Energy storage configuration method for voltage regulation of power distribution network

Abstract

The invention discloses a power distribution network voltage regulation-oriented energy storage configuration method. The method comprises the following steps of: establishing a power distribution network node out-of-limit voltage active sensitivity calculation model; energy storage access positions and number are determined according to node out-of-limit voltage active sensitivity; establishing a target function, and solving the optimal solution of the charge and discharge power value of the energy storage; adding the energy storage charging and discharging power value into historical operation data of the power distribution network, obtaining voltage data after energy storage operation through load flow calculation, if voltage out-of-limit still exists, continuing to calculate node out-of-limit voltage active sensitivity, retaining original energy storage access, accessing new energy storage according to the newest node out-of-limit voltage active sensitivity, and completing the energy storage operation according to the newest node out-of-limit voltage active sensitivity. Recalculating the optimal charging and discharging power of all stored energy, and circulating until no voltage exceeds the limit; and determining all energy storage access positions according to the optimal solution of the charging and discharging power values of the last energy storage, and calculating the rated capacity and the rated power value of the energy storage according to the charging and discharging power values of the energy storage. According to the invention, the accuracy and rationality of energy storage configuration are improved.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage access configuration for distribution network, and in particular relates to an energy storage configuration method for voltage regulation of distribution network. Background Art

[0002] The new power system with new energy as the main body is the country's future development requirement for the power grid, and the medium and low voltage distribution network is an important battlefield for the realization of the new power system. The medium and low voltage distribution network will contain a large number of intermittent distributed photovoltaic and charging loads. When the proportion of distributed photovoltaic and charging load access reaches a certain level, it will have a great impact on the safe and stable operation of the distribution network, and may cause large-scale disturbances or even failures. This is a problem that must be faced for the thorough realization of the new distribution network. Due to the volatility and randomness of distributed photovoltaic output and charging load power, when the number of photovoltaic accesses is large, the voltage of the distribution network will be affected. In severe cases, the voltage will exceed the upper limit. When the charging load scale is large, the voltage may exceed the lower limit, so that the power supply reliability of the distribution network cannot be guaranteed. With the rapid development and increasing maturity of energy storage technology, energy storage systems provide effective technical means for the safe operation of power grid voltage. Since energy storage equipment can effectively solve the problem of "electricity cannot be stored" in power grid operation, through energy storage and rapid two-way regulation, photovoltaic power generation and charging loads become more user-friendly, flexible and controllable. Energy storage can reduce the impact of photovoltaic power generation and charging load power fluctuation and randomness on the power grid by controlling the charging and discharging power, thereby improving the voltage quality of the distribution network. As the cost of energy storage systems continues to decrease, it is a very meaningful solution to reasonably configure energy storage in medium and low voltage distribution networks and perform on-site control to adjust the distribution network voltage.

[0003] At present, the research results on energy storage configuration have the following problems: First, many studies consider establishing an overall optimization model to optimize the energy storage access point and energy storage capacity. When the scale of photovoltaic and charging load access is large, the optimization solution of this method is highly dependent on data, has poor convergence, and is very easy to fall into the local optimal solution. At the same time, the calculation is time-consuming, and slight changes in data can easily cause large differences in the results of energy storage site selection and capacity determination. Second, some studies use voltage sensitivity to select energy storage access locations to improve the speed of energy storage site selection, but the calculation method of its sensitivity index is not detailed enough in considering the voltage support capacity of energy storage for the distribution network, ignores the consideration of line loss, and is very vague in determining the initial number of energy storage access, resulting in unreasonable energy storage site selection. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an energy storage configuration method for voltage regulation of a distribution network.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for configuring energy storage for voltage regulation of a distribution network comprises the following steps:

[0007] According to the distribution network parameters and historical section operation data, a distribution network node over-limit voltage active power sensitivity calculation model is established, including a node over-limit voltage active power sensitivity calculation model and a node over-limit voltage active power sensitivity calculation model;

[0008] Determine the energy storage access location and number based on the active sensitivity of the node over-limit voltage;

[0009] An objective function with the goal of minimizing the total cost of energy storage and voltage over-limit value is established in the distribution network, and the particle swarm optimization algorithm improved by Pareto solution set constraint is used to obtain the optimal solution for the charging and discharging power value of energy storage;

[0010] The calculated energy storage charging and discharging power value is added to the historical section operation data of the distribution network. The voltage data after the energy storage is connected is obtained through power flow calculation. If there is still a voltage over-limit situation, the node over-limit voltage active sensitivity is calculated, and the original energy storage connection is retained. New energy storage is added according to the latest node over-limit voltage active sensitivity calculation value, and the optimal solution of the charging and discharging power value of all energy storage is recalculated. Repeat the cycle until there is no voltage over-limit situation;

[0011] The access locations of all energy storage are determined based on the optimal solution of the energy storage charge and discharge power values ​​calculated last time, and the rated capacity and rated power values ​​of the energy storage are calculated based on the energy storage charge and discharge power values.

