Optimal gas-electric energy system decarbonization planning

Wald, Gregory Von; Sundar, Kaarthik; Sherwin, Evan D.; Zlotnik, Anatoly; Brandt, Adam R.

doi:10.1016/j.adapen.2022.100086

Cited by 22 publications

(4 citation statements)

References 84 publications

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“…In addition, due to the rapid development of meta-heuristic algorithms, many scholars use algorithms to optimize all aspects of energy systems. For example, the optimization of the thermal comfort of the house [ 16 ], the optimization of the stability of the nuclear-renewable energy hybrid system [ 17 ], the optimization of the utilization of renewable energy in the building [ 18 ], the optimization of the equipment capacity configuration of combined heat and power plant [ 19 ], the optimization of the energy cost of the HVAC energy terminal [ 20 ], the optimization of the energy management of the micro-grid [ 21 ], the optimization of the photovoltaic distribution system [ 22 ], carbon emission optimization [ 23 ], thermo-economic and environmental optimization of solar collector [ 24 ], energy subsidy optimization [ 25 ], economic transaction optimization of energy system [ 26 ], Multi-objective optimization of integrated energy systems [ 27 , 28 ], etc. By using the optimization algorithm to optimize the energy system, the energy system is more economical, energy-saving and stable.…”

Section: Introductionmentioning

confidence: 99%

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

Shao,

Yu,

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

Shao,

Yu,

et al. 2023

Heliyon

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“…Kotzur et al 's two-time layers enable seasonal storage calculation while deploying typical days. In (Beck et al 2022;Göke and Kendziorski 2022;Wirtz et al 2021;Neumann et al 2022;von Wald et al 2022;Hoffmann et al 2020) the storage formulation of Kotzur et al was applied to calculate seasonal storages. Gabrielli et al (2018) developed a different approach to calculate seasonal storage with fewer constraints but more variables.…”

Section: Introductionmentioning

confidence: 99%

Time series aggregation for energy system design: review and extension of modelling seasonal storages

et al. 2022

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Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.

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“…A transition to a carbon-constrained world will likely require operating this network in new ways, potentially blending increasing amounts of hydrogen (H 2 ) or biomethane or even retiring part or all of the system in favor of electrification of end uses. , Any of these approaches will place stresses on the network outside of its original design purposes. How will existing steel and plastic assets interact with changing gas composition?…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Representative Sampling for a Natural Gas Distribution System in Transition

Sherwin

Lever²,

Brandt

2022

ACS Omega

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Natural gas distribution systems within municipalities supply a substantial fraction of energy consumed in the United States. As decarbonization of the natural gas system necessitates new modes of operation outside original design purposes, for example, increased hydrogen or biogas blending, it becomes increasingly important to understand in advance how existing infrastructure will respond to these changes. Such an analysis will require detailed information about the existing asset base, such as local soil composition, plastic type, and other characteristics that are not systematically tracked at present or have substantial missing data. Opportunistic sampling, for example, collecting measurements at assets that are already undergoing maintenance, has the potential to substantially reduce the cost of gathering such data but only if the results are representative of the full asset base. To assess prospects for such an approach, we employ a dataset including the entire service line and leak database from a large natural gas distribution utility (∼66,700 km of service pipelines and over 530,000 leaks over decades of observations). This dataset shows that service lines affected by excavation damage produce an approximately random sample of plastic and steel service lines, with similar distributions of component age, operating pressure, and pipeline diameter, as well as a relatively uniform spatial distribution. This means that opportunistic measurements at these locations will produce a first-order estimate of the relative prevalence of key characteristics across the utility's full asset base of service lines. We employ this approach to estimate the plastic type, which is unknown for roughly 80% of plastic service lines in the database. We also find that while 32% of leaks across all components occur in threaded steel junctions, excavation damage accounts for 75% of hazardous grade 1 leaks in plastic service lines and corrosion accounts for 47% in steel service lines. Insights from this sampling approach can thus help natural gas utilities collect the data they need to ensure a safe and reliable transition to a lower-emission system.

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Optimal gas-electric energy system decarbonization planning

Cited by 22 publications

References 84 publications

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

Energy-saving optimization of the parallel chillers system based on a multi-strategy improved sparrow search algorithm

Time series aggregation for energy system design: review and extension of modelling seasonal storages

Low-Cost Representative Sampling for a Natural Gas Distribution System in Transition

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