An Optimization Scheduling Method and Device for a New Energy Power Generation and Energy Storage System

Abstract

The present invention discloses an optimization scheduling method and device for a new energy power generation and energy storage system. The method includes establishing a new energy output and load power prediction model, and obtaining wind power output prediction data, photovoltaic output prediction data, and load power prediction data through prediction; with the goal of minimizing total cost, establishing a day-ahead optimization scheduling model and an intraday optimization scheduling model for the new energy power generation and energy storage system respectively; setting constraints for the day-ahead optimization scheduling model and the intraday optimization scheduling model based on the daily actual working conditions of the new energy power generation and energy storage system; solving the optimization scheduling operation model based on the wind power, photovoltaic output, and load period prediction data, objective function, and constraints corresponding to each time period in the scheduling cycle, and determining the optimal scheduling scheme for the system. The present invention realizes the effective management of new energy power generation and energy storage power stations, improves energy utilization efficiency, and reduces the impact of the intermittent and fluctuating nature of new energy power generation on system stability.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and energy storage system optimization, and in particular to an optimization scheduling method and equipment for a new energy power generation and energy storage system. Background Art

[0002] With the growth of global energy demand and increasingly severe environmental problems, the development and utilization of renewable energy technologies have become an important direction for energy transformation. Renewable energy generation has the advantages of being clean and sustainable and has been widely used in recent years. However, the intermittent and volatile nature of renewable energy generation limits its widespread application in power systems, especially in isolated microgrids, where power supply fluctuations can more easily lead to operational instability. Renewable energy generation combined with energy storage power stations can balance electricity demand and supply, but their high cost and limited charge and discharge life also pose challenges to the economic efficiency and operational efficiency of the system. Therefore, how to effectively integrate renewable energy generation and energy storage devices, optimize scheduling strategies, and improve the operational performance of renewable energy and energy storage power stations is one of the important research topics currently underway.

[0003] Traditional power system dispatch methods primarily rely on static optimization or regularized dispatch strategies. While these methods can achieve some success under stable load conditions, they fail to fully consider real-time load fluctuations, the randomness of renewable energy generation, and the characteristics of energy storage devices, making dynamic dispatch difficult in complex and changing operating environments. Furthermore, some methods, due to their limited consideration of factors, make it difficult to strike a balance between economic efficiency, environmental protection, and user satisfaction in dispatch solutions.

[0004] With the development of intelligent technologies, scheduling methods based on big data analysis and artificial intelligence have gradually become a research hotspot. By integrating historical and real-time data, these methods can effectively predict the volatility of renewable energy generation and load demand, providing support for optimized scheduling. However, the application of existing methods in complex scenarios remains insufficient. On the one hand, the accuracy of prediction models and the computational efficiency of algorithms need to be further improved to cope with the rapidly changing operating conditions of renewable energy power generation systems. On the other hand, although various optimization methods have been proposed in some studies, most remain at the theoretical stage and have not yet formed a mature solution. An algorithm is urgently needed to achieve effective scheduling of energy storage power plants.

[0005] In summary, the scheduling of renewable energy power generation and energy storage systems currently faces multiple challenges: first, how to build a scheduling model that balances real-time performance and stability; second, how to develop efficient intelligent optimization algorithms to achieve economical and efficient system operation. Therefore, there is an urgent need for an innovative intelligent optimization scheduling method for renewable energy power generation and energy storage systems. By integrating adaptive optimization algorithms, economic scheduling models, and intelligent forecasting technologies, this method can achieve efficient scheduling in a dynamic environment and provide technical support for the sustainable development of renewable energy power generation and energy storage systems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and device for optimizing the scheduling of a new energy power generation and energy storage system in response to the deficiencies in the prior art.

[0007] To achieve the above objectives, in a first aspect, the present invention provides a method for optimizing and scheduling a new energy power generation and energy storage system, wherein the new energy power generation and energy storage system includes a wind power generation system, a photovoltaic power generation system, an energy storage device, a diesel generator, and a load. The method comprises:

[0008] Step 1: Using historical data of wind power generation power, photovoltaic power generation power, and load power, a new energy output and load power prediction model is established, and wind power output prediction data, photovoltaic output prediction data, and load power prediction data are obtained through prediction using the new energy output and load power prediction model;

[0009] Step 2: With the goal of minimizing total cost, establish a day-ahead optimization scheduling model and an intraday optimization scheduling model for the renewable energy power generation and energy storage systems, respectively.