[0012] To optimize the above technical solutions, the specific measures taken also include:

[0013] Furthermore, the establishment process of the active sensitivity calculation model of the node exceeding the upper voltage limit is specifically as follows:

[0014] For an n-node distribution network, the relationship between node voltage and node active power is:

[0015]

[0016] Among them, DU i , DP i is the voltage and active power change of node i; l ij is the traditional voltage sensitivity factor of the distribution network between node i and node j, and the matrix formed is the sensitivity matrix;

[0017] Considering that there are H nodes with voltage above the upper limit and L nodes with voltage below the lower limit at a certain section at time t, the active sensitivity calculation model of the node voltage above the upper limit is established as follows:

[0018]

[0019]

[0020] Among them, k hu is the node voltage upper limit deviation weight, k hp is the power flowing from downstream to upstream with node j as the base point, Zh j,t is the active voltage sensitivity value of all nodes whose voltage exceeds the upper limit to the change of active power injected into node j at time t, l ij,t is the traditional voltage sensitivity factor of the distribution network between node i and node j at time t, U i,t is the voltage of node i at time t, which is the per-unit value. Node i belongs to the node set H whose voltage exceeds the upper limit. Uh ref,i,t is the voltage upper threshold value of node i, is the per unit value, P up,j is the upstream inflow power of node j, which is a real value, and its reference positive direction is from upstream to downstream, P PV is the sum of the power of all photovoltaics, which is the real value;

[0021] When there are multiple section moments M, the active sensitivity calculation model of the node crossing the upper voltage limit at multiple moments is established as follows:

[0022]

[0023] In the formula, Zh j It is the sum of the active voltage sensitivity values ​​of all nodes whose voltage exceeds the upper limit to the change of active power injected into node j at all sections at the moment.

[0024] Furthermore, the establishment process of the node lower voltage limit active power sensitivity calculation model is specifically as follows:

[0025]

[0026] Among them, Zl j,t is the active voltage sensitivity value of the active power change injected into node j at time t to all nodes with voltage lower than the lower limit. At this time, node i belongs to the node set L with voltage lower than the lower limit, k lu Indicates the node voltage lower limit deviation weight, k lp It is expressed as the power flowing from upstream to downstream with node j as the base point, Ul ref,i,t is the voltage lower limit threshold of node i, is the per unit value, P dn,j is the outflow power of node j, which is a real value, and its reference positive direction is from upstream to downstream, P LD is the sum of all load powers, which is a real-name value;

[0027] When there are multiple section moments M, the active sensitivity calculation model of the node crossing the lower voltage limit at multiple moments is established as follows:

[0028]

[0029] In the formula, Zl j It represents the sum of the active voltage sensitivity values ​​of all nodes whose voltage exceeds the lower limit to the change of active power injected by node j at all sections at the moment.

[0030] Furthermore, the energy storage access location and number are determined according to the active sensitivity of the node over-limit voltage:

[0031] If only the node voltage exceeds the upper limit, the nodes are sorted according to their active sensitivity values ​​exceeding the upper limit voltage, and the node with the largest value is selected as the energy storage access point, and the number of energy storage access points is 1;

[0032] If only the node voltage exceeds the lower limit, the nodes are sorted according to the active sensitivity value of the node voltage exceeding the lower limit, and the node with the largest value is selected as the energy storage access point, and the number of energy storage access points is 1;

[0033] If both the node voltage upper limit and the node voltage lower limit exist, the nodes are sorted according to the active sensitivity values ​​of the nodes above the upper limit voltage, and the node with the largest active sensitivity value of the nodes above the upper limit voltage is selected as one energy storage access point. The nodes are sorted according to the active sensitivity values ​​of the nodes above the lower limit voltage, and the node with the largest active sensitivity value of the nodes above the lower limit voltage is selected as another energy storage access point. The number of energy storage access points is 2.

