Distributed power supply and energy storage voltage control method and device considering interval coordination

Abstract

The present invention relates to a distributed power supply and energy storage adaptive voltage control method considering interval coordination, comprising the following steps: determining an active distribution network and obtaining parameter information of the active distribution network; partitioning the active distribution network and obtaining node voltage measurement values ​​at time t in each area of ​​the active distribution network and interaction information between each area; setting up a virtual node in each area, uploading the interaction information between each area to the virtual node corresponding to each area, obtaining the voltage difference between the node voltage measurement value of each area and the node voltage control target reference value of each area, and the voltage difference between the virtual node voltage value and the virtual node voltage control target reference value, and judging whether any voltage difference exceeds the limit; if so, establishing a distributed power supply and energy storage adaptive voltage control model considering interval coordination; if not, updating the control time t=t+Δt; the present invention realizes the solution to the voltage optimization control problem of the high penetration rate of distributed power supply access to the distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of voltage control, and in particular relates to a distributed power supply and energy storage voltage control method and device considering interval coordination. Background Art

[0002] With the high penetration rate of distributed power sources connected to the distribution network, the distribution network has gradually transformed from a passive one-way power supply network to a complex active network with bidirectional power flow. On the one hand, the access of distributed power sources brings many challenges to the operation and control of the system, among which the voltage over-limit problem has become a key limiting factor in improving the penetration rate of photovoltaics; on the other hand, obtaining the voltage and power measurement information of all nodes in each area of ​​the distribution network will result in excessive data volume and communication burden. Therefore, how to make full use of controllable resources such as distributed energy storage systems and distributed power sources through reasonable interval information interaction and coordinated control strategies, improve the system operation level and increase the penetration rate of photovoltaics, is a key issue that needs to be urgently solved in building a smart distribution network.

[0003] However, although the traditional centralized control results in less network loss and better voltage control effect, it requires an accurate physical model and cannot perform real-time control to cope with the complex operation scenarios of the distribution network with frequent changes; the coordinated control method of the distributed energy storage system and the distributed power source in the area cannot consider the mutual influence of the control strategies between the areas, which will lead to problems such as large network loss and poor voltage control effect. The data-driven method does not rely on the detailed mathematical model information of the controlled system. It only uses the measured data to replace the general nonlinear system with a dynamic linear time-varying model near the trajectory of the controlled system to achieve the simulation construction of the unknown characteristics of the complex link; the combination of data-driven control and interval coordinated control methods can effectively solve the adverse effects of the conflict of controllable resource output strategies in each area on the distribution network voltage during the control process.

[0004] Therefore, a distributed power supply and energy storage adaptive voltage control method considering interval coordination is proposed, which provides a new idea for the voltage optimization problem of distribution network and helps to improve the safety and user experience on the distribution side. Summary of the invention

[0005] The technical problem to be solved by the present invention is that, for the problem of optimizing the voltage control of the distribution network, when the independent action of the control strategy of the controllable resources in each area will have an adverse effect on the voltage of the distribution network, a regulation mode is constructed based on data-driven and interval coordination means to realize interval information interaction and coordinated control of the distributed energy storage system and the distributed power source, and then an adaptive voltage control strategy for distributed power sources and energy storage that takes into account interval coordination is established.

[0006] The distributed power supply and energy storage adaptive voltage control method considering interval coordination of the present invention is a distribution network dynamic adaptive voltage coordination control method based on boundary information interaction. Firstly, according to the partition information, the real-time measurement information of the measuring device is used to obtain the voltage value with the largest deviation between the node voltage of each zone and the reference value and the active transmission power of the boundary interconnection line. Distributed control is performed according to the coordinated control strategy of active first and reactive later, and the output strategy of distributed energy storage and distributed power supply is adjusted to realize voltage control of the active distribution network.

[0007] The present invention solves the technical problem by adopting the following technical solutions:

[0008] The distributed power supply and energy storage voltage control method considering interval coordination includes the following steps:

[0009] Determine the active distribution network and obtain the parameter information of the active distribution network; set the iteration step Δt and control step ΔT c , prediction step length ΔT p , optimize the duration T, predict the number of steps N, and initialize the control parameter k = 1, and initialize the control time t = 0;

[0010] The active distribution network is divided into zones, and the node voltage measurement values ​​in each zone of the active distribution network at time t and the interaction information between zones are obtained;

