A method for site selection and capacity determination of centralized-distributed energy storage in active distribution networks

Abstract

The present invention proposes a method for site selection and capacity determination of centralized-distributed energy storage for active distribution networks. First, under the premise of ensuring the safety and reliability of the power system, the distribution system containing certain nodes is evenly divided into corresponding zones. According to the improved active-voltage sensitivity analysis method, the node with the highest active-voltage sensitivity of each zone with improved sensitivity is obtained, and the economy and safety issues after the energy storage is connected to the power grid are considered. The voltage offset, the economy of energy storage, and the absorption rate are taken as optimization targets. The analytic hierarchy process and the firefly algorithm are combined to determine the weights of the three indicators in the objective function, and the strategy model is solved. Finally, the energy storage capacity at each location satisfies the maximum economic benefit of the entire system and ensures the safe and stable operation of the distribution system.

Description

Technical Field

[0001] The invention relates to the problem of site selection and capacity determination of energy storage in an active distribution network, and in particular to a method for site selection and capacity determination of centralized-distributed energy storage in an active distribution network. Background Art

[0002] With the massive consumption of traditional fossil fuels, energy shortage and environmental pollution are becoming increasingly serious, and the transformation of energy structure is imperative. Nowadays, clean energy such as photovoltaics and wind power has received considerable attention and development due to its green and environmentally friendly characteristics and easy access.

[0003] Nowadays, the penetration rate of new energy in distribution networks is increasing, and traditional distribution networks are transforming towards active distribution networks. Due to the randomness of distributed new energy, the high penetration rate of distributed new energy grid connection will seriously affect the flow of power between the transmission network and the distribution network, and have an adverse impact on the operation and control of traditional power grids, especially in terms of voltage fluctuations and insufficient absorption rate. This will bring new challenges to the planning and safe and stable operation of the power grid.

[0004] At present, active power regulation of energy storage equipment is commonly used to manage and control the energy of active distribution networks. There are three problems: (1) The cost of energy storage devices is relatively high. Irrational use of energy storage equipment will lead to poor economic efficiency of the system; (2) The capacity of energy storage equipment is limited. If it is not configured properly, it will be difficult to meet the active power demand of the active distribution network when a high proportion of distributed energy is connected to the grid; (3) Both centralized energy storage and distributed energy storage have certain advantages and disadvantages. Centralized energy storage is simple to control, but it occupies a large area and has poor flexibility. Distributed energy storage has better flexibility and occupies a small area, but it is more complex to control. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a centralized-distributed energy storage site selection and sizing method for an active distribution network, which can fully reduce the adverse effects of a high proportion of distributed energy grid connection, appropriately improve the economy of system operation, ensure the safety and stability of the distribution network, and reduce the economic cost of the system.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for selecting a location and determining the capacity of a centralized-distributed energy storage system for an active power distribution network, characterized in that it comprises the following steps:

[0008] (1) Under the premise of ensuring the safety and reliability of the power system, the distribution system with certain nodes is divided into corresponding areas according to the distance from the relative balance node, thereby realizing the partitioning of the active distribution network:

[0009] In step (1), since the farther the node is from the balancing node, the stronger its volatility is, in order to optimize the control, the distribution system containing I nodes is divided into N blocks according to the distance relative to the balancing node, and the first N-1 blocks each contain K nodes, and K can be expressed as:

[0010] K=I / N

[0011] The Nth zone contains C nodes, where C is:

[0012] C = I - (N - 1) * K;

[0013] (2) Establish the connection between active-voltage sensitivity and active-voltage sensitivity matrix. In the traditional sensitivity calculation method, introduce the voltage offset coefficient to reflect the voltage regulation requirements of each node. In each zone, calculate the improved active-voltage sensitivity of each node in the zone, and configure the centralized-distributed energy storage device at the node with the highest value:

[0014] In the step (2), the active power-voltage sensitivity analysis method is specifically as follows:

[0015] First, obtain the active power change ΔP and voltage change ΔU to solve for N -1 The matrix expression is as follows:

[0016] N -1 =ΔUΔP -1

[0017] Among them, N is the Jacobian submatrix, N -1 is the inverse matrix of the N matrix.