[0034] Furthermore, the objective function with the goal of minimizing the total energy storage cost and the voltage over-limit value is specifically:

[0035]

[0036] Among them, F is the overall objective function, f ess 、f ue They are the total energy storage cost target and the voltage over-limit target respectively; E ESS,k , P ESS,k are the capacity and rated power of the kth energy storage system respectively; C w , C p are the unit capacity cost of the energy storage system and the unit power cost of the energy storage system, respectively. The unit capacity cost of energy storage is the converted unit capacity cost considering the energy storage investment cost, installation cost and operation and maintenance cost; U ue,i,t is the voltage over-limit value of node i at time t;

[0037] The system power balance constraint of the objective function is as follows:

[0038]

[0039] Among them, P grid,t , P PV,t , P ESS,tare the active power of the power grid, the active power of distributed photovoltaics, and the charging and discharging power of the energy storage system at time t; P load,t is the load active power at time t; P loss,t is the active power loss of the line at time t, Q grid,t , Q PV,t , Q ESS,t are the reactive power of the power grid, the reactive power of distributed photovoltaics, and the reactive power of the energy storage system at time t; Q load,t is the reactive power of the load at time t; Q loss,t is the reactive power loss of the line at time t;

[0040] The energy storage power constraint of the objective function is as follows:

[0041]

[0042] in, are the maximum and minimum values ​​allowed for the charging and discharging power of the energy storage system, respectively;

[0043] The energy storage state of charge constraints of the objective function are as follows:

[0044] SOC min £SOC t £SOC max

[0045] Among them, SOC t SOC is the state of charge of the energy storage system at time t; max , SOC min They are the upper and lower limits of the energy storage charge state respectively.

[0046] Furthermore, the optimal solution of the charging and discharging power value of the energy storage obtained by using the Pareto solution set constraint improved particle swarm optimization algorithm is specifically:

[0047] The initial particle swarm algorithm is used to encode the charging and discharging power of each energy storage into each particle, set the initial value of each particle, including the position variable x and the speed variable v, calculate the objective function value of each particle, and put it into the non-inferior solution set, so as to determine the current individual extreme value p of each particle and the global optimal solution g;

[0048] The inertia weight of the particle is updated in real time according to the difference distance between each particle. The difference between the particle and the optimal particle and the inertia weight are calculated as follows:

[0049]

[0050] In the formula, represents the updated position of the current particle after optimization, D is the spatial dimension of the solution set; g (k) is the optimal position of the population after the kth optimization; x (k)is the current position of the particle; x max 、x min are the maximum and minimum values ​​of the particle position respectively; Indicates that the inertia weight of the current particle is updated after optimization; w start 、w end are the initial and final values ​​of the inertia weight respectively; I represents the dense distance of particles;

[0051] According to the latest inertia weight, the speed and position of each particle are updated, and the objective function value of each particle is calculated again. According to the dominance relationship of each solution, the optimal solution p of each particle is updated and a new non-inferior solution set is formed. The optimal solution must be updated after each optimization solution. The optimal solution set is selected according to the selectivity of the Pareto solution set. For the two objective functions: the total energy storage cost target f ess and voltage over-limit target f ue The Pareto solution set obtained uses the dense distance to represent the density between the optimal solutions of each particle. The dense distance of the particle I(x i ) is expressed as follows:

[0052]

[0053] In the formula, x j 、x k is the distance x i The two closest particles; f essmax The maximum value of the total energy storage cost target corresponding to all particles, f uemax is the maximum value of the voltage over-limit target corresponding to all particles;

[0054] After solving the dense distance of each Pareto solution, sort them from small to large, first remove the solution with the smallest dense distance, then calculate the dense distance of the remaining Pareto solutions, sort them by dense distance, and then remove the solution with the smallest dense distance, and repeat the calculation until the number of remaining Pareto solutions reaches the set value; select the Pareto solution with the largest dense distance as the global optimal solution of the population, which is used to guide the update of the population, and finally obtain the optimal solution for the charging and discharging power of the energy storage.

[0055] Furthermore, the rated capacity and rated power value of the energy storage are calculated according to the charging and discharging power value of the energy storage as follows:

[0056] The maximum charging and discharging power of the energy storage at all section times is the rated power of the energy storage, and the formula is as follows:

[0057] P ESS,k =max{|P ess,k,t |}

[0058] Where P ess,k,t represents the charging and discharging power of the kth energy storage system at the section time t, PESS,k represents the rated power of the kth energy storage system;

[0059] At all section times, the charge and discharge capacity of the energy storage is calculated as follows:

[0060]

[0061] Among them, E ess,k represents the undetermined value of the kth energy storage rated capacity; T t is the duration of the section moment t;

[0062] The maximum and minimum state of charge of the kth energy storage are SOC max,k , SOC min,k , then the energy storage rated capacity En ESS,k The calculation is as follows:

[0063]

[0064] Among them, max(E ess,k )、min(E ess,k ) are E ess,k The maximum and minimum values ​​that occur during the calculation.