[0011] Taking into account the limitations of actual conditions for information transmission in different intervals, a virtual node is set up in each area, and the interactive information between each area is uploaded to the virtual node corresponding to each area. The interactive information between each area includes the virtual node voltage at time t and the active transmission power of the virtual node tie line at time t; wherein, the virtual node voltage at time t is the node voltage measurement value with the largest deviation from the node voltage control target reference value in each area at time t, and the active transmission power of the virtual node tie line at time t is the active transmission power measurement value of the boundary tie line between each area at time t;

[0012] Obtain the voltage difference between the voltage measurement value of each regional node and the voltage control target reference value of each regional node, and the voltage difference between the virtual node voltage value and the virtual node voltage control target reference value, and determine whether any voltage difference exceeds the limit;

[0013] If so, the objective function is to minimize the control target deviation in each area and the lowest charging and discharging cost of the energy storage system. Under the constraints, a distributed power supply and energy storage adaptive voltage control model considering interval coordination is established;

[0014] If not, the control time t=t+Δt is updated.

[0015] Furthermore,

[0016] After the step of establishing a distributed power supply and energy storage adaptive voltage control model considering interval coordination, the method further includes:

[0017] Solve the distributed power supply and energy storage adaptive voltage control model considering interval coordination, obtain the data-driven energy storage system and distributed power supply output strategy at time t and issue it for execution;

[0018] The interaction information between regions after the output strategy of the energy storage system and distributed generation is issued and executed is obtained, and the target reference value of the voltage control of each regional node and the target reference value of the power control of each regional boundary in the objective function are updated.

[0019] Furthermore,

[0020] After the steps of obtaining the interaction information between the energy storage system and the distributed power supply output strategy after the output strategy is issued and executed, and updating the target reference value of the voltage control of each regional node and the target reference value of the power control of each regional boundary in the objective function, the method further includes:

[0021] Update control time t=t+Δt.

[0022] Furthermore,

[0023] After the step of updating the control time t=t+Δt, the method further comprises:

[0024] Judgment t≥kΔT c Is it established?

[0025] If so, let ΔT p =ΔT p -ΔT c , update the control parameter k=k+1, and determine whether the updated control time t is less than the optimized duration T.

[0026] Furthermore,

[0027] After the step of determining whether the updated control time t is less than the optimized duration T, the method further includes:

[0028] If yes, the active distribution network is partitioned, and the node voltage measurement value at time t in each area of ​​the active distribution network and the interaction information between each area are obtained. Based on the interaction information between each area, a virtual node is established in each area, and the voltage value of the virtual node at time t is obtained.

[0029] If not, then end.

[0030] Furthermore,

[0031] The distributed power supply and energy storage adaptive voltage control model considering interval coordination is:

[0032]

[0033]

[0034]

[0035] Where ΔX m,N [t] represents the energy storage charging and discharging power change vector in region m, starting from time t+Δt in the future N steps, N represents the number of predicted steps, represents t+iΔT c The charging and discharging power of the distributed energy storage system in area m at time; represents the control target reference value at time t+Δt in region m, represents the estimated control target value of region m at time t+Δt, They represent the reactive power output of distributed generation r in region m at time t and time t-Δt respectively. represents the reactive power output of distributed energy storage system l in region m at time t and time t-Δt, λ P , is the weight coefficient;

[0036] It represents the control target reference value vector for the next N steps starting from time t+Δt in region m, and its expression is as follows:

[0037]

[0038] In the formula, represents the node voltage reference value vector in region m, starting from time t+Δt in the future N steps, represents the voltage reference value vector of virtual node α in region m, starting from time t+Δt in the future N steps, represents the net active power reference value vector for the next N steps starting from time t+Δt on the boundary β of region m, and ε represents the conversion coefficient;

[0039] It represents the estimated value vector of the control target in region m for the next N steps starting from time t+Δt, including the voltages of each node and virtual node in region m, and the net active power transmitted at the boundary. Its composition expression is as follows:

[0040]

[0041] Where N represents the number of prediction steps and is the prediction step length ΔT p With the control step length ΔT c The ratio of represents the node voltage estimation vector in region m, starting from time t+Δt in the future N steps, represents the voltage estimation value vector of virtual node α in region m, starting from time t+Δt in the future N steps, It represents the net transmission active power vector predicted N steps from the time t+Δt at the boundary β of region m, and ε represents the conversion coefficient;

[0042] The calculation expression is as follows:

[0043]

[0044] Where E[t] represents the unit column vector, Y m,N [t] represents the vector composed of N groups of control target measurement values ​​in region m at time t, represents the pseudo-Jacobian estimation matrix of region m at time t, ΔX m,N [t] represents the energy storage charging and discharging power change vector in region m, starting from time t+Δt in the future N steps;

[0045] The calculation method is as follows:

[0046]

