Shared energy storage optimization method and device and storage medium

Abstract

The invention discloses a shared energy storage optimization method and device, and a storage medium. The method comprises the following steps: constructing a shared energy storage model; in the shared energy storage model, constructing an upper layer decision model for an energy storage operator according to the interaction and contrary relationship between the energy storage operator and the plurality of parks; in the shared energy storage model, constructing a lower-layer decision model for each park according to the interaction and contrary relationship between an energy storage operator and the plurality of parks; and with an energy storage operator as a dominant and a plurality of parks as followers, optimizing the upper layer decision model and the lower layer decision model according to a master-slave game. In the lease-type shared energy storage mode provided by the embodiment, an energy storage operator can obtain lease income through the short-term use right of the lease energy storage resources, a park can reduce power consumption cost through the short-term use right of the lease energy storage resources, the energy storage operator and the park play a game to obtain optimal configuration, and the utilization rate of energy storage equipment is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a shared energy storage optimization method, device and storage medium. Background Art

[0002] With the rapid development of new energy power generation technology, energy storage systems independently invested and operated by third-party investors have gradually become an important form of energy storage in the market. For this type of energy storage independently invested by third parties, providing leasing and sharing services to energy storage resource demanders on the power generation and consumption side is a feasible operating model.

[0003] For energy storage investors, leasing and sharing energy storage resources provides a new way to make profits, while helping them improve the utilization rate of energy storage equipment and effectively shorten the recovery period of investment costs. For energy storage resource demanders, leasing shared energy storage provides them with a lower threshold for energy storage use, allowing them to obtain short-term use rights of energy storage resources without having to bear independent investment costs and investment risks, thereby improving market performance and obtaining additional market benefits.

[0004] At present, leased shared energy storage mostly operates according to a fixed mechanism, and the feedback between energy storage investors and energy storage resource demanders is relatively slow, resulting in low utilization of energy storage resources. Summary of the invention

[0005] In view of this, the present invention provides a shared energy storage optimization method, device and storage medium, which are used to improve the utilization rate of energy storage resources in a leasing and sharing mode of energy storage resources.

[0006] A first aspect of the present invention provides a shared energy storage optimization method, comprising:

[0007] Constructing a shared energy storage model; the shared energy storage model includes an energy storage operator renting energy storage equipment to multiple parks, and each park renting the energy storage equipment for charging and discharging;

[0008] In the shared energy storage model, an upper-level decision model is constructed for the energy storage operator based on the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0009] In the shared energy storage model, a lower-level decision model is constructed for each of the parks based on the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0010] With the energy storage operator as the leader and the plurality of parks as followers, the upper-level decision model and the lower-level decision model are optimized based on the master-slave game.

[0011] A second aspect of the present invention provides a shared energy storage optimization device, comprising:

[0012] A shared energy storage model construction module is used to construct a shared energy storage model; the shared energy storage model includes an energy storage operator renting energy storage equipment to multiple parks, and each park renting the energy storage equipment for charging and discharging;

[0013] An upper-level decision model construction module, used to construct an upper-level decision model for the energy storage operator in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0014] A lower-level decision model construction module, used to construct a lower-level decision model for each of the parks in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0015] The decision model optimization module is used to optimize the upper decision model and the lower decision model based on the master-slave game with the energy storage operator as the leader and the multiple parks as followers.

[0016] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the shared energy storage optimization method as described in the first aspect above.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the shared energy storage optimization method as described in the first aspect above.

[0021] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the shared energy storage optimization method as described in the first aspect above.

[0022] In this embodiment, a shared energy storage model is constructed; the shared energy storage model includes energy storage operators renting energy storage equipment to multiple parks, and each park renting energy storage equipment for charging and discharging; in the shared energy storage model, an upper-level decision model is constructed for the energy storage operator based on the interaction and opposition between the energy storage operator and the multiple parks; in the shared energy storage model, a lower-level decision model is constructed for each park based on the interaction and opposition between the energy storage operator and the multiple parks; with the energy storage operator as the leader and multiple parks as followers, the upper-level decision model and the lower-level decision model are optimized based on the master-slave game. Under the rental-type shared energy storage model proposed in this embodiment, the energy storage operator can obtain rental income by renting the short-term use rights of energy storage resources, and the park can reduce the electricity cost by renting the short-term use rights of energy storage resources. The two parties compete with each other to obtain the optimal configuration, taking into account the needs of both parties, maximizing the use of energy storage equipment, and effectively improving the utilization rate of energy storage equipment, thereby further promoting the long-term development of the energy storage industry and the shared energy storage model.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 It is a flow chart of a shared energy storage optimization method provided in Example 1 of the present invention.

