New energy collaborative optimization energy storage configuration method and system and storage medium

Abstract

The invention discloses a new energy collaborative optimization energy storage configuration method and system and a storage medium, and the method comprises the steps: collecting historical wind and light output data, and constructing a wind and light combined output scene; a three-layer game model is established in a wind-solar combined output scene, an upper-layer operator energy storage configuration model is used for solving capacity configuration parameters of shared energy storage by taking the lowest operation cost of the shared energy storage as an objective function, and a middle-layer power grid regulation and control model is used for coordinating global optimization through dynamic electricity price and a penalty function, so that the energy storage capacity of the shared energy storage is optimized. The lower-layer new energy station economic model is used for solving an optimal charging and discharging strategy of the new energy station on the basis of the capacity configuration parameters generated by the upper-layer operator energy storage configuration model; solving the three-layer game model to generate a shared energy storage configuration result; reasonable configuration of shared energy storage of the new energy station is realized by considering uncertainty of new energy power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization configuration, and in particular to a method, system and storage medium for collaborative optimization of energy storage configuration with new energy. Background Art

[0002] In recent years, my country has made remarkable progress in renewable energy sectors such as wind power and photovoltaic power generation. However, this progress also presents a more severe challenge for the power system in accommodating these new energies. Due to the uncertainty inherent in generating a high proportion of renewable energy, it is difficult to meet consumers' demand for high-quality power. Furthermore, the distribution network struggles to directly utilize such volatile electricity, leading to a more severe phenomenon of curtailed wind and solar power generation.

[0003] Energy storage is an effective means to solve the above problems. It can effectively improve the absorption capacity of new energy stations for new energy. However, the widespread popularization of energy storage faces the following problems: (1) High cost of use. Ordinary individuals have a small demand for energy storage capacity and it is difficult to enjoy the benefits of energy storage in a short period of time. (2) The types of energy storage needs are diverse. It is difficult for energy storage users to directly buy energy storage products that suit their own needs. Purchasing too large a capacity will result in a waste of investment, and too small a capacity will not meet their own needs. (3) Low utilization rate of energy storage. Users only use energy storage for a limited time during the day, and there is idle energy storage capacity at most times, which leads to the problem of low utilization rate of energy storage equipment.

[0004] The development and popularization of energy storage are hampered by factors including, but not limited to, the three aforementioned factors. To promote its application, some literature, inspired by the sharing economy, has proposed the concept of shared energy storage and elaborated on its business model. For example, the paper "Research on Operation and Configuration Technology of Shared Energy Storage in New Energy Aggregation Areas, Fang Zhijin, Master's Thesis, North China University of Technology" proposes a centralized shared energy storage architecture and concept, and employs game theory to explore the interplay between shared energy storage and multiple entities. However, this proposal fails to analyze the correlation between wind and solar resources in new energy clusters. The document "Research on Optimal Configuration of Shared Energy Storage Based on Game Theory, Diao Yuanpeng, Master's Thesis, North China Electric Power University" proposes a new business model for shared energy storage alliances and new energy clusters. Based on the framework of game theory, a cost-benefit game analysis model for shared energy storage and new energy cluster alliances is established. Patent application publication number CN119010114A proposes a shared energy storage configuration scheme based on hybrid games. The constructed master-slave game model uses both the configuration information of the shared energy storage system and the shared energy storage revenue as decision variables, thereby enabling dynamic pricing and capacity allocation of energy storage when solving the model. However, both schemes rely on market rules for profit distribution. Because market rules aim to maximize individual interests (such as maximizing the revenue of energy storage operators or minimizing the costs of new energy stations), they can easily lead to local optimality while ignoring overall system-level optimization goals (such as the new energy absorption rate and grid stability).

[0005] Furthermore, some current research on shared energy storage configuration within the context of renewable energy sources fails to consider the impact of renewable energy generation uncertainty. This can lead to renewable energy stations being unable to select appropriate storage capacity, impacting the stability of their own systems. However, properly configuring shared energy storage capacity is a prerequisite for reducing investment costs for energy storage operators, avoiding unnecessary cost increases from excessive storage capacity and the impact of insufficient storage capacity on power supply reliability within renewable energy stations.

