Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation

Borghini, Eugenio; Giannetti, Cinzia; Flynn, J.; Todeschini, Grazia

doi:10.3390/en14123453

Cited by 20 publications

(8 citation statements)

References 33 publications

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“…(2) The output of electric vehicles is about: The electric vehicle battery charge and discharge constraints are shown in Eqs. (12)(13)(14)(15)(16)(17). We can see that Eq.…”

Section: Constraintsmentioning

confidence: 88%

“…In this context, the concept of virtual power plant (VPP) emerged. Without changing the distributed energy resources, through certain control and communication strategies, a large number of distributed power sources, energy storage, loads, etc., are assembled to participate in the electricity market [16][17][18][19]. Dispatching operation solves the problem of lack of effective coordination and control between independent distributed energy generation [20][21][22], and provides new ideas for power market operation.…”

Section: Introductionmentioning

confidence: 99%

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A Joint Optimization Algorithm for Renewable Energy System

Khan¹,

Muhammad‐Sukki²,

Rey³

et al. 2023

IASC

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Energy sustainability is a hot topic in both scientific and political circles. To date, two alternative approaches to this issue are being taken. Some people believe that increasing power consumption is necessary for countries' economic and social progress, while others are more concerned with maintaining carbon consumption under set limitations. To establish a secure, sustainable, and economical energy system while mitigating the consequences of climate change, most governments are currently pushing renewable growth policies. Energy markets are meant to provide consumers with dependable electricity at the lowest possible cost. A profit-maximization optimal decision model is created in the electric power market with the combined wind, solar units, loads, and energy storage systems, based on the bidding mechanism in the electricity market and operational principles. This model utterly considers the technological limits of new energy units and storages, as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant's coordinated operation. The accuracy and validity of the optimal decision-making model of combined wind, solar units, loads, and energy storage systems are validated using numerical examples. Under multioperating scenarios, the effects of renewable energy output changes on joint system bidding techniques are compared.

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“…(2) The output of electric vehicles is about: The electric vehicle battery charge and discharge constraints are shown in Eqs. (12)(13)(14)(15)(16)(17). We can see that Eq.…”

Section: Constraintsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

A Joint Optimization Algorithm for Renewable Energy System

Khan¹,

Muhammad‐Sukki²,

Rey³

et al. 2023

IASC

View full text Add to dashboard Cite

show abstract

“…In terms of spatial structure, internally, each distributed energy system includes a renewable energy conversion unit, a distributed energy supply unit and a storage capacity unit to meet the electricity, heat and cooling load requirements of the users within the system. Due to various constraints, wind power is often abandoned and limited, which results in a waste of wind power energy and contradicts the original intention of developing wind power [1][2] . At the same time, abandonment of wind power brings additional costs in addition to reduced wind power revenues.…”

Section: Quantifying Distributed Energy Demand Response Characteristicsmentioning

confidence: 99%

Optimal allocation of distributed energy storage capacity based on smart node terminal technology

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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In the context of the poor time performance of some distributed energy storage capacity optimisation methods, a distributed energy storage capacity optimisation method based on smart node terminal technology is designed. Quantify the distributed energy demand response characteristics, extract the economic index of the energy system, calculate the natural gas consumption of the internal combustion engine and the recovered waste heat respectively, obtain the storage capacity equipment operation coefficient, set the application scenario selection mode based on the intelligent node terminal technology, improve the load factor of the gas internal combustion engine, and design the storage capacity optimization allocation method. Test result: The average time required for the distributed energy storage capacity optimisation allocation method in the paper is: 72.36s, indicating that the designed distributed energy storage capacity optimisation allocation method is more suitable for application to practical tasks after the introduction of smart node terminal technology.

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“…The disadvantage may be the fact that t-SNE may produce some non-interpretable features, which can lead to difficulty in understanding the model. The LSTM method was also used in [21], together with random forest (RF) and convolutional neural networks (CNNs). In the literature, we can find a lot of combinations of the LSTM approach with other neural network models, which are used for electric load forecasing.…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Forecasting for the Digital-Twin Model of the Building

et al. 2022

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The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building.

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Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation

Cited by 20 publications

References 33 publications

A Joint Optimization Algorithm for Renewable Energy System

A Joint Optimization Algorithm for Renewable Energy System

Optimal allocation of distributed energy storage capacity based on smart node terminal technology

Energy Consumption Forecasting for the Digital-Twin Model of the Building

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