A distributed energy storage shared application optimization model method and device

Abstract

The present invention relates to a method and device for optimizing a shared application model of distributed energy storage, wherein the method comprises: constructing a cost model of energy storage facilities, and calculating the shared cost of energy storage facilities according to the cost model of energy storage facilities; constructing a revenue model of energy storage facilities in different scenarios, and calculating the shared revenue of energy storage facilities according to the revenue model; and measuring according to the shared cost of the energy storage facilities and the shared revenue of the energy storage facilities. The present invention can optimize the benefits of distributed energy storage shared applications in different scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy storage sharing application, and in particular to a method and device for optimizing a shared application model of distributed energy storage. Background Art

[0002] In modern society, electricity demand is becoming more and more diversified, and temporary, peak and sudden electricity demand is frequent, such as power construction, outdoor activities, large-scale events, emergency events, etc. These scenarios usually lack stable power supply, and traditional centralized power supply or backup power supply can hardly effectively meet these temporary needs. Distributed energy storage system, as a flexible power dispatching solution, can provide efficient and mobile power supply in these scenarios to meet different electricity needs. The shared use of distributed energy storage system among different users has huge market potential. Traditional energy storage equipment is usually purchased by a single user and occupied for a long time, but the frequency of use may be limited, resulting in waste of resources. Through the sharing model, different users can temporarily use energy storage equipment in different time periods to achieve efficient use of resources. This shared application not only reduces the equipment purchase cost of a single user, but also significantly improves the equipment utilization rate, forming a new business model.

[0003] Distributed energy storage systems face a variety of technical challenges in their application in multiple scenarios (such as temporary power supply, mobile charging, uninterrupted operation, user-side response, temporary capacity expansion, and dual charging and discharging), including power dispatching and power management, which requires dynamic dispatching of energy storage resources in different scenarios to adapt to different power and power demands and ensure power supply stability; intelligent demand response, for user-side response scenarios, the system needs to have intelligent dispatching capabilities that can respond to electricity price fluctuations and load changes in real time; equipment safety and reliability management, under frequent charge and discharge cycles, the safety and reliability management of energy storage equipment is crucial, requiring efficient power management and fault prediction mechanisms; efficient revenue management, the revenue models vary in different application scenarios, and a scientific revenue model is needed to maximize the overall economic benefits to ensure that multi-scenario applications can achieve profitability. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for optimizing a shared application model of distributed energy storage, which can optimize the application benefits of distributed shared energy storage in different scenarios.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a shared application optimization model method of distributed energy storage, comprising the following steps:

[0006] Constructing an energy storage facility cost model, and calculating the shared cost of the energy storage facility according to the energy storage facility cost model;

[0007] Constructing a revenue model for energy storage facilities in different scenarios, and calculating the shared revenue of the energy storage facilities based on the revenue model;

[0008] The calculation is made based on the shared costs and shared benefits of the energy storage facilities.

[0009] The energy storage facility cost model includes a facility purchase cost model, a facility annual depreciation cost model, a facility maintenance cost model, a facility operation cost model under various scenarios, and a power loss cost model.

[0010] The facility acquisition cost model is expressed as: Among them, C f is the facility acquisition cost, p fix is the unit cost of the fixed energy storage facility, is the purchased capacity of fixed energy storage facilities; movable is the unit cost of mobile energy storage facilities, Purchase capacity for mobile energy storage facilities, p charge is the unit cost of charging facilities, c charge The number of charging facilities purchased.

[0011] The annual depreciation cost model of the facility is expressed as: Among them, C d is the annual depreciation cost of the facility, C f is the facility acquisition cost, C residue is the residual value of the facility, and N is the depreciation period of the facility.

[0012] The facility maintenance cost model is expressed as: Among them, C m The cost of facility maintenance, is the purchased capacity of fixed energy storage facilities, c mf is the unit maintenance cost of the fixed energy storage facility, Purchase capacity for mobile energy storage facilities, c mm is the unit maintenance cost of mobile energy storage facilities, c charge is the number of charging facilities purchased, c mc It is the unit maintenance cost of charging facilities.

[0013] The facility operating cost models for the various scenarios described include:

[0014] The facility operation cost model in the mobile charging scenario is expressed as:

[0015] The facility operation cost model under the temporary power supply scenario is expressed as:

[0016] The facility operation cost model under the scenario of non-stop operation is expressed as:

[0017] The facility operation cost model under the temporary capacity increase scenario is expressed as:

[0018] Among them, m m The cost of operating facilities in the mobile charging scenario, Day m The number of days of operation for mobile charging scenarios, p move is the unit transportation cost of mobile energy storage facilities, is the average distance of mobile charging scene; m t The cost of operating the facility in a temporary power supply scenario, Day t Operation days for temporary power supply scenarios, The average distance of temporary power supply scene; m g Day is the facility operating cost in the scenario of non-stop operation. g The number of days of operation for non-stop power supply operation scenarios, is the average distance of the non-stop operation scene; m r The facility operating costs in the temporary capacity increase scenario. The average distance for temporary capacity increase scenarios.

