Energy regulation and control strategy determination method and device, computer equipment, medium and product

Abstract

The invention relates to an energy regulation and control strategy determination method and device, computer equipment, a medium and a product. The method comprises the following steps: constructing a load balance constraint function of a target park according to load demand information and power demand information of the target park in a target time period and equipment output information of a power system to which the target park belongs in the target time period; according to the power utilization cost information, the power utilization demand information, the energy storage cost information and the carbon emission information of the target park in the target time period, constructing an energy storage regulation and control cost function of the target park; and solving the energy storage regulation and control cost function by taking the minimum function value of the energy storage regulation and control cost function as a target and taking the load balance constraint function as a constraint condition to obtain an energy regulation and control strategy of the target park in the future time period. By adopting the method, the energy resources of the park can be accurately and efficiently integrated, regulated and controlled.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution networks, and in particular to a method, device, computer equipment, medium and product for determining an energy regulation strategy. Background Art

[0002] Energy management of smart parks is an important part of the park's smart construction. Through the application of intelligent technology, the park can achieve efficient use and management of energy, thereby reducing energy consumption, reducing emissions, and contributing to sustainable development.

[0003] At present, park energy regulation technology is mostly designed and operated based on a single park, and centralized control and scheduling methods are generally adopted, but many shortcomings are exposed in the scenario of multi-park collaborative operation. First, due to the volatility and intermittency of distributed photovoltaic and other renewable energy sources, they often face the problem of power generation waste. It is difficult for parks to achieve flexible coordination between photovoltaic and energy storage systems to adapt to real-time changes in supply and demand. In addition, the charging and discharging strategies of energy storage systems are mostly based on fixed rules (such as being formulated only based on the difference in peak and valley electricity prices), lacking dynamic adjustment of the park's real-time load, resulting in low energy storage utilization efficiency. Secondly, most parks currently use static scheduling methods, which cannot respond quickly to dynamic changes and lack real-time optimization capabilities, making it difficult to integrate and optimize energy resources. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, medium and product for determining an energy control strategy that can accurately and efficiently integrate and control the energy resources of the park in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for determining an energy regulation strategy, comprising:

[0006] Constructing a load balancing constraint function of the target park according to the load demand information and power demand information of the target park during the target period, and the equipment output information of the power system to which the target park belongs during the target period;

[0007] Constructing an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0008] With the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0009] In one of the embodiments, the equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system;

[0010] The load balancing constraint function of the target park is constructed according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs, including:

[0011] Constructing a first constraint function for constraining power input to the target park according to power demand information of the target park and equipment output information of the power system to which the target park belongs;

[0012] Constructing a second constraint function for constraining the power output of the target park according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs;

[0013] constructing the energy balance constraint function according to the first constraint function and the second constraint function;

[0014] The energy storage state constraint function is constructed according to the charging and discharging information of the energy storage system.

[0015] In one embodiment, the energy storage system charging and discharging information includes the charging efficiency of the energy storage system, the capacity of the energy storage system and the energy storage limit state;

[0016] The step of constructing the energy storage state constraint function according to the energy storage system charging and discharging information includes:

[0017] Obtaining reference energy storage state information, reference charging power, and reference discharging power of the power system to which the target park belongs in a reference period corresponding to the target period; wherein the reference period is a historical period of the target period;

[0018] taking the product of the charging efficiency and the reference charging power as the first reference power;

[0019] taking the difference between the first reference power and the reference discharge power as the second reference power;

[0020] Taking the ratio between the second reference power and the capacity of the energy storage system as a state change value;

[0021] The energy storage state constraint function is constructed according to the reference energy storage state information and the state change value.

[0022] In one embodiment, constructing the energy storage regulation cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period includes:

[0023] Constructing an electricity cost function according to the electricity cost information, electricity demand information and energy storage cost information;

[0024] Constructing a carbon emission cost function based on the carbon emission information and the electricity demand information;

[0025] According to the electricity cost function and the carbon emission cost function, an energy storage regulation cost function of the target park is constructed.

[0026] In one of the embodiments, the carbon emission information includes a carbon emission factor;

[0027] The step of constructing a carbon emission cost function according to the carbon emission information and the electricity demand information includes:

[0028] Constructing a purchased power function representing the power purchased by the target park according to the power demand information;

[0029] A carbon emission cost function is constructed according to the product of the electricity purchase power function and the carbon emission factor.

[0030] In one embodiment, the energy storage regulation cost function is solved with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition to obtain the energy regulation strategy of the target park in the future period, including:

[0031] Based on the Grey Wolf Optimization Algorithm, with the goal of minimizing the function value of the energy storage regulation cost function and the load balance constraint function as a constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0032] In a second aspect, the present application also provides an energy control strategy determination device, comprising:

[0033] A first construction module is used to construct a load balancing constraint function of the target park according to the load demand information and power demand information of the target park in the target period, and the equipment output information of the power system to which the target park belongs in the target period;

[0034] A second construction module is used to construct an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0035] The strategy determination module is used to solve the energy storage regulation cost function with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition to obtain the energy regulation strategy of the target park in the future period.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Constructing a load balancing constraint function of the target park according to the load demand information and power demand information of the target park during the target period, and the equipment output information of the power system to which the target park belongs during the target period;

[0038] Constructing an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0039] With the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0041] Constructing a load balancing constraint function of the target park according to the load demand information and power demand information of the target park during the target period, and the equipment output information of the power system to which the target park belongs during the target period;

[0042] Constructing an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0043] With the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0044] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0045] Constructing a load balancing constraint function of the target park according to the load demand information and power demand information of the target park during the target period, and the equipment output information of the power system to which the target park belongs during the target period;

[0046] Constructing an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0047] With the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is iteratively solved to obtain the energy regulation strategy of the target park in the future period.