[0047] Where Y m [t] represents the control target measurement value of area m at time t, and They represent the number of distributed energy storage systems and distributed generation in region m, represents the pseudo-Jacobian matrix of the distributed generation r in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system l in region m at time t, The calculation expression is as follows:

[0048]

[0049]

[0050] In the formula, They represent the pseudo-Jacobian matrix of the distributed generation r in region m at time t and time t-Δt, respectively. represents the pseudo-Jacobian matrix of the distributed energy storage system l in region m at time t and time t-Δt, ΔY m [t] = Y m [t]-Y m [t-Δt] represents the difference between the control target measurement value of area m at time t and time t-Δt. It represents the difference in reactive power output of distributed generation r in region m at time t-Δt and time t-2Δt, represents the difference in reactive power output of distributed energy storage system l in region m at time t-Δt and time t-2Δt, η DG , μ DG , η ESS and μ ESS Represents the weight coefficient.

[0051] Furthermore,

[0052] The expression of the adaptive voltage control model of distributed generation and energy storage considering interval coordination is as follows:

[0053]

[0054]

[0055] In the formula, represents the reference value of the control target of area m at time t, represents the estimated value of the voltage in region m at time t, They represent the reactive power output of distributed generation r in region m at time t and time t-Δt respectively. represents the reactive output of distributed energy storage system l in region m at time t and time t-Δt, represents the difference in reactive power output of distributed energy storage system b in region m at time t-Δt and time t-2Δt, represents the difference in reactive power output of distributed generation a in region m at time t-Δt and time t-2Δt, and They represent the number of distributed energy storage systems and distributed generation in region m, represents the pseudo-Jacobian matrix of distributed generation a in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system b in region m at time t, ρ DG , DG , ESS and λ ESS Represents the weight coefficient.

[0056] Furthermore,

[0057] The update of the interaction information between the regions is expressed as follows:

[0058]

[0059]

[0060] In the formula, represents the voltage reference value vector of virtual node α in region i, starting from time t in the future N steps, represents the voltage estimation vector in region j, starting from time t for the next N steps. It represents the net active power reference value vector of the future N steps starting from time t on the boundary β of region i, It represents the estimated value vector of the net transmitted active power for N steps in the future starting from time t on the boundary β of region j.

[0061] Furthermore,

[0062] The constraints include the prediction step length ΔT p Internal energy storage system state of charge upper and lower limit constraints, energy storage system active power output upper and lower limits, energy storage system converter operation constraints, distributed power converter operation constraints.

[0063] A computing device, comprising:

[0064] one or more processing units;

[0065] A storage unit for storing one or more programs,

[0066] Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the method as described in any one of claims 1 to 9.

[0067] The advantages and positive effects of the present invention are:

[0068] The invention relates to a method for adaptive voltage control of distributed power sources and energy storage considering interval coordination, which comprehensively considers the information interaction between distribution network zones, the coordinated control of distributed energy storage systems and distributed power sources, and achieves the solution to the voltage optimization control problem of high-penetration distributed power sources connected to distribution networks by establishing an adaptive voltage control strategy of distributed power sources and energy storage considering interval coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, but it should be understood that these drawings are designed only for explanation purposes and are not intended to limit the scope of the present invention. In addition, unless otherwise specified, these drawings are intended only to conceptually illustrate the structural configurations described herein and are not necessarily drawn to scale.

[0070] Figure 1 It is a flow chart of a distributed power supply and energy storage voltage control method considering interval coordination proposed by the present invention;

[0071] Figure 2 It is the interval coordination area topology map in Example 2;

[0072] Figure 3 is the prediction curve of the output and load change of the distributed power source in Example 2;

[0073] Figure 4 This is the voltage result comparison before and after the 24-hour 33-node control in Example 2;

[0074] Figure 5 It is the change of active charging and discharging power and reactive output of the 33-node distributed energy storage system in Example 2 for 24 hours;

[0075] Figure 6 is the 24-hour reactive power output of distributed power sources in each region in Example 2;

[0076] Figure 7 It is the comparison result of the node voltage maximum value before and after the 24-hour voltage control in Example 2;

[0077] Figure 8 It is the change of the charge state of each distributed energy storage system in Example 2 over 24 hours.