[0065] The beneficial effects of the present invention are as follows: the present application introduces the weights of voltage deviation and power, proposes a storage site selection method based on the active sensitivity of node over-limit voltage and a multi-objective sizing method based on the total cost of energy storage and the minimum voltage over-limit value, thereby improving the accuracy and rationality of energy storage configuration, reducing the cost of energy storage configuration, and supporting the effective regulation of distribution network voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of the energy storage configuration method for voltage regulation of distribution network in this application;

[0067] Figure 2 It is a schematic diagram of the topological structure of the IEEE33 system in this application;

[0068] Figure 3 The voltage conditions of each node before optimization at five time sections in this application;

[0069] Figure 4 is a curve diagram of the power sensitivity of each node crossing the upper voltage limit at multiple moments in this application;

[0070] Figure 5 is a graph of the voltage power sensitivity of each node crossing the lower limit at multiple times in this application;

[0071] Figure 6 is a graph of the conventional voltage active power sensitivity of each node in this application;

[0072] Figure 7 is a graph of voltage active power sensitivity including voltage weights of each node in the present application;

[0073] Figure 8 This is the charging and discharging power of the energy storage selected in this application at five time sections;

[0074] Fig. 9 This is the voltage status of each node after optimization at five time sections in this application. DETAILED DESCRIPTION

[0075] The present invention will now be described in further detail with reference to the accompanying drawings.

[0076] Please refer to Figure 1 As shown, the present application provides an energy storage configuration method for distribution network voltage regulation, the steps of which include:

[0077] S1. See Figure 2 There are 4 photovoltaics in the distribution network shown, connected to the 14th, 15th, 16th and 17th nodes of the line in sequence, with photovoltaic installed capacities of 0.9, 0.45, 0.9 and 0.9 MVA respectively. In 24 hours, there were 4 time sections with voltage exceeding the limit, see Figure 3 As shown, time 1 is the case when the voltage does not exceed the limit, time 2 is the case when the voltage exceeds the upper limit, time 3 and time 4 are the cases when the voltage exceeds both the upper limit and the lower limit at the same time, and time 5 is the case when the voltage exceeds the lower limit. According to the distribution network parameters and the historical section operation data, a distribution network node over-limit voltage active sensitivity calculation model is established, including a node over-limit voltage active sensitivity calculation model and a node over-limit voltage active sensitivity calculation model;

[0078] For the n-node distribution network, the energy storage configuration is considered to use its charging and discharging power to adjust the node voltage. Therefore, the reactive power is ignored, and the relationship between the node voltage and the node active power is obtained as follows:

[0079]

[0080] Among them, DU i , DP i is the voltage and active power change of node i; l ij is the traditional voltage sensitivity factor of the distribution network between node i and node j, and the formed matrix is ​​the sensitivity matrix.

[0081] Considering that there are H nodes with voltage above the upper limit and L nodes with voltage below the lower limit at a certain section at time t, the active sensitivity calculation model of the node voltage above the upper limit is established as follows:

[0082]

[0083] Among them, k hu is the node voltage upper limit deviation weight, k hp is the power flowing from downstream to upstream with node j as the base point, Zh j,t is the active voltage sensitivity value of all nodes whose voltage exceeds the upper limit to the change of active power injected into node j at time t, l ij,t is l at time t ij , U i,t That is, the voltage of node i at time t, which is the per-unit value. Node i belongs to the set of nodes with voltage exceeding the upper limit, Uh ref,i,t is the voltage upper limit threshold of node i, taking 1.05 as the per-unit value, P up,j is the upstream inflow power of node j, which is a real value in MW. Its reference positive direction is from upstream to downstream. P PV is the sum of all photovoltaic powers, a real value in MW. The formula uses the degree of node voltage deviation and the power flowing from downstream to upstream as weights to correct the traditional voltage sensitivity factor, and obtains the active sensitivity value of the node exceeding the upper voltage limit. Its purpose is to highlight the nodes that have the greatest impact on voltage regulation and reduce network losses during voltage regulation.

[0084] When there are multiple section moments M, the active sensitivity calculation model of the node crossing the upper voltage limit at multiple moments is established as follows:

[0085]

[0086] The active sensitivity calculation model of the node crossing the lower voltage limit is established as follows:

[0087]

[0088] Among them, Zl j,t is the active voltage sensitivity value of the active power change injected into node j at time t to all nodes with voltage lower than the lower limit. At this time, node i belongs to the set of nodes with voltage lower than the lower limit, k lu Indicates the node voltage lower limit deviation weight, k lp It is expressed as the power flowing from upstream to downstream with node j as the base point, Ul ref,i,t is the voltage lower limit threshold of node i, taking 0.95 as the per unit value, P dn,j is the outflow power of node j, which is a real value in MW. Its reference positive direction is from upstream to downstream. Pn LD is the sum of all load powers, a real value in MW. The formula uses the degree of node voltage deviation and the power flowing from upstream to downstream as weights to modify the traditional voltage sensitivity factor, and obtains the active sensitivity value of the node crossing the lower limit voltage.

