Active power distribution network energy storage configuration optimization method considering demand side response

Abstract

The invention discloses an active power distribution network energy storage configuration optimization method considering demand side response, and the method comprises the steps: solving a constructed power distribution network energy storage configuration model, and obtaining an optimized energy storage configuration result of each transformer area; the power distribution network energy storage configuration model comprises a target function and a constraint model; wherein the target function is that the deviation between the exchange power between the power distribution network and the superior power grid in the scheduling period and the exchange power plan value of the power distribution network is minimum; the constraint model comprises a power distribution network operation constraint, an energy storage configuration constraint, an energy storage operation constraint, an exchange power constraint between the power distribution network and a superior power grid, a power distribution network zone area operation constraint, a demand side response constraint and a power flow equation constraint. According to the method, the influence of the demand side response and the complementarity between the transformer areas are considered at the same time, the energy storage and demand side response resources of the nodes and the transformer areas of the power distribution network are uniformly configured and called, safe and stable autonomous operation of the power distribution network can be ensured, and the economic cost can be reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of energy storage configuration of distribution networks, and specifically relates to an active distribution network energy storage configuration optimization method taking into account demand-side response. Background Art

[0002] For environmental protection reasons, more and more energy storage devices are connected to the distribution network, which also affects the safe and stable operation of the distribution network. Therefore, it is necessary to optimize the energy storage configuration of the distribution network to improve the safety and stability of the distribution network operation and reduce economic costs.

[0003] Demand Side Response (DSR) is a mechanism in the power system that allows power users to adjust their power consumption patterns according to changes in power grid demand, in order to balance supply and demand and relieve grid pressure. However, current distribution network energy storage configuration methods rarely consider demand side response, which can lead to excessive or insufficient energy storage configuration.

[0004] The active distribution network is composed of substation units containing distributed photovoltaics and movable loads. At present, most of the energy storage configuration methods of active distribution networks ignore the complementarity of different substations in terms of load curves, electricity usage habits and geographical locations, resulting in low utilization of energy storage resources. In fact, there may be differences in load fluctuations in different substations at different times. The peak load of some substations may coincide with the trough load of other substations. If these complementarities are not taken into account, the configuration of the energy storage system will be too dispersed and redundant, and the overall investment cost will be high. Summary of the invention

[0005] The purpose of this application is to provide an active distribution network energy storage configuration optimization method considering demand-side response. The active distribution network energy storage configuration optimization method of this application considers the impact of demand-side response and the complementarity between each station, which can reduce economic costs and ensure the safe and stable autonomous operation of the distribution network.

[0006] The present application provides an active distribution network energy storage configuration optimization method considering demand-side response, which comprises: solving a constructed distribution network energy storage configuration model to obtain an optimized energy storage configuration result for each substation;

[0007] The above distribution network energy storage configuration model includes an objective function and a constraint model;

[0008] The objective function is: the deviation between the exchange power between the distribution network and the upper-level power grid and the exchange power plan value of the distribution network within the dispatch period is minimized;

[0009] The constraint model includes distribution network operation constraints, energy storage configuration constraints, energy storage operation constraints, exchange power constraints between the distribution network and the upper power grid, distribution network station operation constraints, demand side response constraints and power flow equation constraints;

[0010] The above-mentioned distribution network substation operation constraints include power balance constraints of each node in the substation, conservation constraints on the sum of active power that can be transmitted outward and the sum of acceptable active power of all nodes in the substation in energy complementary behavior, and upper and lower limit constraints on the active power that can be transmitted outward and the acceptable active power of each node in energy complementary behavior;

[0011] The above-mentioned demand-side response constraints include the constraints on the conservation of the actual power consumption of the node demand-side response load and the total power demand of the user within the scheduling period, as well as the upper and lower limit constraints on the actual power consumption of the node demand-side response load.

[0012] In some embodiments, the objective function is expressed as Among them, P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; P ref (t) represents the reported exchange power plan value of time period t; T represents the number of time periods divided by the scheduling cycle.

[0013] In some embodiments, the distribution network operation constraints include active power balance constraints and reactive power balance constraints of distribution network nodes, and upper and lower limit constraints of distribution network node voltages.

[0014] In some embodiments, the energy storage configuration constraints include power and capacity constraints for configuring energy storage at each node, and constraints on the number of nodes for installing energy storage in the distribution network.

