Urban energy system uncertainty propagation reasoning method based on evidence theory

Abstract

The invention relates to an uncertainty propagation reasoning method for an urban energy system based on an evidence theory. The method is technically characterized by comprising the following steps: establishing a network node topology according to an energy transmission structure; judging a connection relationship between the two nodes; if the two nodes are connected in series, integral uncertain information of the two nodes is calculated; if the two nodes are connected in parallel, respectively calculating the uncertain ranges of the two nodes; forming two pieces of evidence by using the information of the two nodes; the initial evidence is rewritten through a weighted mass function; adopting a D-S synthesis method to obtain a synthesis result; establishing a class probability likelihood function to carry out information distribution; calculating uncertain information based on the information distribution result; integral uncertain information of the two nodes is formed; the method is suitable for solving the problem of uncertainty propagation and updating of the energy equipment in the active power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of active distribution network assessment, and in particular to an uncertainty propagation inference method for urban energy systems based on evidence theory. Background Technique

[0002] With the global emphasis on renewable energy and the rapid development of distributed energy sources such as solar and wind energy, the traditional centralized power supply mode is gradually transforming into distributed generation. This transformation has prompted the distribution network to adapt to the new energy structure and form an active distribution network. The continuous increase in the urban population has led to a sharp rise in energy demand. In order to address climate change and environmental pollution, urban energy systems are gradually transitioning to renewable energy. This requires urban energy systems to have good energy supply capabilities and flexibility to meet the growing electricity, heating, and transportation demands. However, the supply of new energy is uncertain and intermittent, which undoubtedly introduces many uncertain factors in the planning and operation of urban energy systems. The safety boundaries of urban energy systems define the safety limits of system operation. By analyzing these boundaries, it can be ensured that the system can continuously provide reliable energy supply under various load and environmental conditions. However, each energy device in the urban energy system is interconnected, and in current technologies, the safety boundaries and device uncertainties of urban energy systems mostly target individual entities, and there is less analysis of the uncertain propagation between devices in the system. By conducting uncertainty propagation analysis, more reliable basis can be provided for decision-making, helping decision-makers formulate more effective management strategies and emergency plans.

[0003] In the urban energy transmission network system where the production end and the energy consumption end are integrated, dealing with the uncertainty of information becomes a key challenge. The incomplete reliability of information stems from the causal and associative complexity in engineering problems, so a method is needed to measure this information. Evidence theory and credibility methods, as the main tools for dealing with such uncertainties, solve the problems of uncertainty propagation and update in active distribution networks. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art, consider the uncertainties introduced by urban energy systems in accepting new energy, and based on the urban energy system network, propose an uncertainty propagation inference method for urban energy systems based on evidence theory and credibility methods;

[0005] The present invention solves its technical problems by adopting the following technical solutions:

[0006] For the urban power grid system, the network node topology can be established according to the energy transmission structure, and the energy devices are equivalent to the nodes in the network. Different nodes present series and parallel relationships in the network. Let the physical information value of point a be P a , and the uncertain information be [δa- , δ a+ , the physical information value of point b is P b , the uncertainty information is [δ b- , δ b+ , δ - and δ + are the lower and upper floating limits of P

[0007] If node a and node b are in series, the overall uncertainty information of node ab is [δ ab- , δ ab+ , where δ ab- is the minimum value of δ a- and δ b- , and δ ab+ is the maximum value of δ a+ and δ b+ .

[0008] If node a and node b are in parallel, the overall uncertainty information of node ab is [δ ab- , δ ab+ . The calculation steps are as follows

[0009] Step 1, calculate the uncertainty range μ a = δ a+ - δ a- of node a, and calculate the uncertainty range μ b = δ b+ - δ b- .

[0010] Step 2, use the information of node a to form evidence 1, and based on the initial mass function, form the certainty information and uncertainty information {μ a , 1 - μ a} of evidence 1; use the information of node b to form evidence 2, and based on the initial mass function, form the certainty information and uncertainty information {μ b , 1 - μ b} of evidence 2

[0011] Step 3, rewrite the initial evidence of evidence 1 through the weighted mass function to obtain {αμ a , (1 - α)(1 - μ a ); 1 - αμ a - (1 - α)(1 - μ a )}; rewrite the initial evidence of evidence 2 through the weighted mass function to obtain {αμ b , (1 - α)(1 - μ b ); 1 - αμ b - (1 - α)(1 - μb)}.