[0048] The above-mentioned energy regulation strategy determination method, device, computer equipment, medium and product construct a load balance constraint function of the target park according to the load demand information and electricity demand information of the target park in the target period, as well as the equipment output information of the power system to which the target park belongs in the target period; it is equivalent to comprehensively considering the real-time information of load demand, photovoltaic power generation and power grid status, optimizing the charging and discharging behavior of the energy storage system, and greatly improving the dynamic response capability and utilization efficiency of the energy storage system; further, according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period, an energy storage regulation cost function of the target park is constructed, and the carbon emission cost, carbon reduction target and electricity trading decision are deeply integrated, which reduces the carbon emissions of the park and improves the utilization rate of clean energy; finally, with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period, which can flexibly and efficiently adjust the energy allocation strategy to ensure the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A schematic diagram of a flow chart of a method for determining an energy control strategy in one embodiment;

[0051] Figure 2 A schematic diagram of a flow chart of constructing a load balancing constraint function of a target park in one embodiment;

[0052] Figure 3A schematic diagram of a process for constructing an energy storage state constraint function in one embodiment;

[0053] Figure 4 A schematic diagram of a process for constructing an energy storage control cost function in one embodiment;

[0054] Figure 5 A schematic flow chart of a method for determining an energy control strategy in another embodiment;

[0055] Figure 6 It is a structural block diagram of an energy control strategy determination device in one embodiment;

[0056] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] At present, most parks lack standardized interfaces for connecting distributed energy, energy storage systems and load resources to virtual power plants, making it difficult to integrate and optimize energy resources. In addition, existing virtual power plant platforms mainly focus on the economics of power trading, and fail to effectively incorporate carbon emission management into optimization goals, making it impossible to achieve a win-win situation for economic and environmental benefits. In particular, in the face of the high volatility of distributed photovoltaics, the low utilization rate of energy storage, and the complex interaction requirements between multiple parks, traditional control technologies are unable to coordinate resources and optimize operations.

[0059] By combining virtual power plant technology with advanced low-carbon control strategies, the intelligence level of the park's energy system can be significantly improved. On the one hand, the virtual power plant can integrate the distributed energy, energy storage system and flexible load within the park in real time, participate in the electricity market and obtain economic benefits; on the other hand, the virtual power plant can introduce carbon emission costs and low-carbon optimization goals into the control model, dynamically adjust photovoltaic power generation, energy storage charging and discharging, and load response, and achieve low-carbon and efficient coordinated operation. Therefore, developing a low-carbon control solution for photovoltaic energy storage in the park that integrates virtual power plant technology will be a key breakthrough in solving existing problems and improving the level of energy management in the park.

[0060] Based on this, the energy regulation strategy determination method provided in the embodiment of the present application can be applied to the application environment of regulating the energy of the park in the scenario of multi-park collaborative operation. The energy regulation strategy determination method provided in the embodiment of the present application can be executed by a computer device, which can be a server or a terminal with powerful computing power.

[0061] In an exemplary embodiment, Figure 1 As shown, a method for determining an energy regulation strategy is provided, which is described by taking the method applied to a server as an example, and specifically includes the following steps:

[0062] S101, constructing a load balancing constraint function of the target park according to the load demand information and power demand information of the target park in the target period, and the equipment output information of the power system to which the target park belongs in the target period.

[0063] Among them, the load demand information characterizes the load demand of the target park during the target period. The electricity demand information characterizes the electricity demand of the target park during the target period. The equipment output information characterizes the output of each device in the power system. In the embodiment of the present application, the equipment output information specifically characterizes the output of photovoltaic power generation equipment and energy storage equipment in the power system, that is, the equipment processing information at least includes photovoltaic system output information and energy storage system charging and discharging information. The load balance constraint function of the target park constrains the load balance of the load in the target park. In the embodiment of the present application, the load balance constraint function includes but is not limited to an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system.

[0064] Optionally, the load demand information and power demand information of the target park during the target period, as well as the equipment output information of the power system to which the target park belongs during the target period, can be obtained through the virtual electric field platform of the target park. Furthermore, the electricity interaction rules between the target park and the power system to which the target park belongs, as well as the operating rules of all loads in the target park can be comprehensively considered, and an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system can be constructed respectively, and the energy balance constraint function and the energy storage state constraint function can be used together as the load balance constraint function of the target park.

[0065] S102, constructing an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period.

[0066] Among them, the energy storage cost information represents the cost of energy storage used by the target park during the target period; the carbon emission information represents the carbon emission of the target park during the target period. The energy storage regulation cost function of the target park represents the cost of energy storage regulation used by the target park.

[0067] Optionally, the cost of energy storage regulation in the target park can be comprehensively characterized by the electricity cost and carbon emission cost of the target park, that is, the cost of energy storage regulation in the target park can be represented by the sum of the electricity cost and the carbon emission cost. Therefore, in the embodiment of the present application, the energy storage regulation cost function of the target park can be represented by the sum of the electricity cost function and the carbon emission cost function.

[0068] Furthermore, in order to ensure the flexibility of the energy storage control cost function, two weight coefficients can be introduced, which are respectively used as the weight coefficient of the electricity cost function and the weight coefficient of the carbon emission cost function. In this case, the energy storage control cost function of the target park can be a weighted sum of the electricity cost function and the carbon emission cost function. Among them, the two weight coefficients can be adjusted according to actual needs.

[0069] Optionally, an electricity cost function can be constructed based on the electricity cost information, electricity demand information and energy storage cost information of the target park during the target period and based on the operating rules of the load in the target park; further, a carbon emission cost function can be constructed based on the carbon emission information and the cost consumed in the carbon emission process.