[0019] The power loss cost model is expressed as: Among them, C loss is the power loss cost, p loss is the unit capacity power loss cost, T sum Represents the total discharge time, expressed as: T sum =(Day f ·E f +Day free ·E m )·(T f1 +T f2 )+Day m ·T m +Day t ·T t +Day g ·T g , among which Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 The discharge time of peak-valley 2 in the two-charge-two-discharge scenario, Day m The number of days of operation of mobile charging scenarios, T mDaily discharge time for mobile charging, Day t The number of days of operation for temporary power supply scenario, T t Daily discharge time for temporary power supply, Day g The number of days of operation without power outage, T g P is the discharge time of non-stop operation; d is the discharge power of the energy storage facility, η c is the charging efficiency of energy storage facilities, η d The discharge efficiency of the energy storage facility.

[0020] The shared revenue models of energy storage facilities in different scenarios include:

[0021] The profit model of two-charge and two-discharge scenarios is expressed as: R f =(Day f ·E f +Day free ·E m )·P d ·(T f1 ·p f1 +T f2 ·p f2 );

[0022] The profit model of mobile charging scenario is expressed as: R m =Day m ·T m ·P m ·p m ·c charge ;

[0023] The profit model of temporary power supply scenario is expressed as: R t =Day t ·P d ·T t ·p t ;

[0024] The profit model of the non-stop operation scenario is expressed as: R g =Day g ·E g ·p g ;

[0025] The profit model of temporary capacity increase scenario is expressed as: R r =E r ·p r ;

[0026] Among them, R f For the two-charge and two-discharge scenario benefits, Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Dayfree E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, P d is the discharge power of the energy storage facility, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 is the discharge time of peak-valley 2 in the two-charge-two-discharge scenario, p f1 is the price difference between peak and valley 1 in the two-charge and two-discharge scenario, p f2 is the price difference between peak and valley 2 in the two-charge and two-discharge scenario; R m For mobile charging scene benefits, Day m The number of days of operation of mobile charging scenarios, T m Daily discharge time for mobile charging, P m is the discharge power of the mobile energy storage facility, p m The price difference between mobile charging and discharging, c charge is the number of mobile charging facilities; R t For temporary power supply scenario benefits, Day t The number of days of operation for temporary power supply scenario, T t Daily discharge time for temporary power supply, p t The price difference between charging and discharging of temporary power supply; R g For the benefit of non-stop operation scenario, Day g The number of days of operation without power outage, E g is the energy storage capacity for non-stop operation, p g is the unit capacity benefit of non-stop operation, R r is the temporary capacity increase scenario benefit, E r is the energy storage capacity used for temporary capacity expansion operations, p r It is the unit capacity gain of temporary capacity increase operation.

[0027] The calculation is performed based on the shared cost and shared benefit of the energy storage facility, specifically including:

[0028] The annual net income is calculated based on the operating costs in the shared costs of the energy storage facilities and the shared income of the energy storage facilities, expressed as: TP = TR-TC v ;

[0029] The investment payback period is calculated based on the facility acquisition cost and annual net income in the shared cost of the energy storage facility, expressed as:

[0030]

[0031] The net present value is calculated based on the facility acquisition cost and investment payback period in the shared cost of the energy storage facility, expressed as:

[0032]

[0033] The internal rate of return is calculated based on the facility acquisition cost in the shared cost of the energy storage facility and the net income in the tth year, which is expressed as

[0034]

[0035] Among them, TP is the annual net income, TR is the shared income of the energy storage facility, expressed as: TR = R f +R m +R t +R g +R r , R f The benefit of the two-charge and two-discharge scenario, R m For mobile charging scenario benefits, R t The benefit of temporary power supply scenario, R g R is the benefit of the non-stop operation scenario. r For temporary capacity increase scenario benefits; TC v is the operating cost of the shared cost of the energy storage facility, expressed as: TC v =C d +C m +m m +m t +m g +m r +C loss , where C d is the annual depreciation cost of the facility, C m is the facility maintenance cost, m m is the facility operation cost in the mobile charging scenario, m t is the facility operation cost in the temporary power supply scenario, m g is the facility operation cost in the scenario of non-stop operation, m r is the facility operation cost in the temporary capacity increase scenario, C loss is the power loss cost; PP is the investment payback period, TC f is the facility acquisition cost; NPV is the net present value, τ is the discount rate, N is the facility depreciation period; R net (t) is the net income in year t, IRR is the internal rate of return, and T is the time the energy storage facility is used.

[0036] The technical solution adopted by the present invention to solve the technical problem is: to provide a distributed energy storage shared application optimization model method calculation device, including:

[0037] A cost construction calculation module, used to construct an energy storage facility cost model and calculate the shared cost of the energy storage facility according to the energy storage facility cost model;

[0038] A revenue construction and calculation module is used to construct a revenue model of the energy storage facility in different scenarios and calculate the shared revenue of the energy storage facility according to the revenue model;

[0039] The economic calculation module is used to calculate the shared costs and shared benefits of the energy storage facilities.

