电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2014年
3期
63-70
,共8页
杨佳俊%雷宇%龙淼%徐建政%陈红%牟春晓
楊佳俊%雷宇%龍淼%徐建政%陳紅%牟春曉
양가준%뢰우%룡묘%서건정%진홍%모춘효
旋转备用%不确定性%成本效益分析%非线性约束%机会约束规划
鏇轉備用%不確定性%成本效益分析%非線性約束%機會約束規劃
선전비용%불학정성%성본효익분석%비선성약속%궤회약속규화
spinning reserve%uncertainty%cost-benefit analysis%nonlinear constraints%chance constraint programming
为解决含风电场的电力系统中风电与负荷的不确定性对系统的安全经济运行产生的影响,获得既经济又有较高可靠性的解,提出通过配置旋转备用容量来应付系统中的不确定性因素,主要是风电场出力预测的影响。采用成本效益分析的方法,根据备用的效益与成本在目标函数中的相互牵制,自动为系统配置合适的备用。并将构建模型中部分含有{0,1}变量乘积形式的非线性约束转化为由一组线性约束条件来表达,转化后的数学模型为标准的二次混合整数规划问题,建立的模型直接能够用传统数学优化模型求解,因此计算速度较许多机会约束规划方法快,在一定程度上能够满足实际应用。算例分析表明所提模型的有效性和实用性。
為解決含風電場的電力繫統中風電與負荷的不確定性對繫統的安全經濟運行產生的影響,穫得既經濟又有較高可靠性的解,提齣通過配置鏇轉備用容量來應付繫統中的不確定性因素,主要是風電場齣力預測的影響。採用成本效益分析的方法,根據備用的效益與成本在目標函數中的相互牽製,自動為繫統配置閤適的備用。併將構建模型中部分含有{0,1}變量乘積形式的非線性約束轉化為由一組線性約束條件來錶達,轉化後的數學模型為標準的二次混閤整數規劃問題,建立的模型直接能夠用傳統數學優化模型求解,因此計算速度較許多機會約束規劃方法快,在一定程度上能夠滿足實際應用。算例分析錶明所提模型的有效性和實用性。
위해결함풍전장적전력계통중풍전여부하적불학정성대계통적안전경제운행산생적영향,획득기경제우유교고가고성적해,제출통과배치선전비용용량래응부계통중적불학정성인소,주요시풍전장출력예측적영향。채용성본효익분석적방법,근거비용적효익여성본재목표함수중적상호견제,자동위계통배치합괄적비용。병장구건모형중부분함유{0,1}변량승적형식적비선성약속전화위유일조선성약속조건래표체,전화후적수학모형위표준적이차혼합정수규화문제,건립적모형직접능구용전통수학우화모형구해,인차계산속도교허다궤회약속규화방법쾌,재일정정도상능구만족실제응용。산례분석표명소제모형적유효성화실용성。
In order to ensure the safe and economic operation of system which is impacted by the uncertainty of wind power and load, and to acquire economic and reliable solutions, this paper proposes to cope with the uncertainty factors, especially the effect of wind power plant output prediction, through configuring spinning reserve capacity. This paper adopts the method of cost-benefit analysis, based on the mutual restraint of the spare benefit and cost in the objective function, suitable reserve is automatically configured for the system. Part of nonlinear constraints in the model that contains{0, 1}variable product form are transformed to a group of linear constraints. The transformed mathematical model is a standard secondary mixed integer programming, and it can be solved directly by traditional mathematical optimization model, so computing speed is faster than many chance constraint programming methods. To some extent, it can satisfy the practical application. The example results show its effectiveness and practicability.