Research on Clearing Model of Long-Term Transaction Considering Matching Degree between Generation and Load

. With the development of new energy and flexible load, there are many kinds of power and load characters in the power grid. It’s necessary to considerate the similarity between generation output and load when it’s market clearing. First, similarity analysis of generation-load curve based on the grey correlation degree is introduced, correlation between power generation enterprises and power users can be calculated. Then, market clearing model is built. market clearing mechanism is set which considering the matching degree between generation and load. The technological process of market clearing is given. Last, the effectiveness of the proposed clearing model is verified by an example.


Introduction
The power output and load must be balanced in real-time in the power system, and the two curves are identical in actual operation. With the incorporation of new energy and flexible load, there are many kinds of power and load characters in the power grid. So it can't improve the situation of low new energy absorption and large fluctuations of traditional units.
In order to maximize the absorption of new energy, reduce the abandonment of new energy abandonment, and ensure the smooth output of traditional units, market-oriented policies can be used, which can also coordinate the operation of power system source and load, make the curves similar between the load the new energy output. Therefore, it is necessary to consider the load demand and power generation, find a method to measure the similarity between them. A series of characteristic index can be made to express relationship between energy and load [1] .
The power market price of Brazilian is calculated and determined in advance every week. There are peak, valley and normal price in the market. The working day is divided into peak, valley and normal period, and the no-working day is divided into Valley and normal period. Each period of clearing price is according to the realtime economic scheduling [2] . In the Nordic electricity spot market, the trading varieties and bidding mechanism of flexible block trading are introduced, which allows power generation and power users select to appropriate flexible blocks for trading according to their technical characteristics of power generation and the actual demand for electricity [3] .
In reference [4][5][6], a two-way auction bidding model is established by using the bidding model of multi seller and multi buyer in regional electricity market. Reference [7] focuses on the concept and landing scheme of load curve trading from the practical point of view of the "rules for medium and long term electric power trading". The corresponding response strategies of load curve trading are put forward from three angles of power producer, power user and electricity seller respectively. Reference [8] simulates the relevant rules of the domestic monthly electricity centralized bidding market, and makes a quantitative comparative analysis of the common clearing by intersection price and by average price in the domestic monthly centralized bidding.
Currently, few documents mention the consideration of generation-load matching degree when market clearing. So the influencing factors of generation-load curve coordination is not considered. Therefore, based on the medium and long-term electricity trading mechanism, this paper introduces the generation-load matching degree as the influencing factor to study the market clearing.

Similarity analysis of generation-load curve based on the grey correlation degree 2.1 overview of the grey relation degree
Grey relation analysis method is a technical method based on grey theory. As a factor comparison score analysis method, It seeks the relationship among various factors in the system, finds out the main factors influencing the target value by analyzing the limited data sequence in grey system, and then analyzes the relation degree among various factors [9][10][11] .It analyses the degree of similarity between the reference sequence and some comparative sequences, by which judges the degree of correlation of the development trend of grey process. At present, the method of grey correlation analysis has been widely used in various fields.
The evaluation method of grey correlation degree has low requirements for data and is a comprehensive evaluation method with simple principle. The purpose of grey correlation analysis is to reveal the main relationship between the interrelated and influencing factors, find out the important factors that affect the target value, and make the "grey" relationship between the factors clear. As a statistical analysis theory of multifactor comprehensive influence, grey correlation analysis curvilizes the numerical value of the analysis object and the object to be compared. The closer the curves shape are, the closer the change trend is, and the greater the correlation degree between the analysis object and the object to be compared is [12] .

degree Theoretical basis of correlation analysis model
The combination model of grey correlation degree is based on the grey correlation degree analysis to determine the proportion of each single prediction model. It shows the mutual influence of factors from the quantitative. The more consistent the change trend is, the greater the correlation degree is.
Define a set of sequence ' 0 X , ' 1 X … ' l X , a new set of sequence X0, X1…Xlwill be obtained after dimensionless, where X0 is the reference sequence. All the sequence has the same time point division, which is shown as follows: The correlation coefficient λi(k) between Xi(k) and X0(k)at k time is: represents the minimum value of the second level of X0 and X1; represents the maximum value of the second level of X0 and X1; ρ represents the resolution coefficient. Th e value of ρ is generally between 0 and 1.