[0040] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned distributed energy storage shared application optimization model method when executing the computer program.

[0041] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the shared application optimization model method of the above-mentioned distributed energy storage are implemented.

[0042] Beneficial Effects

[0043] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention provides a shared optimization model calculation method for cross-scenario applications of distributed energy storage systems through cost-benefit analysis, dynamic NPV trends and economic benefit calculation for multi-scenario applications. The shared optimization model method can more accurately evaluate the economic benefit composition in different scenarios, helping enterprises and decision makers to make more scientific and reasonable economic decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a shared application optimization model method of distributed energy storage in a first embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of different scenarios in the first embodiment of the present invention;

[0046] Figure 3 is a cost composition diagram in an embodiment of the present invention;

[0047] Figure 4 It is a profit composition diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0049] The first embodiment of the present invention relates to a shared application optimization model method of mobile / fixed distributed energy storage, such as Figure 1 As shown, the following steps are included:

[0050] Step 1: construct an energy storage facility cost model, and calculate the shared cost of the energy storage facility based on the energy storage facility cost model.

[0051] The energy storage facility cost model constructed in this step includes the facility purchase cost model, the facility annual depreciation cost model, the facility maintenance cost model, the facility operation cost model under various scenarios, and the power loss cost model.

[0052] The equipment acquisition cost includes the fixed energy storage facility acquisition cost, the mobile energy storage facility acquisition cost, and the charging facility acquisition cost. Therefore, the facility acquisition cost model in this embodiment is expressed as: Among them, C f is the facility acquisition cost, p fix is the unit cost of the fixed energy storage facility, is the purchased capacity of fixed energy storage facilities; movable is the unit cost of mobile energy storage facilities, Purchase capacity for mobile energy storage facilities, p charge is the unit cost of charging facilities, c charge The number of charging facilities purchased.

[0053] Due to factors such as aging of batteries in energy storage facilities, the energy storage facilities will depreciate. The annual depreciation cost model of the facilities in this implementation is expressed as: Among them, C d is the annual depreciation cost of the facility, C f is the facility acquisition cost, C residue is the residual value of the facility, and N is the depreciation period of the facility.

[0054] Since energy storage facilities require daily maintenance, daily maintenance will also incur corresponding costs. The facility maintenance cost model in this implementation is expressed as: Among them, C m The cost of facility maintenance, is the purchased capacity of fixed energy storage facilities, c mf is the unit maintenance cost of the fixed energy storage facility, Purchase capacity for mobile energy storage facilities, c mm is the unit maintenance cost of mobile energy storage facilities, c charge is the number of charging facilities purchased, c mc It is the unit maintenance cost of charging facilities.

[0055] This implementation also constructs facility operation cost models for different scenarios based on different scenarios, including: a facility operation cost model for a mobile charging scenario, a facility operation cost model for a temporary power supply scenario, a facility operation cost model for an uninterrupted power operation scenario, and a facility operation cost model for a temporary capacity expansion scenario.

[0056] like Figure 2 As shown, mobile charging usually refers to the use of mobile energy storage equipment to provide charging services for electric vehicles. This application scenario is suitable for occasions that require temporary or emergency charging facilities, such as large-scale events, disaster relief sites, etc. Temporary power supply refers to temporary power facilities and power supply methods set up to meet the power demand within a specific time period. It is usually used in short-term projects, exhibitions, celebrations, construction sites and other occasions to cope with specific power needs. In the non-stop operation scenario, the system needs to ensure the continuity of power supply under special circumstances (such as power outages or power outages in the grid). In the temporary capacity increase scenario, in order to cope with the sudden increase in power demand in the short term such as large-scale events or seasonal high loads, the energy storage system needs to provide flexible energy storage leasing services to meet temporary capacity increase needs and maximize economic benefits.

[0057] Among them, the facility operation cost in the mobile charging scenario is related to the number of mobile charging scenarios, the unit cost of distance and the average distance. Therefore, the facility operation cost model in the mobile charging scenario in this embodiment is expressed as: Among them, m m The cost of operating facilities in the mobile charging scenario, Day m The number of days of operation for mobile charging scenarios, p move is the unit transportation cost of mobile energy storage facilities, It is the average distance of mobile charging scenario.

[0058] The facility operation cost in the temporary power supply scenario is related to the number of temporary power supply scenarios, the unit cost of distance, and the average distance. Therefore, the facility operation cost model in the temporary power supply scenario in this implementation is expressed as: Among them, m t The cost of operating the facility in a temporary power supply scenario, Day t Operation days for temporary power supply scenarios, The average distance for temporary power supply scenarios.

[0059] The facility operation cost in the non-stop operation scenario is related to the number of non-stop scenarios, the unit cost of distance, and the average distance. Therefore, the facility operation cost model in the non-stop operation scenario in this implementation is expressed as: Among them, m g Day is the facility operating cost in the scenario of non-stop operation. g The number of days of operation for non-stop power supply operation scenarios, It is the average distance of the non-power-off operation scenario.

