Method, device and equipment for determining energy storage cost of system

Abstract

The invention provides a system energy storage cost determination method, device and equipment, and relates to the technical field of power dispatching, and the method comprises the steps: obtaining user load demand data, spot electricity price data and new energy output data; predicting a future electricity price expected value according to the spot electricity price data, and determining a future highest electricity price; determining a charging and discharging strategy of system energy storage according to the user load demand data, the new energy output data, the future highest electricity price and an operation constraint condition of the energy storage system; and determining the energy storage cost of the system according to the energy storage charging and discharging strategy of the system and the maximum electricity price in the future. According to the scheme, the energy storage and discharge strategy is dynamically optimized, the system economy is ensured to be optimal, the energy storage benefit is ensured to be maximized, and the electricity purchase cost is ensured to be minimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a method, device and equipment for determining system energy storage costs. Background Art

[0002] In the current electricity market environment, the optimal operation of source-grid-load-storage systems requires comprehensive consideration of multiple factors, including spot electricity price fluctuations, load demand, grid peak-shaving capacity, and the uncertainty of renewable energy output. Because medium- and long-term contracts utilize a take-or-pay financial settlement mechanism, contracted electricity consumption must be broken down into hourly periods, with price differentials settled based on the real-time spot market price during that period.

[0003] However, forecast errors in renewable energy output can lead to high electricity prices or curtailed renewable energy generation. Limited load adjustment capabilities and insufficient grid peak-shaving capacity make energy storage a key dynamic regulation tool. Existing energy storage scheduling methods typically focus on discharging to meet load demand, failing to fully optimize the entire energy storage cycle by incorporating factors such as spot electricity price fluctuations, load adjustment capabilities, and grid constraints. This results in low energy storage utilization and reduced economic efficiency. Summary of the Invention

[0004] The present invention provides a method, device and equipment for determining system energy storage cost.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A method for determining system energy storage costs, comprising:

[0007] Obtain user load demand data, spot electricity price data and renewable energy output data;

[0008] Based on the spot electricity price data, predict the expected value of future electricity prices and determine the maximum future electricity price;

[0009] Determining a charging and discharging strategy for the system energy storage based on the user load demand data, the renewable energy output data, the future maximum electricity price, and the operating constraints of the energy storage system;

[0010] Determine the system energy storage cost based on the system energy storage charging and discharging strategy and the future maximum electricity price. Optionally, predict the expected value of future electricity prices based on the spot electricity price data to determine the future maximum electricity price, including:

[0011] By formula λ t =max{π t ,π t+1 ,π t+2 ,...,π T}, get the maximum future electricity price;

[0012] Among them, λt is the maximum electricity price in the future, π t is the spot electricity price data of the tth period, t is the time index, and T is the optimization period.

[0013] Optionally, determining a charging and discharging strategy for system energy storage based on the user load demand data, the new energy output data, future electricity price trends, and operating constraints of the energy storage system includes:

[0014] When the renewable energy output data is higher than the user load demand data, and the remaining energy storage capacity is less than the maximum energy storage capacity, the system energy storage is charged, which is determined as the first system energy storage charging and discharging strategy;

[0015] When the renewable energy output data is lower than the user load demand data, the remaining energy storage capacity is the maximum energy storage capacity, and the spot electricity price data is the highest electricity price in the future, the system energy storage is discharged, which is determined to be the second system energy storage charging and discharging strategy.

[0016] Optionally, the operating constraints of the energy storage system include at least:

[0017] Energy storage remaining power constraint:

[0018] S min ≤S t ≤S max ;

[0019] Among them, S t is the remaining energy storage capacity, S t-1 is the remaining energy storage capacity at the previous moment, P ch,t is the system energy storage charging power, P dis,t is the energy storage discharge power, η dis is the discharge efficiency of energy storage, S min is the minimum energy storage capacity, S max The maximum energy storage capacity.

[0020] Optionally, determining the system energy storage cost according to the system energy storage charging and discharging strategy and the future maximum electricity price includes:

[0021] determining the energy storage cost of the first system based on the energy storage charging and discharging strategy of the first system and the expected maximum electricity price in the future;

[0022] The energy storage cost of the second system is determined according to the energy storage charging and discharging strategy of the second system and the expected maximum electricity price in the future.

