China’s evolving reserve requirements

This paper examines the evolving role of reserve requirements as a policy tool in China. Since 2007, the Chinese central bank (PBC) has relied more on this tool to withdraw domestic liquidity surpluses, as a cheaper substitute for open-market operation instruments in this period of rapid FX accumulation. China’s reserve requirement system has also become more complex and been used to address a range of other policy objectives, not least being macroeconomic management, financial stability and credit policy. The preference for using reserve requirements reflects the size of China’s FX sterilisation task and the associated cost considerations, a quantity-oriented monetary policy framework challenged to reconcile policy dilemmas and tactical considerations. The PBC often finds it easier to reach consensus over reserve requirement decisions than interest rate decisions and enjoys greater discretion in applying this tool. The monetary effects of reserve requirements need to be explored in conjunction with other policy actions and not in isolation. Depending on the policy mix, higher reserve requirements tend to signal a tightening bias, to squeeze excess reserves of banks, to push market interest rates higher, and to help widen net interest spreads, thus tightening domestic monetary conditions. There are, however, costs to using this policy tool, as it imposes a tax burden on Chinese banks that in turn appear to have passed a significant portion of this cost onto their customers, mostly depositors and SMEs. However, the pass-through onto bank customers appears to be partial.reserve requirements; sterilisation tools; monetary policy; net interest margin and spread; tax incidence; Chinese economy

Nonlinear system identification for model-based condition monitoring of wind turbines

This paper proposes a data driven model-based condition monitoring scheme that is applied to wind turbines. The scheme is based upon a non-linear data-based modelling approach in which the model parameters vary as functions of the system variables. The model structure and parameters are identified directly from the input and output data of the process. The proposed method is demonstrated with data obtained from a simulation of a grid-connected wind turbine where it is used to detect grid and power electronic faults. The method is evaluated further with SCADA data obtained from an operational wind farm where it is employed to identify gearbox and generator faults. In contrast to artificial intelligence methods, such as artificial neural network-based models, the method employed in this paper provides a parametrically efficient representation of non-linear processes. Consequently, it is relatively straightforward to implement the proposed model-based method on-line using a field-programmable gate array

Simultaneous fault detection and sensor selection for condition monitoring of wind turbines

Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring

Control systems for WRASPA.

The paper discusses the need for a wave energy converter (WEC) to sense and respond to its environment in order to survive and to produce its maximum useful output. Such systems are described for Wraspa, a WEC being developed at Lancaster University and first reported at ICCEP in 2007. The main control system that continually monitors and optimises the power-take-off is termed ldquoStepwise Controlrdquo and seeks to continually adjust the damping force applied to the collector to suit the wave force that drives it. The complete instrumentation and control system that will be needed is considered briefly, including the above PTO control system; direction sensing and heading control; tide level compensation; condition monitoring and provisions for access and maintenance

Condition monitoring of wind turbines based on extreme learning machine

Wind turbines have been widely installed in many areas, especially in remote locations on land or offshore. Routine inspection and maintenance of wind turbines has become a challenge in order to improve reliability and reduce the energy of cost; thus adopting an efficient condition monitoring approach of wind turbines is desirable. This paper adopts extreme learning machine (ELM) algorithms to achieve condition monitoring of wind turbines based on a model-based condition monitoring approach. Compared with the traditional gradient-based training algorithm widely used in the single-hidden layer feed forward neural network, ELM can randomly choose the input weights and hidden biases and need not be tuned in the training process. Therefore, ELM algorithm can dramatically reduce learning time. Models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains data of the temperature of gearbox oil sump, gearbox oil exchange and generator winding. The results show that the proposed method can efficiently identify faults of wind turbines

Consensus-based Hierachical Demand Side Management in Microgrid

The increasing penetration of renewable power generators has brought a great challenge to develop an appropriate energy dispatch scheme in a microgrid system. This paper presents a hierarchical energy management scheme by integrating renewable energy forecast results and distributed consensus algorithm. A multiple aggregated prediction algorithm (MAPA) is implemented based on satellite weather forecast data to obtain a short-term local solar radiance forecast curve, which outperforms the multiple linear regression model. A distributed consensus algorithm is then incorporated into the HVAC (heating ventilation air conditioning) units as the adjustable loads in order to dynamically regulate power consumption of each HVAC unit, based on solar power forecast in a day. The scheme aims to alleviate the local supply-demand power mismatch by varying demand response of the HVAC units. Two case studies are performed to demonstrate the feasibility and robustness of the algorithms

State-of-the-art forecasting algorithms for microgrids

As a controllable subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. Distributed energy sources employ non-polluted and sustainable resources such as wind and solar power in accordance with local terrain and climate to provide a reliable, consistent power supply for local customers. However, the electricity production in such a system is intermittent in nature, due to the time-varying weather conditions. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to build a smart energy management system. The paper firstly reviews the forecasting algorithms for power supply side and load demand. The feasibly of the current control strategy is discussed. Finally, taking the wind turbine operational at Lancaster University campus as an example, results on power generation forecasting are presented by using a hybrid model combining Radial Basis Function and K-Means clustering. Development of new hybrid techniques aiming at improving model efficiency for online and real time forecasting will be one of the future research directions in this field

Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system’s conditions

A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids

As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed

Distributed control of battery energy storage system in a microgrid

Due to the high penetration of renewable power system with variable generation profiles, the need for flexible demand and flexible energy storage increases. In this paper, a hierarchical energy dispatch scheme incorporating energy storage system is presented to address the uncontrollability of renewable power generation. Statistical-based forecasting techniques are preformed and compared in order to accurately predict solar radiance and estimate solar power generation. Battery energy storage system (BESS) is often deployed as a flexible power supplier to reduce the peak power, emissions and cost. This paper elaborates a multi-agent system (MAS) based distributed algorithm to investigate an energy dispatch scheme for BESS, based on the renewable energy forecasting results. A 24-hour prescheduled energy dispatch scheme is assigned to individual BESSs based on IEEE 5-bus system and IEEE 14-bus system. Simulation results are shown to demonstrate the feasibility and scalability of the algorithm