[0070] S103, taking the minimum function value of the energy storage control cost function as the goal and the load balance constraint function as the constraint condition, solving the energy storage control cost function to obtain the energy control strategy of the target park in the future period.

[0071] Optionally, an optimization algorithm may be used, with the goal of minimizing the function value of the energy storage control cost function and the load balance constraint function as a constraint condition, to iteratively solve the energy storage control cost function and obtain the energy control strategy of the target park in the future period.

[0072] Optionally, in order to ensure the accuracy of the energy control strategy obtained, in an embodiment of the present application, based on the Gray Wolf Optimization Algorithm, the energy storage control cost function can be solved with the minimum function value of the energy storage control cost function as the goal and the load balance constraint function as the constraint condition to obtain the energy control strategy of the target park in the future period.

[0073] In the embodiments of the present application, in addition to energy regulation of the target park, it is also possible to integrate virtual power plant technology to achieve efficient integration and coordinated optimization of distributed energy, energy storage systems and loads among multiple parks, thereby improving resource utilization and overall operating efficiency, and effectively solving the problem of coordinated operation among multiple parks. Secondly, the volatility and intermittency of distributed photovoltaic power generation are fully considered, and the phenomenon of abandoned power generation is reduced by dynamically regulating photovoltaic power generation, energy storage charging and discharging, and load response. Compared with traditional fixed rule regulation, it can flexibly adjust energy allocation strategies according to real-time changes in supply and demand to ensure system stability. In addition, the Gray Wolf optimization algorithm is adopted, combined with the real-time monitoring and data processing capabilities of the VPP platform, which can quickly respond to real-time load fluctuations and energy status changes inside and outside the park, and realize efficient and accurate dynamic optimization and regulation, solving the limitation that traditional static scheduling cannot adapt to complex environments.

[0074] In the above energy regulation strategy determination method, a load balance constraint function of the target park is constructed according to the load demand information and electricity demand information of the target park in the target period, as well as the equipment output information of the power system to which the target park belongs in the target period; this is equivalent to comprehensively considering the real-time information of load demand, photovoltaic power generation and power grid status, optimizing the charging and discharging behavior of the energy storage system, and greatly improving the dynamic response capability and utilization efficiency of the energy storage system; further, according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period, an energy storage regulation cost function of the target park is constructed, and the carbon emission cost, carbon reduction target and electricity trading decision are deeply integrated, which reduces the carbon emissions of the park and improves the utilization rate of clean energy; finally, with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period, which can flexibly and efficiently adjust the energy allocation strategy to ensure the stability of the power system.

[0075] Optionally, in one embodiment, the equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system; in this case, Figure 2 As shown, a method for constructing a load balancing constraint function of a target park is provided, which specifically includes the following steps:

[0076] S201: construct a first constraint function for constraining power input to the target park based on power demand information of the target park and equipment output information of the power system to which the target park belongs.

[0077] The first constraint function is a function used to constrain the power input to the target park.

[0078] Optionally, in the embodiment of the present application, since the first constraint function is a function for constraining the power input to the target park, and the power input to the target park generally includes the power provided by the photovoltaic system, the power purchased from the power grid, and the charging power of the energy storage system. Therefore, the first constraint function for constraining the power input to the target park can be constructed based on the power demand information of the target park and the equipment output information of the power system to which the target park belongs. The specific process is shown in formula (1):

[0079] (1)

[0080] in, represents the first constraint function; for The output power of the photovoltaic system at all times; for The power purchased from the grid during the interaction between the target park and the power system; for The discharge power of the energy storage system at any moment.

[0081] It should be noted that in the embodiments of the present application, in order to make full use of renewable energy, Prioritize meeting the load demand of the target park and reduce the amount of electricity purchased from the power grid. For example, when the load demand is lower than the power generation of the photovoltaic system, priority is given to charging and energy storage, and the remaining power is sold to the grid through the virtual power plant platform. That is, the output of the photovoltaic system gives priority to meeting the load demand of the park, and then decides on the allocation of the remaining power (charging and energy storage or grid-connected sales). Therefore, the output of the photovoltaic system can be expressed by the following formula (2):

[0082] (2)

[0083] in, for The output power of the photovoltaic system at all times; for The photovoltaic system directly supplies power to the target park load at all times; for The power of the photovoltaic system used to charge the energy storage system at any given moment; for The power of the photovoltaic system connected to the power system at any moment.

[0084] The output of the photovoltaic system should first meet the load demand of the target park, that is:

[0085] (3)

[0086] in, is the load demand power of the target park.

[0087] After the output of the photovoltaic system meets the load demand of the target park first, the remaining part is used to charge the energy storage system or connect to the grid, that is:

[0088] (4)

[0089] To further optimize the low-carbon power network in the park, the battery is charged to a high state of charge (SOC) during off-peak electricity prices (nighttime or low load). During peak electricity prices or when photovoltaic power is insufficient, the battery is discharged to reduce the amount of electricity purchased from the grid. At the same time, the depth of charge and discharge is controlled to extend battery life and reduce operating costs. The state of the energy storage system is dynamically adjusted through the charge and discharge power and state variables. The energy storage charge and discharge constraints are shown in the following formula (5), where negative values ​​represent charging and positive values ​​represent discharging:

[0090] (5)

[0091] in, is the charge and discharge power; It is the maximum value of charge and discharge power.

[0092] Furthermore, the charging and discharging strategies are dynamically adjusted according to the electricity price, the output of the photovoltaic system and the load demand. The charging priority condition (Formula (6)) and the discharging priority condition (Formula (7)) are set:

[0093] (6)

[0094] (7)

[0095] in, For electricity price.

