to deviation of forecasted thermal power generation. This paper works on a power management strategy (PMS) where the gap between the day‐ahead scheduled thermal power and the real‐time scheduled power is minimized optimizing the operation cost. Cultural algorithm (CA) is used here very efficiently in a modified UI 62 bus. It is observed from the simulation results that the proposed strategy can effectively reduce the power deviation fluctuations with reducing the operating as well as runtime cost. A comparison of total cost between CA and particle swarm optimization (PSO) is drawn in this paper to observe the efficacy of the proposed method.

show abstract

“…RES are accommodated in optimal scheduling problems while tackling with the uncertainties related with solar and wind plants . A comprehensive review of optimization techniques and comparison among each other is also provided while focusing on the uncertainty and variability of RES . Bhuvanesh et al solved a multi‐objective multistage GEP problem by DE algorithm while taking into account the least green house gas (GHG) emissions .…”

Section: Introductionmentioning

confidence: 99%

“…18 A comprehensive review of optimization techniques and comparison among each other is also provided while focusing on the uncertainty and variability of RES. 19 Bhuvanesh et al solved a multi-objective multistage GEP problem by DE algorithm while taking into account the least green house gas (GHG) emissions. 20 Rajesh et al combined wind power units, 21 solar power units, 22 and solar power with storage facility 23 with the conventional GEP problem and solved using DE algorithm.…”

Section: Introductionmentioning

confidence: 99%

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Nawaz

Malik

Ashraf

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Ever‐increasing energy demand and technology development are continuously pursuing the attention of power system planners to design an optimum strategy for supplying future electrical demand. Generation expansion planning turns out to be a crucial step for an efficient energy management system in a modern power grid. In this paper, a novel whale optimization algorithm with penalty factor approach (WOA‐PFA) framework has been developed to solve a nonlinear, discrete, dynamic, highly complex, heavily constrained GEP problem. Reliability evaluation is accomplished using equivalent energy function approach. A virtual mapping procedure is utilized to add computational strength to the WOA‐PFA framework. Emission reduction is performed by utilizing total emission reduction constraint. The results are organized in four case studies starting with base power plants and adding wind, solar, and solar with storage facility in succession for the rest of case studies. Simulation results prove that the proposed WOA‐PFA shows promising results in terms of the least cost and computational time as compared with the techniques presented in the literature.

show abstract

Review of stochastic optimization methods for smart grid

Cited by 72 publications

References 46 publications

Regression Monte Carlo for microgrid management

Regression Monte Carlo for microgrid management

A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Contact Info

Product

Resources

About