[0010] Step 3: Based on the actual daily operating conditions of the new energy power generation and energy storage system, set the constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model;

[0011] Step 4. Input the electric output forecast data, photovoltaic output forecast data and load power forecast data corresponding to each time period with a scheduling period of 24 hours and a resolution of 1 hour into the day-ahead optimization scheduling model to perform day-ahead power scheduling of the new energy power generation and energy storage system; and input the electric output forecast data, photovoltaic output forecast data and load power forecast data corresponding to each time period with a scheduling period of 2 hours and a resolution of 15 minutes into the intraday optimization scheduling model to perform intraday scheduling of the new energy power generation and energy storage system, and according to the intraday scheduling results, determine the diesel generator output and energy storage charge and discharge expressions for the corresponding time period, and then determine the final optimization scheduling plan according to the energy storage charge and discharge expressions for each time period and the objective function.

[0012] Furthermore, the new energy output and load power prediction model is established based on the extreme learning machine, which is specifically expressed as follows:

[0013]

[0014] Among them, h is the number of hidden nodes, b i and β i are the bias and weight of the i-th hidden layer neuron, g(.) is the activation function, ω i is the i-th input weight of the new energy output and load power forecasting model, ω i =[ω i1 ,ω i2, …,ω in, ] T , n is a natural number greater than 2, x j is the jth input item of the new energy output and load power prediction model, N is the total number of input items, j is the jth output result of the new energy output and load power prediction model.

[0015] Furthermore, the day-ahead optimization scheduling model and the intraday optimization scheduling model are respectively expressed as:

[0016]

[0017] Among them, F1 is the output result of the day-ahead optimization scheduling model, T is the scheduling period corresponding to the day-ahead optimization scheduling model, and its value is 24, indicating that the scheduling period is 24 hours, t represents the t-th scheduling step within the scheduling period T, and the scheduling step of the day-ahead optimization scheduling model is 1 hour, C om is the operation and maintenance cost of the new energy and energy storage system, C D is the cost of diesel generator power generation, C grid is the interaction cost between the power generation system and the large power grid, P i (t) is the dispatch output of the i-th micro-source at the t-th dispatch step, P D,i (t) is the dispatch output of the i-th diesel generator at the t-th dispatch step, F2 is the output result of the 15-minute intraday optimization dispatch model, T' is the dispatch period corresponding to the intraday optimization dispatch model, and its value is 8, indicating that the dispatch period is 2 hours, t' represents the t'th dispatch step within the dispatch period T', and the dispatch step of the intraday optimization dispatch model is 15 minutes, P i (t') is the dispatch output of the i-th micro-source at the t'th dispatch step, P D,i (t') is the dispatched output of the i-th diesel generator at the t'th dispatching step. The above-mentioned micro power source includes a wind power generation device, a photovoltaic power generation device, an energy storage device and a diesel generator.

[0018] Furthermore, the constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model include:

[0019] 1) System power balance constraints:

[0020]

[0021] Among them, P pv,t1 is the dispatch output of the PV power station in the t1th dispatch step; P w,t1 P is the dispatch output of wind power generation in the t1th dispatch step; D,t1 is the dispatch output of the diesel generator in the t1th dispatch step; P c,k,t1 and P d,k,t1 are the discharge power and charging power of electrochemical energy storage respectively; P grid,t1 is the interaction power between the power generation system and the large power grid in the t1th scheduling step, P L,t1 is the load predicted one scheduling cycle ahead of the t1th scheduling step, MG is the installed capacity of the energy storage device, t1 = t, t', t1 = t means that the current constraint condition belongs to the day-ahead optimization scheduling model, t1 = t' means that the current constraint condition belongs to the intraday optimization scheduling model;

[0022] 2) Wind power output limit constraints:

[0023] 0≤P w,t1 ≤P w,N

[0024] P w,min,t1 ≤P w,t1 ≤P w,max,t1

[0025] Among them, P w,N is the installed capacity of the wind farm, P w,t1 is the dispatch output of wind power generation in the t1th dispatch step, P w,min,t1 、P w,max,t1 are the upper and lower limits of the available power of the wind farm in the t1th scheduling step, respectively;

[0026] 3) Photovoltaic output limit constraints:

