CN109598552B - Cross-border power trade market feature analysis method based on complex network theory - Google Patents
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Abstract
The invention discloses a cross-border power trade market feature analysis method based on a complex network theory, which comprises the following steps: combining geographic position information of power trade associated countries and cross-border power trade import-export data of various countries in the past year, establishing a power trade network which takes various countries participating in power trade as nodes and takes inter-country power trade import-export relations as network edges between the nodes based on complex network theory, and adding trade volume information to construct a directional weighting network; calculating the point degree and degree distribution of nodes in the directional weighting network, and calculating a weighting aggregation coefficient and a weighting network structure entropy in the directional weighting network based on network edges and node weights; analyzing and obtaining network structure area characteristics of degree distribution, aggregation and heterogeneity of the power trade network; and selecting a required area and analyzing the characteristics of the network structure area. The method can analyze inter-country cross-border power trade networks in a targeted manner, and the analysis is more accurate.
Description
Technical Field
The invention relates to a cross-border power trade market feature analysis method based on a complex network theory, and belongs to the technical field of cross-border power grid interconnection.
Background
The proposal of the global energy interconnection (Internet of Energy) concept represents the future direction of energy development mode conversion and is also the necessary trend of pushing energy revolution through global co-development and shared resources. The electric energy is used as a junction for mutually converting different energy forms, things in different dimensions and coordinate systems can be connected in a first line, and cross-border electric power trade becomes a key link for constructing global energy interconnection. According to the BP world energy prospect report in 2017, the proportion of the electric sector in primary energy consumption has risen from around 25% in 1965 to 42% in 2015, and is expected to be close to 50% in 2035, and in the hope that there will be more than 2/3 of new world energy consumption to be used for power generation. In order to solve the current situation of unbalanced power supply caused by the ever-increasing global power demand and the country-to-country resource endowment difference, the construction of a global power energy network and the realization of resource allocation in a large range are particularly important. Meanwhile, in order to promote regional economy integration, the utilization efficiency of energy resources is improved, the reliability of power supply is improved, the opening of regional power markets is promoted, and international-range inter-connection and intercommunication of power grids are continuously developed.
Since 2013, regional interconnection engineering has become a key point of cooperation between China and countries along the line. The cross-border power interconnection is an important topic to be solved in the current cooperative key project, the cross-border power trade problem of China and other countries belongs to the cross subjects of power and economy, the research on power trade in the past is less, and the related research is mainly concentrated in the emerging field of the global energy Internet. As an important ring in the global energy internet, the research of cross-border power trade is not only a key subject for researching how the global energy internet is realized, but also a further supplement to the international trade theory.
A complex network refers to a network that has some or all of the properties of self-organizing, self-similar, small world, scaleless. There are a variety of complex networks in nature and human society, and the trade network generated by cross-border power trade activities is just a typical complex network with large aggregate coefficients and power-law distribution of node degrees, conforming to small world characteristics and non-scaled network characteristics.
In the analysis of the characteristics of the electric power trade market, the existing analysis research is mostly focused on pure network characteristics, the specificity of the electric power in the electric power trade network is ignored, and the weight attributes of edges and nodes in the network under the cross-border electric power trade scene are not considered, so that a certain gap exists between the obtained analysis result and the real situation, and the real exploitation of the cross-border electric power trade market characteristics is not facilitated.
In the process of establishing the electric power trade network, various fine changes are likely to influence the topological structure of the network and are influenced by various factors, the electric power trade network has larger difference with other real networks, and when the characteristic analysis research is carried out on the complex network, three characteristic indexes of degree distribution, aggregation coefficient and network structure entropy are generally selected to represent the network structure region characteristics of the complex network, such as order, aggregation, heterogeneity and the like. In the power trade network, however, inter-regional power trade is not limited to the presence or absence of trade, but is more critical in that both parties to trade point to and the amount of electricity in trade. This is not characterizable by conventional methods, resulting in reduced analytical accuracy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and solve the problems that the existing analysis research ignores the specificity of the power in the power trade network, and the weight attribute of the edges and nodes in the network under the cross-border power trade scene is not considered, so that the obtained analysis result has a certain gap with the real situation. Aiming at the specificity of the power trade network, the invention provides a cross-border power trade market feature analysis method based on a complex network theory by improving an aggregation coefficient and network structure entropy calculation method.
