【摘要】：以風(fēng)能、太陽(yáng)能等為代表性的分布式發(fā)電單元受到氣候和天氣影響,發(fā)電功率難以保證平穩(wěn),可能會(huì)引起頻率和電壓不穩(wěn),進(jìn)而引起停電事故。為了解決這一問(wèn)題,在具有分布式電源的系統(tǒng)中引入了儲(chǔ)能裝置。但是受環(huán)境影響,系統(tǒng)供電與用電負(fù)荷會(huì)出現(xiàn)不平衡的情況,從而導(dǎo)致蓄電池處于虧電狀態(tài)或過(guò)充電狀態(tài),長(zhǎng)期運(yùn)行會(huì)降低蓄電池組的使用壽命,增加系統(tǒng)維護(hù)成本,因此選擇合適的蓄電池控制策略具有重要的實(shí)際意義。本文研究了一個(gè)由分布式發(fā)電源、儲(chǔ)能設(shè)備、用電負(fù)荷以及系統(tǒng)的能量管理中心組成的蓄電池儲(chǔ)能系統(tǒng)。該儲(chǔ)能系統(tǒng)可以與電網(wǎng)進(jìn)行交互。發(fā)電量不足時(shí)可以從電網(wǎng)買電,除了供給負(fù)荷需求,系統(tǒng)中多余的電量可以賣給電網(wǎng)或向電網(wǎng)提供頻率調(diào)節(jié)服務(wù)。系統(tǒng)中分布式發(fā)電功率、負(fù)荷需求功率、電價(jià)和調(diào)頻的價(jià)格相互獨(dú)立,具有很大的不確定性,本文將它們分別建模為Markov鏈來(lái)研究。蓄電池儲(chǔ)能裝置從當(dāng)前狀態(tài)轉(zhuǎn)移到空和滿兩個(gè)特殊狀態(tài)的逗留時(shí)間不服從指數(shù)分布,所以將該儲(chǔ)能系統(tǒng)的優(yōu)化控制問(wèn)題建模為半Markov決策過(guò)程。本文采用基于模型的Sarsa算法來(lái)學(xué)習(xí)最優(yōu)策略,從而使系統(tǒng)在滿足負(fù)荷需求的基礎(chǔ)上獲得的長(zhǎng)期收益最大。隨著電動(dòng)汽車產(chǎn)業(yè)的發(fā)展,電動(dòng)汽車入網(wǎng)(vehicle-to-grid, V2G)正在成為研究熱點(diǎn)。本文考慮將分布式發(fā)電裝置引入V2G系統(tǒng)中。當(dāng)發(fā)電量不足時(shí),電動(dòng)車可以從電網(wǎng)買電；當(dāng)發(fā)電能力比較強(qiáng)時(shí),除了供給電動(dòng)車的用電需求,多余的發(fā)電量直接賣給電網(wǎng)。閑置在充電樁上的電動(dòng)車可以與電網(wǎng)交互,根據(jù)自身電量的情況以及電價(jià)和調(diào)頻價(jià)格的高低,決定向電網(wǎng)賣電或是提供頻率調(diào)節(jié)服務(wù)。假設(shè)系統(tǒng)能夠在決策周期的初始時(shí)刻獲得系統(tǒng)的發(fā)電量和電價(jià)信息。將系統(tǒng)的優(yōu)化控制問(wèn)題建模為動(dòng)態(tài)規(guī)劃過(guò)程。用策略迭代的方法來(lái)獲取最優(yōu)策略,從而使該系統(tǒng)能夠在滿足自身需求的同時(shí)獲取最大收益。
[Abstract]:The distributed generation units, such as wind energy and solar energy, are affected by climate and weather, so it is difficult to ensure stable power generation, which may cause frequency and voltage instability, and then cause power outages. In order to solve this problem, energy storage device is introduced into the system with distributed power supply. However, under the influence of the environment, there will be imbalance between the power supply and the power load of the system, which will lead to the battery in the state of power deficit or overcharge, and the long-term operation will reduce the service life of the battery group and increase the maintenance cost of the system. Therefore, the selection of appropriate battery control strategy has important practical significance. In this paper, a battery energy storage system composed of distributed power generation, energy storage equipment, power load and energy management center of the system is studied. The energy storage system can interact with the power grid. Electricity can be bought from the power grid when the power generation is insufficient. In addition to supplying the load demand, the excess power in the system can be sold to the power grid or to provide frequency regulation services to the power network. The distributed generation power, load demand power, electricity price and frequency modulation price are independent of each other. In this paper, they are modeled as Markov chains. The time of stay of storage battery energy storage device from current state to empty state and full state is not satisfied with exponential distribution, so the optimal control problem of the energy storage system is modeled as a semi-Markov decision-making process. In this paper, the model-based Sarsa algorithm is used to learn the optimal strategy, so that the long-term benefits of the system can be maximized on the basis of satisfying the load requirements. With the development of electric vehicle industry, vehicle-to-grid, V 2 G is becoming a research hotspot. In this paper, we consider introducing distributed generation equipment into V2G system. Electric vehicles can buy electricity from the power grid when the generation capacity is low; when the generation capacity is relatively strong, in addition to the electricity demand for electric vehicles, the excess power generation is sold directly to the power grid. Electric vehicles idle on charging piles can interact with the power grid and decide to sell power to the grid or provide frequency regulation services according to their own electricity quantity and the price of electricity price and frequency modulation. It is assumed that the system can obtain the power generation and price information of the system at the beginning of the decision cycle. The optimal control problem of the system is modeled as a dynamic programming process. The method of policy iteration is used to obtain the optimal policy, so that the system can obtain the maximum profit while satisfying its own requirements.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM912

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