[0023] Optionally, determining the first system energy storage cost according to the first system energy storage charging and discharging strategy and the expected maximum future electricity price includes:

[0024] By formula Determine the energy storage cost of the first system;

[0025] Among them, V t (S t )1 is the system energy storage cost at the current moment, P ch,t is the energy storage charging power, G min π t The minimum cost of purchasing electricity from the grid, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

[0026] Optionally, according to the second system energy storage charging and discharging strategy and the expected maximum future electricity price, the system energy storage is discharged to determine the second system energy storage cost, including:

[0027] By formula Determine the second system energy storage cost;

[0028] Among them, V t (S t )2 is the system energy storage cost at the current moment, P dis,t is the energy storage discharge power, π t ·G t is the cost of purchasing electricity from the power grid, (λ t -π t )·P dis,t is the opportunity cost of energy storage discharge, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

[0029] The present invention also provides a device for determining system energy storage cost, comprising:

[0030] Acquisition module, used to obtain user load demand data, spot electricity price data and new energy output data;

[0031] The processing module is used to predict the expected future electricity price based on the spot electricity price data and determine the future maximum electricity price; determine the charging and discharging strategy of the system energy storage based on the user load demand data, the new energy output data, the future maximum electricity price and the operating constraints of the energy storage system; and determine the system energy storage cost based on the system energy storage charging and discharging strategy and the future maximum electricity price.

[0032] The present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the method described above when executed by the processor.

[0033] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0034] The above solution of the present invention includes at least the following beneficial effects:

[0035] The above-mentioned solution of the present invention obtains user load demand data, spot electricity price data, and renewable energy output data; predicts the expected value of future electricity prices based on the spot electricity price data and determines the future maximum electricity price; determines the system energy storage charging and discharging strategy based on the user load demand data, the renewable energy output data, the future maximum electricity price, and the operating constraints of the energy storage system; and determines the system energy storage cost based on the system energy storage charging and discharging strategy and the future maximum electricity price. The solution of the present invention globally optimizes the energy storage charging and discharging strategy, dynamically balances the economic efficiency of energy storage discharge and power purchase from the power grid under the premise of meeting the constraints of stable load operation and power grid peak regulation, and comprehensively considers factors such as renewable energy output, electricity price fluctuations, load adjustment capabilities, and limited power grid peak regulation, and dynamically optimizes the energy storage discharge strategy to ensure optimal system economy, maximize energy storage benefits, minimize power purchase costs, and improve the utilization rate of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of a method for determining system energy storage cost provided by an embodiment of the present invention;

[0037] Figure 2 is a load adjustment calculation flow chart provided by an embodiment of the present invention;

[0038] Figure 3 This is a future electricity price prediction calculation process provided by an embodiment of the present invention;

[0039] Figure 4 The embodiment of the present invention provides a dynamic programming optimization calculation process;

[0040] Figure 5 A flowchart of a scheduling policy recovery provided by an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of modules of a device for determining system energy storage cost provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0043] like Figure 1 As shown, an embodiment of the present invention provides a method for determining system energy storage cost, including:

[0044] Step 11: Obtain user load demand data, spot electricity price data, and renewable energy output data;

[0045] Step 12: predicting the expected value of future electricity prices based on the spot electricity price data and determining the maximum future electricity price;

[0046] Step 13: determining a charging and discharging strategy for the system energy storage based on the user load demand data, the new energy output data, the future maximum electricity price, and the operating constraints of the energy storage system;

[0047] Step 14: Determine the system energy storage cost based on the system energy storage charging and discharging strategy and the future maximum electricity price.

[0048] In this embodiment, a dynamic optimization strategy is used to optimize the discharge strategy of the energy storage system under multiple constraints, such as renewable energy output, load demand, grid peak-shaving capacity, and spot market electricity prices, to ensure that renewable energy is consumed first and to reduce high electricity purchase costs.

[0049] Specifically, it reads the data on renewable energy output, user load demand, and spot electricity price, and initializes the system state based on key constraints such as energy storage parameters, load adjustment capability, and minimum / maximum off-grid power set by the user. Figure 2 As shown, the available power is calculated through the output of new energy and user load demand, and the load is dynamically adjusted according to the available power and user load to calculate the final load gap or surplus, identify the period of insufficient or sufficient new energy, and determine the power purchase demand of the power grid.