[0012] Step 4: Use the D-S combination rule to combine the rewritten Evidence 1 and Evidence 2 to obtain a combination result {ε, γ; 1 - ε - γ}.

[0013] Step 5: Establish a class probability likelihood function, and distribute 1 - ε - γ to ε and γ according to the ratio of ε:γ, obtaining * , γ * .

[0014] Step 6: Let |δ ab- | + δ ab+ = ε * . According to the ratio of |δ a- | + |δ b- |: δ a+ + δ b+ , the distribution can be obtained as follows:

[0015] Starting from the bottom - most nodes of the network through the D - S evidence theory and gradually extending, the reliability propagation process of the urban energy system can be inferred, and the reliability degrees of different nodes in the system [δ - , δ + can be obtained, providing uncertainty theory support for the calculation of the new - energy access safety boundary of the urban power grid.

[0016] The above - mentioned method for inferring the uncertainty propagation of an urban energy system based on evidence theory, the weighted mass function is well - known to those skilled in the art of the present technology;

[0017] The advantages and positive effects of the present invention are:

[0018] 1. The present invention provides a method for dealing with uncertainty and fuzziness, which can perform reasonable inference under incomplete information. This helps decision - makers make more accurate and reliable decisions when facing a complex distribution network environment;

[0019] 2. The method proposed by the present invention can effectively integrate information from different sources, including historical data, expert opinions, and real - time monitoring data. This comprehensive ability makes the evaluation of uncertainty more comprehensive and improves the reliability of the system; BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the drawings and embodiments;

[0021] Figure 1 is a flowchart of a method for inferring the uncertainty propagation of an urban energy system based on evidence theory;

[0022] Figure 2 is an example of the network node topology of the method proposed by the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0023] Figure 1 The process of a method for uncertainty propagation inference of urban energy systems based on the evidence theory is as follows: Start → Establish the network node topology based on the energy transmission structure → Judge the connection relationship between two nodes → If it is in series, calculate the overall uncertain information of the two nodes → If it is in parallel, calculate the uncertain ranges of the two nodes separately → Form two pieces of evidence using the information of the two nodes respectively → Rewrite the initial evidence through the weighted mass function respectively → Obtain the synthesis result using the D-S synthesis rule → → Establish the class probability likelihood function for information allocation → Calculate the uncertain information based on the information allocation result → Form the overall uncertain information of the two nodes → Judge the connection relationship between the synthesized node and the next node and repeat the above steps → End;

[0024] Figure 2 It shows an example of the network node topology of the method proposed in the present invention. The physical information value of point 1 is P 1 , and the uncertain information is [δ 1- , δ 1+ , the physical information value of point 2 is P 2 , and the uncertain information is [δ 2- , δ 2 + ], the physical information value of point 3 is P 3 , and the uncertain information is [δ 3- , δ 3+ , the physical information value of point 4 is P 4 , and the uncertain information is [δ 4- , δ 4+ , δ - and δ + are the floating lower and upper limits of P. Nodes 1 and 3 are bottom-layer nodes, and the energy propagation end point is node 4;

[0025] Embodiment

[0026] A method for uncertainty propagation inference of urban energy systems based on the evidence theory in the present invention has a process as Figure 1 shown, and an example of the network node topology is as Figure 2 shown;

[0027] The propagation inference process is as follows:

[0028] (1) Judge that nodes 1 and 2 are in series. The overall uncertain information of nodes 1-2 is calculated as [δ I- , δ I+ , where δ I- is the minimum value of δ 1- and δ 2- , and δ I+ is the maximum value of δ 1+ and δ 2+ ;

[0029] (2) Determine that nodes 1-2 as a whole and node 3 are in parallel, and record the uncertainty information of the whole of nodes 1-2-3 as [δ II- , δ II+ . The calculation steps are as follows.

[0030] Step 1, calculate the uncertainty range μ I = δ I+ - δ I- of node 1-2, and calculate the uncertainty range μ 3 = δ 3+ - δ 3- .

