Demand-response-based integrated optical-storage scheduling method, device, equipment, and medium

Abstract

The present invention relates to the technical field of intelligent scheduling of energy storage systems, and specifically to a method, device, equipment and medium for demand-response-based integrated photovoltaic and energy storage scheduling. The method obtains parameter information of a photovoltaic and energy storage integrated system within a target scheduling period, and constructs constraints for the photovoltaic and energy storage integrated system during operation and an objective function for optimal economic performance based on the parameter information; the objective function is solved according to the constraints to obtain a power scheduling strategy for energy storage equipment and a battery state of charge configuration strategy for the photovoltaic and energy storage integrated system within a target scheduling period participating in demand response. The method takes into account an optimization scheme for photovoltaic-energy storage integration in the energy storage scheduling strategy, which helps to more efficiently manage and schedule photovoltaic and energy storage resources and cope with the instability of photovoltaic power generation. When formulating an optimized scheduling strategy for photovoltaic and energy storage integration, the specific scenarios in which the system participates in demand response are fully considered, solving the problem of failing to fully tap the potential of the energy storage system in participating in demand response scheduling when formulating a scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent scheduling of energy storage systems, and in particular to a demand-responsive integrated photovoltaic and storage scheduling method, device, equipment, and medium. Background Art

[0002] With the global energy transition and the continued development of renewable energy, the utility of integrated photovoltaic (PV)-storage systems in improving energy efficiency and grid stability is becoming increasingly prominent. These systems can store energy during periods of excess PV generation and release it during periods of insufficient PV output or peak loads. This reduces pressure on the upstream power grid, improves renewable energy utilization and power system flexibility, and reduces electricity costs to a certain extent. These systems can effectively mitigate the challenges to power system stability posed by the intermittent and uncertain nature of PV generation. Currently, the application of integrated PV-storage systems in emerging energy scenarios such as smart campuses and virtual power plants is gradually expanding.

[0003] In order to better utilize the bidirectional flexible scheduling resources of the integrated photovoltaic and storage system and improve the economic efficiency of the energy storage system, the demand response mechanism can be incorporated into the scheduling decision-making basis of the integrated photovoltaic and storage system. By reducing the energy storage charging load during the demand response period, a certain demand response subsidy income can be obtained. However, traditional integrated photovoltaic and storage scheduling strategies often ignore the economic benefits of participating in demand response, and fail to fully tap the potential of energy storage systems in participating in demand response scheduling when formulating scheduling plans. In addition, large users currently only focus on electricity charges when managing electricity consumption, but in fact, the proportion of basic electricity expenses cannot be underestimated, and basic electricity expenses can be saved through demand management. Therefore, when considering the integrated photovoltaic and storage scheduling strategy, it is necessary to incorporate the concept of demand management to better achieve optimal electricity management for users. Summary of the Invention

[0004] In view of this, the present invention provides a demand-response-based integrated photovoltaic and energy storage scheduling method, device, equipment and medium to solve the problem that the existing photovoltaic and energy storage scheduling fails to fully tap the potential of the energy storage system in participating in demand-response scheduling.

[0005] In a first aspect, the present invention provides a photovoltaic and storage integrated scheduling method based on demand response, the method comprising: obtaining parameter information of the photovoltaic and storage integrated system within a target scheduling period, the parameter information including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information; constructing the constraint conditions during operation of the photovoltaic and storage integrated system and the objective function at the time of economic optimization based on the parameter information; solving the objective function according to the constraint conditions to obtain the energy storage equipment power scheduling strategy and battery state of charge configuration strategy of the photovoltaic and storage integrated system within the target scheduling period participating in demand response.

