Search

Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%. This work was supported in part by the Department of Finance and Education of Guangdong Province 2016 [202] through the Key Discipline Construction Program, China, in part by the Education Department of Guangdong Province through the New and Integrated Energy System Theory and Technology Research Group under Project 2016KCXTD022, in part by Guangdong Foshan Power Construction Corporation Group Company Ltd., in part by the National Natural Science Foundation of China under Grant 51907031, and in part by Brunel University London BRIEF Funding, U.K.

Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; Brunei University London BRIEF Funding, UK.

This paper presents an energy arbitrage strategy of a lithium-ion Battery Storage System (BSS) in sequential Day-ahead and Intraday (DA+INT) markets, considering its Cycle Aging Cost (CAC). One of the critical queries of the BSS in such problems is how to tackle the risk of uncertain prices in both market floors. Towards this end, a financial risk management method, i.e., Second-Order Stochastic Dominance Constraints (SOSDCs), is used to control the risk of uncertain market prices. Despite the promising performance of the SOSDCs over a broad range of decision-making problems, the primary challenge for decision-makers taking advantage of this approach is the selection of the minimum profit threshold. To effectively overcome this obstacle, this paper proposes a new benchmark selection approach based on a fuzzy decision-making manner over in-sample and out-of-sample analyses. The idea behind considering both in-sample and out-of-sample studies lies in an unforeseen change of the results by setting various benchmarks in SOSDCs. In this regard, to precisely formulate this problem, with an eye on the battery CAC, a linear two-stage stochastic framework is suggested. The numerical results show the applicability of the developed approach in benchmark selection for the SOSDCs.

Electric vehicles (EVs) play a key role in transport electrification and decarbonizing the society. EVs are becoming popular due to the advancement of drivetrain and battery technologies, with the support from plummeting costs. However, many countries are facing with challenges to accommodate large-scale adoption of electric vehicles due to the great amount of electricity required from the grid for charging. Current research focuses on developing incentives and tariff structure to encourage EV drivers to charge and discharge at appropriate times. However, relying on driver's behavior can be a risky decision for network operators. A regional high electricity demand at a short instance can cause severe technical challenges for the distribution network, including thermal and voltage limit violations. This paper presents and compares the EV development for the UK and China. A research agenda is proposed to consider how large-scale energy storage would benefit the distribution network for rapid charging of electric vehicles.

Currently, multi-view subspace clustering has obtained wide attention in the field of machine learning and pattern recognition. Since different views contain different view-specific information of the same object, how to utilize these views to recover a latent shared representation is important for subsequent clustering. In this paper, we propose a latent shared representation model for multi-view subspace clustering. Our model uses the view-specific generated matrices to recover a latent shared representation from the multi-view data. To consider the correlation information of multi-view data, we adopt an empirical Hilbert-Schmidt independence criterion to constrain these view-specific generated matrices. Based on the inexact augmented Lagrangian method, we also develop an alternating optimization algorithm to solve our model. Experimental results on the real-world multi-view data sets have validated the effectiveness of our model.

With the development of distributed trading mechanism, using Peer-to-Peer (P2P) to realize fair and developed transactions becomes a reality. P2P method is used to schedule the charging behavior of electric vehicles, which can not only achieve the function of peak clipping and valley filling, but also alleviate the influence of three-phase imbalance. This paper proposes an energy transaction model which consists of two parts. The first part is a new auction in which participants can look at historical data to see how competitive they are. The second part is the interaction between the charging station operator and the participants who are not matched in the first part. Case study shows the effectiveness of this distributed trading mechanism.

As the penetration of wind power increases, there is a growing operational demand on Wind Turbine (WT) systems to participate in microgrids frequency regulation. Traditional grid-connected Doubly-Fed Induction Generator (DFIG) control strategies are basically dependent on the power grid which is strong enough through the phase-locked loop (PLL). Traditional control strategies are not applicable to the weak grid with a relatively large short-circuit ratio (SCR). A new method must be introduced to let the wind farm actively respond to system frequency changes. This paper presents a control strategy which enables DFIG to behave as a voltage source. The droop curve is designed to realize the power sharing between DFIGs and battery energy storage system (BESS). Also, the grid frequency and phase angle required for coordinated transformation are no longer obtained through the phase-locked loop, but generated through the droop controller. Simulation results under various operation conditions are presented to verify the effectiveness of the proposed control strategy.

