Matrix modeling of energy hub with variable energy efficiencies

Huang, Wujing; Zhang, Ning; Wang, Yi; Capuder, Tomislav; Kuzle, Igor; Chen, Kang

doi:10.1016/j.ijepes.2020.105876

Cited by 53 publications

(26 citation statements)

References 31 publications

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“…Considering the characteristics of equipment under variable conditions will make the model have nonlinear constraints. Using polynomial fitting for linearization or piecewise linearization can preserve the linear coupling relationship between the input and output of the device, but there are still large errors (Huang et al, 2020). At present, the optimized operation of IES takes less consideration of the characteristics of equipment under different conditions.…”

Section: Equipment Operating Statusmentioning

confidence: 99%

Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

et al. 2022

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The operation of the integrated energy system will be affected by uncertainties, leading to sub-optimal design decisions. Accurate and effective modeling of these uncertainties is essential to ensure the optimal integration of renewable energy in the integrated energy system. This article first gives the basic optimal operation model of the integrated energy system, and analyzes various uncertain factors in the optimal operation of the integrated energy system. The source side mainly considers the intermittent and volatility of renewable energy output. The load side mainly considers the uncertainty of load forecast errors. The network side mainly considers the characteristics of equipment under variable conditions, equipment failures, and the uncertainty of the coupling of multi-energy flow systems. The energy storage side mainly considers the uncertainty of mobile energy storage grid connection and virtual energy storage participation in demand response. The multi-energy trading market mainly considers the uncertainty of market trading methods and market operation mechanisms. Then, various integrated energy system uncertainty modeling techniques and solving methods are summarized. Finally, it looks forward to the future research directions and challenges faced by the optimal operation of the integrated energy system under the influence of uncertainty.

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Section: Equipment Operating Statusmentioning

confidence: 99%

Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

et al. 2022

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“…The EH is the energy conversion main body of the ECU. In this article, the standard matrix model is adopted for the EH model (Wang et al, 2017), and the piecewise linearization method is used to improve the accuracy of the EH model (Huang et al, 2019).…”

Section: Eh Model Considering Off-design Performancementioning

confidence: 99%

Distributed Collaborative Optimization of a Multi-Region Integrated Energy System Based on Edge Computing Unit

et al. 2022

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The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

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“…The internal equipment of entity IESS mainly realizes multi‐energy conversion and storage, and the space distance between them is short. Therefore, the EH model can be used to represent the energy input and output [29]. For virtual IESSs, more attention is given to the coupling between various energy networks and the modelling of multi‐energy flow networks.…”

Section: The Architecture Of Iessmentioning

confidence: 99%

Architecture and function analysis of integrated energy service stations considering cyber‐physical integration

Liu

Wang

Tang

2021

Energy Conversion and Econom

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With the continuing development of the energy internet (EI), the high complexity of multienergy coupling and the dramatic increase in cyber-physical integration impose stricter requirements on the planning and construction of multi-energy systems. The construction of a new energy hub through the intensive use of existing power substation resources is one of the feasible approaches for satisfying these requirements. Integrated energy service stations (IESSs), which comprise substations, multi-energy conversion stations, data centres, communication base stations, and other functional units, constitute the emerging generation of energy and information control centres. IESSs are capable of all-in-one monitoring and energy optimization, and can effectively manage regional energy services involving various energy requirements. This study initially proposes two feasible schemes to realize IESSs: entity IESSs, which require replanning and construction, and virtual IESSs, which involve transformation through existing substation resources. Thereafter, based on the requirements of three types of users, that is high-energy-consumption users, highreliability users, and high-comfort users, the necessity of IESSs in the construction of the EI is analysed in depth. Furthermore, the feasibility of constructing an IESS based on existing power grid resources and the direction for the future development are discussed.

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Matrix modeling of energy hub with variable energy efficiencies

Cited by 53 publications

References 31 publications

Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

Review of Uncertainty Modeling for Optimal Operation of Integrated Energy System

Distributed Collaborative Optimization of a Multi-Region Integrated Energy System Based on Edge Computing Unit

Architecture and function analysis of integrated energy service stations considering cyber‐physical integration

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