Distributed energy storage system scheduling optimization method and system based on nonlinear dung beetle optimization

Abstract

The invention relates to the technical field of power system energy scheduling, and discloses a distributed energy storage system scheduling optimization method and system based on nonlinear dung beetle optimization. According to the method, a dung beetle optimization algorithm based on nonlinear convergence is adopted to carry out optimization solution on constructed double-time-scale models, large-scale data and complex constraint conditions can be effectively processed through a multi-time-scale and multi-optimization-target scheduling strategy, randomness and volatility of renewable energy sources are considered at the same time, and the optimal scheduling effect is achieved. And efficient and stable scheduling of the energy storage system is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy dispatching, and in particular relates to a dispatching optimization method and system for a distributed energy storage system based on nonlinear dung beetle optimization. Background Art

[0002] With the rapid development of the global economy, environmental problems and fossil energy shortages are becoming increasingly severe. In order to meet these challenges, countries around the world are vigorously promoting the development of renewable energy. In this context, the role of distributed energy storage systems (DCESS) in modern power systems has become increasingly significant. DCESS combines the characteristics of centralized and distributed energy storage, and can provide energy storage and release services at multiple locations at the same time, effectively balance the grid load, and improve the stability and reliability of the system. In my country, with the advancement of the "dual carbon" goals, the development and application of distributed energy storage systems have become the key to achieving energy structure transformation. This system can be deployed at different nodes of the power grid, and by coordinating the energy storage resources of each node, it can achieve optimal distribution of the grid load and reduce the operating cost and environmental impact of the grid.

[0003] Distributed energy storage systems are increasingly used in distribution networks, but their dispatch optimization faces multiple challenges. First, the distributed characteristics of distributed energy storage systems require their dispatch strategies to consider the collaborative work of multiple nodes, which increases the complexity of the dispatch model. In order to effectively manage energy storage resources distributed in different geographical locations, the dispatch strategy must be able to coordinate the energy flow of each node to ensure the stability and efficiency of the entire system. This requires the dispatch model to be able to handle complex network topologies and real-time data while taking into account the mutual influence between nodes. Secondly, the output of renewable energy (such as photovoltaic and wind power) is random and volatile, and its prediction error has a significant impact on the dispatch strategy of the energy storage system. Due to the unpredictability of renewable energy, the dispatch of the energy storage system must be able to respond quickly to changes in energy supply to balance the grid load and reduce energy waste. This requires the dispatch model to be highly flexible and adaptable, and to be able to make quick decisions based on real-time data and forecast information.

[0004] At present, existing optimization methods often have problems such as slow iterative convergence and easy falling into local optimal solutions when dealing with large-scale data and complex constraints. Summary of the invention

[0005] In order to overcome the deficiencies and defects mentioned in the above background technology, the present application provides a method and system for optimizing the scheduling of a distributed energy storage system based on nonlinear dung beetle optimization.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0007] In a first aspect, the present application provides a method for optimizing the dispatching of a distributed energy storage system based on nonlinear dung beetle optimization, comprising:

[0008] S1: construct a dual time scale model, wherein the dual time scale model includes a day-ahead optimization scheduling model and an intraday real-time optimization scheduling model;

[0009] S2: using a dung beetle optimization algorithm based on nonlinear convergence to optimize and solve the dual time scale model respectively;

[0010] S3: Perform scheduling optimization according to the solution results.

[0011] In a second aspect, the present application provides a distributed energy storage system scheduling optimization system based on nonlinear dung beetle optimization, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization provided in the present application adopts a dung beetle optimization algorithm based on nonlinear convergence to optimize and solve the constructed dual-time scale model respectively. Through the scheduling strategy of multiple time scales and multiple optimization objectives, it can effectively process large-scale data and complex constraints, while taking into account the randomness and volatility of renewable energy, to achieve efficient and stable scheduling of the energy storage system.

