Energy storage optimization configuration method and device

Abstract

The application provides a kind of energy storage optimization configuration method and device, it is related to energy storage data processing technical field.The method includes: with the minimum of the equalization energy storage cost calculation model of energy storage as objective function to establish optimization target, with the minimum of total cost of complementary system as target to build the configuration model of complementary system containing energy storage, constraint condition is constructed, the configuration capacity of each type of power supply of complementary system is generated, according to the capacity configuration result determined to guide the planning and production of each type of complementary power supply in actual production.The device executes the above method.The energy storage optimization configuration method and device provided in the embodiment of the application can effectively solve the problem that new energy station and new energy collection station are difficult to plan and produce due to high cost, optimize resource allocation, and improve the utilization rate of new energy of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage data processing, in particular to an energy storage optimization configuration method and device. BACKGROUND

[0002] The energy storage application scenarios are diverse, which can be divided into power generation side, power grid side and user side according to different positions, and mainly include improving renewable energy consumption, tracking planned output, frequency modulation, peak shaving, black start and peak clipping (user time-of-use electricity price management, user basic electricity price management), and of course also has the functions of acting as a backup power supply (improving user power supply reliability), improving power quality, providing reactive power support and the like. The above-mentioned energy storage application scenarios can be divided into power type energy storage application scenarios and power type energy storage application scenarios according to the demand of specific energy and specific power, for example, participating in frequency modulation, reactive power support, tracking planned output and improving power quality, which are inclined to power type energy storage application scenarios, while peak shaving, peak clipping demand and load regulation demand are inclined to power type energy storage application scenarios.

[0003] The levelized cost of electricity (LCOE) refers to the discounted generation cost per kilowatt-hour of a power source project in the whole life cycle, which is a widely recognized and high transparency method for calculating generation cost.

[0004] As a special carrier that can be charged and discharged, energy storage is both a "power source" and a "load", and the use of levelized cost of electricity LCOE to describe the cost of each degree of electricity discharged by energy storage may not be appropriate in name, but the method of converting the cost of energy storage into each degree of electricity or each power mileage is feasible, that is, the formation of levelized energy storage cost LCOS.

[0005] In recent years, with the diversified development of energy storage technology, energy storage technology has gradually expanded from traditional pumped storage and electrochemical energy storage to generalized energy storage technology that can realize one-way or bidirectional storage of electric power and thermal energy, chemical energy and the like, such as energy storage power generation, electric-to-gas, electric power hydrogen production and the like; for the application needs of new energy base to smooth output, track planning, system frequency modulation, peak clipping and the like, the adaptability of different energy storage technologies is not the same. Therefore, it is necessary to comprehensively analyze the technical and economic characteristics of multiple types of energy storage suitable for high proportion of new energy external transmission system. Facilitate the power system to comprehensively consider the operation characteristics of different energy storage devices, application scene applicable demand, new energy external transmission system comprehensive benefit, system comprehensive economy and the like factors in the planning and design stage. Provide reference basis for subsequent multi-element energy storage site selection, capacity and selection. SUMMARY

[0006] In view of the problems in the prior art, the embodiment of the present application provides a method and device for optimizing configuration of energy storage, which can at least partially solve the problems in the prior art.

[0007] In one aspect, the present application provides a method for optimizing configuration of energy storage, comprising:

[0008] obtaining energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station;

[0009] analyzing capacity characteristics and power characteristics of energy storage for planning based on energy flow relationships and technical indexes of the energy storage power station;

[0010] establishing an optimization objective with a minimum of a flat-rate energy storage cost calculation model of the energy storage as an objective function, constructing constraint conditions, and calculating scale configurations of each subsystem of the energy storage power station according to energy storage capacity and energy storage hours obtained through simulation calculation; the flat-rate energy storage cost calculation model comprises a cycle degradation parameter and a calendar degradation parameter;

[0011] constructing a complementary system optimization configuration model containing energy storage with a minimum of total cost of the complementary system as an objective, constructing constraint conditions, generating configuration capacities of each type of power source of the complementary system, and guiding planning and production of each type of complementary power source in actual production according to the determined capacity configuration result.

[0012] The cycle degradation parameter is obtained by:

[0013] The cycle degradation parameter is calculated according to cycle life and a preset percentage.

[0014] The calendar degradation parameter is obtained by:

[0015] The calendar degradation parameter is calculated according to a degradation time parameter and a preset percentage.

[0016] The flat-rate energy storage cost calculation model is established by:

[0017] The flat-rate energy storage cost calculation model is established according to a discharge capacity index parameter, each cost item required for flat-rate energy storage cost calculation, a discount rate, a time point of a specific operating year and a service life of energy storage technology.

[0018] The flat-rate energy storage cost calculation model comprises discharge capacity index parameter discount corresponding to the discharge capacity index parameter.

