To improve the overall economy of the wind-energy storage power station, a direct control strategy is proposed to track the deviation of the wind power plan. Compared with the traditional strategy of wind power fluctuation mitigation, the control strategy in this paper can change the charge and discharge power of energy storage in real-time according to the deviation of wind power and the state of charge (SOC). When the power of wind power changes suddenly, the strategy can make the valid judgment and prevent control failure, so that Grid-connected power of wind farm in extreme cases can also meet the requirements of the safe and stable operation of the power system. The strategy uses the discrete Fourier transform (DFT) to analyze the power deviation of the wind farm in the frequency domain and obtains power compensation requirements for different time scales. Energy storage equipment with corresponding characteristics is used to classify control of deviation of wind power. The compensated power deviation can meet the requirements in market competition. At the same time, the power exchange between storage systems is carried out to optimize the state of charge in real-time and make the energy-type energy storage in shallow charge/discharge state, which effectively reduces the repeated regulation of energy storage systems. Finally, this paper establishes a comprehensive economic benefit model of the energy storage system. Combining the Markov Chain Monte Carlo method (MCMC) and backward scenario reduction technology generate multiple scenarios. The calculation results show that the proposed strategy can effectively track the deviation of the wind power plan. Furthermore, prolong the service life of the energy storage system and improve the market competitiveness of wind power.INDEX TERMS Hybrid wind-energy storage (wind-ES) system, tracking wind power schedule output, discrete Fourier transform (DFT), electricity market.
As better wind speeds are available offshore compared to on land, offshore wind power contribution in terms of electricity supplied is higher, thus more and more offshore wind turbines have been and will be deployed. However, the severe offshore conditions make it necessary to develop reliable and cost-effective real-time monitoring system when building offshore wind power farms. This paper proposes an innovative method for designing remote monitoring system for offshore wind turbines based on ZigBee wireless sensor networks. ZigBee networks carrying variety of sensors actively collect dynamic data related to the system operation status, including parameters of the mechanical unit and electrical unit as well as the operation environment. Each wind turbine itself represents a single wireless network, which sends information to remote monitoring center by GPRS module to achieve full wireless communication. To enhance the topologic efficiency and reduce the energy consumption of the networks, an optimized routing algorithm is developed. A physical system based on such method is developed. Analysis and experiment tests with real wind farm data indicate that the developed system works fairly well. The fundamental idea as studied in this work is of great value for building reliable and affordable real-time monitoring systems for wind farms (offshore and on land) with enhanced safety and efficiency.
At present, electric vehicles (EVs), small-scale wind power, and solar power have been increasingly integrated into modern power system via the combined cooling heating and power based microgrid (CCHP-MG). However, inside the microgrid the uncertainties of EVs charging, wind power, and solar power significantly impact the economy of CCHP-MG operation. Therefore to improve the economy deteriorated by the uncertainties, this paper presents a two-stage adjustable robust optimization to achieve the minimal operational cost for CCHP-MG. Before the realizations of the uncertainties, the day-ahead stage as the first stage decides an operational strategy that can withstand the worst-case uncertainties. As long as the uncertainties are observed, the realtime stage as the second stage adjusts the operational units to compensate the errors caused by the day-ahead operational strategy. Due to the difficulties of the model solution, this paper further adopts the duality theory, Big-M method, and column-and-constraint generation (C&CG) decomposition to convert the model into two tractable mixed integer linear programming (MILP) problems. Further, C&CG iteration algorithm is also employed to solve the MILPs, which can ultimately provide an optimal economic day-ahead dispatch strategy capable of handling uncertainties. The experimental results demonstrate the effectiveness of the presented approach.
Newly developed oblique photogrammetry (OP) techniques based on unmanned aerial vehicles (UAVs) equipped with multicamera imaging systems are widely used in many fields. Smartphones cost less than the cameras commonly used in the existing UAV OP system, providing high-resolution images from a built-in imaging sensor. In this paper, we design and implement a novel low-cost and ultralight UAV OP system based on smartphones. Firstly, five digital cameras and their accessories detached from the smartphones are then fitted into a very small device to synchronously shoot images at five different perspective angles. An independent automatic capture control system is also developed to realize this function. The proposed smartphone-based multicamera imaging system is then mounted on a modified version of an existing lightweight UAV platform to form a UAV OP system. Three typical application examples are then considered to evaluate the performance of this system through practical experiments. Our results indicate that both horizontal and vertical location accuracy of the generated 3D models in all three test applications achieve centimeter-level accuracy with respect to different ground sampling distances (GSDs) of 1.2 cm, 2.3 cm, and 3.1 cm. The accuracy of the two types of vector maps derived from the corresponding 3D models also meet the requirements set by the surveying and mapping standards. The textural quality reflected by the 3D models and digital ortho maps (DOMs) are also distinguishable and clearly represent the actual color of different ground objects. Our experimental results confirm the quality and accuracy of our system. Although flight efficiency and the accuracy of our designed UAV OP system are lower than that of the commercial versions, it provides several unique features including very low-cost, ultralightweight, and significantly easier operation and maintenance.
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