Robust optimization-based energy management of hybrid AC/DC microgrids

Mir, Mahdi; Abbasi, Sadeq; Ghadimi, Noradin; Bagherpour, M.

doi:10.1016/b978-0-12-817491-3.00012-x

Cited by 3 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9,10 The climate is constantly changing, especially in the coming years, we will face significant climate change that can cause changes in the flow of rivers. 11 Therefore, predicting the future climate and its impact on the number of water resources and hydropower generation will have a significant impact on the management of hydropower reservoirs, electricity, flow, and control of the relationship between production and energy demand. 12,13 One of the methods of climate forecasting is the use of climate models that are widely used by researchers.…”

Section: Introductionmentioning

confidence: 99%

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

View full text Add to dashboard Cite

One of the most sensitive factors affecting hydropower generation is climate change.The objective of this study is to forecast the hydropower generation under the influence of climate change for the next 50 years. For this purpose, the GFDL-CM3 model is used under three scenarios: RCP2.6, RCP4.5, and RCP8.5 to predict precipitation and temperature. Any change in the inlet flow to the turbine will cause changes in the hydropower output. Therefore, the more accurately the flow is estimated, the hydropower generation is forecasted more accurately. In this research, a hydrological-neural network hybrid model for flow prediction is presented in which the Improved Sparrow Search Algorithm (ISSA) has been used. The results forecasting climate parameters showed that the average annual rainfall, runoff, and evaporation have a downward trend compared to the control period. The minimum and maximum annual temperatures have an increasing trend compared to the control period. To evaluate the hydropower generation Aras dam, the HEC-Ressim reservoir model is utilized. By comparing the average annual hydropower with the control period, it is known that the power generation will decrease in future years. The average annual electricity generated by hydropower under the scenario RCP2.6 and RCP4.5 and RCP8.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Optimization of residential building envelopes using an improved Emperor Penguin Optimizer

Tang

Zafetti

2020

Engineering with Computers

View full text Add to dashboard Cite

An Adaptive Robust Optimization Model for Microgrids Operation Using Convexified AC Power Flow Equations

Mollayousefi Zadeh,

Afshar,

Farahani

et al. 2023

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods.

show abstract

Robust optimization-based energy management of hybrid AC/DC microgrids

Cited by 3 publications

References 9 publications

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Optimization of residential building envelopes using an improved Emperor Penguin Optimizer

An Adaptive Robust Optimization Model for Microgrids Operation Using Convexified AC Power Flow Equations

Contact Info

Product

Resources

About