A multi-objective optimization control method for distribution network considering energy storage life

Abstract

This invention discloses a multi-objective optimization and control method for distribution networks that takes into account energy storage lifetime, comprising: constructing a day-ahead operation optimization model for the distribution network based on day-ahead source-load forecast data; solving for the energy storage charging and discharging status, on-load tap changer (OLTC) tap position, and CB switching group results based on the day-ahead operation optimization model; constructing an energy storage lifetime loss model based on node cycle count, discharge depth, and discharge rate, and calculating the energy storage lifetime loss cost through the energy storage lifetime loss model; constructing an intraday multi-objective operation optimization model for the distribution network based on intraday source-load forecast data; the objective function of the intraday multi-objective operation optimization model for the distribution network includes an economic objective and a voltage deviation objective; wherein, the economic objective is constructed based on the energy storage lifetime loss cost, and the voltage deviation objective is constructed based on the actual node voltage value; and solving for the power commands of photovoltaic and energy storage based on the intraday multi-objective operation optimization model for the distribution network.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically to a multi-objective optimization control method for distribution networks that takes into account energy storage lifespan. Background Technology

[0002] In recent years, distributed power generation has developed rapidly. With its increasing penetration rate in distribution networks, issues such as system economics and voltage limit violations have become increasingly prominent. On-load tap-changing transformers and parallel capacitor banks on the distribution network side can effectively reduce voltage deviations and mitigate the risk of voltage limit violations while improving system economics. Energy storage systems, as temporary energy storage devices, can address the randomness and volatility issues of distributed energy sources. Integrating these resources into the distribution network can effectively solve the system economics and voltage limit violation problems caused by the high penetration rate of distributed energy, promoting the stable and economical operation of the distribution network.

[0003] Currently, some scholars have conducted research on the optimization and control of distribution networks. However, existing studies mainly focus on electricity purchase costs, network loss costs, and equipment operating costs, without considering the lifespan loss costs caused by energy storage operations. Energy storage lifespan is related to its cycle count and discharge depth. To cope with intraday source-load fluctuations, energy storage needs to frequently switch between charge and discharge states, and may experience deep discharge. However, existing research on distribution network optimization and control does not consider the lifespan loss caused by energy storage operations. On the one hand, treating energy storage as a continuous device and updating its charge and discharge state intraday leads to frequent state switching, causing lifespan loss. On the other hand, updating the intraday energy storage output power to smooth source-load fluctuations and regulate node voltages may result in deep discharge, further reducing energy storage lifespan. Therefore, it is necessary to consider the impact of energy storage cycle count and discharge depth on energy storage lifespan to ensure high-quality and long-term operation of energy storage. Summary of the Invention

[0004] In view of this, the present invention provides a multi-objective optimization control method for distribution networks that takes into account the lifespan of energy storage, in order to at least solve the problem that the lifespan loss caused by energy storage operation is not considered in the existing research on distribution network optimization control.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A multi-objective optimization control method for distribution networks that takes into account energy storage lifetime includes the following steps:

[0007] S1. Construct a day-ahead operation optimization model for the distribution network based on day-ahead source-load forecast data;

[0008] S2. Based on the day-ahead operation optimization model of the distribution network, solve for the day-ahead optimization results, which include the energy storage charging and discharging status, the tap position of the on-load tap-changing transformer, the switching results of the capacitor bank, and the output power of photovoltaic and energy storage.

[0009] S3. Using the energy storage charging and discharging status, on-load tap-changing transformer tap position, and capacitor bank switching results from the day-ahead optimization results as known quantities, construct a multi-objective operation optimization model for the distribution network based on the day-ahead source-load prediction data; the objective function of the multi-objective operation optimization model for the distribution network includes an economic objective and a voltage deviation objective; among which, the economic objective considers the energy storage lifetime loss cost, and the voltage deviation objective is constructed based on the actual value of the node voltage;

[0010] S4. Based on the multi-objective operation optimization model of the distribution network during the day, the power commands of photovoltaic and energy storage are obtained by solving.