[0019] The discharge capacity index parameter discount is calculated by:

[0020] The discharge quantity index parameter is discounted according to an annual cycle, a nominal energy storage capacity, a discharge depth, a conversion efficiency, a cycle degradation parameter, a calendar degradation parameter, an external discharge rate, a technical construction time, a specific operating year point in time, and a service life of the energy storage technology.

[0021] The cost items required for calculating the flat-rate energy storage cost include:

[0022] An initial investment cost, an operation and maintenance cost, a charging cost of the energy storage, and a scrap cost.

[0023] In one aspect, the present application provides an energy storage optimization configuration device, comprising:

[0024] An acquisition unit is configured to acquire energy storage data of a new energy station and a new energy collection station, regional resource data, system basic technical data, and complementary system planning data.

[0025] An analysis unit is configured to analyze capacity characteristics and power characteristics of energy storage facing planning based on energy flow relationships and technical indexes of the energy storage power station.

[0026] A construction unit is configured to establish an optimization objective with a minimum flat-rate energy storage cost calculation model of the energy storage as an objective function, to construct a constraint condition, and to calculate scale configurations of each subsystem of the energy storage power station according to energy storage capacity and energy storage hours obtained through simulation calculation; the flat-rate energy storage cost calculation model includes a cycle degradation parameter and a calendar degradation parameter.

[0027] A configuration unit is configured to establish a complementary system optimization configuration model containing energy storage with the lowest total cost of the complementary system as an objective, to construct a constraint condition, to generate configuration capacities of various power sources of the complementary system, and to guide planning and production of various complementary power sources in actual production according to the determined capacity configuration results.

[0028] The construction unit is specifically configured to:

[0029] The cycle degradation parameter is calculated according to a cycle life and a preset percentage.

[0030] The construction unit is specifically configured to:

[0031] The calendar degradation parameter is calculated according to a degradation time parameter and a preset percentage.

[0032] The construction unit is further specifically configured to:

[0033] The flat-rate energy storage cost calculation model is established according to a discharge quantity index parameter, cost items required for calculating the flat-rate energy storage cost, a discount rate, a specific operating year point in time, and a service life of the energy storage technology.

[0034] In still another aspect, an electronic device is provided, comprising: a processor, a memory and a bus, wherein,

[0035] The processor and the memory communicate with each other through the bus;

[0036] The memory stores program instructions executable by the processor, and the processor invoking the program instructions can execute the following method:

[0037] Obtain energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station;

[0038] Based on the energy flow relationship and technical index of the energy storage power station, analyze the capacity characteristics and power characteristics of the energy storage facing planning;

[0039] An optimization objective is established with the minimum of a flat energy storage cost calculation model of the energy storage as the objective function, constraint conditions are constructed, and the scale configuration of each subsystem of the energy storage power station is calculated according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flat energy storage cost calculation model includes cycle degradation parameters and calendar degradation parameters;

[0040] A complementary system optimization configuration model containing energy storage is constructed with the minimum of the total cost of the complementary system as the target, constraint conditions are constructed, the configuration capacity of each type of power supply of the complementary system is generated, and the capacity configuration result determined is used to guide the planning and production of each type of complementary power supply in actual production.

[0041] An embodiment of the present application provides a non-transitory computer readable storage medium, comprising:

[0042] The non-transitory computer readable storage medium stores computer instructions, and the computer instructions make the computer execute the following method:

[0043] Obtain energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station;

[0044] Based on the energy flow relationship and technical index of the energy storage power station, analyze the capacity characteristics and power characteristics of the energy storage facing planning;

[0045] An optimization objective is established with the minimum of a flat energy storage cost calculation model of the energy storage as the objective function, constraint conditions are constructed, and the scale configuration of each subsystem of the energy storage power station is calculated according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flat energy storage cost calculation model includes cycle degradation parameters and calendar degradation parameters;

[0046] The complementary system total cost minimum is taken as a target to construct a complementary optimization configuration model containing energy storage, constraint conditions are constructed, configuration capacities of various power sources of the complementary system are generated, and the determined capacity configuration result is used to guide planning and production of various complementary power sources in actual production.

[0047] The energy storage optimization configuration method and device provided in the embodiments of the present application take the minimum of the flatness energy storage cost calculation model of energy storage as an objective function, construct a complementary optimization configuration model containing energy storage with the complementary system total cost minimum as a target, construct constraint conditions, generate configuration capacities of various power sources of the complementary system, and guide planning and production of various complementary power sources in actual production according to the determined capacity configuration result, which can effectively solve the problem that new energy stations and new energy collection stations are difficult to plan and produce due to high cost, optimize resource configuration, and improve the utilization rate of new energy of the system. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. In the drawings:

[0049] Figure 1 is a flowchart of the energy storage optimization configuration method provided by an embodiment of the present application.

[0050] Figure 2 is a structural schematic diagram of the energy storage optimization configuration device provided by an embodiment of the present application.

