CN105140939B - The multi-objective coordinated control method of active load based on energy-storage system - Google Patents

Abstract

The invention discloses a multi-objective coordinated control method for active loads based on an energy storage system. The multi-objectives include the power sum of the tie line of the regional power grid, the user's economic benefits and the power company's operating benefits, and the user adjusts the weight of the operation target according to actual needs. , by analyzing the power output of the demand side, the state of charge of the energy storage system, the user's energy demand, and the real-time electricity price to automatically plan the real-time output of the energy storage system to achieve the optimal overall result of multi-objective operation. The control steps include: data collection; parameter setting; data screening; step-by-step optimization; instruction execution. The control method described in the present invention can take into account the interests of users and the power grid, and significantly improve the economic and social benefits of the smart demand side. Input-output ratio.

Description

Active load multi-objective coordinated control method based on energy storage system

technical field

The invention relates to the field of electric power technology, in particular to an active load multi-objective coordinated control method based on an energy storage system.

Background technique

The smart demand side is the extension of the smart grid on the power consumption side, and it belongs to the key and hot research field of the smart grid. Different from the traditional demand side, not only distributed renewable energy and energy storage devices appear in the intelligent demand side, but also the power quality problems brought about by the volatility of new energy generation and the two-way flow characteristics of the power flow all affect the original detection and control methods. At the same time, thanks to the rapid development of communication technology, there is the possibility of information interaction between electrical equipment and the grid, which greatly enhances the observability and controllability of the demand side. Realization provides a solid guarantee.

The intelligent demand side combines communication technology with power control technology, and the control object is changed from traditional passive load to active distributed power supply, energy storage, and controllable load. The randomness, intermittence, and volatility of distributed renewable energy output pose challenges to power quality, system stability, and grid scheduling. It is necessary to use energy storage devices to improve power quality, suppress power fluctuations, and improve power utilization efficiency. In addition, the energy storage system can also provide voltage support and uninterrupted power supply (Uninterrupted Power Supply, UPS) for the intelligent demand side, alleviate peak grid congestion and time-of-use electricity price management.

Therefore, combined with the characteristics of the energy storage system participating in the response, an active load coordination control method for the energy storage system participating in the response based on multi-objective optimization to take into account the interests of both the user side and the grid side is studied. On the premise of ensuring the safety of users and the grid, Realize the collection and sorting of electricity price information, weather information and user energy demand, and plan and control the output of the energy storage system. On the premise of not changing the user's willingness to use energy, optimize the power feeding and power consumption behavior to improve economic benefits.

At present, the research on the coordinated control of intelligent demand-side energy storage systems in relevant literature is limited to the theoretical stage. The main defects of the existing control strategies are: on the one hand, there is no integrated control characteristics of demand-side loads and power supply information, and they cannot satisfy users to the maximum extent. On the premise of energy demand, make a reasonable control plan; on the other hand, the existing optimization strategy fails to take into account the interests of grid operators and users at the same time, and fails to achieve the improvement of economic and social benefits at the same time.

Contents of the invention

The technical problem to be solved by the present invention is to overcome the defects of the existing intelligent demand-side control strategy, and to propose an active load multi-level energy storage system based The target coordination control method, under the premise of ensuring the safety of users and the power grid, plans and controls the output of the intelligent demand-side energy storage system by collecting and sorting out electricity price information, weather information and user energy demand, so as to realize the improvement of economic and social benefits.

In order to solve the above technical problems, the present invention provides a multi-objective coordinated control method for active loads based on energy storage systems, including:

Set parameters, the parameters include: control precision T, control weight coefficient and The maximum number of charge-discharge transitions N _cdmax of the energy storage system, the minimum charge/discharge duration of the energy storage system T _min , the state of charge SOC _max at the end of charge of the energy storage system, and the state of charge SOC _min of the end of discharge of the energy storage system; wherein, the control Accuracy T=1440/N, which means dividing 1440 minutes of the day into N time periods of length T, Represents the weight coefficient of tie line power P _pcc , Represents the weight coefficient of the user's economic benefit B _ue , Represents the weight coefficient of the power company's operating benefit B _co , and

Obtaining data, the data includes demand-side power output P _pv (n) for the nth time period of the day based on meteorological forecasts, the state of charge of the energy storage system SOC (n), user power demand L (n), and the power grid peak time period t _p , power grid valley time period t _v , power grid peak power price u ₁₁ , and power grid low power price u ₁₂ ; where, n=1,2,3...N;