The technical scheme adopted by the invention specifically solves the technical problems as follows:
a cross-border power trade market feature analysis method based on complex network theory comprises the following steps:
step 2, calculating the point degree and degree distribution of nodes in the directional weighting network, and calculating a weighting aggregation coefficient and a weighting network structure entropy in the directional weighting network based on network edges and node weights;
step 3, analyzing according to the calculated point degree and degree distribution, the weighted aggregation coefficient and the weighted network structure entropy to obtain the degree distribution condition, aggregation and heterogeneity of the electric power trade network;
and 4, selecting a required area, and analyzing to obtain the network structure area characteristics of the degree distribution condition, the aggregation and the heterogeneity of the electric power trade network of the area.
Further, as a preferable technical scheme of the invention: calculating the point degree k of the node in the network in the step 2 i The formula is adopted:
k i ＝k in,i +k out,i -k t
in the formula, the click-through degree of a node is measured
Wherein a is j,i Representing the exit relationship of node j to node i;
point-out metric for a node
Wherein a is i,j Representing the outlet relation from node i to node j; k (k) t The total number of nodes for import and export trade volume.
Further, as a preferred technical solution of the present invention, in the step 2, the degree distribution of the nodes in the network is calculated by using the formula:
Q Total,i ＝∑Q i,j +∑Q j,i
wherein Q is i,j Representing the amount of export power trade flowing from node i to node j; q (Q) j,i Representing the amount of export power trade flowing from node j to node i.
Further, as a preferred embodiment of the present invention, the step 2 is performed byCalculating a weighted aggregation coefficient C w The formula is adopted:
wherein q i For node weight coefficient, trade quantity Q by node i And the total trade quantity Q of the network N Determining that N is the number of nodes;
weighting an aggregation coefficient for a node i taking into account the power trade network edge weight; w (w) ij The weight coefficient of the network edge between the node i and the node j is obtained; w (w) jk 、w ki The weight coefficients of the network edges between the node j and the node k and between the node k and the node i are respectively.
Further, as a preferred technical solution of the present invention, the step 2 calculates the entropy of the weighted network structure, specifically:
based on the network edge and node weight, the importance degree I of the ith node is corrected i ；
According to the importance degree I of the ith node i Defining the network structure entropy E as
Normalizing the defined network structure entropy E to obtain a network standard entropy as follows:
wherein E is max To take the maximum value of E min Taking the minimum value for E; n is the number of nodes.
Further, as a preferred embodiment of the present invention, the importance I of the ith node is corrected in the step 2 i The method comprises the following steps:
wherein k is i Point degree, q for the i-th node i As node weight coefficient, w ij The weight coefficient of the network edge between the node i and the node j is obtained, and N is the number of the nodes.
By adopting the technical scheme, the invention can produce the following technical effects:
according to the cross-border power trade market feature analysis method based on the complex network theory, in the cross-border power trade network, the weight network considering the transaction amount factors can reflect the intensity of cross-border power trade, so that a directional weighting network is constructed on the basis of a topological network; and aiming at the specificity of the electric power trade network, the aggregation coefficient and the network structure entropy calculation are improved.
Compared with other methods, the method provided by the invention has the following advantages:
(1) The invention increases the representation of the power trade quantity in the power trade network, considers the weights of edges and nodes in the power trade network, and improves the calculation method of the network characteristic aggregation coefficient and the network structure entropy;
(2) The method analyzes the setting scene of the inter-country cross-border power trade network pertinently, is suitable for inter-country power interconnection engineering, is favorable for deep mining of the power trade market characteristics, and provides references for subsequent cross-border power trade market construction;
(3) The method provided by the invention can be expanded to other cross-border power trade network analysis, provides countermeasure suggestions for inter-country cross-border power trade construction, and promotes global energy Internet construction.
Drawings
FIG. 1 is a schematic flow chart of a cross-border power trade market feature analysis method of the present invention.
Fig. 2 is a diagram of the scale of the electric power trade network in 1990-2018 of the country with one road in the embodiment of the invention.
Fig. 3 (a) to (e) show cross-border power trade networks in 1995, 2000, 2005, 2010, 2015, respectively, according to the embodiments of the present invention.
Fig. 4 is a distribution of degree probability of a national power trade node with one-way according to an embodiment of the present invention.
Fig. 5 shows the intensity distribution of the power trade nodes along the country with one path in the embodiment of the invention.
Fig. 6 is a plot of node degree and node strength scatter of the power trade along a country in 2005, 2010, 2015 "one-by-one" in the present embodiment.
Fig. 7 is a plot of the degree and aggregation factor scatter of the power trade nodes along the country of "one-way-by-one" in 2010 in an embodiment of the invention.
