Carbon-Oriented Operational Planning in Coupled Electricity and Emission Trading Markets

Wang, Yunqi; Qiu, Jing; Tao, Yuechuan; Zhao, Junhua

doi:10.1109/tpwrs.2020.2966663

Cited by 177 publications

(45 citation statements)

References 27 publications

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“…The physical meaning of Equation ( 17) is: "carbon emission tax [27]", which can be used to transform the carbon emission of the environmental indicator system into a cost economic indicator to realize the unification of environmental and economic attributes in the measurement.…”

Section: Objective Functionmentioning

confidence: 99%

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties

et al. 2021

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In this paper, a novel methodological framework for energy hub (EH) planning, considering the correlation between renewable energy source (RES) and demand response (DR) uncertainties, is proposed. Unlike other existing works, our study explicitly considers the potential correlation between the uncertainty of integrated energy system operations (i.e., wind speed, light intensity, and demand response). Firstly, an EH single-objective interval optimization model is established, which aims at minimizing investment and operation costs. The model fully considers the correlation between various uncertain parameters. Secondly, the correlation between uncertainties is dealt with by the interval models of multidimensional parallelism and affine coordinate transformation, which are transformed into a deterministic optimization problem by the interval order relationship and probability algorithm, and then solved by a genetic algorithm. Finally, an experimental case is analyzed, and the results show that the research method in this paper has good engineering practicability. At the same time, different correlations among uncertainties have different influences on integrated energy system planning. Correlation and influence are positively correlated.

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Section: Objective Functionmentioning

confidence: 99%

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties

et al. 2021

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“…Another simulation model is done by Ruth et al (2008) to examine the energy and economic implications whereby they conclude modest emissions reductions, despite profit is reduced when conventional plants are retired. Wang et al (2020) propose an agent-based approach with Q-learning algorithm to model 8 interactions between the carbon trading market where carbon emission is regulated from the electricity generators side. On the other hand, since the carbon market is risk prone, it attracts attention in the field of energy economics and finance.…”

Section: Emission Allowances Models In Electricity Marketmentioning

confidence: 99%

Contracts in Electricity Markets under EU ETS: A Stochastic Programming Approach

Abate,

Riccardi,

Ruiz

2021

Preprint

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The European Union Emission Trading Scheme (EU ETS) is a cornerstone of the EU's strategy to fight climate change and an important device for plummeting greenhouse gas (GHG) emissions in an economically efficient manner. The power industry has switched to an auction-based allocation system at the onset of Phase III of the EU ETS to bring economic efficiency by negating windfall profits that have been resulted from grandfathered allocation of allowances in the previous phases. In this work, we analyze and simulate the interaction of oligopolistic generators in an electricity market with a gametheoretical framework where the electricity and the emissions markets interact in a twostage electricity market. For analytical simplicity, we assume a single futures market where the electricity is committed at the futures price, and the emissions allowance is contracted in advance, prior to a spot market where the energy and allowances delivery takes place. Moreover, a coherent risk measure is applied (Conditional Value at Risk) to model both risk averse and risk neutral generators and a two-stage stochastic optimization setting is introduced to deal with the uncertainty of renewable capacity, demand, generation, and emission costs. The performance of the proposed equilibrium model and its main properties are examined through realistic numerical simulations. Our results show that renewable generators are surging and substituting conventional generators without compromising social welfare. Hence, both renewable deployment and emission allowance auctioning are effectively reducing GHG emissions and promoting low-carbon economic path.

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“…Meanwhile, it is also important to power utilities, where understanding the load components helps to model power system operations and schedule the demand response better [2]. Furthermore, it is also meaningful to the development of the entire power industry, e.g., it is the technological base of tracking household energy carbon emissions [3]. Therefore, insights into household electricity usage are emerging as a vital link in the energy consumption chain and are attracting more and more attention in both academic and industrial fields.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Liu

Shi

Wang

et al. 2021

Sensors

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Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

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Carbon-Oriented Operational Planning in Coupled Electricity and Emission Trading Markets

Cited by 177 publications

References 27 publications

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties

Contracts in Electricity Markets under EU ETS: A Stochastic Programming Approach

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

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