With the widespread of consumer electronics, household appliances and electric vehicle (EV), the household energy consumption is gradually increasing. To reduce the burden of distribution grid and meet the growing energy demand, photovoltaic (PV) panels and energy storage could be introduced and deployed at home. Thus, the home energy system is gradually becoming an integrated multiple energy system including the distribution grid, PV panels, battery energy storage, EV and home loads. An efficient energy management is important for the integrated energy system to save cost and comprehensively utilize their distinct characteristics. In this paper, the energy management problem is formulated to minimize the daily electricity purchase cost. The dual attributes of EV, i.e. energy storage and mobility, are both considered in the problem. The numerical result demonstrates that the energy management solution can well meet the demand requirement and significantly reduced the electricity purchase cost. In addition, comparison of results shows that benefits can be acquired from the usage of vehicle to grid and battery energy storage.

Internet of Things (IoT) based infrastructures and applications are essential parts in smart city establishment. The low power wide area network (LPWAN) plays a key role in IoT techniques due to the characteristics of wide coverage and low power consumption. However, it is hard to decide which one of the LPWAN techniques to be implemented in a specific application for best practice. Considering this, we discussed the main characteristics of the three popular LPWAN technologies, LoRaWAN, NB-IoT, and Sigfox, and proposed an LP-INDEX to weighting them according to application requirements. To further distinguish their difference, a comparison test based on parking detection sensors using the three different techniques was implemented as a use case.

One of the key domains in smart cities is smart energy in which smart grid is a main focus. In recent years, with the development of smart grid, controllable air conditioning load participating in demand response projects and the application of renewable energy sources have drawn wide research interests. The integration of photovoltaic (PV) system and electric vehicles into the micro grid has also brought vitality to the stable operation of smart grids. In this paper, a novel control scheme is proposed to optimize the scheduling of building micro grid that integrate controllable air conditioner loads, PV panels and electric vehicles. The optimal operation problem is modeled and further converted into a mixed integer linear programming (MILP) problem whose objective function is minimizing the electricity cost of the building. The stochastic characteristics of electric vehicles are also considered in this paper to better model electric vehicle behaviors. Simulations are conducted on an office building micro grid and the simulation results verify the feasibility of proposed control strategy.

Power lines are essential for electricity transmission between power plant and consumption point. Periodical inspection and assessment of the power line damages are critical to ensure the uninterrupted power delivery and grid stability. With the recent development of the unmanned aerial vehicles technology, the aerial images of power lines are adopted for broken strand detection. A huge challenge is the lack of the fatal but rare broken strand images. Thus, an oversampling strategy is proposed to increase the data diversity and reduce the data imbalance between the normal and broken lines. In addition, image background noises are filtered through image transformation to facilitate the anomaly detection. After that, five popular machine learning models are trained on four representative views of the aerial images. The experiments results show that the models can achieve a remarkable performance when they are trained and specialized for images from the same view. Second, the model can be generalized from one view to the other views sharing similar features, where the neural network solutions show remarkable knowledge transfer capability. Third, the impact of data size is discussed. More data does help promote the detection accuracy, but the performance gain diminishes in data size. Guangdong Academy of Sciences

The application of artificial neural network to load forecasting can overcome the problem of dynamic load change, and its ability to adapt to nonlinear relationships makes the prediction result satisfactory. This paper firstly reviews and introduces the concepts and basic principles of load prediction, discusses various methods for load forecasting, and then selects artificial neural network to establish a predictive model. In this paper, the European electric load is predicted with a BP neural network. From the prediction results, it is feasible to use BP neural network for load forecasting, and its accuracy can meet the needs of real-life engineering work. However, BP neural networks have the problem of slow convergence and easily falling into local minimum points. Therefore, this paper also uses three other neural networks for load forecasting, which are Radial Basis Network (RBF), Elman Network, and Long-Short Term Memory Network (LSTM). In the experiment, the four neural networks achieved expected prediction results, and the LSTM network had the best prediction effect. Scientific discussions are offered.