[0006] The demand-response-based photovoltaic-storage integrated scheduling method provided by an embodiment of the present invention obtains parameter information of the photovoltaic-storage integrated system within the target scheduling period, including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information, and constructs the constraint conditions during the operation of the photovoltaic-storage integrated system and the objective function at the time of economic optimization based on the parameter information; solves the objective function according to the constraint conditions to obtain the energy storage device power scheduling strategy and battery state of charge configuration strategy of the photovoltaic-storage integrated system within the target scheduling period participating in demand response. Therefore, this method takes into account the optimization scheme of photovoltaic-energy storage integration in the energy storage scheduling strategy at the park and microgrid levels, which helps to more efficiently manage and schedule photovoltaic storage resources and cope with the instability of photovoltaic power generation. At the same time, when formulating the photovoltaic-storage integrated optimization scheduling strategy, the specific scenarios of the system participating in demand response are fully considered, which solves the problem of not fully tapping the potential of the energy storage system in participating in demand response scheduling when formulating the scheduling plan.

[0007] In an optional embodiment, the objective function is determined based on the difference between the total cost, total revenue and total benefit of the photovoltaic storage integrated system within the target scheduling period.

[0008] In this embodiment, the benefits, revenue, and costs of the integrated photovoltaic and energy storage system are integrated into the objective function, covering multiple economic indicator dimensions. This allows the optimized objective function to more accurately and comprehensively reflect the economic benefits of the energy storage scheduling cycle.

[0009] In an optional embodiment, the total benefit of the integrated photovoltaic and energy storage system within the target scheduling period is determined by the sum of the benefits of photovoltaic self-use, the demand management benefits generated by system-wide demand management, and the peak-valley arbitrage benefits of energy storage;

[0010] The benefits of self-use photovoltaic power generation are calculated using the following formula:

[0011]

[0012] P load (t) = P station (t)+P building (t)+P char (t)

[0013] Where, represents the self-use benefit of photovoltaic power generation, λ(t) represents the time-of-use electricity price at time t, and P PV (t) represents the photovoltaic power at time t, P load (t) represents the sum of all types of loads in the system at time t, P station (t) represents the load of the charging pile connected to the photovoltaic storage integrated system at time t, P vuilding (t) represents the building load connected to the photovoltaic storage integrated system at time t, P char(t) represents the energy storage charging load at time t, and T represents the target scheduling period;

[0014] The demand management benefits generated by system-wide demand management are calculated using the following formula:

[0015] C DM_0 =p base ×P m

[0016] C DM =p base ×max{P spike ,P peak ,P shoulder ,P xalley}

[0017] ΔC DM =(C DM0 -C DM ) / D

[0018] Where C DM_0 It represents the historical highest basic electricity fee calculated based on the maximum load limit demand of the system, p base Indicates the basic electricity price, P m is the maximum load limit of the system, C DM Indicates the basic electricity fee calculated according to actual demand when performing demand management, P spike 、P peak 、P shoulder 、P valley Respectively represent the actual value of demand during the peak, flat, and valley periods within the calculation cycle, ΔC DM is the average daily demand management benefit after demand management is allocated in the corresponding month, and D represents the number of days in the corresponding month;

[0019] The peak-valley arbitrage benefits of energy storage are calculated using the following formula:

[0020]

[0021] Where, ΔC bat represents the peak-valley arbitrage benefit of energy storage, P dis (t) represents the energy storage discharge power at time t, P char (t) represents the energy storage charging power at time t.

[0022] In this embodiment, when determining the total benefit, the demand management benefit generated by system-wide demand management is taken into account, thereby making the final optimized scheduling strategy more executable;

[0023] In an optional embodiment, the total revenue of the photovoltaic integrated storage system within the target scheduling period is determined by the sum of the photovoltaic surplus grid-connected revenue and the system's demand response subsidy revenue;

[0024] The income from the photovoltaic surplus grid connection is calculated using the following formula:

[0025]

[0026] Where, sale (t) is the photovoltaic grid price at time t;

[0027] The system's demand response subsidy income is calculated using the following formula:

[0028]

[0029] Where C DR represents the subsidy income of the system participating in demand response, i represents the i-th demand response participated by the system, n represents the number of demand response participated in on a single day on the target day, T i,start 、T i,end are the start and end time of the i-th demand response respectively; λ DR,i (t i ) represents the demand response compensation unit price corresponding to the i-th demand response at time t, P DR,i (t i ) represents the effective response amount of the corresponding period of the i-th demand response; P DR,i (t i ) is calculated using the following formula:

[0030] P DR,i (t i )=P base,i (t i )-P load (t i )

[0031] Where, P base,i (t i ) represents the response baseline, P load (t i ) represents t i The sum of all types of loads in the system during a period of time.