[0050] Calculate the expected value of future electricity prices, and use it to identify the highest electricity price in the future period as a reference for the opportunity cost of energy storage discharge, avoid accidental discharge during low-price periods, and ensure that the stored energy is released when prices are high in the future to optimize economic efficiency.

[0051] A dynamic programming model is constructed, using the remaining energy storage capacity (SOC) as the state variable to calculate the optimal energy storage charging and discharging strategy at each moment. When renewable energy is insufficient, discharge is prioritized to reduce the demand for high-priced grid electricity purchases. The opportunity cost of discharge is also calculated to ensure optimal discharge. When renewable energy is abundant, charging is prioritized to increase the renewable energy consumption rate, avoid renewable energy curtailment, and ensure that the SOC is not excessive. Finally, by optimizing the energy storage charging and discharging strategy, combined with future electricity prices, load adjustment / downward adjustment capabilities, and maximum / minimum grid power limits, discharge at low prices is avoided. When discharging at low prices, the opportunity cost is calculated to ensure that electricity is released when prices are high, thereby reducing the total electricity purchase cost and increasing energy storage benefits.

[0052] This embodiment globally optimizes the energy storage charging and discharging strategy. It dynamically balances the economics of energy storage discharge and grid power purchase, while ensuring stable load operation and grid peak regulation constraints. It comprehensively considers factors such as renewable energy output, electricity price fluctuations, load adjustment capabilities, and grid peak regulation constraints. It dynamically optimizes the energy storage discharge strategy to ensure optimal system economics, maximize energy storage benefits, minimize power purchase costs, and improve renewable energy utilization.

[0053] In an optional embodiment of the present invention, step 12 includes:

[0054] By formula λ t =max{π t ,π t+1 ,π t+2 ,...,π T}, get the maximum future electricity price;

[0055] Among them, λ t is the maximum electricity price in the future, π t is the spot electricity price data of the tth period, t is the time index, and T is the optimization period.

[0056] In this embodiment, Figure 3 As shown, using the formula λ t =max{π t ,π t+1 ,π t+2 ,...,π T}, traverse all times from t = 0 to t = T-1, calculate the market electricity price, and take the maximum value as the expected maximum electricity price at future moments to avoid low-price discharge. When discharging at low prices, calculate the opportunity cost to ensure that the electricity is released at high prices, so as to reduce the total electricity purchase cost and increase the energy storage income.

[0057] In an optional embodiment of the present invention, step 13 includes:

[0058] Step 131: When the new energy output data is higher than the user load demand data and the remaining energy storage capacity is less than the maximum energy storage capacity, the system energy storage is charged, and the first system energy storage charging and discharging strategy is determined;

[0059] In step 132 , when the new energy output data is lower than the user load demand data, the remaining energy storage capacity is the maximum energy storage capacity, and the spot electricity price data is the highest electricity price in the future, the system energy storage is discharged, and the second system energy storage charging and discharging strategy is determined.

[0060] In this embodiment, Figure 4 and Figure 5As shown, a backward-calculation optimization approach is employed, working backwards from the end of the optimization cycle, calculating the optimal charging and discharging decision at each moment from time t = T-1 to t = 0. Specifically, when the renewable energy output exceeds the user load demand, the system energy storage is charged, which is determined as the first system energy storage charging and discharging strategy. When the renewable energy output is lower than the user load demand and the grid power supply is at its maximum, the system energy storage is discharged, which is determined as the second system energy storage charging and discharging strategy. Based on the first and second system energy storage charging and discharging strategies, a complete scheduling plan is generated, including energy storage SOC changes, grid power purchases, discharge strategies, and opportunity cost calculations, ensuring optimal energy storage operation throughout the entire cycle.

[0061] Furthermore, by factoring in future electricity prices, we can avoid discharging at low prices. When discharging at low prices, we can calculate the opportunity cost and ensure that electricity is released when prices are high, thereby reducing the total electricity purchase cost and increasing energy storage returns. Furthermore, by factoring in the grid's maximum and minimum off-grid power constraints, we can optimize the energy storage discharge strategy during peak user load periods, improving grid operational stability.

[0062] At the same time, the charging and discharging strategies are constrained by constraints:

[0063] Energy storage remaining power constraint:

[0064] S min ≤S t ≤S max ;

[0065] Power balance constraints ensure energy supply and demand matching:

[0066] P new,t +G t +P dis,t =L t +P ch,t +C t ;

[0067] Among them, C t The amount of abandoned electricity from new energy sources, P new,t is the new energy output data for the t period, G t is the amount of electricity purchased from the power grid in period t, P ch,t is the system energy storage charging power, P dis,t is the energy storage discharge power, L t is the user load demand in period t.

