本文選題：購售電綜合策略模型 + 隨機價格模型��； 參考：《華北電力大學(北京)》2017年碩士論文

【摘要】：我國新一輪電改指出要有序向社會資本開放配售電業(yè)務,售電試點地區(qū)的售電公司和用戶積極開展購售電交易,電力改革發(fā)展態(tài)勢良好。售電商作為電力市場的主體,可以選擇不同的市場購電,再將電力出售給終端用戶,它的利潤是售電收入與供電成本之差。但由于電力市場中價格是不斷變動的,用戶的購電需求量也不確定,因此售電商在交易中要承擔一定的風險。在目前的改革形勢下,本文針對面向大工業(yè)用戶的售電公司的購售電策略的研究有著重要的研究價值和實際意義。本文的研究對象為不擁有自身輸配電資產(chǎn)的獨立售電商,其客戶目標群體為大工業(yè)用戶,從購電和售電兩方面研究其在市場中的最優(yōu)購售電策略。在購電方面,考慮售電公司從中長期、日前和實時三個市場中購電,主要研究其購電成本,包含在各個市場中的購電量及相應價格。其中,日前和實時市場為現(xiàn)貨市場,設為每小時結算一次電價和電量,而中長期市場中的合約依據(jù)其負荷特點的不同分為工作日峰時、非工作日峰時和谷時三類,具有不同的價格和購買量限制。售電方面,主要研究了銷售電價的定價方法,考慮了固定電價和峰谷分時電價兩種定價法,同時在定價過程中考慮了輸配電成本的回收,并將其體現(xiàn)在銷售電價上。在購售電交易過程中,售電商所承擔的風險采用CVaR風險約束條件來控制。結合購電和售電這兩方面,本文建立了售電公司的購售電綜合策略模型。此外,該購售電模型涉及到各個市場的購電價格,其中,中長期合約是在時間跨度一開始就簽訂的,其電價可視為已知;而日前市場和實時市場的電價是時刻波動的,視為隨機參數(shù),本文建立了一種隨機價格模型,結合拉丁超立方抽樣法來獲得隨機價格樣本,可以模擬現(xiàn)貨市場價格的不確定性。最后,基于美國德州電力市場數(shù)據(jù),采用遺傳算法,在銷售電價采用固定電價形式時,對售電公司代理不同負荷特點的大工業(yè)用戶可獲最大利潤做出對比分析,得出不同負荷的供電成本差異;在銷售電價采用峰谷分時電價形式時,求解模型得到售電公司的最優(yōu)購售電策略。
[Abstract]:The new round of electricity reform in China points out that it is necessary to open the distribution business to the social capital in an orderly manner, and the power sale companies and customers in the pilot area of electricity sale have actively carried out the purchase and sale of electricity, and the electric power reform has developed in a good situation. As the main body of electricity market, electricity sellers can choose different markets to buy electricity, and then sell electricity to end users. Its profit is the difference between electricity sales income and power supply cost. However, because the price in the electricity market is constantly changing and the demand for electricity purchase is uncertain, the seller has to bear certain risks in the transaction. Under the current situation of reform, this paper has important research value and practical significance to study the strategy of purchasing and selling electricity to large industrial customers. The research object of this paper is the independent power seller who does not own its own transmission and distribution assets, and the target group of its customers is the large industrial users. The optimal strategy of purchasing and selling electricity in the market is studied from the two aspects of electricity purchase and power sale. In the aspect of purchasing electricity, we consider that the company buys electricity from three markets: medium and long term, day before and real time. It mainly studies the cost of purchasing electricity, including the purchase quantity in each market and the corresponding price. Among them, the pre-day and real-time market is the spot market, which is set to settle the electricity price and electricity quantity once per hour, while the contracts in the medium and long term markets are divided into three categories according to their load characteristics: working peak, non-working peak and valley time. There are different price and purchase limits. In the aspect of electricity sale, this paper mainly studies the pricing method of sale electricity price, considering two pricing methods: fixed electricity price and peak-valley time-sharing price. At the same time, the recovery of transmission and distribution cost is considered in the process of pricing, and it is reflected in the sale price. In the process of purchase and sale of electricity, the risk borne by the seller is controlled by the CVaR risk constraint. Combining the two aspects of electricity purchase and power sale, this paper establishes a comprehensive strategy model of power purchase and sale. In addition, the model relates to the purchase price of electricity in various markets, in which medium- and long-term contracts are signed at the beginning of the time span, and the price of electricity can be considered as known, while the price of electricity in the pre-day market and in the real-time market is always fluctuating. As a random parameter, this paper establishes a stochastic price model, which combines Latin hypercube sampling method to obtain the random price sample, which can simulate the uncertainty of the spot market price. Finally, based on the data of Texas Electric Power Market and genetic algorithm, when the electricity price is fixed, the paper makes a comparative analysis on the maximum profit of the large industrial customers who represent the different load characteristics of the power sale company. When the price of electricity is in the form of peak-valley time-sharing price, the model is solved to get the optimal purchasing and selling strategy of the power company.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F416.61

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