Simulation and performance evaluation of concurrent Java program execution have been difficult due to the lack of proper model and tools. Previous modeling and simulation approaches cannot simultaneously achieve three tasks: (1) support the direct simulation of unmodified multi-thread Java programs; (2) guarantee deterministic simulation results; and (3) offer low-overhead and scalable simulation. This paper first presents a novel simulation model based on the Java memory model. The model axiomatically defines action ordering, relationships, and constraints to ensure the well-formedness and determinism of a simulated execution. Then, based on the model, we implement the DecompositionJ simulation framework (deterministic, concurrent multi-processsing simulation for Java programs) which enables the direct-execution simulation of target program by using compiler-based source-to-source transformation and a purposely designed runtime library. The framework is compatible with any JVM that complies with the Java specifications, and does not require manual modifications on the target program code. The performance of the framework has been evaluated with the Grande Java concurrency benchmark suite, results have shown a geometric mean of 98.9% overhead over all cases, which significantly outperforms full-system simulation techniques.

In recent years, electric vehicles (EVs) have been widely applied to improve environment. The EV could provide environmentally friendly transportation but have the demerit of low battery capacity. Rapid charging by charging stations (CS) is critically needed especially for those drivers in long distance trip. Thus, a routing optimization problem for EVs charging should be addressed. Furthermore, this problem becomes more practical when the EV density is high at peak. In this scenario EVs are only allowed to obtain energy that render them able to arrive at the destination. In this paper, we formulate an emergent EV charging optimization problem in EV high density area which has not been discussed in related work and a novel genetic algorithm based emergent charging scheduling (GECS) scheme is proposed. The genetic algorithm (GA) is presented to simplify the multi-objectives optimization process in this case. Furthermore, incorporation of the Earliest Deadline First (EDF) which indicates the minimum recharging deadline time as the subject and Nearest Job First (NJF) which indicates the minimum recharging path as the subject into genetic optimization process can relieve the charging emergent condition and improve optimized results. The simulation results show that the proposed scheme can provide an optimal solution to minimize the average distance and waiting time for emergent charging in EV high density region.

This paper focuses on the development of Security- Constrained Unit Commitment (SCUC) with natural gas systems. This paper proposes an Integrated Electricity and Natural Gas System (IEGS) to describe the day-ahead scheduling including natural gas purchase contract and transmission constraints. An Second-order Cone Programming (SOCP) is proposed to handle the nonlinear steady-state natural gas flow And through penalty and linearization method, the original Mixed-Integer Non-linear Programming (MINLP) is converted into Mixed-Integer Second- Order Cone Programming (MISOCP) problem. Case study simulations demonstrate the workable solution of the scheduling method.

For microgrids (MGs) optimal operation, one heated topic is the uncertainty management associated with renewable variations and electricity load forecasting errors. On the other hand, the networking of MGs is receiving an increasing attention in recent years. In this paper, an interactive energy management strategy is developed for high renewable-penetrated MGs. The control method includes two steps. In the first step, a local optimization is proposed for each microgrid to minimize the operation cost during the whole scheduling periods. In the second step, a global optimization is conducted for networked microgrids. CVaR based risk averse measure is introduced here to provide a risk-hedging strategy for microgrids energy management. Formulated models are solved by the easily implemented and computationally inexpensive mix integer linear programming (MILP) solver. Case studies demonstrate the feasibility of the proposed method by identifying optimal scheduling results.

With the increase popularity of multi-view data, multi-view learning has attracted vital attentions in pattern recognition as well as machine learning. Most of existing methods apply in traditional single view learning. However, these methods neglect the complementary information among the views. The aim of multi-view is to discover complementary information and enhance the single view learning result. Multi-view is capable of capture incomplete and different types of information from multiple sources. However, multi-views may contain redundant information. Many multi-view methods assume that multi-views are generated from various view-specific generation matrices. This paper proposes the multi-view collaborative representation classification (MVCRC) algorithm which contains the information of different views and the connection of view-to-view. Experimental results conducted on five practical databases are used to confirm the effectiveness of the proposed approach.

This paper presents some results and discussions on the use of phase change materials (PCM) performance for energy storage. Different parameters which affect the energy distribution and also the distribution of thermal energy will be illustrated. Future work has also been discussed on the importance of local distributed energy trading in the hope to reduce energy losses and maximize energy efficiency.

