Taxing Strategies for Carbon Emissions: A Bilevel  Optimization Approach

Wei, Wei; Liang, Yile; Liu, Feng; Mei, Shengwei; Tian, Fang-Bao

doi:10.3390/en7042228

Cited by 34 publications

(22 citation statements)

References 28 publications

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“…In [25] an integrated power generation expansion planning model towards low-carbon economy is proposed. In [26] a short term optimization method is proposed to determine the optimal tax rate among generating units.…”

Section: Introductionmentioning

confidence: 99%

The Impact of a Carbon Tax on the Chilean Electricity Generation Sector

et al. 2015

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This paper aims to analyse the economy-wide implications of a carbon tax applied on the Chilean electricity generation sector. In order to analyse the macroeconomic impacts, both an energy sectorial model and a Dynamic Stochastic General Equilibrium model have been used. During the year 2014 a carbon tax of 5 US$/tCO2e was approved in Chile. This tax and its increases (10, 20, 30, 40 and 50 US$/tCO2e) are evaluated in this article. The results show that the effectiveness of this policy depends on some variables which are not controlled by policy makers, for example, non-conventional renewable energy investment cost projections, natural gas prices, and the feasibility of exploiting hydroelectric resources. For a carbon tax of 20 US$/tCO2e, the average annual emission reduction would be between 1.1 and 9.1 million tCO2e. However, the price of the electricity would increase between 8.3 and 9.6 US$/MWh. This price shock would decrease the annual GDP growth rate by a maximum amount of 0.13%. This article compares this energy policy with others such as the introduction of non-conventional renewable energy sources and a sectorial cap. The results show that the same global OPEN ACCESSEnergies 2015, 8 2675 greenhouse gas (GHG) emission reduction can be obtained with these policies, but the impact on the electricity price and GDP are lower than that of the carbon tax.

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Section: Introductionmentioning

confidence: 99%

The Impact of a Carbon Tax on the Chilean Electricity Generation Sector

et al. 2015

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“…These insects basically damage the crops. Swimming behaviour of grasshoppers can be written as [24]:…”

Section: Overviewmentioning

confidence: 99%

“…GOA is also initialized with some random solutions like other evolutionary algorithms using following equation [24]. min max min () * ( )…”

Section: Initializationmentioning

confidence: 99%

Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms

Goyal¹,

Vadhera²

2019

JESA

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This paper deals with application of meta-heuristic algorithms to resolve the problem of combined economic emission dispatch (CEED) with peak load management for a mediumsized power system in an efficient manner. The objective is to optimize the fuel cost of generation simultaneously minimizing the environmental pollution caused by fossil fuel based power generating units working at their peak limits. In this paper combined problem of minimizing fuel cost and emission of flue gases (NOx, CO2, SO2), is solved using Cuckoo search (CS) and Grasshopper optimization algorithm (GOA) via composite function of all four objectives with help of weight ratios and price penalty factors. Demand Side Management (DSM) measure is also applied at least expensive areas to manage the peak load condition at generating units. The problem is implemented on IEEE 30-bus system with 6 generator units. The simulation results of the CS algorithm for CEED with and without DSM have been compared with the results of GOA algorithm. The compound results obtained by CS algorithm for the problem of CEED with DSM validated its potential.

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“…It is evident that the manufacturing industry has become one of the major sources of energy consumption and CO 2 emissions, which will continue to increase by 1.9% annually if no effective action is taken [2]. Improved industrial energy efficiency is a critical cornerstone in climate change mitigation [3].…”

Section: Introductionmentioning

confidence: 99%

Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

et al. 2017

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Abstract:Energy demand modeling of machining processes is the foundation of energy optimization. Energy demand of machining state transition is integral to the energy requirements of the machining process. However, research focus on energy modeling of state transition is scarce. To fill this gap, an energy demand modeling methodology of key state transitions of the turning process is proposed. The establishment of an energy demand model of state transition could improve the accuracy of the energy model of the machining process, which also provides an accurate model and reliable data for energy optimization of the machining process. Finally, case studies were conducted on a CK6153i CNC lathe, the results demonstrating that predictive accuracy with the proposed method is generally above 90% for the state transition cases.

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Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach

Cited by 34 publications

References 28 publications

The Impact of a Carbon Tax on the Chilean Electricity Generation Sector

The Impact of a Carbon Tax on the Chilean Electricity Generation Sector

Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms

Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

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