Shared energy storage and carbon neutralization park optimization scheduling method and system based on double-layer game

Abstract

The invention discloses a shared energy storage and carbon neutralization park optimization scheduling method and system based on a double-layer game. The method comprises the following steps: constructing a double-layer game optimization framework according to the characteristics of a carbon neutralization park and a power transaction strategy; constructing a power transaction and energy storage scheduling strategy model according to the characteristics of the carbon neutralization park; constructing a park power dispatching strategy model according to the carbon emission requirement and the power transaction strategy; the output results of the power transaction and energy storage scheduling model and the park power scheduling model are optimized through a double-layer game optimization framework to obtain an optimal scheduling scheme, and the system adopts the method. According to the shared energy storage and carbon neutralization park optimal scheduling method and system based on the double-layer game, effective balance of operation is ensured, the utilization efficiency of an energy storage system is improved, technical support is provided for energy management of a carbon neutralization park, the cost of the carbon neutralization park is reduced, and the energy utilization rate of the carbon neutralization park is improved. And the benefit of an operator of the shared energy storage equipment can be improved to the greatest extent.

Description

Technical Field

[0001] The present invention relates to the field of shared energy storage operation technology, and in particular to a shared energy storage and carbon-neutral park optimization scheduling method and system based on double-layer game. Background Art

[0002] Carbon neutrality has become a key component of national policies. To achieve this goal, the development and application of low-carbon technologies and green energy have garnered widespread attention. In urban power grids, effectively regulating energy supply and demand, reducing carbon emissions, and improving energy efficiency has become a hot topic of research. Shared energy storage, as an emerging technology, can help grids balance power supply and demand across varying load periods through the flexible scheduling of energy storage devices, alleviating the burden on the grid and providing strong support for achieving carbon neutrality.

[0003] With the gradual expansion of clean energy, the proportion of renewable energy sources such as solar and wind power in the power grid is increasing year by year. However, the supply of these renewable energy sources is highly volatile and intermittent, leading to significant uncertainty in power grid scheduling. To address this uncertainty, shared energy storage systems, as a flexible energy scheduling tool, have been gradually adopted in power dispatch. Shared energy storage systems can store excess electricity and release it during peak power demand, effectively alleviating the imbalance between power supply and demand.

[0004] In carbon-neutral parks, optimizing the scheduling of shared energy storage systems to balance power demand with renewable energy supply has become a key technical issue. Currently, shared energy storage devices and carbon-neutral parks each have their own scheduling logic for power scheduling, making it difficult to effectively integrate and balance these two logics, and therefore, unable to achieve an optimal scheduling solution. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a shared energy storage and carbon-neutral park optimization scheduling method and system based on two-layer game, which ensures the effective balance of operations between carbon-neutral parks and shared energy storage equipment operators, not only improves the utilization efficiency of energy storage systems, but also provides technical support for the energy management of carbon-neutral parks, not only reduces the cost of carbon-neutral parks, but also maximizes the benefits of shared energy storage equipment operators.

[0006] The present invention provides a method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a two-layer game, comprising the following steps:

[0007] Construct a two-tier game optimization framework based on the characteristics of the carbon neutral park and the power trading strategy;

[0008] Build a power trading and energy storage scheduling strategy model based on the characteristics of the carbon-neutral park to adjust the charging and discharging power strategy, capacity price, and charging and discharging price of shared energy storage equipment;

[0009] Construct a park power dispatch strategy model based on carbon emission requirements and power trading strategies to adjust the power purchasing strategy, power dispatch mechanism, and power usage strategy of the carbon-neutral park;

[0010] The output results of the power trading and energy storage scheduling model and the park power scheduling model are optimized through the two-layer game optimization framework to obtain the optimal scheduling solution.

[0011] In one embodiment, constructing a power trading and energy storage scheduling strategy model based on the characteristics of the carbon neutral park further includes:

[0012] Define strategies for adjusting the charge and discharge power of shared energy storage devices, and choose to charge during low-demand periods and discharge during peak periods based on the spatiotemporal characteristics of power demand.

[0013] Define dynamic adjustment of capacity price and charging and discharging price of shared energy storage equipment.

[0014] In one embodiment, the power trading and energy storage scheduling strategy model is expressed as:

[0015]

[0016] Where: x is the decision variable of the upper-level shared energy storage operator, y i is the decision variable of the i-th park in the lower layer, g O ≤0 includes pricing range constraints, h O =0 includes power security constraints for shared energy storage devices.

