Distributed energy storage aggregation regulation and control operation method for service insurance supply and absorption promotion

Abstract

The invention relates to a distributed energy storage aggregation regulation and control operation method for service guarantee supply and absorption promotion, and the method comprises the following steps: 1, considering the influence of distributed photovoltaic access on different main body power consumption behaviors, and constructing a distributed energy storage coordination operation strategy under demand response based on distributed energy storage regulation and control demands and aggregation response characteristics; 2, analyzing a distributed energy storage aggregation operation mode, and constructing a distribution network distributed energy storage aggregation regulation optimization scheduling model based on an aggregation effect; step 3, considering an interaction mode with a superior power grid, and establishing a distributed energy storage multi-time scale energy storage peak regulation and frequency modulation cooperative control strategy oriented to power grid flexibility improvement; 4, considering the influence of distributed energy storage regulation and control operation on the economical efficiency and low-carbon property of system operation, and establishing a distributed energy storage regulation and control benefit evaluation index; the method has the advantages that the demand response characteristics are considered, the aggregation regulation and control model is established, and the regulation and control efficiency evaluation index is constructed.

Description

Technical Field

[0001] The present invention belongs to the field of distributed energy storage technology, and specifically relates to a distributed energy storage aggregation control operation method for ensuring supply and promoting consumption. Background Art

[0002] With the rapid development of new energy power generation technologies represented by wind and light, a high proportion of new energy grid connection will become a basic feature of the new power system. Distributed energy is an important part of the future new power system. In order to deal with the randomness and high load problems brought about by the access of a large number of distributed power sources to the distribution network, distributed energy storage has become increasingly important in the modern energy system due to its technical and economic advantages such as flexible access location, short construction period and low investment cost. However, the distributed energy storage system still faces some problems. Compared with the traditional operation mode of large power grids, the access and output of distributed energy storage have the characteristics of decentralized layout and poor controllability. From the perspective of power grid dispatching, there is a lack of effective dispatching means, which is not conducive to the distributed energy storage to play its role in improving the frequency, voltage and power quality of the power grid, resulting in the waste of energy storage resources. There is a large waste. In contrast, aggregated distributed energy storage has the characteristics of considerable quantity and large controllable potential, and is more suitable for participating in grid operation. Current research focuses on maximizing the interests of energy storage aggregators, and lacks consideration of the economic and environmental benefits it brings to grid operation. In general, current research on distributed energy storage aggregation is mostly focused on the optimization of control strategies based on the aggregate's own interests, and lacks research on multi-time scale control strategies for distributed energy storage aggregation participating in grid operation. There is a lack of evaluation indicators and quantitative methods for the economy and low-carbon nature of aggregated regulation of distributed energy storage; therefore, it is very necessary to provide a distributed energy storage aggregation regulation and operation method that takes into account demand response characteristics, establishes an aggregated regulation model, and constructs a regulation efficiency evaluation index to ensure supply and promote consumption. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a distributed energy storage aggregation control operation method that takes into account demand response characteristics, establishes an aggregation control model, and constructs a control efficiency evaluation index to ensure supply and promote consumption.

[0004] The object of the present invention is achieved by providing a distributed energy storage aggregation control operation method for serving supply guarantee and consumption promotion, the method comprising the following steps:

[0005] Step 1: Considering the impact of distributed photovoltaic access on the electricity consumption behavior of different entities, a coordinated operation strategy of distributed energy storage under demand response is constructed based on the distributed energy storage regulation demand and aggregate response characteristics;

[0006] Step 2: Analyze the distributed energy storage aggregation operation mode and build a distributed energy storage aggregation control optimization dispatching model for distribution network based on the aggregation effect;

[0007] Step 3: Considering the interaction mode with the upper power grid, establish a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy for improving power grid flexibility;

[0008] Step 4: Consider the impact of distributed energy storage regulation and operation on the economy and low carbon nature of system operation, and establish distributed energy storage regulation benefit evaluation indicators.

[0009] The step 1 of constructing a coordinated operation strategy of distributed energy storage under demand response includes the following steps:

[0010] Step 1.1: Energy storage demand response model based on time-of-use electricity price: With the goal of minimizing the user's electricity cost, establish an energy storage battery demand response model based on time-of-use electricity price;

[0011] Step 1.2: Multi-point energy storage charging and discharging coordination optimization model: With the goal of minimizing network losses, considering the energy storage battery charging and discharging power constraints, energy constraints, line overload constraints, and node voltage constraints, a coordinated optimization model is established when multi-point energy storage systems participate in demand response.

[0012] The energy storage demand response model based on time-of-use electricity price in step 1.1 is specifically as follows: ① Objective function: In the formula, the power consumption cycle is T, which is divided into N time periods according to the step length Δt; a i is the time-of-use electricity price in the i-th period; P L ' ,i is the net load in the ith period; ② Net load balance constraint: P L ' ,i =P L,i +P E,i (4), where P L,i is the electricity consumption plan for the i-th period; P E,i is the energy storage battery charging and discharging plan for the i-th period; ③ Load transfer constraints: P L,i ≥P LB,i (6), where P L0,i is the original load demand in the ith period; P LB,i is the basic load demand in the i-th period; ④Battery charging and discharging power constraint: P E,min ≤|P E,i |≤P E,max (7), where P E,max , P E,min They are the upper and lower limits of the energy storage battery charging and discharging power; ⑤ Battery charging and discharging depth constraint: SOC min ≤SOC≤SOC max (8), where SOC max , SOCmin They are the upper and lower limits of the charging and discharging power depth of the energy storage battery; SOC is defined as: Where, E is the current energy state of the energy storage battery; E rate It is the rated energy space of the energy storage battery.

[0013] The multi-point energy storage charging and discharging coordination optimization model in step 1.2 is specifically as follows: ① Objective function: Where M is the number of lines; P loss,j is the line loss of the jth line; ②Charge and discharge plan constraints: Where K is the number of distributed energy storage battery facilities; P k is the charging and discharging power of the k-th energy storage facility; ΔP E is the total charging and discharging plan under demand response; ③ Power balance constraint: Where N is the number of network nodes; M is the number of network branches; S is the number of energy storage facilities; P Gi is the power generation of the ith node; P Li is the load power of the ith node; P Ek is the charge and discharge power of the energy storage battery at the kth location; ④ Single-point energy storage battery charge and discharge power constraint: P k,min ≤|P k |≤P k,max (13), where P k,max , P k,min are the upper and lower limits of the charge and discharge power of the energy storage battery at the kth location; ⑤ Single-point energy storage battery charge and discharge depth constraint: SOC k,min ≤SOC k ≤SOC k,max (14), where SOC k,max , SOC k,min are the upper and lower limits of the charge and discharge depth of the energy storage battery at the kth location; ⑥ Line overload constraint: I j <I j,max (15), where I j is the current of the jth line; I j,max is the thermal stability current of the jth line; ⑦Node voltage constraint: U i,min ≤U i ≤U i,max (16), where U i,max , U i,min are the upper and lower limits of the voltage at the ith node respectively; U i is the voltage of the ith node.