[0096] In addition, the operation time of high-energy-consuming equipment (such as air conditioners and production equipment) is adjusted through demand response technology. In addition, the adjustable load is used to achieve peak load shifting and valley filling to reduce electricity costs. The adjustment of adjustable load is optimized through demand response technology, with the goal of smoothing the load curve and reducing electricity costs. First, the load is decomposed:

[0097] (8)

[0098] in, is the non-adjustable load of the target park; It is the adjustable load of the target park.

[0099] Furthermore, the load adjustment range of the adjustable load of the target park is constrained:

[0100] (9)

[0101] in, Maximum value for the secondary adjustment of adjustable load.

[0102] Set an optimization goal for load adjustment: minimize electricity consumption during peak electricity prices and transfer part of the load to periods with low electricity prices by optimizing time period switching. Specifically:

[0103] (10)

[0104] (11)

[0105] in, is the total load response cost; For electricity price.

[0106] Furthermore, in order to reduce electricity consumption during peak electricity prices, energy storage release and load regulation are used to respond to demand. In the embodiment of the present application, an interaction strategy between the target park and the power grid is set. This optimization strategy participates in auxiliary services (such as frequency regulation and peak regulation) to obtain additional benefits. The power exchange between the target park and the power grid aims to reduce carbon emissions and optimize benefits. The power purchased from the power grid is expressed by the following formula (12):

[0107] (12)

[0108] The power sold to the grid is expressed by the following formula (13):

[0109] (13)

[0110] in, Selling electricity to the grid.

[0111] The carbon emission formula (14) is as follows:

[0112] (14)

[0113] in, is the carbon emission success rate; is the carbon emission factor per unit of electricity in the power grid.

[0114] S202: Construct a second constraint function for constraining the power output of the target park according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs.

[0115] The second constraint function is a function used to constrain the power output by the target park.

[0116] Optionally, in the embodiment of the present application, since the second constraint function is a function for constraining the power output of the target park, and the power output of the target park generally includes the load demand power of the target park, the power sold to the power grid, and the discharge power of the energy storage system. Therefore, the load demand power can be calculated based on the load demand information of the target park, the power sold to the power grid can be constructed based on the power demand information, and finally the discharge power of the energy storage system can be constructed based on the equipment processing information. Specifically, the construction process of the second constraint function is shown in the following formula (15):

[0117] (15)

[0118] S203: construct an energy balance constraint function according to the first constraint function and the second constraint function.

[0119] Optionally, since the first constraint function is a function for constraining the power input to the target park, and the second constraint function is a function for constraining the power output from the target park, in order to ensure the energy balance of the target park, the first constraint function should be equal to the second constraint function. In this case, the difference between the first constraint function and the second constraint function can be used as the energy balance constraint function. Specifically, the energy balance constraint function can be expressed by the following formula (16):

[0120] (16)

[0121] In addition, it should be noted that the output power of the photovoltaic system, the charging and discharging power of the energy storage system, and the power purchased and sold from the power grid need to meet the rated power, that is:

[0122] (17)

[0123] in, To purchase and sell electricity from the power grid; is the rated power.

[0124] S204: construct an energy storage state constraint function according to the energy storage system charging and discharging information.

[0125] Optionally, since the energy storage state constraint function is a function for constraining the operating state of the energy storage system in the power system, the energy storage state of the energy storage system can be comprehensively considered to construct an energy storage state constraint function that dynamically constrains the operating state of the energy storage system.

[0126] In this embodiment, by comprehensively considering the power demand information of the target park and the equipment output information of the power system to which the target park belongs, it is ensured that the constructed first constraint function can accurately constrain the power of the target park; at the same time, by comprehensively considering the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, it is ensured that the constructed second constraint function can accurately constrain the power output of the target park; finally, by combining the first constraint function and the second constraint function, it is ensured that the constructed energy balance constraint function can constrain the energy balance of the target park. In addition, the charging and discharging information of the energy storage system is also considered to ensure the accuracy of the constructed energy storage state constraint function.

[0127] Optionally, in one embodiment, the energy storage system charging and discharging information includes the charging efficiency of the energy storage system, the capacity of the energy storage system and the energy storage limit state; in this case, if Figure 3 As shown, a method for constructing an energy storage state constraint function is provided, which specifically includes the following steps:

[0128] S301, obtaining reference energy storage state information, reference charging power, and reference discharging power of a power system to which a target park belongs in a reference time period corresponding to a target time period.

[0129] Among them, the reference period is the historical period of the target period; the reference energy storage status information describes the energy storage status of the power system of the target park during the reference period. The reference charging power is the charging power of the power system of the target park during the reference period; the reference discharging power is the discharging power of the power system of the target park during the reference period.

[0130] Optionally, the virtual power plant platform of the target park can be used to obtain reference energy storage status information, reference charging power and reference discharging power of the power system of the target park in a reference period corresponding to the target period.

[0131] S302: Taking the product of the charging efficiency and the reference charging power as the first reference power.

[0132] Optionally, the process of taking the product of the charging efficiency and the reference charging power as the first reference power can be expressed by the following formula (18):

[0133] (18)

[0134] in, is the first reference power; For charging efficiency; is the reference charging power.

[0135] S303: Taking the difference between the first reference power and the reference discharge power as the second reference power.

[0136] Optionally, the process of using the difference between the first reference power and the reference discharge power as the second reference power can be expressed by the following formula (19):

[0137] (19)

[0138] in, is the second reference power; is the reference discharge power.

[0139] S304: Taking the ratio between the second reference power and the capacity of the energy storage system as the state change value.

[0140] Optionally, the process of using the ratio between the second reference power and the energy storage system capacity as the state change value can be expressed by the following formula (20):

[0141] (20)

[0142] in, is the state change value; is the capacity of the energy storage system.