[0017] In one embodiment, constructing a park power dispatch strategy model based on carbon emission requirements and power trading strategies further includes:

[0018] Define a carbon emission reward and penalty mechanism, including adjusting the carbon neutral park's electricity purchasing strategy based on the carbon emissions of the carbon neutral park and implementing a carbon emission cost and reward system strategy;

[0019] Define a power dispatch mechanism whereby shared energy storage devices are used to supplement power demand and reduce grid purchases when renewable energy is insufficient;

[0020] Define a mechanism for dynamically adjusting the power usage of the park, and dynamically adjust the power usage strategy of the carbon-neutral park based on various types of equipment and power usage patterns in the park.

[0021] In one embodiment, the park power dispatch strategy model is expressed as:

[0022]

[0023] Where: represents the operating cost of the i-th carbon neutral park, The cost of purchasing electricity from the grid, is the cost of carbon emissions, To share the cost of using energy storage equipment, Service fees for shared energy storage equipment.

[0024] In one embodiment, optimizing the output results of the power trading and energy storage scheduling model and the park power scheduling model through the two-layer game optimization framework to obtain the optimal scheduling solution further includes:

[0025] The Lagrange multiplier method is used to establish the revenue function of shared energy storage operators, and by adding constraints, an optimizable objective function is formed;

[0026] The objective function of the power trading and energy storage scheduling strategy model is solved using the Lagrange multiplier method, and the optimal combination of the power trading and energy storage scheduling strategy model is solved using the KKT condition.

[0027] By applying KKT conditions, the park power dispatch strategy model is optimized.

[0028] The present invention also provides a shared energy storage and carbon neutral park optimization scheduling system based on a two-layer game, which adopts any of the above methods, including:

[0029] Shared energy storage equipment to provide electricity services for carbon-neutral parks;

[0030] Power trading and energy storage scheduling strategy model, used to adjust the charging and discharging power strategy, capacity price, and charging and discharging price of shared energy storage equipment;

[0031] The park power dispatch strategy model is used to adjust the power purchasing strategy, power dispatch mechanism and power usage strategy of the carbon-neutral park;

[0032] An interaction module, used for data interaction between the power transaction and the energy storage scheduling strategy model and the park power scheduling strategy model;

[0033] A two-layer game optimization framework is used to optimize the output results of the power trading and energy storage scheduling model and the park power scheduling model to obtain the optimal scheduling solution.

[0034] The shared energy storage and carbon-neutral park optimization scheduling method and system based on double-layer game provided by the present invention ensures an effective balance in operations between the carbon-neutral park and the shared energy storage equipment operator, which not only improves the utilization efficiency of the energy storage system, but also provides technical support for the energy management of the carbon-neutral park. It not only reduces the cost of the carbon-neutral park, but also maximizes the benefits of the shared energy storage equipment operator. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A flow chart of the shared energy storage and carbon-neutral park optimization scheduling method based on two-layer game provided by the present invention.

[0037] Figure 2 A schematic diagram of the real-time pricing results of shared energy storage equipment within one day in a test scenario for the shared energy storage and carbon-neutral park optimization scheduling method based on two-layer game provided by the present invention.

[0038] Figure 3 The shared energy storage and carbon-neutral park optimization scheduling method based on two-layer game provided by the present invention is used to test the changes in the charging and discharging power and battery power of shared energy storage equipment in different carbon-neutral parks at different times of the day under the test scenario. DETAILED DESCRIPTION

[0039] Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are merely some, and not all, of the embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the description of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0040] In the description of the present invention, unless otherwise specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; and direct or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of these terms based on the specific circumstances.

[0041] The directions or positional relationships indicated by terms such as "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inside" and "outside" are based on the directions or positional relationships shown in the accompanying drawings, or are the directions or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience and simplification of description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0042] The terms "first," "second," "third," etc. are merely used to distinguish between elements of similar nature and do not indicate or imply relative importance or a particular order.

[0043] The terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than the listed elements and may also include additional elements not specifically listed.

[0044] Example 1

[0045] See also Figure 1 The present invention provides a method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a two-layer game, comprising the following steps:

[0046] S1, construct a two-layer game optimization framework based on the characteristics of the carbon neutral park and the power trading strategy.