Several renewable energies such as wind and solar are intermittent. To increase renewable energy penetration, Electrical Energy Storage (EES) systems are becoming increasingly important. There is an increasing need for wide deployment of EES from generation to distribution systems. This requires relevant financial resources, and economics and finance are the important factors to determine if installing EES is profitable. Hence, this paper reviews the recent economics and financial analyses performed for EES in the energy system context. The paper begins with examining the EES technologies. Then, the difference between energy economics and finance are explained. The recent EES techno-economic studies and financial studies are reviewed. Under the uncertain economic, financing, and technical environment, it is important to examine EES projects with real options analysis. Finally, the paper concludes with future research directions for EES finance and economics.

To accurate predict valve regulated lead acid battery (VRLA) temperature changes to prevent overheat without information of battery’s mechanical structure, a thermal model for VRLA is developed. Four thin film platinum resistances (RTDs) temperature detectors are located on the battery’s core, case’s surface, terminal and outer surface of insulating material for extracting thermal parameters with using multi-objective genetic algorithm. Nine tests are completed to verify the model accuracy is acceptable for long-term prediction, 30000 seconds. A model-based prediction with RMSE of about 0.5°C and a maximum error of less than 1°C is created. The prediction method can be done without requiring any thermocouple or RTDs on battery after the parameters are identified. The method can also be used to estimate the battery’s internal temperature to prevent some tragic situations such as thermal runaway. The proposed model can run on microcontroller after the parameters have been obtained which means that it can be embedded into a battery management system for continuous temperature prediction and internal temperature estimation.

Representation-based classifiers have shown the impressive results for pattern classification. In this paper, we propose a joint collaborative representation and discriminative projection model (JCRDP) for subspace learning. We aim to seek a linear projection matrix to effectively reveal or maintain the underlying structure of original data and well fit collaborative representation classifier simultaneously. Unlike previous representation-based subspace learning methods, in which the linear reconstruction and the generalized eigenvalue decomposition are two independent steps, our proposed JCRDP integrates these two tasks into one single optimization step to learn a more discriminative linear projection matrix. To effectively solve JCRDP, we develop an alternative strategy to deal with the optimization problem. Extensive experimental results demonstrate the effectiveness of our proposed method.

In Day-ahead Power Market (DAM), Load Serving Entities (LSEs) needs to submit their load schedule to market operator beforehand. For reduction of the total cost, the disparity of the price of DAM and the price of RDM (Real Day Market) should be considered by the LSEs. Therefore, the problem is that a more accurate load-forecasting model sometimes provide a price that has an interspace will lead to a lower cost. Facing this issue, this paper initiates a load forecasting model considering the Costing Correlated Factor (CCF) with deep Long Short-term Memory (LSTM). The target of the forecast model contains both accuracy section and power cost section. At the same time, the construct of LSTM can of fset the sacrificed accuracy. Also, this paper uses an Adaptive Moment Estimation algorithm for network training and the type of neuron is Rectified Linear Unit (ReLU). A numerical study based on practical data is presented and the result shows that LSTM with CCF can reduce energy cost with acceptable accuracy level.

Causing Reason Locating (CRL) is a negative going decision making process. It provides specific individual causing reasons to events or abnormal variation so that decision maker can find out clear-cut points for updates or maintenance. Traditional CRL is usually implemented with self-correlation analysis on Value Comparable Data (VCD). Property Data (PD) is not covered in CRL for its nominal evaluations. This paper initiates a new scheme for PD data correlation analysis, including virtual data creation method and correlation balancing method for Kendall correlation computation. A numerical study is implemented for model support on equipment retire analysis.

It is common to perform feature selection for pattern recognition and image processing. However, most of conventional methods often convert the image matrix into a vector for feature selection, which fails to consider the spatial location of image. To address this issue, we propose a graph-based sparse matrix regression for feature selection on matrix. We incorporate a graph regularization term into the objective function of the sparse matrix regression model. The role of this graph structure is to make the matrix samples sharing the same labels keep close together in the transformed space. Extensive experimental results can demenstrate the effectiveness of our proposed method.