[0068] Load adjustment constraints:

[0069]

[0070] Among them, L t is the load demand in period t (MW), is the load increase (MW), is the load reduction (MW);

[0071] Energy storage power constraints:

[0072] 0≤P ch,t ≤P ch,max ,0≤P dis,t ≤P dis,max ;

[0073] Among them, P ch,max is the maximum charging efficiency of energy storage, P dis,max is the maximum discharge efficiency of energy storage.

[0074] Constraints on power purchases from the power grid:

[0075] G min ≤G t ≤G max ;

[0076] Among them, G min is the minimum grid power (MW), G max is the maximum grid-connected power (MW);

[0077] Power curtailment constraints:

[0078] C t ≥0;

[0079] Among them, S t is the remaining energy storage capacity, S t-1 is the remaining energy storage capacity at the previous moment, P ch,t is the system energy storage charging power, P dis,t is the energy storage discharge power, η dis is the discharge efficiency of energy storage, S min is the minimum energy storage capacity, S max The maximum energy storage capacity.

[0080] Among them, through the energy storage charging and discharging strategy, according to the power balance constraint, the new energy abandoned power C is calculated. t :

[0081] By formula C t =max{P new,t +G t -P ch,t -L t ,0}, calculate the amount of abandoned electricity from new energy;

[0082] And the new energy replacement rate is calculated based on the abandoned new energy power:

[0083] where R new is the new energy replacement rate.

[0084] By calculating the amount of curtailed electricity from new energy and the replacement rate of new energy, changes in the remaining amount of energy storage can be planned in advance, reducing curtailment due to energy storage overflow, improving the utilization rate of new energy, and avoiding high-priced electricity purchases or curtailment of new energy.

[0085] In an optional embodiment of the present invention, step 14 includes:

[0086] Step 141: determining the energy storage cost of the first system according to the energy storage charging and discharging strategy of the first system and the expected maximum electricity price in the future;

[0087] Step 142: Determine the energy storage cost of the second system according to the energy storage charging and discharging strategy of the second system and the expected maximum electricity price in the future.

[0088] In this embodiment, when the new energy output data is higher than the user load demand data, the system stores energy and charges, thereby reducing electricity purchases and lowering costs. The electricity purchase cost at this time is the first system energy storage cost.

[0089] When the renewable energy output data is lower than the user load demand data and the grid power supply is at its maximum value, the system energy storage is discharged to reduce the purchase of electricity during high-price periods. At this time, the electricity purchase cost is the second system energy storage cost.

[0090] In an optional embodiment of the present invention, step 141 includes:

[0091] By formula Determine the energy storage cost of the first system;

[0092] Among them, V t (S t )1 is the system energy storage cost at the current moment, P ch,t is the energy storage charging power, G min π t The minimum cost of purchasing electricity from the grid, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

[0093] In this embodiment, when there is sufficient renewable energy generation, the energy storage system chooses to charge:

[0094] S t+1 =S t +P ch,t Δt, but it is necessary to ensure that the energy storage limit is not exceeded (SOC is limited) and that future charge and discharge scheduling still conforms to the optimal strategy.

[0095] Among them, S t is the energy storage SOC at the current moment, S t+1 It is the energy storage SOC at the next moment.

[0096] The energy storage cost of the first system is calculated as:

[0097] In an optional embodiment of the present invention, step 142 includes:

[0098] By formula Determine the second system energy storage cost;

[0099] Among them, V t (S t )2 is the system energy storage cost at the current moment, P dis,t is the energy storage discharge power, π t ·G t is the cost of purchasing electricity from the power grid, (λ t -π t )·P dis,t is the opportunity cost of energy storage discharge, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

[0100] In this embodiment, when the new energy is insufficient, the current moment is a power shortage, and the grid can be selected to purchase electricity G t To meet the power gap or discharge P dis,t To reduce online electricity purchases;

[0101] At this time, opportunity costs still need to be considered. Dynamic planning is used to optimize the changes in energy storage SOC, ensuring that energy storage is charged first when renewable energy is sufficient, and accurately discharged when renewable energy is insufficient, thereby optimizing returns:

[0102] S t+1 =S t -P dis,t ·Δt / η dis ;

[0103] Calculate the optimal cost V at the future moment t+1 (S t+1 ) to ensure the global optimization of the energy storage state, and further calculate the energy storage cost of the second system:

[0104]

[0105] Among them, η dis is the discharge efficiency of energy storage.

