A robust optimization framework for energy hub operation considering different time resolutions: A real case study

Honarmand, Hamed Asgarian; Shamim, Ahmad Ghaderi; Meyar‐Naimi, Hassan

doi:10.1016/j.segan.2021.100526

Cited by 21 publications

(7 citation statements)

References 34 publications

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“…Robust optimization approaches have been provided in [29,30] for optimal operation and scheduling of MCESs in an uncertain condition of renewable energy and electricity prices and gas fuel consumption by gas-fired units considering DR programs.…”

Section: Multi-carrier Energy Systemsmentioning

confidence: 99%

Optimization of transmission lines congestion management in integrated electricity and natural gas multi‐carrier energy systems

Khoshbouy,

Kazemzadeh

2024

IET Generation Trans & Dist

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The increasing interactivity between energy carrier networks such as electrical, natural gas (NG), and heating can be appropriately used for improving the operation optimality along with overcoming some challenges of those systems. With such a view, this paper studies the practical issue of power transmission lines congestion management in integrated multicarrier energy systems (MCESs) and makes a comparison with the only electrical operation mode. The MCES consisting of electrical and NG network infrastructures with exchanged energy between them is modelled by considering the several local energy hubs and their electrical and heat loads. Rescheduling of energy sources (power plants and gas wells) and demand side management in both electrical and NG networks have been utilized as the two important tools in the developed congestion alleviation framework. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to a remarkable reduction in the congestion management cost index. Implementing the proposed congestion management framework on an assumed test MCES with a combined 39‐bus New England power system and 20‐node Belgium NG system demonstrates its effectiveness and applicability for the system operators.

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Section: Multi-carrier Energy Systemsmentioning

confidence: 99%

Optimization of transmission lines congestion management in integrated electricity and natural gas multi‐carrier energy systems

Khoshbouy,

Kazemzadeh

2024

IET Generation Trans & Dist

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“…Several articles have only taken into account the uncertainty of the renewable energy sources, i.e., wind power and PV generation [104], [115], [124]. In addition, [80] utilized classic RO to deal with the uncertainty of PV generation in a real case study. Other studies have only focused on the uncertainty of the electricity market prices e.g.…”

Section: ) Classic Romentioning

confidence: 99%

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

et al. 2023

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Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

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“…In this study, Information-gap Decision Theory (IGDT) and risk measures are considered for better decision-making in energy generation with diverse levels of risk values. The energy optimization of a hospital building in the energy hub in Iran with consideration of the solar irradiance and wind speed uncertainties by robust approach is implemented in [28]. The bi-level optimization approach for energy scheduling and optimal bidding is modeled in [29] with optimal performance of the compressed air energy storage and cascaded consumption of heat in an energy hub.…”

Section: Related Studiesmentioning

confidence: 99%

Energy scheduling based-two layers interval optimization in a smart energy hub system with multi-performance of the hydrogen storage system and demand side management

Javid

Derakhshan

Hakimi

2023

Preprint

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A smart energy hub (SEH) is one of the significant opportunities in smart grids as a distinguished framework for optimal energy consumption and generation. The energy price uncertainty in energy markets for energy optimization is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a SEH under uncertainty of electricity prices in the day ahead in the energy market. The proposed optimization approach is implemented by a two-layer interval approach considering uncertainties for minimizing energy generation cost. In first layer optimization, electrical demand subject to the optimal level of electrical consumption is modified by the demand shifting strategy by using the demand side management (DSM). In second-layer optimization, the interval approach is used for modeling uncertainty; and energy generation cost is converted to Bi-objective functions including average and deviation costs with conflict nature than each other. Also, the multi-performance of the hydrogen storage system based-electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as Bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the three case studies to verify obtained results by implementing the DSM and hydrogen storage system participation.

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A robust optimization framework for energy hub operation considering different time resolutions: A real case study

Cited by 21 publications

References 34 publications

Optimization of transmission lines congestion management in integrated electricity and natural gas multi‐carrier energy systems

Optimization of transmission lines congestion management in integrated electricity and natural gas multi‐carrier energy systems

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Energy scheduling based-two layers interval optimization in a smart energy hub system with multi-performance of the hydrogen storage system and demand side management

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