[0047] What is clear is that by combining the park's power demand with the characteristics of renewable energy generation, energy storage equipment can be flexibly adjusted to meet power demand and comply with carbon emission control requirements. The two-tiered game optimization framework considers the carbon-neutral park's power load, energy storage capacity, renewable energy volatility, and carbon emission limits. By rationally planning energy storage charging and discharging strategies, it ensures the efficient operation of shared energy storage equipment.

[0048] A two-tiered game optimization framework is used to analyze the demands and behaviors of different entities within a carbon-neutral park and construct a pricing strategy for shared energy storage based on this game model. This two-tiered game optimization framework considers power demand fluctuations, the economic viability of shared energy storage, and carbon emission targets to set reasonable power purchase and storage prices, thereby optimizing the interests of all entities and maximizing the benefits of shared energy storage.

[0049] S2, builds a power trading and energy storage scheduling strategy model based on the characteristics of the carbon-neutral park, which is used to adjust the charging and discharging power strategy, capacity price and charging and discharging price of shared energy storage equipment.

[0050] It is understandable that the above steps may further include:

[0051] S201, defining a strategy for adjusting the charge and discharge power of the shared energy storage device, and selecting charging during low power demand periods and discharging during peak power demand periods based on the spatiotemporal characteristics of power demand;

[0052] S202, defining a method for dynamically adjusting the capacity price and charging and discharging price of a shared energy storage device.

[0053] It can be seen that the above energy storage scheduling strategy model can be expressed as:

[0054]

[0055] Where: x is the decision variable of the upper-level shared energy storage operator, y i is the decision variable of the i-th park in the lower layer, g O ≤0 includes pricing range constraints, h O =0 includes power security constraints for shared energy storage devices.

[0056] The above-mentioned shared energy storage equipment power safety constraints include constraints including SOC constraints, state of charge constraints, charge and discharge power constraints and energy storage power station charge and discharge power balance constraints.

[0057] The SOC constraints are described as follows:

[0058]

[0059] Where: For charging efficiency, is the discharge efficiency, is the self-discharge rate, is the maximum capacity of the shared energy storage device.

[0060] The charge and discharge power constraints are described in detail as follows:

[0061]

[0062]

[0063]

[0064] Where: The charging power of the shared energy storage device in the nth carbon-neutral park at time t; The discharge power provided by the shared energy storage device to the nth carbon-neutral park at time t; 、 、 、 They are the upper and lower limits of charging power and discharging power of the shared energy storage equipment in period t respectively; and They are respectively the charging status bit and discharging status bit of the shared energy storage device used by the nth carbon-neutral park in time period t, both of which are Boolean variables.

[0065] The state of charge constraints are described as follows:

[0066]

[0067]

[0068]

[0069]

[0070] Where: is the self-discharge efficiency of the shared energy storage system; and The electric energy storage capacity of the shared energy storage device at time t and time t-1; and are the charging efficiency and discharging efficiency of the shared energy storage system respectively; and The power consumption of the shared energy storage device at the beginning and end of the period.

[0071] S3, constructs a park power dispatching strategy model based on carbon emission requirements and power trading strategies, which is used to adjust the power purchasing strategy, power dispatching mechanism and power usage strategy of the carbon-neutral park.

[0072] It is understandable that the above steps may further include:

[0073] S301, defining a carbon emission reward and penalty mechanism, wherein the carbon neutral park's electricity purchasing strategy is adjusted based on the carbon emission situation of the carbon neutral park, and a carbon emission cost and reward system strategy is implemented;

[0074] S302, defining a power dispatch mechanism, wherein when renewable energy is insufficient, power demand is supplemented by shared energy storage equipment to reduce power purchases from the grid;

[0075] S303, define the mechanism for dynamically adjusting the power usage of the park, and dynamically adjust the power usage strategy of the carbon-neutral park based on various types of equipment and power usage patterns in the park.

[0076] It can be known that the above power dispatch strategy model can be expressed as:

[0077]

[0078] in, represents the operating cost of the i-th carbon neutral park, The cost of purchasing electricity from the grid, is the cost of carbon emissions, To share the cost of using energy storage equipment, Service fees for shared energy storage equipment.

[0079] The above-mentioned power purchase costs of the power grid are described in detail as follows:

[0080]

[0081] Where: the cost of purchasing electricity for the grid; is the electricity price purchased from the power grid at time t; is the power purchased from the grid by the nth park at time t.

[0082] The total carbon emissions are described as follows:

[0083]

[0084] Where: is the total carbon emissions of the park (unit: tons CO2e). is the carbon emissions of activity type i (in tons CO2e); Average carbon emission factor for grid electricity supply.

