|Title:||Advanced modelling and control for demand side management implementation in smart grid|
|Subject:||Hong Kong Polytechnic University -- Dissertations|
Smart power grids -- Design
Electric power distribution
Electric power systems
|Department:||Department of Electrical Engineering|
|Pages:||xviii, 184 pages : color illustrations|
|Abstract:||In order to increase the utilization of renewable energy sources and to reduce the need of generator-provided ancillary services and inefficient peaking generation, electricity consumers are progressively transforming into active participants in power system operations via various demand side management programs. Flexible load response is expected to be utilized to achieve lower grid operating costs, increased system reliability and improved energy efficiency, by modulating power consumption in response to various supply conditions, such as high market price, peak demand, overloading, regulation signal, etc. However, the effects of changing the load profiles on the system performances need to be identified and assessed before implementation, so as to determine the optimal control strategy to maximize the network benefits. Besides, although some demand responsive loads can participate in demand side management schemes by simple connection and disconnection, some require more sophisticated control algorithms because physical constraints of load devices and consumer behavior need to be taken into account. This thesis firstly proposes a novel hourly electric load of a building based on radial basis function neural network and validate the high accuracy using real building data under various weather conditions.|
Next, the most updated statistic information on load composition in residential and commercial load sectors in UK is used to develop the aggregate load models with time-varying model coefficients using a component-based load aggregation methodology, which is able to represent the temporal variations due to the time-varying load composition caused by normal consumer behavior or demand side management actions. Then a general methodology is presented to model DSM scenarios to assess the corresponding impacts on the network performances, using the wet load in residential sector as an example. In addition, a conservation voltage reduction optimization problem based on the developed time-varying exponential load models is formulated to demonstrate the importance of accurate load modelling in the VVO analysis. Furthermore, the problem of the optimal locations for demand side management deployment is studied by formulating a Optimal Power Flow problem to minimize the amount of load needed (i.e. minimize the number of affected customers) to participate in DSM scheme to relieve an upstream network contingency. There are various demand manageable loads which require more advanced control algorithms in order to play a significant role in demand side management, such as thermostatically controlled loads. In order to allow more heating, ventilation and air-conditioning (HVAC) system loads to participate in supply-demand balancing and ancillary service market, this thesis introduces a mathematical R-C thermodynamic model of building that can accurately capture the temperature dynamics which is strongly related to the power consumption of HVAC systems. Economic model predictive controllers are then proposed to effectively and optimally modify the power consumption of HVAC systems to reduce the peak load and increase energy efficiency by minimizing the electricity costs while maintaining the temperature within satisfactory levels. Lastly, this work proposes a novel contract framework between buildings, building aggregator and utility that can maximize and reward the flexible power reserve provided by aggregate HVAC loads, and under this developed contract framework, an optimal building aggregator that can aggregate the HVAC loads to declare the power flexibility to the utility for providing fast regulating power is proposed, and dispatch the load according to the power regulation signal from utility in the real-time scheduling stage.
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