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Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the issues of reactive power must-run capacity in power system operations, hence in electricity markets. A must-run index-based method is proposed in the paper to measure the market power held by reactive power suppliers. The Nordic 32-bus system and the IEEE 118-bus system are used to test the proposed method. The market power holders of reactive power found using the proposed method are in accord with that found in the realistic Nordic system operation and in the existing analysis of IEEE 118-bus system. The paper identifies through must-run indices possible conditions that could lead to market power in the case of applying a bid structure within a market framework. Furthermore, market structure drawbacks can cause the appearance of market power even in a topologically ideal system. [ABSTRACT FROM AUTHOR]

-A power supplier in a pool-based market needs to allocate his generation capacities to participate in contract and spot markets. In this paper, the optimal portfolio selection theory is introduced for this purpose. A model applying this theory is proposed to solve the supplier asset allocation problem. Real market data are used in a numerical study to test the proposed model. The results show that different asset allocation solutions can yield very different risk-return tradeoffs for a supplier, and the proposed method can be potentially useful in suppliers' decision making. [ABSTRACT FROM AUTHOR]

Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850-1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015-2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a 'tertiary class echelon' condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021). [ABSTRACT FROM AUTHOR]

Abstract: The application of wind energy in electric power systems is growing rapidly due to enhanced public concerns to adverse environmental impacts and escalation in energy costs associated with the use of conventional energy sources. Electric power from wind energy is quite different from that of conventional resources. The fundamental difference is that the wind power is intermittent and uncertain. Therefore, it affects the reliability of power system in a different manner from that of the conventional generators. This paper, from available literatures, presents the model of wind farms and the methods of wind speed parameters assessment. Two main categories of methods for evaluating the wind power reliability contribution, i.e., the analytical method and the Monte Carlo simulation method have been reviewed. This paper also summarizes factors affecting the reliability of wind power system, such as wake effect, correlation of output power for different windturbines, effect of windturbine parameters, penetration and environment. An example has been used to illustrate how these factors affect the reliability of wind power system. Finally, mainstream reliability indices for evaluating reliability are introduced. Among these reliability indices, some are recently developed, such as wind generation interrupted energy benefit (WGIEB), wind generation interruption cost benefit (WGICB), Equivalent Capacity Rate (ECR), load carrying capacity benefit ratio (LCCBR). [Copyright &y& Elsevier]

The expected rise of plug-in electric vehicles (PEVs) will lead to a significant additional demand on low voltage distribution systems, and PEVs can challenge power quality and reliability of power systems if their charging is not coordinated. The PEV aggregator, acting as an intermedium between customers and the grid operator, can solve the charging management problem effectively. In this study, a decentralized scheduling scheme of aggregators for PEV charging based on Lagrange Relaxation method is proposed. At the same time, a centralized model of aggregators is also formulated in the same situation in order to make comparisons with the proposed scheme. The two charging models are both aimed at maximizing the charging profit of PEV aggregators considering constraints including users' electricity demand, charging time, available capacity of distribution transformers, etc. A number of charging scenarios are simulated to evaluate the proposed method. The economic benefits, computational complexity and efficiency are compared and analyzed between the centralized charging and the decentralized charging. Simulation results verify that the decentralized charging strategy based on Lagrange Relaxation method gains charging profit close to the centralized charging yet is more efficient and realizable than the centralized charging. [ABSTRACT FROM AUTHOR]