[0143] S305: construct an energy storage state constraint function according to the reference energy storage state information and the state change value.

[0144] Optionally, the sum of the reference energy storage state and the state change value may be used as the energy storage state constraint function, which may be specifically expressed as the following formula (21):

[0145]

[0146] (twenty one)

[0147] in, is a reference energy storage state, which is determined by reference energy storage state information; and They are the minimum state of charge and maximum state of charge allowed by the energy storage system.

[0148] In this embodiment, by introducing the charging efficiency, capacity and energy storage limit state of the energy storage system, it is equivalent to comprehensively considering the charging and discharging conditions and energy storage limit of the energy storage system, thereby ensuring the accuracy of the constructed energy storage state constraint function.

[0149] Optionally, in one embodiment, Figure 4 As shown, a method for constructing an energy storage control cost function of a target park is provided, which specifically includes the following steps:

[0150] S401, constructing an electricity cost function according to electricity cost information, electricity demand information and energy storage cost information.

[0151] Among them, the electricity cost function includes the electricity purchase cost function used to describe the cost of the target park purchasing electricity from the power grid, the electricity sales cost function used to describe the target park selling photovoltaic surplus power to the power grid through the virtual power plant platform, and the energy storage system cost function used to describe the operation and maintenance cost of the energy storage system.

[0152] Optionally, the electricity purchase price from the power grid can be determined based on the electricity cost information, and the electricity purchase cost function can be constructed in combination with the electricity purchase power from the power grid. Specifically, the electricity purchase cost function can be expressed by the following formula (22):

[0153] (twenty two)

[0154] in, is the electricity purchase cost function; The price of electricity purchased from the grid.

[0155] Furthermore, the electricity price sold to the power grid can be determined based on the electricity cost information and the electricity demand information, and the electricity selling cost function can be constructed in combination with the electricity selling power to the power grid. Specifically, the electricity selling cost function can be expressed by the following formula (23):

[0156] (twenty three)

[0157] In addition, the energy storage system cost function can be constructed based on the energy storage cost information. On this basis, the electricity cost function can be expressed as:

[0158] (twenty four)

[0159] in, is the electricity cost function; is the cost function of the energy storage system.

[0160] S402: construct a carbon emission cost function based on the carbon emission information and the electricity demand information.

[0161] Among them, carbon emission information includes carbon emission factors.

[0162] Optionally, a power purchase function representing the power purchased by the target park can be constructed based on the power demand information; and a carbon emission cost function can be constructed based on the product of the power purchase function and the carbon emission factor. Specifically, the method for constructing the carbon emission cost function can be expressed by the following formula (25):

[0163] (25)

[0164] in, is the carbon emission cost function; The carbon price.

[0165] S403: Constructing an energy storage control cost function for the target park based on the electricity cost function and the carbon emission cost function.

[0166] Optionally, the sum of the electricity cost function and the carbon emission cost function can be used as the energy storage control cost function of the target park. Furthermore, in order to ensure the flexibility and accuracy of the constructed energy storage control cost function, two weight coefficients can be introduced as the weights of the electricity cost function and the carbon emission cost function, respectively, and the weighted sum between the electricity cost function and the carbon emission cost function is used as the energy storage control cost function. Specifically, the energy storage control cost function of the target park can be expressed by the following formula (26):

[0167] (26)

[0168] in, and All are weight coefficients; is the energy storage regulation cost function.

[0169] In this embodiment, by comprehensively considering electricity cost information, electricity demand information and energy storage cost information, it is ensured that the constructed electricity cost function can accurately describe the cost of electricity incurred by the load; at the same time, by comprehensively considering carbon emission information and electricity demand information, it is ensured that the constructed carbon emission cost function can accurately describe the carbon emission cost on the load side; finally, by combining the electricity cost function and the carbon emission cost function, it is ensured that the constructed energy storage regulation cost function can accurately describe the cost of energy storage regulation in the target park.

[0170] Based on the above embodiment, the optimization process of the gray wolf optimization algorithm can be divided into the following steps:

[0171] Step 1: Population initialization

[0172] Each individual represents a set of scheduling strategies, and then the population is randomly generated to meet the initial constraints:

[0173] (27)

[0174] Step 2: Objective function calculation: Calculate the comprehensive objective value F of each individual and determine the one with the highest fitness. Wolf, Wolf and Wolf.

[0175] Step 3: Position update: Dynamically adjust the distribution of solutions through the hunting mechanism of the gray wolf optimization algorithm:

[0176] (28)

[0177] in, Indicates the distance between the prey's position and the current solution; and Both represent parameters that control the search scope.

[0178] Step 4: Constraint correction: If the individual does not meet the constraint conditions (such as SOC exceeds the limit or the load cannot be met), it is corrected.

[0179] Step 5: Convergence determination: Stop after reaching the maximum number of iterations or the objective function value converges, and output the optimal solution.

[0180] Figure 5 FIG. 1 is a flow chart of a method for determining an energy control strategy in another embodiment. Based on the above embodiment, this embodiment provides an optional example of a method for determining an energy control strategy. Figure 5 The specific implementation process is as follows:

[0181] S501: construct a first constraint function for constraining power input to the target park according to power demand information of the target park and equipment output information of the power system to which the target park belongs.

[0182] S502: Construct a second constraint function for constraining the power output of the target park according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs.

[0183] S503: construct an energy balance constraint function according to the first constraint function and the second constraint function.

[0184] S504, obtaining reference energy storage state information, reference charging power, and reference discharging power of the power system to which the target park belongs in a reference time period corresponding to the target time period.