Feature selection has obtained dramatic attentions in the recent years. In this paper, we propose a semi-supervised graph learning robust feature selection model (SGL-RFS). Our method can merge the procedures of sparse regression and graph construction as a whole to learn an optimal sparse regression matrix for feature selection. To solve our propose method, we also develop an effective alternating optimization algorithm. Experimental results on face and digit databases confirm the effectiveness of our proposed method.

In data streaming environments such as a smart grid, it is impossible to restrict each data chunk to have the same number of samples in each class. Hence, in addition to the concept drift, classification problems in streaming data environments are inherently imbalanced. However, streaming imbalanced and concept drifting problems in the power system and smart grid have rarely been studied. Incremental learning aims to learn the correct classification for the future unseen samples from the given streaming data. In this paper, we propose a new incremental ensemble learning method to handle both concept drift and class imbalance issues. The class imbalance issue is tackled by an imbalance-reversed bagging method that improves the true positive rate while maintains a low false positive rate. The adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers according to their classification performances and stochastic sensitivities. The proposed method is applied to a case study for the electricity pricing in Australia to predict whether the price of New South Wales will be higher or lower than that of Victorias in a 24-h period. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.

Joint sparse representation (JSR) models have been widely applied into the field of hyperspectral image (HSI) classification. However, most of JSR-based models adopt the Frobenius norm to measure the reconstruction error, which ignores the structural information of the small patch. In this paper, we propose a nuclear-norm joint sparse representation (NuJSR) model for hyperspectral image classification. The main motivation of NuJSR is to utilize the nuclear-norm to measure the reconstruction error, so as to reflect the low-rank structural information of the small patch. To optimize our proposed NuJSR, an efficient algorithm is proposed. Experiments results confirm the effectiveness of NuJSR.

Water management is an important issue in economics and environment. Recently, amount of water control system has been proposed and developed. For the type of intelligent water control, the related parameters will be the input of the control system. Hence, there is a need of developing a scalable, flexible and reliable sensor network for related parameters monitoring. To install and replace water sensors in building networks, wireless connection will be the first priority. However, improper time synchronization in the network will cause packet loss and long latency which degrades the network performance. In this paper, time-synchronized ZigBee building network (TS-ZBN) is proposed for water management. The node-to-node time synchronization is proposed. The concept is to calculate the clock difference by studying the propagation delay model. The simulation result shows that the mean synchronization error and variance are low.

Energy crisis and carbon emissions are two major problems for human. As a viable solution, the Energy Internet (EI) has attracted worldwide attention. EI is the Internet development vision for smart grid. In this paper, we have introduced a representative EI architecture, a future renewable energy supply, management system and how artificial intelligence and pattern recognition methods could be applied to smart city. After highlighting the key EI features, the basic energy storage is summarized. Besides, this paper also included some applications.

Multiagent Systems (MAS) are used extensively in Smart Grid research as a means of developing distributed control systems comprised of a network of communicating units. Many previous works have made implicit or explicit assumptions that lead to simplified modeling and simulation of the underlying communication systems. While these assumptions are valid under some cases, they may not hold true when system size and complexity scale up exponentially, which can be anticipated given the rapid development of smart grid technologies. In this paper, we present a more comprehensive modeling and simulation approach that accounts for the MAS related protocols as described in the FIPA (Foundation for Intelligent Physical Agents) specification; together with a co-simulation platform for the investigation of multi-disciplinary interdependencies between MAS, communication, and electrical systems. To show the benefits of comprehensive modeling and simulation, we demonstrate how different network configurations affect system performance in a Fault Location, Isolation and Service Restoration (FLISR) scenario.

The network reconfiguration during power system restoration after blackouts usually takes a long and complex procedure. To address the uncertainties in the restoration steps and time involved, a CCP (Chance Constrained Programming) based method for network reconfiguration scheme optimization is proposed in this paper. The proposed method can generate the best restoration sequence to maximize the benefit of the reconfiguration scheme by taking into account the number of restarted generator-nodes and the cost of power outage saved by the load restoration accordingly. The Differential Evolution (DE) is employed to solve for the optimal solution subject to special requirements of the network reconfiguration operation. A numerical example over the New England 39-bus power system is conducted to demonstrate the effectiveness of the proposed method.