[0051] Figure 3 is an electronic device entity structure schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation of the present application. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other at will.

[0053] Figure 1 is a flowchart of the energy storage optimization configuration method provided by an embodiment of the present application, as shown in Figure 1 The energy storage optimization configuration method provided by the embodiments of the present application comprises:

[0054] Step S1: Obtain the energy storage data of the new energy station and the new energy collection station, the regional resource data, the system basic technical data, and the complementary system planning data.

[0055] Step S2: Based on the energy flow relationship and technical index of the energy storage power station, analyze the capacity characteristics and power characteristics of the energy storage facing planning.

[0056] Step S3: Establish an optimization objective with the minimum target function of the flatness energy storage cost calculation model of the energy storage, construct a constraint condition, and calculate the scale configuration of each subsystem of the energy storage power station according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flatness energy storage cost calculation model includes cyclic degradation parameters and calendar degradation parameters.

[0057] Step S4: Build a complementary optimization configuration model containing energy storage with the lowest total cost of the complementary system as the target, construct a constraint condition, generate the configuration capacity of each type of power supply of the complementary system, and guide the planning and production of each type of complementary power supply in actual production according to the determined capacity configuration result.

[0058] In the above step S1, the device obtains the energy storage data of the new energy station and the new energy collection station, the regional resource data, the system basic technical data, and the complementary system planning data. The device can be a computer device executing the method, the regional resource data includes historical wind speed data, historical illumination data, and historical water inflow data; the system basic technical data includes load data, technical and economic data of each type of power supply, tie line data, and environmental and economic data; the energy storage power station planning data includes the annual design utilization hours AUH CSP of the energy storage power station, the confidence capacity of the energy storage power station, the minimum power generation time Tmin of the energy storage power station, and the maximum power generation proportion x of the backup system; the complementary system planning data includes the system standby rate D, the minimum utilization rate γ of the tie line, the minimum proportion of new energy power generation of the system, and the maximum allowed power conversion amount of the unit time to the transmission capacity ratio ε.

[0059] In the above step S2, the device analyzes the capacity characteristics and power characteristics of the energy storage facing planning based on the energy flow relationship and technical index of the energy storage power station.

[0060] Based on the energy flow relationship and technical index of the energy storage power station, the capacity characteristics and power characteristics of the energy storage facing planning can also be analyzed, as well as the economy, reliability, flexibility, and environmental protection of the energy storage facing planning.

[0061] Capacity characteristics Used to evaluate the ability of power supply to provide power, and the specific calculation is as follows:

[0062]

[0063] The parameters of the above formula are conventional technical parameters in the art, and the calculation process is also conventional technology in the art, which will not be repeated.

[0064] Electricity characteristic: annual electricity generation E of the power source CSP , for electricity balance analysis of system planning, the specific calculation is as follows:

[0065] E CSP = Cap CSP AUH CSP

[0066] The parameters of the above formula are conventional technical parameters in the art, and the calculation process is also conventional technology in the art, which will not be repeated.

[0067] Economic: economic is evaluated by the flat storage cost calculation model of storage, and the specific calculation formula is as follows:

[0068]

[0069] Wherein, LCOS is the flat storage cost, C invest , C O&M , C charge and C end are collectively referred to as each cost item required for flat storage cost calculation.

[0070] C invest , C O&M , C charge and C end are initial investment cost, operation and maintenance cost, charging cost of storage and scrap cost respectively.

[0071] and are operation and maintenance cost discount, charging cost of storage discount and scrap cost discount respectively.

[0072] is the discharge capacity index parameter discount.

[0073] Q dis is the discharge capacity index parameter. r is the discount rate, n is the time point of the specific operation year, and N is the service life of the storage technology.

[0074] The discharge capacity index parameter discount is calculated according to the following formula:

[0075]

[0076] Wherein, Cyc pa is annual cycle, Cap nom,E is nominal storage capacity, DoD is discharge depth, η RT is conversion efficiency, Cyc DegCyc is a cycle degradation parameter, T Deg η is a calendar degradation parameter, T out η is a self-discharge rate, T C T is a technical construction time.

[0077] The preset percentage can be set autonomously according to actual conditions, and can be 75%, indicating that the preset scrap value corresponds to the proportion, and the preset scrap value = nominal energy storage capacity x 75%.

[0078] For the cycle life Cyc life There are:

[0079]

[0080] Cyc life It can be understood as the actual service life of the battery determined according to the rated service life of the battery, and it can be understood that the actual service life of the battery is different due to different objective conditions such as use mode and use environment during continuous cycle use, and there is a certain difference with the rated service life of the battery.

[0081] Further, the cycle life Cyc life of the same type of battery can be calculated according to historical use data and historical actual service life of the same type of battery.

[0082] According to the above formula:

[0083]

[0084] Similarly, for the calendar degradation parameter T Deg , there are:

[0085]

[0086] T life is a degradation time parameter, which is a general professional term in the art.