[0185] S505: Taking the product of the charging efficiency and the reference charging power as the first reference power.

[0186] S506: Taking the difference between the first reference power and the reference discharge power as the second reference power.

[0187] S507: Use the ratio between the second reference power and the capacity of the energy storage system as the state change value.

[0188] S508: construct an energy storage state constraint function according to the reference energy storage state information and the state change value.

[0189] S509: construct an electricity cost function according to the electricity cost information, the electricity demand information and the energy storage cost information.

[0190] S510, constructing a carbon emission cost function according to the carbon emission information and the electricity demand information.

[0191] Among them, carbon emission information includes carbon emission factors.

[0192] Optionally, based on the electricity demand information, a power purchase function is constructed to characterize the power purchased by the target park; and based on the product of the power purchase function and the carbon emission factor, a carbon emission cost function is constructed.

[0193] S511, constructing an energy storage control cost function for the target park based on the electricity cost function and the carbon emission cost function.

[0194] S512, with the minimum function value of the energy storage control cost function as the goal, and with the energy balance constraint function and the energy storage state constraint function as constraints, the energy storage control cost function is solved to obtain the energy control strategy of the target park in the future period.

[0195] The specific process of S501-S512 can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0196] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0197] Based on the same inventive concept, the embodiment of the present application also provides an energy control strategy determination device for implementing the energy control strategy determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more energy control strategy determination device embodiments provided below can refer to the limitations of the energy control strategy determination method above, and will not be repeated here.

[0198] In an exemplary embodiment, Figure 6 As shown, an energy regulation strategy determination device 600 is provided, comprising: a first construction module 610, a second construction module 620 and a strategy determination module 630, wherein:

[0199] The first construction module 610 is used to construct a load balancing constraint function of the target park according to the load demand information and power demand information of the target park in the target period, and the equipment output information of the power system to which the target park belongs in the target period.

[0200] The second construction module 620 is used to construct an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period.

[0201] The strategy determination module 630 is used to solve the energy storage regulation cost function with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition to obtain the energy regulation strategy of the target park in the future period.

[0202] The above-mentioned energy regulation strategy determination device constructs a load balance constraint function of the target park according to the load demand information and electricity demand information of the target park in the target period, as well as the equipment output information of the power system to which the target park belongs in the target period; it is equivalent to comprehensively considering the real-time information of load demand, photovoltaic power generation and power grid status, optimizing the charging and discharging behavior of the energy storage system, and greatly improving the dynamic response capability and utilization efficiency of the energy storage system; further, according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period, an energy storage regulation cost function of the target park is constructed, and the carbon emission cost, carbon reduction target and electricity trading decision are deeply integrated, which reduces the carbon emissions of the park and improves the utilization rate of clean energy; finally, with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period, which can flexibly and efficiently adjust the energy allocation strategy to ensure the stability of the power system.

[0203] In one embodiment, the equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system; the first construction module 610 includes:

[0204] The first construction unit is used to construct a first constraint function for constraining the power input to the target park according to the power demand information of the target park and the equipment output information of the power system to which the target park belongs.

[0205] The second construction unit is used to construct a second constraint function for constraining the power output of the target park according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs.

[0206] The third construction unit is used to construct an energy balance constraint function according to the first constraint function and the second constraint function.

[0207] The fourth constructing unit is used to construct an energy storage state constraint function according to the charging and discharging information of the energy storage system.

[0208] In one embodiment, the energy storage system charging and discharging information includes the charging efficiency, capacity and energy storage limit state of the energy storage system; the fourth construction unit is specifically used to:

[0209] Obtain the reference energy storage state information, reference charging power and reference discharging power of the power system to which the target park belongs in the reference time period corresponding to the target time period; take the product of the charging efficiency and the reference charging power as the first reference power; take the difference between the first reference power and the reference discharging power as the second reference power; take the ratio between the second reference power and the capacity of the energy storage system as the state change value; and construct an energy storage state constraint function based on the reference energy storage state information and the state change value.

[0210] In one embodiment, the second building block 620 includes:

[0211] The fifth constructing unit is used to construct an electricity cost function according to the electricity cost information, the electricity demand information and the energy storage cost information.

[0212] The sixth construction unit is used to construct a carbon emission cost function according to the carbon emission information and the electricity demand information.

[0213] The seventh construction unit is used to construct an energy storage control cost function of the target park according to the electricity cost function and the carbon emission cost function.

[0214] In one embodiment, the carbon emission information includes a carbon emission factor; and the sixth construction unit is specifically used to:

[0215] According to the electricity demand information, a power purchase function is constructed to characterize the power purchased by the target park; according to the product between the power purchase function and the carbon emission factor, a carbon emission cost function is constructed.

[0216] In one embodiment, the policy determination module 630 is specifically configured to:

[0217] Based on the Grey Wolf Optimization Algorithm, with the goal of minimizing the function value of the energy storage regulation cost function and the load balance constraint function as the constraint condition, the energy storage regulation cost function is iteratively solved to obtain the energy regulation strategy of the target park in the future period.

[0218] Each module in the above energy control strategy determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0219] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining an energy regulation strategy is implemented.

[0220] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0221] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0222] According to the load demand information and power demand information of the target park in the target period, as well as the equipment output information of the power system to which the target park belongs in the target period, the load balance constraint function of the target park is constructed;

[0223] Construct the energy storage control cost function of the target park based on the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0224] Taking the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0225] In one embodiment, the equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system; when the processor executes the computer program to construct the load balance constraint function of the target park according to the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, the following steps are also implemented:

[0226] According to the power demand information of the target park and the equipment output information of the power system to which the target park belongs, a first constraint function is constructed for constraining the power input to the target park; according to the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, a second constraint function is constructed for constraining the power output of the target park; according to the first constraint function and the second constraint function, an energy balance constraint function is constructed; according to the charging and discharging information of the energy storage system, an energy storage state constraint function is constructed.