[0060] The facility operation cost in the temporary capacity increase scenario is related to the distance unit cost and the average distance. Therefore, the facility operation cost model in the temporary capacity increase scenario in this implementation is expressed as: Among them, m r The facility operating costs in the temporary capacity increase scenario. The average distance for temporary capacity increase scenarios.

[0061] Since the charging and discharging of energy storage facilities will generate line losses, the power loss cost model in this embodiment is expressed as: Among them, C loss is the power loss cost, p loss is the unit capacity power loss cost, T sum Represents the total discharge time, expressed as: T sum =(Day f ·E f +Day free ·E m )·(T f1 +T f2 )+Day m ·T m +Day t ·T t +Day g ·T g , among which Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 The discharge time of peak-valley 2 in the two-charge-two-discharge scenario, Day m The number of days of operation of mobile charging scenarios, T m Daily discharge time for mobile charging, Day t The number of days of operation for temporary power supply scenario, T t Daily discharge time for temporary power supply, Day g The number of days of operation without power outage, T g P is the discharge time of non-stop operation; d is the discharge power of the energy storage facility, η c is the charging efficiency of energy storage facilities, η d The discharge efficiency of the energy storage facility.

[0062] Step 2: construct a revenue model for the energy storage facility under different scenarios, and calculate the shared revenue of the energy storage facility based on the revenue model.

[0063] The profit models for different scenarios constructed in this step include the two-charge-two-discharge scenario profit model, the mobile charging scenario profit model, the temporary power supply scenario profit model, the non-stop operation scenario profit model and the temporary capacity increase scenario profit model.

[0064] like Figure 2 As shown, the two-charge and two-discharge system mainly utilizes the fluctuations in electricity demand and electricity prices, and performs charging and discharging operations in different time periods through intelligent scheduling, mainly charging during two electricity low consumption periods and discharging during two electricity peak consumption periods, so as to obtain price difference benefits and maximize economic benefits.

[0065] The two-charge and two-discharge scenario mainly makes profits from the idle time of fixed energy storage facilities and mobile energy storage facilities. Therefore, the two-charge and two-discharge scenario profit model in this implementation is expressed as: R f =(Day f ·E f +Day free ·E m )·P d ·(T f1 ·p f1 +T f2 ·p f2 ), where R f For the two-charge and two-discharge scenario benefits, Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, P d is the discharge power of the energy storage facility, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 is the discharge time of peak-valley 2 in the two-charge-two-discharge scenario, p f1 is the price difference between peak and valley 1 in the two-charge and two-discharge scenario, p f2 It is the price difference of peak-valley 2 in the two-charge and two-discharge scenario.

[0066] The benefit of the mobile charging scenario is related to the number of days of the mobile scenario, the daily discharge time, the discharge power of the charging pile, the charge-discharge price difference, and the number of charging facilities. Therefore, the mobile charging scenario benefit model in this implementation is expressed as: R m =Day m ·T m ·P m ·p m ·c charge , where Rm For mobile charging scene benefits, Day m The number of days of operation of mobile charging scenarios, T m Daily discharge time for mobile charging, P m is the discharge power of the mobile energy storage facility, p m The price difference between mobile charging and discharging, c charge The number of mobile charging facilities.

[0067] The benefit of the temporary charging scenario is related to the number of days of the temporary power supply scenario, the daily discharge time, the discharge power of the facility, and the charge-discharge price difference. Therefore, the profit model of the temporary power supply scenario in this implementation is expressed as: R t =Day t ·P d ·T t ·p t , where R t For temporary power supply scenario benefits, Day t The number of days of operation for temporary power supply scenario, T t Daily discharge time for temporary power supply, p t The price difference between charging and discharging for temporary power supply.

[0068] The revenue of the non-stop power supply scenario is related to the number of days of non-stop power supply operation, the energy storage capacity invested in non-stop power supply operation, and the charge-discharge price difference. Therefore, the revenue model of the non-stop power supply operation scenario in this implementation is expressed as: R g =Day g ·E g ·p g , where R g For the benefit of non-stop operation scenario, Day g The number of days of operation without power outage, E g is the energy storage capacity for non-stop operation, p g Unit capacity revenue for non-stop operation.

[0069] The revenue of the temporary capacity increase scenario is calculated on an annual basis, which is related to the mobile energy storage capacity of the temporary capacity increase and the annual rental price. In this implementation, the revenue model of the temporary capacity increase scenario is expressed as: R r =E r ·p r , where R r is the temporary capacity increase scenario benefit, E r is the energy storage capacity used for temporary capacity expansion operations, p r It is the unit capacity gain of temporary capacity increase operation.

[0070] Step 3, calculating based on the shared cost and shared benefit of the energy storage facility, this step specifically includes:

[0071] The annual net income is calculated based on the operating costs in the shared costs of the energy storage facilities and the shared income of the energy storage facilities, expressed as: TP = TR-TC v ; Where TP is the annual net income, TR is the shared income of the energy storage facility, expressed as: TR = R f +R m +R t +R g +R r , TC v is the operating cost of the shared cost of the energy storage facility, expressed as: TC v =C d +C m +m m +m t +m g +m r +C loss .