[0014] The step 2 of constructing a distribution network distributed energy storage aggregation control optimization scheduling model based on the aggregation effect includes the following steps:

[0015] Step 2.1: Distributed energy storage aggregation model;

[0016] Step 2.2: Mathematical model for optimal scheduling of distributed energy storage;

[0017] Step 2.3: Solving the optimal scheduling.

[0018] The distributed energy storage aggregation model in step 2.1 includes the following steps:

[0019] Step 2.11: Mathematical model of energy storage: ① Energy storage charging and discharging model: When the energy storage is charged, it absorbs active power, and the SOC increases; when it is discharged, it emits active power, and the SOC decreases; the SOC at time t t The value calculation formula is: Where SOC0 is the SOC value at the initial moment of energy storage; P ch , P dis are the charge and discharge power during the period from t to t-1, and at most one of them can be non-zero; η ch , η dis are the charge and discharge efficiency respectively; Δt is the duration of charge and discharge; S rate is the rated capacity of energy storage; ② Energy storage constraints: including SOC constraints and charge and discharge constraints, SOC constraints: SOC min ≤SOC i ≤SOC max (18), where SOC min , SOC max They are the minimum and maximum values ​​allowed by the energy storage SOC; charging and discharging constraints: Where P ch,max , P dis,max are the maximum charging power and maximum discharging power of energy storage respectively;

[0020] Step 2.12: Power allocation of distributed energy storage: The specific determination method is as follows: Charging: Discharge: Where P irate is the rated power of energy storage i; P ich , P idis are the charging and discharging power of energy storage i respectively; P allch , P alldis is the total charging and discharging power required by the dispatching; N is the total number of energy storages; f ch (x), f dis (x) are the charge and discharge SOC functions of energy storage respectively; the specific expressions of charge and discharge SOC functions are:

[0021] Step 2.13: Equivalent centralized energy storage parameters under aggregation effect: After aggregating each energy storage using equations (20) and (21), only the equivalent centralized rated power, equivalent centralized rated capacity and equivalent centralized charging and discharging efficiency are needed to represent the entire distributed energy storage system. When each energy storage has the same charging and discharging time at rated power, the corresponding equivalent centralized energy storage parameters are defined as follows: The equivalent centralized rated power is: Where P allrate is the rated power of the equivalent centralized energy storage; P irate is the rated power of the i-th energy storage; the equivalent concentrated rated capacity is: In the formula, S allrate is the rated capacity of the equivalent centralized energy storage; S irate is the rated capacity of the i-th energy storage; the equivalent centralized charging efficiency and the equivalent centralized discharging efficiency are: Where η allch , η alldis are the equivalent concentrated charging efficiency and the equivalent concentrated discharging efficiency respectively; η ich , η idis are the charging efficiency and discharging efficiency of the i-th energy storage respectively.

[0022] The distributed energy storage optimization scheduling mathematical model in step 2.2 includes the following steps:

[0023] Step 2.21: Division of charging and discharging time periods and load power intervals;

[0024] Step 2.22: Objective function: According to the peak shaving and valley filling and voltage management functions of distributed energy storage in the distribution network, load fluctuation and voltage deviation are used as the objective functions of optimal scheduling, respectively. ① Load fluctuation: In the formula, f ΔP is the load fluctuation of the distribution network in all periods; K is the number of load power intervals; M j is the number of loads in the jth load power interval; P javrload is the average power in the jth load power interval; P avrload is the average power of the distribution network during all periods; P jess is the total active output of the distributed energy storage system in the jth load power interval; ② Voltage deviation: In the formula, f ΔU is the voltage deviation of the distribution network in all periods; R is the total number of nodes in the distribution network; U ji is the voltage amplitude of node i in the jth load power interval; U exp is the expected voltage of the node;

[0025] Step 2.23: Constraints: The power flow constraints are: The voltage range constraint is: U min ≤U ji ≤U max (29); The charging and discharging power constraints of the distributed energy storage system are: The SOC constraint of the distributed energy storage system is: SOC min ≤SOC all ≤SOC max (31), SOC first =SOC last (32), where P jis , Q jis are the active power and reactive power injected into node i in the jth load power interval respectively; G ik , B ik are the conductance and susceptance from node i to node k respectively; θ ik is the phase angle difference between nodes i and k; P jiavrload , Q jiavrload are respectively the active power and reactive power of the node i load in the jth load power interval; P jiess , Q jiess are respectively the active power and reactive power of the energy storage installed at node i in the jth load power interval; U min , U max are the minimum and maximum values ​​allowed for the node voltage respectively; SOC all is the SOC value of equivalent centralized energy storage; SOC min , SOC max They are the minimum and maximum values ​​allowed by the distributed energy storage system SOC; SOC first , SOC last are the initial SOC and final SOC of the equivalent centralized energy storage, respectively.

[0026] The step 3 of establishing a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy includes the following steps:

[0027] Step 3.1: Collaborative control work area division;

[0028] Step 3.2: ES peak-shaving and frequency-regulation coordinated control strategy.

[0029] The ES peak-shaving and frequency-shaving coordinated control strategy in step 3.2 includes the following steps:

[0030] Step 3.21: Monotonic peak control strategy;

[0031] Step 3.22: Single frequency modulation control strategy;

[0032] Step 3.23: Peak and frequency regulation coordinated control strategy;

[0033] Step 3.24: Peak and frequency regulation evaluation indicators.

[0034] The step 4 of establishing the distributed energy storage regulation benefit evaluation index comprises the following steps:

[0035] Step 4.1: Cost analysis indicators;

[0036] Step 4.2: Benefit-cost indicators.

[0037] Beneficial effects of the invention: The invention provides a distributed energy storage aggregation control operation method for serving to ensure supply and promote consumption. During use, the invention aims at the coordinated optimization of multi-point distributed battery energy storage systems participating in grid demand response, analyzes the regulation response characteristics of energy storage systems, and proposes a strategy analysis step for multi-point distributed energy storage participating in demand response. With the goal of minimizing user electricity costs, an energy storage battery demand response model based on time-of-use electricity prices is established, and on this basis, a multi-point distributed energy storage participating in demand response coordinated optimization model with the goal of minimizing network losses is constructed; the invention proposes a distributed energy storage optimization scheduling method for distribution networks based on aggregation effects; the invention proposes The peak-shaving and frequency-regulation working area division method and collaborative control strategy based on the ES charge state can realize the switching of ES between peak-shaving and frequency-regulation control, thereby improving the utilization rate. Secondly, in order to further improve the peak-shaving and frequency-regulation effects of ES, a variable power peak-shaving strategy and a comprehensive frequency-regulation control strategy combining virtual inertia and droop control are proposed in a single peak-shaving scenario. Then, on this basis, a peak-shaving and frequency-regulation collaborative control strategy is further proposed, and an evaluation index system for distributed energy storage in distribution networks is proposed based on typical application scenarios, which can effectively evaluate its operating benefits. The present invention has the advantages of considering demand response characteristics, establishing an aggregated control model, and constructing a control efficiency evaluation index. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the PQ control of the energy storage converter of the present invention.

[0039] Figure 2 It is a schematic diagram of the charge and discharge SOC function of the present invention.

[0040] Figure 3 This is the ES peak regulation principle diagram of the present invention.

[0041] Figure 4 It is a schematic diagram of the division of collaborative working areas at different time scales of the present invention.