[0026] Figure 2 It is a structural schematic diagram of a shared energy storage optimization device provided in Example 2 of the present invention.

[0027] Figure 3 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] See also Figure 1 , shows a flow chart of a shared energy storage optimization method provided by the first embodiment of the present invention, the method can be executed by a shared energy storage optimization device, the shared energy storage optimization device can be implemented in the form of hardware and / or software, and the shared energy storage optimization device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] Step 101: Build a shared energy storage model.

[0033] In practical applications, a shared energy storage model for multi-park leasing is constructed based on the electricity market environment.

[0034] In the shared energy storage model, energy storage operators invest in and build energy storage power stations based on the energy storage needs of the park (i.e., users). The energy storage power stations are equipped with multiple energy storage devices. One of their operating purposes is to make profits for themselves. They provide energy storage services to multiple parks and rent out energy storage equipment to ensure the charging and discharging needs of the parks.

[0035] In the shared energy storage model, each park is a producer and seller of electricity. The park has new energy power generation systems such as wind energy and solar energy. In the leased shared energy storage operation, the park can lease energy storage equipment with a certain capacity and charging and discharging power from the energy storage operator. After completing the lease, the park can plan its own charging and discharging strategy in real time, just like self-built energy storage devices. When its own electricity production is higher than the load demand of the park, the park can store electricity in the leased energy storage equipment. When its own electricity production is lower than the load demand of the park, it can purchase electricity from the power grid or release its own electricity stored in the shared energy storage equipment. By leasing energy storage equipment, it is possible to stagger peak electricity prices, reduce electricity costs, and fully absorb its own new energy output.

[0036] In the shared energy storage model, the energy storage operator and multiple parks mutually constrain the transaction process of shared energy storage through contracts, and the decisions between the energy storage operator and multiple parks are mutually influential. On the one hand, the capacity and power price specified by the energy storage operator for the energy storage equipment will affect the park's energy storage equipment leasing strategy; on the other hand, the overall charging and discharging decision of the park will affect the energy storage operator's configuration of energy storage equipment.

[0037] In the shared energy storage model, the transaction process of shared energy storage can be divided into the following steps:

[0038] (1) Energy storage operators set the power price and capacity price for energy storage equipment leasing based on energy storage investment costs, expected rate of return, real-time electricity prices, etc.

[0039] (2) The park determines the leasing capacity of energy storage based on its own load demand, power output and time-of-use electricity price, after learning the price set by the energy storage operator, with the goal of minimizing its own electricity cost, and formulates a charging and discharging strategy based on its own needs.

[0040] (3) The park will feed back its real-time charging and discharging strategies and leasing needs to the shared energy storage platform.

[0041] (4) Energy storage operators collect and integrate feedback from all parks through their own shared energy storage platforms to obtain the real-time net charging / discharging efficiency of energy storage equipment.

[0042] (5) With the goal of maximizing profits, energy storage operators formulate optimal capacity configuration and energy storage operation strategies for energy storage equipment, and meet the real-time charging and discharging needs of all parks through the charging and discharging of energy storage equipment and real-time transactions with the power grid.

[0043] Step 102: In the shared energy storage model, an upper-level decision model is constructed for the energy storage operator based on the interaction and opposition relationship between the energy storage operator and multiple parks.

[0044] In practical applications, there is an interactive and antagonistic relationship between energy storage operators and multiple parks. There is interaction between energy storage operators and multiple parks in renting and leasing energy storage equipment. Energy storage operators and parks are two independent decision-making entities, with the goals of increasing their own profits and reducing costs respectively. There is a conflict of interest (i.e., opposition), and a two-layer decision-making optimization model can be constructed based on this.

[0045] The energy storage operator is the leader, who makes decisions on leasing prices, configuration capacity, and operation arrangements with the goal of maximizing its own profits. The park, as a follower, makes decisions on energy storage capacity leasing and scheduling operations with the goal of minimizing its own electricity costs. A two-layer decision optimization model is constructed to characterize the game behavior of the leader and the follower.

[0046] In the two-layer decision optimization model, an upper-level decision model is constructed for energy storage operators.

[0047] In one embodiment of the present invention, step 102 may include the following steps:

[0048] Step 1021: In the shared energy storage model, the net profit of the energy storage operator for leasing energy storage equipment is calculated as the first objective function based on the interaction and opposition relationship between the energy storage operator and multiple parks.

[0049] In practical applications, the goal of energy storage operators is to maximize their own net profit based on the optimal decision of the underlying decision model, thereby constructing the first objective function.

