本文選題：微電網(wǎng)(微網(wǎng)) + 多時間尺度��； 參考：《電力系統(tǒng)自動化》2017年22期

【摘要】：微電網(wǎng)中分布式電源出力具有間歇性、波動性等特點,并且負荷形式多樣,分布式電源和負荷的不確定性會導致微電網(wǎng)能量優(yōu)化的不確定性。為了更好地解決上述問題,提出了一種基于改進模型預測控制(MPC)的雙層多時間尺度微電網(wǎng)優(yōu)化策略。傳統(tǒng)MPC優(yōu)化中某些模型參數(shù)固定,難以及時處理系統(tǒng)中的突發(fā)擾動;并且傳統(tǒng)MPC的單個優(yōu)化周期時長有限,難以處理一些與時間相關或影響優(yōu)化結(jié)果全局性的復雜約束。根據(jù)微電網(wǎng)中不確定性因素及復雜約束提出MPC的自適應改進,能更好地適應微電網(wǎng)設備投切靈活、發(fā)電功率受外界影響大等特性,更好地保障系統(tǒng)的魯棒性與優(yōu)化的精確性。基于日前計劃優(yōu)化出未來的能量分配及負荷調(diào)度;在日內(nèi)基于改進MPC并參考日前優(yōu)化結(jié)果進行實時能量優(yōu)化,從而使目標更優(yōu),提高結(jié)果的準確性。最后應用MATLAB進行仿真,證明了所提策略的適用性和準確性。
[Abstract]:Distributed power generation in microgrid has the characteristics of intermittent and fluctuating, and the load forms are varied. The uncertainty of distributed power generation and load will lead to the uncertainty of energy optimization of microgrid. In order to solve the above problems, an improved model predictive control (MPC) based multi-time scale microgrid optimization strategy is proposed. In the traditional MPC optimization, some model parameters are fixed, it is difficult to deal with the burst disturbance in the system in time, and the single optimization period of the traditional MPC is limited, so it is difficult to deal with some complex constraints related to time or affecting the global optimization results. According to the uncertainty factors and complex constraints in microgrid, the adaptive improvement of MPC is proposed, which can better adapt to the characteristics such as flexible switching of micro-grid equipment, large external influence on generating power, and better guarantee the robustness and optimization accuracy of the system. Based on the pre-day plan, the future energy distribution and load scheduling are optimized, and the real-time energy optimization based on the improved MPC and the pre-day optimization results is carried out in order to make the target better and improve the accuracy of the results. Finally, MATLAB simulation is used to verify the applicability and accuracy of the proposed strategy.
【作者單位】： 東南大學電氣工程學院;國網(wǎng)江蘇省電力公司電力科學研究院;南京師范大學電氣與自動化工程學院;
【基金】：國家重點研發(fā)計劃資助項目(2016YFB0900500) 國家自然科學基金資助項目(51777031)~~
【分類號】：TM727

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