[0032] In an optional embodiment, the total cost of the integrated photovoltaic and storage system during the target scheduling period is determined by the sum of the cost of electricity purchased by the integrated photovoltaic and storage system from the upper power grid, the target day energy storage life cycle cost, and the target day photovoltaic life cycle cost;

[0033] The cost of purchasing electricity from the upper grid for the integrated photovoltaic and storage system is calculated using the following formula:

[0034]

[0035] Where C buyrepresents the cost of the photovoltaic and storage integrated system purchasing electricity from the upper power grid, C DM Indicates the basic electricity fee calculated according to actual demand when performing demand management, P pre (t) is the net load forecast value of the time period, P bat (t) is the charge and discharge power of the energy storage system during period t;

[0036] The target daily energy storage life cycle cost is calculated using the following formula:

[0037] C ESS =LCOE bat E0

[0038] In the formula, LCOE bat represents the energy storage life cycle electricity cost, E0 represents the target daily energy storage processing power, LCOE bat It is expressed by the following formula:

[0039]

[0040] Where C inv represents the initial investment cost of the energy storage system, C O&M represents the operation and maintenance cost of the energy storage system, C repl represents the energy storage system replacement cost, C rec Represents the residual value of energy storage equipment, E sum The total amount of electricity processed during the entire life cycle of the energy storage system;

[0041] The initial investment cost of the energy storage system is calculated using the following formula:

[0042] C inv =γ P P ESS +γ E E ESS

[0043] Where, γ P Represents the unit power investment cost of energy storage, P ESS is the rated power of energy storage, γ E represents the investment cost per unit capacity of energy storage, E ESS is the rated capacity of the energy storage;

[0044] The operation and maintenance costs of the energy storage system are calculated using the following formula:

[0045]

[0046] Where,∈ O is the annual operation and maintenance cost per unit power of energy storage, ∈ Mis the annual operation and maintenance cost coefficient per unit capacity of energy storage, r is the discount rate, m is the number of years the energy storage system is in operation, including the year after battery replacement, and M represents the life cycle;

[0047] The replacement cost of the energy storage system is calculated using the following formula:

[0048]

[0049] Where N repl represents the number of replacements, π repl is the replacement cost per unit capacity;

[0050] The residual value of energy storage equipment is calculated using the following formula:

[0051] C rec =σ rec R rec

[0052] Where σ rec R is the ratio of the residual value of the energy storage power station to the system cost, rec It is the system residual value of energy storage technology in a capacity scenario;

[0053] The total power consumption of the energy storage system over its entire life cycle is calculated using the following formula:

[0054] E sum =kDODρτE ess

[0055] Where k represents the number of cycles of the energy storage system under the designed DOD, DOD is the depth of discharge (DOD) of the energy storage battery, ρ is the energy efficiency of the energy storage system, and τ is the equivalent capacity retention rate of the energy storage system per cycle;

[0056] The target daily life cycle cost of photovoltaics is calculated using the following formula:

[0057]

[0058] In the formula, LCOE PV Indicates the cost of electricity for the entire photovoltaic life cycle;

[0059] The cost of electricity for the entire photovoltaic life cycle is calculated using the following formula:

[0060]

[0061] Where C inv_PV represents the initial investment cost of photovoltaics, C O&M_pV Indicates the total operation and maintenance cost during the photovoltaic life cycle, E sum_pV represents the total power generation during the photovoltaic life cycle, where

[0062] Cinv_PV =c inv_pv E PV

[0063]

[0064] Where c inv_pv is the initial investment cost per unit capacity of the photovoltaic power station, in yuan / W; E PV Indicates the rated capacity of the photovoltaic power station, in W; c O&M_pv It represents the annual unit operation and maintenance cost of the photovoltaic power station, in yuan / (W·year).

