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The volatility and uncertainty of wind energy has brought great challenges to its accommodation in electric power systems. To determine a wind power trading strategy in the real-time power market, the accurate assessment of wind energy accommodation capability (WAC) is of great significance. However, most current studies investigate the wind energy accommodation potential from the perspective of dispatch, but rarely provide quantitative assessment results. Additionally, the non-anticipativity of decisions has not been considered in the assessment yet. This paper proposes a novel quantitative assessment method for WAC based on the multi-stage robust optimization (RO), which addresses the anticipativity issues in the traditional two-stage method while maintaining the decision-making process' robustness. In the assessment method, the operational risk is integrated via a tractable scheme to obtain dynamic admissible WAC boundaries. To obtain the optimal solution of the proposed multi-stage RO problem, a fast robust dual dynamic programming (FRDDP) algorithm is employed, where the adaptive techniques are developed to accelerate the computation. Numerical studies on the modified IEEE 14-Bus system and a real-world system in Zhejiang Province of China validate the effectiveness of the proposed assessment model and adaptive acceleration techniques. The simulation results demonstrate the presented method brings a 18.87% reduction in operational cost, and reduces 29.34% curtailment of wind energy. Compared with the original FRDDP, the adaptive technique significantly reduces the computational consumption by 73.34% on the real-world test system. • A novel assessment method for WAC is proposed based on the multi-stage robust optimization. • Tractable risk evaluation method is integrated in the multi-stage framework. • Adaptive techniques are designed for FRDDP method to accelerate the optimal solution. • The proposed method achieves both non-anticipative and robust assessment decisions. [ABSTRACT FROM AUTHOR]

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers "prosumers" arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs. • A non-intrusive carbon emission accounting method for industrial corporations. • A novel perspective of modern power systems for carbon emission accounting. • Calculating carbon emissions considering distributed photovoltaics' impacts. • The method becomes superior as the photovoltaic output proportion increases. [ABSTRACT FROM AUTHOR]

[Display omitted] • Relative energy efficiency of regional wastewater treatment plants was assessed. • Designed scale and quality of wastewater influence energy efficiency significantly. • Area surrounding Taihu Lake is energy efficiency through in the Yangtze River Delta. With priorities to reach a carbon emission peak and integrated development involving ecological demonstration, a systematic evaluation on the energy efficiency and internal discrepancies of wastewater treatment plants in the Yangtze River Delta region is needed. In this study, a slacks-based measure data envelopment analysis model was applied to quantify the relative energy efficiency of 270 regional wastewater treatment plants. Based on the score of relative energy efficiency, the internal discrepancies of the region were identified through a method combining spatial and sensitivity analysis. Although the wastewater treatment plants had met the pollutant limits of discharge standards, the result of data envelopment analysis showed that 253 wastewater treatment plants were inefficient (with efficiency scores less than 1) due to input excess or output shortfall, indicating substantial potential for improving the energy efficiency. Besides, binary logistic regression demonstrated the significant impact from explanatory factors including designed capacity (104 m3/d), loading rate (%), influent ratio of chemical oxygen demand to total nitrogen, and influent concentration (mg/L) of chemical oxygen demand. Moreover, with regards to regional discrepancies, proportion of efficient wastewater treatment plants for Shanghai, Jiangsu, Zhejiang, and Anhui differs significantly. The area surrounding Taihu Lake was recognized as the trough of the Yangtze River Delta region in terms of the relative energy efficiency of wastewater treatment plants. This paper would give a reference for optimization of wastewater treatment plants in the study area and the evaluation framework on internal discrepancies might also be useful for other regions worldwide. [ABSTRACT FROM AUTHOR]