[0027] 0≤P PV,t1 ≤P PV,N

[0028] P PV,min,t1 ≤P pv,t1 ≤P PV,max,t1

[0029] Among them, P PV,N is the installed capacity of photovoltaic power generation; P PV,t1 is the dispatch output of photovoltaic power generation in the t1th dispatch step; P PV,min,t1 、P PV,max,t1 They are the upper and lower limits of the photovoltaic available power during period t1 respectively;

[0030] 4) State of charge constraints of electrochemical energy storage devices:

[0031] SOC min ≤SOC t1 ≤SOC max

[0032] Among them, SOC t1 SOC is the state of charge of the energy storage device of the energy storage power station in the t1th scheduling step. min , SOC max are the minimum and maximum charge states of the energy storage device of the energy storage power station respectively;

[0033] 5) Power limit constraints of energy storage devices during charging and discharging:

[0034]

[0035] Among them, P c,k,min 、P c,k,t1 、P c,k,max The minimum power, current power and maximum power of the kth electrochemical energy storage device discharged in the t1th scheduling step respectively; P d,k,min 、P d,k,t1 、P d,k,max The minimum, real-time, and maximum charging power of the k-th energy storage device in the t1-th scheduling step respectively;

[0036] 6) Climbing constraints:

[0037]

[0038] in, is the active power output by the diesel generator in the t1th scheduling step, is the active power output by the diesel generator in the t1-1th scheduling step, They are the upper and lower limits of the diesel generator’s output active power respectively;

[0039] 7) Interaction power constraints between the system and the large power grid:

[0040]

[0041] Among them, P Grid (t1) is the interaction power between the power generation system and the large power grid in the t1th scheduling step, are the maximum and minimum values of the interaction power, respectively.

[0042] Furthermore, the objective function is a hunter-prey algorithm function.

[0043] In a second aspect, the present invention provides an optimization scheduling device for a new energy power generation and energy storage system, comprising a storage medium and a processor, wherein the storage medium stores a computer program, and the computer program is used to implement the above method when executed by the processor.

[0044] Beneficial effects: 1. This application realizes the effective management of new energy power generation and energy storage power stations, and improves energy utilization efficiency;

[0045] 2. This application aims to minimize the total cost, achieving the minimization of operation and maintenance costs, diesel generator power generation costs, and interaction costs with the large power grid;

[0046] 3. This application reduces the impact of intermittent and volatile renewable energy generation on system stability through prediction models and optimized scheduling strategies;

[0047] 4. This application improves the absorption capacity of new energy power generation and reduces energy waste through intelligent scheduling.

[0048] 5. This application reduces dependence on fossil fuels and reduces carbon emissions by optimizing the use of renewable energy;

[0049] 6. This application combines multiple intelligent technologies such as neural network models, extreme learning machines and hunter-prey algorithms to achieve integrated technological innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of an optimization scheduling method for a new energy power generation and energy storage system;

[0051] Figure 2 It is a schematic diagram of the hunter-prey algorithm used in this application. DETAILED DESCRIPTION

[0052] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solutions of the present invention. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0053] like Figure 1 As shown, an embodiment of the present invention provides an optimization scheduling method for a new energy power generation and energy storage system, wherein the new energy power generation and energy storage system includes a wind power generation system, a photovoltaic power generation system, an energy storage device, a diesel generator, and a load. The optimization scheduling method for the system includes:

[0054] Step 1: Use historical data on wind power generation, photovoltaic power generation, and load power to establish a new energy output and load power prediction model, and use the new energy output and load power prediction model to predict wind power output prediction data, photovoltaic output prediction data, and load power prediction data. Specifically, the new energy output and load power prediction model is established based on an extreme learning machine and is specifically expressed as:

[0055]

[0056] Among them, h is the number of hidden nodes, b i and β i are the bias and weight of the i-th hidden layer neuron, g(.) is the activation function, ω i is the i-th input weight of the new energy output and load power forecasting model, ω i =[ω i1 ,ω i2, …,ω in, ] T , n is a natural number greater than 2, x j is the jth input item of the new energy output and load power prediction model, N is the total number of input items, j is the jth output result of the new energy output and load power prediction model.