[0006] Currently, the two most common methods for addressing the uncertainty of renewable energy generation are stochastic optimization and robust optimization. Stochastic optimization methods select generated typical scenarios as data input, while robust optimization methods determine the worst-case energy storage capacity and propose day-ahead scheduling strategies for wind and thermal power units, increasing their flexibility. However, stochastic optimization typically requires numerous iterations and computations, while robust optimization often sacrifices economic benefits due to its overly conservative strategies. Summary of the Invention

[0007] The technical problem solved by the present invention is how to take into account the uncertainty of renewable energy power generation and realize the reasonable configuration of shared energy storage at renewable energy sites.

[0008] The present invention solves the above technical problems through the following technical means:

[0009] A new energy collaborative optimization energy storage configuration method is proposed, including:

[0010] Collect historical wind and solar power output data and build wind and solar power combined output scenarios;

[0011] A three-layer game model is established for the wind-solar combined output scenario. The upper-layer operator energy storage configuration model is used to solve the capacity configuration parameters of shared energy storage with the lowest shared energy storage operating cost as the objective function. The middle-layer grid regulation model is used to coordinate global optimization through dynamic electricity prices and penalty functions. The lower-layer new energy station economic model is used to solve the optimal charging and discharging strategy of new energy stations based on the capacity configuration parameters generated by the upper-layer operator energy storage configuration model.

[0012] The three-layer game model is solved to generate the shared energy storage configuration results.

[0013] Furthermore, the collecting of historical wind and solar power output data and the calculation of wind and solar power combined output data include:

[0014] Collect historical wind and solar power output data, perform kernel density estimation on the historical wind and solar power output data, and generate probability density functions of wind and solar power outputs;

[0015] Based on the Copula theory, the probability density function of wind and solar power output is modeled separately to generate the probability density function of wind and solar power combined output;

[0016] The inverse function is used to calculate the probability density function of the wind-solar combined output and construct the joint distribution function of the wind-solar combined output scenario.

[0017] Furthermore, the objective function of the upper-layer operator's energy storage configuration model is:

[0018] minC ses =C inv +C grid

[0019] Where C ses is the shared energy storage operating cost; C inv is the annual investment cost of energy storage power station equipment; C grid The annual cost of electricity purchased from the power grid by new energy stations;

[0020] The constraints of the objective function of the upper-layer operator's energy storage configuration model include energy storage power station power balance constraints, energy storage power station SOC state continuity constraints, and energy storage power station charging and discharging power constraints.

[0021] Furthermore, the formula of the intermediate layer power grid control model is expressed as:

[0022]

[0023] Where C Grid is the objective function of the power grid; base is the benchmark electricity price; is the power loss; k is the volatility risk cost coefficient; is the fluctuating load; T is the scheduling period, which is 24h.

[0024] Furthermore, the objective function of the lower-level new energy station economic model is:

[0025] max C new =C sell -C serve -C pen -C curtail

[0026]

[0027] Where: C new is the economic objective function of the new energy station; C sell The income from electricity sales at new energy stations; t is a dynamic electricity price; is the actual output of cluster i in period t; C serve C is the service fee for shared energy storage; pen Penalty cost for the power grid; c pen is the unit prediction deviation penalty coefficient; is the predicted output of cluster i in time period t; μ is the output smoothing penalty coefficient; P RES,i is the current daily power value of cluster i; is the average daily output benchmark value of cluster i; C curtail Cost of curtailing wind and solar power; is the amount of wind and solar curtailment of cluster i in period t;

[0028] The constraints of the objective function of the lower-level new energy station economic model are:

[0029]

[0030] Where, is the actual wind power output value of cluster i during period t; is the actual photovoltaic output value of cluster i during period t; The electric power discharged by cluster i using energy storage during period t; The electric power used by cluster i to charge energy storage during period t; is the electric load power of cluster i during period t; is the amount of power wasted by cluster i during period t.