[0087] The self-discharge rate η out can be calculated according to the following formula:

[0088]

[0089] DD is the discharge duration, η self,idle is the daily self-discharge in the idle state, Cyc pa is the annual cycle.

[0090] The initial investment cost C invest can be calculated according to the following formula:

[0091]

[0092] wherein Cap nom,P is the nominal power, C p is the specific power cost, C E is the specific capacity cost, C pr is the replacement cost, T r is the interval time, rep is the number of replacements in the whole technical life cycle.

[0093] Operation and maintenance cost C O&M includes power-specific operation and maintenance cost C p-OM and energy-specific operation and maintenance cost C E-OM .

[0094] The operation and maintenance cost is calculated according to the following formula:

[0095]

[0096] The parameters in the formula can refer to the above description.

[0097] The ratio between the charge cost discount of the energy storage and the discharge quantity index parameter discount is equal to the ratio of the electricity price P el and the conversion efficiency η RT :

[0098]

[0099] According to the above formula, the charge cost discount of the energy storage can be calculated as follows:

[0100] The scrap cost discount is calculated according to the following formula:

[0101]

[0102] wherein F EOL is the scrap cost coefficient.

[0103] Reliability: a model is established based on the long-term statistical characteristics of the energy storage, and the calculation is as follows:

[0104]

[0105] The parameters in the above formula are conventional technical parameters in the field, and the calculation process is also conventional technology in the field, which will not be described in detail.

[0106] Flexibility: the operation range of the unit, the climbing rate and the unit start-stop, and the specific calculation is as follows:

[0107]

[0108]

[0109] The parameters of the above formula are conventional technical parameters in the art, and the calculation process is also conventional technology in the art, which will not be described here.

[0110] Environmental protection: pollutant emissions and emission costs of energy storage power station are small, the specific calculation is as follows:

[0111]

[0112] Wherein, x is the capacity factor of the energy storage power station; Cap CSP is the rated capacity of the energy storage power station; CRF is the capital recovery factor; is the discounted value of the investment cost of the energy storage power station; is the annual operation and maintenance cost of the energy storage power station; Y is the state probability function of the energy storage power station; X CSP is the output state variable of the energy storage power station; is the i th output state of the energy storage power station: is the probability of the energy storage power station being in the i th output state; is the state variable of the energy storage power station at time h, and 1 indicates operation, and 0 indicates shutdown; respectively represent the minimum and maximum output of the energy storage power station; is the output of the energy storage power station at time h; RD CSP , RU CSP respectively are the maximum down ramp rate and the maximum up ramp rate of the energy storage power station; x is the type of pollutant; ρ x is the emission cost of unit mass of pollutant x; is the emission equivalent of pollutant x.

[0113] In the above step S3, the device establishes an optimization objective with the minimum of the levelized energy storage cost calculation model of energy storage as the objective function, constructs constraint conditions, and calculates the scale configuration of each subsystem of the energy storage power station according to the energy storage capacity and energy storage hours obtained by simulation calculation; the levelized energy storage cost calculation model includes cyclic degradation parameters and calendar degradation parameters.

[0114] The optimization objective min f is established with the minimum of the levelized energy storage cost calculation model of the energy storage power station as the objective function.

[0115] min f=LCOS

[0116] Wherein, LCOS is the levelized energy storage cost calculation model.

[0117] The constructed constraint conditions include resource constraints of the energy storage power station; operation constraints of the energy storage power station; external characteristic constraints of the energy storage power station;

[0118] The resource constraints of the energy storage power station are:

[0119]

[0120] wherein, A SF is the area of the energy storage station; is the planning area of the energy storage station;

[0121] The operation constraints of the energy storage station are:

[0122]

[0123]

[0124]

[0125]

[0126] wherein, is the electric energy transmitted to the energy storage station at time h; n is the energy conversion efficiency; DNI h is the electric power at time h: is the abandoned electric energy at time h; is the energy transmitted from the energy storage system to the power generation system at time h, is the electric energy transmitted from the energy storage system to the power generation system at time h; is the stored electric energy of the energy storage system at time h; η Heat is the loss coefficient of the energy storage system; η ST is the energy storage efficiency of the energy storage system; η PB is the efficiency of the power generation system; η TP is the discharging efficiency of the energy storage system; is the electric energy provided by the backup system at time h;

[0127] The external characteristic constraints of the energy storage station include the following:

[0128] The confidence capacity constraint is:

[0129]

[0130] The annual power generation constraint is:

[0131]

[0132] The reliability constraint is:

[0133]

[0134] The environmental protection constraint is:

[0135]

[0136] wherein, τ is the peak load period; T τis the length of the peak period; T is the simulation length; x is the capacity factor of the energy storage power station; k is the maximum generation proportion of the backup system: T min is the minimum generation time of the energy storage power station; AUH CSP is the annual design utilization hours of the energy storage power station; is the confidence capacity of the energy storage power station; E CSP is the annual generation of the energy storage power station.