[0227] In one embodiment, the energy storage system charging and discharging information includes the charging efficiency of the energy storage system, the capacity of the energy storage system and the energy storage limit state; when the processor executes the computer program to construct the energy storage state constraint function according to the energy storage system charging and discharging information, the processor also implements the following steps:

[0228] Obtain the reference energy storage state information, reference charging power and reference discharging power of the power system to which the target park belongs in the reference time period corresponding to the target time period; take the product of the charging efficiency and the reference charging power as the first reference power; take the difference between the first reference power and the reference discharging power as the second reference power; take the ratio between the second reference power and the capacity of the energy storage system as the state change value; and construct an energy storage state constraint function based on the reference energy storage state information and the state change value.

[0229] In one embodiment, when the processor executes the computer program to construct the energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period, the following steps are also implemented:

[0230] Based on electricity cost information, electricity demand information and energy storage cost information, an electricity cost function is constructed; based on carbon emission information and electricity demand information, a carbon emission cost function is constructed; based on the electricity cost function and the carbon emission cost function, an energy storage regulation cost function of the target park is constructed.

[0231] In one embodiment, the carbon emission information includes a carbon emission factor; when the processor executes the computer program to construct a carbon emission cost function according to the carbon emission information and the electricity demand information, the processor also implements the following steps:

[0232] According to the electricity demand information, a power purchase function is constructed to characterize the power purchased by the target park; according to the product between the power purchase function and the carbon emission factor, a carbon emission cost function is constructed.

[0233] In one embodiment, when the processor executes the computer program to minimize the function value of the energy storage control cost function and to use the load balance constraint function as a constraint condition, and to solve the energy storage control cost function to obtain the energy control strategy of the target park in the future period, the following steps are also implemented:

[0234] Based on the Grey Wolf Optimization Algorithm, with the goal of minimizing the function value of the energy storage regulation cost function and the load balance constraint function as the constraint condition, the energy storage regulation cost function is iteratively solved to obtain the energy regulation strategy of the target park in the future period.

[0235] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0236] According to the load demand information and power demand information of the target park in the target period, as well as the equipment output information of the power system to which the target park belongs in the target period, the load balance constraint function of the target park is constructed;

[0237] Construct the energy storage control cost function of the target park based on the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0238] Taking the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0239] In one embodiment, the equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system; when the processor executes the computer program to construct the load balance constraint function of the target park according to the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, the following steps are also implemented:

[0240] According to the power demand information of the target park and the equipment output information of the power system to which the target park belongs, a first constraint function is constructed for constraining the power input to the target park; according to the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, a second constraint function is constructed for constraining the power output of the target park; according to the first constraint function and the second constraint function, an energy balance constraint function is constructed; according to the charging and discharging information of the energy storage system, an energy storage state constraint function is constructed.

[0241] In one embodiment, the energy storage system charging and discharging information includes the charging efficiency of the energy storage system, the capacity of the energy storage system and the energy storage limit state; when the processor executes the computer program to construct the energy storage state constraint function according to the energy storage system charging and discharging information, the processor also implements the following steps:

[0242] Obtain the reference energy storage state information, reference charging power and reference discharging power of the power system to which the target park belongs in the reference time period corresponding to the target time period; take the product of the charging efficiency and the reference charging power as the first reference power; take the difference between the first reference power and the reference discharging power as the second reference power; take the ratio between the second reference power and the capacity of the energy storage system as the state change value; and construct an energy storage state constraint function based on the reference energy storage state information and the state change value.

[0243] In one embodiment, when the processor executes the computer program to construct the energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period, the following steps are also implemented:

[0244] Based on electricity cost information, electricity demand information and energy storage cost information, an electricity cost function is constructed; based on carbon emission information and electricity demand information, a carbon emission cost function is constructed; based on the electricity cost function and the carbon emission cost function, an energy storage regulation cost function of the target park is constructed.

[0245] In one embodiment, the carbon emission information includes a carbon emission factor; when the processor executes the computer program to construct a carbon emission cost function according to the carbon emission information and the electricity demand information, the processor also implements the following steps:

[0246] According to the electricity demand information, a power purchase function is constructed to characterize the power purchased by the target park; according to the product between the power purchase function and the carbon emission factor, a carbon emission cost function is constructed.

[0247] In one embodiment, when the processor executes the computer program to minimize the function value of the energy storage control cost function and to use the load balance constraint function as a constraint condition, and to solve the energy storage control cost function to obtain the energy control strategy of the target park in the future period, the following steps are also implemented:

[0248] Based on the Grey Wolf Optimization Algorithm, with the goal of minimizing the function value of the energy storage regulation cost function and the load balance constraint function as the constraint condition, the energy storage regulation cost function is iteratively solved to obtain the energy regulation strategy of the target park in the future period.

[0249] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0250] According to the load demand information and power demand information of the target park in the target period, as well as the equipment output information of the power system to which the target park belongs in the target period, the load balance constraint function of the target park is constructed;

[0251] Construct the energy storage control cost function of the target park based on the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period;

[0252] Taking the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

[0253] In one embodiment, the equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system; when the processor executes the computer program to construct the load balance constraint function of the target park according to the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, the following steps are also implemented:

[0254] According to the power demand information of the target park and the equipment output information of the power system to which the target park belongs, a first constraint function is constructed for constraining the power input to the target park; according to the load demand information and power demand information of the target park, as well as the equipment output information of the power system to which the target park belongs, a second constraint function is constructed for constraining the power output of the target park; according to the first constraint function and the second constraint function, an energy balance constraint function is constructed; according to the charging and discharging information of the energy storage system, an energy storage state constraint function is constructed.