From the output control curve of the energy storage system, select the effective control curve combination C(n)={C ₁ (n), C ₂ (n), ..., C _i (n), ..., C _m ( n)}, where m is the number of effective control curves;

According to the combination of the effective control curve and the collected data, the tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co are obtained;

After the tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co are normalized, the optimization objective function F is obtained, where,

In the formula, Indicates the normalized tie-line power, Indicates the normalized user economic benefits, Indicates the normalized operating benefits of the power company, and Respectively represent the normalization coefficients of tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co ;

Bring the effective control curve combination into the optimization objective function F in turn, gradually calculate and obtain the effective control curve when the optimization objective function F is the minimum value, as the optimal output curve;

The energy storage system performs rated power charging/discharging according to the control instruction of the optimal output curve.

Further, the tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co are calculated according to the combination of the effective control curve and the collected data, specifically including:

According to the combination of the effective control curves and the collected data, the tie line power P _pcc is calculated, wherein,

In the formula, P _pcc (n)=P _pv (n)+C(n)·P _es -P _fix (n)-(P _ac (n)-P _acr (n)), P _pcc (n) represents the The power of the tie line in n time periods, P _es represents the rated charging/discharging power of the energy storage system, P _ac (n) is the total power of loads that can be transferred in the nth time period, P _acr (n) is the The sum of the active load power participating in the response in the n time period, P _fix (n) the total power of the load with a fixed power consumption time in the nth time period;

According to the effective control curve and the collected data, the user economic benefit B _ue is calculated, wherein,

In the formula, B _ue (n)=B _rc (n)+B _ss (n)-C _be (n),

B _ue (n) represents the user's economic benefits in the nth time period, P _n (n) is the surplus electricity grid power in the nth time period, and C _be (n) is the electricity purchased by the user in the nth time period Cost, B _rc (n) is the income of the user’s surplus electricity on-grid subsidy in the nth time period, B _ss (n) is the user’s self-use subsidy income in the nth time period, u ₂ is the user’s surplus electricity on-grid electricity price, u ₃ is the user's self-use electricity price;

According to the collected data, the operating benefit B _co of the power company is calculated,

in, In the formula,

B _co (n)＝C _bt (n)-C′ _bt (n)+C _al (n)-C′ _al (n)+B′ _se (n)-B _se (n)+B′ _id (n )-B _id (n),

B _co (n) represents the operating benefit of the power company in the nth time period, C _bt (n) represents the cost of electricity purchase and transmission from the power company to the power plant in the nth time period before the active load response, and C _bt '(n) represents The power purchase cost and transmission cost of the power company from the power plant in the nth time period after the active load response, C _al (n) represents the cost of the power company's implementation of active load compensation in the nth time period before the active load response, C _al '(n) Indicates the cost of the power company implementing active load compensation in the nth time period after the active load response, B _se (n) represents the power company’s electricity sales income in the nth time period before the active load response, B _se '(n) represents the active load response The power company’s income from electricity sales in the next nth time period, B _id (n) represents the indirect revenue of the power company in the nth time period before the active load response, and B _id '(n) represents the power company’s nth time period after the active load response Indirect income; u ₀₁ and u ₀₂ are the purchase price of active power and reactive power of the power company respectively, u ₁₀ is the active load compensation price, k _u is the unit price of apparent power revenue, P(n) is the energy consumption of each time period The total demand remains unchanged, ΔP(n) is the active load response power in the nth time period, S(n) and P _l (n)+jQ _l (n) are the power companies in the nth time period before the active load response Purchased power and line loss power, S'(n) and P _l '(n)+jQ _l '(n) are the purchase power and line loss power of the power company in the nth time period after the active load response.

Further, the constraints are specifically:

t _d0 -t _c ≥T _min , t _c0 -t _d ≥T _min , N _cd ≤N _cdmax , L(n)=-(1-k(n))·P _l , 0≤P _acr (n)≤ P _ac , -P _es -P _pv (n)<ΔP(n)≤P(n)+P _es , P _acr (n)=k(n)·P _l =ΔP(n)-C(n)· P _es -P _pv (n), 0≤k(n)≤1, C(n)∈Ω _C(n)

N _c and N _d are the number of continuous charging and discharging time periods respectively, N _cd is the total number of charging and discharging times in one day, t _d0 is the time point when the energy storage system changes from charging state to discharging or hot standby state, t _c0 is the storage time t _d is the time point when the energy storage system changes from the charging or hot standby state to the discharging state, and _tc is the time point when the energy storage system changes from the discharging or hot standby state to At the time point of the charging state, P _ac is the total power of the load that can transfer the power consumption time, and Ω _C(n) is the value constraint of C(n).