[0011] Preferably, the day-ahead operation optimization model for the distribution network in S1 aims to minimize the total operating cost of the distribution network and controllable resources, where the distribution network operating cost includes the electricity purchase cost C. grid and power distribution network loss cost C loss The operating costs of controllable resources include photovoltaic operation and maintenance costs C. pv Energy storage operation and maintenance costs C ess OLTC Action Cost C OLTC CB action cost C CB and the cost of abandoned light C pl ;

[0012] The objective function of the day-ahead operation optimization model for the distribution network is:

[0013] minC=C grid +C loss +C pv +C ess +C OLTC +C CB +C pl (1)

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula: Let be the electricity purchase price coefficient at time t; Let t be the power purchased by the distribution network from the main grid at time t; Δt1 is the time scale for day-ahead dispatching. R represents the current flowing from node i to node j at time t; ij c is the resistance value of line ij; pv This is the photovoltaic operating cost coefficient; c is the actual photovoltaic absorption value at node j at time t; ess This is the energy storage operating cost coefficient; and Let c be the charging power and discharging power of the energy stored at node j at time t; OLTC This represents the cost coefficient for OLTC actions. c is the OLTC tap position on branch ij at time t; CB This is the cost coefficient for CB actions; c is the number of capacitor banks in operation at node j at time t; pl This is the cost coefficient for abandoned light. Let be the output power of the photovoltaic at node j at time t;

[0022] The day-ahead operating constraints of the distribution network are as follows:

[0023] 1) Distribution network power flow constraints:

[0024]

[0025]

[0026] In the formula, and These represent the injected active power and reactive power of node j at time t, respectively. and Let be the active power and reactive power of branch ij at time t, respectively; σ(j) represents the set of downstream nodes of node j; δ(j) represents the set of upstream nodes of node j. X represents the voltage value at node j at time t; ij Let be the reactance value of line ij;

[0027] 2) Constraints on safe operation of the power grid:

[0028]

[0029] In the formula, U min and U max The lower and upper limits of full operation; This represents the upper limit of the current in branch ij;

[0030] 3) Photovoltaic output constraints:

[0031]

[0032] 4) Energy storage operation constraints:

[0033]

[0034]

[0035] In the formula, and These are binary variables representing the charging and discharging states of the energy storage at node j, respectively. A value of 1 indicates that the energy storage is operating in both charging and discharging states, thus preventing the energy storage from being in both states simultaneously. j,max This represents the upper limit of the energy storage charging and discharging power at node j; E represents the energy stored at node j at time t. jmax and E jmin These are the upper and lower limits of the energy storage capacity of node j, respectively;

[0036] 5) Operating constraints of on-load tap-changing transformers:

[0037]

[0038] In the formula, Let be the adjustable ratio of the OLTC on branch ij at time t; and Δk ij These represent the standard ratio and adjustment step size of OLTC on branch ij, respectively; K ijmax This represents the maximum value of the adjustable gear of OLTC in branch ij;

[0039] 6) Operating constraints of parallel capacitor banks:

[0040]

[0041] In the formula, This refers to the reactive power output of the capacitor bank. Y represents the reactive power output of a single capacitor bank at node j; j,CBmax This represents the upper limit of the number of capacitor banks at node j.

[0042] Preferably, the specific content of calculating the energy storage lifetime loss cost in S3 includes:

[0043] The total equivalent discharge capacity of energy storage over its entire life cycle under rated conditions is expressed as:

[0044] E j,eq =E j,max N j,max D j,max (17)

[0045] Where: E j,eq N represents the total equivalent discharge over the entire lifecycle of energy storage at node j under rated conditions; j,max The rated number of cycles for energy storage at node j, D j,max The rated depth of discharge for energy storage at node j;

[0046] Considering the charge / discharge rate and depth of discharge during actual operation, and neglecting the lifespan loss caused by energy storage charging, the equivalent discharge amount of energy stored at node j at time t is expressed as:

