Visible to the public Optimal microgrid energy management integrating intermittent renewable energy and stochastic load

TitleOptimal microgrid energy management integrating intermittent renewable energy and stochastic load
Publication TypeConference Paper
Year of Publication2015
AuthorsJi, Y., Wang, J., Yan, S., Gao, W., Li, H.
Conference Name2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Date Publisheddec
Keywordscontinuous random variable, DG energy management, discrete stochastic scenario, distributed generator energy management, distributed power generation, EMS, energy management systems, energy storage, energy storage system, ESS, integer programming, intermittent renewable energy, Load modeling, LV network, Markov chain, Markov processes, Matlab optimization toolbox, MG energy management problem, microgrid, mixed integer quadratic programming problem, Optimization, optimization principle, Programming, ptimal microgrid energy management, pubcrawl170110, renewable energy sources, Scenarios generation approach, stochastic, stochastic load, stochastic programming, time series, time-homogeneous Markov chain model, two-stage stochastic programming

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.

Citation Keyji_optimal_2015