[0015] In some embodiments, the energy storage operation constraints include power constraints, capacity constraints, and charge and discharge state constraints when each node is in energy storage operation;

[0016] The expression of the exchange power capacity constraint is:

[0017]

[0018] The expression of the exchange power charge and discharge state constraint is:

[0019]

[0020] in, and They represent the storage capacity of node i during period t and the initial capacity respectively; η ch and η dis Respectively represent the charging and discharging efficiency of energy storage; P ch,i (t) and P dis,i (t) represents the energy storage charging power and energy storage discharging power of node i in period t respectively; Δt represents the time step; μ down and μ upThey respectively represent the minimum capacity factor and the maximum capacity factor allowed during the operation of energy storage; represents the rated capacity of energy storage at node i; μ start Indicates the initial capacity coefficient of energy storage; u ess,i,K 、u ess,i,t-1 、u ess,i,t Respectively represent the charge and discharge status mark of the energy storage at node i in time periods K, t-1, and t; and They represent the minimum charging duration and the minimum discharging duration of the energy storage at node i respectively.

[0021] In some embodiments, the expression of the exchange power constraint between the distribution network and the upper-level power grid is:

[0022]

[0023] Among them, P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; P ch,i (t), P DR,i (t), P load,i (t), P pv,i (t), P loss (t), P dis,i (t) respectively represent the energy storage charging power of node i in period t, the movable load power participating in demand-side response, load power, distributed photovoltaic output power, loss power, and energy storage discharge power; B represents the total number of distribution network nodes.

[0024] In some embodiments, the expression of the distribution network area operation constraint is:

[0025]

[0026] Among them, P L,i (t) represents the power of the immovable load on node i during period t; P DR,i (t) represents the power of the movable load participating in the demand-side response at node i during period t; P ch,i (t) represents the energy storage charging power of node i during period t; P output,i (t) represents the active power that can be transmitted outward from node i in the energy complementation behavior during time period t; P pv,i (t) represents the distributed photovoltaic output power at node i during period t; P dis,i (t) represents the energy storage discharge power of node i during period t; P input,i (t) represents the acceptable active power at node i in the energy complementation behavior during period t.

[0027] In some embodiments, the expression of the demand-side response constraint is:

[0028]

[0029] Among them, P DR,i (t) represents the actual power of the demand-side response load of node i during period t; E DR,i It represents the total electricity demand of node i participating in the demand-side response of users during the scheduling period; and are the minimum and maximum electricity demands of the demand-side response load of node i in period t respectively; Δt represents the unit scheduling time.

[0030] In order to reduce the complexity of model solution and improve solution efficiency, in some embodiments, a non-negative auxiliary variable P is introduced. opt,1 (t), P opt,2 (t), the exchange power objective function is constructed as a linear objective function as follows:

[0031]

[0032] Among them, the non-negative auxiliary variable P opt,1 (t), P opt,2 (t) and P grid (t), P ref (t) constitutes an equality constraint.

[0033] Furthermore, in some embodiments, the exchange power flow equation constraint is also linearized, including: constructing an exchange power linearized flow equation and a voltage linearized flow equation, which are:

[0034]

[0035] Among them, P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; U grid (t) represents the voltage amplitude of the root node of the distribution network during period t; U i (t) represents the voltage amplitude of node i in period t; P(t) represents the column vector of active power injected by the nodes except the root node in period t; Q(t) represents the column vector of reactive power injected by the nodes except the root node in period t; and are the undetermined coefficient vectors of the exchange power linearization power flow equation and the voltage linearization power flow equation respectively;

[0036] Get P grid (t), P(t), Q(t), U i (t) historical measurement data, through P grid (t), U gridThe historical measurement data of (t), P(t), and Q(t) are analyzed by least squares regression to determine the unknown coefficient vector Through U i (t), U grid The historical measurement data of (t), P(t), and Q(t) are used for least squares regression analysis to obtain the unknown coefficient vector

[0037] Compared with the prior art, this application has the following advantages and beneficial effects:

[0038] 1. This application takes the active distribution network composed of several photovoltaic storage substation units as the object, and considers the impact of demand-side response and the complementarity between substations. It uniformly configures and calls the energy storage and demand-side response resources of each node and each substation in the distribution network, which can ensure the safe and stable autonomous operation of the distribution network and reduce economic costs.