[0072] The investment payback period is calculated based on the facility acquisition cost and annual net income in the shared cost of the energy storage facility, expressed as: Among them, PP is the investment payback period, TC f The cost of acquiring the facility.

[0073] The net present value is calculated based on the facility acquisition cost and investment payback period in the shared cost of the energy storage facility, expressed as: Among them, NPV is the net present value, τ is the discount rate, and N is the depreciation period of the facility.

[0074] The internal rate of return is calculated based on the facility acquisition cost in the shared cost of the energy storage facility and the net income in the tth year, expressed as: Among them, R net (t) is the net income in year t, IRR is the internal rate of return, and T is the time the energy storage facility is used.

[0075] According to the method of this embodiment, through the analysis and verification of sample data, significant application effects are shown, and excellent results are achieved in cost structure analysis, benefit evaluation, dynamic NPV trend analysis, and economic benefit optimization guidance. The specific effects are as follows:

[0076] Clarification of cost structure: This implementation method refines each cost so that decision makers can clearly understand the cost distribution in different scenarios. Figure 3Taking the sample data in Table 1 as an example, the fixed cost in the shared cost is RMB 1,310,000, while the maintenance cost in the variable cost is as high as RMB 150,250, accounting for the main part of the variable cost. At the same time, the operating costs of mobile charging, temporary power supply, non-stop operation and temporary capacity increase are quantified in detail, which are RMB 6,500, RMB 15,000, RMB 7,500 and RMB 7,500 respectively, realizing the refined management of costs and making the enterprise more targeted in cost control.

[0077] Table 1 Cost data composition table

[0078] name Amount (Yuan) Fixed cost (yuan) 1310000 Maintenance costs 150250 Mobile charging operating costs 6500 Temporary power supply operating costs 15000 Non-stop operation cost 7500 Temporary capacity increase operating costs 7500

[0079] Accuracy of revenue assessment: Figure 4 The cost-benefit shown in Table 2 can clearly show the benefits of each scenario, making the economic benefits clear at a glance. The model quantifies the benefits under different application scenarios and provides specific data support. For example, the benefit of the "two charging and two discharging" scenario is 490,374 yuan, the benefit of the mobile charging scenario is 52,000 yuan, the benefit of the temporary power supply scenario is 60,000 yuan, the benefit of the non-stop operation scenario is 162,000 yuan, the benefit of the temporary capacity increase scenario is 64,800 yuan, and the shared benefit is 829,174 yuan. This benefit evaluation method makes the economic value of different scenarios more transparent and provides a scientific basis for calculation for decision makers.

[0080] Table 2 Income data composition table

[0081] Scenario income Two charging and two discharging 490374 Mobile Charging 52000 Temporary power supply 60000 No power outages 162000 Temporary capacity increase 64800 Shared Profits 829174

[0082] According to the dynamic net profit change trend: This implementation method can clearly reflect the dynamic change trend of net profit under different times or conditions, so that enterprises can effectively predict long-term economic benefits and optimize investment returns. The net profit calculation table of the example data in Table 3 shows the net profit change trend in different project periods, providing more forward-looking data support for the company's investment decisions. For example, the project's net profit was negative in the first two cycles, but gradually turned positive from the third cycle, and finally reached 1,272,240 yuan in the fifth cycle. This dynamic trend analysis allows the time series changes of investment benefits to be fully displayed, providing strong support for enterprises to make long-term strategic decisions.

[0083] Table 3 Economic calculation data table

[0084] Project Period Net Profit 1 -793552 2 -277104 3 239344 4 755792 5 1272240

[0085] This implementation is not limited to static analysis of cost-benefit, but can also provide guidance for decision makers to optimize resource allocation. In the cost composition analysis, it is found that maintenance costs account for the main part of variable costs, which can guide enterprises to give priority to reducing maintenance costs, thereby further improving economic benefits. In addition, through the analysis of economic benefits in different scenarios, it can help enterprises reduce unnecessary cost expenditures, allocate resources more reasonably, and maximize benefits.

[0086] In summary, the method of this implementation has shown significant application effects in many aspects. It can not only provide refined cost and benefit analysis, but also present long-term investment value through dynamic trends. At the same time, it can also provide scientific support for enterprise resource allocation and economic benefit improvement through optimization guidance, and has wide application value and market potential.

[0087] The second embodiment of the present invention relates to a distributed energy storage shared application optimization model method calculation device, comprising:

[0088] A cost construction calculation module, used to construct an energy storage facility cost model and calculate the shared cost of the energy storage facility according to the energy storage facility cost model;

[0089] A revenue construction and calculation module is used to construct a revenue model of the energy storage facility in different scenarios and calculate the shared revenue of the energy storage facility according to the revenue model;

[0090] The economic calculation module is used to calculate the shared costs and shared benefits of the energy storage facilities.

[0091] The energy storage facility cost model includes a facility purchase cost model, a facility annual depreciation cost model, a facility maintenance cost model, a facility operation cost model under various scenarios, and a power loss cost model.

