Optimized scheduling method and equipment for new energy power generation and energy storage system

Abstract

The invention discloses an optimal scheduling method and equipment for a new energy power generation and energy storage system. The method comprises the steps that 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; respectively establishing a day-ahead optimal scheduling model and an intra-day optimal scheduling model of the new energy power generation and energy storage system by taking the lowest total cost as a target; setting constraint conditions of the day-ahead optimization scheduling model and the intra-day optimization scheduling model based on daily actual working conditions of the new energy power generation and energy storage system; and based on the wind power and photovoltaic output corresponding to each time period in the scheduling period, the load time period prediction data, the objective function and the constraint condition, solving the optimal scheduling operation model, and determining an optimal scheduling scheme of the system. According to the invention, effective management of the new energy power generation and energy storage power station is realized, the energy utilization efficiency is improved, and the influence of intermittency and volatility of new energy power generation on the system stability is reduced.

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 power 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 power generation limits its widespread application in power systems, especially in isolated microgrids, which are more likely to cause operational instability due to power supply fluctuations. The combination of renewable energy power generation and energy storage power stations can balance electricity demand and supply, but its high cost and limited charge and discharge life also pose challenges to the economy and operating efficiency of the system. Therefore, how to effectively integrate renewable energy power generation and energy storage devices, optimize scheduling strategies, and improve the operating performance of renewable energy and energy storage power stations is one of the important research topics at present.

[0003] Traditional power system dispatching methods mainly rely on static optimization or regularized dispatching strategies. Although these methods can achieve certain results under stable load conditions, they fail to fully consider real-time load changes, the randomness of renewable energy generation, and the characteristics of energy storage equipment, making it difficult to achieve dynamic dispatching under complex and changeable operating environments. In addition, some methods consider only a single factor, making it difficult to strike a balance between economic efficiency, environmental protection, and user satisfaction in dispatching schemes.

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

[0005] In summary, the scheduling of new energy power generation and energy storage systems currently faces multiple challenges: first, how to build a scheduling model that takes into account both real-time and stability, and second, how to develop an efficient intelligent optimization algorithm to achieve economical and efficient operation of the system. Therefore, there is an urgent need for an innovative intelligent optimization scheduling method for new energy power generation and energy storage systems, which can achieve efficient scheduling in a dynamic environment by integrating adaptive optimization algorithms, economic scheduling models, and intelligent prediction technologies, and provide technical support for the sustainable development of new 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 view of the deficiencies in the prior art.

[0007] To achieve the above objectives, in a first aspect, 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 comprises a wind power generation system, a photovoltaic power generation system, an energy storage device, a diesel generator and a load, and the optimization scheduling method comprises:

[0008] Step 1: Establish a new energy output and load power prediction model by using historical data of wind power generation power, photovoltaic power generation power and load power, and obtain wind power output prediction data, photovoltaic output prediction data and load power prediction data through the new energy output and load power prediction model;

[0009] Step 2: With the goal of minimizing the total cost, establish a day-ahead optimization dispatching model and an intraday optimization dispatching 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 cycle of 24h and a resolution of 1h 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 cycle of 2h and a resolution of 15min 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 of the corresponding time period, and then determine the final optimization scheduling plan according to the energy storage charge and discharge expressions of 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 prediction 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 It 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 in 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 is the dispatch output of wind power generation in the t1th dispatch step; P 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 by 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 They 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 available photovoltaic power during period t1 respectively;

[0030] 4) Constraints on the state of charge of electrochemical energy storage devices:

[0031] SOC min ≤SOC t1 ≤SOC max

[0032] Among them, SOC t1 is the state of charge of the energy storage device of the energy storage power station in the t1th scheduling step, SOC min , SOC max They 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 unit discharged in the t1th scheduling step; P d,k,min , P d,k,t1 , P d,k,max The minimum, real-time and maximum charging power of the kth energy storage device in the t1th 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 active power output of the diesel generator 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, 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.

[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, and achieves 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 adopted in this application. DETAILED DESCRIPTION

[0052] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. The embodiments are implemented based on the technical solution 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, and the optimization scheduling method for the system includes:

[0054] Step 1: Use the historical data of wind power generation, photovoltaic power generation and load power to establish a new energy output and load power prediction model, and obtain wind power output prediction data, photovoltaic output prediction data and load power prediction data through the new energy output and load power prediction model. Specifically, the new energy output and load power prediction model is established based on the extreme learning machine, which 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 prediction 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 It is the jth output result of the new energy output and load power prediction model.