[0050] Exemplarily, the first objective function is:

[0051]

[0052] Among them, I SES is the net profit of the energy storage operator for renting energy storage equipment, max means the optimization goal is to maximize the net profit, I Rent is the rental income, C Inv For investment cost, is the electricity purchase cost of the park at time t, is the electricity purchase cost of the energy storage operator at time t, Δt is the time interval, and T is the set of times.

[0053] The daily rental income of the energy storage operator can be expressed as:

[0054]

[0055] Among them, I Rent is the rental income, β E is the rent per unit capacity, β P is the rental per unit power, is the capacity leased for park i, is the maximum power leased by park i, S Cen A collection of parks.

[0056] When calculating the daily operating cost of energy storage equipment, the original investment cost of the energy storage equipment is converted to daily cost. Therefore, the daily equivalent investment cost can be expressed as:

[0057]

[0058] Among them, C Inv is the investment cost, r is the discount rate, Y is the maximum service life of the energy storage equipment, γ P is the investment cost per unit power, γ E is the investment cost per unit capacity, PCap is the total power of the energy storage device, E Cap is the total capacity of the energy storage device.

[0059] The energy storage operator's single-period electricity charge is:

[0060]

[0061] in, is the electricity purchase cost of the park at time t, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t, Δt is the time interval, S Cen A collection of parks.

[0062] The actual cost of purchasing electricity for energy storage operators is:

[0063]

[0064] in, is the electricity purchase cost of the energy storage operator at time t, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by the energy storage operator at time t, is the electricity sales power of the energy storage operator at time t, and Δt is the time interval.

[0065] Step 1022: In the shared energy storage model, a first constraint condition is added to the parameters in the first objective function according to the interaction and opposition relationship between the energy storage operator and the multiple parks.

[0066] In the shared energy storage model, first constraints may be added to some parameters in the first objective function based on the interaction and opposition relationship between the energy storage operator and multiple parks.

[0067] Exemplarily, the first constraint is:

[0068] The maximum power limit of the energy storage investment cannot be exceeded during charging and discharging, that is:

[0069]

[0070] in, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, P Cap is the total power of the energy storage device.

[0071] The real-time power of the energy storage device cannot exceed the maximum power allowed by the energy storage, nor can it be lower than the minimum power allowed by the energy storage, that is:

[0072] E min ≤E t ≤E max

[0073] Among them, E t is the amount of electricity stored in the energy storage device at time t, E min E is the minimum amount of electricity that the energy storage device is allowed to store. max The maximum amount of electricity that an energy storage device is allowed to store.

[0074] The battery state of charge (SOC) is used to describe the remaining power percentage of the battery in the energy storage device. The following constraints exist:

[0075] E o =E Cap ·SOC o

[0076] E max =E Cap ·SOC max

[0077] E min =E Cap ·SOC min

[0078] Among them, E min E is the minimum amount of electricity that the energy storage device is allowed to store. max E is the maximum amount of electricity that the energy storage device is allowed to store. o is the initial storage capacity of the energy storage device, E Cap is the total capacity of the energy storage device, SOC o SOC is the initial state of charge of the energy storage device. max SOC is the maximum state of charge allowed by the energy storage device. min It is the minimum state of charge allowed for the energy storage device.

[0079] The power in the energy storage device changes continuously, and in order to keep the energy storage device working stably, the initial power of the battery in the energy storage device is kept consistent with the final power, so the constraints are as follows:

[0080]

[0081] E N =E O =E Cap .SOC o

[0082] Among them, Et is the amount of electricity stored in the energy storage device at time t, E t-1 is the amount of electricity stored in the energy storage device at time t-1, η C is the charging efficiency of the energy storage device, η D is the discharge efficiency of the energy storage device, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, Δt is the time interval, E O E is the initial energy storage capacity of the energy storage device in one day, N is the amount of energy stored by the energy storage device at the end of the day, E Cap is the total capacity of the energy storage device, SOC o is the initial state of charge of the energy storage device.

[0083] Energy storage operators meet the charging and discharging needs of all parks in real time, namely:

[0084]

[0085] in, is the power purchased by the energy storage operator at time t, is the electricity sales power of the energy storage operator at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, is the discharge power of park i at time t, is the charging power of park i at time t, is the electricity sales power of park i at time t, is the power purchased by park i at time t, S Cen A collection of parks.

[0086] Step 1023: Combine the first objective function and the first constraint condition into an upper-level decision model.

[0087] Step 103: In the shared energy storage model, a lower-level decision model is constructed for each park based on the interaction and opposition relationship between the energy storage operator and multiple parks.