[0137] The scale configuration of each subsystem of the energy storage power station is calculated as follows:

[0138] Cap SF = SM SF Cap CSP / η PB

[0139]

[0140] Cap SF is the capacity of the energy storage system; is the capacity of the energy storage subsystem; SM SF is the ratio of the photovoltaic capacity of the energy storage power station; H TES is the energy storage hours; η PB is the efficiency of the power generation system; η TP is the heat dissipation efficiency of the energy storage system; η Heat is the loss coefficient of the energy storage system; Cap CSP is the rated capacity of the energy storage power station.

[0141] In the above step S4, the device constructs an energy storage containing complementary optimization configuration model with the lowest total cost of the complementary system as the target, constructs a constraint condition, generates the configuration capacity of each type of power source of the complementary system, and guides the planning and production of each type of complementary power source in actual production according to the determined capacity configuration result.

[0142] The objective function of the energy storage containing complementary optimization configuration model with the lowest total cost of the complementary system as the target is as follows:

[0143]

[0144] N is the number of types of complementary power sources; K n is the number of units of the nth type of power source: is the investment cost of a single unit of the nth type of power source: is the operation and maintenance cost of a single unit of the nth type of power source; and are the sending and input powers of the tie line at h time; p SE and p BE are the selling and buying electricity prices respectively: C spill is the new energy disposal fee.

[0145] The constraint conditions for constructing the capacity of various power supply configurations in the complementary system include system power backup and balance constraints, operation constraints of various power supplies, system resource constraints, system new energy generation capacity proportion constraints, and system external area power transmission constraints.

[0146] The system power backup and balance constraints are:

[0147]

[0148]

[0149] wherein x n is the capacity factor of the nth type of power supply: Cap n is the rated capacity of the nth type of power supply; Ω R and Ω C are the new energy and conventional energy sets, respectively: maximum load; and represent the load power at time h and the power of the nth type of power supply, respectively;

[0150] The intermittent power supply such as wind power and photovoltaic power is:

[0151]

[0152] wherein P is the output of the intermittent power supply at time h; K w / s is the number of intermittent units; Z h is the output of a single unit at time h: is the abandoned power of the intermittent power supply at time h;

[0153] The operation constraints of the conventional power supply are:

[0154]

[0155]

[0156] wherein, is the number of conventional units in operation: is the minimum and maximum power of the conventional unit; is the output of the conventional unit at time h; RU C , RD C represent the up and down ramp rates of the conventional unit, respectively;

[0157] The system resource constraints are:

[0158]

[0159] wherein, represent the minimum and maximum number of configurations of the nth type of power supply, respectively;

[0160] The new energy generation capacity proportion constraint is:

[0161]

[0162] The system external area power transmission constraint is:

[0163]

[0164]

[0165] Cap line is the transmission capacity of the tie line.

[0166] The configuration capacity of the nth type of power supply in the complementary system is n as follows:

[0167] П n = K n Cap n

[0168] K n is the number of units of the nth type of power supply; and Cap n is the rated capacity of the nth type of power supply.

[0169] It can be understood that the method is based on a flat storage cost calculation model, and is a general model suitable for power type storage application scenarios and electricity type storage application scenarios, has the beneficial effects of facilitating the acquisition of flat storage cost data and improving the efficiency of acquiring flat storage cost data.

[0170] The energy storage optimization configuration method provided by the embodiment of the application takes the minimum flat storage cost calculation model of the energy storage as an objective function, constructs an optimization target with the lowest total cost of the complementary system, constructs an energy storage complementary optimization configuration model, constructs a constraint condition, generates the configuration capacity of each type of power supply in the complementary system, and guides the planning and production of each type of complementary power supply in actual production according to the determined capacity configuration result, so that the problem that new energy stations and new energy collection stations are difficult to plan and produce due to high cost can be effectively solved, resource allocation can be optimized, and the utilization rate of new energy of the system can be improved.

[0171] Further, the cycle degradation parameter is obtained by:

[0172] The cycle degradation parameter is calculated according to the cycle life and the preset percentage. Refer to the above embodiment description, which will not be repeated here.

[0173] Further, the calendar degradation parameter is obtained by:

[0174] The calendar degradation parameter is calculated according to the degradation time parameter and a preset percentage. Refer to the above embodiments for illustration, and no further elaboration is made.

[0175] Further, the establishment of the levelized energy storage cost calculation model comprises:

[0176] The levelized energy storage cost calculation model is established according to the discharge capacity index parameter, each cost item required for the levelized energy storage cost calculation, a discount rate, a time point of a specific operation year and a service life of the energy storage technology. Refer to the above embodiments for illustration, and no further elaboration is made.

[0177] Further, the levelized energy storage cost calculation model comprises a discharge capacity index parameter discount corresponding to the discharge capacity index parameter. Refer to the above embodiments for illustration, and no further elaboration is made.

