Flexible Co-Scheduling of integrated electrical and gas energy networks under continuous and discrete uncertainties

Nikoobakht, Ahmad; Aghaei, Jamshid; Fallahzadeh-Abarghouei, Hossein; Hemmati, Rasul

doi:10.1016/j.energy.2019.06.053

Cited by 23 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar study has been accomplished in [7] for a WPA as a price-maker in the balancing market while being a price-taker in the DA market. A hybrid stochastic-information gap decision theory algorithm is used to handle the wind uncertainty and power equipment failure in [8,9].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

Ghavidel

Rajabi

Ghadi

et al. 2021

IET Energy Syst Integration

View full text Add to dashboard Cite

This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi-objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57-bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

Ghavidel

Rajabi

Ghadi

et al. 2021

IET Energy Syst Integration

View full text Add to dashboard Cite

show abstract

“…Finally, a robust planning method is adopted for optimization. Except for load uncertainty, Reference [17] also proposes a method to handle the uncertainties of wind power and power generation equipment failure, which uses the hybrid stochastic-information gap decision theory (HS-IGDT) algorithm to deal with the uncertain set, and verifies the feasibility of the model through a 10-node Institute of Electrical and Electronics Engineers (IEEE) standard test system. Moreover, a two-stage stochastic optimization model is proposed in Reference [18] to solve the uncertainty of power demand and to further help CCHP system operators formulate reasonable price strategies at different demand levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Liu

Nie

2019

Sustainability

View full text Add to dashboard Cite

With the rapid transformation of energy structures, the Integrated Energy System (IES) has developed rapidly. It can meet the complementary needs of various energy sources such as cold, thermal, and electricity in industrial parks; can realize multi-energy complements and centralized energy supplies; and can further improve the use efficiency of energy. However, with the extensive access of renewable energy, the uncertainty and intermittentness of renewable energy power generation will greatly reduce the use efficiency of renewable energy and the supply flexibility of IES so as to increase the operational risk of the system operator. With the goal of minimum sum of the system-operating cost and the carbon-emission penalty cost, this paper analyzes the combined supply of cooling, heating, and power (CCHP) influence on system efficiency, compared with the traditional IES. The flexible modified IES realizes the decoupling of cooling, thermal, and electricity; enhances the flexibility of the IES in a variety of energy supply; at the same time, improves the use efficiency of multi-energy; and reasonably avoids the occurrence of energy loss and resource waste. With the aim of reducing the risk that the access of renewable energy may bring to the IES, this paper introduces the fuzzy c-mean-clustering comprehensive quality (FCM-CCQ) algorithm, which is a novel method superior to the general clustering method and performs cluster analysis on the output scenarios of wind power and photovoltaic. Meanwhile, conditional value at risk (CVaR) theory is added to control the system operation risk, which is rarely applied in the field of IES optimization. The model is simulated in a numerical example, and the results demonstrate that the availability and applicability of the presented model are verified. In addition, the carbon dioxide emission of the traditional operation mode; thermoelectric decoupling operation mode; and cooling, thermal, and electricity decoupling operation mode of the IES decrease successively. The system flexibility is greatly enhanced, and the energy-use rate of the system is improved as a whole. Finally, IES, after its flexible transformation, significantly achieve energy conservation, emission reduction, and environmental protection.

show abstract

“…Many other researches have been conducted in this field by Gharib [32] in quantitative-fuzzy Controller Design for Multivariable Systems with Uncertainty. Hemmati et al [33][34][35] have used metaheuristic optimization techniques for solving problems in this field. In this paper, we intend to present a method for load balancing in the cloud computation spaces based on the line length of virtual machine's transactions, the average waiting time, the average response time, the number of failed transactions, the used memory and the level of processor.…”

Section: Introductionmentioning

confidence: 99%

Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks<strong><em> </em></strong>

Zakeri¹,

Krishnasamy²,

Gomathi³

2020

Preprint

View full text Add to dashboard Cite

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

show abstract

Flexible Co-Scheduling of integrated electrical and gas energy networks under continuous and discrete uncertainties

Cited by 23 publications

References 21 publications

Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Multi-Variable Stress Balancing Wireless Method Based on the Status of the Machines in the Cloud Spaces by Neural Networks<strong><em> </em></strong>

Contact Info

Product

Resources

About