[0088] In the two-layer decision optimization model, a lower-level decision model is constructed for each park.

[0089] In one embodiment of the present invention, step 103 may include the following steps:

[0090] Step 1031: In the shared energy storage model, the electricity cost of leasing energy storage equipment in each park is calculated based on the interaction and opposition relationship between the energy storage operator and multiple parks as the second objective function.

[0091] The lower-level decision of shared energy storage is made by each park individually. The park’s goal is to achieve the minimum electricity cost based on the optimal decision of the upper-level decision model, thereby constructing the second objective function.

[0092] Generally speaking, the electricity cost of a park can be divided into two parts. The first part is the cost of renting energy storage equipment, and the second part is the cost of purchasing electricity from the power grid.

[0093] Then, the second objective function is:

[0094]

[0095] in, is the electricity cost of park i, min means the optimization goal is to minimize the electricity cost, β E is the rent per unit capacity, β P is the rental per unit power, is the capacity leased for park i, is the maximum power leased by park i, Δt is the time interval, T is the set of time, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t.

[0096] Step 1032: In the shared energy storage model, add second constraints to the parameters in each second objective function based on the interaction and opposition relationship between the energy storage operator and the multiple parks.

[0097] In the shared energy storage model, second constraints are added to some parameters in each second objective function based on the interaction and opposition relationship between the energy storage operator and multiple parks.

[0098] Exemplarily, the second constraint is:

[0099] Charge and discharge power constraints, namely:

[0100]

[0101] in, is the charging power of park i at time t, is the discharge power of park i at time t, P Cap is the total power of the energy storage device.

[0102] The remaining energy constraint of energy storage is:

[0103]

[0104] Among them, e i,t is the energy storage capacity of park i at time t, e i,t-1 is the energy storage capacity of park i at time t-1, is the minimum energy storage capacity allowed in park i, is the maximum energy storage capacity allowed in park i, η C is the charging efficiency of the energy storage device, η D is the discharge efficiency of the energy storage device, is the charging power of park i at time t, is the discharge power of park i at time t, Δt is the time interval, e i,o is the initial energy storage capacity in one day, e i,N is the energy storage capacity at the end of the day, SOC o is the initial state of charge of the energy storage device.

[0105] The park rents energy storage equipment to meet its own power needs, that is, the power balance constraint:

[0106]

[0107] in, is the power purchased by park i at time t, is the electricity sales power of park i at time t, is the discharge power of park i at time t, is the charging power of park i at time t, d i,t is the load of park i at time t.

[0108] Step 1033: Combine the second objective function and the second constraint condition to form a lower-level decision model.

[0109] Step 104: With the energy storage operator as the leader and multiple parks as followers, the upper-level decision model and the lower-level decision model are optimized based on the master-slave game.

[0110] In this embodiment, the master-slave game is introduced to characterize the interaction and opposition relationship between the energy storage operator and the park.

[0111] In the two-layer decision optimization model, the upper-layer decision model uses the solution of the lower-layer decision model as the parameter in optimizing the first objective function; the lower-layer decision model uses the decision variables of the upper-layer decision model as the parameter in optimizing the second objective function. The decisions between the energy storage operator and the park influence each other. When solving the two-layer decision optimization model, it can be decoupled into three steps according to the decision order.

[0112] Step 1: Initialize the rental price of shared energy storage. When reviewing the two-layer decision optimization model of shared energy storage, it can be seen that the lower-layer decision model is affected by the price set by the energy storage operator in the upper-layer decision model, while the upper-layer decision model is more affected by the lower-layer decision model. Therefore, the lower-layer decision model is considered first to make decisions, that is, it is assumed that the energy storage operator first sets the shared energy storage capacity and power price.

[0113] Step 2: In the lower-level decision model, each park makes energy storage leasing capacity decisions based on its own power production and load demand, with the goal of minimizing electricity costs.

[0114] Step 3: The lower-level decision model is passed to the energy storage operator through the shared energy storage intermediate platform. The energy storage operator collects the capacity requirements of all park users and formulates the configuration of shared energy storage equipment with the goal of maximizing its own net profit.

[0115] Through decoupling analysis, a set price can be input, and after two optimizations, the physical configuration and optimal profit of shared energy storage at a fixed price can be obtained. Therefore, the net profit of energy storage operators when the price changes is analyzed: Since the purpose of the park's demand for energy storage is to reduce its own electricity costs, as the energy storage rental price increases, the cost of leasing energy storage in the park continues to increase. When the cost of leasing can no longer be reduced by mobilizing energy storage, the park is not inclined to lease energy storage, and the net profit of the operator will stagnate. However, when the set price is too low, the energy storage operator cannot make a profit through leasing. Therefore, it can be known that the operator's optimal net profit changes with the fixed range of rental prices.