[0178] Further, the calculation of the discharge capacity index parameter discount comprises:

[0179] The discharge capacity index parameter discount is calculated according to an annual cycle, a nominal energy storage capacity, a depth of discharge, a conversion efficiency, a cycle degradation parameter, a calendar degradation parameter, an external discharge rate and a technical construction time, a time point of a specific operation year and a service life of the energy storage technology. Refer to the above embodiments for illustration, and no further elaboration is made.

[0180] Further, each cost item required for the levelized energy storage cost calculation comprises:

[0181] An initial investment cost, an operation and maintenance cost, a charging cost of the energy storage and a scrap cost. Refer to the above embodiments for illustration, and no further elaboration is made.

[0182] Figure 2 is a structural schematic diagram of an energy storage optimization configuration device provided by an embodiment of the present application, as Figure 2 shown, the energy storage optimization configuration device provided by the embodiment of the present application comprises an acquisition unit 201, an analysis unit 202, a construction unit 203 and a configuration unit 204, wherein:

[0183] The acquisition unit 201 is configured to acquire energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station; the analysis unit 202 is configured to analyze capacity characteristics and power characteristics of energy storage facing planning based on an energy flow relationship and technical indexes of an energy storage power station; the construction unit 203 is configured to establish an optimization target with a minimum of a flat storage cost calculation model of energy storage as an objective function, construct a constraint condition, and calculate scale configurations of each subsystem of the energy storage power station according to energy storage capacity and energy storage hours obtained through simulation calculation; the flat storage cost calculation model comprises a cycle degradation parameter and a calendar degradation parameter; and the configuration unit 204 is configured to construct a complementary optimization configuration model containing energy storage with the lowest total cost of the complementary system as an objective, construct a constraint condition, generate configuration capacities of various power sources of the complementary system, and guide planning and production of various complementary power sources in actual production according to the determined capacity configuration result.

[0184] Specifically, the acquisition unit 201 in the device is configured to acquire energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station; the analysis unit 202 is configured to analyze capacity characteristics and power characteristics of energy storage facing planning based on an energy flow relationship and technical indexes of an energy storage power station; the construction unit 203 is configured to establish an optimization target with a minimum of a flat storage cost calculation model of energy storage as an objective function, construct a constraint condition, and calculate scale configurations of each subsystem of the energy storage power station according to energy storage capacity and energy storage hours obtained through simulation calculation; the flat storage cost calculation model comprises a cycle degradation parameter and a calendar degradation parameter; and the configuration unit 204 is configured to construct a complementary optimization configuration model containing energy storage with the lowest total cost of the complementary system as an objective, construct a constraint condition, generate configuration capacities of various power sources of the complementary system, and guide planning and production of various complementary power sources in actual production according to the determined capacity configuration result.

[0185] Further, the construction unit 203 is specifically configured to:

[0186] The cycle degradation parameter is calculated according to a cycle life and a preset percentage.

[0187] Further, the construction unit 203 is specifically configured to:

[0188] The calendar degradation parameter is calculated according to a degradation time parameter and a preset percentage.

[0189] Further, the construction unit 203 is specifically configured to:

[0190] The flat storage cost calculation model is established according to a discharge capacity index parameter, each cost item required for flat storage cost calculation, a discount rate, a time point of a specific operation year and a service life of energy storage technology.

[0191] The energy storage optimization configuration device provided by the embodiment of the present application takes the minimum of a flat energy storage cost calculation model of energy storage as an objective function, constructs an optimization target, constructs an energy storage complementary optimization configuration model with the lowest total cost of a complementary system as a target, constructs a constraint condition, generates configuration capacities of various power sources of the complementary system, and guides the planning and production of various complementary power sources in actual production according to the determined capacity configuration result, so that the problem that new energy stations and new energy collection stations are difficult to plan and produce due to too high cost can be effectively solved, resource allocation is optimized, and the utilization rate of new energy of the system is improved.

[0192] The embodiment of the energy storage optimization configuration device provided by the embodiment of the present application can be specifically used to execute the processing procedures of the above-mentioned method embodiments, and the functions thereof will not be repeated here, and the detailed description of the above-mentioned method embodiments can be referred to.

[0193] Figure 3 The electronic device entity structure schematic diagram provided by the embodiment of the present application is shown as Figure 3 The electronic device includes a processor 301, a memory 302 and a bus 303.

[0194] The processor 301, the memory 302 and the bus 303 complete mutual communication through the bus 303.

[0195] The processor 301 is used to call program instructions in the memory 302 to execute the method provided by the above-mentioned method embodiment, for example, including:

[0196] The energy storage data, regional resource data, system basic technical data and complementary system planning data of the new energy station and the new energy collection station are acquired.

[0197] Based on the energy flow relationship and technical index of the energy storage power station, the capacity characteristics and power characteristics of the energy storage facing planning are analyzed.