[0031] Step 2, use the information of node 1-2 to form evidence 1, and based on the initial mass function, form the certainty information and uncertainty information of evidence 1 {μ I , 1 - μ I}. Use the information of node 3 to form evidence 2, and based on the initial mass function, form the certainty information and uncertainty information of evidence 2 {μ 3 , 1 - μ 3}.

[0032] Step 3, rewrite the initial evidence of evidence 1 through the weighted mass function to obtain {αμ I , (1 - α)(1 - μ I ); 1 - αμ I - (1 - α)(1 - μ I )}. Rewrite the initial evidence of evidence 2 through the weighted mass function to obtain {αμ 3 , (1 - α)(1 - μ 3 ); 1 - αμ 3 - (1 - α)(1 - μ 3 )}.

[0033] Step 4, adopt the D-S combination rule to combine the rewritten evidence 1 and evidence 2 to obtain the combination result {ε, γ; 1 - ε - γ}.

[0034] Step 5, establish a class probability likelihood function, and distribute 1 - ε - γ to ε and γ according to the ratio of ε:γ to obtain [ε * , γ * .

[0035] Step 6, let |δ I3- | + δ I3+ = ε * , and according to the ratio of |δ I- | + |δ 3- | : δ I+ + δ 3+ for distribution, it can be obtained that

[0036] (3) It is determined that nodes 1-2-3 as a whole and node 4 are in series, and the uncertainty information of nodes 1-2-3-4 as a whole is [δ III- , δ III+ , where δ III- is the minimum value of δ II- and δ 4- , and δ III+ is the maximum value of δ II+ and δ 4+ .

[0037] In the above embodiments, the mass function, D-S combination rule, and method for establishing the class probability likelihood function are prior arts and are well-known to those skilled in the art;

[0038] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention is not limited to the embodiments described in the specific embodiments. Any other embodiments obtained by those skilled in the art according to the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for uncertainty propagation reasoning of urban energy systems based on evidence theory, characterized by The following steps are involved: The network node topology is established based on the energy transmission structure, and the energy equipment is equivalent to the nodes in the network; different nodes are connected in series and in parallel in the network, and the physical information value of point a is calculated as P a , the uncertain information is [δ a- , δ a+ ], the physical information value of point b is P b , the uncertain information is [δ b- , δ b+ ], δ - and δ + is the floating lower and upper limits of P; If nodes a and b are connected in series, the uncertainty information of node ab as a whole is [δ ab- , δ ab+ ]; where δ ab- is δ a- and δ b- The minimum value in ab+ is δ a+ and δ b+ The maximum value in ; If nodes a and b are connected in parallel, the overall uncertainty information of nodes ab is [δ ab- , δ ab+ ]; The calculation steps are as follows; Step 1: Calculate the uncertainty range μ of node a a =δ a+ -δ a- , calculate the uncertainty range μ of node b b =δ b+ -δ b- ; Step 2: Use the information of node a to form evidence 1, and form the certain information and uncertain information of evidence 1 based on the initial mass function {μ a , 1-μ a }; Use the information of node b to form evidence 2, and form the certain information and uncertain information of evidence 2 based on the initial mass function {μ b , 1-μ b }; Step 3: Rewrite the initial evidence of evidence 1 through the weighted mass function to obtain {αμ a , (1-α)(1-μ a ); 1-αμ a -(1-α)(1-μ a )}; rewrite the initial evidence of evidence 2 through the weighted mass function to obtain {αμ b , (1-α)(1-μ b ); 1-αμ b -(1-α)(1-μ b )}; Step 4, use the DS synthesis rule to synthesize the rewritten evidence 1 and evidence 2 to obtain the synthesis result {ε, γ; 1-ε-γ}; Step 5, establish the class probability likelihood function, distribute 1-ε-γ to ε and γ according to the ratio of ε:γ, and obtain [ε*, γ*]; Step 6, let |δ ab- |+δ ab+ =ε*, according to |δ a- |+|δ b- |:δ a+ +δ b+ The proportion distribution can be obtained, 2. According to claim 1, an urban energy system uncertainty propagation reasoning method based on evidence theory is characterized by: The uncertainty propagation reasoning process starts from the lowest node in the network and gradually extends to the reliability propagation process of the urban energy system, updating the reliability of different nodes in the system [δ - , δ + ].

Images