[0047]

[0048] In the formula: Let be the equivalent discharge amount of energy stored at node j at time t; Let P be the actual number of energy storage cycles at node j at time t. j,max This represents the upper limit of the energy storage charging and discharging power at node j; , respectively, represent the discharge power of the energy stored at node j at time t; The actual discharge amount of the energy stored at node j at time t;

[0049] The actual cycle life of energy storage is obtained through fitting and is expressed as:

[0050]

[0051] In the formula: u1 and u2 are the coefficients of the fitted curve, Let be the actual discharge depth of energy stored at node j at time t;

[0052] The energy storage lifespan loss cost is:

[0053]

[0054] In the formula: c el This refers to the investment cost per unit capacity of energy storage.

[0055] Preferably, the energy storage lifetime loss cost F for nonlinear functions is... el Perform linearization:

[0056] A piecewise linearization method is used to linearize the nonlinear function energy storage lifetime loss cost.

[0057] Due to variables The value of is within [0,1]. Divide the interval [0,1] into N-1 segments, corresponding to N segmentation points dod1, dod2, ..., dod N Introduce a continuous variable η within [0,1]. n The linearized energy storage lifetime loss model is then expressed as:

[0058]

[0059]

[0060] In the formula: f[dod n ] is the dod corresponding to the nth segment pointn Section F el function.

[0061] Preferably, the objective function of the intraday multi-objective operation optimization model for the distribution network in S3 is:

[0062] minF=ω1F1+ω2F2 (23)

[0063] minF1=F grid +F loss +F pv +F ess +F el (twenty four)

[0064]

[0065] In the formula: ω1 and ω2 are the weights of targets F1 and F2, respectively; F1 is the intraday operating economic target; F2 is the intraday operating voltage deviation target; F grid For electricity purchase costs, F loss For network loss costs, F pv For the cost of curtailment, F ess For energy storage operation and maintenance costs, F el Cost of energy storage lifespan loss; U j This represents the actual value of the node voltage;

[0066]

[0067] In the formula: Δt2 is the time scale for intraday scheduling;

[0068] The constraints of the distribution network intraday operation optimization model include: energy storage operation constraints and curtailment cost constraints C. pl Constraints include power flow constraints in the distribution network, safe operation constraints in the power grid, and energy storage operation constraints. Among these, the cost constraint of curtailment of solar power is C. pl As shown in equation (8);

[0069] Energy storage operation constraints are:

[0070]

[0071] In the formula, and This is a binary variable representing the intraday energy storage charge / discharge state, and its value is equal to the optimized result of the day-ahead energy storage charge / discharge state variable, i.e.

[0072]

[0073] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a multi-objective optimization control method for distribution networks that takes into account energy storage lifetime, which has the following beneficial effects:

[0074] Based on the existing two-stage optimization scheduling of distribution networks, this invention proposes a multi-objective optimization control method for distribution networks that considers energy storage lifetime. This method addresses the damage to energy storage lifetime caused by frequent intraday switching of charging and discharging states and the deep discharge resulting from source-load fluctuations and node voltage regulation. Building upon existing distribution network optimization control strategies, this method considers the damage to energy storage lifetime caused by frequent intraday switching of charging and discharging states. The energy storage charging and discharging states are executed according to the previous day's optimization results and are not updated intraday. Furthermore, the intraday impact of energy storage discharge depth and rate on energy storage lifetime is fully considered. In addition, considering the problem of distribution network node voltage exceeding limits caused by source-load fluctuations, a node voltage deviation target is considered in addition to economic objectives. A multi-objective intraday operation optimization model for the distribution network is established to update the power of photovoltaic and energy storage. This method comprehensively considers energy storage lifetime and voltage regulation, extending energy storage lifetime and reducing node voltage deviation while ensuring the economic efficiency of the distribution network. This is of great significance for the safe and economical operation of the distribution network. Attached Figure Description

[0075] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 This is a flowchart of the multi-objective optimization and control strategy for distribution networks that takes into account energy storage lifespan, as described in this invention.