[0042] Figure 5 It is a schematic diagram of the division of the SOC working area under the coordinated control of the present invention.

[0043] Figure 6 It is a schematic diagram of the SOC-based collaborative control working area division structure of the present invention.

[0044] Figure 7 This is a flow chart of the ES variable power charging and discharging strategy of the present invention.

[0045] Figure 8 This is the ES comprehensive control model diagram of the present invention.

[0046] Fig. 9 This is a schematic diagram of the integrated frequency control strategy of the present invention.

[0047] Fig.10 It is a schematic diagram of the division of the collaborative control working area based on load forecasting of the present invention.

[0048] Fig.11 This is a flow chart of the collaborative control strategy of the present invention.

[0049] Fig.12 It is a schematic diagram of the ES collaborative control output strategy of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Example 1

[0052] like Figure 1-12 As shown, a distributed energy storage aggregation control operation method for ensuring supply and promoting consumption is provided, and the method comprises the following steps:

[0053] Step 1: Considering the impact of distributed photovoltaic access on the electricity consumption behavior of different entities, a coordinated operation strategy of distributed energy storage under demand response is constructed based on the distributed energy storage regulation demand and aggregate response characteristics;

[0054] In this embodiment, the regulation and response characteristics of the energy storage battery are analyzed: a typical battery energy storage system is connected to the external power grid through a power electronic converter. When the converter adopts the PQ control mode, the grid voltage is used as the reference voltage, an inner loop reference current is given according to the PQ control target, and the output current is output after PI regulation and dq conversion. The PQ control of the energy storage converter is as follows Figure 1 As shown; In the formula, P and Q are respectively the active and reactive power output by the three-phase converter; U and I are respectively the phase voltage and phase current at the three-phase converter port; is the phase angle; since the output voltage of the three-phase converter is the grid voltage, the injection or output of the active power and reactive power of the energy storage converter can be controlled by controlling its output current.

[0055] Analysis of distributed energy storage participating in demand response strategy: For multi-point distributed battery energy storage facilities under the same investment and operation entity, the specific steps for participating in time-of-use electricity price response are: 1) Predict the load demand in each period of the next cycle based on the type of power equipment and production and living needs; 2) Consider the total energy storage capacity and status of the energy storage batteries at each point, respond to the time-of-use price signal as a whole, optimize the electricity consumption plan in the next cycle with the goal of minimizing electricity costs, and at the same time obtain the overall charging and discharging plan of the energy storage batteries; 3) According to the overall charging and discharging plan of the energy storage batteries, consider the energy storage status of the energy storage batteries at each point, the charging and discharging power limit and the corresponding grid constraints, and reasonably allocate the adjustment amount of the energy storage batteries at each point.

[0056] The specific steps include:

[0057] Step 1.1: Energy storage demand response model based on time-of-use electricity price: With the goal of minimizing the user's electricity cost, an energy storage battery demand response model based on time-of-use electricity price is established. This model considers the distributed energy storage batteries participating in the demand response as a whole, and the parameters involved are all lumped parameters that describe the overall characteristics;

[0058] ①Objective function: In the formula, the power consumption cycle is T, which is divided into N time periods according to the step length Δt; a i is the time-of-use electricity price in the i-th period; P L ' ,i is the net load in the ith period; ② Net load balance constraint: P L ' ,i =P L,i +P E,i (4), where P L,i is the electricity consumption plan for the i-th period; P E,i is the energy storage battery charging and discharging plan for the i-th period (charging is positive, discharging is negative); ③ Load transfer constraints: P L,i ≥P LB,i (6), where P L0,i is the original load demand in the ith period; P LB,i is the basic load demand in the i-th period (not adjustable); ④Battery charging and discharging power constraint: P E,min ≤|P E,i |≤P E,max (7), where P E,max , P E,min They are the upper and lower limits of the energy storage battery charging and discharging power; ⑤ Battery charging and discharging depth constraint: SOC min ≤SOC≤SOC max (8), where SOC max , SOC minThey are the upper and lower limits of the charging and discharging power depth of the energy storage battery; SOC is defined as: Where, E is the current energy state of the energy storage battery; E rate It is the rated energy space of the energy storage battery.

[0059] Step 1.2: Coordinated optimization model for multi-point energy storage charging and discharging: For the same investment and operation entity, the core issue of multi-point distributed energy storage participating in demand response is how to allocate the power of each energy storage facility after the overall charging and discharging plan is determined. Therefore, with the goal of minimizing network losses, the energy storage battery charging and discharging power constraints, energy constraints, line overload constraints, and node voltage constraints are considered to establish a coordinated optimization model for multi-point distributed energy storage systems participating in demand response.

[0060] ①Objective function: Where M is the number of lines; P loss,j is the line loss of the jth line; ②Charge and discharge plan constraints: Where K is the number of distributed energy storage battery facilities; P k is the charging and discharging power of the k-th energy storage facility; ΔP E is the total charging and discharging plan under demand response; ③ Power balance constraint: Where N is the number of network nodes; M is the number of network branches; S is the number of energy storage facilities; P Gi is the power generation of the ith node; P Li is the load power of the ith node; P Ek is the charge and discharge power of the energy storage battery at the kth location; ④ Single-point energy storage battery charge and discharge power constraint: P k,min ≤|P k |≤P k,max (13), where P k,max , P k,min are the upper and lower limits of the charge and discharge power of the energy storage battery at the kth location; ⑤ Single-point energy storage battery charge and discharge depth constraint: SOC k,min ≤SOC k ≤SOC k,max (14), where SOC k,max , SOC k,min are the upper and lower limits of the charge and discharge depth of the energy storage battery at the kth location; ⑥ Line overload constraint: I j <I j,max (15), where I j is the current of the jth line; I j,max is the thermal stability current of the jth line; ⑦Node voltage constraint: U i,min ≤U i ≤U i,max (16), where U i,max , U i,minare the upper and lower limits of the voltage at the ith node respectively; U i is the voltage of the ith node.

[0061] Step 2: Analyze the distributed energy storage aggregation operation mode and build a distributed energy storage aggregation control optimization dispatching model for distribution network based on the aggregation effect;

[0062] Step 3: Considering the interaction mode with the upper power grid, establish a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy for improving power grid flexibility;

[0063] Step 4: Consider the impact of distributed energy storage regulation and operation on the economy and low carbon nature of system operation, and establish distributed energy storage regulation benefit evaluation indicators.

[0064] The present invention is a distributed energy storage aggregation regulation and operation method for serving to ensure supply and promote consumption. During use, the present invention aims at the coordinated optimization of multi-point distributed battery energy storage systems participating in power grid demand response, analyzes the regulation response characteristics of the energy storage system, and proposes a strategy analysis step for multi-point distributed energy storage to participate in demand response. With the goal of minimizing user electricity costs, a storage battery demand response model based on time-of-use electricity prices is established, and on this basis, a multi-point distributed energy storage participating in demand response coordination optimization model with the goal of minimizing network losses is constructed. The present invention has the advantages of considering demand response characteristics, establishing an aggregate regulation model, and constructing regulation efficiency evaluation indicators.

