Multi-criteria decision analysis of electricity sector transition policy in Korea

Choi, Donghyun; Ahn, Young-Hwan; Choi, Dong Gu

doi:10.1016/j.esr.2020.100485

Cited by 26 publications

(15 citation statements)

References 37 publications

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“…The study suggested that potential power shortages require more sustainable and renewable energy [66]. Another research has shown that there may be a comprehensive change in energy consumption in Korea in the future and ways to address complex sustainability issues [67]. Electricity demand was assessed using the LA approach, aiming to upgrade the estimates using the past pattern.…”

Section: 73mentioning

confidence: 99%

A review on short‐term load forecasting models for micro‐grid application

Kondaiah

Saravanan

Sanjeevikumar

et al. 2022

The Journal of Engineering

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Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role throughout the operation of the conventional power system. The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives. This article presents a survey of the latest analytical and approximation techniques reported in the literature to model STLF in an MG environment. This article mainly focusses on the review on important methods applied to forecast renewable energy availability, energy demand, and price and load demand. Different models, their main objectives, methodology, error percentage, and so forth, are critically reviewed and analysed. For quick reference, we have highlighted the important points in the form tables. The researchers can quickly identify and frame their research problem related to the LF area by reading this review paper.

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Section: 73mentioning

confidence: 99%

A review on short‐term load forecasting models for micro‐grid application

Kondaiah

Saravanan

Sanjeevikumar

et al. 2022

The Journal of Engineering

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“…These studies have focused on improving system efficiency, competitiveness, and installed generating capacity Example from the extant literature includes hybrid NG‐RE‐powered generating systems (e.g., Xu et al, 2017), analysis of efficiency and distributional effects of electricity rate designs and grid support services under high DERs penetration (Byrne et al, 2022; Byrne & Taminiau, 2016; Zhang & Giannakis, 2016), regulatory policy innovations (e.g., Carley et al, 2018), and diversified utility customer choice and generation mix (Stewart, 2020; e.g., Nyangon & Byrne, 2018). Similar studies include expanding rooftop solar PV development (e.g., Byrne & Taminiau, 2018), investigating sociotechnical dynamics of energy transitions (e.g., Choi et al, 2020; Jenkins et al, 2018; Sovacool, 2017; Turnheim & Sovacool, 2020), and promoting integrated coordination of electric and NG power systems (Jiang et al, 2018; e.g., Brandstätt et al, 2017). Additionally, Jenner et al (2012) assessed the drivers of RE electricity generation at the state level, including market structure and existence of solar energy associations, while Lee and Zhong (2015) investigated policy effectiveness of net‐metering mechanisms for distributed solar PV systems.…”

Section: Low‐carbon Flexible Generationmentioning

confidence: 99%

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Nyangon

Byrne

2022

WIREs Energy & Environment

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Expansion of distributed solar photovoltaic (PV) and natural gas-fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas-fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania-New Jersey-Maryland (PJM) Interconnection in the United States over the period 2008-2018. We developed an empirical dynamic panel data model using the systemgeneralized method of moments (system-GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions-level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost-related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non-wire alternative policy technique.

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“…Returning to physical and financial carbon assets at risk of being stranded, ML and AI techniques can provide appealingly pragmatic Pareto-optimal solutions for mitigating stranding risks among different policy aspects instead of using scalarization, thereby creating a balanced transition to lower-carbon technology [93][94][95]. What causes assets to strand?…”

Section: Mitigating Stranded Assets Risks Using ML and Ai Techniquesmentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

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The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

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Multi-criteria decision analysis of electricity sector transition policy in Korea

Cited by 26 publications

References 37 publications

A review on short‐term load forecasting models for micro‐grid application

A review on short‐term load forecasting models for micro‐grid application

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

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