[0057] Step 2: With the goal of minimizing total cost, establish a day-ahead optimization scheduling model and a day-intraday optimization scheduling model for the renewable energy power generation and energy storage systems. Specifically, the day-ahead optimization scheduling model and the day-intraday optimization scheduling model are expressed as:

[0058]

[0059] Among them, F1 is the output result of the day-ahead optimization scheduling model, T is the scheduling period corresponding to the day-ahead optimization scheduling model, and its value is 24, indicating that the scheduling period is 24 hours, t represents the t-th scheduling step within the scheduling period T, and the scheduling step of the day-ahead optimization scheduling model is 1 hour, C om is the operation and maintenance cost of the new energy and energy storage system, C D is the cost of diesel generator power generation, C grid is the interaction cost between the power generation system and the large power grid, P i (t) is the dispatch output of the i-th micro-source at the t-th dispatch step, P D,i (t) is the dispatch output of the i-th diesel generator at the t-th dispatch step, F2 is the output result of the 15-minute intraday optimization dispatch model, T' is the dispatch period corresponding to the intraday optimization dispatch model, and its value is 8, indicating that the dispatch period is 2 hours, t' represents the t'th dispatch step within the dispatch period T', and the dispatch step of the intraday optimization dispatch model is 15 minutes, Pi (t') is the dispatch output of the i-th micro-source at the t'th dispatch step, P D,i (t') is the dispatched output of the i-th diesel generator at the t'th dispatching step. The above-mentioned micro power source includes a wind power generation device, a photovoltaic power generation device, an energy storage device and a diesel generator.

[0060] Step 3: Based on the actual daily operating conditions of the new energy power generation and energy storage system, set the constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model. The constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model include:

[0061] 1) System power balance constraints:

[0062]

[0063] Among them, P pv,t1 is the dispatch output of the PV power station in the t1th dispatch step; P w,t1 P is the dispatch output of wind power generation in the t1th dispatch step; D,t1 is the dispatch output of the diesel generator in the t1th dispatch step; P c,k,t1 and P d,k,t1 are the discharge power and charging power of electrochemical energy storage respectively; P grid,t1 is the interaction power between the power generation system and the large power grid in the t1th scheduling step, P L,t1 is the load predicted for the t1th scheduling step one scheduling period (T, T') in advance, MG is the installed capacity of the energy storage device, t1 = t, t', t1 = t means that the current constraint condition belongs to the day-ahead optimization scheduling model, and t1 = t' means that the current constraint condition belongs to the intraday optimization scheduling model.

[0064] 2) Wind power output limit constraints:

[0065] 0≤P w,t1 ≤P w,N

[0066] P w,min,t1 ≤P w,t1 ≤P w,max,t1

[0067] Among them, P w,N is the installed capacity of the wind farm, P w,t1 is the dispatch output of wind power generation in the t1th dispatch step, P w,min,t1 、P w,max,t1 are the upper and lower limits of the available power of the wind farm in the t1th scheduling step, respectively.

[0068] 3) Photovoltaic output limit constraints:

[0069] 0≤PPV,t1 ≤P PV,N

[0070] P PV,min,t1 ≤P pv,t1 ≤P PV,max,t1

[0071] Among them, P PV,N is the installed capacity of photovoltaic power generation; P PV,t1 is the dispatch output of photovoltaic power generation in the t1th dispatch step; P PV,min,t1 、P PV,max,t1 They are the upper and lower limits of the photovoltaic available power during period t1 respectively.

[0072] 4) State of charge constraints of electrochemical energy storage devices:

[0073] SOC min ≤SOC t1 ≤SOC max

[0074] Among them, SOC t1 SOC is the state of charge of the energy storage device of the energy storage power station in the t1th scheduling step. min , SOC max They are the minimum and maximum charge states of the energy storage device of the energy storage power station.

[0075] 5) Power limit constraints of energy storage devices during charging and discharging:

[0076]

[0077] Among them, P c,k,min 、P c,k,t1 、P c,k,max The minimum power, current power and maximum power of the kth electrochemical energy storage device discharged in the t1th scheduling step respectively; P d,k,min 、P d,k,t1 、P d,k,max The minimum, real-time, and maximum charging powers of the k-th energy storage device in the t1-th scheduling step, respectively.

[0078] 6) Climbing constraints:

[0079]

[0080] in, is the active power output by the diesel generator in the t1th scheduling step, is the active power output by the diesel generator in the t1-1th scheduling step, They are the upper and lower limits of the diesel generator output active power respectively.

[0081] 7) Interaction power constraints between the system and the large power grid:

[0082]

[0083] Among them, P Grid (t1) is the interaction power between the power generation system and the large power grid in the t1th scheduling step, are the maximum and minimum values of the interaction power, respectively.