[0065] Example 2

[0066] like Figure 1-12 As shown, a distributed energy storage aggregation control operation method for ensuring supply and promoting consumption is provided, and the method comprises the following steps:

[0067] Step 1: Considering the impact of distributed photovoltaic access on the electricity consumption behavior of different entities, a coordinated operation strategy of distributed energy storage under demand response is constructed based on the distributed energy storage regulation demand and aggregate response characteristics;

[0068] Step 2: Analyze the distributed energy storage aggregation operation mode and build a distributed energy storage aggregation control optimization dispatching model for distribution network based on the aggregation effect;

[0069] In this embodiment, step 2 includes the following steps:

[0070] Step 2.1: Distributed energy storage aggregation model, including the following steps:

[0071] Step 2.11: Mathematical model of energy storage: ① Energy storage charging and discharging model: When the energy storage is charged, it absorbs active power, and the SOC increases; when it is discharged, it emits active power, and the SOC decreases; the SOC at time t t The value calculation formula is: Where SOC0 is the SOC value at the initial moment of energy storage; P ch , P dis are the charge and discharge power during the period from t to t-1, and at most one of them can be non-zero; η ch , η dis are the charge and discharge efficiency respectively; Δt is the duration of charge and discharge; S rate The rated capacity of energy storage; energy storage is generally connected to the grid through PCS (power conversion system). While absorbing or emitting active power, it can also absorb or emit reactive power, providing certain reactive support and voltage management for the grid. In the actual operation process, its reactive power can be controlled according to the situation; ② Constraints of energy storage: including SOC constraints and charge and discharge constraints. SOC constraints: SOC min ≤SOC i ≤SOC max (18), where SOC min , SOC max They are the minimum and maximum values ​​allowed by the energy storage SOC; charging and discharging constraints: Where P ch,max , P dis,max are the maximum charging power and maximum discharging power of energy storage respectively;

[0072] Step 2.12: Power allocation of distributed energy storage: Aggregate multiple energy storage into an equivalent centralized energy storage for dispatching by the dispatching center. Only one centralized energy storage charge and discharge variable is required in the dispatching optimization calculation. In order to make the power allocation of each energy storage reasonable and maintain the relative balance of SOC during the dispatching process, the rated power and SOC value of each energy storage are used to jointly determine its charge and discharge power. The specific determination method is as follows: Charging: Discharge: Where P irate is the rated power of energy storage i; P ich , P idis are the charging and discharging power of energy storage i respectively; P allch , P alldis is the total charging and discharging power required by the dispatching; N is the total number of energy storages; f ch (x), f dis (x) are respectively the charging and discharging SOC functions of energy storage; in order to make the energy storage with lower SOC charge more and discharge less, and the energy storage with higher SOC charge less and discharge more, the Sigmoid function is transformed by scaling, translation and other transformations as the charging and discharging SOC functions of energy storage; the specific expressions of the charging and discharging SOC functions are: The curves of charge and discharge SOC functions are shown in Figure 2 ,from Figure 2It can be seen that the selected charging and discharging SOC function can make the energy storage with higher SOC have lower charging power and higher discharging power, while the energy storage with lower SOC has higher charging power and lower discharging power, thus realizing the reasonable distribution of total charging and discharging power among various energy storages.

[0073] Step 2.13: Equivalent centralized energy storage parameters under aggregation effect: After aggregating each energy storage using equations (20) and (21), only the equivalent centralized rated power, equivalent centralized rated capacity and equivalent centralized charging and discharging efficiency are needed to represent the entire distributed energy storage system. When each energy storage has the same charging and discharging time at rated power, the corresponding equivalent centralized energy storage parameters are defined as follows: The equivalent centralized rated power is: Where P allrate is the rated power of the equivalent centralized energy storage; P irate is the rated power of the i-th energy storage; the equivalent concentrated rated capacity is: In the formula, S allrate is the rated capacity of the equivalent centralized energy storage; S irate is the rated capacity of the i-th energy storage; the equivalent centralized charging efficiency and the equivalent centralized discharging efficiency are: Where η allch , η alldis are the equivalent concentrated charging efficiency and the equivalent concentrated discharging efficiency respectively; η ich , η idis are the charging efficiency and discharging efficiency of the i-th energy storage, respectively. After defining the corresponding equivalent centralized parameters, only one equivalent centralized energy storage is needed to represent all energy storages during scheduling, and the power of each energy storage is allocated using equations (20) and (21).

[0074] Step 2.2: The mathematical model of distributed energy storage optimal scheduling includes the following steps:

[0075] Step 2.21: Division of charging and discharging time periods and load power intervals: Before optimizing the scheduling of distributed energy storage in the distribution network, it is necessary to first determine the scheduling cycle of the distributed energy storage system and the charging and discharging options of energy storage in different time periods. Therefore, when the load of the distribution network is less than the average value of the entire time period, the energy storage can be charged; when the load of the distribution network is greater than the average value of the entire time period, the energy storage can be discharged; when scheduling the energy storage of the distribution network, the charging and discharging power of the energy storage is not required to have a high accuracy. Therefore, before optimizing the calculation, the load of the distribution network can be divided into different power intervals, so that the energy storage charging and discharging power in different time periods within the same load power interval is the same. When optimizing the scheduling calculation, the load power and energy storage charging and discharging power in the same load power interval are regarded as the same value. In the same power interval, only one calculation can represent multiple calculations that need to be repeated in the interval. Therefore, after adopting the load power partition, the variables in the optimization scheduling model can be further reduced;

[0076] Step 2.22: Objective function: According to the peak shaving and valley filling and voltage management functions of distributed energy storage in the distribution network, load fluctuation and voltage deviation are used as the objective functions of optimal scheduling, respectively. ① Load fluctuation: In the formula, f ΔP is the load fluctuation of the distribution network in all periods; K is the number of load power intervals; M j is the number of loads in the jth load power interval; P javrload is the average power in the jth load power interval; P avrload is the average power of the distribution network during all periods; P jess is the total active output of the distributed energy storage system in the jth load power interval. A positive value indicates discharge and a negative value indicates charging. ② Voltage deviation: In the formula, f ΔU is the voltage deviation of the distribution network in all periods; R is the total number of nodes in the distribution network; U ji is the voltage amplitude of node i in the jth load power interval; U exp is the expected voltage of the node, which is taken as 1 in the present invention;

[0077] Step 2.23: Constraints: The power flow constraints are: The voltage range constraint is: U min ≤U ji ≤U max (29); The charging and discharging power constraints of the distributed energy storage system are: The SOC constraint of the distributed energy storage system is: SOC min ≤SOC all ≤SOC max (31), SOC first=SOC last (32), where P jis , Q jis are the active power and reactive power injected into node i in the jth load power interval respectively; G ik , B ik are the conductance and susceptance from node i to node k respectively; θ ik is the phase angle difference between nodes i and k; P jiavrload , Q jiavrload are respectively the active power and reactive power of the node i load in the jth load power interval; P jiess , Q jiess are respectively the active power and reactive power of the energy storage installed at node i in the jth load power interval; U min , U max are the minimum and maximum values ​​allowed for the node voltage respectively; SOC all is the SOC value of equivalent centralized energy storage; SOC min , SOC max They are the minimum and maximum values ​​allowed by the distributed energy storage system SOC; SOC first , SOC last are the initial SOC and final SOC of the equivalent centralized energy storage, respectively.