[0039] 2. This application utilizes a data-driven linearized flow method to perform regression analysis on historical flow data, linearizes the flow model, and uses the linearized flow equation as a flow equation constraint; the use of a linearized flow equation can avoid full-node measurement and phase angle data measurement; the complex nonlinear flow model is approximated as a linear flow model, which simplifies the nonlinear flow model, reduces the complexity of the model solution, and improves the efficiency of the model solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] The present application obtains the optimized energy storage configuration results of each substation by solving the constructed distribution network energy storage configuration model. The distribution network energy storage configuration model includes an objective function and a constraint model. The distribution network energy storage configuration model constructed in this embodiment will be described in detail below.

[0043] In the present application, the objective function of the distribution network energy storage configuration model is: the deviation between the exchange power between the distribution network and the upper-level power grid and the planned value of the exchange power of the distribution network during the dispatching period is minimized.

[0044] In this embodiment, the objective function is expressed as follows:

[0045]

[0046] In formula (1): P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; P ref (t) represents the reported exchange power plan value of time period t; T represents the number of time periods divided by the scheduling cycle.

[0047] In this embodiment, the scheduling period is set to 1 day, and the scheduling period is divided into each hour as a time period. Then, the number of time periods T into which the scheduling period is divided is 24.

[0048] In this application, the constraint model of the distribution network energy storage configuration model includes distribution network operation constraints, energy storage configuration constraints, energy storage operation constraints, distribution network and upper grid power exchange constraints, substation operation constraints, demand side corresponding constraints and power flow equation constraints.

[0049] Each constraint model will be described in detail below.

[0050] (1) Distribution network operation constraints, which are used to ensure the normal operation of the distribution network, and further include node power balance constraints and node voltage constraints of the distribution network;

[0051] In this embodiment, the node power balance constraint further includes the active power balance constraint and reactive power balance constraint of the node, and the expression is:

[0052]

[0053] In formula (2): P i (t) and Q i (t) respectively represent the active power and reactive power injected by node i in period t; U i (t) and U j (t) represents the voltage amplitude of nodes i and j in time period t; G ij represents the conductance between nodes i and j; B ij represents the voltage between nodes i and j; δ ij represents the phase angle difference between nodes i and j; B represents the total number of distribution network nodes.

[0054] In this embodiment, the expression of the node voltage constraint is:

[0055]

[0056] In formula (3): and They represent the upper and lower voltage limits allowed for node i respectively.

[0057] (2) Energy storage configuration constraints. Energy storage configuration is affected by investment, planning and safety conditions. There are certain restrictions on the number of nodes allowed to install energy storage in the distribution network and the power and capacity of energy storage configured at each node.

[0058] In this embodiment, the energy storage configuration constraints include the power, capacity and number of installed nodes of the energy storage configuration, and the expression is:

[0059]

[0060] In formula (4): and They represent the power and capacity of the energy storage configuration of node i respectively; and They represent the maximum power and maximum capacity allowed by the energy storage configuration of node i; u i Indicates the energy storage configuration status of node i. Its value is 1, indicating that the node is configured with energy storage, and its value is 0, indicating that the node is not configured with energy storage; u i The value is determined according to the actual situation of the photovoltaic project; n ess Indicates the maximum number of nodes that the distribution network is allowed to configure energy storage.

[0061] (3) Energy storage operation constraints refer to the power and capacity constraints that the energy storage connected to each node must meet, including the power constraint, capacity constraint, and charge and discharge state constraint of the energy storage at each node. Considering that energy storage can provide reactive power support through bidirectional converters, the power constraint of the energy storage at each node in this embodiment also introduces reactive power constraints.

[0062] In this embodiment, the expression of the power constraint of each node energy storage is:

[0063]

[0064] In formula (5): P dis,i (t) and P ch,i (t) represents the energy storage discharge power and energy storage charging power of node i in period t respectively; represents the rated power of energy storage at node i; Q ch,i (t) and Q dis,i (t) represents the energy storage charging reactive power and energy storage discharging reactive power of node i in period t respectively; represents the maximum reactive power of energy storage discharged at node i; u ess,i It is a symbol used to describe the charging and discharging status of energy storage. Its value is 1, which represents charging; its value is 0, which represents discharging.

[0065] In this embodiment, the expression of the capacity constraint of each node energy storage is:

[0066]

[0067] In formula (6): and They represent the storage capacity of node i during period t and the initial capacity respectively; η ch and η dis Respectively represent the charging and discharging efficiency of energy storage; P ch,i (t) and P dis,i (t) represents the energy storage charging power and energy storage discharging power of node i in period t respectively; Δt represents the time step; μ down and μ up They respectively represent the minimum capacity factor and the maximum capacity factor allowed during the operation of energy storage; represents the rated capacity of energy storage at node i; and They represent the minimum capacity and maximum capacity allowed in energy storage operation; μ start The initial capacity factor of energy storage is used to characterize the ratio of the capacity of the energy storage device in the initial period to the rated capacity.