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of a distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization provided by the present application;

[0016] Figure 2 It is a flow chart of the dung beetle optimization algorithm provided by this application;

[0017] Figure 3 It is a modified IEEE 33-node system schematic diagram provided by this application;

[0018] Figure 4This is a schematic diagram of the day-ahead market electricity purchase provided in this application;

[0019] Figure 5 This is a schematic diagram of the output of the day-ahead aggregated and distributed energy storage system provided in this application;

[0020] Figure 6 This is a schematic diagram of the real-time dispatching output of the clustered energy storage system provided by this application;

[0021] Figure 7 This is a schematic diagram of the voltages of each node at 19:00 provided by this application. DETAILED DESCRIPTION

[0022] In order to facilitate the understanding of the present invention, the present invention will be described more comprehensively and meticulously below in conjunction with the accompanying drawings and preferred embodiments of the present invention, but the protection scope of the present invention is not limited to the following specific embodiments.

[0023] Unless otherwise defined, all professional terms used below have the same meanings as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention.

[0024] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate quantity restrictions, but indicate the existence of at least one. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0025] It should be understood that the output of renewable energy (such as photovoltaic and wind power) is random and volatile, and its prediction has a certain error, which will have a significant impact on the dispatching strategy of the distributed energy storage system, and thus affect the safe and stable operation of the entire power grid. Therefore, in order to reduce the impact of the uncertainty of renewable energy output on the dispatching strategy of the energy storage system, the present invention proposes a dispatching optimization method for a distributed energy storage system based on a nonlinear dung beetle optimization algorithm for a distributed energy storage system containing large-scale renewable energy. This method adopts different optimization dispatching targets for the day-ahead and intra-day real-time time scales through a dispatching strategy with multiple time scales and multiple optimization targets, effectively responds to the uncertainty of renewable energy output, optimizes the charging and discharging strategy of the distributed energy storage system, reduces the adverse impact on the distribution network, and achieves the economically optimal operation of the power company.

[0026] See also Figure 1 The present application provides a method for optimizing the dispatching of a distributed energy storage system based on nonlinear dung beetle optimization, comprising:

[0027] S1: construct a dual time scale model, wherein the dual time scale model includes a day-ahead optimization scheduling model and an intraday real-time optimization scheduling model;

[0028] S2: using a dung beetle optimization algorithm based on nonlinear convergence to optimize and solve the dual time scale model respectively;

[0029] S3: Perform scheduling optimization according to the solution results.

[0030] The above-mentioned distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization adopts a dung beetle optimization algorithm based on nonlinear convergence to optimize and solve the constructed dual-time scale model respectively. Through the scheduling strategy of multiple time scales and multiple optimization objectives, it can effectively handle large-scale data and complex constraints, while taking into account the randomness and volatility of renewable energy, to achieve efficient and stable scheduling of the energy storage system.

[0031] Below, the steps of the above-mentioned grid-connected control method of the cluster-dispersed energy storage system are described in detail using a complete example:

[0032] (1) The day-ahead optimization scheduling model is described as follows:

[0033] The power company estimates the results of the day's electricity price and optimizes the dispatch of distributed energy storage systems and interruptible loads in the distribution network to arrange the energy storage charging and discharging plan for the next day and determine the amount of electricity purchased and interruptible loads.

[0034] 1) The first objective function is as follows:

[0035] The first objective function aims to minimize the total operating cost of the power company, including the cost of purchasing electricity, the charging and discharging cost of the energy storage system, and the management cost of interruptible loads. By accurately predicting electricity prices and load demand, the optimization model can develop the most economical energy storage charging and discharging plan, while reasonably arranging interruptible loads to reduce the peak load and operating costs of the power grid. Therefore, the optimization goal of this stage is to minimize the day-ahead operating cost of the power company:

[0036]

[0037] In the formula, C DA is the total cost of the power company's day-ahead operation; T is the dispatch date; ΔT is the time interval; NDG is the number of distributed energy storage systems connected to the distribution network. The present invention assumes that the power company can dispatch all distributed energy storage systems; a i ,b i ,c i is the secondary output cost coefficient of the i-th distributed energy storage system; is the power generated by the i-th distributed energy storage system at time t; is the market electricity purchase price at time t the day before; is the market electricity purchase amount at time t the day before; is the quotation for the interruptible load to be reduced at time t on the next day; is the reduction amount of the interruptible load pair at time t.

[0038] It is assumed that the power company has two renewable energy sources, wind power and photovoltaic power, so the output cost is not considered.