[0116] In one embodiment of the present invention, in order to find the best configuration and the best price corresponding to the highest profit, a one-dimensional search algorithm may be used to achieve the goal of finding the best configuration through iteration within a certain input price range. Then, step 104 may include the following steps:

[0117] Step 1041: Initialize the price range for leasing energy storage equipment, and set a first search point and a second search point in the range.

[0118] During the initialization process, the initial range of the shared energy storage rental price is set [β min ,β max ], initialize the minimum rental price β in each one-dimensional search iteration min and the maximum rental price β max , and generate the corresponding first search point β L and the second search point β H , which is the first stage decision.

[0119] Step 1042: Search between the first search point and the second search point to optimize the lower-level decision model.

[0120] In the process of solving the lower-level decision model, at the first search point β L and the second search point β H Next, we search for the best strategy for all parks based on several pieces of information, which is the second stage decision.

[0121] Step 1043: When the lower-level decision model is optimized, the upper-level decision model is optimized at the first search point and the second search point according to the lower-level decision model.

[0122] In the process of solving the upper-level decision model, after the follower completes the optimization, the energy storage operator optimizes the first search point β based on the leasing demand and scheduling decision information of all parks. L and the second search point β H The planning and operation strategy under this is the third stage decision.

[0123] Step 1044: When the upper-level decision model completes the optimization, the interval range is converged, and the first search point and the second search point are updated in the interval range after convergence.

[0124] In the process of convergence judgment, the range of the shared energy storage rental price can be narrowed down [β min ,β max ], that is, update the minimum rental price β min and the maximum rental price β max , and update the corresponding first search point β L and the second search point β H .

[0125] Step 1045, determine whether the distance of the interval range is less than or equal to a preset threshold; if so, execute step 1046; if not, return to execute steps 1042-1044.

[0126] Step 1046: Determine whether the upper-level decision model and the lower-level decision model have been optimized.

[0127] In the process of judging the convergence, it is judged whether the distance of the interval range is less than or equal to a preset threshold, which is usually a specified proportion (such as 1%) of the distance of the initial interval range.

[0128] If the distance of the interval range is less than or equal to a preset threshold, it means that the interval range has converged, and it can be determined that the optimization of the two-layer decision model is completed.

[0129] If the distance of the interval range is greater than the preset threshold, it means that the interval range has not converged, and the process returns to step 1042 to step 1044 to perform a one-dimensional search process.

[0130] In this embodiment, a shared energy storage model is constructed; the shared energy storage model includes energy storage operators renting energy storage equipment to multiple parks, and each park renting energy storage equipment for charging and discharging; in the shared energy storage model, an upper-level decision model is constructed for the energy storage operator based on the interaction and opposition between the energy storage operator and the multiple parks; in the shared energy storage model, a lower-level decision model is constructed for each park based on the interaction and opposition between the energy storage operator and the multiple parks; with the energy storage operator as the leader and multiple parks as followers, the upper-level decision model and the lower-level decision model are optimized based on the master-slave game. Under the rental-type shared energy storage model proposed in this embodiment, the energy storage operator can obtain rental income by renting the short-term use rights of energy storage resources, and the park can reduce the electricity cost by renting the short-term use rights of energy storage resources. The two parties compete with each other to obtain the optimal configuration, taking into account the needs of both parties, maximizing the use of energy storage equipment, and effectively improving the utilization rate of energy storage equipment, thereby further promoting the long-term development of the energy storage industry and the shared energy storage model.

[0131] Embodiment 2

[0132] See also Figure 2 , shows a schematic diagram of the structure of a shared energy storage optimization device provided by Embodiment 2 of the present invention. Figure 2 As shown, the device comprises:

[0133] A shared energy storage model construction module 201 is used to construct a shared energy storage model; the shared energy storage model includes an energy storage operator renting energy storage equipment to multiple parks, and each park renting the energy storage equipment for charging and discharging;

[0134] An upper-level decision model construction module 202 is used to construct an upper-level decision model for the energy storage operator in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0135] A lower-level decision model construction module 203 is used to construct a lower-level decision model for each of the parks in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0136] The decision model optimization module 204 is used to optimize the upper decision model and the lower decision model based on the master-slave game, with the energy storage operator as the leader and the multiple parks as followers.