[0255] In one embodiment, the energy storage system charging and discharging information includes the charging efficiency of the energy storage system, the capacity of the energy storage system and the energy storage limit state; when the processor executes the computer program to construct the energy storage state constraint function according to the energy storage system charging and discharging information, the processor also implements the following steps:

[0256] Obtain the reference energy storage state information, reference charging power and reference discharging power of the power system to which the target park belongs in the reference time period corresponding to the target time period; take the product of the charging efficiency and the reference charging power as the first reference power; take the difference between the first reference power and the reference discharging power as the second reference power; take the ratio between the second reference power and the capacity of the energy storage system as the state change value; and construct an energy storage state constraint function based on the reference energy storage state information and the state change value.

[0257] In one embodiment, when the processor executes the computer program to construct the energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park in the target period, the following steps are also implemented:

[0258] Based on electricity cost information, electricity demand information and energy storage cost information, an electricity cost function is constructed; based on carbon emission information and electricity demand information, a carbon emission cost function is constructed; based on the electricity cost function and the carbon emission cost function, an energy storage regulation cost function of the target park is constructed.

[0259] In one embodiment, the carbon emission information includes a carbon emission factor; when the processor executes the computer program to construct a carbon emission cost function according to the carbon emission information and the electricity demand information, the processor also implements the following steps:

[0260] According to the electricity demand information, a power purchase function is constructed to characterize the power purchased by the target park; according to the product between the power purchase function and the carbon emission factor, a carbon emission cost function is constructed.

[0261] In one embodiment, when the processor executes the computer program to minimize the function value of the energy storage control cost function and to use the load balance constraint function as a constraint condition, and to solve the energy storage control cost function to obtain the energy control strategy of the target park in the future period, the following steps are also implemented:

[0262] Based on the Grey Wolf Optimization Algorithm, with the goal of minimizing the function value of the energy storage regulation cost function and the load balance constraint function as the constraint condition, the energy storage regulation cost function is iteratively solved to obtain the energy regulation strategy of the target park in the future period.

[0263] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0264] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0265] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0266] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining an energy regulation strategy, characterized in that: The method comprises: Constructing a load balancing constraint function of the target park according to the load demand information and power demand information of the target park during the target period, and the equipment output information of the power system to which the target park belongs during the target period; Constructing an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period; With the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition, the energy storage regulation cost function is solved to obtain the energy regulation strategy of the target park in the future period.

2. The method according to claim 1, characterized in that The equipment output information includes photovoltaic system output information and energy storage system charging and discharging information; the load balance constraint function includes an energy balance constraint function for constraining the energy balance in the target park and an energy storage state constraint function for constraining the operating state of the energy storage system in the power system; The load balancing constraint function of the target park is constructed according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs, including: Constructing a first constraint function for constraining power input to the target park according to power demand information of the target park and equipment output information of the power system to which the target park belongs; Constructing a second constraint function for constraining the power output of the target park according to the load demand information and power demand information of the target park and the equipment output information of the power system to which the target park belongs; constructing the energy balance constraint function according to the first constraint function and the second constraint function; The energy storage state constraint function is constructed according to the charging and discharging information of the energy storage system.

3. The method according to claim 2, characterized in that The energy storage system charging and discharging information includes the charging efficiency of the energy storage system, the capacity of the energy storage system and the energy storage limit state; The step of constructing the energy storage state constraint function according to the energy storage system charging and discharging information includes: Obtaining reference energy storage state information, reference charging power, and reference discharging power of the power system to which the target park belongs in a reference period corresponding to the target period; wherein the reference period is a historical period of the target period; taking the product of the charging efficiency and the reference charging power as the first reference power; taking the difference between the first reference power and the reference discharge power as the second reference power; Taking the ratio between the second reference power and the capacity of the energy storage system as a state change value; The energy storage state constraint function is constructed according to the reference energy storage state information and the state change value.

4. The method according to claim 1, characterized in that The step of constructing the energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period includes: Constructing an electricity cost function according to the electricity cost information, electricity demand information and energy storage cost information; Constructing a carbon emission cost function based on the carbon emission information and the electricity demand information; According to the electricity cost function and the carbon emission cost function, an energy storage regulation cost function of the target park is constructed.

5. The method according to claim 4, characterized in that The carbon emission information includes a carbon emission factor; The step of constructing a carbon emission cost function according to the carbon emission information and the electricity demand information includes: Constructing a purchased power function representing the power purchased by the target park according to the power demand information; A carbon emission cost function is constructed according to the product of the electricity purchase power function and the carbon emission factor.

6. The method according to claim 1, characterized in that The energy storage regulation cost function is solved with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition to obtain the energy regulation strategy of the target park in the future period, including: Based on the Grey Wolf Optimization Algorithm, with the goal of minimizing the function value of the energy storage regulation cost function and the load balance constraint function as a constraint condition, the energy storage regulation cost function is iteratively solved to obtain the energy regulation strategy of the target park in the future period.

7. An energy control strategy determination device, characterized in that: The device comprises: A first construction module is used to construct a load balancing constraint function of the target park according to the load demand information and power demand information of the target park in the target period, and the equipment output information of the power system to which the target park belongs in the target period; A second construction module is used to construct an energy storage control cost function of the target park according to the electricity cost information, electricity demand information, energy storage cost information and carbon emission information of the target park during the target period; The strategy determination module is used to solve the energy storage regulation cost function with the minimum function value of the energy storage regulation cost function as the goal and the load balance constraint function as the constraint condition to obtain the energy regulation strategy of the target park in the future period.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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