[0065] In an optional implementation, the constraints include: power balance constraints, demand management constraints, energy storage battery power constraints, demand response load constraints, response times and response time constraints, and energy storage battery state of charge constraints.

[0066] In an optional implementation, the demand management constraint is expressed using the following formula:

[0067] P station (t)+P building (t)+P char (t)≤P MAX

[0068] Where, P MAX is the maximum load limit set, P station (t) represents the charging pile load, P building (t) represents the building load, P char (t) represents the energy storage charging power at time t.

[0069] In this embodiment, the constraints include system power, demand management, energy storage battery power, demand response load, demand response times and duration, and energy storage battery state of charge, further improving the feasibility and effectiveness of the scheduling strategy in actual application scenarios.

[0070] In the second aspect, the present invention provides a photovoltaic and storage integrated scheduling device based on demand response, which includes: a parameter acquisition module for obtaining parameter information of the photovoltaic and storage integrated system within the target scheduling period, the parameter information including load and power information, electricity price information, energy storage and photovoltaic parameter information and demand response information; a condition and function construction module for constructing the constraint conditions during the operation of the photovoltaic and storage integrated system and the objective function at the optimal economic performance based on the parameter information; a scheduling strategy determination module for solving the objective function according to the constraint conditions to obtain the energy storage equipment power scheduling strategy and battery state of charge configuration strategy of the photovoltaic and storage integrated system within the target scheduling period participating in demand response.

[0071] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the demand-response-based integrated photovoltaic and storage scheduling method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0072] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the demand-response-based integrated photovoltaic and storage scheduling method of the first aspect or any corresponding embodiment thereof.

[0073] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for enabling a computer to execute the demand-response-based photovoltaic and storage integrated scheduling method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0075] Figure 1 1 is a flow chart of a demand-response-based photovoltaic and storage integrated scheduling method according to an embodiment of the present invention;

[0076] Figure 2 is a flow chart of another demand-response-based photovoltaic and storage integrated scheduling method according to an embodiment of the present invention;

[0077] Figure 3 is a schematic diagram of a scheduling situation according to an embodiment of the present invention;

[0078] Figure 4 2 is a block diagram of a demand-response-based photovoltaic and storage integrated scheduling device according to an embodiment of the present invention;

[0079] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0081] According to an embodiment of the present invention, an embodiment of a demand-responsive integrated photovoltaic and storage scheduling method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0082] In this embodiment, a demand-responsive integrated photovoltaic and storage scheduling method is provided, which can be used for electronic devices such as computers, mobile phones, and tablet computers. Figure 1 is a flow chart of a demand-response-based photovoltaic storage integrated scheduling method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0083] Step S101: Obtain parameter information for the integrated photovoltaic and energy storage system within the target scheduling period. This parameter information includes load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information. Specifically, this parameter information includes external data information and internal parameter information. External data information includes target scheduling day load, power forecast data, baseline load, time-of-use electricity price information, and demand response information. Internal parameter information includes energy storage device parameters, demand management upper limit, target scheduling day energy storage initial power and SOC, and cost per kilowatt-hour.

[0084] In an optional embodiment, the parameter information includes the load prediction value P(t) of each component in the target scheduling period, the photovoltaic power prediction value P(t) and the photovoltaic power prediction value P(t). PV (t), time-of-use electricity price information λ(t), basic electricity unit price p base , Rated energy storage capacity E ess , energy storage charge and discharge conversion rate η c h ar and η dis , various costs of energy storage life cycle, energy storage SOC and DOD limits, number of cycles in the energy storage life cycle, initial energy consumption E of energy storage target scheduling cycle ini , Photovoltaic grid-connected electricity price v sale (t), various costs of photovoltaic life cycle, demand response subsidy price P DR,i (t i ) and time period information ti , response baseline P base,i (t i )wait.