[0078] in, Figure 3 In: Load stands for load, PV stands for photovoltaic, and WT stands for wind turbine; Figure 5 In: P-ES represents the active output of the energy storage system, and Q-ES represents the reactive output of the energy storage system; DETAILED DESCRIPTION

[0079] First of all, it should be noted that the specific structure, characteristics and advantages of the present invention will be specifically described below by way of example, but all descriptions are only for illustration and should not be understood as limiting the present invention. In addition, any single technical feature described or implied in each embodiment mentioned herein can still be combined or deleted between these technical features (or their equivalents) to obtain more other embodiments of the present invention that may not be directly mentioned herein. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0080] Example 1

[0081] The present invention relates to a distributed power supply and energy storage adaptive voltage control method considering interval coordination, such as Figure 1 As shown, the following steps are included:

[0082] 1) According to the selected active distribution network, obtain the parameter information of the distribution network, set the iteration step Δt and control step ΔT c , prediction step length ΔT p, optimization time T, prediction step number N, and initialize control parameter k = 1, initialize control time t = 0; where the distribution network parameter information includes: active distribution network partition information, energy storage system type, capacity, access location and initial charge state and charge and discharge power limit, distributed power type, capacity, access location; each area control target reference value Including: voltage control target reference value of each area node Reference value of voltage control target of virtual nodes in each region Power control target reference value for each area boundary

[0083] 2) Partition the active distribution network and obtain the node voltage measurement value in each area of ​​the active distribution network at time t and the interaction information between each area;

[0084] Taking into account the limitations of actual conditions for information transmission in different intervals, a virtual node is set up in each area, and the interactive information between each area is uploaded to the virtual node corresponding to each area. The interactive information between each area includes the virtual node voltage at time t and the active transmission power of the virtual node tie line at time t; wherein, the virtual node voltage at time t is the node voltage measurement value with the largest deviation from the node voltage control target reference value in each area at time t, and the active transmission power of the virtual node tie line at time t is the active transmission power measurement value of the boundary tie line between each area at time t;

[0085] The role of the virtual node is: to coordinate the control of the distribution network in each area, and taking into account the limitations of the actual conditions of information transmission in different areas, the method of setting virtual nodes in each area is adopted to upload the node voltage measurement value and the boundary tie line active transmission power measurement value with the largest deviation from the node voltage control target reference value in each area to the virtual node corresponding to each area, and transmit and interact the information contained in the virtual nodes of each area, so as to realize the coordinated voltage control between the distribution network areas under the background of limited information transmission conditions;

[0086] 3) According to the control target reference value of each area described in step 1), and the node voltage measurement value and the virtual node voltage value in each area at time t described in step 2), calculate the voltage difference between the node voltage measurement value of each area and the node voltage control target reference value of each area, and the voltage difference between the virtual node voltage value and the virtual node voltage control target reference value, and determine whether any voltage difference exceeds the limit. If yes, execute step 4), otherwise, go to step 7);

[0087] 4) Taking the minimum deviation of each control target in each area and the lowest charging and discharging cost of the energy storage system as the objective function, considering the prediction step length ΔT pThe upper and lower limits of the state of charge of the internal energy storage system, the upper and lower limits of the active power output of the energy storage system, the operation constraints of the energy storage system converter, the constraints of the distributed power converter, and the establishment of a distributed power and energy storage adaptive voltage control model considering interval coordination;

[0088] The objective function described in this step, which takes the minimum node voltage deviation in each area, the minimum charging and discharging cost of the energy storage system, and the minimum boundary net transmission power as the control objectives, is expressed as follows.

[0089]

[0090]

[0091]

[0092] In formula (1), represents t+iΔT c The charging and discharging power of the distributed energy storage system in region m at time. In formulas (2) and (3), represents the control target reference value at time t+Δt in region m, represents the estimated control target value of region m at time t+Δt, They represent the reactive power output of distributed generation r in region m at time t and time t-Δt respectively. represents the reactive output of distributed energy storage system l in region m at time t and time t-Δt, λ P , is the weight coefficient;

[0093] It represents the control target reference value for the next N steps starting from time t+Δt in region m, and its expression is as follows:

[0094]

[0095] In the formula, represents the node voltage reference value vector in region m for the next N steps starting from time t+Δt, represents the voltage reference value vector of virtual node α in region m, starting from time t+Δt in the future N steps, represents the net active power reference value vector for the next N steps starting from time t+Δt on the boundary β of region m, and ε represents the conversion coefficient;

[0096] It represents the estimated value vector of the control target for the next N steps starting from time t+Δt in region m, including the voltages of each node and virtual node in region m, and the net active power transmitted at the boundary. Its composition expression is as follows:

[0097]

[0098] Where N represents the number of prediction steps and is the prediction step length ΔT p With the control step length ΔT c The ratio of represents the node voltage estimation vector in region m, starting from time t+Δt in the future N steps, represents the voltage estimation value vector of virtual node α in region m, starting from time t+Δt in the future N steps, It represents the net transmission active power vector predicted N steps from the time t+Δt at the boundary β of region m, and ε represents the conversion coefficient;