[0089] When there are multiple section moments M, the active sensitivity calculation model of the node crossing the lower voltage limit at multiple moments is established as follows:

[0090]

[0091] According to the above model, the voltage power sensitivity of each node at multiple times exceeding the upper limit is calculated, see Figure 4 As shown, after comparison, it is found that the node 14 has the highest power sensitivity exceeding the upper voltage limit.

[0092] According to the above model, the voltage power sensitivity of each node at multiple times crossing the lower limit is calculated, see Figure 5 As shown, after comparison, it is found that the node 29 has the highest power sensitivity across the lower voltage limit.

[0093] Compared with the traditional voltage active power sensitivity calculation formula, the traditional sensitivity is calculated, refer to Figure 6 As shown, the highest point is node 18.

[0094] Compare the voltage active sensitivity when only voltage weight is considered, when power weight is not considered, refer to Figure 7 , at this time, the point with the highest sensitivity is node 33.

[0095] Therefore, it can be seen that the present invention introduces node voltage over-limit deviation weights and power weights to calculate voltage active sensitivity, highlighting the nodes with the greatest impact on voltage regulation while reducing network losses during voltage regulation.

[0096] S2. Determine the energy storage access location and number based on the active sensitivity of the node over-limit voltage;

[0097] (1) Only when the node voltage exceeds the upper limit:

[0098] Sort the nodes by their active sensitivity values ​​that exceed the upper voltage limit, and select the node with the largest value as the energy storage access point. The number of energy storage access points is 1.

[0099] (2) When only the node voltage exceeds the lower limit:

[0100] Sort the nodes by their active sensitivity values ​​across the lower voltage limit, and select the node with the largest value as the energy storage access point. The number of energy storage access points is 1.

[0101] (3) There are cases where the node voltage exceeds both the upper and lower limits:

[0102] Sort by the active sensitivity value of the nodes above the upper voltage limit, and select the node with the largest value as one energy storage access point. Sort by the active sensitivity value of the nodes above the lower voltage limit, and select the node with the largest value as another energy storage access point. The number of energy storage access points is 2.

[0103] According to the above calculation results of the node active voltage sensitivity, the above method for determining the energy storage access position and number is applied. At this time, the number of energy storage access is 2, and the energy storage access positions are nodes 14 and 29.

[0104] S3, establishing an objective function with the goal of minimizing the total cost of energy storage and voltage over-limit value in the distribution network;

[0105]

[0106] where f ess 、f ue They are the total energy storage cost target and the voltage over-limit target respectively; E ESS,k , P ESS,k are the capacity and rated power of the kth energy storage system respectively; C w , C p are the unit capacity cost of the energy storage system and the unit power cost of the energy storage system, respectively. The unit capacity cost of energy storage is the converted unit capacity cost considering the energy storage investment cost, installation cost and operation and maintenance cost; U ue,i,t is the voltage exceeding the limit value of node i at time t.

[0107] The system power balance constraint of the objective function is as follows:

[0108]

[0109] Among them, P grid,t , P PV,t , P ESS,t are the active power of the power grid, the active power of distributed photovoltaics, and the charging and discharging power of the energy storage system at time t; P load,t is the load active power at time t; P loss,t is the active power loss of the line at time t, Q grid,t , Q PV,t , Q ESS,t are the reactive power of the power grid, the reactive power of distributed photovoltaics, and the reactive power of the energy storage system at time t; Q load,t is the reactive power of the load at time t; Q loss,t is the reactive power loss of the line at time t.

[0110] The energy storage power constraints are as follows:

[0111]

[0112] in, They are respectively the maximum and minimum values ​​allowed for the charging and discharging power of the energy storage system.

[0113] The energy storage state of charge constraints are as follows:

[0114] SOC min ≤SOCt ≤SOC max

[0115] Among them, SOC t SOC is the state of charge of the energy storage system at time t; max , SOC min They are the upper and lower limits of the energy storage charge state respectively.

[0116] S4, the process of solving and calculating the optimal solution of the charging and discharging power value of the energy storage by using the Pareto solution set constraint improved particle swarm optimization algorithm;

[0117] The initial particle swarm algorithm is used to first encode the charging and discharging power of each energy storage into each particle, set the initial value of each particle, including the position variable x and the speed variable v, calculate the objective function of each particle, and put it into the non-inferior solution set, so as to determine the current individual extreme value p of each particle and the global optimal solution g.