[0137] In one embodiment of the present invention, the upper layer decision model construction module 202 includes:

[0138] A first objective function calculation module is used to calculate the net profit of the energy storage operator for renting the energy storage equipment as the first objective function in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0139] A first constraint condition adding module, used for adding a first constraint condition to a parameter in the first objective function in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0140] An upper-level decision model composition module is used to combine the first objective function and the first constraint condition into an upper-level decision model.

[0141] In one embodiment of the present invention, the first objective function is:

[0142]

[0143] Among them, I SES is the net profit of the energy storage operator for renting the energy storage equipment, max means the optimization goal is to maximize the net profit, I Rent is the rental income, C Inv For investment cost, is the electricity purchase cost of the park at time t, is the electricity purchase cost of the energy storage operator at time t, Δt is the time interval, and T is the set of time;

[0144]

[0145] Among them, I Rent is the rental income, β E is the rent per unit capacity, β P is the rental per unit power, is the capacity leased for park i, is the maximum power leased by park i, S Cen For the collection of parks;

[0146]

[0147] Among them, C Inv is the investment cost, r is the discount rate, Y is the maximum service life of the energy storage equipment, γ P is the investment cost per unit power, γ E is the investment cost per unit capacity, P Cap is the total power of the energy storage device, E Cap is the total capacity of the energy storage device;

[0148]

[0149] in, is the electricity purchase cost of the park at time t, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t, Δt is the time interval, S Cen is a collection of said parks;

[0150]

[0151] in, is the electricity purchase cost of the energy storage operator at time t, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by the energy storage operator at time t, is the electricity sales power of the energy storage operator at time t, and Δt is the time interval.

[0152] In one embodiment of the present invention, the first constraint condition is:

[0153]

[0154] in, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, P Cap is the total power of the energy storage device;

[0155] E min ≤E t ≤E max

[0156] Among them, E t is the amount of electricity stored in the energy storage device at time t, E min is the minimum amount of electricity that the energy storage device is allowed to store, E max The maximum amount of electricity that the energy storage device is allowed to store;

[0157] E o =E Cap ·SOC o

[0158] E ma x=E Cap ·SOC max

[0159] E min =E Cap ·SOC min

[0160] Among them, E min is the minimum amount of electricity that the energy storage device is allowed to store, E max is the maximum amount of electricity that the energy storage device is allowed to store, E o is the initial storage capacity of the energy storage device, E Cap is the total capacity of the energy storage device, SOC o is the initial state of charge of the energy storage device, SOC max SOC is the maximum state of charge allowed by the energy storage device. min The minimum state of charge allowed by the energy storage device;

[0161]

[0162] E N =E O =E Cap ·SOC o

[0163] Among them, E t is the amount of electricity stored in the energy storage device at time t, E t-1 is the amount of electricity stored in the energy storage device at time t-1, η C is the charging efficiency of the energy storage device, η D is the discharge efficiency of the energy storage device, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, Δt is the time interval, E O is the amount of energy stored by the energy storage device at the beginning of the day, E N is the amount of energy stored by the energy storage device at the end of the day, E Cap is the total capacity of the energy storage device, SOC o is the initial state of charge of the energy storage device;

[0164]

[0165] in, is the power purchased by the energy storage operator at time t, is the electricity sales power of the energy storage operator at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, is the discharge power of park i at time t, is the charging power of park i at time t, is the electricity sales power of park i at time t, is the power purchased by park i at time t, S Cen is a collection of the parks.

[0166] In one embodiment of the present invention, the lower layer decision model construction module 203 includes:

[0167] A second objective function calculation module is used to calculate the electricity cost of leasing the energy storage equipment for each park as a second objective function based on the interaction and opposition relationship between the energy storage operator and the plurality of parks in the shared energy storage model;

[0168] A second constraint condition adding module, used for adding a second constraint condition to the parameters in each of the second objective functions in the shared energy storage model according to the interaction and opposition relationship between the energy storage operator and the plurality of parks;

[0169] The lower-level decision model composition module is used to combine the second objective function and the second constraint condition into a lower-level decision model.

[0170] In one embodiment of the present invention, the second objective function is:

[0171]

[0172] in, is the electricity cost of park i, min means the optimization goal is to minimize the electricity cost, β E is the rent per unit capacity, β P is the rental per unit power, is the capacity leased for park i, is the maximum power leased by park i, Δt is the time interval, T is the set of time, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t.