Fig. 8 is a diagram of a power trade network in the united body and in the middle eastern european countries 2005 and 2015 according to an embodiment of the present invention.
Fig. 9 is a diagram of a power trade network in the southwest asian countries 2005 and 2015 according to an embodiment of the present invention.
Fig. 10 is a diagram of a power trade network in the chinese and western asia countries, china 2005 and 2015 according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described below with reference to the drawings.
As shown in fig. 1, the invention designs a cross-border power trade market feature analysis method based on a complex network theory, which specifically comprises the following steps:
firstly, the geographical position information of all countries associated by power trade is marked, a power trade network diagram is drawn by using cross-border power trade import-export data of all countries along the line, the nodes are used for describing country units participating in cross-border power trade, each country participating in trade is a node, and the countries coexistAnd connecting N nodes and the nodes through the electric power trade relation to form a network side of the electric power trade network. If the direction indicated by the arrow is the power outlet direction, the inter-country power transaction can be represented by a network of N nodes connected by one edge. The trade relationship is converted into an adjacency matrix A, and if trade activities exist between the countries i and j, a ij =1, if there is no transaction activity, a ij ＝0。
After adding the trade volume information, the network can be expressed as a directionally weighted network W, wherein,
and (3) information indicating the amount of electric power transaction from the export country i to the import country j in the t-th year. The cross-border power trade conditions among 1990-2018 of all countries along the line with one path are analyzed from the time dimension by taking 5 years as a time span, so that the forming and developing process of the cross-border power trade is disclosed.
Step 2, calculating the point degree and degree distribution of nodes in the directional weighting network, and calculating a weighting aggregation coefficient and a weighting network structure entropy in the directional weighting network based on network edges and node weights; and combining the weighted aggregation coefficient and the weighted network structure entropy formula, and calculating the weighted aggregation coefficient and the weighted network structure entropy of the spatial structure of the power trade. Wherein, referring to the complex network small world effect (Small world Effect) and the Scale-free Property, the selected power trade network characteristic indexes mainly comprise:
(1) And calculating the degree and degree distribution of the points.
The point number refers to the number of nodes connected with a certain node in the network, and the point strength is the connection strength of the node and the adjacent node. In the invention, the point number represents the number of countries which trade power with one country, and the point intensity reflects the power trade intensity of one country and other countries. In a directional weighted network, the degree of a point is divided into a point-in degree and a point-out degree. The click-through degree of the node is measured by the import relation between the trade node and other countries, and is obtained by the same column number value in the adjacent matrix, namely
a j,i Representing the exit relationship of node j to node i; the click-through metric is the exit relationship between trade nodes, which is derived by summing the same row numbers in the adjacency matrix, i.e. +.>
a i,j Representing the exit relationship of node i to node j.
The node degree of node i is determined by:
k i ＝k in,i +k out,i -k t (1)
wherein k is t The total number of nodes for import and export trade volume is the number of nodes for both import and export trade volume.
The point intensity is calculated by trade quantity among nodes, namely Q Total,i ＝∑Q i,j +∑Q j,i ，Q i,j Representing the amount of export power trade flowing from node i to node j; q (Q) j,i Representing the amount of export power trade flowing from node j to node i.
By examining the degree and intensity distribution between nodes in the area along the line, the interconnections between the countries in the power trading network and the change in the network over time can be depicted.
(2) A weighted aggregate coefficient is calculated.
Aggregation coefficient C of one node i Is the ratio of the number of connections between its neighboring nodes to the number of all of their possible connections, i.e.:
where x is the number of connections between the node and the neighboring nodes, and n is the number of neighboring nodes of the node.
The aggregation factor describes the degree of clustering of nodes in the network, i.e. it is examined how many of the respective neighbors of the clustered nodes that are connected together are in commonIs a neighbor of (a). The average aggregate factor for all nodes in the network is the average of the aggregate factors for all nodes, i.e
For the average aggregation coefficient C, 0 < C < 1 is evident. Many practical large-scale networks have significant aggregation effects, with their aggregation coefficients, although less than 1, being much greater than 0. This means that the actual network is not completely random, and that there are factors that attract aggregation in the actual network and conditions that constrain aggregation.