[0118] The inertia weight of a particle is updated in real time based on the difference distance between particles. The difference between a particle and the optimal particle and the inertia weight are calculated as follows:

[0119]

[0120] In the formula, represents the updated position of the current particle after optimization, D is the spatial dimension of the solution set; g (k) is the optimal position of the population after the kth optimization; x (k) is the current position of the particle; Indicates the updated inertia weight of the current particle after optimization, x max 、x min are the maximum and minimum values ​​of the particle position respectively; w start 、w end They refer to the initial and final values ​​of the inertia weight respectively. I represents the dense distance of particles.

[0121] According to the latest inertia weight and the initial particle swarm formula, the speed and position of each particle are updated, and the objective function of each particle is calculated again. According to the dominance relationship of each solution, the optimal solution p of each particle is updated and a new non-inferior solution set is formed. The optimal solution must be updated after each optimization. The optimal solution set is selected according to the selectivity of the Pareto solution set to ensure that the Pareto solution set is evenly distributed. For the two objective functions f in the present invention, ess and f ue The Pareto solution set obtained uses the dense distance to represent the density between the optimal solutions of each particle and describes the characteristics of uniform distribution of solutions. The dense distance of particles I(x i ) is expressed as follows:

[0122]

[0123] In the formula, x j 、x k is the distance x i The two closest particles; f essmax and f uemax is the maximum value of the objective function corresponding to all particles.

[0124] After solving the dense distance of each Pareto solution, sort them from small to large, remove the solution with the smallest dense distance first, and then calculate the dense distance of the remaining Pareto solutions, sort them by dense distance, and then remove the solution with the smallest dense distance, and repeat the calculation until the number of remaining Pareto solutions reaches the set value. The Pareto solution with the largest dense distance is selected as the global optimal solution of the population to guide the update of the population, and finally the optimal charging and discharging power of the energy storage is obtained.

[0125] S5, add the calculated energy storage charging and discharging power value to the historical section operation data of the distribution network, and obtain new voltage data through power flow calculation. If there is still a voltage over-limit situation, the calculation is repeated from step S1, the original energy storage connection is retained, and new energy storage connection is added according to the latest node over-limit voltage active sensitivity calculation value, and the optimal solution of the charging and discharging power value of all energy storage is recalculated, and the cycle is repeated until there is no voltage over-limit situation;

[0126] After S3-S5, the optimal charging and discharging power of the two energy storages at five time sections are obtained after optimization calculation. Figure 8 As shown, it can be seen from the figure that the energy storage at node 14 is charged to lower the voltage when the voltage exceeds the upper limit, and the energy storage at node 29 is discharged to raise the voltage when the voltage exceeds the lower limit.

[0127] S6. Determine all energy storage access locations based on the optimal solution of the energy storage charge and discharge power value calculated last time, and calculate the capacity and rated power value of the energy storage based on the energy storage charge and discharge power value as follows:

[0128] (1) Rated power of energy storage:

[0129] The maximum charging and discharging power of the energy storage at all section times is the rated power of the energy storage.

[0130] P ESS,k =max{|P ess,k,t |}

[0131] Where P ess,k,t represents the charging and discharging power of the kth energy storage system at the section time t, P ESS,k represents the rated power of the kth energy storage system.

[0132] (2) Rated capacity of energy storage:

[0133] At all section times, the charge and discharge capacity of the energy storage is calculated as follows:

[0134]

[0135] Among them, E ess,k represents the undetermined value of the kth energy storage rated capacity, T t It is the duration of the section moment t, in hours h.

[0136] Assume that the maximum and minimum state of charge of energy storage k are SOC max,k , SOC min,k , then the energy storage rated capacity En ESS,k The calculation is as follows:

[0137]

[0138] Among them, max(E ess,k )、min(E ess,k ) are E ess,k The maximum and minimum values ​​that occur during the calculation.

[0139] According to the above method, the capacities of the two energy storages are calculated to be 0.875MWh and 0.776MWh respectively.

[0140] According to the optimal value of energy storage charging and discharging power, the voltage values ​​of each node after the power flow calculation under five time sections are obtained. Fig. 9 As shown in the figure, the voltage returns to the safety limit, achieving effective regulation of the distribution network.