[0085] Step S102 constructs the operating constraints and economically optimized objective function for the integrated photovoltaic and energy storage system based on the parameter information. Specifically, the acquired parameter information can be combined to construct operating constraints for the system, such as power constraints and state-of-charge constraints. When constructing the objective function, the goal is to achieve optimal economic performance. This optimal economic performance, or optimal economic utility, can be determined by considering the benefits, revenue, and costs of the integrated photovoltaic and energy storage system.

[0086] Step S103 solves the objective function based on the constraints to obtain the energy storage device power scheduling strategy and battery state of charge configuration strategy for the integrated photovoltaic and energy storage system within the target scheduling period for participating in demand response. Specifically, the objective function is solved based on the established constraints to obtain data such as charge and discharge power that minimizes the objective function under the constraints, i.e., optimizes economic efficiency. Based on this data, the energy storage device power scheduling strategy and battery state of charge configuration strategy are then formulated, thereby optimizing the energy storage device power scheduling strategy and battery state of charge configuration strategy within the target scheduling period.

[0087] The demand-response-based photovoltaic-storage integrated scheduling method provided by an embodiment of the present invention obtains parameter information of the photovoltaic-storage integrated system within the target scheduling period, including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information, and constructs the constraint conditions during the operation of the photovoltaic-storage integrated system and the objective function at the time of economic optimization based on the parameter information; solves the objective function according to the constraint conditions to obtain the energy storage device power scheduling strategy and battery state of charge configuration strategy of the photovoltaic-storage integrated system within the target scheduling period participating in demand response. Therefore, this method takes into account the optimization scheme of photovoltaic-energy storage integration in the energy storage scheduling strategy at the park and microgrid levels, which helps to more efficiently manage and schedule photovoltaic storage resources and cope with the instability of photovoltaic power generation. At the same time, when formulating the photovoltaic-storage integrated optimization scheduling strategy, the specific scenarios of the system participating in demand response are fully considered, which solves the problem of not fully tapping the potential of the energy storage system in participating in demand response scheduling when formulating the scheduling plan.

[0088] This embodiment provides a demand-responsive integrated photovoltaic and storage scheduling method, which includes the following steps:

[0089] Step S201: Obtain parameter information of the photovoltaic integrated system within the target scheduling period, including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0090] Step S202 : constructing the constraint conditions of the photovoltaic-storage integrated system during operation and the objective function for optimal economic performance based on the parameter information.

[0091] Specifically, the objective function is determined based on the difference between the total cost, total revenue and total benefit of the photovoltaic storage integrated system within the target scheduling period.

[0092] Therefore, the objective function can be expressed as follows:

[0093] min(-C eff -C income +C cost )

[0094] Where C eff is the total benefit of the system during the scheduling period, C income is the total revenue of the system during the scheduling period, C cost is the total cost of the system during the scheduling period.

[0095] The total benefit of the integrated photovoltaic and energy storage system within the target scheduling period refers to the economic benefits generated through system energy management, specifically the cost savings. This total benefit can be determined by the sum of the benefits of self-consumption of photovoltaic power, the benefits of demand management generated by system-wide demand management, and the benefits of peak-valley arbitrage from energy storage.

[0096] The benefits of self-use photovoltaic power generation are calculated using the following formula:

[0097]

[0098] P load (t) = P station (t)+P building (t)+P char (t)

[0099] Where, represents the self-use benefit of photovoltaic power generation, λ(t) represents the time-of-use electricity price at time t, and P PV (t) represents the photovoltaic power at time t, P load (t) represents the sum of all types of loads in the system at time t, P station (t) represents the load of the charging pile connected to the photovoltaic storage integrated system at time t, P building (t) represents the building load connected to the photovoltaic storage integrated system at time t, P char (t) represents the energy storage charging load at time t, and T represents the target scheduling period;