After the weight of the connecting edges between the nodes is increased, the aggregation coefficient of the node i should consider the weight of the edges between the nodes connected with the node i, and the weight of the node is not negligible, so the invention proposes to weight the aggregation coefficient C w The improvement is as follows:
wherein q i For node weight coefficient, trade quantity Q by node i And the total trade quantity Q of the network N It is decided that the method comprises the steps of,
to consider node i weighting aggregation coefficient, w, of power trade network edge weights ij The weight coefficient of the network edge between the node i and the node j is the ratio of the current edge power trade amount to the total network trade amount, and the same is true, w jk 、w ki The weight coefficients of the network edges between the node j and the node k and between the node k and the node i are respectively.
(3) And calculating the entropy of the weighted network structure.
The network structure entropy is defined to quantitatively study the non-uniformity of the complex network, the node degree determines the importance degree of the nodes in the network in a certain sense, but after the weight of the connecting edges among the nodes and the weight of the nodes are increased, the node importance degree is not only determined by the node degree, so that the node importance degree calculation method is required to be corrected.
Correcting importance I of ith node i The method comprises the following steps:
wherein N is the number of nodes in the network, k i Degree of the ith node, q i As node weight coefficient, w ij Is the weight coefficient of the network edge between the node i and the node j.
Entropy is a measure of "disorder", and a network is considered to be disordered if it is randomly connected, the importance of each node being approximately comparable, whereas a network is considered to be ordered if it is unscaled, there being a small number of core nodes and a large number of end nodes in the network, the importance of the nodes being different. The network structure entropy E used to quantitatively measure this "order" is defined as:
it can be easily demonstrated that when the network is completely homogeneous, i.e
When E takes the maximum value:
in order to eliminate the influence of the number N of the nodes on E, the network structure entropy is normalized, and then the network standard entropy is as follows:
wherein E is max To take the maximum value of E min Take the minimum value for E. I i Importance for the modified i-th node.
And step 3, analyzing according to the calculated point degree and degree distribution, the weighted aggregation coefficient and the weighted network structure entropy to obtain the degree distribution condition, aggregation and heterogeneity network structure area characteristics of the electric power trade network.
In the analysis process, the nodes with higher point-in degree, point-out degree and node degree of the electric power trade network are few, the characteristics of the non-scale network are met, the weighted aggregation coefficient value corresponding to the node is also in a high position, aggregation phenomenon is generated in the network, the entropy value of the corresponding weighted network structure is lower, and the network heterogeneity is indicated to be stronger. Otherwise, the same is true. Thus, the degree distribution, aggregation and heterogeneity of the power trade network diagram are revealed by combining the three index values.
And 4, selecting a required area, and analyzing to obtain the network structure area characteristics of the degree distribution condition, the aggregation and the heterogeneity of the electric power trade network of the area. The characteristics of the trade network in a specific area are further analyzed on the basis of the analysis of the characteristics of the space of the electric power trade between the countries of Asia and European parts.
In order to verify that the method can analyze the inter-country cross-border power trade market characteristics with high precision, a verification example is specifically listed for explanation, and the verification example is based on analyzing the inter-country cross-border power trade data along the line of 'one-way-with-one', and specifically comprises the following steps:
verification example 1,
The cross-border power trade market feature analysis method based on the complex network theory of the verification example comprises the following steps:
As shown in fig. 2, according to the number and variation trend of countries along the "one-way-by-one" participating in the national power trade in 1999-2018, the national units participating in the "one-way-by-one" inter-country power trade are described by nodes by using the cross-border power trade import-export data of the various countries along the line, and the nodes are connected through power trade to form the network side of the power trade network. The direction indicated by the arrow is defined as the power outlet direction, and a cross-border power trade directional weighting network is established. The cross-border power trade conditions among 1990-2015 of all countries along the line of 'one-way' are analyzed from the time dimension by taking 5 years as a time span, so that the forming and developing process of the cross-border power trade is revealed, and the results are shown in (a) to (e) in fig. 3, and respectively represent the cross-border power trade networks in 1995, 2000, 2005, 2010 and 2015.
Table 1 shows the directional weighted network data of the regional power trade along the line of "one-way" every five years from 1995 to 2015, and it is known from the table that the overall scale of the regional power trade has grown from 22 nodes, 22 edges in 1995, to 55 nodes, 152 edges in 2015, and the multilateral trade gradually becomes the main form of the regional power trade network along the line of "one-way".
Table 1 Main node, "one-way-one-way" along country Power trade Directional weighting network data in 1990-2015
And 2, 3, combining the weighted aggregation coefficient and the weighted network structure entropy formula, performing actual-check calculation on three indexes of the degree distribution, the weighted aggregation coefficient and the weighted network structure entropy of the power trade network between the countries with one path by utilizing the formulas (1) to (9), and comparing the three index values with an aggregation coefficient and a network standard entropy calculation method without considering the weighted information to reveal the degree distribution, aggregation and heterogeneity of the power trade network graph.