[0141] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for configuring energy storage for voltage regulation of a distribution network, characterized in that: The following steps are involved: According to the distribution network parameters and historical section operation data, a distribution network node over-limit voltage active power sensitivity calculation model is established, including a node over-limit voltage active power sensitivity calculation model and a node over-limit voltage active power sensitivity calculation model; Determine the energy storage access location and number based on the active sensitivity of the node over-limit voltage; An objective function with the goal of minimizing the total cost of energy storage and voltage over-limit value is established in the distribution network, and the particle swarm optimization algorithm improved by Pareto solution set constraint is used to obtain the optimal solution for the charging and discharging power value of energy storage; The calculated energy storage charging and discharging power value is added to the historical section operation data of the distribution network. The voltage data after the energy storage is connected is obtained through power flow calculation. If there is still a voltage over-limit situation, the node over-limit voltage active sensitivity is calculated, and the original energy storage connection is retained. New energy storage is added according to the latest node over-limit voltage active sensitivity calculation value, and the optimal solution of the charging and discharging power value of all energy storage is recalculated. Repeat the cycle until there is no voltage over-limit situation; The access locations of all energy storage are determined based on the optimal solution of the energy storage charge and discharge power values ​​calculated last time, and the rated capacity and rated power values ​​of the energy storage are calculated based on the energy storage charge and discharge power values.

2. The energy storage configuration method for distribution network voltage regulation according to claim 1, characterized in that: The establishment process of the active sensitivity calculation model of the node exceeding the upper voltage limit is specifically as follows: For an n-node distribution network, the relationship between node voltage and node active power is: Among them, DU i , DP i is the voltage and active power change of node i; ij is the traditional voltage sensitivity factor of the distribution network between node i and node j, and the matrix formed is the sensitivity matrix; Considering that there are H nodes with voltage above the upper limit and L nodes with voltage below the lower limit at a certain section at time t, the active sensitivity calculation model of the node voltage above the upper limit is established as follows: Among them, k hu is the node voltage upper limit deviation weight, k hp is the power flowing from downstream to upstream with node j as the base point, Zh j,t is the active voltage sensitivity value of all nodes whose voltage exceeds the upper limit to the change of active power injected into node j at time t, λ ij,t is the traditional voltage sensitivity factor of the distribution network between node i and node j at time t, U i,t is the voltage of node i at time t, which is the per-unit value. Node i belongs to the node set H whose voltage exceeds the upper limit. Uh ref,i,t is the voltage upper threshold value of node i, is the per unit value, P up,j is the upstream inflow power of node j, which is a real value, and its reference positive direction is from upstream to downstream, P PV is the sum of the power of all photovoltaics, which is the real value; When there are multiple section moments M, the active sensitivity calculation model of the node crossing the upper voltage limit at multiple moments is established as follows: In the formula, Zh j It is the sum of the active voltage sensitivity values ​​of all nodes whose voltage exceeds the upper limit to the change of active power injected into node j at all sections at the moment.

3. The energy storage configuration method for distribution network voltage regulation according to claim 1, characterized in that: The establishment process of the node lower voltage limit active power sensitivity calculation model is specifically as follows: Among them, Zl j,t is the active voltage sensitivity value of the active power change injected into node j at time t to all nodes with voltage lower than the lower limit. At this time, node i belongs to the node set L with voltage lower than the lower limit, k lu Indicates the node voltage lower limit deviation weight, k lp It is expressed as the power flowing from upstream to downstream with node j as the base point, Ul ref,i,t is the voltage lower limit threshold of node i, is the per unit value, P dn,j is the outflow power of node j, which is a real value, and its reference positive direction is from upstream to downstream, P LD is the sum of all load powers, which is a real-name value; When there are multiple section moments M, the active sensitivity calculation model of the node crossing the lower voltage limit at multiple moments is established as follows: In the formula, Zl j It represents the sum of the active voltage sensitivity values ​​of all nodes whose voltage exceeds the lower limit to the change of active power injected by node j at all sections at the moment.

4. The energy storage configuration method for distribution network voltage regulation according to claim 1, characterized in that: The specific method of determining the energy storage access location and number according to the active sensitivity of the node over-limit voltage is as follows: If only the node voltage exceeds the upper limit, the nodes are sorted according to their active sensitivity values ​​exceeding the upper limit voltage, and the node with the largest value is selected as the energy storage access point, and the number of energy storage access points is 1; If only the node voltage exceeds the lower limit, the nodes are sorted according to the active sensitivity value of the node voltage exceeding the lower limit, and the node with the largest value is selected as the energy storage access point, and the number of energy storage access points is 1; If both the node voltage upper limit and the node voltage lower limit exist, the nodes are sorted according to the active sensitivity values ​​of the nodes above the upper limit voltage, and the node with the largest active sensitivity value of the nodes above the upper limit voltage is selected as one energy storage access point. The nodes are sorted according to the active sensitivity values ​​of the nodes above the lower limit voltage, and the node with the largest active sensitivity value of the nodes above the lower limit voltage is selected as another energy storage access point. The number of energy storage access points is 2.

