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The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world's energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs. [ABSTRACT FROM AUTHOR]

Line loss is inherent in transmission and distribution stages, which can cause certain impacts on the profits of power-supply corporations. Thus, it is an important indicator and a benchmark value of which is needed to evaluate daily line loss rates in low voltage transformer regions. However, the number of regions is usually very large, and the dataset of line loss rates contains massive outliers. It is critical to develop a regression model with both great robustness and efficiency when trained on big data samples. In this case, a novel method based on robust neural network (RNN) is proposed. It is a multi-path network model with denoising auto-encoder (DAE), which takes the advantages of dropout, L2 regularization and Huber loss function. It can achieve several different outputs, which are utilized to compute benchmark values and reasonable intervals. Based on the comparison results, the proposed RNN possesses both superb robustness and accuracy, which outperforms the testing conventional regression models. According to the benchmark analysis, there are about 13% outliers in the collected dataset and about 45% regions that hold outliers within a month. Hence, the quality of line loss rate data should still be further improved. [ABSTRACT FROM AUTHOR]

Featured Application: The method proposed in this paper can be applied to the dispatch of power system with the large-scale integration of wind power, under the background of proposing of virtual synchronous generator technology. With the large-scale integration of renewable energy sources (e.g., wind power), the system inertial response and primary frequency regulation are affected. The virtual synchronous generator technology, which makes it possible for wind power units to adjust the frequency, provides a new way of approaching this problem. In this paper, we set up the related constraints that fit the optimal dispatch framework with a primary reserve representing the primary frequency response between the conventional synchronous generator and the wind power units, and the technical parameters of the virtual synchronous generator. Meanwhile, we use the robust day-ahead optimization dispatch model considering the wind power integrated primary frequency control in the real-time operation with the scenario of the wind power output showing the uncertainty of wind power. Based on the model, we identify the key set using an iterative method and obtain the maximum power loss. Through the proposed model, we could provide the day-ahead unit output and real-time primary reserve, thus ensuring the reliable operation of the system. Finally, the effectiveness of the proposed method is verified by a computational experiment. [ABSTRACT FROM AUTHOR]

The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user’s travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new output power smoothing control strategy for a wind farm based on the allocation of wind turbines. The wind turbines in the wind farm are divided into control wind turbines (CWT) and power wind turbines (PWT), separately. The PWTs are expected to output as much power as possible and a maximum power point tracking (MPPT) control strategy combining the rotor inertia based power smoothing method is adopted. The CWTs are in charge of the output power smoothing for the whole wind farm by giving the calculated appropriate power. The battery energy storage system (BESS) with small capacity is installed to be the support and its charge and discharge times are greatly reduced comparing with the traditional ESSs based power smoothing strategies. The simulation model of the permanent magnet synchronous generators (PMSG) based wind farm by considering the wake effect is built in Matlab/Simulink to test the proposed power smoothing method. Three different working modes of the wind farm are given in the simulation and the simulation results verify the effectiveness of the proposed power smoothing control strategy. [ABSTRACT FROM AUTHOR]

In modern power systems with more renewable energy sources connected, the consideration of both security and economy becomes the key to research on power system optimal dispatch, especially when more participants from the source and load sides join in the interaction response activities. In this paper, we propose a two-stage dispatch model that contains a day-ahead multi-objective optimization scheduling sub-model that combines a hyper-box and hyper-ellipse space theory-based system security index in the first stage, and an intraday adjustment scheduling sub-model that considers active demand response (DR) behavior in the second stage. This model is able to quantitatively analyze the relationship between the security and economy of the system dispatch process, as well as the impacts of the interaction response behavior on the wind power consumption and the system's daily operating cost. The model can be applied to the evaluation of the response mechanism design for interactive resources in regional power systems. [ABSTRACT FROM AUTHOR]

Integrated electrical and heating networks (IEHNs) effectively improve energy utilization efficiency, reduce environmental pollution and realize sustainable development of energy. To realize the accurate, comprehensive and fast perception of the integrated electrical and heating networks, it is necessary to build a state estimation model. However, the robust state estimator of IEHNs based on the temperature drop equation, flow balance equation and power balance equation still have the problems of convergence and low computational efficiency. In this paper, a fast state estimation method based on weighted least absolute value is proposed, which makes partition calculation of ring-shaped heating network and radiant heating network under certain assumptions. Simulation results show that the method improves the efficiency of the robust state estimator on the premise of high accuracy. [ABSTRACT FROM AUTHOR]

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models. [ABSTRACT FROM AUTHOR]

Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages. [ABSTRACT FROM AUTHOR]

This paper develops a nonlinear analytical algorithm for predicting the probabilistic mass flow of radial district heating networks based on the principle of heat transfer and basic pipe network theory. The use of a nonlinear mass flow model provides more accurate probabilistic operation information for district heating networks with stochastic heat demands than existing probabilistic power flow analytical algorithms based on a linear mass flow model. Moreover, the computation is efficient because our approach does not require repeated nonlinear mass flow calculations. Test results on a 23-node district heating network case indicate that the proposed approach provides an accurate and efficient estimation of probabilistic operation conditions. [ABSTRACT FROM AUTHOR]

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as sub-models for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deep-learning-based sub-models. Lastly, variances are obtained from sub-models and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting. [ABSTRACT FROM AUTHOR]

A correction to the article "Sensorless Control for the EVT-Based New Dual Power Flow Wind Energy Conversion System" is presented.