[0018] The active-voltage sensitivity is N -1 Column sum of elements in a matrix:

[0019]

[0020] Among them, i,j are nodes, Sen j is the active power-voltage sensitivity; e ij N -1 Elements in

[0021] (3) Comprehensively considering the economic efficiency, voltage offset and consumption rate of the distribution system, the judgment matrix is ​​constructed using the hierarchical analysis method, and the characteristic vector is solved. Then, through the consistency test, the weights of the three indicators of voltage offset, economic efficiency and consumption rate in the objective function are obtained, and finally the energy storage capacity configuration model is constructed:

[0022] In the step (3), the judgment matrix A is constructed by using the hierarchical analysis method. First, the element a in the matrix A is obtained. ij , a ijThe acquisition of was carried out using a 1 to 9 scale;

[0023] In step (3), the eigenvector is solved by first solving the maximum eigenvalue λ corresponding to the A matrix. max , and then solve the eigenvector w corresponding to the maximum eigenvalue, w is:

[0024] w=[w1,w2,w3]

[0025] The consistency check in step (3) refers to the check coefficient CR, which is expressed as follows:

[0026]

[0027] Where CI is:

[0028]

[0029] CR is the reference test coefficient, CI is the consistency test index, and RI is the random consistency index.

[0030] The value of RI is related to the order of the matrix, and the relationship is:

[0031] When the matrix order is 1 or 2, RI = 0; when the matrix order is 3, RI = 0.58; when the matrix order is 4, RI = 0.90;

[0032] When the matrix order is 5, RI = 1.12; when the matrix order is 6, RI = 1.24;

[0033] If CR is less than 0.1, it is considered that the consistency test of the judgment matrix can be passed. Otherwise, the consistency test fails and the judgment matrix needs to be re-obtained:

[0034] In step (3), an energy storage capacity configuration model is constructed, and the objective function of the model is expressed as follows:

[0035] min f=w1U+w2C-w3P

[0036] Among them, f is the energy storage capacity configuration function, w1, w2, and w3 are the indicator weights corresponding to voltage offset, economy, and absorption rate, respectively. U represents the voltage offset factor, C represents the economic influencing factor, and P represents the absorption rate factor in the energy storage capacity configuration.

[0037] (4) According to the given objective function and constraints, a method for solving the energy storage capacity based on the firefly optimization algorithm is established to finally complete the capacity configuration of centralized-distributed energy storage.

[0038] As a further improvement of the present invention, in step (2), by introducing a voltage offset coefficient, the voltage regulation demand of each node in the system is reflected, wherein the active voltage sensitivity Sen of the j node in the n phase of the ath region at time t is a,j,n,t and voltage offset coefficient k a,j,n,t The expression is as follows:

[0039]

[0040] k a,j,n,t =(V a,j,n,t -V ref,a,j,n,t ) 2

[0041] Among them, e a,i(3j-3+n),t is the value of the active power-voltage sensitivity matrix at time t in region a, V a,j,n,t and V ref,a,j,n,t are the voltage amplitude and expected voltage amplitude at time t in region a respectively.

[0042] As a further improvement of the present invention, according to the 1 to 9 scaling method, if the quantization value is 1, it means that factor i is equally important as factor j; if the quantization value is 2 or 3, it means that factor i is slightly more important than factor j; if the quantization value is 4 or 5, it means that factor i is more important than factor j; if the quantization value is 6 or 7, it means that factor i is strongly important than factor j; if the quantization value is 8 or 9, it means that factor i is extremely important than factor j.

[0043] As a further improvement of the present invention, in step (4), the objective function of the firefly optimization algorithm is the objective function minf in claim 5, and its constraint condition is:

[0044] ① Ensure that the node voltage offset of the system is less than the specified maximum value:

[0045] ΔU≤ΔU max

[0046] Among them, ΔU max is the maximum value of the specified voltage offset;

[0047] ②The capacity and power of energy storage cannot exceed the maximum value:

[0048]

[0049] Among them, E E,k and P E,k,t They represent the maximum capacity of the k-th energy storage and the power of the k-th energy storage at time t respectively; E max and P max They represent the maximum capacity and maximum power of energy storage respectively.

