IGDT-Based Robust Operation of Integrated Electricity and Natural Gas Networks for Managing the Variability of Wind Power

Mirzaei, Mohammad Amin; Sadeghi-Yazdankhah, Ahmad; Nazari-Heris, Morteza; Mohammadi‐Ivatloo, Behnam

doi:10.1007/978-3-030-04296-7_8

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…A common approach to joint optimization is to model wind generation as stochastic and uncertain, and develop two-stage stochastic formulations for joint gas and electricity system operations [92]- [94], [150], [151]. Other approaches use interval optimization [152] or draw on robust optimization techniques [64], [153]- [158]. Most of these studies consider operational scheduling, and some include emerging power-to-gas technologies [154], [159]- [161] or expansion planning [64], [156], [162].…”

Section: Uncertainty Management In Gas-electric Energy Systemsmentioning

confidence: 99%

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

Roald¹,

Sundar²,

Zlotnik³

et al. 2020

Preprint

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In many parts of the world, electric power systems have seen a significant shift towards generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines, and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants, and only consider predetermined gas consumption. The results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.

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Section: Uncertainty Management In Gas-electric Energy Systemsmentioning

confidence: 99%

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

Roald¹,

Sundar²,

Zlotnik³

et al. 2020

Preprint

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“…In [86], unit commitment problem has been solved subject to uncertainties of demand and renewable units generation via robust optimization method and IGDT and the results of employed methodologies are presented to derive the necessary conclusions. Unit commitment problem has been also solved for integrated electricity and gas networks in [87] in which the impact of uncertainty of wind generation on performance of operating system has been assessed with IGDT. Similarly, the unit commitment problem has been investigated without considering security constraints in [53] where demand response program has been used to improve economic performance of system.…”

Section: • Unit Commitmentmentioning

confidence: 99%

Application of information gap decision theory in practical energy problems: A comprehensive review

2019

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The uncertainty and risk modeling is a hot topic in operation and planning of energy systems. The system operators and planners are decision makers that need to handle the uncertainty of input data of their models. As an example, energy consumption has always been a critical problem for operators since the forecasted values, and the actual consumption is never expected to be the same. The penetration of renewable energy resources is continuously increasing in recent and upcoming years.These technologies are not dispatch-able and are highly dependent on natural resources. This would make the real-time energy balancing more complicated. Another source of uncertainty is related to energy market prices which are determined by the market participants' behaviors. To consider these issues, the uncertainty modeling should be performed. Various approaches have been previously utilized to model the uncertainty of these parameters such as probabilistic approaches, possibilistic approaches, hybrid possibilisticprobabilistic approach, information gap decision theory, robust and interval optimization techniques. This paper reviews the research works that used information gap decision theory for uncertainty modeling in energy and power systems.

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“…In instances of low or no curtailed wind power, three effects may occur: 1) low production of gas leading to reduced revenue, 2) longer nonoperational time, and 3) additional costs resulting from increased flushing of the electrolyzer due to more shutdown and startup events (Wulf and Zapp, 2018). In the study conducted by (Li et al, 2018;Mirzaei et al, 2019;Sawas and Farag, 2019;Zhang et al, 2022), curtailed wind power is utilized to supply P2G to facilitate the production of natural gas. The obtained natural gas is used by gas fired generators to generate electricity in the process called power to gas to power (P2G2P).…”

Section: Introductionmentioning

confidence: 99%

Coordinated operation and optimal sizing strategy of P2G—CNG trucks scheme on wind farm for participating in retail gas market

Salimu,

Zhang,

Zhang

et al. 2024

Front. Energy Res.

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The increase in wind penetration and the stochastic nature of wind can cause some wind power to be curtailed, leading to a loss of revenue. Wind farm owners can invest in power to gas (P2G)—compressed natural gas (CNG) truck systems to utilize the curtailed wind power that would otherwise have been wasted. This paper proposes a coordinated operation and optimal sizing strategy of P2G and CNG trucks. P2G derives its power from curtailed wind power and also purchases electricity at low prices to facilitate methane (natural gas) production. The methane is directly stored in CNG trucks, which are used as temporary storage and as a means of transporting the gas, thereby reducing costs by eliminating storage tank. Firstly, a coordinated operation strategy for P2G and CNG trucks considering curtailed wind power and electricity purchased from the grid is presented. Secondly, an optimal sizing optimization model to determine the capacity of P2G and the number of CNG trucks using particle swarm optimization is presented, considering the coordinated operation strategy and operation constraints to maximize profit when the proposed system participates in the retail gas market. Finally, case studies were conducted to verify the viability of the proposed strategy using curtailed wind power data, electricity price data, and gas price data. Result show that optimal capacity of the P2G plant is 9.9 MW, and the optimal number of CNG trucks is 2. There is a 19.8% cost reduction with a payback period of 5.728 years compared to the conventional strategy with a payback period of 8.52 years. 89.74% of curtailed wind power is utilized, while only 10.26% of curtailed wind power is unutilized. Higher utilization of curtailed wind power that would otherwise have been wasted is achieved. The system earns a profit by selling the produced natural gas to a retail gas market.

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IGDT-Based Robust Operation of Integrated Electricity and Natural Gas Networks for Managing the Variability of Wind Power

Cited by 6 publications

References 24 publications

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

Application of information gap decision theory in practical energy problems: A comprehensive review

Coordinated operation and optimal sizing strategy of P2G—CNG trucks scheme on wind farm for participating in retail gas market

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