[0084] Step 4. Input the electric output forecast data, photovoltaic output forecast data and load power forecast data corresponding to each time period with a scheduling period of 24 hours and a resolution of 1 hour into the day-ahead optimization scheduling model to perform day-ahead power scheduling of the new energy power generation and energy storage system; and input the electric output forecast data, photovoltaic output forecast data and load power forecast data corresponding to each time period with a scheduling period of 2 hours and a resolution of 15 minutes into the intraday optimization scheduling model to perform intraday scheduling of the new energy power generation and energy storage system, and according to the intraday scheduling results, determine the diesel generator output and energy storage charge and discharge expressions for the corresponding time period, and then determine the final optimization scheduling plan according to the energy storage charge and discharge expressions for each time period and the objective function.

[0085] Specifically, the objective function is the hunter-prey algorithm function (HPO). Figure 2 , the specific process of the hunter-prey algorithm:

[0086] The game between hunters and prey in nature can be modeled as an optimization process. The hunter or prey is regarded as a candidate solution. In the process of hunting and anti-hunting, the solution space is explored. Assume that the population is X = {X1, X2, ..., X N}, each hunter or prey X i Each solution is represented by a D-dimensional vector, where each dimension corresponds to a decision variable in the problem to be solved. The evolution of HPO can be summarized into the following two rules.

[0087] a) Hunter rule: In each iteration, the hunter will hunt the prey and replace the prey. The hunting behavior of HPO is as follows:

[0088] X i (t2+1)=X i (t2)+0.5[2CP pos -X i (t2)+2(1-C)Zμ-X i (t2)]

[0089] Where: X i (t2) is the hunter's current position; X i (t2+1) is the next position of the hunter; μ is the mean of all individual positions; Z is a random vector; P posrepresents the position of the prey to be eaten; C is the control variable, and t2 is the current iteration number.

[0090] P pos =X i |i is storted D euc (k best )|

[0091] k best =round(C×N)

[0092] C=1-t2(0.98 / T2)

[0093] Where N is the number of individuals in the population; D euc is the set of Euclidean distances of each individual to the mean μ; T2 is the maximum number of iterations.

[0094] b) Prey Rule: When prey escape from predators, they will move to the safest location in the environment. HPO considers the currently found optimal solution as the safest area in the environment. The prey update model is as follows:

[0095] X i (t+1)=T pos +CZcos(2πR1)×[T pos -X i (t)]

[0096] Among them, T pos represents the position of the global optimal value, and R1 is a random vector in [0,1).

[0097] The HPO algorithm uses a probability parameter β to determine whether the individual being updated is a hunter or prey, thus selecting the appropriate update strategy. Before updating an individual's position, a random number r1 is calculated. If r1 < β, the individual is considered a hunter and updated according to step a). Otherwise, the position is updated according to step b) as if it were a prey. In HPO, β is set to 0.1.

[0098] Based on the above embodiments, those skilled in the art can easily understand that the present invention also provides an optimization scheduling device for a new energy power generation and energy storage system, including a storage medium and a processor, wherein the storage medium stores a computer program, and the computer program is used to implement the above method when executed by the processor.

[0099] The above description is merely a preferred embodiment of the present invention. It should be noted that any other aspects not specifically described are considered prior art or common knowledge to those skilled in the art. Improvements and modifications may be made without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention.