[0068] Above It is used to constrain the energy storage equipment to have the same regulation performance in each scheduling cycle.

[0069] In this embodiment, the expression for the charge and discharge state constraint of each node energy storage is:

[0070]

[0071] In formula (7), u ess,i,K 、u ess,i,t-1 、u ess,i,t Respectively represent the charge and discharge status flag of the energy storage at node i in time periods K, t-1, and t. Its value is 1, which represents charging; its value is 0, which represents discharging. Time periods t-1 and t are adjacent time periods. and They represent the minimum charging duration and minimum discharging duration of the energy storage at node i respectively, and their values ​​are related to the type of energy storage device.

[0072] (4) Power exchange constraint between the distribution network and the upper power grid. In this embodiment, the power exchange constraint between the distribution network and the upper power grid is expressed as:

[0073]

[0074] In formula (8), P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; P ch,i (t) represents the energy storage charging power of node i during period t; P DR,i (t) represents the power of the movable load participating in the demand-side response at node i during period t; P load,i(t) represents the load power of node i during period t; P pv,i (t) represents the distributed photovoltaic output power of node i during period t; P loss (t) represents the power loss of node i during period t; P dis,i (t) represents the energy storage discharge power of node i in period t; B represents the total number of distribution network nodes.

[0075] (5) Substation operation constraints, which are used to ensure that the energy complementarity between substations does not affect the normal balance of power supply and demand;

[0076] In this embodiment, the substation operation constraint includes the substation power balance constraint, which is expressed as:

[0077]

[0078] In formula (9), P L,i (t) represents the immovable load power on node i during period t; P DR,i (t) represents the power of the movable load participating in the demand-side response at node i during period t; P ch,i (t) represents the energy storage charging power of node i during period t; P output,i (t) represents the active power that node i can transmit to the outside in the energy complementation behavior during time period t; P pv,i (t) represents the distributed photovoltaic output power at node i during period t; P dis,i (t) represents the energy storage discharge power of node i during period t; P input,i (t) represents the acceptable active power of node i in the energy complementation behavior during period t; P pv,i (t) represents the distributed photovoltaic output power of node i during period t.

[0079] (6) Demand-side response constraints. In this application, demand-side response constraints include the constraints on the actual power consumption of the node demand-side response load during the scheduling period and the total power demand of the user, as well as the upper and lower limit constraints on the actual power consumption of the node demand-side response load.

[0080] In this embodiment, the expression of the demand side response constraint is:

[0081]

[0082] In formula (10), P DR,i (t) represents the actual power of the demand-side response load of node i during period t; E DR,i It represents the total electricity demand of node i participating in the demand-side response of users during the scheduling period; and are the minimum and maximum electricity demands of the demand-side response load of node i in period t respectively; Δt represents the unit scheduling time.

[0083] (7) The power flow equation is used to describe the relationship between power flow variables such as voltage, power, phase angle, etc. at each node in the distribution network. In this application, the power flow equation constraint adopts the branch power flow model.

[0084] Solve the above-constructed distribution network energy storage configuration model to obtain the optimized energy storage configuration plan for each substation. The energy storage configuration plan includes the optimal power configuration of each energy storage system in the substation. and capacity And the optimal exchange power P between the distribution network and the upper power grid grid (t); By regulating the charging and discharging behavior of each energy storage system, the purpose of regulating the exchange power between the distribution network and the upper power grid to the optimal exchange power is achieved. The solution of the distribution network energy storage configuration model can be solved by mature mixed integer linear programming algorithms, genetic algorithms, etc.

[0085] Since the above distribution network energy storage configuration model includes nonlinear objective functions and constraint models, the model solution process is relatively complex and the solution efficiency is not high. To improve the solution efficiency, in this embodiment, the objective function and the power flow equation constraints are first linearized.

[0086] In this embodiment, the linearization process of the objective function is:

[0087] Introduce a non-negative auxiliary variable P opt,1 (t), P opt,2 (t), the objective function is constructed into the following linear form:

[0088]

[0089] In formula (11): non-negative auxiliary variable P opt,1 (t), P opt,2 (t) and the original variable P grid (t), P ref (t) constitutes an equality constraint.