Implement the present invention, have following beneficial effect:

1. The concept of energy storage system output control curve is introduced. As a bipolar active load, the energy storage system can be used as both a load and a power source. Its control is mainly based on the stability of the grid power, and the energy storage system is also considered cost-effective in itself. Based on the weather forecast and the day-ahead load, the demand-side generation power curve and load power curve corresponding to each period are made; after obtaining the above parameters, the multi-objective function corresponding to the different output control curves of the energy storage system is calculated in turn, and then obtained by using the bubbling method The output control curve corresponding to the minimum F is the target control curve to be planned, and finally this curve is used as the output control command of the energy storage system for the day;

2. On the premise of ensuring the safety of the power grid and not changing the user's willingness to use energy, optimize the power feeding and power consumption behavior, and establish the most optimal system that simultaneously realizes the three goals of regional power grid tie line power, user economic benefits and power company operating benefits. An optimization model whose strategies can significantly improve the economic and social benefits of the smart demand side;

3. The multi-objective active load coordination control technology proposed in this invention can also be applied to the energy storage capacity planning investment in the initial stage of intelligent demand side construction to obtain the optimal input/output ratio.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a schematic flowchart of an active load multi-objective coordinated control method based on an energy storage system provided by an embodiment of the present invention;

Fig. 2 is a system structure diagram realizing the method shown in Fig. 1;

Fig. 3 is a schematic flow chart of step S106 in Fig. 1;

Figure 4 is a diagram of line transmission power before active load response;

Figure 5 is a diagram of line transmission power after active load response.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

An embodiment of the present invention provides a multi-objective coordinated control method for active loads based on an energy storage system, as shown in FIG. 1 , and a system structure for implementing the method is shown in FIG. 2 .

Embodiments of the present invention include steps:

S101, setting parameters.

The parameters include: control precision T, control weight coefficient and The maximum number of charge-discharge transitions N _cdmax of the energy storage system, the minimum charge/discharge duration of the energy storage system T _min , the state of charge SOC _max at the end of charge of the energy storage system, and the state of charge SOC _min of the end of discharge of the energy storage system; wherein, the control Accuracy T=1440/N, which means dividing 1440 minutes of the day into N time periods of length T, Represents the weight coefficient of tie line power P _pcc , Represents the weight coefficient of the user's economic benefit B _ue , Represents the weight coefficient of the power company's operating benefit B _co , and

S102. Obtain data.

The data includes the demand-side power output P _pv (n) of the nth time period of the day based on the weather forecast, the state of charge of the energy storage system SOC(n), the user's electricity demand L(n), and the grid peak time period t _p , grid valley time period t _v , grid peak electricity price u ₁₁ and grid valley electricity price u ₁₂ ; where, n=1,2,3...N.

P _pv (n) is the total power output of the demand side based on the weather forecast, SOC (n) is the SOC state based on the real-time detection of the energy storage system, L (n) is the load at the corresponding time of the user one day before the planning, real-time electricity price and power grid The peak and valley hours are determined by the local power company.

S103. Select an effective control curve combination C(n) that meets the constraints from the output control curves of the energy storage system.

Among them, in the whole process, it is assumed that the SOC of the energy storage system is always between the charge cut-off state of charge SOC _max and the discharge cut-off state of charge SOC _min , and the effective control curve that meets the constraint conditions is selected from the output control curve of the energy storage system Combination C(n)={{C ₁ (n), C ₂ (n), . . . , C _i (n), . . . , C _m (n)}. Among them, m is the number of effective control curves, and its maximum value is 3 ^N in theory. Since the value of C(n) is limited by its constraints, the actual number of effective combinations is far less than 3 ^N .

S104. Calculate tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co according to the effective control curve combination and the collected data.