Claims

1. A method for optimizing the scheduling of a new energy power generation and energy storage system, wherein the new energy power generation and energy storage system includes a wind power generation system, a photovoltaic power generation system, an energy storage device, a diesel generator, and a load, characterized in that: The optimization scheduling method includes: Step 1: Using historical data of wind power generation power, photovoltaic power generation power, and load power, a new energy output and load power prediction model is established, and wind power output prediction data, photovoltaic output prediction data, and load power prediction data are obtained through prediction using the new energy output and load power prediction model; Step 2: With the goal of minimizing total cost, establish a day-ahead optimization scheduling model and an intraday optimization scheduling model for the renewable energy power generation and energy storage systems, respectively. Step 3: Based on the actual daily operating conditions of the new energy power generation and energy storage system, set the constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model; Step 4: Input the electric output forecast data, photovoltaic output forecast data, and load power forecast data corresponding to each time period with a scheduling period of 24 hours and a resolution of 1 hour into the day-ahead optimization scheduling model to perform day-ahead power scheduling of the renewable energy power generation and energy storage system; and input the electric output forecast data, photovoltaic output forecast data, and load power forecast data corresponding to each time period with a scheduling period of 2 hours and a resolution of 15 minutes into the intraday optimization scheduling model to perform intraday scheduling of the renewable energy power generation and energy storage system. According to the intraday scheduling results, the diesel generator output and energy storage charge and discharge expressions for the corresponding time period are determined. Then, according to the energy storage charge and discharge expressions for each time period, the objective function is used to determine the final optimization scheduling plan. The day-ahead optimization scheduling model and the intraday optimization scheduling model are respectively expressed as: ； ； in, is the output result of the day-ahead optimization scheduling model, The scheduling period corresponding to the day-ahead optimization scheduling model is 24, which means the scheduling period is 24 hours. Indicates that in the scheduling cycle The first The scheduling step length of the day-ahead optimization scheduling model is 1 hour. The operation and maintenance costs of new energy and energy storage systems, The cost of diesel generator power generation, is the interaction cost between the power generation system and the large power grid, For the i A micro power supply The dispatch output of the dispatch step length is For the i A diesel generator in the The dispatch output of the dispatch step length is This is the output result of the 15-minute optimization scheduling model within the day. The scheduling period corresponding to the intraday optimization scheduling model is 8, which means the scheduling period is 2 hours. Indicates that in the scheduling cycle The first The scheduling step length of the intraday optimization scheduling model is 15 minutes. For the i A micro power supply The dispatch output of the dispatch step length is For the i A diesel generator in the The above-mentioned micro power sources include wind power generation devices, photovoltaic power generation devices, energy storage devices and diesel generators.

2. The optimization scheduling method of a new energy power generation and energy storage system according to claim 1, characterized in that: The new energy output and load power prediction model is established based on the extreme learning machine, which is specifically expressed as follows: ； Among them, h is the number of hidden nodes, and are the bias and weight of the i-th hidden layer neuron, is the activation function, is the i-th input weight of the new energy output and load power prediction model, , n is a natural number greater than 2, is the jth input item of the new energy output and load power prediction model, N is the total number of input items, is the jth output result of the new energy output and load power prediction model.

3. The optimization scheduling method of a new energy power generation and energy storage system according to claim 1, characterized in that: The constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model include: 1) System power balance constraints: ； in, For photovoltaic power station t The dispatch output within 1 dispatch step; For wind power generation t The dispatch output within 1 dispatch step; For diesel generators t The dispatch output within 1 dispatch step; and are the discharge power and charging power of electrochemical energy storage, respectively; For the power generation system and the large power grid t Interaction power within 1 scheduling step, For the t 1 scheduling step is the load predicted one scheduling cycle ahead, MG is the installed capacity of the energy storage device, , Indicates that the current constraints belong to the day-ahead optimization scheduling model, Indicates that the current constraints belong to the intraday optimization scheduling model; 2) Wind power output limit constraints: ； ； in, is the installed capacity of the wind farm, For wind power generation t The dispatch output within 1 dispatch step, 、 In the t The upper and lower limits of the wind farm's available power within one dispatching step; 3) Photovoltaic output limit constraints: ； ； in, is the installed capacity of photovoltaic power generation; Photovoltaic power generation t The dispatch output within 1 dispatch step; 、 They are t 1. The upper and lower limits of photovoltaic available power in a period; 4) State of charge constraints of electrochemical energy storage devices: ； in, The energy storage device of the energy storage power station is t The state of charge within 1 scheduling step, 、 are the minimum and maximum charge states of the energy storage device of the energy storage power station respectively; 5) Power limit constraints of energy storage devices during charging and discharging: ； in, 、 、 In the t The minimum power, current power and maximum power of the kth electrochemical energy storage device discharged within one scheduling step; 、 、 In the t The minimum, real-time, and maximum charging power of the kth energy storage device within one scheduling step; 6) Climbing constraints: ； in, For diesel generators t Active power output within 1 scheduling step, For diesel generators t 1-1 active power output within the scheduling step, 、 They are the upper and lower limits of the diesel generator’s output active power respectively; 7) Power constraints of interaction between the system and the large power grid: ； in, For the power generation system and the large power grid t Interaction power within 1 scheduling step, 、 are the maximum and minimum values of the interaction power, respectively.

4. The optimization scheduling method of a new energy power generation and energy storage system according to claim 1, characterized in that: The objective function is a hunter-prey algorithm function.

5. An optimization scheduling device for a new energy power generation and energy storage system, comprising a storage medium and a processor, wherein the storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the method according to any one of claims 1 to 4.

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