[0050] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0051] Beneficial effects: The voltage requirements of each node are comprehensively considered in site selection. In terms of capacity setting, the economy of active distribution network operation is improved, the voltage deviation of each node is reduced, and the absorption rate of the distribution system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of a process flow of a method for site selection and capacity determination of a centralized-distributed energy storage of an active power distribution network provided by the present invention;

[0053] Figure 2 A flow chart for solving the energy storage capacity in the present invention;

[0054] Figure 3 A schematic diagram of site selection and capacity determination for the centralized-distributed energy storage of the active power distribution network provided by the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0056] Figure 1 The figure shows a flow chart of a method for site selection and capacity determination of a centralized-distributed energy storage of an active distribution network provided by the present invention, wherein:

[0057] (1) The nodes of the active distribution network are divided into zones, the improved active power-voltage sensitivity of each node in each zone is calculated, and centralized-distributed energy storage is configured at the node with the highest value to achieve the site selection of energy storage.

[0058] (2) The analytic hierarchy process is used to determine the weights of the three indicators in the objective function, and a storage capacity configuration model is established. The firefly optimization algorithm is combined to solve the storage capacity configuration function. Ultimately, the storage capacity of each location is optimized to maximize the economic benefits of the entire system, ensure the safe and stable operation of the distribution system, and achieve the fixed capacity of energy storage.

[0059] Figure 2 The flowchart of solving the energy storage capacity in the present invention is shown. First, the structural model of hierarchical analysis is constructed, and then the judgment matrix is ​​established and the corresponding weights are solved. After the consistency test, the objective function f of energy storage optimization can be obtained. Then, the firefly algorithm is used to solve the objective function. Finally, when the required accuracy is achieved, the capacity configuration of the energy storage is obtained.

[0060] Figure 3 The figure shows a schematic diagram of site selection and capacity determination for centralized-distributed energy storage of an active power distribution network provided by the present invention, which specifically includes the following steps:

[0061] (1) Under the premise of ensuring the safety and reliability of the power system, the distribution system containing certain nodes is divided into corresponding areas according to the distance from the relative balance node, thereby realizing the partitioning of the active distribution network.

[0062] (2) Establish the connection between active-voltage sensitivity and active-voltage sensitivity matrix. In the traditional sensitivity calculation method, introduce the voltage offset coefficient to reflect the voltage regulation requirements of each node. In each zone, calculate the improved active-voltage sensitivity of each node in the zone, and configure the centralized-distributed energy storage device at the node with the highest value.

[0063] (3) Taking into account the economy, voltage offset and absorption rate of the distribution system, the judgment matrix is ​​constructed using the hierarchical analysis method, and the eigenvector is solved to obtain the weights of the three indicators of voltage offset, economy and absorption rate in the objective function, and finally the energy storage capacity configuration model is constructed.

[0064] (4) According to the given objective function and constraints, a method for solving the energy storage capacity based on the firefly optimization algorithm is established to finally complete the capacity configuration of centralized-distributed energy storage.

[0065] In step (1), since the farther the node is from the balancing node, the stronger its volatility is, in order to optimize the control, the distribution system containing I nodes is divided into N blocks according to the distance relative to the balancing node, and the first N-1 blocks each contain K nodes, and K can be expressed as:

[0066] K=I / N

[0067] The Nth zone contains C nodes, and C can be expressed as:

[0068] C=I-(N-1)*K

[0069] In step (2), the active power-voltage sensitivity analysis method first obtains the active power change ΔP and the voltage change ΔU to solve for N -1 The matrix expression is as follows:

[0070] N -1 =ΔUΔP -1

[0071] Among them, N is the Jacobian submatrix, N -1 is the inverse matrix of the N matrix.