[0077] Figure 2 This is a schematic diagram of the system topology provided in an embodiment of the present invention;

[0078] Figure 3 This is a schematic diagram of the photovoltaic predicted output power and daily load provided in an embodiment of the present invention;

[0079] Figure 4 This is a schematic diagram of the charging and discharging states of three energy storage devices according to Scheme 1 provided in the embodiment of the present invention. (a) shows the charging and discharging state of the energy storage device connected to 11 nodes, (b) shows the charging and discharging state of the energy storage device connected to 24 nodes, and (c) shows the charging and discharging state of the energy storage device connected to 32 nodes.

[0080] Figure 5 This is a schematic diagram of the charging and discharging states of three energy storage devices in Scheme 2 provided in this embodiment of the invention. (a) shows the charging and discharging states of the energy storage device connected to 11 nodes, (b) shows the charging and discharging states of the energy storage device connected to 24 nodes, and (c) shows the charging and discharging states of the energy storage device connected to 32 nodes.

[0081] Figure 6This is a schematic diagram of the output power of three energy storage devices provided in Scheme 1 of the present invention. (a) is the output power of the energy storage device connected to 11 nodes, (b) is the output power of the energy storage device connected to 24 nodes, and (c) is the output power of the energy storage device connected to 32 nodes.

[0082] Figure 7 This is a schematic diagram of the output power of three energy storage devices in Scheme 2 provided in this embodiment of the invention. (a) is the output power of the energy storage device connected to 11 nodes, (b) is the output power of the energy storage device connected to 24 nodes, and (c) is the output power of the energy storage device connected to 32 nodes.

[0083] Figure 8 This is a voltage deviation diagram of each node in a 24-hour 33-node distribution network provided in the embodiment of the present invention.

[0084] Figure 9 This is a voltage deviation diagram of each node in a 24-hour 33-node distribution network provided in Scheme 2 of the present invention. Detailed Implementation

[0085] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] This invention provides a multi-objective optimization control method for distribution networks that takes into account energy storage lifetime, comprising the following steps:

[0087] S1. Construct a day-ahead operation optimization model for the distribution network based on day-ahead source-load forecast data;

[0088] S2. Based on the day-ahead operation optimization model of the distribution network, solve for the day-ahead optimization results, which include the energy storage charging and discharging status, the tap position of the on-load tap-changing transformer, the switching results of the capacitor bank, and the output power of photovoltaic and energy storage.

[0089] S3. Using the energy storage charging and discharging status, on-load tap-changing transformer tap position, and capacitor bank switching results from the day-ahead optimization results as known quantities, construct a multi-objective operation optimization model for the distribution network based on the day-ahead source-load prediction data; the objective function of the multi-objective operation optimization model for the distribution network includes an economic objective and a voltage deviation objective; among which, the economic objective considers the energy storage lifetime loss cost, and the voltage deviation objective is constructed based on the actual value of the node voltage;

[0090] S4. Based on the multi-objective operation optimization model of the distribution network during the day, the power commands of photovoltaic and energy storage are obtained by solving.

[0091] It should be noted that:

[0092] In this embodiment, based on the aforementioned daily operation optimization model of the distribution network, the Gurobi solver is called using MATLAB to solve the model, and the output of the energy storage charging and discharging status, OLTC tap position, and CB switching group results are input into the daily multi-objective operation optimization model of the distribution network. Based on the daily multi-objective operation optimization model of the distribution network that takes into account the energy storage lifetime, the Gurobi solver is called using MATLAB to solve the model, and the power of photovoltaic and energy storage is obtained.