[0031] Furthermore, solving the three-layer game model to generate a shared energy storage configuration result includes:

[0032] The KKT condition is used to transform the optimization problem of the lower-level new energy station economic model into the constraint form of the upper-level operator energy storage configuration model;

[0033] The decision variables of the middle-layer grid control model are embedded into the objective function of the upper-layer operator's energy storage configuration model to form a single-layer optimization model.

[0034] The single-layer optimization model is linearized using the Big-M method and solved using the Cplex solver to obtain the shared energy storage configuration result.

[0035] Furthermore, the formula of the three-layer game model is expressed as:

[0036] G={M,(S k ) k∈M ,(I k ) k∈M};

[0037] Where M is the set of shared energy storage operators, power grids, and new energy clusters; S K is a strategy set that includes operators sharing energy storage, power grid and new energy clusters; I K It is a payment function set that includes operators’ shared energy storage, power grid and new energy clusters.

[0038] Furthermore, the operator's strategy sets for sharing energy storage, power grid, and new energy clusters are:

[0039]

[0040] Where S SES Rated strategy set for shared energy storage operators, is the charging and discharging state of energy storage, is the charging and discharging price; is the charge and discharge power, The price of electricity purchased or sold from the power grid; S Grid is the strategy set of the coordinator grid, λ t is the dynamic electricity price, θ p is the assessment item; S NEC is the strategy set of new energy stations, C i,t For the assessment cost of the power grid, To use the charging and discharging power of shared energy storage, E i,t The capacity rented for each time period.

[0041] In addition, the present invention also proposes a new energy collaborative optimization energy storage configuration system, including:

[0042] The joint output construction module is used to collect historical wind and solar power output data and build wind and solar power joint output scenarios;

[0043] A game model building module is used to establish a three-layer game model in the scenario of wind and solar combined output. Among them, the upper-layer operator energy storage configuration model is used to solve the capacity configuration parameters of shared energy storage with the lowest shared energy storage operating cost as the objective function. The middle-layer power grid regulation model is used to coordinate global optimization through dynamic electricity prices and penalty functions. The lower-layer new energy station economic model is used to solve the optimal charging and discharging strategy of new energy stations based on the capacity configuration parameters generated by the upper-layer operator energy storage configuration model;

[0044] The model solving module is used to solve the three-layer game model and generate the shared energy storage configuration results.

[0045] In addition, the present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned new energy collaborative optimization energy storage configuration method is implemented.

[0046] The advantages of the present invention are:

[0047] (1) The present invention starts with an analysis of the uncertainty of renewable energy output, constructs a wind-solar joint output scenario from the perspective of wind-solar joint output correlation, prepares conditions for the coordinated operation of new energy storage systems and renewable energy, and fully considers the uncertainty of wind-solar output and the impact of correlation in the planning stage; then designs a three-layer game model of shared energy storage operators-grids-new energy stations to achieve global joint collaborative optimization operation, which can effectively improve the benefits of all parties. Since the grid is added as a subject in the three-layer game model, it can balance the interests of multiple parties and avoid the limitations of relying solely on market bidding.

[0048] (2) The grid assessment mechanism sets indicators such as new energy fluctuation penalties and load fluctuation sensitivity coefficients. Under the grid coordination mechanism, it takes system-level global optimization as the core goal (such as new energy absorption rate and grid stability), directly links the operation of energy storage resources with the overall needs of the grid, and promotes the coordination of interests of multiple parties.