[0106] The method for determining the system energy storage cost described in the above embodiment of the present invention comprehensively considers spot electricity prices, grid power purchases, and energy storage discharge timing to ensure that energy storage is charged and discharged under the optimal conditions for the entire system, rather than simply serving load matching. Different from simple peak-valley arbitrage, in the spot market environment, it combines electricity price forecasting + dynamic scheduling + full-cycle optimization to ensure the optimal economic efficiency of energy storage discharge. It adopts an economic optimization scheduling strategy to ensure that energy storage discharge is carried out at the economically optimal time through future electricity price forecasts, opportunity cost calculations, and load adjustment optimization, thereby improving the benefits of the energy storage system. In combination with load adjustment capabilities, it prioritizes load adjustment within the adjustable range and uses energy storage discharge only when load adjustment is limited, reducing reliance on high-priced electricity purchases and lowering overall electricity costs. In combination with the peak-shaving capabilities of the grid, it optimizes energy storage charging and discharging to reduce grid electricity costs.

[0107] An embodiment of the present invention further provides a system energy storage cost determination device 60, comprising:

[0108] Acquisition module 61, used to obtain user load demand data, spot electricity price data and new energy output data;

[0109] Processing module 62 is used to predict the expected future electricity price based on the spot electricity price data and determine the future maximum electricity price; determine the system energy storage charging and discharging strategy based on the user load demand data, the new energy output data, the future maximum electricity price and the operating constraints of the energy storage system; and determine the system energy storage cost based on the system energy storage charging and discharging strategy and the future maximum electricity price.

[0110] Optionally, predicting an expected future electricity price based on the spot electricity price data to determine a maximum future electricity price includes:

[0111] By formula λ t =max{π t ,π t+1 ,π t+2 ,...,π T}, get the expected value of future electricity price;

[0112] Among them, λ t is the expected maximum electricity price in the future, π t is the spot electricity price data of the tth period, t is the time index, and T is the optimization period.

[0113] Optionally, determining a charging and discharging strategy for system energy storage based on the user load demand data, the new energy output data, the future maximum electricity price, and operating constraints of the energy storage system includes:

[0114] When the renewable energy output data is higher than the user load demand data, and the remaining energy storage capacity is less than the maximum energy storage capacity, the system energy storage is charged, which is determined as the first system energy storage charging and discharging strategy;

[0115] When the renewable energy output data is lower than the user load demand data, the remaining energy storage capacity is the maximum energy storage capacity, and the spot electricity price data is the highest electricity price in the future, the system energy storage is discharged, which is determined to be the second system energy storage charging and discharging strategy.

[0116] Optionally, the operating constraints of the energy storage system include at least:

[0117] Energy storage remaining power constraint:

[0118] S min ≤S t ≤S max ;

[0119] Among them, S t is the remaining energy storage capacity, S t-1 is the remaining energy storage capacity at the previous moment, P ch,t is the system energy storage charging power, P dis,t is the energy storage discharge power, η dis is the discharge efficiency of energy storage, S min is the minimum energy storage capacity, S max The maximum energy storage capacity.

[0120] Optionally, determining the system energy storage cost according to the system energy storage charging and discharging strategy and the future maximum electricity price includes:

[0121] determining the energy storage cost of the first system based on the energy storage charging and discharging strategy of the first system and the expected maximum electricity price in the future;

[0122] The energy storage cost of the second system is determined according to the energy storage charging and discharging strategy of the second system and the expected maximum electricity price in the future.