[0093] To further implement the above technical solutions, the day-ahead operation optimization model for the distribution network in S1 aims to minimize the total operating cost of the distribution network and controllable resources. The distribution network operating cost includes the electricity purchase cost C. grid and power distribution network loss cost C loss The operating costs of controllable resources include photovoltaic operation and maintenance costs C. pv Energy storage operation and maintenance costs C ess OLTC Action Cost C OLTC CB action cost C CB and the cost of abandoned light C pl ;

[0094] The objective function of the day-ahead operation optimization model for the distribution network is:

[0095] minC=C grid +C loss +C pv +C ess +C OLTC +C CB +C pl (1)

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] In the formula: Let be the electricity purchase price coefficient at time t; Let t be the power purchased by the distribution network from the main grid at time t; Δt1 is the time scale for day-ahead dispatching. R represents the current flowing from node i to node j at time t;ij c is the resistance value of line ij; pv This is the photovoltaic operating cost coefficient; c is the actual photovoltaic absorption value at node j at time t; ess This is the energy storage operating cost coefficient; and Let c be the charging power and discharging power of the energy stored at node j at time t; OLTC This represents the cost coefficient for OLTC actions. c is the OLTC tap position on branch ij at time t; CB This is the cost coefficient for CB actions; c is the number of capacitor banks in operation at node j at time t; pl This is the cost coefficient for abandoned light. Let be the output power of the photovoltaic at node j at time t;

[0104] The day-ahead operating constraints of the distribution network are as follows:

[0105] 1) Distribution network power flow constraints:

[0106]

[0107]

[0108] In the formula, and These represent the injected active power and reactive power of node j at time t, respectively. and Let be the active power and reactive power of branch ij at time t, respectively; σ(j) represents the set of downstream nodes of node j; δ(j) represents the set of upstream nodes of node j. X represents the voltage value at node j at time t; ij Let be the reactance value of line ij;

[0109] 2) Constraints on safe operation of the power grid:

[0110]

[0111] In the formula, U min and U max The lower and upper limits of full operation; This represents the upper limit of the current in branch ij;

[0112] 3) Photovoltaic output constraints:

[0113]

[0114] 4) Energy storage operation constraints:

[0115]

[0116]

[0117] In the formula, and These are binary variables representing the charging and discharging states of the energy storage at node j, respectively. A value of 1 indicates that the energy storage is operating in both charging and discharging states, thus preventing the energy storage from being in both states simultaneously. j,max This represents the upper limit of the energy storage charging and discharging power at node j; E represents the energy stored at node j at time t. jmax and E jmin These are the upper and lower limits of the energy storage capacity of node j, respectively;

[0118] 5) Operating constraints of on-load tap-changing transformers:

[0119]

[0120] In the formula, Let be the adjustable ratio of the OLTC on branch ij at time t; and Δk ij These represent the standard ratio and adjustment step size of OLTC on branch ij, respectively; K ijmax This represents the maximum value of the adjustable gear of OLTC in branch ij;

[0121] 6) Operating constraints of parallel capacitor banks:

[0122]

[0123] In the formula, This refers to the reactive power output of the capacitor bank. Y represents the reactive power output of a single capacitor bank at node j; j,CBmax This represents the upper limit of the number of capacitor banks at node j.

[0124] To further implement the above technical solution, the specific details of calculating the energy storage lifetime loss cost in S3 include:

[0125] The total equivalent discharge capacity of energy storage over its entire life cycle under rated conditions is expressed as:

[0126] E j,eq =E j,max N j,max D j,max (17)

[0127] Where: E j,eq N represents the total equivalent discharge over the entire lifecycle of energy storage at node j under rated conditions; j,max The rated number of cycles for energy storage at node j, D j,max The rated depth of discharge for energy storage at node j;

[0128] Considering the charge / discharge rate and depth of discharge during actual operation, and neglecting the lifespan loss caused by energy storage charging, the equivalent discharge amount of energy stored at node j at time t is expressed as:

[0129]

[0130] In the formula: Let be the equivalent discharge amount of energy stored at node j at time t; Let P be the actual number of energy storage cycles at node j at time t. j,max This represents the upper limit of the energy storage charging and discharging power at node j; , respectively, represent the discharge power of the energy stored at node j at time t; The actual discharge amount of the energy stored at node j at time t;

[0131] The actual cycle life of energy storage is obtained through fitting and is expressed as:

[0132]

[0133] In the formula: u1 and u2 are the coefficients of the fitted curve, Let be the actual discharge depth of energy stored at node j at time t;

[0134] The energy storage lifespan loss cost is:

[0135]

[0136] In the formula: c el This refers to the investment cost per unit capacity of energy storage.

