CubeSats have been gaining significant interest as a cost-effective solution that can be built with low power requirements for different mission types. The most critical subsystem in is the Electrical Power Subsystem (EPS), which provides required to operate remaining subsystems. This paper presents an approach optimizing load management and scheduling CubeSat applications ensure optimal coordi...

TAbstractT—With the high-speed economic development in China, the transition of structural function in the urban land system highly effects the development of the urban electric load. Forecasting the urban electric load accurately is the foundation of decision making scientifically for the development and planning of the urban power grid in China. This paper improves the decision method of Tran...

A comprehensive model for stochastic load flow analysis in electric power systems is proposed for the purpose of estimating the statistics of bus voltage magnitudes, circuit currents and transmission line power flows. The proposed method is driven by a realistic nonconforming stochastic electric load model, defined in terms of few independent stochastic processes; it is based on the quadratized...

California is a leader in automating demand response (DR) to promote low-cost, consistent, and predictable electric grid management tools. Over 250 commercial and industrial facilities in California participate in fully-automated programs providing over 60 MW of peak DR savings. This paper presents a summary of Open Automated DR (OpenADR) implementation by each of the investor-owned utilities i...

The presence of an electrical de-regulated market reinforces the need of forecast. Actions like network management, load dispatch and network reconfiguration under quality of service constraints, require reliable load forecasts. This paper establishes a methodological approach based on a Gaussian Process Model in order to choose an efficient endogenous information to be included in the input ve...

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Ei...

As accurate Short Term Load Forecasting (STLF) is very important for improvement of the management performance of the electric industry, various short term loads forecasting methods have been developed. This paper addresses an issue of the optimal design of a neural network based short term load forecaster. A new hybrid evolutionary algorithm combining the Particle Swarm Optimization (PSO) algo...

With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction o...