[0099] The calculation expression is as follows:

[0100]

[0101] Where E[t] represents the unit column vector, Y m,N [t] represents the vector composed of N groups of control target measurement values ​​in region m at time t, represents the pseudo-Jacobian estimation matrix of region m at time t, ΔX m,N [t] represents the energy storage charging and discharging power change vector in region m, starting from time t+Δt in the future N steps;

[0102] The calculation method is as follows:

[0103]

[0104] Where Y m [t] represents the control target measurement value of area m at time t, and They represent the number of distributed energy storage systems and distributed generation in region m, represents the pseudo-Jacobian matrix of the distributed generation r in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system l in region m at time t, The calculation expression is as follows:

[0105]

[0106]

[0107] In the formula, represents the pseudo-Jacobian matrix of the distributed generation r in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system l in region m at time t, ΔY m [t] = Ym [t]-Y m [t-Δt] represents the difference between the control target measurement value of area m at time t and time t-Δt. It represents the difference in reactive power output of distributed generation r in region m at time t-Δt and time t-2Δt, represents the difference in reactive power output of distributed energy storage system l in region m at time t-Δt and time t-2Δt, η DG , μ DG , η ESS and μ ESS represents the weight coefficient;

[0108] The calculation method is as follows:

[0109]

[0110] In the formula, The iterative calculation expression is as follows:

[0111]

[0112] In the formula, represents the pseudo-Jacobian matrix of the lth distributed energy storage in region m at time t and time t-Δt, respectively. ΔU[t]=U[t]-U[t-Δt] represents the difference between the voltage measurements of each node at time t and time t-Δt. represents the difference in reactive power output of the lth distributed energy storage in region m at time t-Δt and time t-2Δt, η ESS and μ ESS Represents the weight coefficient.

[0113] In formula (10) represents t+wΔT c The pseudo-Jacobian prediction matrix of the lth distributed energy storage in the time zone m, w = 1, ..., N, is calculated as follows:

[0114]

[0115] In the formula, represents t+wΔT c The pseudo-Jacobian prediction matrix of the lth distributed energy storage in area m at time (w=1,…,N), θ q [t] represents the prediction coefficient at time t, where q = 1,…,ξ, m represents the number of days of historical measurement data required to construct the estimation sequence, T d Indicates the time interval of historical data, and They represent t+wΔT calculated using the historical measurement data of the active distribution network at the same time the day before.c -T d Time, t+wΔT c -2T d Time and t+wΔT c -ξT d The pseudo-Jacobian matrix of the lth distributed energy storage in region m at time. θ[t]=(θ1[t],…,θ ξ [t]) T , the iterative solution formula of θ[t] is as follows:

[0116]

[0117] In the formula, δ represents the weight coefficient.

[0118] The state of charge constraint of the energy storage system in this step is expressed as follows:

[0119]

[0120]

[0121] In formula (14), SOC0 represents the initial value of the state of charge of the distributed energy storage, SOC max and SOC min Respectively represent the upper and lower limits of the state of charge, represents the converter capacity of the lth distributed energy storage in region m, E[t] represents the unit column vector, represents the active output of the first distributed energy storage in region m at time t, ΔT c To control the step size, N represents the number of prediction steps, and Respectively represent t-Δt+ΔT c ,t-Δt+2ΔT c and t-Δt+NΔT c The change in the charging and discharging power of the lth distributed energy storage in area m at time. In formula (15), represents the initial value of the state of charge of the lth distributed energy storage in area m, Indicates the state of charge value of the lth distributed energy storage in area m after one cycle of operation.

[0122] The active power output constraint of distributed energy storage is expressed as follows:

[0123]

[0124] In the formula, They represent the upper and lower limits of the active output of the lth distributed energy storage in region m, E[t] represents the unit column vector, Represents the active output of the lth distributed energy storage in region m at time t.

[0125] The expressions of the reactive power output constraint of the energy storage system, the capacity constraint of the energy storage system converter, the reactive power output constraint of the distributed power source and the capacity constraint of the distributed power source converter described in this step are as follows.

[0126] The reactive power output constraint of the energy storage system and the converter capacity constraint of the energy storage system are expressed as:

[0127]

[0128] In the formula, They represent the upper and lower limits of the reactive output of the lth distributed energy storage in region m, E[t] represents the unit column vector, represents the reactive output of the lth distributed energy storage in region m at time t, represents the converter capacity of the lth distributed energy storage in area m, Represents the active output of the lth distributed energy storage in area m at time t.

