Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

Pan, Qiao; Liu, Weilong; Zhang, Yong; Zeng, Shihong; Graham, Byron

doi:10.1016/j.rser.2023.113209

Cited by 16 publications

(7 citation statements)

References 68 publications

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“…Over the years, numerous planning models and optimization techniques have been developed to address the complexity and uncertainty associated with power systems. These models and procedures have evolved from traditional deterministic programming to stochastic programming [1] and from single-stage to multistage optimization frameworks [2]. Deterministic models assume that all the inputs and parameters are known with certainty, whereas stochastic models consider the uncertainty associated with load growth, fuel prices, and other factors [3].…”

Section: Technical Literature Reviewmentioning

confidence: 99%

“…Based on the literature review, there is a gap to fill in the SDDiP methodology regarding the algorithm cut settings. This study makes the following contributions: (1) We propose a pattern of cuts to be used in GTCEP problems, and (2) We validate the performance in terms of convergence and simulation time solving two IEEE test power systems. Several simulations are conducted to determine the performance and solver simulation time applying these cuts.…”

Section: Contributionsmentioning

confidence: 99%

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Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

Vergara,

Gil,

Hinojosa

2024

Energies

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This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.

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Section: Technical Literature Reviewmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

Vergara,

Gil,

Hinojosa

2024

Energies

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“…With the rapid development of artificial intelligence, machine learning and computational platforms [8], machine-learning-based methods have become a powerful solution to management issues in batteries [9,10]. Numerous machine-learning-based solutions have been developed to estimate batteries' internal states [11][12][13][14], forecast batteries' future ageing dynamics [15][16][17] and remaining useful life (RUL) [18,19], diagnose battery faults [20][21][22] and optimize battery charging [23][24][25][26] and energy management [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Dai,

Xiang,

Zhou

et al. 2024

WEVJ

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Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications.

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“…Studies on using renewable energy in electricity generation have been extensively conducted as an alternative policy for reducing CO 2 emissions on a national scale [16]. On a regional scale, using renewable energy in electricity generation can help to create a sustainable electricity supply system [17].…”

Section: Introductionmentioning

confidence: 99%

Towards a Net Zero-Emission Electricity Generation System by Optimizing Renewable Energy Sources and Nuclear Power Plant

Rahmanta,

Hasibi,

Tambunan

et al. 2024

Energies

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Greenhouse gas emissions, including CO2 emissions, are an issue in the energy sector that must be addressed urgently. The energy sector, including electricity, has been given a global aim of net zero emissions (NZE). This article examines three scenarios for reaching net-zero emissions in power supply. These scenarios are baseline, NZE1, and NZE2. The baseline scenario represents power plant capacity planning based on existing regulations in the base year. The net zero emissions consisting of the NZE1 and NZE2 scenarios aim to achieve net zero emissions by 2060. The NZE1 and NZE2 scenarios differ in the usage of nuclear power plant technology. The NZE1 scenario employs advanced costs for small modular reactors and large reactors technology, whilst the NZE2 scenario employs the low cost of small modular reactors and large reactors. The three scenarios were implemented and examined using the low emissions analysis platform software. The analytical results demonstrate that the NZE1 and NZE2 scenarios can meet the net zero emission objective by 2058. The baseline scenario results in power plant capacity planning with an average annual CO2 emission growth rate of 3.58%. On the other hand, the baseline scenario has the lowest investment expenses, at only 44 billion USD.

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Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

Cited by 16 publications

References 68 publications

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Towards a Net Zero-Emission Electricity Generation System by Optimizing Renewable Energy Sources and Nuclear Power Plant

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