[0137] To further implement the above technical solution, the actual cycle life of energy storage represented by equation (18) is a nonlinear function, and the life loss cost is also a nonlinear function. For the nonlinear function energy storage life loss cost F... el Perform linearization:

[0138] A piecewise linearization method is used to linearize the nonlinear function energy storage lifetime loss cost.

[0139] Due to variables The value of is within [0,1]. Divide the interval [0,1] into N-1 segments, corresponding to N segmentation points dod1, dod2, ..., dod N Introduce a continuous variable η within [0,1]. n The linearized energy storage lifetime loss model is then expressed as:

[0140]

[0141]

[0142] In the formula: f[dod n ] is the dod corresponding to the nth segment point n Section F el function.

[0143] To further implement the above technical solutions, the objective function of the intraday multi-objective operation optimization model for the distribution network in S3 is:

[0144] minF=ω1F1+ω2F2 (23)

[0145] minF1=F grid +F loss +F pv +F ess +F el (twenty four)

[0146]

[0147] In the formula: ω1 and ω2 are the weights of targets F1 and F2, respectively; F1 is the intraday operating economic target; F2 is the intraday operating voltage deviation target; F grid For electricity purchase costs, F loss For network loss costs, F pv For the cost of curtailment, F ess For energy storage operation and maintenance costs, F el Cost of energy storage lifespan loss; U j This represents the actual value of the node voltage;

[0148]

[0149] In the formula: Δt2 is the time scale for intraday scheduling;

[0150] The constraints of the distribution network intraday operation optimization model include: energy storage operation constraints and curtailment cost constraints C. pl Constraints include power flow constraints in the distribution network, safe operation constraints in the power grid, and energy storage operation constraints. Among these, the cost constraint of curtailment of solar power is C. pl As shown in equation (8);

[0151] Energy storage operation constraints are:

[0152]

[0153] In the formula, and This is a binary variable representing the intraday energy storage charge / discharge state, and its value is equal to the optimized result of the day-ahead energy storage charge / discharge state variable, i.e.

[0154]

[0155] To verify the effectiveness and superiority of the method involved in this invention, this embodiment takes an IEEE 33-node distribution network system as an example, and the system topology diagram is as follows. Figure 2 As shown, the normalized photovoltaic predicted power and daily load curves are as follows: Figure 3 As shown, the load power fluctuation is set within ±5%, and the photovoltaic power prediction fluctuation is set within ±10%. The connection nodes and parameters of each device are shown in Table 1, the cost coefficients for each component are shown in Table 2, and the time-of-use tariffs are shown in Table 3.

[0156] Table 1 Device Access Nodes and Parameters

[0157]

[0158] Table 2 Cost coefficient values ​​for each component

[0159]

[0160] Table 3 Time-of-use Electricity Prices

[0161]

[0162]

[0163] To verify the day-to-day optimization control method for distribution networks that takes into account energy storage lifespan and voltage regulation proposed in this invention, the following schemes were set up for comparative analysis to illustrate the effectiveness of the proposed scheme.

[0164] Option 1: Without considering energy storage lifespan, the energy storage charge and discharge status is executed according to the intraday optimization results, and the intraday optimization does not consider the energy storage lifespan loss cost; without considering node voltage deviation, the intraday optimization only aims at economic efficiency.

[0165] Option 2 (the present invention): Considering the energy storage lifespan, the energy storage charging and discharging state is executed according to the day-ahead optimization results, and the day-ahead optimization takes into account the energy storage lifespan loss cost; considering the node voltage deviation, the day-ahead optimization aims at economic efficiency and node voltage deviation.

