As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncert...As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.展开更多
基金partially supported by Hong Kong RGC Theme-based Research Scheme(No.T23-407/13N and No.T23-701/14N)SUSTech Faculty Startup Funding(No.Y01236135 and No.Y01236235).
文摘As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.