[0039] 2) The first constraint is as follows:

[0040] ① Power balance constraints:

[0041]

[0042] Where P t wind is the predicted wind power output at time t the next day; P t PV is the predicted value of photovoltaic power output at time t the next day;

[0043] It is the power demand value of the load in the distribution network at time t the next day.

[0044] ② System backup constraints:

[0045]

[0046] Where P i max is the maximum output of the i-th distributed energy storage system; The upper limit for purchasing electricity from the electricity market; is the maximum interruptible load reduction at time t.

[0047] ③Power generation constraints:

[0048]

[0049] Where P i min is the minimum output of the i-th distributed energy storage system; U i,t is the start / stop status of the i-th distributed energy storage system at time t, 1 indicates start and 0 indicates shutdown.

[0050] ④Minimum start and stop time constraints:

[0051]

[0052]

[0053] In the formula, the minimum operating time of the i-th distributed energy storage system is expressed as MUP i The minimum supply time of the i-th distributed energy storage system is expressed as MDN i .

[0054] ⑤Climbing constraint:

[0055]

[0056]

[0057] In the formula, the upper limit of the ramp rate of the i-th distributed energy storage system is expressed as R up,i , the lower limit of which is R nd,i To express.

[0058] ⑥ Market power purchase constraints:

[0059]

[0060] ⑦Interruptible load constraints:

[0061]

[0062] (2) The description of the intraday real-time optimization scheduling model is as follows:

[0063] Real-time dispatching belongs to advanced optimization dispatching, which needs to be estimated based on factors such as photovoltaic and wind power, and the output is adjusted in a cycle of 5 to 15 minutes. It is to optimize the next period t=t+1 at t=t, so as to eliminate the error between the estimated result and the dispatching plan. It can be seen that real-time dispatching is carried out for a specific time section, so it is still a static optimal power flow problem fundamentally. The corresponding power flow calculation must be completed to determine the specific demand for reactive power, so as to minimize the active power loss of the distribution network and ensure that the node voltage of the distributed energy storage system and the adjustable load are still in the normal range after being connected to the power grid. This type of dispatching does not completely change the mode of day-ahead dispatching. It is just that on the basis of the day-ahead plan, the intraday real-time optimization dispatching model dynamically adjusts the charging and discharging strategy of the energy storage system according to the real-time electricity price and renewable energy output.

[0064] 1) The second optimization function:

[0065]

[0066] In the formula, The operating optimization cost of the power supply company at time t in the real-time dispatch phase; as well as They represent the reduction of interruptible load at time t in the real-time stage, the real-time quotation of interruptible load at time t, the power of the i-th distributed energy storage system at time t, and the power purchase from the large power grid at time t; ρ gc Reactive power purchased by the power supply company from the auxiliary market; It is the reactive power purchased by the power supply company from the auxiliary market. The optimization target in the real-time stage is different from the day-ahead dispatch. It is the optimal value of the distribution network operation at a certain moment, rather than the optimal value in certain time periods.

[0067] The third item of the objective function is compared with the market power purchase in the day-ahead stage because in the day-ahead dispatching stage, the power company adjusts the output of its distributed power sources according to the changes in load and renewable energy output, and determines the market power purchase and interruptible load reduction. When the power purchase of the large power grid changes in the real-time dispatching stage and is different from the market power purchase in the day-ahead stage, the energy company can sell the excess power purchased in the power market in real time at the electricity price in the power market in the future. When the power purchased by the power company in the daily power market is insufficient, the power company can purchase it in real time at the electricity price in the power market. In the real-time optimization dispatching stage, it is necessary to calculate the optimal power flow of the distribution network to determine the reactive power purchased from the auxiliary power market required to adjust the voltage within the limit.

[0068] 2) The second constraint:

[0069] ① Flow constraints:

[0070]

[0071]

[0072] Where P i grid represents the effective power injected by node i into the power grid; P i DG represents the active output of the distributed energy storage system of node i; P i IL P represents the industrial load reduction of the distributed energy storage system at node i. i L represents the active power demand of the distributed energy storage system load at node i; represents the reactive power demand of the load at node i, represents the compensation capacity of the reactive power compensator at node i, represents the reactive output of the distributed energy storage system at node i, Y represents the reactive power of the grid injected by node i. ij is the conductance between nodes i and j, θ ij Y ij The phase angle, δ i The voltage phase angle of node i is represented by δ, and the voltage phase angle of node j is represented by δ j To express.