[0166] The energy storage lifetime loss cost and node voltage deviation under the two schemes are calculated separately, as shown in Table 4 below. In Scheme 1, the energy storage lifetime loss cost and node voltage deviation are obtained by substituting the optimization results into the numerical solution.

[0167] Table 4 Comparison of Results of the Two Schemes

[0168]

[0169] As shown in Table 4, under the scheme of the present invention, for the three nodes [11,24,32] connected to the energy storage device, the life-cycle loss cost of energy storage is reduced by 8.18% compared with Scheme 1. Figure 4 , 5The diagrams shown are the charge and discharge states of the energy storage connected to the three nodes under Scheme 1 and the scheme of this invention, respectively. Figure 6 , 7 The figures show the output power diagrams of the energy storage connected to the three nodes under Scheme 1 and the scheme of this invention, respectively. As can be seen from the figures, the energy storage under the scheme of this invention can reduce the number of charge / discharge state transitions and deep discharge, making the output power curve of the energy storage smoother and improving the lifespan of the energy storage; for example... Figure 8 , 9 The figures show the node voltages under Scheme 1 and the scheme of the present invention, respectively. As can be seen from the table and figures, the scheme of the present invention can reduce the system node voltage deviation and maintain stable system operation. That is, the scheme of the present invention can ensure the safe and economical operation of the distribution network.

[0170] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A multi-objective optimization control method for distribution networks considering energy storage lifetime, characterized in that, Includes the following steps: S1. Construct a day-ahead operation optimization model for the distribution network based on day-ahead source-load forecast data; S2. Based on the day-ahead operation optimization model of the distribution network, solve for the day-ahead optimization results, which include the energy storage charging and discharging status, the tap position of the on-load tap-changing transformer, the switching results of the capacitor bank, and the output power of photovoltaic and energy storage. S3. Using the energy storage charging and discharging status, on-load tap-changing transformer tap positions, and capacitor bank switching results from the previous day's optimization results as known quantities, a multi-objective operation optimization model for the distribution network is constructed based on the intraday source-load forecast data. The objective function of the multi-objective operation optimization model for the distribution network includes an economic objective and a voltage deviation objective. The economic objective considers the energy storage lifetime loss cost, and the voltage deviation objective is constructed based on the actual node voltage values. The specific content for calculating the energy storage lifetime loss cost includes: The total equivalent discharge capacity of energy storage over its entire life cycle under rated conditions is expressed as: （17） In the formula: For nodes j The total equivalent discharge capacity of energy storage over its entire life cycle under rated conditions; For nodes j The upper limit of the energy storage capacity; For nodes j The rated number of cycles for energy storage For nodes j Rated depth of discharge for energy storage; Considering the actual charge / discharge rate and depth of discharge during operation, and neglecting the lifespan loss caused by energy storage charging, t Time Node j The equivalent discharge capacity of energy storage is expressed as: （18） In the formula: for t Time Node j The equivalent discharge of stored energy; for t Time Node j Actual number of energy storage cycles For nodes j The upper limit of energy storage charging and discharging power; They are respectively t Time Node j The discharge power of the stored energy; for t Time Node j The actual discharge amount of the stored energy; The actual cycle life of energy storage is obtained through fitting and is expressed as: （19） In the formula: and These are the coefficients of the fitted curve. for t Time Node j Actual depth of discharge of energy storage; Energy storage lifespan loss cost for: （20） In the formula: The scheduling cycle is per day. The investment cost per unit capacity of energy storage; S4. Update the output power commands for photovoltaic and energy storage based on the daily multi-objective operation optimization model of the distribution network.

