Optimal Operation Strategy of Port Integrated Energy System Considering Demand Response

Pu, Yue; Chen, Wei; Zhang, Ruicong; Liu, Haoming

doi:10.1109/ei250167.2020.9346904

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…In addition to supplying electrical energy to the system, it also has the capability to provide heating and cooling energy. This makes it a versatile and valuable asset for enhancing the reliability and resilience of the seaport energy system [11]. By providing multiple forms of energy, the CCHP system can mitigate the impacts of potential disruptions in the system driven by random contingencies or natural disasters.…”

Section: Motivation and Rationalementioning

confidence: 99%

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Xie,

Dehghanian,

Estebsari

2023

IET Generation Trans & Dist

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The ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios.

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Section: Motivation and Rationalementioning

confidence: 99%

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Xie,

Dehghanian,

Estebsari

2023

IET Generation Trans & Dist

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“…Wei et al [6] introduce a thermal sensation average scale prediction index to describe user heating comfort, transforming the traditional heat load curve into a load demand interval, thereby increasing the flexibility of the load schedule. Pu et al [7] propose a CHP scheduling strategy that considers heat supply storage and thermal inertia to flexibly adjust the thermal load. Tian et al [8] maintain indoor thermal comfort by changing the duty ratios of central air conditioning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Building Integrated Energy System Considering Demand Response and User Comfort

Zhou,

Qin,

Wang

et al. 2024

Frontiers in Artificial Intelligence and Applications

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The building itself has a certain thermal inertia, which makes the energy supply process of the energy system flexible. At the same time, the comfort of the user should be ensured in the process of building energy supply adjustment. To solve the above problems, this paper proposes a considering demand response (DR) and user comfort of building integrated energy system (BIES) multi-objective optimization method. Firstly, a typical BIES model and a device power model are constructed. Secondly, according to the price elasticity matrix and building thermal inertia, the price-type and regulation-type DR models are constructed. Then, according to the economic, environmental protection, and comfort requirements of the BIES, considering the constraints of power balance, equipment operation, DR, and so on, the BIES optimal scheduling model is established. The multi-objective gray wolf optimizer (MOGWO) algorithm is optimized by chaos operator and t distribution. The model is solved and the optimized algorithm is to obtain the Pareto optimal solution set, using the VIKOR method for the optimal decision solution set. Finally, the BIES in a building is used as an example for analysis, and the results show that the proposed method can effectively balance the efficiency of system operation, environmental protection, and energy efficiency.

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“…Electricity accounts for an increasing proportion of energy consumption in ports, making energy management and optimization in ports particularly important. 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].…”

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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Optimal Operation Strategy of Port Integrated Energy System Considering Demand Response

Cited by 12 publications

References 15 publications

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Multi-Objective Optimization of Building Integrated Energy System Considering Demand Response and User Comfort

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

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