[0085] The formula for calculating carbon emissions for each type of activity is:

[0086]

[0087] Where: is the amount of activity in category (i) (e.g. electricity consumption, fuel use, etc.). is the emission factor for activity type (i) (in tonnes CO2e per unit of activity).

[0088] Carbon offsetting is an important tool for combating climate change and is seen as a complementary measure that offsets the carbon dioxide emissions of a specific activity or region by investing in environmental projects.

[0089]

[0090] Where: is the amount of carbon offset (unit: tonnes CO2e). It is Compensation amount of each carbon offset project (unit: tonnes CO2e).

[0091] S4, optimizes the output results of the power trading and energy storage scheduling model and the park power scheduling model through a two-layer game optimization framework to obtain the optimal scheduling solution.

[0092] It is understandable that the above steps may further include:

[0093] S401, using the Lagrange multiplier method to establish the revenue function of the shared energy storage operator, and by adding constraints, forming an optimizable objective function;

[0094] S402, solving the objective function of the power trading and energy storage scheduling strategy model using the Lagrange multiplier method, and solving the optimal combination of the power trading and energy storage scheduling strategy model using the KKT condition;

[0095] S403, optimize the park power dispatch strategy model by applying KKT conditions.

[0096] The constraints that need to be met for the optimized operation of the carbon-neutral park at the lower level include the distributed photovoltaic power generation model, the carbon capture system operation constraints, the power purchase constraints from the power grid, the carbon emission flow constraints of the distribution network, and the power balance constraints on the bus. The specific constraints are as follows:

[0097] The distributed photovoltaic power generation model is described as follows:

[0098]

[0099]

[0100] Where: is the photovoltaic power generation power during period t; The configured capacity of the photovoltaic unit; is the light intensity during period t; is the rated light intensity. Therefore, the photovoltaic power generation in period t is:

[0101]

[0102] Where: is the photovoltaic power generation in period t; is the time interval of period t.

[0103] The power purchase power constraints of the power grid are described as follows:

[0104]

[0105] Where: The rated power for electricity trading between the park and the upper-level power grid.

[0106] The power balance constraints of the campus distribution network are described in detail as follows:

[0107]

[0108] Where, is the green power in period t; is the park's electrical load power during period t.

[0109] See also Figure 3 , this embodiment is based on the research on the electricity demand characteristics of the carbon-neutral park and the scheduling optimization of the energy storage system. Combined with the actual operation of the park, an effective balance between electricity supply and carbon emission control is achieved through a shared energy storage scheduling optimization model. The total capacity of the shared energy storage system is set to 10MW, the maximum charging and discharging power of the energy storage equipment is 2MW, the initial charging state is 50%, and the hourly charging and discharging power is 0.05MW. Each energy storage device is scheduled according to the renewable energy input of the park. Taking into account the volatility and unpredictability of renewable energy such as solar energy and wind energy, efficient scheduling of power supply is achieved through scheduling data based on ground and regional loads. According to the changes in real-time electricity demand and renewable energy power generation in the park, the scheduling model dynamically adjusts the charging and discharging strategy of the energy storage equipment. While meeting the power demand of the park, carbon emissions are minimized to ensure that the park's carbon emission targets are achieved. The method of the present invention can make optimization decisions on time period pricing and charging and discharging strategies for each time period for energy storage systems shared in carbon-neutral parks, such as Figure 2 At the same time, the method of the present invention also optimizes the charging and discharging power of shared energy storage in different parks during each time period, ensuring that the battery power is always within a safe range, ensuring the long-term stable operation of the energy storage system.

[0110] Example 2

[0111] This embodiment provides a shared energy storage and carbon-neutral park optimization scheduling system based on a two-layer game, using any of the above methods, including:

[0112] Shared energy storage equipment to provide electricity services for carbon-neutral parks;

[0113] Power trading and energy storage scheduling strategy model, used to adjust the charging and discharging power strategy, capacity price, and charging and discharging price of shared energy storage equipment;

[0114] The park power dispatch strategy model is used to adjust the power purchasing strategy, power dispatch mechanism and power usage strategy of the carbon-neutral park;

[0115] Interaction module, used for data interaction between power trading, energy storage scheduling strategy model, and campus power scheduling strategy model;

[0116] A two-layer game optimization framework is used to optimize the output results of the power trading and energy storage scheduling model and the park power scheduling model to obtain the optimal scheduling solution.