[0123] Optionally, determining the first system energy storage cost according to the first system energy storage charging and discharging strategy and the expected maximum future electricity price includes:

[0124] By formula Determine the energy storage cost of the first system;

[0125] Among them, V t (S t )1 is the system energy storage cost at the current moment, P ch,t is the energy storage charging power, G min π t The minimum cost of purchasing electricity from the grid, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

[0126] Optionally, according to the second system energy storage charging and discharging strategy and the expected maximum future electricity price, the system energy storage is discharged to determine the second system energy storage cost, including:

[0127] By formula Determine the second system energy storage cost;

[0128] Among them, V t (S t )2 is the system energy storage cost at the current moment, P dis,t is the energy storage discharge power, π t ·G t is the cost of purchasing electricity from the power grid, (λ t -π t )·P dis,t is the opportunity cost of energy storage discharge, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

[0129] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0130] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0131] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to execute the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0137] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0138] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0139] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0140] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for determining system energy storage cost, characterized in that: include: Obtain user load demand data, spot electricity price data and renewable energy output data; Based on the spot electricity price data, predict the expected value of future electricity prices and determine the maximum future electricity price; Determining a charging and discharging strategy for the system energy storage based on the user load demand data, the renewable energy output data, the future maximum electricity price, and the operating constraints of the energy storage system; The system energy storage cost is determined based on the system energy storage charging and discharging strategy and the future maximum electricity price.

2. The method for determining the system energy storage cost according to claim 1, characterized in that: Based on the spot electricity price data, the expected value of future electricity prices is predicted and the maximum future electricity price is determined, including: By formula λ t =max{π t ,π t+1 ,π t+2 ,...,π T }, get the maximum future electricity price; Among them, λ t is the maximum electricity price in the future, π t is the spot electricity price data of the tth period, t is the time index, and T is the optimization period.

3. The method for determining the system energy storage cost according to claim 1, characterized in that: Determine the charging and discharging strategy of the system energy storage based on the user load demand data, the new energy output data, the future maximum electricity price, and the operating constraints of the energy storage system, including: When the renewable energy output data is higher than the user load demand data, and the remaining energy storage capacity is less than the maximum energy storage capacity, the system energy storage is charged, which is determined as the first system energy storage charging and discharging strategy; When the renewable energy output data is lower than the user load demand data, the remaining energy storage capacity is the maximum energy storage capacity, and the spot electricity price data is the highest electricity price in the future, the system energy storage is discharged, which is determined to be the second system energy storage charging and discharging strategy.

4. The method for determining the system energy storage cost according to claim 3, characterized in that: The operating constraints of the energy storage system include at least: Energy storage remaining power constraint: S min ≤S t ≤S max ; Among them, S t is the remaining energy storage capacity, S t-1 is the remaining energy storage capacity at the previous moment, P ch,t is the system energy storage charging power, P dis,t is the energy storage discharge power, η dis is the discharge efficiency of energy storage, S min is the minimum energy storage capacity, S max The maximum energy storage capacity.

5. The method for determining the system energy storage cost according to claim 3, characterized in that: The system energy storage cost is determined based on the system energy storage charging and discharging strategy and the future maximum electricity price, including: determining the energy storage cost of the first system based on the energy storage charging and discharging strategy of the first system and the expected maximum electricity price in the future; The energy storage cost of the second system is determined according to the energy storage charging and discharging strategy of the second system and the expected maximum electricity price in the future.

6. The method for determining the system energy storage cost according to claim 5, characterized in that: Determining the energy storage cost of the first system according to the energy storage charging and discharging strategy of the first system and the expected maximum electricity price in the future includes: By formula Determine the energy storage cost of the first system; Among them, V t (S t )1 is the system energy storage cost at the current moment, P ch,t is the energy storage charging power, G min π t The minimum cost of purchasing electricity from the grid, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

7. The method for determining the system energy storage cost according to claim 5, characterized in that: According to the second system energy storage charging and discharging strategy and the expected maximum future electricity price, the system energy storage is discharged to determine the second system energy storage cost, including: By formula Determine the second system energy storage cost; Among them, V t (S t )2 is the system energy storage cost at the current moment, P dis,t is the energy storage discharge power, π t ·G t is the cost of purchasing electricity from the power grid, (λ t -π t )·P dis,t is the opportunity cost of energy storage discharge, V t+1 (S t+1 ) is the optimal cumulative cost at the next moment.

8. A device for determining system energy storage cost, characterized in that: include: Acquisition module, used to obtain user load demand data, spot electricity price data and new energy output data; The processing module is used to predict the expected future electricity price based on the spot electricity price data and determine the future maximum electricity price; determine the charging and discharging strategy of the system energy storage based on the user load demand data, the new energy output data, the future maximum electricity price and the operating constraints of the energy storage system; and determine the system energy storage cost based on the system energy storage charging and discharging strategy and the future maximum electricity price.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: The device stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

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