[0049] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a method for collaboratively optimizing energy storage configuration using new energy sources, as proposed in one embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of the combined operation of new energy storage and new energy in one embodiment of the present invention;

[0052] Figure 3 This is a scene diagram of wind and solar power combined output in one embodiment of the present invention;

[0053] Figure 4 It is a structural diagram of a new energy collaborative optimization energy storage configuration system proposed in one embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] like Figure 1 As shown, the first embodiment of the present invention proposes a method for collaboratively optimizing energy storage configuration with new energy, the method comprising the following steps:

[0056] S10. Collect historical wind and solar power output data and build a wind and solar power combined output scenario;

[0057] S20. Establish a three-layer game model in the wind-solar combined output scenario, where the upper-layer operator energy storage configuration model is used to solve the capacity configuration parameters of shared energy storage with the lowest shared energy storage operating cost as the objective function; the middle-layer grid control model is used to coordinate global optimization through dynamic electricity prices and penalty functions; and the lower-layer new energy station economic model is used to solve the optimal charging and discharging strategy of the new energy station based on the capacity configuration parameters generated by the upper-layer operator energy storage configuration model;

[0058] S30. Solve the three-layer game model to generate a shared energy storage configuration result.

[0059] Compared with traditional energy storage configuration methods, the embodiments of the present invention fully consider the uncertainty of wind and solar power output as well as the shared energy storage capacity and power configuration under the active regulation of the power grid. Through the Stackelberg game, the various entities among the shared energy storage operator, the power grid, and the new energy station meet the energy storage usage needs of the new energy station, realize the rational configuration of energy storage resources, and achieve the coordinated optimization of interests.

[0060] As a further preferred technical solution, step S10: collecting historical wind and solar power output data and constructing a wind and solar power combined output scenario specifically includes the following steps:

[0061] S11. Collect historical wind and solar power output data, perform kernel density estimation on the historical wind and solar power output data, and generate probability density functions of wind and solar power outputs;

[0062] Specifically, this embodiment collects historical wind and solar power output data per hour, and then, based on kernel density estimation, selects a Gaussian kernel function that meets the characteristics to generate the probability density function of the wind and solar power output at each time period of the day. The formula is expressed as follows:

[0063]

[0064] Where, are the kernel density estimation functions of wind and solar output respectively; n is the number of historical data samples; h is the window width; K(x) is the selected kernel function; x 1 ,....,x 24 It is the wind power output value point at different time periods; The historical data of wind power output in different periods is: 1 、....、y 24 Y is the photovoltaic output value point at different time periods; i 1 、....、Y i 24 It is the historical data of photovoltaic output in different periods.

[0065] S12. Based on the Copula theory, the probability density function of wind and solar power output is modeled to generate the probability density function of wind and solar power combined output;

[0066] Specifically, in this embodiment, the probability density function of the wind-solar combined output is obtained by modeling the wind-solar combined output based on the Copula theory, and the formula is expressed as follows:

[0067]

[0068] [F 1 (u 1 ,v 1 ),F 2 (u 2 ,v 2 ),…,F 24 (u 24 ,v 24 )]

[0069]

[0070] Where, F n (x i ,y i ) is the joint distribution function containing the two variables of wind and solar, is the distribution function of wind power output only, is the distribution function of photovoltaic output only, C() is the connection function, F 1 (u 1 ,v1 ),F 2 (u 2 ,v 2 ),…,F 24 (u 24 ,v 24 ) is the wind and solar joint distribution function in each period, u i 、v i The wind and solar output prediction values ​​taking into account the correlation.

[0071] S13. Use an inverse function to calculate the probability density function of the wind-solar combined output to construct a joint distribution function of the wind-solar combined output scenario.

[0072] Specifically, this embodiment performs an inverse function operation on the probability density function of the wind-solar combined output, and obtains the joint distribution function scenario as follows:

[0073] x i =F i -1 (u i )

[0074] y i =F i -1 (v i )

[0075] Where, F i -1 () is the inverse function operator.

[0076] As a further preferred technical solution, the first objective function of the upper-layer operator energy storage configuration model is:

[0077] minC ses =C inv +C grid

[0078]

[0079] Where C ses is the shared energy storage operating cost; C inv is the annual investment cost of energy storage power station equipment; C grid is the annual cost of electricity purchased from the power grid by the new energy station; r is the annual interest rate of funds; γ is the life cycle of energy storage; g p is the unit energy storage power investment cost; g E is the unit energy storage capacity investment cost; g M is the maintenance cost per unit energy storage power; P max is the maximum charging and discharging power of the energy storage station; E max is the maximum configuration capacity of the energy storage power station; baseis the benchmark electricity price; α is the load fluctuation sensitivity coefficient; β is the new energy fluctuation sensitivity coefficient; P load,t is the real-time system load; is the typical daily average load; ΔP RES,t To generate fluctuating power for new energy sources; It is the rated capacity of new energy output.

