A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

Dehghani, Majid; Taghipour, Mohammad; Gougheri, Saleh Sadeghi; Nikoofard, Amirhossein; Gharehpetian, Gevork B.; Khosravy, Mahdi

doi:10.3390/en14238035

Cited by 12 publications

(6 citation statements)

References 46 publications

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“…Figure 8 shows the plotted graph for the multichannel CNN model. As can be observed, the model starts with a convolutional layer with the input shape (3,4), which means that the model receives three previous time steps consisting of four input features.…”

Section: Multichannel Cnnmentioning

confidence: 99%

“…The study concluded that under the full scenario, offshore wind power could become a major energy source towards the end of the decarbonization pathway. In their study [4], Dehghani et al recently demonstrated the importance of including the power plants' lifetime as a factor in the generation expansion planning problem, guided by a deep learning model to predict annual peak demand growth. Later, Zhao and You extended their work in [3] by introducing a novel robust optimization framework that integrated multiple machine learning techniques to provide more realistic transition pathways for New York [5].…”

Section: Introductionmentioning

confidence: 99%

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A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

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Section: Multichannel Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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“…Despite its complex structure and the diversity of registered user data, the researchers [30] work here assumes the MIA in a semi-white box scenario where system model structures and parameters are available but no user data information is available, and verifies it as a serious threat even for a deep-learning-based face recognition system. The impact of power plants on GEP over their lifetime is studied in this article [31]. Deep learning-based techniques are also commonly employed for time series forecasting.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

A Review on Deep Learning Techniques for IoT Data

et al. 2022

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Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

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“…For this reason, the generation expansion planning (GEP) problem can be considered to ensure that a fraction of future load demand would be supplied. For years, different optimization techniques have been investigated in order to provide an optimal plan for the expansion of generation [1].…”

Section: Introductionmentioning

confidence: 99%

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Jalalzad¹,

Yektamoghadam

Haghighi

et al. 2022

Energies

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In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

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A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

Cited by 12 publications

References 46 publications

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

A Review on Deep Learning Techniques for IoT Data

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

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