(1) The degree distribution is analyzed.
By examining the node degree distribution and the node intensity distribution among all nodes in the line area with one path, the mutual connection condition among all countries in the electric power trade network and the evolution condition of the trade network along with the time can be depicted. Table 2 is the data for the degrees of the directionally weighted network along the national power trade for "one-way" between 1990 and 2015.
Table 2 Main node "one-way-by-one" along line national Power trade Directional weighting network degree data in 1990-2015
| Index (I) | 1995 | 2000 | 2005 | 2010 | 2015 |
| Maximum point incidence | 2 | 6 | 9 | 11 | 11 |
| Maximum point output | 3 | 10 | 8 | 11 | 12 |
| |
5 | 16 | 17 | 20 | 23 |
| Maximum point intensity | 83752494 | 309344814 | 388174845 | 255374765 | 38955701 |
Fig. 4, 5 depict the point degree and point intensity distribution of "one-by-one" along a country in 2005, 2010 and 2015. From the coupled features of the trade network, "one-by-one" along the line regional power trade network has non-scaled features, a few countries play a more important role in power trade, existing as a trade core in the system: first, from the perspective of the scaled features, the distribution curve of node intensities is approximated as a straight line in these three years, conforming to the features of a scaleless network. In a non-scaled network, the connection situation between nodes has a severe non-uniform distribution characteristic, a few nodes have extremely many connections, and most nodes have only a few connections. This illustrates that a minority of countries contribute more power trading in a "one-by-one" line area power trading network. Second, from the point of view analysis of the node degree and intensity distribution characteristics, the node degree distribution and the node intensity distribution have a certain similarity, as shown in fig. 6. The correlation coefficient of the node degree and the node intensity verifies the correspondence between the two. The correlation coefficients of 2010 and 2015 are over 0.7, namely 0.8138 and 0.7746 respectively, which shows that countries with the same number of nodes tend to have inter-country power trade with the same strength. Third, from the analysis of the node degree and intensity scatter point relationship, the power trade node degree and node intensity scatter point relationship between the countries along the line with one path in the correlation coefficient of the power trade node degree and node intensity between the countries along the line in table 3 is not a linear relationship. Only a few countries have strong node degree and node strength at the same time, such as russia, selveya and the like, and the countries have more remarkable influence on the trade of power among the countries along the line of one-way and occupy a central position in the network.
Table 3 correlation coefficient of node degree and node strength for national Power trade along "one-way-with-one" line
| 2005 | 2010 | 2015 | |
| Average node degree | 4.24 | 5.70 | 5.53 |
| Average sectionPoint strength | 797237 | 3568690 | 6012832 |
| Correlation coefficient | 0.6221 | 0.8138 | 0.7746 |
(2) Aggregation was analyzed.
The data in table 4 shows that in the "one-way" line area power trade network, the average aggregate factor without considering the network edge and node weights and the weighted aggregate factor with considering the network edge and node are both around 0.3 and far greater than the density. In a random network, the density should be equal to the aggregation factor, indicating that there is a clustering in the "one-way" line area power trading network. The clustering of such trade networks is due to the non-uniform distribution of electrical energy and the high cost of long distance delivery points. The trade of electric power among countries along the line with one path is more prone to be spread among countries with adjacent geographic positions and similar cultures, and further forms a plurality of trade sub-networks which are relatively aggregated.
Table 4 "one-way along-the-way" national Power trade network Density and aggregation coefficient index
| Index (I) | 2005 | 2010 | 2015 |
| Density of | 0.052 | 0.062 | 0.051 |
| Standard deviation of | 0.221 | 0.242 | 0.220 |
| Average aggregation coefficient | 0.316 | 0.313 | 0.302 |
| Weighted aggregate coefficients | 0.298 | 0.343 | 0.328 |
From the comparison of the average aggregation coefficient and the weighted aggregation coefficient, it can be found that the aggregation coefficient value obtained by the calculation method considering the network edge and the node weight is generally larger, because on one hand, the aggregation property of the regional-centered trade aggregation center after adding the trade amount weight is stronger, and on the other hand, the aggregation property of the whole regional power trade network with one path along the whole 'line' is also stronger. The trade amount in part of trade relations in the electric power trade network in 2005 is smaller, and the network connection is loose, so that after the network side and the node weight are considered, the aggregation coefficient becomes smaller; in 2010, the trade amount in the electric power trade network is increased, the network structure is complex, and the aggregation coefficient is increased compared with the previous one; the trade volume in the 2015 electric power trade network is reduced compared with 2010, so that the aggregation degree is reduced compared with 2010, but the connection relation in the network is increased, and the trade is frequently carried out, so that the network aggregation degree is improved compared with the aggregation coefficient calculation method taking the weighting information into consideration.
