Loi Lei Lai

Powering China's Sustainable Development with Renewable Energies: Current Status and Future Trend

Abstract China experienced a rapid economic growth in the past few decades, whereas it has been facing a critical environmental situation. It is palpable that creating a sustainable energy structure in China is urgently required. As reported in Chinas 12th Five-year (2011–2015) Plan (which is a five-year development plan that outlines the development focus and strategic plan for different sectors in the next five-year period, published by the State Council of the PRC every five years), integrating more renewable energy, especially from solar and wind sources, becomes an essential part of their smart grid development. This article comprehensively reviews the existing achievements of renewable technology development and current status including relevant policies from central government and some on-going demonstration projects. Promoting new energy vehicles (e.g., electric vehicles) as an effective way of reducing carbon emissions is addressed in this article as well. Finally, the difficulties at the current stage are analyzed and future trends for a subsequent phase of development are also outlined.

Risk-Based Power System Security Analysis Considering Cascading Outages

Successful development of smart grid demands strengthened system security and reliability, which requires effective security analysis in conducting system operation and expansion planning. Classical N - 1 criterion has been widely used to examine every creditable contingency through detailed computations in the past. The adequacy of such approach becomes doubtful in many recent blackouts where cascading outages are usually involved. This may be attributed to the increased complexities and nonlinearities involved in operating conditions and network structures in context of smart grid development. To address security threats, particularly from cascading outages, a new and efficient security analysis approach is proposed, which comprises cascading failure simulation module (CFSM) for post-contingency analysis and risk evaluation module (REM) based on a decorrelated neural network ensembles (DNNE) algorithm. This approach overcomes the drawbacks of high computational cost in classical N-k-induced cascading contingency analysis. Case studies on two different IEEE test systems and a practical transmission system-Polish 2383-bus system have been conducted to demonstrate the effectiveness of the proposed approach for risk evaluation of cascading contingency.

An overview on wind power forecasting methods

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.

Guest Editorial Enabling Technologies and Methodologies for Knowledge Discovery and Data Mining in Smart Grids

The advance in the research of Smart Grid methodologies opens the doors toward the conceptualization of new tools aimed at effectively addressing most challenging issues of modern power distribution systems, including the massive pervasion of renewable power generators, the strictest power quality limits, the complex interactions with the energy markets, the raising levels of security and reliability constraints, and the need for maximizing the exploitation of existing electrical infrastructures

Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence

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.

Security constrained unit commitment-based power system dispatching with plug-in hybrid electric vehicles

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.

Power smooth control for DFIG under extreme operating gust

This paper presents a new power smooth control scheme to enable Double Fed Induction Generator (DFIG) based wind turbine system to safely and smoothly ride through the extreme operating gusts. Under the normal condition, the control scheme will have few impacts on the maximum power point tracking control of DFIG. In the event of extreme operating gust, the new scheme controls the electromagnetic torque directly so that the generated power can be effectively limited and smoothed. Consequently, the negative impacts on grid and turbine itself can be reduced. The scheme involves a detection module which can detect the extreme operating gust and activate the power smooth control as quickly as possible. When the gust terminates, the control scheme can ensure smooth transition back to normal control. The simulation results verify the effectiveness of the proposed scheme.

Coordinated Dispatch of Virtual Energy Storage Systems in LV Grids for Voltage Regulation

The growth in installed solar photovoltaic (PV) capacity and the ever-increasing power demand due to the use of energy-hungry appliances have caused voltage issues. In this paper, a hierarchical dispatch strategy is proposed for coordinating multiple groups of virtual energy storage systems (VESSs), i.e., residential houses with air conditioners, to regulate voltage in low-voltage (LV) grids with high solar PV penetration. Specifically, the two levels of the proposed model are: 1) in the lower level, VESSs within each intelligent residential district are controlled locally by individual aggregator; 2) in the upper level, multiple aggregators are coordinated to achieve voltage regulation through a consensus control strategy. By exchanging information through sparse communication links, each aggregator shares the required active power adjustment among all participating groups, without compromising users’ thermal comfort. Simulation result demonstrates that the proposed control scheme can effectively regulate voltage in LV grids with greater robustness and scalability.

Power Market Load Forecasting on Neural Network with Beneficial Correlated Regularization

In day-ahead market (DAM), load serving entities (LSEs) are required to submit their future load schedule to market operator. Due to the cost computation, we have found the inconformity between load accuracy and cost of power purchase. It means that more accurate load forecasting model may not lead to a lower cost for LSEs. Accuracy pursuing load forecast model may not target a solution with optimal benefit. Facing this issue, this paper initiates a beneficial correlated regularization (BCR) for neural network (NN) load prediction. The training target of NN contains both accuracy section and power cost section. Also, this paper establishes a virtual neuron and a modified Levenberg–Marquardt algorithm for network training. A numerical study with practical data is presented and the result shows that NN with BCR can reduce power cost with acceptable accuracy level.

Guest Editorial Special Section on Emerging Informatics for Risk Hedging and Decision Making in Smart Grids

THE development of smart grids worldwide aims at tackling various challenges facing power system operation and planning due to increased penetration of many new technologies of diversified properties. On the one hand, system operators and many other participants have to deal with increased uncertainties and risks involved in daily operation and planning activities. On the other hand, applications of many new metering and measurement devices, capable of closely monitoring and sensing grid operation in real time, result in overwhelming amount of measurement data of high precision and resolution. By far, how to make the best use of the massive data remains quite a challenging task facing power system researchers and practitioners [1]. It is recently realized that the availability of the high-quality data could potentially facilitate risk hedging and decision making in system operation and planning, of which the prerequisite calls for innovative informatics approaches that are intelligent, data driven, and capable of handling various complex problems. For instance, the application of advanced data mining techniques has enabled better prognosis of renewable generations that are highly uncertain and intermittent. Specially, recent trend has seen a revolutionary change of forecasting paradigm moving from the point-based forecasting approaches toward the probabilistic interval based ones by utilizing advanced data analytic techniques such as extreme learning machine (ELM) and granule computing [2]–[4], in order to achieve better prognosis and representation of the uncertainties and intermittencies involved in renewable energies. Another example is the wide area measurement systems data measured by widely deployed phasor measurement units has been exploited to develop advanced online power system security surveillance and visualization tools by many utilities [5]–[7]. Recently, there are also industrial applications of intelligent and data-oriented approaches dedicated for resolving complex control and operation problems in transmission or distribution grids with high renewable energy penetration [8]–[10]. In terms of electricity market operation, applications of advanced data analytic techniques have been popular to construct optimal bidding strategies for generators or electric vehicle aggregators, as well as short load forecasting and electricity market price forecasting, etc. [11], [12]. There are also many applications of risk-based techniques to counterbalance the uncertainties facing construction/expansion planning, as well as operation planning in context of deregulated electricity markets.