[0072] The active-voltage sensitivity is N -1 Column sum of elements in a matrix:

[0073]

[0074] Among them, i,j are nodes, Sen jis the active power-voltage sensitivity; e ij N -1 The elements in .

[0075] In step (2), the voltage offset coefficient is introduced to reflect the voltage regulation demand of each node in the system. a,j,n,t and voltage offset coefficient k a,j,n,t The expression is as follows:

[0076]

[0077] k a,j,n,t =(V a,j,n,t -V ref,a,j,n,t ) 2

[0078] Among them, e a,i(3j-3+n),t is the value of the active power-voltage sensitivity matrix at time t in region a, V a,j,n,t and V ref,a,j,n,t are the voltage amplitude and expected voltage amplitude at time t in region a respectively.

[0079] In the step (3), the judgment matrix A is constructed by using the hierarchical analysis method. First, the element a in the matrix A is obtained. ij , a ij The 1 to 9 scaling method is used to obtain the value. According to the 1 to 9 scaling method, if the quantitative value is 1, it means that factor i is equally important as factor j; if the quantitative value is 2 or 3, it means that factor i is slightly more important than factor j; if the quantitative value is 4 or 5, it means that factor i is more important than factor j; if the quantitative value is 6 or 7, it means that factor i is strongly important than factor j; if the quantitative value is 8 or 9, it means that factor i is extremely important than factor j.

[0080] In step (3), the eigenvector is solved by first solving the maximum eigenvalue λ corresponding to the A matrix. max , and then solve the eigenvector w corresponding to the maximum eigenvalue, w can be expressed as

[0081] w=[w1,w2,w3]

[0082] The consistency check in step (3) refers to the check coefficient CR, which is expressed as follows:

[0083]

[0084] The CI is:

[0085]

[0086] CR is the reference test coefficient, CI is the consistency test index, and RI is the random consistency index.

[0087] The value of RI is related to the order of the matrix. The commonly used corresponding relationship is shown in the following table:

[0088]

[0089] Generally, if CR is less than 0.1, it is considered that the consistency test of the judgment matrix can be passed, otherwise the consistency test fails and the judgment matrix needs to be re-obtained.

[0090] In step (3), an energy storage capacity configuration model is constructed, and the objective function of the model can be expressed by the following formula:

[0091] min f=w1U+w2C-w3P

[0092] Among them, f is the energy storage capacity configuration function, w1, w2, and w3 are the indicator weights corresponding to voltage offset, economy, and absorption rate, respectively. U represents the voltage offset factor, C represents the economic influencing factor, and P represents the absorption rate factor in the energy storage capacity configuration.

[0093] In step (4), the objective function of the firefly optimization algorithm is the objective function minf in claim 5, and the constraint condition is:

[0094] ① Ensure that the node voltage deviation of the system is less than the specified maximum value

[0095] ΔU≤ΔU max

[0096] Among them, ΔU max is the maximum value of the specified voltage offset.

[0097] ②The capacity and power of energy storage cannot exceed the maximum value

[0098]

[0099] Among them, E E,k and P E,k,t They represent the maximum capacity of the k-th energy storage and the power of the k-th energy storage at time t respectively; E max and P max They represent the maximum capacity and maximum power of energy storage respectively.