Fig. 7 shows a scatter diagram of node degrees and aggregation coefficients of the power trade in countries along the line of "one-way" in 2010, and the two are approximately in a positive correlation relationship, namely, the higher the node degree is, the higher the aggregation coefficient is, which indicates that the power trade between the country with more power trade objects and the surrounding countries is more active. On one hand, the importance of energy safety of all countries in the world is described, and the national defense and economic development of one country can be seriously endangered by the energy dependence of a single country, so that the risk can be effectively avoided by carrying out power exchange with more trading partners. On the other hand, the electric power energy has the characteristics of difficult storage, high loss and the like, is different from fossil energy such as petroleum, natural gas and the like, and is unsuitable for long-distance transmission, so that the transnational electric power exchange with adjacent countries is even more efficient and economical than the long-distance electric power transmission in the home country.
In general, the trade of electricity between countries along a "one-way" line is in line with the structure of the core edge, i.e. most countries build a network of electricity trade groups around the core country, while trade is frequent between different countries within an area. As a core country of the regional network, some countries of electric power energy source play a role of trans-regional communication, such as russia, ukraine, and other countries. Therefore, the electric power trade network with one path along the line has the characteristics of large-range division and small-range clustering, and the electric power trade is easier to develop between countries with similar territories and similar cultural economy.
(3) Heterogeneity was analyzed.
Table 5 "one-way along with" national Power trade network Structure entropy
| Index (I) | 2005 | 2010 | 2015 |
| Entropy of network structure | 0.272 | 0.290 | 0.277 |
| Weighted network structure entropy | 0.288 | 0.294 | 0.312 |
According to the step 2 provided in the method, the entropy of the network structure of the electric trade along the country with one line and the entropy of the weighted network structure are calculated as shown in table 5 in 2005-2015, when the weights of the network edges and the nodes are not considered, the entropy values of the network structure of the electric trade along the country with one line in 2005-2015 are all between 0.27 and 0.29, which indicates that the heterogeneity of the network is strong, the entropy values of the structure of the electric trade after weighting are between 0.28 and 0.31, which indicates that the heterogeneity of the network is reduced after the weights of the network edges and the nodes are considered, because the part of the trade volume of the weighted electric trade network is distributed in disorder, and meanwhile, the number of the edges in the electric trade network is continuously increased from 2005-2015, the network structure is gradually complex, so that the entropy of the weighted network structure is increased. But overall, the entropy of the network structure is still at a relatively low level, which means that the difference of the power trade relations among the countries is relatively large. The development of the electric power trade network of the country along the area with one path is greatly influenced by space factors, namely, the electric power trade between the countries with similar territories is more intimate, such as the trade between the eastern European country and the independent body country, and the southeast Asia and the western Asia countries have more frequent electric power trade between the adjacent countries on the space range. Unlike oil and gas trade, electricity trade is more susceptible to space and territory. The regional power network has the conclusion of regional aggregation characteristic in combination with the analysis, a few countries play a more important role in the regional power network, and a plurality of power trade sub-networks are formed in regional power trade around the countries, so that regional aggregation is generated, and further, the relative balance of power supply and demand in a small range is realized. Thus, the heterogeneity of the "one-by-one" line regional power trade network is most likely derived from the heterogeneity of regional subnetworks. In general, the power trade networks in the area along the line with one path have large heterogeneity, and the power trade relationship of each sub-network is in an unbalanced state for a long time due to the regional aggregation.
And 4, selecting a required area, analyzing and obtaining network structure area characteristics of degree distribution, aggregation and heterogeneity of the power trade network of the area, and verifying the aggregation and heterogeneity characteristics of the cross-border power trade network. That is, "one-way-in-one" power trade network aggregation starts to be enhanced, four more prominent local trade networks are increasingly differentiated inside the network, including eastern europe and independent body trade areas, southeast asia trade areas, western asia trade areas and trade areas with china as a core, since south asia and middle asia areas are most populated with developing countries without traditional energy source large countries, power infrastructure is not developed, more formed power trade local area networks are also present, and most of the countries in these areas are more dependent on power trade between other regional countries to meet the requirements of the development of the country, and the specific processes are as follows:
(1) And analyzing the network structure area characteristics of the eastern European region and the independent body country.