[0198] The minimum of a flat energy storage cost calculation model of energy storage is taken as an objective function to establish an optimization target, a constraint condition is constructed, and the scale configuration of each subsystem of the energy storage power station is calculated according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flat energy storage cost calculation model includes a cycle degradation parameter and a calendar degradation parameter.

[0199] The lowest total cost of a complementary system is taken as a target to construct an energy storage complementary optimization configuration model, a constraint condition is constructed, and the configuration capacity of various power sources of the complementary system is generated, and the planning and production of various complementary power sources in actual production are guided according to the determined capacity configuration result.

[0200] The embodiment discloses a computer program product, the computer program product comprises a computer program stored on a non-transitory computer readable storage medium, the computer program comprises program instructions, when the program instructions are executed by a computer, the computer can execute the method provided by each method embodiment, for example, comprising:

[0201] Obtain the energy storage data, regional resource data, system basic technical data and complementary system planning data of the new energy station and the new energy collection station;

[0202] Based on the energy flow relationship and technical index of the energy storage power station, analyze the capacity characteristics and power characteristics of the energy storage facing planning;

[0203] The optimization target is established with the minimum of the flatness energy storage cost calculation model of the energy storage as the objective function, the constraint condition is constructed, and the scale configuration of each subsystem of the energy storage power station is calculated according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flatness energy storage cost calculation model comprises a cycle degradation parameter and a calendar degradation parameter;

[0204] The complementary optimization configuration model containing energy storage is constructed with the minimum of the total cost of the complementary system as the target, the constraint condition is constructed, the configuration capacity of each type of power supply of the complementary system is generated, and the planning and production of each type of complementary power supply in actual production are guided according to the determined capacity configuration result.

[0205] The embodiment provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program enables the computer to execute the method provided by each method embodiment, for example, comprising:

[0206] Obtain the energy storage data, regional resource data, system basic technical data and complementary system planning data of the new energy station and the new energy collection station;

[0207] Based on the energy flow relationship and technical index of the energy storage power station, analyze the capacity characteristics and power characteristics of the energy storage facing planning;

[0208] The optimization target is established with the minimum of the flatness energy storage cost calculation model of the energy storage as the objective function, the constraint condition is constructed, and the scale configuration of each subsystem of the energy storage power station is calculated according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flatness energy storage cost calculation model comprises a cycle degradation parameter and a calendar degradation parameter;

[0209] The complementary optimization configuration model containing energy storage is constructed with the minimum of the total cost of the complementary system as the target, the constraint condition is constructed, the configuration capacity of each type of power supply of the complementary system is generated, and the planning and production of each type of complementary power supply in actual production are guided according to the determined capacity configuration result.

[0210] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0211] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0212] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for performing the function specified by the flowchart illustrations and / or block diagrams block or blocks.

[0214] In the description of the present specification, the description of the terms "one embodiment", "one specific embodiment", "some embodiments", "for example", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples.

[0215] The specific embodiments described above further illustrate the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for energy storage optimization configuration, characterized in that, The method comprises the following steps: obtaining energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station; the regional resource data comprises historical wind speed data, historical illumination data and historical water inflow data; the system basic technical data comprises load data, technical and economic data of various power sources, tie-line data and environmental and economic data; the complementary system planning data comprises system reserve rate, minimum utilization rate of tie-line, minimum proportion of new energy power generation of system and maximum allowed power conversion amount of unit time to transmission capacity ratio; the energy storage data is energy storage station planning data, which comprises annual design utilization hours of the energy storage station, confidence capacity of the energy storage station, minimum power generation time of the energy storage station and maximum power generation proportion of the backup system; based on the energy flow relationship and technical index of the energy storage station, the capacity characteristics and power characteristics of the energy storage station are analyzed; an optimization objective is established by taking the minimum of a flat energy storage cost calculation model of the energy storage as an objective function, constraint conditions are constructed, and the scale configuration of each subsystem of the energy storage station is calculated according to the energy storage capacity and energy storage hours obtained through simulation calculation; the flat energy storage cost calculation model comprises a cycle degradation parameter and a calendar degradation parameter; a complementary optimization configuration model containing energy storage is constructed by taking the minimum of the total cost of the complementary system as an objective, constraint conditions are constructed, the configuration capacity of each type of power source of the complementary system is generated, and the planning and production of each type of complementary power source in actual production are guided according to the determined capacity configuration result; the cycle degradation parameter is obtained, comprising: the cycle degradation parameter is calculated according to the cycle life and a preset percentage; the calendar degradation parameter is obtained, comprising: the calendar degradation parameter is calculated according to the degradation time parameter and a preset percentage; the flat energy storage cost calculation model is established, comprising: the flat energy storage cost calculation model is established according to the discharge capacity index parameter, each cost item required for flat energy storage cost calculation, discount rate, time point of specific operation year and service life of energy storage technology; the flat energy storage cost calculation model comprises discharge capacity index parameter discount corresponding to the discharge capacity index parameter; the calculation of the discharge capacity index parameter discount comprises: the discharge capacity index parameter discount is calculated according to annual cycle, nominal energy storage capacity, discharge depth, conversion efficiency, cycle degradation parameter, calendar degradation parameter, external discharge rate and technical construction time, time point of specific operation year and service life of energy storage technology; the calculation of the discharge capacity index parameter discount according to annual cycle, nominal energy storage capacity, discharge depth, conversion efficiency, cycle degradation parameter, calendar degradation parameter, external discharge rate and technical construction time, time point of specific operation year and service life of energy storage technology comprises: the discharge capacity index parameter discount is calculated according to the following formula: Wherein, Cyc pa is the annual cycle, Cap nom,E is the nominal energy storage capacity, DoD is the depth of discharge, η RT is the conversion efficiency, Cyc Deg is the cycle degradation parameter, T Deg is the calendar degradation parameter, η out is the external discharge rate, T C is the technical construction time, Q dis is the discharge quantity index parameter, r is the discount rate, n is the specific point in time of the operating year, and N is the service life of the energy storage technology. wherein Cyc life is the actual battery life determined from the battery rated life, T life is the degradation time parameter.