[0129] The reactive power output constraint of distributed generation and the capacity constraint of distributed generation converter are expressed as:

[0130]

[0131] In the formula, represents the upper and lower limits of the reactive power output of the rth distributed generation, E[t] represents the unit column vector, represents the active output of the rth distributed generation at time t, represents the capacity of the rth distributed generation converter in region m, Represents the reactive output of the rth distributed generation in region m at time t.

[0132] 5) Solve the distributed power supply and energy storage adaptive voltage control model considering interval coordination in step 4), obtain the data-driven energy storage system and distributed power supply output strategy at time t and issue it for execution;

[0133] The expression of the adaptive voltage control model of distributed generation and energy storage considering interval coordination described in this step is as follows.

[0134]

[0135]

[0136] In the formula, represents the reference value of the control target of area m at time t, represents the estimated value of the voltage in region m at time t, They represent the reactive power output of distributed generation r in region m at time t and time t-Δt respectively. represents the reactive output of distributed energy storage system l in region m at time t and time t-Δt, represents the difference in reactive power output of distributed energy storage system b in region m at time t-Δt and time t-2Δt, represents the difference in reactive power output of distributed generation a in region m at time t-Δt and time t-2Δt, and They represent the number of distributed energy storage systems and distributed generation in region m, represents the pseudo-Jacobian matrix of distributed generation a in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system b in region m at time t, ρ DG , DG , ESS and λ ESS Represents the weight coefficient.

[0137] 6) Obtain the interaction information between the regions after the energy storage system and the distributed power generation output strategy are issued and executed, and update the target reference value of the voltage control of each regional node and the target reference value of the power control of each regional boundary in the objective function;

[0138] The update of the interaction information between the regions in this step is expressed as follows.

[0139]

[0140]

[0141] In the formula, represents the voltage reference value vector of virtual node α in region i, starting from time t in the future N steps, represents the voltage estimation vector in region j, starting from time t for the next N steps. It represents the net active power reference value vector of the future N steps starting from time t on the boundary β of region i, It represents the estimated value vector of the net transmitted active power for N steps in the future starting from time t on the boundary β of region j.

[0142] 7) Update control time t = t + Δt, and judge t ≥ kΔT c Is it true? If so, let ΔT p =ΔT p -ΔT c , k=k+1, execute step 8), if not, directly execute step 8);

[0143] 8) Determine whether the updated control time t is less than the optimized duration T. If so, go to step 2); if not, end.

[0144] Example 2

[0145] For this embodiment, the distribution network includes 33 nodes, and the topological connection is as follows: Figure 2 As shown, nodes 18, 25, and 33 are connected to a distributed energy storage system with a capacity of 4MVA, an upper and lower limit of active charge and discharge power of 5kW and -5kW, and an upper and lower limit of reactive output power of 5kvar and -5kvar respectively; nodes 4, 12, and 21 are connected to photovoltaics; nodes 9, 15, 24, and 29 are connected to wind turbines; the iteration step length Δt = 5min, and the control step length ΔT c =1h, initial value of prediction step length ΔT p = 24h, optimization time T = 24h; the voltage reference value of the distribution network is set to 1.0pu. The active power charging and discharging cost of the distributed energy storage system per unit time C ESS = 0.05 yuan / kWh. Weight coefficient λ P The value is 15. The value is 1, DG , ESS The value is 1, DG , η ESS The value is 0.8, μ DG , μ ESS The value is 1, the threshold value ε is 4; P It is an active power control parameter, and its value is generally between 10 and 20; is the reactive power control parameter, which is generally between 0 and 1; η DG , η ESS The value of is generally between 0 and 1; DG , ESS , generally takes a value between 0-1; ε is the conversion coefficient, generally takes a value between 1-5. The data-driven distributed energy storage system and distributed power supply coordinated voltage control method is used for optimization. After the above steps, the distributed energy storage system charging and discharging strategy and the distributed power supply reactive output strategy can be obtained. To verify the effectiveness of this method, the following two control schemes are used for comparison for the distribution network:

[0146] Solution 1: Do not control the controllable resources of the distribution network and obtain the initial operating state of the active distribution network;

[0147] Solution 2: adopt the distributed power supply and energy storage data driven voltage control method considering interval coordination of the present invention.

[0148] The computer hardware environment for performing optimization calculations is Intel(R) Core(TM) CPU i5-10210U, with a main frequency of 1.6 GHz and a memory of 16 GB; the software environment is the Windows 10 operating system.

