The Impact of Carbon Emission Quota Allocation Regulations on the Investment of Low-Carbon Technology in Electric Power Industry Under Peak-Valley Price Policy

Chen, Wei; Ma, Yongkang; Bai, Chunguang

doi:10.1109/tem.2021.3121002

Cited by 19 publications

(9 citation statements)

References 39 publications

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“…They find that BM is more conducive to renewable energy investment than GM. Chen et al (2021b) discuss the carbon abatement technology investment under cap-and-trade mechanisms for an electricity supply chain with a peak-valley pricing policy. They find that the peak-valley pricing policy will encourage the electricity generator to invest more carbon abatement technology than the flat pricing policy.…”

Section: Carbon Abatement Innovation In the Electric Power Industrymentioning

confidence: 99%

“…As the unit carbon quota ( e 0 ) under the benchmarking mechanism (BM)and the total carbon quota ( E i ) under the grandfathering mechanism (GM) for Firm i are restricted by the government,e 0 ≥ 0 and E i ≥ 0, a > max w(2bdi+ci(2b+di))dj ej −2bwdi(cj +dj )ei cidi(cj +dj ) , w(2bdi+ci(2b+di))dj ej −bwdi(cj +dj )ei (bdi(cj+dj)+ci(b+di)(cj+dj)) , i = 1, 2; j = 3 − i , as in Ji et al (2017) and Chen et al (2021b).…”

Section: Model Frameworkmentioning

confidence: 99%

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Competition vs cooperation: renewable energy investment under cap-and-trade mechanisms

Chen

2022

Financ Innov

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This paper explores the incentives of investment in renewable energy of two utility firms who compete or cooperate under either a cap-and-trade grandfathering mechanism (GM) or benchmarking mechanism (BM). We find that utility firms will invest in renewable energy more under BM than under GM, in both competitive and cooperative markets, and they will invest more in a competitive market than in a cooperative market, under either GM or BM. Furthermore, utility firms will produce more electricity and generate more total carbon emissions under BM than under GM. The profits of two firms, however, are higher in cooperative market than in competitive market. The government will benefit from implementing a BM to encourage utility firms to invest in renewable energy in a competing market.

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Section: Carbon Abatement Innovation In the Electric Power Industrymentioning

confidence: 99%

Section: Model Frameworkmentioning

confidence: 99%

Competition vs cooperation: renewable energy investment under cap-and-trade mechanisms

Chen

2022

Financ Innov

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“…On the Power technology progress other hand, with the further increase of carbon emission reduction constraints, scholars believe that carbon emission reduction is an institutional constraint of electric power production and consumption. Especially in the market environment without adequate supervision, carbon emission reduction constraints are necessary to encourage economic entities to improve the utilization of electric energy (Chen et al, 2021;Pan et al, 2020). To confirm the constraint effect of carbon emission reduction on the utilization of electric power resources, scholars incorporate carbon emissions into the process of electric power consumption, construct an electric energy efficiency measurement model including unexpected output based on the total factor productivity framework and discuss the impact of carbon emission reduction constraints on electric power consumption (Sun et al, 2020;Chen et al, 2017;Li et al, 2014;Zheng, 2014;Sueyoshi and Goto, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of nonlinear evolution mechanism of power technology progress under the constraints of net-zero carbon dioxide emissions in China

Zheng¹

2022

IJCCSM

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Purpose Striving to achieve the goal of carbon neutrality before 2060 indicates that China, as the most extensive power system in the world and a country based on coal power, is imperative to improve the technical level of electric power utilization. This paper aims to explore the nonlinear evolution mechanism of power technology progress under the constraints of net-zero carbon dioxide emissions in China. Design/methodology/approach This paper, first, based on China’s provincial panel data from 2000 to 2019, uses global direction distance function to measure power technological progress. Second, the threshold regression model is used to explore the nonlinear relationship between carbon emission reduction constraints on electric power technological progress. Findings There is a significant inverted U-shaped relationship between China’s provincial carbon emission reduction constraints and electric power technological progress. Meanwhile, the scale of regional economic development has a significant moderating effect on the relationship between carbon emission reduction constraints and power technological progress. Research limitations/implications This paper puts forward targeted suggestions for perfecting regional carbon emission reduction policy and improving electric power technological progress. Originality/value Based on the global directional distance function, this paper extracts power as a production factor in total factor productivity and calculates the total factor electric power technological progress. This paper objectively reveals the influence mechanism of carbon emission reduction constraints on electric power technology progress based on the threshold regression model.

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“…The decarbonization of industrial sectors, significant technological advances, improved efficiency, and increased affordability represent some of the main drivers for customers (and producer-consumers, also called 'prosumers') to acquire DER, mainly connected to LV distribution networks [3]. Technical constraints from DER are compounded with a low (or null) observability of LV electrical networks, which leads DNO to face unprecedented operational challenges [4].…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

et al. 2023

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Increasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utility sides. Non-intrusive load monitoring (NILM) methods provide an opportunity to add value to conventional electric measurements and to increase the observability of LV networks for the implementation of active management network techniques and intelligent control of DER. This work proposes a novel implementation of NILM methods for the identification of DER electrical signatures from aggregated measurements taken at the LV side of a distribution transformer. The implementation evaluates three machine learning algorithms such as k Nearest Neighbours (kNN), random forest and a multilayer perceptron under 100 scenarios of DER integration. A year of minutely reported values of electric current, voltage, active power, and reactive power are used to train and test the proposed model. The F 1 scores achieved of 73% and 93% for Electrical Vehicles (EV) and rooftop photovoltaic (PV) respectively and processing times below 314 µs on an Intel Core i7-8700 machine. These results confirm the relevance of the NILM method based on low frequency electric measurements from the real-time identification of DER.INDEX TERMS Distributed energy resources (DER), non-intrusive load monitoring (NILM), low voltage networks distribution networks (LVN), random forest, supervised machine learning.The associate editor coordinating the review of this manuscript and approving it for publication was Wentao Fan .

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The Impact of Carbon Emission Quota Allocation Regulations on the Investment of Low-Carbon Technology in Electric Power Industry Under Peak-Valley Price Policy

Cited by 19 publications

References 39 publications

Competition vs cooperation: renewable energy investment under cap-and-trade mechanisms

Competition vs cooperation: renewable energy investment under cap-and-trade mechanisms

Analysis of nonlinear evolution mechanism of power technology progress under the constraints of net-zero carbon dioxide emissions in China

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

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