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Microgrids can serve to integrate distributed energy resources (DERs) and controllable loads in a smarter and more reliable fashion. The operation of residential microgrids with DER during islanded situation is of great significance to both customers and utility providers. This paper proposes two control strategies for a residential microgrid that has a shared energy storage (ES) in islanded mode...
Machine learning methods are main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey...
This paper presents the results of a temperature-load sensitivity study carried out at the Midcontinent Independent System Operator (MISO) for adjusting MISO day-ahead load forecast. As the area operated by MISO is growing rapidly, operators at MISO have to examine the day-ahead load forecast results generated by commercial software packages on a daily basis and manually correct anticipated discrepancies...
This paper presents the architecture and modeling approach of a Matlab-based toolbox for developing and testing home energy management (HEM) algorithms under a number of typical operation conditions. This toolbox serves as a developer platform that includes a graphical user interface, a model database, a computational engine, and an input-output database. The model database consists of home appliance...
This paper presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts considering users' comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem is described using an electrical water heater load as an example. The thermal...
This paper describes a modeling approach that simulates the impacts of different climate change mitigation technologies on power grids for power system planning purposes. Because the historical data is less credible when new technologies are being deployed to the system, it is then critical to model them to address their impacts. This paper illustrates how to integrate modeling results obtained from...
This paper presents a smart distribution grid testbed to test or compare designs of integrated information management systems (I2MSs). An I2MS extracts and synthesizes information from a wide range of data sources to detect abnormal system behaviors, identify possible causes, assess the system status, and provide grid operators with response suggestions. The objective of the testbed is to provide...
This paper presents a smart distribution grid testbed to test or compare designs of integrated information management systems (I2MSs). An I2MS extracts and synthesizes information from a wide range of data sources to detect abnormal system behaviors, identify possible causes, assess the system status, and provide grid operators with response suggestions. The objective of the testbed is to provide...
Support vector machine (SVM) is based on the statistical learning theory. It has recently been successfully used to solve nonlinear regression and time series problems and has been applied to predict values. The key problem of SVM is the choice of SVM parameters. Particle swarm optimization (PSO) algorithm has the ability of global optimization. This paper proposed an improved PSO algorithm based...
Support Vector Machine (SVM) is a type of learning machine which has been proved to be available in solving the problems of nonlinear regression. The decision of SVM parameters is essential. In this paper a new SVM model based on particle swarm optimization (PSO) for parameter optimization has been proposed. PSO algorithm has extensive capability of global optimization. Once the PSO finds the optimal...
Electric power system load forecasting plays an important role in the energy management system (EMS), which has great effect on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will lead to economic cost saving and right decisions on generating electric power. Electric power load is difficult to be forecasted accurately for...
This paper discusses the development of a load component database for household appliances and office equipment. To develop more accurate load models at both the transmission and distribution levels, a better understanding of the behaviors of home appliances and office equipment associated with the variations of the power system voltage becomes more and more critical. The Bonneville Power Administration...
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