In fig. 8, eastern european countries and independent body countries are the ones that "take one way" along the line that originally developed the power trade. Because the countries in the area are numerous, the distribution is dense, and meanwhile, the resource endowment difference among different countries is large, so that large electric power trade demands exist. Examples of the core countries of the eastern European power trade network include Czech, stokes, storev, selvia, romania, hungary, and Irania. Among them, polish, czech, romania, hungarian and isaniya are important power energy output countries, selveya is the most active country along the line of power trade "with one line", the point degree of 2015 is 23, the power energy is exported to 11 surrounding countries, and there are power energy imports from 12 countries. The trading partners greek, slavariac and crotamia are relatively concentrated, with the main power inlets being derived from turkish and slalomian.
In the aspect of the independent body countries, russian, uclean and white Russian are core countries of regional power energy trade. Large amounts of electrical energy in russia and ukraina are also being transported to peripheral trade countries. White russia is an important electric energy import country, and a large amount of electric energy needs to be imported from other countries. Russia and ukraina play an important role in the power trade networks in eastern europe and northeast asia from the aspect of the characteristics of the trade network architecture.
In general, power trade in eastern europe and the independent body countries is most active along the "one-by-one" line. The eastern European country has established regional power trade networks centered on the countries of Czochralski, stlover, selvia, romania, hungary, and Irania, while the independent body countries also develop their own power trade around Russia and Ukraut. The power trade networks in the subareas are connected with each other through russia and the large country of energy sources such as ukraine. Because of the difference of the resource endowments, the power energy supply of different countries has great difference, so in order to meet the domestic power energy supply, many countries participate in a small-range power trade network, and the power consumption of productivity and consumption is met through the exchange of the power energy. Meanwhile, the higher socioeconomic development level provides perfect electric power infrastructure construction for countries in the area, which provides possibility for realizing electric network interconnection in the area and developing active electric power trade. From the results, active power trade improves the power consumption per person in eastern europe and the independent body countries. The countries of these regions achieve higher power supply and demand balances at the same time than other "one-by-one" countries and regions.
(2) And analyzing the characteristics of the network structure area in southeast Asia.
The southeast Asia power local area network has relatively slow development speed. In the southeast asia power trade network of fig. 9, thailand and india are major power energy export countries. The electric energy of thailand is exported to the surrounding 9 countries, and the main connection of the indian electric network is the adjacent southeast asia countries such as the mangladesh and the nephel. In terms of energy import, laos, garland, nephel, and manglara are pure input countries for electric energy, and electric power trade has only a single import of electric power from countries such as thailand, india, china, and the like.
By comparing the power trade developments in southeast asia and eastern european regions, it is readily apparent that the power trade networks in southeast asia have not yet formed a large radiation range, the trade of which is an active region of power trade. On the one hand, due to the limitation of the social development level, the electric power infrastructure of the southeast asia countries is still quite behind, and many countries have no gap in the capability of meeting the electric power energy requirements of the home through the interconnection of electric grids. On the other hand, the current supply and demand of the whole electric power energy source in the southeast Asia are in a rapid growth stage, and the electric power energy source supply still has a lot of potential development space, and in the aspect of electric power demand, along with the rapid development of the economy of a plurality of developing countries in the area and the increasing of the electric power demand, the supply and demand gap is gradually revealed. The activity of future power trade in southeast asia will depend largely on the gap situation caused by the unsynchronized growth of power infrastructure construction and power supply and demand in the area.
(3) And analyzing the characteristics of the network structure area in the western Asia area.
There is also some electricity trade in western asia countries. The left side of fig. 10 is a diagram of a power trade network in the western asia country, and as can be seen from the diagram, the western asia area is an area with a path along which the power trade network construction development speed is slower. The network snapshot in 2015 shows that only 7 frameworks are involved in the power trade network. From the network structure, strong aggregation is not formed, about denier and sauter are two dispersed nodes, and power trade is respectively carried out between 2-3 countries, and power trade is not carried out between other countries, so that the aggregation coefficient is not strong. The lower level of western electricity trade is due in large part to the abundant reserves of petroleum energy in most countries of the area, and fossil energy power generation is basically responsible for the domestic electricity supply. From the view of the power production structure given by world banks, the power production of western asia countries is most dependent on fossil energy, and the power generation ratio of fossil energy is over 90%. Meanwhile, the population base of the western Asia country is far less than the level of the southeast Asia country, fossil energy power generation of the home country is enough to meet the production and consumption demands of the home country, a large power supply and demand gap does not exist, and the countries in the region do not have strong power energy exchange demands. More power trade in a few areas is to meet the transient and intermittent power gap caused by power peak staggering.