[0078] Step 2.3: Solving the optimal scheduling.

[0079] Aiming at the day-ahead scheduling problem of distributed energy storage in distribution network, the particle swarm algorithm in intelligent algorithm is used to solve the optimization scheduling model, specifically: 1) read the network structure and parameters of distribution network, parameters of each energy storage, initial power of energy storage and related constraints, and calculate the centralized parameters corresponding to equivalent centralized energy storage; 2) predict the load data of distribution network on the day-ahead, calculate the load average value of distribution network in the whole period; compare the load of each period of distribution network with the load average value, if it is greater than the load average value, the energy storage in this period is set to discharge, if it is less than the load average value, the energy storage in this period is set to charge; at the same time, the total load data in each period is distributed to each node according to the proportion of the load of each node in distribution network, as the load of each node in different periods; 3) according to the established optimization scheduling model, the particle swarm algorithm is used to solve it and output the relevant results, such as load fluctuation and voltage deviation results, and charging and discharging power of each energy storage.

[0080] Step 3: Considering the interaction mode with the upper power grid, establish a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy for improving power grid flexibility;

[0081] Step 4: Consider the impact of distributed energy storage regulation and operation on the economy and low carbon nature of system operation, and establish distributed energy storage regulation benefit evaluation indicators.

[0082] The present invention is a distributed energy storage aggregation control operation method for serving to ensure supply and promote consumption. During use, the present invention proposes a method based on aggregation effect to schedule each energy storage for the distributed energy storage optimization scheduling problem in the distribution network: through a reasonable power allocation method, energy storage with different charging and discharging efficiencies can be aggregated into an equivalent centralized energy storage for scheduling by the upper-level scheduling center, and only one equivalent centralized energy storage is needed to represent multiple energy storages; in addition, before establishing the optimization scheduling model, the total load of the distribution network is divided into power intervals, and the interval average value is used to represent all loads in the interval, which further reduces the variables required in the optimization scheduling model. While greatly reducing the variables, the distributed energy storage in the distribution network can be reasonably and correctly scheduled, reducing the load fluctuation and voltage deviation of the distribution network; the present invention has the advantages of considering demand response characteristics, establishing an aggregation control model, and constructing a control efficiency evaluation index.

[0083] Example 3

[0084] like Figure 1-12 As shown, a distributed energy storage aggregation control operation method for ensuring supply and promoting consumption is provided, and the method comprises the following steps:

[0085] Step 1: Considering the impact of distributed photovoltaic access on the electricity consumption behavior of different entities, a coordinated operation strategy of distributed energy storage under demand response is constructed based on the distributed energy storage regulation demand and aggregate response characteristics;

[0086] Step 2: Analyze the distributed energy storage aggregation operation mode and build a distributed energy storage aggregation control optimization dispatching model for distribution network based on the aggregation effect;

[0087] Step 3: Considering the interaction mode with the upper power grid, establish a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy for improving power grid flexibility;

[0088] In this embodiment, step 3 includes the following steps:

[0089] Step 3.1: Collaborative control work area division, including the following steps:

[0090] Step 3.11: Work area division: Figure 3Figure 1 is a schematic diagram of ES peak-shaving principle. However, when ES is only applied to a single peak-shaving scenario, it only performs charging and discharging actions during load peak periods or load valley periods, and is idle in other periods, which greatly reduces the ES utilization rate. Therefore, the present invention adopts the "idle time multiplexing" method for ES (that is, in the same time period, there is only one working mode (peak shaving or frequency modulation)). In the non-peak-shaving stage of ES, idle ES is used to improve the grid frequency, thereby improving the utilization rate, so that the ES can be divided into multiple working areas, such as Figure 4 As shown;

[0091] Step 3.12: Division of working areas based on SOC: When ES works in the valley filling area, ES is in a charging state, and its SOC increases from 0.1 to 0.9; when ES works in the peak clipping area, it is in a discharging state, and SOC decreases from 0.9 to 0.1; at this time, when ES switches to the frequency modulation area, the initial SOC value may be 0.1 or 0.9; however, the frequency deviation has a bidirectional possibility, so when SOC is 0.1, the frequency drop problem cannot be improved by discharging, and when SOC is 0.9, the frequency rise problem cannot be improved by charging; the present invention sets the SOC peak modulation working interval to 0.15-0.85, and reserves 0.05 for frequency modulation, Figure 5 The schematic diagram of SOC partition under cooperative control is shown in Figure 2. The working area of ​​SOC peak-shaving and frequency-modulation cooperative control is divided as follows: Figure 6 shown.

[0092] Step 3.2: ES peak-shaving and frequency-shaving coordinated control strategy, including the following steps:

[0093] Step 3.21: Single peak load control strategy: The present invention proposes a variable power charge and discharge control strategy, which enables ES to dynamically adjust the charge and discharge power according to the load peak load demand. The ES variable power charge and discharge control strategy uses one day as the time scale and is based on the principle of power balance, that is, the capacity absorbed by ES during valley filling is equal to the capacity released by peak load shaving. The control process of the variable power charge and discharge strategy is as follows: Figure 7 As shown, the specific control steps are: 1) Import the original load P1 and obtain the peak value P of the load max and valley value P min ; 2) Set the rated power P of ES m With rated capacity E m , according to the SOC limit of ES, the available capacity E of ES power station is obtained n , take the initial clipping line P x =P max and moves downward with a set small value step size ΔP, clipping the peak line P x =P max The intersection points of -k·ΔP and the load curve are t1 and t2, and the amount of electricity released by peak shaving can be determined: When the peak capacity is S fWith available capacity E n When they are equal, the peak clipping line P is determined x , if the peak clipping capacity S f Less than available capacity E n , the iteration coefficient k = k + 1, the peak clipping line P x Continue to move down until S f With E n Equal; ES participates in the peak load scenario with variable power charging and discharging strategy to reduce peak output: P bess-x =P1-P x (34); 3) Take the initial valley filling line P t =P min , move upward with a set small value step size ΔP, filling the valley line P t =P min The intersection points of -k·ΔP and the load curve are t3 and t4, and the amount of electricity absorbed by valley filling is: When the valley filling capacity S g With available capacity E n When they are equal, the valley filling line P g It is also determined that if S g Less than E n , then continue to iterate and move the valley filling line upward until S g With S f Equal, ES participates in the peak load scenario variable power charging and discharging strategy to reduce peak output: P bess-t =P1-P t (36); The above steps can determine the action depth and action time of the variable power charging and discharging strategy of ES participating in the peak load regulation scenario;