[0173] In one embodiment of the present invention, the second constraint condition is:

[0174]

[0175] in, is the charging power of park i at time t, is the discharge power of park i at time t, p Cap is the total power of the energy storage device;

[0176]

[0177] Among them, e i，t is the energy storage capacity of park i at time t, e i，t-1 is the energy storage capacity of park i at time t-1, is the minimum energy storage capacity allowed in park i, is the maximum energy storage capacity allowed in park i, η C is the charging efficiency of the energy storage device, η D is the discharge efficiency of the energy storage device, is the charging power of park i at time t, is the discharge power of park i at time t, Δt is the time interval, e i，o is the initial energy storage capacity in one day, e i，N is the energy storage capacity at the end of the day, SOC o is the initial state of charge of the energy storage device;

[0178]

[0179] in, is the power purchased by park i at time t, is the electricity sales power of park i at time t, is the discharge power of park i at time t, is the charging power of park i at time t, d i,t is the load of park i at time t.

[0180] In one embodiment of the present invention, the decision model optimization module 204 includes:

[0181] A search range initialization module, used to initialize the price range of leasing the energy storage device, and set a first search point and a second search point in the range;

[0182] A lower-level decision model optimization module, used for performing optimization between the first search point and the second search point to optimize the lower-level decision model;

[0183] An upper-layer decision model optimization module, configured to optimize the upper-layer decision model at the first search point and the second search point according to the lower-layer decision model when the lower-layer decision model is optimized;

[0184] A search range updating module, configured to converge the interval range when the upper decision model is optimized, and to update the first search point and the second search point in the interval range after convergence;

[0185] A convergence judgment module is used to judge whether the distance of the interval range is less than or equal to a preset threshold; if so, the optimization completion determination module is executed; if not, the lower decision model optimization module is returned to execute;

[0186] The optimization completion determination module is used to determine whether the upper decision model and the lower decision model have completed optimization.

[0187] The shared energy storage optimization device provided in the embodiment of the present invention can execute the shared energy storage optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the shared energy storage optimization method.

[0188] Embodiment 3

[0189] See also Figure 3 , showing a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0190] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0191] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0192] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the shared energy storage optimization method.

[0193] In some embodiments, the shared energy storage optimization method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the shared energy storage optimization method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the shared energy storage optimization method in any other appropriate manner (e.g., by means of firmware).

[0194] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0195] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0196] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0197] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0198] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0199] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0200] Embodiment 4

[0201] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the shared energy storage optimization method provided in any embodiment of the present invention.

[0202] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0203] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0204] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A shared energy storage optimization method, characterized in that: include: Constructing a shared energy storage model; the shared energy storage model includes an energy storage operator renting energy storage equipment to multiple parks, and each park renting the energy storage equipment for charging and discharging; In the shared energy storage model, an upper-level decision model is constructed for the energy storage operator based on the interaction and opposition relationship between the energy storage operator and the plurality of parks; In the shared energy storage model, a lower-level decision model is constructed for each of the parks based on the interaction and opposition relationship between the energy storage operator and the plurality of parks; With the energy storage operator as the leader and the plurality of parks as followers, the upper-level decision model and the lower-level decision model are optimized based on the master-slave game.

2. The method according to claim 1, characterized in that In the shared energy storage model, an upper-level decision model is constructed for the energy storage operator based on the interaction and opposition relationship between the energy storage operator and the plurality of parks, including: In the shared energy storage model, the net profit of the energy storage operator for renting the energy storage equipment is calculated as the first objective function based on the interaction and opposition relationship between the energy storage operator and the plurality of parks; In the shared energy storage model, a first constraint condition is added to the parameters in the first objective function according to the interaction and opposition relationship between the energy storage operator and the plurality of parks; The first objective function and the first constraint condition form an upper-level decision model.

3. The method according to claim 2, characterized in that The first objective function is: Among them, I SES is the net profit of the energy storage operator for renting the energy storage equipment, max means the optimization goal is to maximize the net profit, I Rent is the rental income, C Inv For investment cost, is the electricity purchase cost of the park at time t, is the electricity purchase cost of the energy storage operator at time t, Δt is the time interval, and T is the set of time; Among them, I Rent is the rental income, β E is the rent per unit capacity, β P is the rental per unit power, is the capacity leased for park i, is the maximum power leased by park i, S Cen For the collection of parks; Among them, C Inv is the investment cost, r is the discount rate, Y is the maximum service life of the energy storage equipment, γ P is the investment cost per unit power, γ E is the investment cost per unit capacity, P Cap is the total power of the energy storage device, E Cap is the total capacity of the energy storage device; in, is the electricity purchase cost of the park at time t, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t, Δt is the time interval, S Cen is a collection of said parks; in, is the electricity purchase cost of the energy storage operator at time t, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by the energy storage operator at time t, is the electricity sales power of the energy storage operator at time t, and Δt is the time interval.