[0090] In this embodiment, the linearization process of the power flow equation constraint is:

[0091] The data-driven linearized power flow method is used to perform regression analysis on the historical data of power flow data, and a linear function relationship between power flow variables is established, namely, the linearized power flow equation. The linearized power flow equation is used as a power flow equation constraint. The linearized power flow equation can avoid full node measurement and phase angle data measurement. The data-driven linearized power flow method can be used to approximate complex nonlinear models into linear models, thereby simplifying nonlinear models.

[0092] The following will provide ideas for constructing the linearized power flow equation.

[0093] For a single-phase distribution network with n+1 nodes, the root node is numbered 0, and the power flow variable y is related to x=[x0,x1,x2,...x 2n ] T The functional relationship is expressed as:

[0094] y=f(x0,x1,x2,...x 2n ) (12)

[0095] Mapping the above functional relationship to the general form of the linearized power flow equation:

[0096]

[0097] In formula (13), b=[b0,b1,b2,...b 2n+1 ] T is the coefficient vector to be determined.

[0098] The historical measurement data of the power flow variable x is represented as matrix X, the historical measurement data of the power flow variable y is represented as matrix Y, b is the coefficient vector to be determined, and equation (13) is expressed as:

[0099] Xb=Y (14)

[0100] Xb represents the predicted data of the power flow variables, and Y represents the actual data of the power flow variables.

[0101] Using the least squares method, construct the loss function and find the minimum value. At this time, we have:

[0102]

[0103] By mathematically transforming equation (15), taking the partial derivative of b and setting it equal to zero, we can obtain the expression of the extreme value:

[0104]

[0105] Formula (16) is expressed as follows:

[0106] 2X T Xb-2X T Y=0 (17)

[0107] The matrices X are linearly independent, so X T If X is reversible, then equation (17) is simplified to:

[0108] b=(X T X) -1 X T Y (18)

[0109] Formula (18) is the analytical solution of the unknown coefficient vector b.

[0110] By performing regression analysis on the historical measured data of the flow variables x and y, the unknown coefficient vector b is determined, and the linearized flow equation of the flow variables x and y is constructed.

[0111] Through the above method, the root node exchange power P of the distribution network is constructed grid The linearized power flow equation between the active power P(t) and the reactive power Q(t) injected by the remaining nodes is constructed. The remaining nodes refer to all nodes except the root node in the distribution network. The voltage amplitude U of each node is also constructed by the above method. i (t) and the voltage amplitude U of the root node grid The linearized power flow equation between P(t), P(t), and Q(t) is constructed. The constructed linearized power flow equation is shown in formula (19).

[0112]

[0113] In formula (19), P grid (t) and U grid (t) represent the exchange power and voltage amplitude of the root node in time period t, respectively. The exchange power of the root node is the exchange power between the distribution network and the upper power grid; U i (t) represents the voltage amplitude of node i in period t; P(t) represents the column vector of active power injected by the nodes except the root node in period t; Q(t) represents the column vector of reactive power injected by the nodes except the root node in period t.

[0114] and They are the undetermined coefficient vectors in the exchange power linearization power flow equation and the voltage linearization power flow equation, respectively, and are calculated by performing least squares regression analysis on the historical measurement data of power flow variables. Specifically, Through P grid (t), U grid The historical measurement data of (t), P(t), and Q(t) are calculated by least squares regression analysis. Specifically, Through U i (t), U grid The historical measurement data of P(t), Q(t) are calculated by least square regression analysis.

[0115] The linearized power flow equation of formula (19) is used as the power flow equation constraint in the distribution network energy storage configuration model, which can reduce the complexity of solving the distribution network energy storage configuration model.

[0116] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present application, all of which belong to the protection scope of the present application.

Claims

1. A method for optimizing energy storage configuration in an active distribution network considering demand-side response, characterized in that: Solve the constructed distribution network energy storage configuration model to obtain the optimized energy storage configuration results for each substation; The distribution network energy storage configuration model includes an objective function and a constraint model; The objective function is: the deviation between the exchange power between the distribution network and the upper-level power grid and the exchange power plan value of the distribution network within the dispatch period is minimized; The constraint model includes distribution network operation constraints, energy storage configuration constraints, energy storage operation constraints, exchange power constraints between the distribution network and the upper power grid, distribution network station operation constraints, demand side response constraints and power flow equation constraints; The distribution network substation operation constraints include power balance constraints of each node in the substation, conservation constraints on the sum of active power that can be transmitted outward and the sum of acceptable active power of all nodes in the substation in energy complementary behavior, and upper and lower limit constraints on the active power that can be transmitted outward and the acceptable active power of each node in energy complementary behavior; The demand side response constraints include the constraints on the conservation of the actual power consumption of the node demand side response load and the total power demand of the user within the scheduling period, as well as the upper and lower limit constraints on the actual power consumption of the node demand side response load.

2. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The objective function is Among them, P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; P ref (t) represents the reported exchange power plan value of time period t; T represents the number of time periods divided by the scheduling cycle.

3. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The distribution network operation constraints include active power balance constraints and reactive power balance constraints of distribution network nodes, and upper and lower limit constraints of distribution network node voltages.

4. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The energy storage configuration constraints include power and capacity constraints for energy storage configuration at each node, and constraints on the number of nodes for energy storage installation in the distribution network.

5. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The energy storage operation constraints include power constraints, capacity constraints, and charge and discharge state constraints when each node is in energy storage operation; The expression of the capacity constraint is: The expression of the charge and discharge state constraint is: in, and They represent the storage capacity of node i during period t and the initial capacity respectively; η ch and η dis Respectively represent the charging and discharging efficiency of energy storage; P ch,i (t) and P dis,i (t) represents the energy storage charging power and energy storage discharging power of node i in period t respectively; Δt represents the time step; μ down and μ up They respectively represent the minimum capacity factor and the maximum capacity factor allowed during the operation of energy storage; represents the rated capacity of energy storage at node i; μ start Indicates the initial capacity coefficient of energy storage; u ess,i,K 、u ess,i,t-1 、u ess,i,t Respectively represent the charge and discharge status mark of the energy storage at node i in time periods K, t-1, and t; and They represent the minimum charging duration and the minimum discharging duration of the energy storage at node i respectively.

6. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The expression of the exchange power constraint between the distribution network and the upper power grid is: Among them, P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; P ch,i (t), P DR,i (t), P load,i (t), P pv,i (t), P loss (t), P dis,i (t) respectively represent the energy storage charging power of node i in period t, the movable load power participating in demand-side response, load power, distributed photovoltaic output power, loss power, and energy storage discharge power; B represents the total number of distribution network nodes.

7. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The expression of the distribution network area operation constraint is: Among them, P L,i (t) represents the power of the immovable load on node i during period t; P DR,i (t) represents the power of the movable load participating in the demand-side response at node i during period t; P ch,i (t) represents the energy storage charging power of node i during period t; P output,i (t) represents the active power that can be transmitted outward from node i in the energy complementation behavior during time period t; P pv,i (t) represents the distributed photovoltaic output power at node i during period t; P dis,i (t) represents the energy storage discharge power of node i during period t; P input,i (t) represents the acceptable active power at node i in the energy complementation behavior during period t.

8. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The expression of the demand side response constraint is: Among them, P DR,i (t) represents the actual power of the demand-side response load of node i during period t; E DR,i It represents the total electricity demand of node i participating in the demand-side response of users during the scheduling period; and are the minimum and maximum electricity demands of the demand-side response load of node i in period t respectively; Δt represents the unit scheduling time.

9. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 2, characterized in that: Introduce a non-negative auxiliary variable P opt,1 (t), P opt,2 (t), the objective function is constructed as a linear objective function as follows: Among them, the non-negative auxiliary variable P opt,1 (t), P opt,2 (t) and P grid (t), P ref (t) constitutes an equality constraint.

10. The method for optimizing energy storage configuration of active distribution network considering demand-side response according to claim 1, characterized in that: The power flow equation constraints are linearized, including: The exchange power linearization power flow equation and voltage linearization power flow equation are constructed as follows: Among them, P grid (t) represents the exchange power between the distribution network and the upper power grid during period t; U grid (t) represents the voltage amplitude of the root node of the distribution network during period t; U i (t) represents the voltage amplitude of node i in period t; P(t) represents the column vector of active power injected by the nodes except the root node in period t; Q(t) represents the column vector of reactive power injected by the nodes except the root node in period t; and are the undetermined coefficient vectors of the exchange power linearization power flow equation and the voltage linearization power flow equation respectively; Get P grid (t), P(t), Q(t), U i (t) historical measurement data, through P grid (t), U grid The historical measurement data of (t), P(t), and Q(t) are analyzed by least squares regression to determine the unknown coefficient vector Through U i (t), U grid The historical measurement data of (t), P(t), and Q(t) are used for least squares regression analysis to obtain the unknown coefficient vector

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