Energy-Efficient Buildings Facilitated by Microgrid

By X. Guan, Zhanbo Xu, Q. Jia. IEEE Transactions on Smart Grid, 2010

509 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/5dfad314620feb714030b5d053a49ccc99f69bc1

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In Control and Systems Engineering

In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

State of the Art in Research on Microgrids: A Review (2015, background): “The study in [164] presents a scheduling of autonomous generators, renewable resources, ESS devices, and schedulable loads in a microgrid for buildings.”
Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization (2012, methodology): “This work was supported in part by the U.S. National Science Foundation Grant ECCS-1102064.”
Smart Grid — The New and Improved Power Grid: A Survey (2012, unknown): “Researchers realize these objectives in various levels and from various perspectives, such as individual user cost/bill or profit [32], [40], [53], [69], [95], [120], [170], [171], [188], single energy bill or aggregate utility of a group of users [98], [220], cost or utility of electricity industry”
A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings (2014, background): “As expected, the EMSs of these disaggregated environments must know in advance the generation and the demand they are in charge of, as shown in Guan et al. (2010) [125], Chen et al. (2011) [126], and Kanchev et al. (2011) [127].”
Robust Energy Management for Microgrids With High-Penetration Renewables (2012, unknown): “Considering the uncertainties of demand proﬁles and PV generation, a stochastic program is formulated to minimize the overall cost of electricity and natural gas for a building in [5].”
Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response (2015, background): “For simplicity, the forecasting errors of wind speed , system load, DA and RT electricity prices are assumed to follow normal distributions with zero-mean and the standar d deviations are 5 % [17], 10%, 20% [18], and 25% of their corresponding forecast values.”