[0073] ② Voltage constraint:

[0074] V imin ≤V i ≤V imax (14);

[0075] Where V imax and V imin They represent the upper and lower limits of the voltage at node i respectively.

[0076] ③Reactive power compensator capacity constraints:

[0077]

[0078] In the formula, represents the upper capacity limit of the reactive power compensation device installed at node i, and its lower limit is To express.

[0079] ④Distributed power output constraints:

[0080]

[0081]

[0082] In the formula, To optimize the output of distributed power generation at time t in real-time stage; They are respectively the ramp rates of the distributed energy storage system on a real-time time scale (5 to 15 minutes).

[0083] The output of the distributed energy storage system in the real-time stage is based on the day-ahead stage. This constraint limits the output of the distributed energy storage system in the real-time optimization stage from being adjusted too much. After real-time scheduling, on a smaller time scale, such as seconds, the operation of the distribution network will only change slightly.

[0084] (3) Model optimization algorithm

[0085] In order to solve the problems of multiple objective function parameters, increased computational dimensions, and greatly increased computational workload, the present invention uses a dung beetle optimization algorithm based on nonlinear convergence to optimize and solve the constructed dual-time scale model, thereby improving computational efficiency and convergence speed. Figure 2 shown.

[0086] The steps of the dung beetle optimization algorithm based on nonlinear convergence are as follows:

[0087] 1) Initialize the parameters of the dung beetle swarm and DBO algorithm;

[0088] 2) Calculate the fitness values ​​of all dung beetles according to the objective function;

[0089] 3) Update the location of all dung beetles:

[0090] The inspiration of DBO mainly comes from the dung beetle's behaviors such as rolling, dancing, foraging, stealing, and reproduction. Therefore, the entire dung beetle population is divided into four roles: rolling dung beetles, breeding dung beetles, small dung beetles and stealing dung beetles.

[0091] ① When there are no obstacles ahead, the rolling dung beetle can use celestial clues to navigate during the rolling process, making the dung ball roll in a straight line. Sunlight and other factors will affect the motion trajectory, making it curved or elliptical. Natural factors (such as wind and uneven ground) will cause the dung beetle to deviate from its original direction. In order to simulate the rolling behavior, the dung beetle is required to move in a given direction throughout the search space. During the rolling process, the position of the rolling dung beetle is updated, which can be expressed as:

[0092]

[0093] In the formula, iter represents the number of iterations; x i(iter) represents the position information of the i-th dung beetle at the iter-th iteration, k∈(0,0.2] is a constant, indicating the deflection coefficient; b∈(0,1) is a constant; α is a natural coefficient, which takes 1 or -1, where -1 indicates deviation from the original direction and 1 indicates no deviation; X w represents the global worst position; Δx is used to simulate the change of light intensity.

[0094] When the ball-rolling dung beetle encounters an obstacle and cannot move forward, it needs to reposition itself by dancing in order to obtain a new route. To simulate dancing behavior, a tangent function is used to obtain a new rolling direction. Once the dung beetle successfully determines a new direction, it continues to move the ball backward. Therefore, the position update of the dung beetle is defined as follows:

[0095] x i (iter+1)=x i (iter)+tan(θ)|x i (iter)-x i (iter-1)| (19);

[0096] Where θ∈[0,π] is the rotation angle; if θ=0,π / 2,π, the position of the rolling dung beetle will not be updated.

[0097] ② In nature, dung balls are rolled by dung beetles to a safe place and hidden. In order to provide a safe environment for their offspring, it is crucial for dung beetles to choose a suitable place to lay eggs. The regional boundary selection strategy for simulating female dung beetles to lay eggs is defined as:

[0098]

[0099] Where, X * is the current local optimal position; Lb * and Ub * are the lower and upper bounds of the spawning area respectively; R = 1-iter / T max , T max is the maximum number of iterations; Lb and Ub represent the lower and upper bounds of the optimization parameters respectively.