[0094] Step 3.22: Single frequency modulation control strategy: The present invention proposes a comprehensive frequency modulation control strategy (virtual droop control and virtual inertia control), the specific process of which is as follows: Figure 8 As shown by Figure 8 It can be seen that when the two control strategies are combined, the ES output is: ΔP(s)=-(K DR +K IR s)G(s)ΔF(s)(37), where K DR , K IR are inertia and droop control coefficients respectively; under the comprehensive control strategy, the system frequency is: In the formula, K G is the frequency regulation coefficient of the traditional unit; the system initial frequency deviation change rate and steady-state frequency deviation can be obtained by Laplace transform: It can be seen from formula (39) that by combining droop control with inertia control, the steady-state frequency deviation can be reduced on the one hand, and the frequency deviation change rate can be suppressed on the other hand; when the frequency deviation is in the deterioration stage, the virtual inertia control strategy is adopted; and when the frequency deviation is in the recovery stage, the virtual droop control is adopted; Fig. 9 The schematic diagram of the integrated frequency control strategy in different frequency modulation stages is shown in Figure 2. Fig. 9 In the period from t1 to t2, Δf·df / dt>0 is satisfied, and the frequency deviation has a tendency to continue to increase, which belongs to the frequency deterioration condition, and the virtual inertia control strategy needs to be adopted; in the period from t3 to t4, the frequency deviation has a tendency to decrease, gradually approaching 0, which belongs to the frequency recovery condition. At this time, Δf·df / dt<0, and droop control will slow down the frequency recovery speed, so droop control is adopted in this condition;

[0095] Step 3.23: Collaborative control strategy for peak-shaving and frequency regulation: The present invention adopts the "idle time reuse" approach to the collaborative control strategy of ES participating in peak-shaving and frequency regulation. In the non-peak-shaving stage of ES, idle ES is used to improve the grid frequency, thereby improving the utilization rate. Therefore, it is necessary to obtain the peak-shaving and valley-filling lines based on the day-ahead predicted load curve, and then divide the outer peak-shaving area and the inner frequency regulation area. The outer peak-shaving area is further divided into the valley-filling area and the peak-shaving area. The period of the outer peak-shaving area projected to the x-axis is the peak-shaving period, thereby obtaining the collaborative working period based on load forecasting, such as Fig.10 As shown by Fig.10 It can be seen that three working areas can be divided based on the day-ahead load forecast curve, so as to determine the time period when ES participates in peak load regulation and frequency regulation on the next day. Its peak load regulation and frequency regulation coordinated control strategy mainly includes five links: 1) Importing predicted load and real-time load disturbance data; 2) Based on the predicted load data, the peak shaving line and valley filling line are determined by the variable power strategy, and the frequency deviation is determined based on the real-time load disturbance data; 3) When the predicted load data is outside the peak shaving line and valley filling line, it means that ES is in the outer peak load regulation area. At this time, the ES output is determined according to the variable power peak load regulation control strategy; 4) If ES is not in the outer peak load regulation area, it means that it is in the inner frequency regulation area, and it is judged whether the frequency deviation crosses the dead zone. If it crosses the dead zone, the ES output is determined according to the comprehensive frequency regulation control strategy, otherwise the ES output is 0; 5) Output the final output of ES, and get the peak load regulation and frequency regulation coordinated control strategy as shown in Fig.11 As shown;

[0096] According to the load forecast, the planned valley-filling output P of ES at time t is obtained g (t), planned peak shaving output P f (t), the ES frequency modulation output caused by the real-time load disturbance is P PFR (t), the load disturbance sampling interval is 1min, then t=1440 in the one-day time scale, thus the ES output in each working area can be obtained: 1) Valley filling area: during this period, the load is at the valley value, the power demand is low, the power supply pressure is small, the ES is in the charging state, and the excess power is stored, P bess (t) = P g (t)<0; 2) Frequency regulation area: There is no peak regulation demand during this period. If the frequency deviation is greater than the set upper limit, ES is in charging state, PPFR (t)<0; if the frequency deviation is less than the set lower limit, ES is in discharge state, P bess (t) = P PRF (t)>0; if the frequency deviation is within the dead zone, the ES output is 0, P bess (t) = 0; 3) Peak shaving area: During this period, the load is at its peak, the power demand is large, and the ES is in a discharging state. bess (t) = P f (t)>0; According to the division of working areas and output rules of ES participating in peak and frequency regulation coordinated control, the ES output at each moment can be obtained. Among them, the coordinated output strategies under different time scales are as follows: Fig.12 shown.

[0097] Step 3.24: Peak and frequency regulation evaluation index: ① Peak regulation evaluation index: In order to quantitatively measure the peak regulation effect, it can be quantified by defining evaluation indicators, specifically: 1) Absolute peak-to-valley difference: ΔP = P max -P min (40), where P max , P min They are the peak and valley values ​​of the load respectively. The smaller the value of this indicator is, the smaller the absolute deviation of the load is within the 24h time scale; 2) Peak-valley coefficient: This indicator reflects the degree of load fluctuation. The smaller the value, the greater the degree of fluctuation. 3) Peak-to-valley difference rate: This indicator reflects the fluctuation range of the load. The smaller the value, the smaller the fluctuation range. 4) Load variation variance: Where n is the number of samples for load forecasting; i is the corresponding sampling time; P avg It is the average value of the predicted load; this indicator reflects the flatness of the load, the smaller the value, the flatter it is; 5) Peak reduction rate: Where P m ' ax It is the load peak after ES participates in peak load regulation. This indicator reflects the degree of ES participation in peak load reduction.

[0098] ② Frequency modulation evaluation index: In order to measure the frequency modulation effect, it is necessary to define evaluation indicators to quantify it. For step load disturbance, the frequency deterioration speed and recovery speed are defined: In the formula, v d Refers to the frequency deterioration rate, v d The smaller the value, the slower the frequency deterioration speed, and the better the frequency modulation effect; Δf max , Δf0 represent the maximum frequency deviation and the initial frequency deviation respectively; t max , t0 represent the time corresponding to the maximum frequency deviation and the initial time respectively; v r Refers to the frequency deterioration rate, v rThe larger the value, the faster the frequency recovery, and the better the frequency modulation effect; Δf sta is the steady-state frequency deviation; t sta is the moment when the steady-state frequency deviation occurs; for continuous disturbances, the frequency deviation and SOC root mean square value are used as evaluation indicators for the frequency modulation scenario: In the formula, f i , SOC i are the frequency and SOC at time i respectively; f0 is the reference frequency; SOC0 is 0.5; R f , R SOC Represents the degree to which the system frequency and SOC deviate from the baseline value. The smaller the deviation value, the better the frequency modulation effect and SOC maintenance effect.

[0099] Step 4: Consider the impact of distributed energy storage regulation and operation on the economy and low carbon nature of system operation, and establish distributed energy storage regulation benefit evaluation indicators.