[0080] The constraints of the first objective function include the energy storage power station power balance constraint, the energy storage power station SOC state continuity constraint, and the energy storage power station charge and discharge power constraint, specifically:

[0081] 1) Power balance constraints of energy storage power stations

[0082]

[0083] Where, is the discharge power of new energy station i; is the charging power of new energy station i; P relea is the discharge power of the shared energy storage power station; P abs The charging power of shared energy storage.

[0084] 2) SOC state continuity constraints of energy storage power stations

[0085]

[0086] Where, E ess (t) is the SOC state of the energy storage at time t; η abs ,η relea is the charging and discharging efficiency of energy storage; P abs 、P relea is the rated charge and discharge power of the energy storage.

[0087] 3) Energy storage power station charging and discharging power constraints

[0088]

[0089] Where U abs 、U relea They are the charging and discharging states of the energy storage power station respectively.

[0090] It should be noted that the constraints set for the first objective function in this embodiment can improve model quality, such as physical feasibility, grid compliance, and solution stability, to ensure the normal operation of the shared energy storage system.

[0091] As a further preferred technical solution, the second objective function of the intermediate layer power grid control model is expressed as:

[0092]

[0093] Where C Grid is the objective function of the power grid, which quantifies the impact of load fluctuation on the power grid and is only used as an optimization indicator; base is the benchmark electricity price; is the power loss; k is the volatility risk cost coefficient; is the fluctuating load; T is the scheduling period, 24 hours.

[0094] It should be noted that the second objective function is only the objective function of the power grid, involving decision variables that affect the dynamic electricity price and the penalty function. The dynamic electricity price is reflected in the shared energy storage objective function, i.e. the first objective function, and the penalty function is reflected in the third objective function of the new energy station.

[0095] It should be noted that this embodiment adds the power grid as a coordinator to the master-slave game model of traditional shared energy storage and new energy clusters, forming a three-layer dynamic Stackelberg game model with shared energy storage operators, the power grid, and the new energy cluster as the main bodies. The coordinator can influence the decision-making of other entities and balance the interests of multiple parties such as upper-level leaders and lower-level followers, avoiding the limitations of the power grid that relies solely on market bidding.

[0096] As a further preferred technical solution, the third objective function of the lower-level new energy station economic model is:

[0097] maxC new =C sell -C serve -C pen -C curtail

[0098]

[0099] Where: C new is the economic objective function of the new energy station; C sell The income from electricity sales at new energy stations; t is a dynamic electricity price; is the actual output of cluster i in period t; C serve C is the service fee for shared energy storage; pen Penalty cost for the power grid; c pen is the unit prediction deviation penalty coefficient; is the predicted output of cluster i in time period t; μ is the output smoothing penalty coefficient; P RES,i is the current daily power value of cluster i; is the average daily output benchmark value of cluster i; C curtail Cost of curtailing wind and solar power; is the amount of wind and solar curtailment of cluster i in period t;

[0100] The constraints of the third objective function are:

[0101]

[0102] Where, is the actual wind power output value of cluster i during period t; is the actual photovoltaic output value of cluster i during period t; The electric power discharged by cluster i using energy storage during period t; The electric power used by cluster i to charge energy storage during period t; is the electric load power of cluster i during period t; is the amount of power wasted by cluster i during period t.

[0103] It should be noted that this embodiment adds grid assessment parameters to the lower layer of the power balance constraint of the new energy station to ensure that the model can be solved and that the new energy cluster provides high-quality electricity.