[0100] Once the egg-laying area is determined, the female dung beetle will choose the egg ball in this area to lay eggs. For the DBO algorithm, each female dung beetle only lays one egg in each iteration. In addition, it can be clearly seen that the boundary range of the egg-laying area changes dynamically, which is mainly determined by the R value. It can be seen that the position of the egg ball is also dynamic during the iteration process, which is expressed as:

[0101] B i (iter+1)=X * +b1(B i (iter)-Lb* )+b2(B i (iter)-Ub * ) (twenty one);

[0102] In the formula, B i (iter) is the position of the i-th egg ball at the iter-th iteration; b1 and b2 represent two independent random vectors of size 1×D that obey the normal distribution, and D represents the dimension of the optimization problem.

[0103] ③ Some small dung beetles that have grown into adults will come out from the ground to find food. It is necessary to establish an optimal dung beetle area to guide the small dung beetles to find food. The boundary of the optimal dung beetle area is defined as follows:

[0104]

[0105] Where, X b represents the global optimal dung beetle position; Lb b and Ub b are the lower and upper bounds of the optimal foraging area. Therefore, the position of the dung beetle is updated as follows:

[0106] x i (iter+1)=x i (iter)+C1(x i (iter)-Lb b )+C2(x i (iter)-Ub b ) (twenty three);

[0107] Where C1 and C2 represent two independent random vectors of size 1×D that follow a normal distribution.

[0108] Although the boundary convergence factor R of the spawning and foraging areas is dynamically adjusted with the number of iterations, the linear reduction strategy is difficult to characterize the complex situation during reproduction and foraging. In order to make the algorithm conduct a more extensive search of the global feasible area in the early stage, and pay more attention to the local development near the optimal solution in the later stage, so as to speed up its convergence speed. Therefore, the boundary convergence factor R is redefined, so that the algorithm conducts a more extensive search of the global feasible area in the early stage, and pays more attention to the local development near the optimal solution in the later stage, so as to speed up its convergence speed.

[0109]

[0110] ④There are also some dung beetles called thieving dung beetles, which steal dung balls from other dung beetles. b To compete for the best location for food, during the iteration, the position update of the stealing dung beetle is defined as follows:

[0111] x i(iter+1)=X b +S×g×(|x i (iter)-X * |+|x i (iter)-X b |) (25);

[0112] Where g is a 1×D-dimensional random vector that follows a normal distribution, and S represents a constant.

[0113] 4) Determine whether each dung beetle is beyond the boundary; if the dung beetle is beyond the boundary, set the element of the dung beetle beyond the boundary to the boundary value;

[0114] 5) Update the current optimal solution and its fitness value;

[0115] 7) Update the current optimal solution, the global optimal solution and their corresponding fitness values;

[0116] 8) Repeat the above steps and output the global optimal solution after reaching the maximum number of iterations and C DA .

[0117] In summary, the present invention provided by the present application solves the dispatch planning of the distributed energy storage system based on the nonlinear convergence of the dung beetle optimization algorithm, so that the algorithm conducts a more extensive search of the global feasible area in the early stage, and pays more attention to the local development near the optimal solution in the later stage, which can better tap the potential global optimal point and accelerate its convergence speed, thereby reducing the possibility of falling into the local optimal solution to a certain extent. The present invention can effectively deal with the uncertainty of renewable energy output, improve the utilization efficiency of the energy storage system, reduce the operating cost of the power grid, and ensure the safe and stable operation of the power grid. In addition, the method can also improve the economic benefits of power companies and achieve the optimal allocation of power resources.

[0118] In one example, the modified IEEE 33-node distribution system is used to verify the correctness of the proposed multi-time scale decision optimization scheduling algorithm for distributed energy storage systems and interruptible loads, so as to explore the regulation characteristics of distributed energy storage systems and interruptible loads. In this example, the locations where each adjustable distributed energy storage source is connected are nodes 18, 22, 25 and 33, the location of the wind turbine is node 13, the location of the photovoltaic is node 29, and the IL user is node 8. Figure 3 shown.

[0119] Among them, the characteristics of each distributed energy storage power supply are shown in Table 1.

[0120] Table 1 Basic parameters of distributed energy storage power supply

[0121]

[0122] The results of this example are analyzed as follows:

[0123] 1) Day-ahead scheduling stage:

[0124] The day-ahead load and electricity price forecasts are as follows: Figure 3 shown.