The high penetration of solar PV generations brings about significant challenges for decision-makers of power system operation due to high volatility and uncertainty it involves. In recent years, it has been demonstrated by many researchers that the probabilistic interval forecast could significantly facilitate some decision-making cases, such as storage optimization, market bidding, reserves setting, as it can provide the uncertainty information associated with the point estimations. This paper proposes a nonparametric conditional interval forecast method for PV power generation which can capture the interdependence among the real power output and their point forecasts within all forecasting horizons of interests. The proposed model is tested using the dataset of PV generation power measurements and day-ahead point forecasts in Belgium. The results based on reliability and interval score performance metrics illustrate the effectiveness of the proposed model.

Uncertainty and variability are the inherent factors in modern power systems with increasing number of intermittent power resources such as wind and solar integration. This paper presents a novel probabilistic approach to evaluate the power grid with Fault Ride-Through consideration of the wind generation. Low Voltage Ride-Through regulation applied in China grid is investigated in this paper. Critical Clearing Time, which is applied to indicate the transient stability of the system, is considered as the transient stability constraint. The probabilistic factors of the fault occurring in the system are taken into consideration for assessing the number of wind generators tripped from the power grid. Promising results were obtained.

In this paper, the state-of-The-Art of big data is reviewed. Challenges, opportunities and tools will be discussed. Some emerging technologies will be looked to promote big data applications. The applications of big data in smart grid in some countries will be summarized too. State Grid Corporation of China

Smart grid development highlights the "self-healing" capability as it enables a power system to efficiently and automatically react to disturbances and guide the system to the best possible state. Effectively partitioning the power network (PN) into suitable areas or zones to accommodate subsequent control actions is useful. In this paper, a novel partitioning approach that combines Laplacian spectrum of a PN and self organizing map (SOM) algorithm is proposed. This approach aims to optimize the partitioning solution so as to minimize the real power imbalance and simultaneously maintain a satisfactory voltage profile. Case study is carried out on New England 39-bus system, which demonstrates the effectiveness of the proposed approach.

With more uncertainty and variability existing in smart grid, deterministic load flow, which is used to analyze the operation conditions on a daily routine and planning the power systems for future investment, could not solve the problems with consideration of renewable generation intermittence and load variation. Nowadays, with more attentions are paid to the environment, carbon emission problem is one of the main concerns in smart grid strategies and planning. It is a great opportunity and challenge for planners to take good care of all stakeholders' interests. Multi-objectives need to be considered when making a critical decision. This paper presents result comparisons between probabilistic optimal load flow and probabilistic load flow by considering both carbon emission and minimization of power loss of the entire grid. The framework is applied to the modified IEEE 14-bus system, which is modeled in Power Factory Dig SILENT, with intermittent wind energy source and load variation consideration. State Grid Corporation of China

With the continually increasing growth in wind generation being integrated into the electric networks, it brings about significant challenges for decision-makers of power system operation due to its high volatility and uncertainty. One efficient approach to tackling such a problem is using reliable forecasting tools. As the conventional point forecasting can only provide a deterministic predicted value, instead, the probabilistic interval forecasting was attracted broad attention in the last few years since it can reflect the information of the uncertainties associated with wind power generation, which can significantly facilitate a large number of decision-making problems in power system operation. This paper presents an overview of current methods used in wind power forecasting. First of all, the frequently-used traditional point forecasting methods are reviewed Afterwards, various state-of-the-art techniques in terms of probabilistic forecasting are discussed. The indications for future development in wind power forecasting approaches and conclusions are given in the end.

As plug-in hybrid electric vehicles (PHEVs) are expected to be widely used in the near future, a mathematical model is developed based on the traditional security constrained unit commitment (SCUC) formulation to address the power system dispatching problem with PHEVs taken into account. With the premise of power system secure operation, both the economic benefit for PHEV users and the carbon-emission costs are taken into account. Then, the features of PHEVs as mobile energy storage units are exploited to decouple the developed model into two sub-models, involving the unit commitment model and the charging and discharging scheduling model that includes AC power flow constraints. The optimal plug-in capacities for PHEVs and the schemes, including when and where charging and discharging occur, are obtained through a mixed integer programming algorithm and the Newton-Raphson load flow algorithm in addition to the optimal day-ahead unit commitment scheme. Finally, the feasibility and efficiency of the proposed model are verified with a 6-bus test system.