[0104] As a further preferred technical solution, step S30: solving the three-layer game model to generate a shared energy storage configuration result, specifically includes the following steps:

[0105] S31. Use KKT conditions to transform the optimization problem of the lower-level new energy station economic model into the constraint form of the upper-level operator energy storage configuration model;

[0106] S32. Embed the decision variables of the middle-layer grid control model into the objective function of the upper-layer operator's energy storage configuration model to form a single-layer optimization model;

[0107] S33. Linearize the single-layer optimization model using the Big-M method and use the Cplex solver to solve it, obtaining the shared energy storage configuration result.

[0108] It should be noted that this embodiment combines the objective functions and constraints of each subject in a typical wind-solar joint output scenario to construct a three-layer dynamic Stackelberg game model with shared energy storage operators, power grids, and new energy clusters as the main bodies, taking into account the day-ahead shared energy storage leasing demand, charging and discharging strategies, dynamic electricity prices, and power grid assessment projects; uses the KKT condition solution to generate energy storage configuration information and shared energy storage service fees, and then transmits the generated energy storage configuration information and shared energy storage service fees to the new energy cluster to adjust its own strategy, and the power grid mandatory parameters are adjusted to optimize the game strategy.

[0109] As a further preferred technical solution, the basic elements of the game include participants, strategies, and payments. The formula of the three-layer game model is expressed as follows:

[0110] G={M,(S k ) k∈M,(I k ) k∈M};

[0111] Where M is the set of shared energy storage operators, power grids, and new energy clusters; S K is a strategy set that includes operators sharing energy storage, power grid and new energy clusters; I K It is a payment function set that includes operators’ shared energy storage, power grid and new energy clusters.

[0112] Among them, the participant set M = {SES∪Grid∪NEC}, and the set M includes the leader shared energy storage operator, the coordinator power grid and the follower new energy station.

[0113] Strategy set S K Specifically including S SES 、S Grid and S NEC :

[0114]

[0115] Where S SES Rated strategy set for shared energy storage operators, is the charging and discharging state of energy storage, is the charging and discharging price; is the charge and discharge power, The price of electricity purchased or sold from the power grid; S Grid is the strategy set of the coordinator grid, λ t is the dynamic electricity price, θ p is the assessment item; S NEC is the strategy set of new energy stations, C i,t For the assessment cost of the power grid, To use the charging and discharging power of shared energy storage, E i,t The capacity rented for each time period.

[0116] Payment Function Set I K Includes: Shared Energy Storage Payment Function I SES That is, the first objective function, the grid payment function I Grid That is, the second objective function, the new energy station payment function I user That is the third objective function.

[0117] It should be noted that the Stackelberg game models constructed in this embodiment are coupled with each other, and the underlying optimization problem can be transformed using KKT conditions and modeled using Yalmip, and then the Cplex solver can be called for collaborative solution.

[0118] It should be noted that in order to verify the optimized configuration method for the coordinated operation of the designed new energy storage and new energy, a simulation example was set up: a new energy station consisting of three new energy power generation systems was selected, and the wind and solar power generation power and load power measured data of new energy stations in a certain region of my country were used as historical data sets. One day was taken as the scheduling day, and one hour was taken as the scheduling period. The power selling price of the power grid is a dynamic electricity price model, and according to relevant policy regulations, the electricity generated by the new energy station cannot be directly transmitted to the upper power grid. Figure 2 The structure diagram of the new energy storage and new energy joint operation shown in the figure is used as an example to construct and solve the master-slave game model, and the following is generated: Figure 3 After collecting the 500 sets of wind and solar output data shown in the figure, they are clustered into wind and solar scene diagrams of 5 typical scenarios; Tables 1 and 2 show that the new energy storage and new energy coordinated operation optimization configuration method designed by the present invention significantly reduces the operating costs of various new energy stations and improves the utilization efficiency of energy storage resources.

[0119] Table 1

[0120]

[0121] Table 2

[0122]

[0123] Therefore, incorporating the concept of the "sharing economy," this paper designs a three-layer dynamic Stackelberg game model: a shared energy storage operator, a power grid, and a new energy station. In this model, multiple new energy stations can share energy storage resources, enabling more flexible and efficient energy scheduling and improving overall energy efficiency. Simulation results demonstrate that the proposed novel method for the coordinated operation of energy storage and new energy sources has low operating costs and good stability.