Step S104 comprises steps:

S1041. Calculate tie line power P _pcc according to the combination of the effective control curves and the collected data, wherein,

In the formula, P _pcc (n)=P _pv (n)+C(n)·P _es -P _fix (n)-(P _ac (n)-P _acr (n))

P _pcc (n) represents the power of the tie line in the nth time period, P _es represents the rated charging/discharging power of the energy storage system, which is read from the manual of the energy storage device, and P _ac (n) is the transferable power of the nth time period The total power of the load during the power consumption time, P _acr (n) is the active load power participating in the response in the nth time period, and P _fix (n) is the total power of the load with a fixed power consumption time in the nth time period. The parameters P _ac (n), P _acr (n), and P _fix (n) are the actual power consumption data of the previous day.

S1042. According to the combination of the effective control curves and the collected data, calculate and obtain the user's economic benefit B _ue , wherein,

In the formula, B _ue (n)=B _rc (n)+B _ss (n)-C _be (n),

B _ue (n) represents the user's economic benefits in the nth time period, P _n (n) is the surplus electricity grid power in the nth time period, and C _be (n) is the electricity purchased by the user in the nth time period Cost, B _rc (n) is the income of the user’s surplus electricity on-grid subsidy in the nth time period, B _ss (n) is the user’s self-use subsidy income in the nth time period, u ₂ is the user’s surplus electricity on-grid electricity price, u ₃ is the user's self-use electricity price;

S1043. Calculate and obtain the operation benefit B _co of the power company according to the collected data.

in,

In the formula, B _co (n)=C _bt (n)-C′ _bt (n)+C _al (n)-C′ _al (n)+B′ _se (n)-B _se (n)+B′ _id (n)-B _id (n),

B _co (n) represents the operating benefit of the power company in the nth time period, C _bt (n) represents the cost of electricity purchase and transmission from the power company to the power plant in the nth time period before the active load response, and C _bt '(n) represents The power purchase cost and transmission cost of the power company from the power plant in the nth time period after the active load response, C _al (n) represents the cost of the power company's implementation of active load compensation in the nth time period before the active load response, C _al '(n) Indicates the cost of the power company implementing active load compensation in the nth time period after the active load response, B _se (n) represents the power company’s electricity sales income in the nth time period before the active load response, B _se '(n) represents the active load response The power company’s income from electricity sales in the next nth time period, B _id (n) represents the indirect revenue of the power company in the nth time period before the active load response, and B _id '(n) represents the power company’s nth time period after the active load response Indirect income; u ₀₁ and u ₀₂ are the purchase price of active power and reactive power of the power company respectively, u ₁₀ is the active load compensation price, k _u is the unit price of apparent power revenue, P(n) is the energy consumption of each time period The total demand remains unchanged, ΔP(n) is the active load response power in the nth time period, S(n) and P _l (n)+jQ _l (n) are the power companies in the nth time period before the active load response Purchased power and line loss power, S'(n) and P _l '(n)+jQ _l '(n) are the purchased power and line loss power of the power company in the nth time period after the active load response, refer to Figure 4 and Figure 4 5. C _al (n), B _id (n), u ₀₁ , u ₀₂ , u ₁₀ , and k _u are given by the power company, and other power parameters are the actual power consumption data of the previous day.

S105. After performing normalization processing on tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co , an optimization objective function F is obtained.

Specifically, the grid-connection of intelligent demand-side power has changed the traditional grid-user power supply and consumption mode, and at the same time effectively alleviated the peak load pressure on the distribution network. However, when the bidirectional power flow transmission power is too large, it will have adverse effects on the grid tie line. Therefore, the minimum tie line power P _pcc , the maximum user economic benefit B _ue and the maximum operating benefit B _co of the power company are taken as the optimization objectives, and the optimization objective function F is obtained, where, In the formula, Indicates the normalized tie-line power, Indicates the normalized user economic benefits, Indicates the normalized operating benefits of the power company, and Respectively represent the normalization coefficients of tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co .

S106. Bring the effective control curves in the combination of effective control curves into the optimization objective function F in sequence, gradually calculate and obtain the effective control curve when the optimization objective function F is the minimum value, as the target control curve.

Specifically, the processing of step S106 is as shown with reference to FIG. 3 .

S107. The reserve system performs charging/discharging at rated power according to the control of the target control curve.

Among them, when the SOC of the energy storage system does not meet the charging/discharging conditions, the execution is suspended, mainly to protect the energy storage system.