5. The energy storage configuration method for distribution network voltage regulation according to claim 1, characterized in that: The objective function with the goal of minimizing the total energy storage cost and the voltage over-limit value is specifically: Among them, F is the overall objective function, f ess 、f ue They are the total energy storage cost target and the voltage over-limit target respectively; E ESS,k , P ESS,k are the capacity and rated power of the kth energy storage system respectively; C w , C p are the unit capacity cost of the energy storage system and the unit power cost of the energy storage system, respectively. The unit capacity cost of energy storage is the converted unit capacity cost considering the energy storage investment cost, installation cost and operation and maintenance cost; U ue,i,t is the voltage over-limit value of node i at time t; The system power balance constraint of the objective function is as follows: Among them, P grid,t , P PV,t , P ESS,t are the active power of the power grid, the active power of distributed photovoltaics, and the charging and discharging power of the energy storage system at time t; P load,t is the load active power at time t; P loss,t is the active power loss of the line at time t, Q grid,t , Q PV,t , Q ESS,t are the reactive power of the power grid, the reactive power of distributed photovoltaics, and the reactive power of the energy storage system at time t; Q load,t is the reactive power of the load at time t; Q loss,t is the reactive power loss of the line at time t; The energy storage power constraint of the objective function is as follows: in, are the maximum and minimum values ​​allowed for the charging and discharging power of the energy storage system, respectively; The energy storage state of charge constraints of the objective function are as follows: SOC min ≤SOC t ≤SOC max Among them, SOC t is the state of charge of the energy storage system at time t; SOC max , SOC min They are the upper and lower limits of the energy storage charge state respectively.

6. The energy storage configuration method for distribution network voltage regulation according to claim 1, characterized in that: The optimal solution of the charging and discharging power value of energy storage obtained by using the Pareto solution set constraint improved particle swarm optimization algorithm is specifically: The initial particle swarm algorithm is used to encode the charging and discharging power of each energy storage into each particle, set the initial value of each particle, including the position variable x and the speed variable v, calculate the objective function value of each particle, and put it into the non-inferior solution set, so as to determine the current individual extreme value p of each particle and the global optimal solution g; The inertia weight of the particle is updated in real time according to the difference distance between each particle. The difference between the particle and the optimal particle and the inertia weight are calculated as follows: In the formula, represents the updated position of the current particle after optimization, D is the spatial dimension of the solution set; g (k) is the optimal position of the population after the kth optimization; x (k) is the current position of the particle; x max 、x min are the maximum and minimum values ​​of the particle position respectively; Indicates that the inertia weight of the current particle is updated after optimization; w start 、w end are the initial and final values ​​of the inertia weight respectively; I represents the dense distance of particles; According to the latest inertia weight, the speed and position of each particle are updated, and the objective function value of each particle is calculated again. According to the dominance relationship of each solution, the optimal solution p of each particle is updated and a new non-inferior solution set is formed. The optimal solution must be updated after each optimization solution. The optimal solution set is selected according to the selectivity of the Pareto solution set. For the two objective functions: the total energy storage cost target f ess and voltage over-limit target f ue The Pareto solution set obtained uses the dense distance to represent the density between the optimal solutions of each particle. The dense distance of the particle I(x i ) is expressed as follows: In the formula, x j 、x k is the distance x i The two closest particles; f essmax The maximum value of the total energy storage cost target corresponding to all particles, f uemax is the maximum value of the voltage over-limit target corresponding to all particles; After solving the dense distance of each Pareto solution, sort them from small to large, first remove the solution with the smallest dense distance, then calculate the dense distance of the remaining Pareto solutions, sort them by dense distance, and then remove the solution with the smallest dense distance, and repeat the calculation until the number of remaining Pareto solutions reaches the set value; select the Pareto solution with the largest dense distance as the global optimal solution of the population, which is used to guide the update of the population, and finally obtain the optimal solution for the charging and discharging power of the energy storage.

7. The energy storage configuration method for distribution network voltage regulation according to claim 1, characterized in that: The rated capacity and rated power value of the energy storage are calculated according to the charging and discharging power value of the energy storage as follows: The maximum charging and discharging power of the energy storage at all section times is the rated power of the energy storage, and the formula is as follows: P ESS,k =max{|P ess,k,t |} Where P ess,k,t represents the charging and discharging power of the kth energy storage system at the section time t, P ESS,k represents the rated power of the kth energy storage system; At all section times, the charge and discharge capacity of the energy storage is calculated as follows: Among them, E ess,k represents the undetermined value of the kth energy storage rated capacity; T t is the duration of the section moment t; The maximum and minimum state of charge of the kth energy storage are SOC max,k , SOC min,k , then the energy storage rated capacity En ESS,k The calculation is as follows: Among them, max(E ess,k )、min(E ess,k ) are E ess,k The maximum and minimum values ​​that occur during the calculation.

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