grey correlation analysis of power generation and load curve
The typical daily curves of generation and power users are described by 96-point curve.
The typical output of generations can be described as The dimensionless curves can be obtained through dividing each electric power of generation and load divided by its 96 point average value.
Through formula (1) -(4), the correlation coefficient between each power generation output curve and power consumption load curve is calculated. The correlation coefficient matrix  is as follow: 11 12 1n In formula (11), λmn is the correlation coefficient between them-th generation and the n-th power user.
3 Research on market clearing model considering similarity

market mechanism
The market bidding mechanism constructed in this paper is based on the traditional medium and long-term power market (such as 'two-way bidding, centralized bidding, price difference pair pricing'), and adopts the organization mode of 'two-way bidding, centralized bidding, price difference pair pricing, and distribution collaboration'. Under certain constraints (such as system constraints, unit constraints, transaction constraints, etc.), power generation companies rank according to the declared price from low to high, power users / power selling companies rank according to the declared price from high to low. The clearing price is the average price of the declared price of the transaction pair. When the declared price is the same, the generation and power user with high correlation coefficient have priority in transaction.

objective function
The goal of power trade is to maximize social welfare and coordinate generation-load power. Its objective function is as follows: In formula (12), λ is the correlation coefficient of generation-load curve of trade pair; Pui presents the declared price of the power user; Pfi presents the declared price of the generator.

Balance of power generation and consumption constraint
The electricity purchased is equal to the electricity sold: 2. market clearing (1) According to the declared price, Generations are ranked from low to high, and power users are ranked from high to low.
(2) Suppose that the corresponding market subject of the m-th trade pair are M-th generation and N-th power user. It is assumed that the closest and lower than M is M+1, and the closest and higher than N is N+1. The next trade pair (m+1)-th are:

collaboration degree index
Collaboration degree K is the difference ratio calculated by the correlation degree analysis model and the traditional clearing model， In formula (21),λij is the correlation coefficient of generation-load curve of trade pair; QC presents trade quantity of trade pair C; S1 indicates the clearing scene of 'two-way quotation, centralized bidding, price difference pricing, and distribution collaboration'; S2 indicates the clearing scene of 'two-way quotation, centralized bidding, price difference pricing'.

Technological process
Technological process picture is showed as follow:

Start
Finding the most high declare price of user and the lowest declare price of generations

Example analysis
In this paper, the scenario of monthly centralized power trade in the medium and long term is simulated. Four power generation enterprises and six power users participate in the market-oriented trading. The declaration is as table 1 and table 2: In this paper, Matlab simulation is used to carry out dimensionless typical daily curves of generation enterprises and power users, as shown in Figure 2 and  In order to reduce the influence of the maximum value on the correlation coefficient, the value of resolution coefficient is 0.2. By using formula (1)formula (11), the correlation coefficient is obtained as follows:  The declared prices of user 1 and 5 is the same, and that of power generation enterprises 1 and 4 is the same. Beacause λ25>λ21 and λ42>λ12, user 5 has the priority to deal with power generation enterprise 2, and power generation enterprise 4 has the priority to deal with power user 2.
Calculate the collaboration index according to equation (21), as shown in the table-5: In table 5 we can see collaboration degree k=4.73%, which shows that the collaboration rate of power generation and consumption can be greatly improved after considering the correlation analysis while market clearing.

Conclusion
There are many kinds of power and load characters in the power grid. It's necessary to make generation-load matching degree as the influencing factor to study the market clearing. Market clearing model is built. market clearing mechanism is set which considering the matching degree between generation and load. The example shows that the collaboration rate of power generation and consumption can be greatly improved after considering the correlation analysis while market clearing.