2. The energy storage optimization configuration method of claim 1, wherein, each cost item required for flat energy storage cost calculation comprises: initial investment cost, operation and maintenance cost, charging cost of energy storage and scrap cost.

3. An energy storage optimization configuration device, characterized by, The method comprises the following steps: an obtaining unit is configured to obtain energy storage data, regional resource data, system basic technical data and complementary system planning data of a new energy station and a new energy collection station; The regional resource data includes historical wind speed data, historical illumination data and historical water inflow data; the system basic technical data includes load data, technical and economic data of various power sources, tie line data and environmental and economic data; the complementary system planning data includes system reserve rate, minimum utilization rate of tie line, minimum proportion of new energy power generation of system and maximum allowed power conversion amount of unit time to proportion of transmission capacity; the energy storage data is energy storage power station planning data, which includes annual design utilization hours of energy storage power station, confidence capacity of energy storage power station, minimum power generation time of energy storage power station and maximum power generation proportion of backup system; The analysis unit is configured to analyze capacity characteristics and power characteristics of the energy storage for planning based on the energy flow relationship and technical index of the energy storage power station; The construction unit is configured to establish an optimization objective with a minimum as an objective function of a flat rate energy storage cost calculation model of the energy storage, construct a constraint condition, and calculate the scale configuration of each subsystem of the energy storage power station according to the energy storage capacity and energy storage hours obtained by simulation calculation; the flat rate energy storage cost calculation model includes a cycle degradation parameter and a calendar degradation parameter; The configuration unit is configured to establish a complementary optimization configuration model containing energy storage with the minimum total cost of the complementary system as an objective, construct a constraint condition, generate the configuration capacity of each type of power source of the complementary system, and guide the planning and production of each type of complementary power source in actual production according to the determined capacity configuration result; The construction unit is specifically configured to: calculate the cycle degradation parameter according to the cycle life and the preset percentage; The construction unit is specifically configured to: calculate the calendar degradation parameter according to the degradation time parameter and the preset percentage; The construction unit is further specifically configured to: establish the flat rate energy storage cost calculation model according to the discharge capacity index parameter, each cost item required for flat rate energy storage cost calculation, discount rate, time point of specific operation year and service life of energy storage technology; The flat rate energy storage cost calculation model includes a discharge capacity index parameter discount corresponding to the discharge capacity index parameter; The calculation of the discharge capacity index parameter discount includes: calculating the discharge capacity index parameter discount according to annual cycle, nominal energy storage capacity, discharge depth, conversion efficiency, cycle degradation parameter, calendar degradation parameter, external discharge rate and technical construction time, time point of specific operation year and service life of energy storage technology; The calculation of the discharge capacity index parameter discount according to annual cycle, nominal energy storage capacity, discharge depth, conversion efficiency, cycle degradation parameter, calendar degradation parameter, external discharge rate and technical construction time, time point of specific operation year and service life of energy storage technology includes: calculating the discharge capacity index parameter discount according to the following formula: wherein Cyc pa is the annual cycle, Cap nom,E is the nominal energy storage capacity, DoD is the depth of discharge, η RT is the conversion efficiency, Cyc Deg is the cycle degradation parameter, T Deg is the calendar degradation parameter, η out is the external discharge rate, T C is the technical construction time, Q dis is the discharge quantity index parameter, r is the discount rate, n is the specific point in time of the operating year, and N is the service life of the energy storage technology. wherein Cyc life is the actual battery life determined from the battery rated life, T life is the degradation time parameter.

4. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method of claim 1 or 2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of claim 1 or 2.

Images