[0149] The interval coordination area topology adopted in this embodiment is as follows Figure 2 The prediction curve of distributed power output and load information changes as shown in Figure 3 The voltage results before and after the 24-hour energy storage access node 33 control are shown in Figure 2. Figure 4 As shown in Figure 2, the active charging and discharging power and reactive output changes of the 33-node distributed energy storage system in 24 hours are as follows: Figure 5 The 24-hour reactive power output of distributed power generation in area 2 is as follows: Figure 6 The comparison results of the node voltage maximum values ​​before and after 24-hour voltage control are shown in Figure 7 As shown. At 16 o'clock, the global voltage deviation distribution is as follows Figure 8 As shown. Figures 4 to 8 It can be seen that Scheme 2 can effectively adjust the voltage level of the distribution network in this embodiment. The distributed power supply and energy storage data-driven voltage control method considering the interval coordination can effectively solve the voltage optimization problem.

[0150] The results of the above two solutions are compared as shown in Table 1:

[0151] Table 1

[0152]

[0153] Example 3

[0154] A computing device comprising:

[0155] one or more processing units;

[0156] A storage unit for storing one or more programs,

[0157] Among them, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the above-mentioned distributed power supply and energy storage voltage control method considering interval coordination; it should be noted that the computing device may include but is not limited to a processing unit and a storage unit; those skilled in the art can understand that the computing device including a processing unit and a storage unit does not constitute a limitation on the computing device, and may include more components, or a combination of certain components, or different components. For example, the computing device may also include input and output devices, network access devices, buses, etc.

[0158] A computer-readable storage medium having a non-volatile program code executable by a processor, wherein when the computer program is executed by the processor, the steps of the above-mentioned distributed power supply and energy storage voltage control method considering interval coordination are implemented; it should be noted that the readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above; the program contained on the readable medium can be transmitted using any appropriate medium, including, but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. For example, the program code for performing the operation of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

Claims

1. A distributed power supply and energy storage voltage control method considering interval coordination, characterized in that: The steps include: Determine the active distribution network and obtain the parameter information of the active distribution network; set the iteration step Δt and control step ΔT c , prediction step length ΔT p , optimize the duration T, predict the number of steps N, and initialize the control parameter k = 1, and initialize the control time t = 0; The active distribution network is divided into zones, and the node voltage measurement values ​​in each zone of the active distribution network at time t and the interaction information between zones are obtained; Taking into account the limitations of actual conditions for information transmission in different intervals, a virtual node is set up in each area, and the interactive information between each area is uploaded to the virtual node corresponding to each area. The interactive information between each area includes the virtual node voltage at time t and the active transmission power of the virtual node tie line at time t; wherein, the virtual node voltage at time t is the node voltage measurement value with the largest deviation from the node voltage control target reference value in each area at time t, and the active transmission power of the virtual node tie line at time t is the active transmission power measurement value of the boundary tie line between each area at time t; Obtain the voltage difference between the voltage measurement value of each regional node and the voltage control target reference value of each regional node, and the voltage difference between the virtual node voltage value and the virtual node voltage control target reference value, and determine whether any voltage difference exceeds the limit; If so, the objective function is to minimize the control target deviation in each area and the lowest charging and discharging cost of the energy storage system. Under the constraints, a distributed power supply and energy storage adaptive voltage control model considering interval coordination is established; If not, update the control time t=t+Δt; The distributed power supply and energy storage adaptive voltage control model considering interval coordination is: Where ΔX m,N [t] represents the energy storage charging and discharging power change vector in region m, starting from time t+Δt in the future N steps, N represents the number of predicted steps, represents t+iΔT c The charging and discharging power of the distributed energy storage system in area m at time; represents the control target reference value at time t+Δt in region m, represents the estimated control target value of region m at time t+Δt, They represent the reactive power output of distributed generation r in region m at time t and time t-Δt respectively. represents the reactive power output of distributed energy storage system l in region m at time t and time t-Δt, λ P , is the weight coefficient; It represents the control target reference value vector for the next N steps starting from time t+Δt in region m, and its expression is as follows: In the formula, represents the node voltage reference value vector in region m, starting from time t+Δt in the future N steps, represents the voltage reference value vector of virtual node α in region m, starting from time t+Δt in the future N steps, represents the net active power reference value vector for the next N steps starting from time t+Δt on the boundary β of region m, and ε represents the conversion coefficient; It represents the estimated value vector of the control target in region m for the next N steps starting from time t+Δt, including the voltages of each node and virtual node in region m, and the net active power transmitted at the boundary. Its composition expression is as follows: Where N represents the number of prediction steps and is the prediction step length ΔT p With the control step length ΔT c The ratio of represents the node voltage estimation vector in region m, starting from time t+Δt in the future N steps, represents the voltage estimation value vector of virtual node α in region m, starting from time t+Δt in the future N steps, It represents the net transmission active power vector predicted N steps from the time t+Δt at the boundary β of region m, and ε represents the conversion coefficient; The calculation expression is as follows: Where E[t] represents the unit column vector, Y m,N [t] represents the vector composed of N groups of control target measurement values ​​in region m at time t, represents the pseudo-Jacobian estimation matrix of region m at time t, ΔX m,N [t] represents the energy storage charging and discharging power change vector in region m, starting from time t+Δt in the future N steps; The calculation method is as follows: Where Y m [t] represents the control target measurement value of area m at time t, and They represent the number of distributed energy storage systems and distributed generation in region m, represents the pseudo-Jacobian matrix of the distributed generation r in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system l in region m at time t, The calculation expression is as follows: In the formula, They represent the pseudo-Jacobian matrix of the distributed generation r in region m at time t and time t-Δt, respectively. represents the pseudo-Jacobian matrix of the distributed energy storage system l in region m at time t and time t-Δt, ΔY m [t] = Y m [t]-Y m [t-Δt] represents the difference between the control target measurement value of area m at time t and time t-Δt. It represents the difference in reactive power output of distributed generation r in region m at time t-Δt and time t-2Δt, represents the difference in reactive power output of distributed energy storage system l in region m at time t-Δt and time t-2Δt, η DG , μ DG , η ESS and μ ESS Represents the weight coefficient.

2. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 1 is characterized in that: After the step of establishing a distributed power supply and energy storage adaptive voltage control model considering interval coordination, the method further includes: Solve the distributed power supply and energy storage adaptive voltage control model considering interval coordination, obtain the data-driven energy storage system and distributed power supply output strategy at time t and issue it for execution; The interaction information between regions after the output strategy of the energy storage system and distributed generation is issued and executed is obtained, and the target reference value of the voltage control of each regional node and the target reference value of the power control of each regional boundary in the objective function are updated.

3. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 2 is characterized in that: After the steps of obtaining the interaction information between the energy storage system and the distributed power supply output strategy after the output strategy is issued and executed, and updating the target reference value of the voltage control of each regional node and the target reference value of the power control of each regional boundary in the objective function, the method further includes: Update control time t=t+Δt.

4. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 1 or 3 is characterized in that: After the step of updating the control time t=t+Δt, the method further comprises: Judgment t≥kΔT c Is it established? If so, let ΔT p =ΔT p -ΔT c , update the control parameter k=k+1, and determine whether the updated control time t is less than the optimized duration T.

5. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 4 is characterized in that: After the step of determining whether the updated control time t is less than the optimized duration T, the method further includes: If yes, the active distribution network is partitioned, and the node voltage measurement value at time t in each area of ​​the active distribution network and the interaction information between each area are obtained. Based on the interaction information between each area, a virtual node is established in each area, and the voltage value of the virtual node at time t is obtained. If not, then end.

6. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 2 is characterized in that: The expression of the adaptive voltage control model of distributed generation and energy storage considering interval coordination is as follows: In the formula, represents the reference value of the control target of area m at time t, represents the estimated value of the voltage in region m at time t, They represent the reactive power output of distributed generation r in region m at time t and time t-Δt respectively. represents the reactive output of distributed energy storage system l in region m at time t and time t-Δt, represents the difference in reactive power output of distributed energy storage system b in region m at time t-Δt and time t-2Δt, represents the difference in reactive power output of distributed generation a in region m at time t-Δt and time t-2Δt, and They represent the number of distributed energy storage systems and distributed generation in region m, represents the pseudo-Jacobian matrix of distributed generation a in region m at time t, represents the pseudo-Jacobian matrix of the distributed energy storage system b in region m at time t, ρ DG , DG , ESS and λ ESS Represents the weight coefficient.

7. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 2 is characterized in that: The update of the interaction information between the regions is expressed as follows: In the formula, represents the voltage reference value vector of virtual node α in region i, starting from time t in the future N steps, represents the voltage estimation vector in region j, starting from time t for the next N steps. represents the net active power reference vector for the next N steps starting from time t on the boundary β of region i, It represents the estimated value vector of the net transmitted active power for N steps in the future starting from time t on the boundary β of region j.

8. The distributed power supply and energy storage voltage control method considering interval coordination according to claim 1 is characterized in that: The constraints include the prediction step length ΔT p Internal energy storage system state of charge upper and lower limit constraints, energy storage system active power output upper and lower limits, energy storage system converter operation constraints, distributed power converter operation constraints.

9. A computing device, characterized in that: include: one or more processing units; A storage unit for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the method as described in any one of claims 1 to 8.

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