[0125] By solving the day-ahead optimization dispatch model mentioned in the present invention, the power purchased by the power company from the day-ahead market and the output of the day-ahead distributed energy storage system can be obtained, such as Figure 4 , 5 As shown:

[0126] 2) Real-time scheduling during the day

[0127] Here, the real-time load and electricity price data in the period of 19:00-20:00 are selected to solve the real-time dispatch example, when the four DCESSs are outputting power at the same time. 15 minutes is taken as a dispatch cycle of real-time dispatch.

[0128] Figure 6 It is the active output of each distributed energy storage power source.

[0129] Figure 7 The voltage amplitude of each node at 19:00 is selected. It can be seen from the figure that the voltage amplitude of all nodes is within a reasonable range.

[0130] The present application also provides a distributed energy storage system scheduling optimization system based on nonlinear dung beetle optimization, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program. The distributed energy storage system scheduling optimization system based on nonlinear dung beetle optimization can implement various embodiments of the above distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization, and can achieve the same beneficial effects, which will not be described in detail here.

[0131] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization, characterized in that: include: S1: Constructing a dual-time-scale model, wherein the dual-time-scale model includes a day-ahead optimization scheduling model and an intraday real-time optimization scheduling model; S2: using a dung beetle optimization algorithm based on nonlinear convergence to optimize and solve the dual-time-scale model respectively; S3: Perform scheduling optimization based on the solution results.

2. The distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization according to claim 1 is characterized in that: The day-ahead optimization scheduling model satisfies the following first objective function: Where C DA is the total cost of the power company's day-ahead operation; T is the dispatch date; ΔT is the time interval; NDG is the number of distributed energy storage systems connected to the distribution network, assuming that the power company can dispatch all distributed energy storage systems; a i ,b i ,c i is the secondary output cost coefficient of the i-th distributed energy storage system; is the power generated by the i-th distributed energy storage system at time t; is the market electricity purchase price at time t the day before; is the market electricity purchase amount at time t the day before; The price quoted for load reduction at time t on the next day; is the reduction amount of the interruptible load pair at time t.

3. The distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization according to claim 1, characterized in that: The day-ahead optimization scheduling model satisfies the first constraint condition, which includes: power balance constraint, system reserve constraint, power generation constraint, minimum start and stop time constraint, ramp constraint, market power purchase constraint, and interruptible load constraint.

4. The distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization according to claim 1, characterized in that: The intraday real-time optimization scheduling model satisfies the following second objective function: Where, The operation optimization cost of the power supply company at time t in the real-time dispatch phase; as well as They represent the reduction amount of interruptible load at time t in the real-time stage, the real-time quotation of interruptible load at time t, the power of the i-th distributed energy storage system at time t, and the power purchase amount from the large power grid at time t; ρ gc Reactive power purchased by the power company from the auxiliary market; It is the reactive power purchased by the power supply company from the auxiliary market. The optimization goal in the real-time stage is different from the day-ahead dispatch. It is the optimal value of the distribution network operation at a certain moment, rather than the optimal value in a certain period of time.

5. The distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization according to claim 1, characterized in that: The intraday real-time optimization scheduling model satisfies the second constraint condition, which includes: power flow constraint, voltage constraint, reactive power compensator capacity constraint and distributed power output constraint.

6. The distributed energy storage system scheduling optimization method based on nonlinear dung beetle optimization according to claim 1, characterized in that: The S2 includes: S21: Initialize the parameters of the dung beetle swarm and DBO algorithm; S22: Calculating the fitness values ​​of all dung beetles according to the first objective function and / or the second objective function; S23: Update the positions of all dung beetles; S24: judging whether each dung beetle is beyond the boundary according to the positions of all dung beetles; if the dung beetle is beyond the boundary, setting the element of the dung beetle that is beyond the boundary to the boundary value; S25: Update the current optimal solution, the global optimal solution and their corresponding fitness values; S26: Repeat the above steps, and after reaching the maximum number of iterations, output the global optimal solution of the first objective function and / or the global optimal solution of the second objective function.

7. A distributed energy storage system scheduling optimization system based on nonlinear dung beetle optimization, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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