4. The method according to claim 2, characterized in that: The first constraint condition is: in, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, P Cap is the total power of the energy storage device; AND min ≤E t ≤E max Among them, E t is the amount of electricity stored in the energy storage device at time t, E min is the minimum amount of electricity that the energy storage device is allowed to store, E max The maximum amount of electricity that the energy storage device is allowed to store; IN o =E Cap ·SOC o IN max =E Cap ·SOC max IN min =E Cap ·SOC min Among them, E min is the minimum amount of electricity that the energy storage device is allowed to store, E max is the maximum amount of electricity that the energy storage device is allowed to store, E o is the initial storage capacity of the energy storage device, E Cap is the total capacity of the energy storage device, SOC o is the initial state of charge of the energy storage device, SOC max SOC is the maximum state of charge allowed by the energy storage device. min The minimum state of charge allowed by the energy storage device; AND N =And O =And Cap ·SOC o Among them, E t is the amount of electricity stored in the energy storage device at time t, E t-1 is the amount of electricity stored in the energy storage device at time t-1, η C is the charging efficiency of the energy storage device, η D is the discharge efficiency of the energy storage device, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, Δt is the time interval, E O is the amount of energy stored by the energy storage device at the beginning of the day, E N is the amount of energy stored by the energy storage device at the end of the day, E Cap is the total capacity of the energy storage device, SOC o is the initial charge state of the energy storage device; in, is the power purchased by the energy storage operator at time t, is the electricity sales power of the energy storage operator at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t, is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t, is the discharge power of park i at time t, is the charging power of park i at time t, is the electricity sales power of park i at time t, is the power purchased by park i at time t, S Cen is a collection of the parks.

5. The method according to claim 1, characterized in that In the shared energy storage model, a lower-level decision model is constructed for each park based on the interaction and opposition relationship between the energy storage operator and the plurality of parks, including: In the shared energy storage model, the electricity cost of leasing the energy storage equipment for each park is calculated as the second objective function based on the interaction and opposition relationship between the energy storage operator and the plurality of parks; In the shared energy storage model, a second constraint condition is added to the parameters in each of the second objective functions according to the interaction and opposition relationship between the energy storage operator and the plurality of parks; The second objective function and the second constraint condition form a lower-level decision model.

6. The method according to claim 5, characterized in that The second objective function is: in, is the electricity cost of park i, min means the optimization goal is to minimize the electricity cost, β E is the rent per unit capacity, β P is the rental per unit power, is the capacity leased for park i, is the maximum power leased by park i, Δt is the time interval, T is the set of time, λ t is the electricity purchase price at time t, θ t is the electricity price at time t, is the power purchased by park i at time t, is the electricity sales power of park i at time t.

7. The method according to claim 5, characterized in that The second constraint is: in, is the charging power of park i at time t, is the discharge power of park i at time t, P Cap is the total power of the energy storage device; Among them, e i,t is the energy storage capacity of park i at time t, e i,t-1 is the energy storage capacity of park i at time t-1, is the minimum energy storage capacity allowed in park i, is the maximum energy storage capacity allowed in park i, η C is the charging efficiency of the energy storage device, η D is the discharge efficiency of the energy storage device, is the charging power of park i at time t, is the discharge power of park i at time t, Δt is the time interval, e i,o is the initial energy storage capacity in one day, e i,N is the energy storage capacity at the end of the day, SOC o is the initial charge state of the energy storage device; in, is the power purchased by park i at time t, is the electricity sales power of park i at time t, is the discharge power of park i at time t, is the charging power of park i at time t, d i,t is the load of park i at time t.

8. The method according to any one of claims 1 to 7, characterized in that The energy storage operator is the leader and the plurality of parks are followers, and the upper decision model and the lower decision model are optimized according to the master-slave game, including: Initializing a price interval range for leasing the energy storage device, and setting a first search point and a second search point in the interval range; Performing optimization between the first search point and the second search point to optimize the lower-layer decision model; When the lower-level decision model is optimized, optimizing the upper-level decision model at the first search point and the second search point according to the lower-level decision model; When the upper decision model is optimized, converge the interval range, and update the first search point and the second search point in the interval range after convergence; Determine whether the distance of the interval range is less than or equal to a preset threshold; if so, determine that the upper decision model and the lower decision model have completed optimization, if not, return to execute the optimization between the first search point and the second search point to optimize the lower decision model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the shared energy storage optimization method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the shared energy storage optimization method according to any one of claims 1 to 8 is implemented.

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