2. The multi-objective optimization control method for distribution networks considering energy storage lifetime as described in claim 1, characterized in that, The S1 distribution network day-ahead operation optimization model aims to determine the total operating cost of the distribution network and controllable resources. C Minimum, where the operating cost of the distribution network includes the cost of electricity purchase. and power distribution network loss costs The operating costs of controllable resources include photovoltaic operation and maintenance costs. Energy storage operation and maintenance costs OLTC action cost CB action cost and the cost of abandoned light ; The objective function of the day-ahead operation optimization model for the distribution network is: （1） （2） （3） （4） （5） （6） （7） （8） In the formula: for t Electricity purchase price coefficient at any given time; for t The amount of electricity purchased by the distribution network from the main grid at any given time; The time scale for the current day's scheduling. for t Time Node i Flow to Node j The current value; For the line ij The resistance value; This is the photovoltaic operating cost coefficient; for t Time Node j The actual absorption value of photovoltaic power; This is the energy storage operating cost coefficient; for t Time Node j The charging power of energy storage; This is the cost coefficient for OLTC actions; for t Time Branch ij OLTC tap position; This is the cost coefficient for CB actions; for t Time Node j The number of capacitor banks in operation; This is the cost coefficient for abandoned light. for t Time Node j The output power of photovoltaics; The day-ahead operating constraints of the distribution network are as follows: 1) Distribution network power flow constraints: （9） （10） In the formula, and They are respectively t Time Node j The injected active and reactive power; and They are respectively t Time Branch ij Active power and reactive power; Represents a node j The set of downstream nodes; Represents a node j The set of upstream nodes; for t Time Node j The voltage value; For the line ij The reactance value; 2) Constraints on safe operation of the power grid: （11） In the formula, and The lower and upper limits of full operation; branch road ij The upper limit of the current; 3) Photovoltaic output constraints: （12） 4) Energy storage operation constraints: （13） （14） In the formula, and They are nodes j The binary variables representing the charging and discharging states of energy storage, with a value of 1 indicating that the energy storage is operating in both charging and discharging states, thus preventing the energy storage from being in both charging and discharging states simultaneously. for t Time Node j The energy storage charge capacity; For nodes j The lower limit of the energy storage capacity; 5) Operating constraints of on-load tap-changing transformers: （15） In the formula, for t Time Branch ij The adjustable ratio of the OLTC; and Branch roads ij The standard ratio and adjustment step size of the OLTC; branch road ij The maximum value of the adjustable gears in the OLTC; 6) Operating constraints of parallel capacitor banks: （16） In the formula, This refers to the reactive power output of the capacitor bank. For nodes j The reactive power output of a single capacitor bank; For nodes j The upper limit on the number of capacitor banks.

3. The multi-objective optimization control method for distribution networks considering energy storage lifetime as described in claim 1, characterized in that, Cost of energy storage lifetime loss for nonlinear functions Perform linearization: A piecewise linearization method is used to linearize the nonlinear function energy storage lifetime loss cost; Due to variables The value of is within [0,1]. Divide the interval [0,1] into N-1 segments, corresponding to N segmentation points. dod 1, dod 2, ... dod N Introduce continuous variables within [0,1] The linearized energy storage lifetime loss model is then expressed as: （21） （22） In the formula: For the first n Each segmentation point corresponds to dod n part function.

4. The multi-objective optimization and control method for distribution networks considering energy storage lifetime as described in claim 2, characterized in that, S3 Intraday Multi-Objective Operation Optimization Model for Distribution Networks F The objective function is: （23） （24） （26） In the formula: and The target F 1 and F A weight of 2; F 1 represents the intraday economic target; F 2 represents the target for intraday operating voltage deviation; For electricity purchase costs, For network loss costs, To cover the cost of light curtailment, For energy storage operation and maintenance costs, Costs related to energy storage lifespan depletion; Actual node voltage value; （25） In the formula: The timescale for intraday scheduling; The constraints of the daily operation optimization model for the distribution network include: energy storage operation constraints and curtailment cost constraints. Constraints include power flow constraints in the distribution network, power grid safety operation constraints, and energy storage operation constraints, among which the cost of curtailing solar power is a key constraint. As shown in equation (8); Energy storage operation constraints are: （27） In the formula, and This is a binary variable representing the intraday energy storage charge / discharge state, and its value is equal to the optimized result of the day-ahead energy storage charge / discharge state variable, i.e.: （28）。

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