[0057] Step 2: With the goal of minimizing the total cost, establish the day-ahead optimization dispatch model and the intraday optimization dispatch model for the new energy power generation and energy storage system. Specifically, the day-ahead optimization dispatch model and the intraday optimization dispatch model are respectively 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 in 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 is the dispatch output of wind power generation in the t1th dispatch step; P 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 cycle (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 They 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 available photovoltaic power during period t1 respectively.

[0072] 4) Constraints on the state of charge of electrochemical energy storage devices:

[0073] SOC min ≤SOC t1 ≤SOC max

[0074] Among them, SOC t1 is the state of charge of the energy storage device of the energy storage power station in the t1th scheduling step, SOC min , SOC max They are respectively 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 unit discharged in the t1th scheduling step; P d,k,min , P d,k,t1 , P d,k,max The minimum, real-time and maximum charging powers of the kth energy storage device in the t1th 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 respectively the upper and lower limits of the diesel generator output active power.

[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 cycle of 24h and a resolution of 1h 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 cycle of 2h and a resolution of 15min 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 of the corresponding time period, and then determine the final optimization scheduling plan according to the energy storage charge and discharge expressions of each time period and the objective function.

[0085] Specifically, the above 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 Corresponding to a solution, each solution is represented by a D-dimensional vector, and each dimension corresponds to a decision variable of the problem to be solved. The evolution process 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 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 from each individual to the mean μ; T2 is the maximum number of iterations.

[0094] b) Prey rule: When the prey escapes from the predator, it will move to the safest position in the environment. HPO regards the currently found optimal solution as the safest area in the environment. The update model of the prey 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 to be updated belongs to the hunter or the prey, and thus selects the corresponding update strategy. Before updating the individual position, a random number r1 is calculated. When r1<β, the current individual is regarded as a hunter and updated according to a), otherwise b) the position is updated according to the prey method. In HPO, the value of β is 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 is only a preferred embodiment of the present invention. It should be noted that for ordinary technicians in this technical field, other parts not specifically described belong to the prior art or common knowledge. Several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope 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 comprises 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 comprises: Step 1: Establish a new energy output and load power prediction model by using historical data of wind power generation power, photovoltaic power generation power and load power, and obtain wind power output prediction data, photovoltaic output prediction data and load power prediction data through the new energy output and load power prediction model; Step 2: With the goal of minimizing the total cost, establish a day-ahead optimization dispatching model and an intraday optimization dispatching 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 cycle of 24h and a resolution of 1h 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 cycle of 2h and a resolution of 15min 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 of the corresponding time period, and then determine the final optimization scheduling plan according to the energy storage charge and discharge expressions of each time period and the objective function.

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, 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 prediction 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 It 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 is characterized in that: The day-ahead optimization scheduling model and the intraday optimization scheduling model are respectively expressed as: 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 in 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.

4. The optimization scheduling method of a new energy power generation and energy storage system according to claim 3 is characterized in that: The constraints of the day-ahead optimization scheduling model and the intraday optimization scheduling model include: 1) System power balance constraints: Among them, P pv,t1 is the dispatch output of the PV power station in the t1th dispatch step; P w,t1 is the dispatch output of wind power generation in the t1th dispatch step; P 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 by 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; 2) Wind power output limit constraints: 0≤P w,t1 ≤P w,N P w,min,t1 ≤P w,t1 ≤P w,max,t1 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 They are the upper and lower limits of the available power of the wind farm in the t1th scheduling step, respectively; 3) Photovoltaic output limit constraints: 0≤P PV,t1 ≤P PV,N P PV,min,t1 ≤P pv,t1 ≤P PV,max,t1 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 available photovoltaic power during period t1 respectively; 4) Constraints on the state of charge of electrochemical energy storage devices: SOC min ≤SOC t1 ≤SOC max Among them, SOC t1 is the state of charge of the energy storage device of the energy storage power station in the t1th scheduling step, SOC min , SOC max They 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: 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 unit discharged in the t1th scheduling step; P d,k,min , P d,k,t1 , P d,k,max The minimum, real-time and maximum charging power of the kth energy storage device in the t1th scheduling step respectively; 6) Climbing constraints: -P i down Δt1≤P i t1 -P i t1-1 ≤P i up Δt1 Among them, P i t1 is the active power output of the diesel generator in the t1th scheduling step, P i t1-1 is the active power output of the diesel generator in the t1-1th scheduling step, P i up Δt1、P i down Δt1 are the upper and lower limits of the active power output of the diesel generator; 7) Interaction power constraints between the system and the large power grid: 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.

5. 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.

6. 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 5.

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