Wherein, the constraints described in step S103 are specifically:

t _d0 -t _c ≥T _min , t _c0 -t _d ≥T _min , N _cd ≤N _cdmax , L(n)=-(1-k(n))·P _l , 0≤P _acr (n)≤ P _ac , -P _es -P _pv (n)<ΔP(n)≤P(n)+P _es , P _acr (n)=k(n)·P _l =ΔP(n)-C(n)· P _es -P _pv (n), 0≤k(n)≤1, C(n)∈Ω _C(n) ;

N _c and N _d are the number of continuous charging and discharging time periods respectively, N _cd is the total number of charging and discharging times in one day, t _d0 is the time point when the energy storage system changes from charging state to discharging or hot standby state, t _c0 is the storage time t _d is the time point when the energy storage system changes from the charging or hot standby state to the discharging state, and _tc is the time point when the energy storage system changes from the discharging or hot standby state to At the time point of the charging state, P _ac is the total power of the load that can transfer the power consumption time, and Ω _C(n) is the value constraint of C(n).

Implement the present invention, have following beneficial effect:

1. The concept of energy storage system output control curve is introduced. As a bipolar active load, the energy storage system can be used as both a load and a power source. Its control is mainly based on the stability of the grid power, and the energy storage system is also considered cost-effective in itself. Based on the weather forecast and the day-ahead load, the demand-side generation power curve and load power curve corresponding to each period are made; after obtaining the above parameters, the multi-objective function corresponding to the different output control curves of the energy storage system is calculated in turn, and then obtained by using the bubbling method The output control curve corresponding to the minimum F is the target control curve to be planned, and finally this curve is used as the output control command of the energy storage system for the day;

2. On the premise of ensuring the safety of the power grid and not changing the user's willingness to use energy, optimize the power feeding and power consumption behavior, and establish the most optimal system that simultaneously realizes the three goals of regional power grid tie line power, user economic benefits and power company operating benefits. An optimization model whose strategies can significantly improve the economic and social benefits of the smart demand side;

3. The multi-objective active load coordination control technology proposed in this invention can also be applied to the energy storage capacity planning investment in the initial stage of intelligent demand side construction to obtain the optimal input/output ratio.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

The serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

Professionals can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the possible For interchangeability, in the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be directly implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known storage medium.

The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (3)