[0100] In this embodiment, step 4 includes the following steps:

[0101] Step 4.1: Cost analysis indicators: ① Initial investment cost: refers to the total amount of capital invested at the beginning of the construction of the distributed energy storage system project. This part of the capital is usually mainly used for the purchase of equipment, including battery energy storage power supplies, flywheels and other infrastructure, as well as the purchase of supporting auxiliary facilities; in addition, when some components of the energy storage equipment need to be scrapped, they also need to be recycled. The fixed asset investment cost is composed of the P of the energy storage system. max The power part and E max The capacity part is determined by the power part, which usually depends on the grid-connected power conversion system: C1 = (k P ×P max +k E ×E max )(49), where k P is the unit power investment cost; k E = Investment cost per unit capacity; ② Operation and maintenance cost: refers to the cost investment incurred by the energy storage system during its total life cycle due to equipment loss, aging, or when the operating parameters reach the scrap standard and need maintenance or replacement. The fixed part is also composed of the rated power and rated capacity of the energy storage system, while the variable part is determined by the annual charge and discharge capacity of the energy storage system. The capacity part of the operation and maintenance cost can usually be equivalent to the capacity part of the initial investment cost, that is, the overall replacement of the energy storage equipment; and the variable part refers to the funds invested dynamically to ensure the normal operation of the energy storage system during its life cycle. Due to the changes in dynamic parameters, it needs to be calculated separately every year, usually including the installation, commissioning, manpower, maintenance and other costs of the energy storage system; the annual operation and maintenance cost of the energy storage equipment is related to its operating conditions and can be expressed as: In the formula, C rpis the unit power operation and maintenance cost; C re is the unit capacity operation and maintenance cost; C d is the dynamic cost per unit of electricity; W c (t), W d (t) are the annual charging capacity and annual discharging capacity of the energy storage system respectively;

[0102] Step 4.2: Income-cost indicators: ① Low storage and high generation income: Based on the implementation of the time-of-use electricity price policy, energy storage equipment is encouraged to choose charging at load troughs, that is, when electricity prices are low, and to choose discharging at load peaks, that is, when electricity prices are high. At the same time, power grids and enterprises with energy storage equipment can make profits by using the time-of-use electricity price policy. The mathematical model of the income obtained by the energy storage system is: Where W1 is the low storage and high generation benefit of the energy storage system; Respectively represent the discharge and charging power of the energy storage system in the i-th time period; P i is the real-time on-grid electricity price in time period i; ② Capacity benefit: During peak load periods, power can be generated to provide power output and serve as the peak capacity of the power grid; during low power consumption periods, the excess power during the low power consumption period can be absorbed to store energy and reduce the peak-to-valley difference in load; the distributed energy storage system can make up for the working capacity and standby capacity of the system, thereby reducing the installed capacity of traditional thermal power and saving the investment and operating costs of the power system. The resulting economic benefits are called capacity benefits; the application of energy storage systems can improve the ability to accept renewable energy output and reduce the amount of abandoned light and wind; W2 is used to represent the added value of renewable energy power generation income, which is: In the formula, E res It represents the annual increase in distributed grid-connected power generation; = represents the amount of renewable energy power generation added to the grid during time period i; ③ Environmental benefits: Distributed energy storage systems have environmental benefits in the process of participating in the peak load regulation of the power grid. Coal-fired power units not only consume a large amount of fossil fuels during operation, but also emit a large amount of pollutants into the environment, which is very unfriendly to the environment. The application of distributed energy storage systems can improve this problem, thereby bringing considerable environmental benefits. The main environmental pollutants generated by coal-fired power generation are SO2, NO x , CO, CO2 and smoke, etc., SO2 and NO x The calculation is based on the emission of as an example: 1) SO2 daily emission: The composition of coal contains sulfur, and after combustion, sulfide is produced, the main component of which is SO2. The daily emission is calculated as follows: Where b yx S is the unit power supply coal consumption of the replaced thermal power unit; ar is the sulfur content of the coal; The proportion of SO2 generated by coal combustion; is the efficiency of the desulfurization device; W tgd(d) is the daily peak-shaving charging power of clean energy by energy storage on day d, i.e., the wasted power when no energy storage is used; 2) NO x Daily emissions: The main components of nitrogen oxides are NO and NO x In the actual combustion process, the nitrogen in the coal components is not completely converted into NO x， The ratio of converted nitrogen to total nitrogen in the fuel is expressed as λ N express: Where N N is the mass fraction of nitrogen in the coal; m N NO generated from fuel nitrogen x Accounts for all NO x Emission ratio; η N The environmental benefits of the energy storage system are defined as the benefits of reducing pollutants and greenhouse gas emissions when the energy storage system replaces coal-fired power units to participate in the peak load regulation of the power system: Where W3 is the environmental benefit of the energy storage system; V ei is the environmental value of the i-th pollutant; n is the total number of pollutants; Q i is the emission of the i-th pollutant; I is the annual value of the investment cost of the desulfurization device and the smoke filtration device.

[0103] The present invention provides a distributed energy storage aggregation regulation and operation method for ensuring supply and promoting consumption. During use, the present invention proposes a peak-shaving and frequency-regulation working area division method and a collaborative control strategy based on the ES charge state, which can realize the switching of ES between peak-shaving and frequency-regulation control, thereby improving the utilization rate. Secondly, in order to further improve the peak-shaving and frequency-regulation effect of ES, a variable power peak-shaving strategy and a comprehensive frequency-regulation control strategy combining virtual inertia and droop control are proposed in a single peak-shaving scenario. Then, on this basis, a peak-shaving and frequency-regulation collaborative control strategy is further proposed, and an evaluation index system for distributed energy storage in distribution networks is proposed based on typical application scenarios, which can effectively evaluate its operating benefits. The present invention has the advantages of considering demand response characteristics, establishing an aggregate regulation model, and constructing a regulation efficiency evaluation index.

Claims

1. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption, characterized by: The method comprises the following steps: Step 1: Considering the impact of distributed photovoltaic access on the electricity consumption behavior of different entities, a coordinated operation strategy of distributed energy storage under demand response is constructed based on the distributed energy storage regulation demand and aggregate response characteristics; Step 2: Analyze the distributed energy storage aggregation operation mode and build a distribution network distributed energy storage aggregation control optimization dispatching model based on the aggregation effect; Step 3: Considering the interaction mode with the upper power grid, establish a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy for improving power grid flexibility; Step 4: Consider the impact of distributed energy storage regulation and operation on the economy and low carbon nature of system operation, and establish distributed energy storage regulation benefit evaluation indicators.

2. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 1, characterized in that: The step 1 of constructing a coordinated operation strategy of distributed energy storage under demand response includes the following steps: Step 1.1: Energy storage demand response model based on time-of-use electricity price: With the goal of minimizing the user's electricity cost, establish an energy storage battery demand response model based on time-of-use electricity price; Step 1.2: Multi-point energy storage charging and discharging coordination optimization model: With the goal of minimizing network losses, considering the energy storage battery charging and discharging power constraints, energy constraints, line overload constraints, and node voltage constraints, a coordinated optimization model is established when multi-point energy storage systems participate in demand response.

3. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 2, characterized in that: The energy storage demand response model based on time-of-use electricity price in step 1.1 is specifically as follows: ① Objective function: In the formula, the power consumption cycle is T, which is divided into N time periods according to the step length Δt; a i is the time-of-use electricity price in the ith period; P L ' ,i is the net load in the ith period; ② Net load balance constraint: P L ' ,i =P L,i +P E,i (4), where P L,i is the electricity consumption plan for the ith period; P E,i is the energy storage battery charging and discharging plan for the i-th period; ③ Load transfer constraints: P L,i ≥P LB,i (6), where P L0,i is the original load demand in the ith period; P LB,i is the basic load demand in the i-th period; ④Battery charging and discharging power constraint: P E,min ≤|P E,i |≤P E,max (7), where P E,max , P E,min They are the upper and lower limits of the energy storage battery charging and discharging power; ⑤ Battery charging and discharging depth constraint: SOC min ≤SOC≤SOC max (8), where SOC max , SOC min They are the upper and lower limits of the charging and discharging power depth of the energy storage battery; SOC is defined as: Where, E is the current energy state of the energy storage battery; E rate It is the rated energy space of the energy storage battery.

4. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 2, characterized in that: The multi-point energy storage charging and discharging coordination optimization model in step 1.2 is specifically as follows: ① Objective function: Where M is the number of lines; P loss,j is the line loss of the jth line; ②Charge and discharge plan constraints: Where K is the number of distributed energy storage battery facilities; P k is the charging and discharging power of the k-th energy storage facility; ΔP E is the total charging and discharging plan under demand response; ③ Power balance constraint: Where N is the number of network nodes; M is the number of network branches; S is the number of energy storage facilities; P Gi is the power generation of the ith node; P Li is the load power of the ith node; P Ek is the charge and discharge power of the energy storage battery at the kth location; ④ Single-point energy storage battery charge and discharge power constraint: P k,min ≤|P k |≤P k,max (13), where P k,max , P k,min are the upper and lower limits of the charge and discharge power of the energy storage battery at the kth location; ⑤ Single-point energy storage battery charge and discharge depth constraint: SOC k,min ≤SOC k ≤SOC k,max (14), where SOC k,max , SOC k,min are the upper and lower limits of the charge and discharge depth of the energy storage battery at the kth location; ⑥ Line overload constraint: I j <I j,max (15), where I j is the current of the jth line; I j,max is the thermal stability current of the jth line; ⑦Node voltage constraint: U i,min ≤U i ≤U i,max (16), where U i,max , U i,min are the upper and lower limits of the voltage at the ith node respectively; U i is the voltage of the node at the ith location.

5. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 1, characterized in that: The step 2 of constructing a distribution network distributed energy storage aggregation control optimization scheduling model based on the aggregation effect includes the following steps: Step 2.1: Distributed energy storage aggregation model; Step 2.2: Mathematical model for optimal scheduling of distributed energy storage; Step 2.3: Solving the optimal scheduling.

6. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 5, characterized in that: The distributed energy storage aggregation model in step 2.1 includes the following steps: Step 2.11: Mathematical model of energy storage: ① Energy storage charging and discharging model: When the energy storage is charged, it absorbs active power, and the SOC increases; when it is discharged, it emits active power, and the SOC decreases; the SOC at time t t The value calculation formula is: Where SOC0 is the SOC value at the initial moment of energy storage; P ch , P dis are the charge and discharge power during the period from t to t-1, and at most one of them can be non-zero; η ch , η dis are the charge and discharge efficiency respectively; Δt is the duration of charge and discharge; S rate is the rated capacity of energy storage; ② Energy storage constraints: including SOC constraints and charge and discharge constraints, SOC constraints: SOC min ≤SOC i ≤SOC max (18), where SOC min , SOC max They are the minimum and maximum values ​​allowed by the energy storage SOC; the charging and discharging constraints are: Where P ch,max , P dis,max are the maximum charging power and maximum discharging power of energy storage respectively; Step 2.12: Power allocation of distributed energy storage: The specific determination method is as follows: Charging: Discharge: Where P irate is the rated power of energy storage i; P ich , P idis are the charging and discharging power of energy storage i respectively; P allch , P alldis is the total charging and discharging power required by the dispatching; N is the total number of energy storages; f ch (x), f dis (x) are the charge and discharge SOC functions of energy storage respectively; the specific expressions of the charge and discharge SOC functions are: Step 2.13: Equivalent centralized energy storage parameters under aggregation effect: After aggregating each energy storage using equations (20) and (21), only the equivalent centralized rated power, equivalent centralized rated capacity and equivalent centralized charging and discharging efficiency are needed to represent the entire distributed energy storage system. When each energy storage has the same charging and discharging time at rated power, the corresponding equivalent centralized energy storage parameters are defined as follows: The equivalent centralized rated power is: Where P allrate is the rated power of the equivalent centralized energy storage; P irate is the rated power of the i-th energy storage; the equivalent concentrated rated capacity is: In the formula, S allrate is the rated capacity of the equivalent centralized energy storage; S irate is the rated capacity of the i-th energy storage; The equivalent concentrated charging efficiency and the equivalent concentrated discharging efficiency are: Where η allch , η alldis are the equivalent concentrated charging efficiency and the equivalent concentrated discharging efficiency respectively; η ich , η idis are the charging efficiency and discharging efficiency of the i-th energy storage respectively.

7. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 5, characterized in that: The distributed energy storage optimization scheduling mathematical model in step 2.2 includes the following steps: Step 2.21: Division of charging and discharging time periods and load power intervals; Step 2.22: Objective function: According to the peak shaving and valley filling and voltage management functions of distributed energy storage in the distribution network, load fluctuation and voltage deviation are used as the objective functions of optimal scheduling, respectively. ① Load fluctuation: In the formula, f ΔP is the load fluctuation of the distribution network in all periods; K is the number of load power intervals; M j is the number of loads in the jth load power interval; P javrload is the average power in the jth load power interval; P avrload is the average power of the distribution network during all periods; P jess is the total active output of the distributed energy storage system in the jth load power interval; ② Voltage deviation: In the formula, f ΔU is the voltage deviation of the distribution network in all periods; R is the total number of nodes in the distribution network; U ji is the voltage amplitude of node i in the jth load power interval; U exp is the expected voltage of the node; Step 2.23: Constraints: The power flow constraints are: The voltage range constraint is: U min ≤U ji ≤U max (29); The charging and discharging power constraints of the distributed energy storage system are: The SOC constraint of the distributed energy storage system is: SOC min ≤SOC all ≤SOC max (31), SOC first =SOC last (32), where P jis , Q jis are the active power and reactive power injected into node i in the jth load power interval respectively; G ik , B ik are the conductance and susceptance from node i to node k respectively; θ ik is the phase angle difference between nodes i and k; P jiavrload , Q jiavrload are respectively the active power and reactive power of the node i load in the jth load power interval; P jiess , Q jiess are respectively the active power and reactive power of the energy storage installed at node i in the jth load power interval; U min , U max are the minimum and maximum values ​​allowed for the node voltage respectively; SOC all is the SOC value of equivalent centralized energy storage; SOC min , SOC max They are the minimum and maximum values ​​allowed by the distributed energy storage system SOC; SOC first , SOC last are the initial SOC and final SOC of the equivalent centralized energy storage, respectively.

8. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 1, characterized in that: The step 3 of establishing a distributed energy storage multi-time scale energy storage peak and frequency regulation coordinated control strategy includes the following steps: Step 3.1: Collaborative control work area division; Step 3.2: ES peak-shaving and frequency-regulation coordinated control strategy.

9. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 8, characterized in that: The ES peak-shaving and frequency-shaving coordinated control strategy in step 3.2 includes the following steps: Step 3.21: Monotonic peak control strategy; Step 3.22: Single frequency modulation control strategy; Step 3.23: Peak and frequency regulation coordinated control strategy; Step 3.24: Peak and frequency regulation evaluation indicators.

10. A distributed energy storage aggregation control operation method for ensuring supply and promoting consumption as claimed in claim 1, characterized in that: The step 4 of establishing the distributed energy storage regulation benefit evaluation index comprises the following steps: Step 4.1: Cost analysis indicators; Step 4.2: Benefit-cost indicators.

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