A Two-Stage Algorithm of Locational Marginal Price Calculation Subject to Carbon Emission Allowance

Wu, Mingxing; Lu, Zhilin; Chen, Qing; Zhu, Tao; Lu, En; Lu, Wentian; Liu, Mingbo

doi:10.3390/en13102510

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…For this purpose, it is now considered that the regression output has M dimensions, such that y ∈ R M . This consideration modifies the objective function of the single-output SVR optimization problem as seen in Equation (7), since it implies that the parameters w, ξ, and b have increased their dimensional spaces:…”

Section: Multi-output Regression With Svrmentioning

confidence: 99%

“…Likewise, the LMPs are the shadow prices for real power restrictions for the optimal power flow (OPF) [6]. The OPF is an optimization model to find the most economic dispatch of real power considering the network characteristics, the available generation, demand, and operational restrictions [7,8]. The OPF could be expressed as DCOPF or ACOPF [9] for a single period and multi-period evaluation [10].…”

Section: Introductionmentioning

confidence: 99%

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Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

et al. 2022

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Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

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Section: Multi-output Regression With Svrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

et al. 2022

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“…The allocation mechanism sets the upper limit for the enterprises and sends a carbon emission reduction signal to the enterprise. Wu et al (2020) [ 36 ] used the two-step goal programming allocation model to allocate the quota of carbon emission for the power industry. They analyze the impact of carbon emission quota allocation on the electricity market, propose a two-stage algorithm, and establish a multi-objective optimization model that minimizes the total cost and carbon emission cost of the power system.…”

Section: Literature Reviewmentioning

confidence: 99%

Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019

Ren

Zhang

Fan

2022

IJERPH

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2011–2019 was the critical period of the low-carbon transformation of the power industry, reflecting the deepening influence of market mechanisms. Decarbonization of the new power system is a systematic project that needs to strengthen the top-level design and overall planning. Therefore, the paper first evaluates the decarbonization of the existing power system and controls the grid architecture, power structure, energy utilization, supply chain, and trading market to further optimize the system by strengthening the basic theoretical research of the new power system, exploring the key elements of the low-carbon development of the power system, promoting the breakthrough of the key subjects, and formulating the new power system decarbonization path. In the international push for carbon neutrality goals, identifying key factors in the decarbonization of the power system is critical to achieving low-carbon development in the power sector. Combined with the characteristics and development trends of the power industry, the five dimensions of “Power generation decarbonization (SP)”, “Energy utilization efficiency (EU)”, “Supply chain decarbonization (SC)”, and “Power grid decarbonization (PG)”, and “the Trading system (TS)” are selected to construct an evaluation index system for the power decarbonization and identify the key factors. The Analytic Network Process (ANP) Method is used to calculate the index weight and measure the decarbonization level of the power industry in 30 provinces in China from 2011 to 2019. The evaluation results reveal that the overall decarbonization level of the power industry is on the rise and has stabilized after peaking in 2016. The regression results of the systematic GMM estimation show that “the intensity of cross-regional transmission”, “the degree of carbon market participation”, “technology innovation”, and “policy support” can significantly promote power decarbonization, and different regions have heterogeneity. Therefore, we propose to achieve technological innovation and upgrading in the eastern region, strengthen the construction of smart grids in the central region, optimize the power structure in the western region, and improve the market mechanism as a whole, to form a low-carbon development path for the power industry.

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“…Deep learning can alleviate the problems of low detection and recognition accuracy in the case of detecting multiple complex types of samples due to its advantages of high precision and strong adaptability [18]. Broadly speaking, one-stage algorithms can directly extract features from images in an end-to-end framework to predict object location and classification [19]. The accuracies of the earlier one-stage algorithms are not as high as the two-stage algorithms [20].…”

Section: Introductionmentioning

confidence: 99%

Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

Zhu

et al. 2022

Agriculture

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Insect pest management is one of the main ways to improve the crop yield and quality in agriculture and it can accurately and timely detect insect pests, which is of great significance to agricultural production. In the past, most insect pest detection tasks relied on the experience of agricutural experts, which is time-consuming, laborious and subjective. In rencent years, various intelligent methods have emerged for detection. This paper employs three frontier Deep Convolutional Neural Network (DCNN) models—Faster-RCNN, Mask-RCNN and Yolov5, for efficient insect pest detection. In addition, we made two coco datasets by ourselves on the basis of Baidu AI insect detection dataset and IP102 dataset, and compared these three frontier deep learning models on the two coco datasets. In terms of Baidu AI insect detection dataset whose background is simple, the experimental results strongly recommend Yolov5 for the insect pest detection, because its accuracy reaches above 99% while Faster-RCNN’s and Mask-RCNN’s reach above 98%. Meanwhile, Yolov5 has the faster computational speed than Faster-RCNN and Mask-RCNN. Comparatively speaking, with regard to the IP102 dataset whose background is complex and categories are abundant, Faster-RCNN and Mask-RCNN have the higher accuracy, reaching 99%, than Yolov5 whose accuracy is about 97%.

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A Two-Stage Algorithm of Locational Marginal Price Calculation Subject to Carbon Emission Allowance

Cited by 5 publications

References 20 publications

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019

Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

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