[0092] The facility acquisition cost model is expressed as: Among them, C f is the facility acquisition cost, p fix is the unit cost of the fixed energy storage facility, is the purchased capacity of fixed energy storage facilities; movable is the unit cost of mobile energy storage facilities, Purchase capacity for mobile energy storage facilities, p charge is the unit cost of charging facilities, c charge The number of charging facilities purchased.

[0093] The annual depreciation cost model of the facility is expressed as: Among them, C d is the annual depreciation cost of the facility, C f is the facility acquisition cost, C residueis the residual value of the facility, and N is the depreciation period of the facility.

[0094] The facility maintenance cost model is expressed as: Among them, C m The cost of facility maintenance, is the purchased capacity of fixed energy storage facilities, c mf is the unit maintenance cost of the fixed energy storage facility, Purchase capacity for mobile energy storage facilities, c mm is the unit maintenance cost of mobile energy storage facilities, c charge is the number of charging facilities purchased, c mc It is the unit maintenance cost of charging facilities.

[0095] The facility operating cost models for the various scenarios described include:

[0096] The facility operation cost model in the mobile charging scenario is expressed as:

[0097] The facility operation cost model under the temporary power supply scenario is expressed as:

[0098] The facility operation cost model under the scenario of non-stop operation is expressed as:

[0099] The facility operation cost model under the temporary capacity increase scenario is expressed as:

[0100] Among them, m m The cost of operating facilities in the mobile charging scenario, Day m The number of days of operation for mobile charging scenarios, p move is the unit transportation cost of mobile energy storage facilities, is the average distance of mobile charging scene; m t The cost of operating the facility in a temporary power supply scenario, Day t Operation days for temporary power supply scenarios, The average distance of temporary power supply scene; m g Day is the facility operating cost in the scenario of non-stop operation. g The number of days of operation for non-stop power supply operation scenarios, is the average distance of the non-stop operation scene; m r The facility operating costs in the temporary capacity increase scenario. The average distance for temporary capacity increase scenarios.

[0101] The power loss cost model is expressed as: Among them, C loss is the power loss cost, p lossis the unit capacity power loss cost, T sum Represents the total discharge time, expressed as: T sum =(Day f ·E f +Day free ·E m )·(T f1 +T f2 )+Day m ·T m +Day t ·T t +Day g ·T g , among which Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 The discharge time of peak-valley 2 in the two-charge-two-discharge scenario, Day m The number of days of operation of mobile charging scenarios, T m Daily discharge time for mobile charging, Day t The number of days of operation for temporary power supply scenario, T t Daily discharge time for temporary power supply, Day g The number of days of operation without power outage, T g P is the discharge time of non-stop operation; d is the discharge power of the energy storage facility, η c is the charging efficiency of energy storage facilities, η d The discharge efficiency of the energy storage facility.

[0102] The revenue models of energy storage facilities in different scenarios include:

[0103] The profit model of two-charge and two-discharge scenarios is expressed as: R f =(Day f ·E f +Day free ·E m )·P d ·(T f1 ·p f1 +T f2 ·p f2 );

[0104] The profit model of mobile charging scenario is expressed as: R m =Day m ·T m ·Pm ·p m ·c charge ;

[0105] The profit model of temporary power supply scenario is expressed as: R t =Day t ·P d ·T t ·p t ;

[0106] The profit model of the non-stop operation scenario is expressed as: R g =Day g ·E g ·p g ;

[0107] The profit model of temporary capacity increase scenario is expressed as: R r =E r ·p r ;

[0108] Among them, R f For the two-charge and two-discharge scenario benefits, Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, P d is the discharge power of the energy storage facility, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 is the discharge time of peak-valley 2 in the two-charge-two-discharge scenario, p f1 is the price difference between peak and valley 1 in the two-charge and two-discharge scenario, p f2 is the price difference between peak and valley 2 in the two-charge and two-discharge scenario; R m For mobile charging scene benefits, Day m The number of days of operation of mobile charging scenarios, T m Daily discharge time for mobile charging, P m is the discharge power of the mobile energy storage facility, p m The price difference between mobile charging and discharging, c charge is the number of mobile charging facilities; R t For temporary power supply scenario benefits, Day t The number of days of operation for temporary power supply scenario, T t Daily discharge time for temporary power supply, p t The price difference between charging and discharging of temporary power supply; R g For the benefit of non-stop operation scenario, Day g The number of days of operation without power outage, E gis the energy storage capacity for non-stop operation, p g is the unit capacity benefit of non-stop operation, R r is the temporary capacity increase scenario benefit, E r is the energy storage capacity used for temporary capacity expansion operations, p r It is the unit capacity gain of temporary capacity increase operation.

