Optimization-based economic analysis of energy storage technologies in a coupled electricity and natural gas market

Dimitriadis, Christos N.; Tsimopoulos, Evangelos G.; Georgiadis, Michael C.

doi:10.1016/j.est.2022.106332

Cited by 31 publications

(7 citation statements)

References 39 publications

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“…Simultaneously, these community entities extensively and massively utilize renewable energy sources within the region, coordinating the utilization of their own combined heating and power (CHP) systems with those of surrounding IESs. Dimitriadis et al [8] proposed a coupled electricity and natural gas market model that includes a hybrid energy storage system and wind power generation. The model, by simulating the volatility of wind power generation, has demonstrated that such volatility can effectively increase the economic benefits of power systems equipped with energy storage technology.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Electricity and Carbon in Multi-Park Integrated Energy Systems

Li,

Ying,

et al. 2024

Energies

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In order to maximize the utilization efficiency of renewable energy resources and reduce carbon costs in multi-park integrated energy systems (MIESs), this paper proposes an electricity–carbon energy scheduling method for MIESs, where a electricity–carbon joint trading market is established to allow energy interactions between IESs so as to satisfy their energy deficiencies and surpluses. Simultaneously, through leveraging differences in carbon prices among regions, carbon quotas are shared between all IESs, thereby reducing the overall carbon trading costs within the region. The paper also suggests that to encourage carbon cooperation between IESs, incentive measures such as government subsidies could be provided to foster collaboration. The simulation results demonstrate that the proposed electricity–carbon energy scheduling method for MIESs can effectively improve the utilization flexibility of various energy resources and obtain the higher economic benefits, compared with the traditional method where each IES operates independently.

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Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Electricity and Carbon in Multi-Park Integrated Energy Systems

Li,

Ying,

et al. 2024

Energies

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“…In recent years, the growing usage of renewable energy sources such as wind and solar power in ports, the development of energy substitution technologies, virtual power plants, and energy storage technologies has necessitated the need for accurate short-term forecasting of port load [3,4]. Energy storage technologies have an important role to play during peak load hours in ports and by discharging during peak demand and charging during low demand, which can balance the energy supply and demand and maintain system stability [5]. Accurate port load forecasting and fast response capabilities enable energy storage systems to optimize dispatch and improve the reliability and economics of port area power systems.…”

Section: Introductionmentioning

confidence: 99%

Multifeature-Based Variational Mode Decomposition–Temporal Convolutional Network–Long Short-Term Memory for Short-Term Forecasting of the Load of Port Power Systems

Chen,

Ma,

Wei

2024

Sustainability

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Accurate short-term forecasting of power load is essential for the reliable operation of the comprehensive energy systems of ports and for effectively reducing energy consumption. Owing to the complexity of port systems, traditional load forecasting methods often struggle to capture the non-linearity and multifactorial interactions within the factors creating power load. To address these challenges, this study combines variational mode decomposition (VMD), temporal convolutional network (TCN), and long short-term memory (LSTM) network to develop a multi-feature-based VMD-TCN-LSTM model for the short-term forecasting of the power load of ports. VMD is first used to decompose the power load series of ports into multiple, relatively stable components to mitigate volatility. Furthermore, meteorological and temporal features are introduced into the TCN-LSTM model, which combines the temporal feature extraction capability of the TCN and the long term-dependent learning capability of the LSTM. Comparative analyses with other common forecasting models using the observed power load data from a coastal port in China demonstrate that the proposed forecasting model achieves a higher prediction accuracy, with an R-squared value of 0.94, mean squared error of 3.59 MW, and a mean absolute percentage error of 2.36%.

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“…In fact, such programs are only presented with the aim of fattening the demand profle, while in case of natural disasters, the charging/discharging behaviour of the CS must be changed to improve the resilience of the network. In recent studies, grid resiliency refers to the ability of the grid to prepare, survive, and rapidly recover itself during unexpected fault inception, like extreme weather conditions [21][22][23]. It should be noted that the development and implementation of the charging/discharging program with the aim of improving network resilience can reduce energy not supplied (ENS) to an acceptable level and thus reduce the cost of the DSO [24].…”

Section: Introductionmentioning

confidence: 99%

“…However, in the matter of the CS, particularly integrated with DERs, developing an optimal and proportionate charging/discharging schedule can efectively improve network resilience. Risk-based operational planning for enhancing grid resiliency has been proposed in the paper [21]. Furthermore, in [21,22], resilience improvement is obtained via infrastructure and minimization of outage duration.…”

Section: Introductionmentioning

confidence: 99%

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Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

Armaghan,

Asgharzadeh,

Yousefi-Khanghah

et al. 2023

International Transactions on Electrical Energy Systems

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This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail.

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Optimization-based economic analysis of energy storage technologies in a coupled electricity and natural gas market

Cited by 31 publications

References 39 publications

Optimal Scheduling of Electricity and Carbon in Multi-Park Integrated Energy Systems

Optimal Scheduling of Electricity and Carbon in Multi-Park Integrated Energy Systems

Multifeature-Based Variational Mode Decomposition–Temporal Convolutional Network–Long Short-Term Memory for Short-Term Forecasting of the Load of Port Power Systems

Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

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