[0124] In addition, if Figure 4 As shown, another embodiment of the present invention further proposes a new energy collaborative optimization energy storage configuration system, the system comprising:

[0125] The combined output construction module 10 is used to collect historical wind and solar power output data and construct a combined wind and solar power output scenario;

[0126] A game model establishment module 20 is used to establish a three-layer game model in a wind-solar combined power generation scenario, wherein the upper-layer operator energy storage configuration model is used to solve the capacity configuration parameters of shared energy storage with the lowest shared energy storage operating cost as the objective function; the middle-layer power grid regulation model is used to coordinate global optimization through dynamic electricity prices and penalty functions; and the lower-layer new energy station economic model is used to solve the optimal charging and discharging strategy of the new energy station based on the capacity configuration parameters generated by the upper-layer operator energy storage configuration model;

[0127] The model solving module 30 is used to solve the three-layer game model and generate a shared energy storage configuration result.

[0128] As a further preferred technical solution, the combined output construction module 10 includes:

[0129] A data collection unit is used to collect historical wind and solar power output data, and perform kernel density estimation on the historical wind and solar power output data to generate probability density functions of wind and solar power outputs;

[0130] The joint modeling unit is used to model the probability density function of wind and solar power outputs based on the Copula theory and generate the probability density function of wind and solar power outputs;

[0131] The inverse function operation unit is used to use the inverse function to operate the probability density function of the wind-solar combined output and construct the joint distribution function of the wind-solar combined output scenario.

[0132] As a further preferred technical solution, the model solving module 30 includes:

[0133] A conversion unit is used to convert the optimization problem of the lower-level new energy station economic model into the constraint form of the upper-level operator energy storage configuration model using the KKT condition;

[0134] An embedding unit is used to embed the decision variables of the middle-layer grid control model into the objective function of the upper-layer operator's energy storage configuration model to form a single-layer optimization model;

[0135] The solving unit is used to linearize the single-layer optimization model using the Big-M method and call the Cplex solver to solve it to obtain the shared energy storage configuration result.

[0136] It should be noted that other embodiments or specific implementation methods of the three-layer game model used by the new energy collaborative optimization energy storage configuration system described in the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0137] In addition, the present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for collaboratively optimizing energy storage configuration for new energy as described in the above embodiment is implemented.

[0138] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0139] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0140] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0141] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0142] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for collaborative optimization of energy storage configuration using new energy, characterized in that: include: Collect historical wind and solar power output data and build wind and solar power combined output scenarios; A three-layer game model is established for the wind-solar combined output scenario. The upper-layer operator energy storage configuration model is used to solve the capacity configuration parameters of shared energy storage with the lowest shared energy storage operating cost as the objective function. The middle-layer grid regulation model is used to coordinate global optimization through dynamic electricity prices and penalty functions. The lower-layer new energy station economic model is used to solve the optimal charging and discharging strategy of new energy stations based on the capacity configuration parameters generated by the upper-layer operator energy storage configuration model. The three-layer game model is solved to generate the shared energy storage configuration results.

2. The method for collaborative optimization of energy storage configuration using new energy sources according to claim 1, wherein: The collection of historical wind and solar power output data and calculation of wind and solar power combined output data includes: Collect historical wind and solar power output data, perform kernel density estimation on the historical wind and solar power output data, and generate probability density functions of wind and solar power outputs; Based on the Copula theory, the probability density function of wind and solar power output is modeled separately to generate the probability density function of wind and solar power combined output; The inverse function is used to calculate the probability density function of the wind-solar combined output and construct the joint distribution function of the wind-solar combined output scenario.

3. The method for collaborative optimization of energy storage configuration using new energy sources according to claim 1, wherein: The objective function of the upper-layer operator energy storage configuration model is: minC ses =C inv +C grid Where C ses is the operating cost of shared energy storage; C inv is the annual investment cost of energy storage power station equipment; C grid The annual cost of electricity purchased from the power grid by new energy stations; The constraints of the objective function of the upper-layer operator's energy storage configuration model include energy storage power station power balance constraints, energy storage power station SOC state continuity constraints, and energy storage power station charging and discharging power constraints.