[0109] The economic calculation module includes:

[0110] The annual net income calculation unit is used to calculate the annual net income based on the operating cost in the shared cost of the energy storage facility and the shared income of the energy storage facility, expressed as: TP = TR - TC v ;

[0111] The investment payback period calculation unit is used to calculate the investment payback period based on the facility acquisition cost and annual net income in the shared cost of the energy storage facility, and is expressed as:

[0112] The net present value calculation unit is used to calculate the net present value according to the facility acquisition cost and investment payback period in the shared cost of the energy storage facility, which is expressed as:

[0113] The internal rate of return calculation unit is used to calculate the internal rate of return based on the facility acquisition cost in the shared cost of the energy storage facility and the net income in the tth year, expressed as:

[0114] Among them, TP is the annual net income, TR is the shared income of the energy storage facility, expressed as: TR = R f +R m +R t +R g +R r , R f The benefit of the two-charge and two-discharge scenario, R m For mobile charging scenario benefits, R t The benefit of temporary power supply scenario, R g R is the benefit of the non-stop operation scenario. r For temporary capacity increase scenario benefits; TC v is the operating cost of the shared cost of the energy storage facility, expressed as: TC v =C d +C m +m m +m t +m g +m r +C loss , where C d is the annual depreciation cost of the facility, C m is the facility maintenance cost, m mis the facility operation cost in the mobile charging scenario, m t is the facility operation cost in the temporary power supply scenario, m g is the facility operation cost in the scenario of non-stop operation, m r is the facility operation cost in the temporary capacity increase scenario, C loss is the power loss cost; PP is the investment payback period, TC f is the facility acquisition cost; NPV is the net present value, τ is the discount rate, N is the facility depreciation period; R net (t) is the net income in year t, IRR is the internal rate of return, and T is the time the energy storage facility is used.

[0115] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the shared application optimization model method for mobile / fixed distributed energy storage of the first embodiment when executing the computer program.

[0116] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the shared application optimization model method of mobile / fixed distributed energy storage of the first embodiment.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed energy storage shared application optimization model method, characterized in that: The following steps are involved: Constructing an energy storage facility cost model, and calculating the shared cost of the energy storage facility according to the energy storage facility cost model; Constructing a revenue model for energy storage facilities in different scenarios, and calculating the shared revenue of the energy storage facilities based on the revenue model; The calculation is made based on the shared costs and shared benefits of the energy storage facilities.

2. The distributed energy storage shared application optimization model method according to claim 1 is characterized in that: The energy storage facility cost model includes a facility purchase cost model, a facility annual depreciation cost model, a facility maintenance cost model, a facility shared operation cost model under various scenarios, and a power loss cost model.

3. The distributed energy storage shared application optimization model method according to claim 2 is characterized in that: The facility acquisition cost model is expressed as: Among them, C f is the facility acquisition cost, p fix is the unit cost of the fixed energy storage facility, is the purchased capacity of fixed energy storage facilities; movable is the unit cost of mobile energy storage facilities, Purchase capacity for mobile energy storage facilities, p charge is the unit cost of charging facilities, c charge The number of charging facilities purchased.

4. The distributed energy storage shared application optimization model method according to claim 2 is characterized in that: The annual depreciation cost model of the facility is expressed as: Among them, C d is the annual depreciation cost of the facility, C f is the facility acquisition cost, C residue is the residual value of the facility, and N is the depreciation period of the facility.

5. The distributed energy storage shared application optimization model method according to claim 2 is characterized in that: The facility maintenance cost model is expressed as: Among them, C m The cost of facility maintenance, is the purchased capacity of fixed energy storage facilities, c mf is the unit maintenance cost of the fixed energy storage facility, Purchase capacity for mobile energy storage facilities, c mm is the unit maintenance cost of mobile energy storage facilities, c charge is the number of charging facilities purchased, c mc It is the unit maintenance cost of charging facilities.

6. The distributed energy storage shared application optimization model method according to claim 2 is characterized in that: The facility sharing operating cost models under the various scenarios include: The facility operation cost model in the mobile charging scenario is expressed as: The facility operation cost model under the temporary power supply scenario is expressed as: The facility operation cost model under the scenario of non-stop operation is expressed as: The facility operation cost model under the temporary capacity increase scenario is expressed as: Among them, m m The cost of operating facilities in the mobile charging scenario, Day m The number of days of operation for mobile charging scenarios, p move is the unit transportation cost of mobile energy storage facilities, is the average distance of mobile charging scene; m t The cost of operating the facility in a temporary power supply scenario, Day t Operation days for temporary power supply scenarios, The average distance of temporary power supply scene; m g Day is the facility operating cost in the scenario of non-stop operation. g The number of days of operation for non-stop operation scenarios, is the average distance of the non-stop operation scene; m r The facility operating costs in the temporary capacity increase scenario. The average distance for temporary capacity increase scenarios.

7. The distributed energy storage shared application optimization model method according to claim 2 is characterized in that: The power loss cost model is expressed as: Among them, C loss is the power loss cost, p loss is the unit capacity power loss cost, T sum represents the total discharge time, expressed as: T sum =(Day f ·E f +Day free ·E m )·(T f1 +T f2 )+Day m ·T m +Day t ·T t +Day g ·T g , where Day f is Number of days of operation of fixed energy storage facilities in two-charge and two-discharge scenarios, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 The discharge time of peak-valley 2 in the two-charge-two-discharge scenario, Day m The number of days of operation of mobile charging scene, T m Daily discharge time for mobile charging, Day t The number of days for temporary power supply operation, T t Daily discharge time for temporary power supply, Day g The number of days of operation without power outage, T g P is the discharge time of non-stop operation; d is the discharge power of the energy storage facility, η c is the charging efficiency of energy storage facilities, η d The discharge efficiency of the energy storage facility.