[0100] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A method for selecting and sizing centralized and distributed energy storage for an active distribution network, characterized in that: The steps include: (1) Under the premise of ensuring the safety and reliability of the power system, the distribution system with certain nodes is divided into corresponding areas according to the distance from the relative balance node, thereby realizing the partitioning of the active distribution network: In step (1), since the farther the node is from the balancing node, the stronger its volatility is, in order to optimize the control, the distribution system containing I nodes is divided into N blocks according to the distance relative to the balancing node, and the first N-1 blocks each contain K nodes, and K can be expressed as: K=I / N The Nth zone contains C nodes, where C is: C = I - (N - 1) * K; (2) Establish the connection between active-voltage sensitivity and active-voltage sensitivity matrix. In the traditional sensitivity calculation method, introduce the voltage offset coefficient to reflect the voltage regulation requirements of each node. Calculate the improved active-voltage sensitivity of each node in each zone, and configure the centralized-distributed energy storage device at the node with the highest value: In step (2), the voltage offset coefficient is introduced to reflect the voltage regulation demand of each node in the system, where the active voltage sensitivity Sen of the j node in the n phase of the ath region at time t is a,j,n,t and voltage offset coefficient k a,j,n,t The expression is as follows: k a,j,n,t =(V a,j,n,t -V ref,a,j,n,t ) 2 Among them, e a,i(3j-3+n),t is the value of the active power-voltage sensitivity matrix at time t in region a, V a,j,n,t and V ref,a,j,n,t are the voltage amplitude and expected voltage amplitude at time t in region a respectively; In the step (2), the active power-voltage sensitivity analysis method is specifically as follows: First, obtain the active power change ΔP and voltage change ΔU to solve for N -1 The matrix expression is as follows: N -1 =ΔUΔP -1 Among them, N is the Jacobian submatrix, N -1 is the inverse matrix of the N matrix; The active-voltage sensitivity is N -1 Column sum of elements in a matrix: Among them, i,j are nodes, Sen j is the active power-voltage sensitivity; e ij N -1 Elements in (3) Comprehensively considering the economic efficiency, voltage offset and consumption rate of the distribution system, the judgment matrix is ​​constructed using the hierarchical analysis method, and the characteristic vector is solved. Then, through the consistency test, the weights of the three indicators of voltage offset, economic efficiency and consumption rate in the objective function are obtained, and finally the energy storage capacity configuration model is constructed: In the step (3), the judgment matrix A is constructed by using the hierarchical analysis method. First, the element a in the matrix A is obtained. ij , a ij The acquisition of was carried out using a 1 to 9 scale; According to the 1 to 9 scale, if the quantitative value is 1, it means that factor i is equally important as factor j; if the quantitative value is 2 or 3, it means that factor i is slightly more important than factor j; if the quantitative value is 4 or 5, it means that factor i is more important than factor j; if the quantitative value is 6 or 7, it means that factor i is strongly important than factor j; if the quantitative value is 8 or 9, it means that factor i is extremely important than factor j; In step (3), the eigenvector is solved by first solving the maximum eigenvalue λ corresponding to the A matrix. max , and then solve the eigenvector w corresponding to the maximum eigenvalue, w is: w=[w1,w2,w3] The consistency check in step (3) refers to the check coefficient CR, which is expressed as follows: Where CI is: CR is the reference test coefficient, CI is the consistency test index, and RI is the random consistency index; The value of RI is related to the order of the matrix, and the relationship is: When the matrix order is 1 or 2, RI = 0; when the matrix order is 3, RI = 0.58; when the matrix order is 4, RI = 0.90; When the matrix order is 5, RI = 1.12; when the matrix order is 6, RI = 1.24; If CR is less than 0.1, it is considered that the consistency test of the judgment matrix can be passed. Otherwise, the consistency test fails and the judgment matrix needs to be re-obtained: In step (3), an energy storage capacity configuration model is constructed, and the objective function of the model is expressed as follows: min f=w1U+w2C-w3P Among them, f is the energy storage capacity configuration function, w1, w2, w3 are the indicator weights corresponding to voltage offset, economy, and consumption rate, respectively, U represents the voltage offset factor, C represents the economic influencing factor, and P represents the consumption rate factor in the energy storage capacity configuration; (4) According to the given objective function and constraint conditions, a method for solving the energy storage capacity based on the firefly optimization algorithm is established, and finally the capacity configuration of the centralized-distributed energy storage is completed; in the step (4), the objective function of the firefly optimization algorithm is the objective function minf, and the constraint condition is: ① Ensure that the node voltage offset of the system is less than the specified maximum value: ΔU≤ΔU max Among them, ΔU max is the maximum value of the specified voltage offset; ②The capacity and power of energy storage cannot exceed the maximum value: Among them, E E,k and P E,k,t They represent the maximum capacity of the k-th energy storage and the power of the k-th energy storage at time t respectively; E max and P max They represent the maximum capacity and maximum power of energy storage respectively.

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