4. The method for collaborative optimization of energy storage configuration using new energy sources according to claim 1, wherein: The formula of the intermediate layer power grid control model is expressed as: Where C Grid is the objective function of the power grid; base is the base electricity price; is the power loss; k is the volatility risk cost coefficient; is the fluctuating load; T is the scheduling period.

5. The method for collaborative optimization of energy storage configuration using new energy sources according to claim 1, wherein: The objective function of the lower-level new energy station economic model is: maxC new =C sell -C serve -C pen -C curtail Where: C new is the economic objective function of the new energy station; C sell The income from electricity sales at new energy stations; t is a dynamic electricity price; is the actual output of cluster i in period t; C serve C is the service fee for shared energy storage; pen Penalty cost for the power grid; c pen is the unit prediction deviation penalty coefficient; is the predicted output of cluster i in time period t; μ is the output smoothing penalty coefficient; P RES,i is the current daily power value of cluster i; is the average daily output benchmark value of cluster i; C curtail Cost of curtailing wind and solar power; is the amount of wind and solar curtailment of cluster i in period t; The constraints of the objective function of the lower-level new energy station economic model are: Where, is the actual wind power output value of cluster i during period t; is the actual photovoltaic output value of cluster i during period t; The electric power discharged by cluster i using energy storage during period t; The electric power used by cluster i to charge energy storage during period t; is the electric load power of cluster i during period t; is the amount of power wasted by cluster i during period t.

6. The method for collaborative optimization of energy storage configuration using new energy sources according to claim 1, wherein: Solving the three-layer game model to generate a shared energy storage configuration result includes: The KKT condition is used to transform the optimization problem of the lower-level new energy station economic model into the constraint form of the upper-level operator energy storage configuration model; The decision variables of the middle-layer grid control model are embedded into the objective function of the upper-layer operator's energy storage configuration model to form a single-layer optimization model. The single-layer optimization model is linearized using the Big-M method and solved using the Cplex solver to obtain the shared energy storage configuration result.

7. The method for collaboratively optimizing energy storage configuration using new energy sources according to claim 6, wherein: The formula of the three-layer game model is expressed as: G={M,(S k ) k∈M ,(I k ) k∈M }; Where M is the set of shared energy storage operators, power grids, and new energy clusters; S K is a strategy set that includes operators sharing energy storage, power grid and new energy clusters; I K It is a payment function set that includes operators’ shared energy storage, power grid and new energy clusters.

8. The method for collaboratively optimizing energy storage configuration using new energy sources according to claim 7, wherein: The operator's strategy sets for sharing energy storage, power grid, and new energy clusters are: Where S SES Rated strategy set for shared energy storage operators, is the charging and discharging state of energy storage, is the charging and discharging price; is the charge and discharge power, The price of electricity purchased or sold from the power grid; S Grid is the strategy set of the coordinator grid, λ t is the dynamic electricity price, θ p is the assessment item; S NEC is the strategy set of new energy stations, C i,t For the assessment cost of the power grid, To use the charging and discharging power of shared energy storage, E i,t The capacity rented for each time period.

9. A new energy collaborative optimization energy storage configuration system, characterized in that: include: The joint output construction module is used to collect historical wind and solar power output data and build wind and solar power joint output scenarios; A game model building module is used to establish a three-layer game model in the scenario of wind and solar combined output. Among them, the upper-layer operator energy storage configuration model is used to solve the capacity configuration parameters of shared energy storage with the lowest shared energy storage operating cost as the objective function. The middle-layer power grid regulation model is used to coordinate global optimization through dynamic electricity prices and penalty functions. The lower-layer new energy station economic model is used to solve the optimal charging and discharging strategy of new energy stations based on the capacity configuration parameters generated by the upper-layer operator energy storage configuration model; The model solving module is used to solve the three-layer game model and generate the shared energy storage configuration results.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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