本文選題：發(fā)電自調(diào)度 + 分布魯棒優(yōu)化　； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：在解除管制的電力市場中,發(fā)電公司為了在競爭中使自身獲得最大收益,需要依據(jù)某種優(yōu)化理論得到的競標(biāo)曲線進(jìn)行投標(biāo)。在聯(lián)營交易模式中,發(fā)電公司和消費(fèi)者分別提出供給和需求,然后由系統(tǒng)調(diào)度員(ISO)根據(jù)電網(wǎng)傳輸容量和網(wǎng)絡(luò)約束等條件建立合適的市場出清規(guī)。發(fā)電公司在聯(lián)營交易模式的電力市場中參與競爭時(shí),只能通過對LMPs(節(jié)點(diǎn)邊際電價(jià))的預(yù)測,向ISO提供相應(yīng)的發(fā)電計(jì)劃。而在充分競爭的電力市場中,隨著系統(tǒng)投標(biāo)的市場化運(yùn)作,必然會(huì)產(chǎn)生更多的不確定因素,因此發(fā)電公司面臨的挑戰(zhàn)是如何根據(jù)電力市場預(yù)測的電價(jià),建立有效的發(fā)電自調(diào)度計(jì)劃。發(fā)電自調(diào)度模型是一類含有不確定性隨機(jī)變量的規(guī)劃問題,模型中的負(fù)荷、節(jié)點(diǎn)邊際電價(jià)等參數(shù)具有一定的不確定性,求解難度會(huì)隨著系統(tǒng)規(guī)模的增大、約束條件和不確定參數(shù)的增多而增大。對于含有不確定性隨機(jī)變量的規(guī)劃問題,其建模方法主要是隨機(jī)規(guī)劃方法和魯棒優(yōu)化方法。前者通過引入隨機(jī)變量來描述不確定性,但這種方法需要得知隨機(jī)變量的完整分布統(tǒng)計(jì)特性,并且會(huì)產(chǎn)生大量的離散樣本點(diǎn),導(dǎo)致計(jì)算規(guī)模過于龐大;后者不需要參數(shù)的概率分布,僅需要假設(shè)不確定參數(shù)屬于一個(gè)不確定集,但這種方法可能過于保守,且沒有利用一些可以取得的概率統(tǒng)計(jì)信息。本文考慮節(jié)點(diǎn)邊際電價(jià)的大小既有一定的范圍,又具有一定的概率分布。因此可以使用一種將隨機(jī)規(guī)劃與魯棒優(yōu)化相結(jié)合地方法,即分布魯棒優(yōu)化方法,對含節(jié)點(diǎn)邊際電價(jià)不確定的發(fā)電自調(diào)度問題進(jìn)行求解。雖然電價(jià)的概率分布是不確定的,但通過長期統(tǒng)計(jì)可以得到電價(jià)的期望及協(xié)方差等確定的矩參數(shù)。電價(jià)短期的概率分布與長期統(tǒng)計(jì)的概率分布相似,但其矩一般是不同的,即矩具有不確定性。因此,利用矩不確定分布魯棒優(yōu)化方法來解決此類問題可以很好地處理節(jié)點(diǎn)邊際電價(jià)這一特性。該方法首先定義一個(gè)以節(jié)點(diǎn)邊際電價(jià)地期望、協(xié)方差為中心地不確定集合,構(gòu)建一個(gè)min-max問題,然后通過Lagrange對偶原理將原問題轉(zhuǎn)換為一個(gè)半正定規(guī)劃進(jìn)行求解。通過IEEE30節(jié)點(diǎn)系統(tǒng)進(jìn)行計(jì)算分析,可以驗(yàn)證本文所提方法的可行性有有效性。
[Abstract]:In the deregulated electricity market, in order to obtain the maximum profit in the competition, the power generation company needs to bid according to the bidding curve obtained by some optimization theory. In the joint trading mode, the power generation company and the consumer put forward the supply and demand, and then the system dispatcher ISOO (system dispatcher ISOO) established the appropriate market rules according to the transmission capacity and network constraints of the power network. When the generation company participates in the competition in the electricity market of the joint trading mode, it can only provide the corresponding generation plan to the ISO by forecasting the LMPs (nodal marginal electricity price). In the fully competitive electricity market, with the market-oriented operation of the system bidding, there will inevitably be more uncertain factors. Therefore, the challenge faced by the power generation companies is how to forecast the electricity price according to the electricity market. Establish effective self-dispatch plan. Power generation self-dispatch model is a kind of programming problem with uncertain random variables. The load and marginal electricity price of the model are uncertain, and the difficulty of solving the problem will increase with the scale of the system. The increase of constraint conditions and uncertain parameters increases. For programming problems with uncertain random variables, the modeling methods are mainly stochastic programming method and robust optimization method. The former describes uncertainty by introducing random variables, but this method needs to know the complete distribution statistical characteristics of random variables, and it will produce a large number of discrete sample points, resulting in a large scale of calculation. The latter does not need the probability distribution of the parameter, but only needs to assume that the uncertain parameter belongs to an uncertain set, but this method may be too conservative and does not make use of some available probability and statistics information. In this paper, the size of the marginal price of the node is considered in both a certain range and a certain probability distribution. Therefore, a local method combining stochastic programming with robust optimization, that is, distributed robust optimization method, can be used to solve the generation self-dispatch problem with uncertain marginal price of electricity nodes. Although the probability distribution of electricity price is uncertain, the moment parameters such as the expectation of electricity price and covariance can be obtained by long-term statistics. The short-term probability distribution of electricity price is similar to the probability distribution of long-term statistics, but the moment is generally different, that is, the moment is uncertain. Therefore, using the moment uncertain distribution robust optimization method to solve this kind of problem can well deal with the characteristic of node marginal price. In this method, we first define a set of uncertainties with marginal electricity price expectation and covariance as the center to construct a min-max problem, and then transform the original problem into a semi-positive definite programming by means of the Lagrange duality principle. The feasibility and validity of the proposed method can be verified by the calculation and analysis of the IEEE30 node system.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM73

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