[0117] From the above description, it can be seen that the shared energy storage and carbon-neutral park optimization scheduling method and system based on two-layer game provided by the present invention ensures the effective balance of operations between the carbon-neutral park and the shared energy storage equipment operator, which not only improves the utilization efficiency of the energy storage system, but also provides technical support for the energy management of the carbon-neutral park. It not only reduces the cost of the carbon-neutral park, but also maximizes the benefits of the shared energy storage equipment operator.

[0118] The foregoing description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the technical scope disclosed herein are intended to be encompassed within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a two-layer game, characterized in that: The steps include: Construct a two-tier game optimization framework based on the characteristics of the carbon neutral park and the power trading strategy; Build a power trading and energy storage scheduling strategy model based on the characteristics of the carbon-neutral park to adjust the charging and discharging power strategy, capacity price, and charging and discharging price of shared energy storage equipment; Construct a park power dispatch strategy model based on carbon emission requirements and power trading strategies to adjust the power purchasing strategy, power dispatch mechanism, and power usage strategy of the carbon-neutral park; The output results of the power trading and energy storage scheduling model and the park power scheduling model are optimized through the two-layer game optimization framework to obtain the optimal scheduling solution.

2. The method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a two-layer game as claimed in claim 1, characterized in that: The said constructing a power trading and energy storage scheduling strategy model based on the characteristics of the carbon neutral park further includes: Define strategies for adjusting the charge and discharge power of shared energy storage devices, and choose to charge during low-demand periods and discharge during peak periods based on the spatiotemporal characteristics of power demand. Define dynamic adjustment of capacity price and charging and discharging price of shared energy storage equipment.

3. The shared energy storage and carbon neutral park optimization scheduling method based on double-layer game as claimed in claim 2 is characterized in that: The power transaction and energy storage scheduling strategy model is expressed as: Where: x is the decision variable of the upper-level shared energy storage operator, y i is the decision variable of the i-th park in the lower layer, g O ≤0 includes pricing range constraints, h O =0 includes power security constraints for shared energy storage devices.

4. The method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a two-layer game as claimed in claim 1, characterized in that: The construction of a park power dispatch strategy model based on carbon emission requirements and power trading strategies further includes: Define a carbon emission reward and penalty mechanism, including adjusting the carbon neutral park's electricity purchasing strategy based on the carbon emissions of the carbon neutral park and implementing a carbon emission cost and reward system strategy; Define a power dispatch mechanism whereby shared energy storage devices are used to supplement power demand and reduce grid purchases when renewable energy is insufficient; Define a mechanism for dynamically adjusting the power usage of the park, and dynamically adjust the power usage strategy of the carbon-neutral park based on various types of equipment and power usage patterns in the park.

5. The method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a double-layer game as claimed in claim 4 is characterized in that: The park power dispatch strategy model is expressed as: Where: represents the operating cost of the i-th carbon neutral park, The cost of purchasing electricity from the grid, is the cost of carbon emissions, To share the cost of using energy storage equipment, Service fees for shared energy storage equipment.

6. The method for optimizing the scheduling of shared energy storage and carbon-neutral parks based on a double-layer game as claimed in claim 1, characterized in that: Optimizing the output results of the power trading and energy storage scheduling model and the park power scheduling model through the two-layer game optimization framework to obtain the optimal scheduling solution further includes: The Lagrange multiplier method is used to establish the revenue function of shared energy storage operators, and by adding constraints, an optimizable objective function is formed; The objective function of the power trading and energy storage scheduling strategy model is solved using the Lagrange multiplier method, and the optimal combination of the power trading and energy storage scheduling strategy model is solved using the KKT condition. By applying KKT conditions, the park power dispatch strategy model is optimized.

7. A shared energy storage and carbon neutral park optimization scheduling system based on a two-layer game, characterized by: The method according to any one of claims 1 to 6, comprising: Shared energy storage equipment to provide electricity services for carbon-neutral parks; Power trading and energy storage scheduling strategy model, used to adjust the charging and discharging power strategy, capacity price, and charging and discharging price of shared energy storage equipment; The park power dispatch strategy model is used to adjust the power purchasing strategy, power dispatch mechanism and power usage strategy of the carbon-neutral park; An interaction module, used for data interaction between the power transaction and the energy storage scheduling strategy model and the park power scheduling strategy model; A two-layer game optimization framework is used to optimize the output results of the power trading and energy storage scheduling model and the park power scheduling model to obtain the optimal scheduling solution.

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