8. The distributed energy storage shared application optimization model method according to claim 1 is characterized in that: The shared revenue models of energy storage facilities in different scenarios include: The profit model of two-charge and two-discharge scenarios is expressed as: R f =(Day f ·E f +Day free ·E m )·P d ·(T f1 ·p f1 +T f2 ·p f2 ); The profit model of mobile charging scenario is expressed as: R m =Day m ·T m ·P m ·p m ·c charge ; The profit model of temporary power supply scenario is expressed as: R t =Day t ·P d ·T t ·p t ; The profit model of the non-stop operation scenario is expressed as: R g =Day g ·E g ·p g ; The profit model of temporary capacity increase scenario is expressed as: R r =E r ·p r ; Among them, R f For the two-charge and two-discharge scenario benefits, Day f is the number of days for the two-charge and two-discharge scenarios of fixed energy storage facilities, E f For the effective capacity of fixed energy storage facilities, Day free E is the number of days for the mobile energy storage facility to operate in two-charge and two-discharge scenarios. m is the effective capacity of mobile energy storage facilities, P d is the discharge power of the energy storage facility, T f1 is the discharge time of peak-valley 1 in the two-charge-two-discharge scenario, T f2 is the discharge time of peak-valley 2 in the two-charge-two-discharge scenario, p f1 is the price difference between peak and valley 1 in the two-charge and two-discharge scenario, p f2 is the price difference between peak and valley 2 in the two-charge and two-discharge scenario; R m For mobile charging scene benefits, Day m The number of days of operation of mobile charging scene, T m Daily discharge time for mobile charging, P m is the discharge power of the mobile energy storage facility, p m The price difference between mobile charging and discharging, c charge is the number of mobile charging facilities; R t For temporary power supply scenario benefits, Day t The number of days for temporary power supply operation, T t Daily discharge time for temporary power supply, p t The price difference between charging and discharging of temporary power supply; R g For the benefit of non-stop operation scenario, Day g The number of days of operation without power outage, E g is the energy storage capacity for non-stop operation, p g is the unit capacity benefit of non-stop operation, R r is the temporary capacity increase scenario benefit, E r is the energy storage capacity used for temporary capacity expansion operations, p r It is the unit capacity gain of temporary capacity increase operation.

9. The distributed energy storage shared application optimization model method according to claim 1 is characterized in that: The sharing optimization according to the sharing cost and the sharing income of the energy storage facility specifically includes: The annual net income is calculated based on the operating costs in the shared costs of the energy storage facilities and the shared income of the energy storage facilities, expressed as: TP = TR-TC v ; The investment payback period is calculated based on the facility acquisition cost and annual net income in the shared cost of the energy storage facility, expressed as: The net present value is calculated based on the facility acquisition cost and investment payback period in the shared cost of the energy storage facility, expressed as: The internal rate of return is calculated based on the facility acquisition cost in the shared cost of the energy storage facility and the net income in the tth year, expressed as: Among them, TP is the annual net income, TR is the shared income of the energy storage facility, expressed as: TR = R f +R m +R t +R g +R r , R f The benefit of the two-charge and two-discharge scenario, R m For mobile charging scenario benefits, R t The benefit of temporary power supply scenario, R g The benefit of the non-stop operation scenario, R r For temporary capacity increase scenario benefits; TC v is the operating cost of the shared cost of the energy storage facility, expressed as: TC v =C d +C m +m m +m t +m g +m r +C loss , where C d is the annual depreciation cost of the facility, C m is the facility maintenance cost, m m is the facility operation cost in the mobile charging scenario, m t is the facility operation cost in the temporary power supply scenario, m g is the facility operation cost in the scenario of non-stop operation, m r is the facility operation cost in the temporary capacity increase scenario, C loss is the power loss cost; PP is the investment payback period, TC f is the facility acquisition cost; NPV is the net present value, τ is the discount rate, N is the facility depreciation period; R net (t) is the net income in year t, IRR is the internal rate of return, and T is the time the energy storage facility is used.

10. A shared optimization model device for distributed energy storage sharing applications, characterized in that: include: A cost construction calculation module, used to construct an energy storage facility cost model and calculate the shared cost of the energy storage facility according to the energy storage facility cost model; A revenue construction and calculation module is used to construct a revenue model of the energy storage facility in different scenarios and calculate the shared revenue of the energy storage facility according to the revenue model; The shared optimization model module is used to calculate the shared cost and shared benefit of the energy storage facility.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the shared application optimization model method of distributed energy storage as described in any one of claims 1-9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the shared application optimization model method of distributed energy storage as described in any one of claims 1-9 are implemented.

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