(4) Analyzing the network structure area characteristics of China.
The participation level of China in the 'one-way-with-one' power trade is still low. The right diagram of fig. 10 shows a graph of the power trade network developed by china in 2015 along the country with "one-way-one". From the figure, it is easily found that by 2015, china has only developed electricity trade with 6 surrounding countries. In fact, china began the earliest electricity trade in 1992, from russian importation of electric energy to northeast areas to meet the seasonal shortage of electricity. In terms of power supply, china surpasses the United states in 2011, becomes the largest power generation country in the world, and has strong power energy export capability. In terms of power demand, the power production of the home country can meet the production and consumption needs of the home country, and the dependency on the power energy of other countries does not exist. China imports only part of the electric energy from russia, more of which exports domestic electric energy to southeast asian countries, while the subject of electric power trade is mainly in sub-regional countries. Therefore, china has strong strategic significance for power grid intercommunication and interconnection in southeast Asia, middle Asia and other areas.
In summary, the invention increases the representation in the network of the electric power trade volume, considers the weight of the edges and nodes in the network to improve the network characteristic calculation method, and specifically analyzes the inter-country cross-border electric power trade network, so that the characteristic analysis is more accurate, can be expanded to other cross-border electric power trade network analysis, and provides countermeasure suggestions for the inter-country cross-border electric power trade construction.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (2)
1. A cross-border power trade market feature analysis method based on a complex network theory is characterized by comprising the following steps:
step 1, combining geographic position information of power trade associated countries and cross-border power trade import and export data of each country in years, establishing a power trade network which takes each country as a node and takes inter-country power trade import and export relations as network edges between the nodes based on a complex network theory, and adding trade quantity information to construct a directional weighting network;
step 2, calculating the point degree and degree distribution of nodes in the directional weighting network, and calculating a weighting aggregation coefficient and a weighting network structure entropy in the directional weighting network based on network edges and node weights;
wherein, calculating the point degree k of the node in the network i The formula is adopted:
k i ＝k in,i +k out,i -k t
in the formula, the click-through degree of a node is measured
Wherein a is j,i Representing the exit relationship of node j to node i;
point-out metric for a node
Wherein a is i,j Representing the outlet relation from node i to node j; k (k) t The total number of nodes for import and export trade volume;
and, calculate the degree distribution of the node in the network, adopt the formula:
Q Total,i ＝∑Q i,j +∑Q j,i
wherein Q is i,j Representing the amount of export power trade flowing from node i to node j; q (Q) j,i Representing an export power trade amount from node j to node i;
and, calculating a weighted aggregation coefficient C w The formula is adopted:
wherein q i For node weight coefficient, trade quantity Q by node i And the total trade quantity Q of the network N Determining that N is the number of nodes;
weighting an aggregation coefficient for a node i taking into account the power trade network edge weight; w (w) ij The weight coefficient of the network edge between the node i and the node j is obtained; w (w) jk 、w ki The weight coefficients of the network edges between the node j and the node k and between the node k and the node i are respectively;
and calculating the entropy of the weighted network structure, specifically:
based on network edge and node weight, the importance of the ith node is correctedDegree I i ；
According to the importance degree I of the ith node i Defining the network structure entropy E as
Normalizing the defined network structure entropy E to obtain a network standard entropy as follows:
wherein E is max To take the maximum value of E min Taking the minimum value for E; n is the number of nodes;
step 3, analyzing according to the calculated point degree and degree distribution, the weighted aggregation coefficient and the weighted network structure entropy to obtain the degree distribution condition, aggregation and heterogeneity of the electric power trade network;
and 4, selecting a required area, and analyzing to obtain the network structure area characteristics of the degree distribution condition, the aggregation and the heterogeneity of the electric power trade network of the area.
2. The method for analyzing characteristics of cross-border power trade market based on complex network theory according to claim 1, wherein the importance degree I of the ith node is corrected in the step 2 i The method comprises the following steps:
wherein k is i Point degree, q for the i-th node i As node weight coefficient, w ij The weight coefficient of the network edge between the node i and the node j is obtained, and N is the number of the nodes.
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