1. A multi-objective coordinated control method for active loads based on an energy storage system, characterized in that it comprises: Set parameters, the parameters include: control precision T, control weight coefficient and The maximum number of charge-discharge transitions N _cdmax of the energy storage system, the minimum charge/discharge duration of the energy storage system T _min , the state of charge SOC _max at the end of charge of the energy storage system, and the state of charge SOC _min of the end of discharge of the energy storage system; wherein, the control Accuracy T=1440/N, which means dividing 1440 minutes of the day into N time periods of length T, Represents the weight coefficient of tie line power P _pcc , Represents the weight coefficient of the user's economic benefit B _ue , Represents the weight coefficient of the power company's operating benefit B _co , and Obtaining data, the data includes demand-side power output P _pv (n) for the nth time period of the day based on meteorological forecasts, the state of charge of the energy storage system SOC (n), user power demand L (n), and the power grid peak time period t _p , power grid valley time period t _v , power grid peak power price u ₁₁ , and power grid low power price u ₁₂ ; where, n=1,2,3...N; From the output control curve of the energy storage system, select the effective control curve combination C(n)={C ₁ (n), C ₂ (n), ..., C _i (n), ..., C _m ( n)}, where m is the number of effective control curves; According to the combination of the effective control curve and the collected data, the tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co are obtained; After the tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co are normalized, the optimization objective function F is obtained, where, <mrow><mi>F</mi><mo>=</mo><munder><mrow><mi>m</mi><mi>i</mi><mi>n</mi></mrow><mi>u</mi></munder><mo>&amp;Sigma;</mo><mrow><mo>(</mo><msub><mi>&amp;omega;</mi><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub></msub><mo>&amp;CenterDot;</mo><msub><mi>I</mi><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub></msub><mo>+</mo><msub><mi>&amp;omega;</mi><msub><mi>B</mi><mrow><mi>u</mi><mi>e</mi></mrow></msub></msub><mo>&amp;CenterDot;</mo><msub><mi>I</mi><msub><mi>B</mi><mrow><mi>u</mi><mi>e</mi></mrow></msub></msub><mo>+</mo><msub><mi>&amp;omega;</mi><msub><mi>B</mi><mrow><mi>c</mi><mi>o</mi></mrow></msub></msub><mo>&amp;CenterDot;</mo><msub><mi>I</mi><msub><mi>B</mi><mrow><mi>c</mi><mi>o</mi></mrow></msub></msub><mo>)</mo></mrow></mrow> In the formula, Indicates the normalized tie-line power, Indicates the normalized user economic benefits, Indicates the normalized operating benefits of the power company, and Respectively represent the normalization coefficients of tie line power P _pcc , user economic benefit B _ue and power company operating benefit B _co ; Bring the effective control curve combination into the optimization objective function F in turn, gradually calculate and obtain the effective control curve when the optimization objective function F is the minimum value, as the optimal output curve; The energy storage system performs rated power charging/discharging according to the control instruction of the optimal output curve. 2. The active load multi-objective coordinated control method based on energy storage system according to claim 1, characterized in that the tie line power P _pcc , user Economic benefits B _ue and power company operating benefits B _co , including: According to the combination of the effective control curves and the collected data, the tie line power P _pcc is calculated, wherein, In the formula, P _pcc (n)=P _pv (n)+C(n)·P _es -P _fix (n)-(P _ac (n)-P _acr (n)), P _pcc (n) represents the The power of the tie line in n time periods, P _es represents the rated charging/discharging power of the energy storage system, P _ac (n) is the total power of loads that can be transferred in the nth time period, P _acr (n) is the The sum of the active load power participating in the response in the n time period, P _fix (n) the total power of the load with a fixed power consumption time in the nth time period; According to the effective control curve and the collected data, the user economic benefit B _ue is calculated, wherein, In the formula, B _ue (n)=B _rc (n)+B _ss (n)-C _be (n), <mrow><mfenced open = "{" close = ""><mtable><mtr><mtd><mrow><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>P</mi><mrow><mi>p</mi><mi>v</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>+</mo><mi>C</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>&amp;CenterDot;</mo><msub><mi>P</mi><mrow><mi>e</mi><mi>s</mi></mrow></msub><mo>-</mo><msub><mi>P</mi><mrow><mi>f</mi><mi>i</mi><mi>x</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>-</mo><mrow><mo>(</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>c</mi></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>-</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>c</mi><mi>r</mi></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mtable><mtr><mtd><mrow><msub><mi>C</mi><mrow><mi>b</mi><mi>e</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mo>-</mo><mrow><mo>(</mo><msub><mi>u</mi><mn>11</mn></msub><mo>+</mo><msub><mi>u</mi><mn>12</mn></msub><mo>)</mo></mrow><mo>&amp;CenterDot;</mo><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>,</mo></mrow></mtd><mtd><mrow><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>&lt;</mo><mn>0</mn></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>B</mi><mrow><mi>r</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>u</mi><mn>2</mn></msub><mo>&amp;CenterDot;</mo><msub><mi>P</mi><mi>n</mi></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>,</mo></mrow></mtd><mtd><mrow><mn>0</mn><mo>&lt;</mo><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>B</mi><mrow><mi>s</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><msub><mi>u</mi><mn>3</mn></msub><mo>&amp;CenterDot;</mo><mrow><mo>(</mo><msub><mi>P</mi><mrow><mi>p</mi><mi>v</mi></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>+</mo><msub><mi>P</mi><mrow><mi>e</mi><mi>s</mi></mrow></msub><mo>)</mo></mrow><mo>,</mo></mrow></mtd><mtd><mrow><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>&lt;</mo><mn>0</mn></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>B</mi><mrow><mi>s</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>u</mi><mn>3</mn></msub><mo>&amp;CenterDot;</mo><mrow><mo>(</mo><msub><mi>P</mi><mrow><mi>f</mi><mi>i</mi><mi>x</mi></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>+</mo><mrow><mo>(</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>c</mi></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>-</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>c</mi><mi>r</mi></mrow></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></mrow><mo>,</mo></mrow></mtd><mtd><mrow><mn>0</mn><mo>&lt;</mo><msub><mi>P</mi><mrow><mi>p</mi><mi>c</mi><mi>c</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></mtd></mtr></mtable></mtd></mtr></mtable></mfenced><mo>,</mo></mrow> B _ue (n) represents the user's economic benefits in the nth time period, P _n (n) is the surplus electricity grid power in the nth time period, and C _be (n) is the electricity purchased by the user in the nth time period Cost, B _rc (n) is the income of the user’s surplus electricity on-grid subsidy in the nth time period, B _ss (n) is the user’s self-use subsidy income in the nth time period, u ₂ is the user’s surplus electricity on-grid electricity price, u ₃ is the user's self-use electricity price; According to the collected data, the operating benefit B _co of the power company is calculated, wherein, In the formula, B _co (n)=C _bt (n)-C _b ′ _t (n)+C _al (n)-C _a ′ _l (n)+B _s ′ _e (n)-B _se (n) +B _i ′ _d (n)-B _id (n), <mrow><mfenced open = "{" close = ""><mtable><mtr><mtd><mrow><msub><mi>C</mi><mrow><mi>b</mi><mi>t</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>u</mi><mn>01</mn></msub><mo>&amp;CenterDot;</mo><mrow><mo>(</mo><msub><mi>P</mi><mi>l</mi></msub><mo>(</mo><mi>n</mi><mo>)</mo><mo>+</mo><mi>P</mi><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></mrow><mo>+</mo><msub><mi>u</mi><mn>02</mn></msub><mo>&amp;CenterDot;</mo><msub><mi>Q</mi><mi>l</mi></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msubsup><mi>C</mi><mrow><mi>b</mi><mi>t</mi></mrow><mo>&amp;prime;</mo></msubsup><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>u</mi><mn>01</mn></msub><mo>&amp;CenterDot;</mo><mrow><mo>(</mo><msubsup><mi>P</mi><mi>l</mi><mo>&amp;prime;</mo></msubsup><mo>(</mo><mi>n</mi><mo>)</mo><mo>+</mo><mi>P</mi><mo>(</mo><mi>n</mi><mo>)</mo><mo>-</mo><mi>&amp;Delta;</mi><mi>P</mi><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></mrow><mo>+</mo><msub><mi>u</mi><mn>02</mn></msub><mo>&amp;CenterDot;</mo><msubsup><mi>Q</mi><mi>l</mi><mo>&amp;prime;</mo></msubsup><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>C</mi><mrow><mi>a</mi><mi>l</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mn>0</mn></mrow></mtd></mtr><mtr><mtd><mrow><msubsup><mi>C</mi><mrow><mi>a</mi><mi>l</mi></mrow><mo>&amp;prime;</mo></msubsup><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>u</mi><mn>10</mn></msub><mo>&amp;CenterDot;</mo><mi>&amp;Delta;</mi><mi>P</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>B</mi><mrow><mi>s</mi><mi>e</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mrow><mo>(</mo><msub><mi>u</mi><mn>11</mn></msub><mo>+</mo><msub><mi>u</mi><mn>12</mn></msub><mo>)</mo></mrow><mo>&amp;CenterDot;</mo><mi>P</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msubsup><mi>B</mi><mrow><mi>s</mi><mi>e</mi></mrow><mo>&amp;prime;</mo></msubsup><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mrow><mo>(</mo><msub><mi>u</mi><mn>11</mn></msub><mo>+</mo><msub><mi>u</mi><mn>12</mn></msub><mo>)</mo></mrow><mo>&amp;CenterDot;</mo><mrow><mo>(</mo><mi>P</mi><mo>(</mo><mi>n</mi><mo>)</mo><mo>-</mo><mi>&amp;Delta;</mi><mi>P</mi><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>B</mi><mrow><mi>i</mi><mi>d</mi></mrow></msub><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mn>0</mn></mrow></mtd></mtr><mtr><mtd><mrow><msubsup><mi>B</mi><mrow><mi>i</mi><mi>d</mi></mrow><mo>&amp;prime;</mo></msubsup><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>k</mi><mi>u</mi></msub><mrow><mo>(</mo><mi>S</mi><mo>(</mo><mi>n</mi><mo>)</mo><mo>-</mo><msup><mi>S</mi><mo>&amp;prime;</mo></msup><mo>(</mo><mi>n</mi><mo>)</mo><mo>)</mo></mrow></mrow></mtd></mtr></mtable></mfenced><mo>,</mo></mrow> B _co (n) represents the operating benefit of the power company in the nth time period, C _bt (n) represents the cost of electricity purchase and transmission from the power company to the power plant in the nth time period before the active load response, and C _bt '(n) represents The power purchase cost and transmission cost of the power company from the power plant in the nth time period after the active load response, C _al (n) represents the cost of the power company's implementation of active load compensation in the nth time period before the active load response, C _al '(n) Indicates the cost of the power company implementing active load compensation in the nth time period after the active load response, B _se (n) represents the power company’s electricity sales income in the nth time period before the active load response, B _se '(n) represents the active load response The power company’s income from electricity sales in the next nth time period, B _id (n) represents the indirect revenue of the power company in the nth time period before the active load response, and B _id '(n) represents the power company’s nth time period after the active load response Indirect income; u ₀₁ and u ₀₂ are the purchase price of active power and reactive power of the power company respectively, u ₁₀ is the active load compensation price, k _u is the unit price of apparent power revenue, P(n) is the energy consumption of each time period The total demand remains unchanged, ΔP(n) is the active load response power in the nth time period, S(n) and P _l (n)+jQ _l (n) are the power companies in the nth time period before the active load response Purchased power and line loss power, S'(n) and P _l '(n)+jQ _l '(n) are the purchase power and line loss power of the power company in the nth time period after the active load response. 3. The energy storage system-based multi-objective coordinated control method for active loads according to claim 2, wherein the constraint conditions are specifically: <mrow><mi>C</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow><mo>=</mo><mfenced open = "{" close = ""><mtable><mtr><mtd><mrow><mo>-</mo><mn>1</mn><mo>,</mo></mrow></mtd><mtd><mrow><mi>t</mi><mo>&amp;Element;</mo><msub><mi>t</mi><mi>v</mi></msub><mo>,</mo></mrow></mtd><mtd><mrow><mi>S</mi><mi>O</mi><mi>C</mi><mo>&amp;le;</mo><msub><mi>SOC</mi><mi>max</mi></msub></mrow></mtd></mtr><mtr><mtd><mrow><mn>0</mn><mo>,</mo></mrow></mtd><mtd><mrow></mrow></mtd><mtd><mrow></mrow></mtd></mtr><mtr><mtd><mrow><mn>1</mn><mo>,</mo></mrow></mtd><mtd><mrow><mi>t</mi><mo>&amp;Element;</mo><msub><mi>t</mi><mi>p</mi></msub><mo>,</mo></mrow></mtd><mtd><mrow><msub><mi>SOC</mi><mi>min</mi></msub><mo>&amp;le;</mo><mi>S</mi><mi>O</mi><mi>C</mi></mrow></mtd></mtr></mtable></mfenced><mo>,</mo></mrow> <mrow><mfenced open = "{" close = ""><mtable><mtr><mtd><mrow><msub><mi>u</mi><mn>12</mn></msub><mo>=</mo><mn>0</mn><mo>,</mo></mrow></mtd><mtd><mrow><mi>t</mi><mo>&amp;Element;</mo><msub><mi>t</mi><mi>p</mi></msub></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>u</mi><mn>11</mn></msub><mo>=</mo><mn>0</mn><mo>,</mo></mrow></mtd><mtd><mrow><mi>t</mi><mo>&amp;Element;</mo><msub><mi>t</mi><mi>v</mi></msub></mrow></mtd></mtr></mtable></mfenced><mo>,</mo></mrow> t _d0 -t _c ≥T _min , t _c0 -t _d ≥T _min , N _cd ≤N _cdmax , L(n)=-(1-k(n))·P _l , 0≤P _acr (n)≤ P _ac , -P _es -P _pv (n)<ΔP(n)≤P(n)+P _es , P _acr (n)=k(n)·P _l =ΔP(n)-C(n)· P _es -P _pv (n), 0≤k(n)≤1, C(n)∈Ω _C(n) N _c and N _d are the number of continuous charging and discharging time periods respectively, N _cd is the total number of charging and discharging times in one day, t _d0 is the time point when the energy storage system changes from charging state to discharging or hot standby state, t _c0 is the storage time t _d is the time point when the energy storage system changes from the charging or hot standby state to the discharging state, and _tc is the time point when the energy storage system changes from the discharging or hot standby state to At the time point of the charging state, P _ac is the total power of the load that can transfer the power consumption time, and Ω _C(n) is the value constraint of C(n).