[0100] For large industrial users who calculate their base electricity charges based on actual demand, the demand management benefit refers to the cost savings from calculating the base electricity charge based on actual demand compared to calculating the base electricity charge based on the contracted limit demand. However, the contracted limit metering scope covers all loads connected to the user's grid. For the demand management benefit of an integrated solar-storage system, this embodiment calculates the base electricity charge based on actual demand, with the system's maximum load limit as the comparison standard. Therefore, the demand management benefit generated by system-wide demand management is calculated using the following formula:

[0101] C DM_0 =p base ×P m

[0102] C DM =p base ×max{P spike ,P peak ,P shoulder ,P valley}

[0103]

[0104] Where C DM_0 It represents the historical highest basic electricity fee calculated based on the maximum load limit demand of the system, p base Indicates the basic electricity price, P m is the maximum load limit of the system, C DM Indicates the basic electricity fee calculated according to actual demand when performing demand management, P spike 、P peak 、P shoulder 、P valley Respectively represent the actual value of demand during the peak, flat, and valley periods within the calculation cycle, ΔC DM is the average daily demand management benefit after demand management is allocated in the corresponding month, and D represents the number of days in the corresponding month;

[0105] The peak-valley arbitrage benefit of energy storage is specifically manifested as the reduction in electricity purchase costs achieved by microgrid energy storage equipment through the "low storage, high discharge" mechanism. Therefore, the peak-valley arbitrage benefit of energy storage is calculated using the following formula:

[0106]

[0107] Where, ΔC bat represents the peak-valley arbitrage benefit of energy storage, P dis (t) represents the energy storage discharge power at time t, P char (t) represents the energy storage charging power at time t.

[0108] The total revenue of the integrated photovoltaic and storage system during the target dispatch period is the revenue generated by the energy exchange between the system and the upper-level grid. Therefore, this total revenue can be determined by the sum of the revenue from the grid connection of surplus photovoltaic power and the subsidy revenue from the system's participation in demand response.

[0109] The income from the surplus photovoltaic power generation is specifically the income from the return of the excess photovoltaic power generation to the upper power grid after the system's internal photovoltaic power generation meets the "self-generation and self-use" requirement. Therefore, the income from the surplus photovoltaic power generation is calculated using the following formula:

[0110]

[0111] Where, v sale (t) is the photovoltaic grid price at time t;

[0112] The system's demand response subsidy income is calculated using the following formula:

[0113]

[0114] Where C DR represents the subsidy income of the system participating in demand response, i represents the i-th demand response participated by the system, n represents the number of demand response participated in on a single day on the target day, T i,start 、T i,end are the start and end time of the i-th demand response respectively; λ DR,i (t i ) represents the demand response compensation unit price corresponding to the i-th demand response at time t, P DR,i (t i ) represents the effective response amount of the corresponding period of the i-th demand response; P DR,i (t i ) is calculated using the following formula:

[0115] P DR,i (t i )=P base,i (t i )-P load (t i )

[0116] Where, P base,i (t i ) represents the response baseline, P load (t i ) represents t i The sum of all types of loads in the system during a period of time.

[0117] The total cost of the integrated photovoltaic and storage system within the target scheduling period is the cost incurred by the system in exchanging energy with the upper-level power grid. Therefore, this cost is determined by the sum of the cost of the integrated photovoltaic and storage system purchasing electricity from the upper-level power grid, the target day energy storage life cycle cost, and the target day photovoltaic life cycle cost.

[0118] The cost of purchasing electricity from the upstream power grid for the integrated photovoltaic and storage system includes the basic electricity price and the electricity price per unit volume. Therefore, the cost is calculated using the following formula:

[0119]

[0120] Where C buy represents the cost of the photovoltaic and storage integrated system purchasing electricity from the upper power grid, C DM Indicates the basic electricity fee calculated according to actual demand when performing demand management, P pre (t) is the net load forecast value of the day before the t period, P bat (t) is the charging and discharging power of the energy storage system during period t. station (t), building load P building (t), energy storage load P bat (t), photovoltaic power P PV (t) composed of the integrated photovoltaic and storage system structure, P pre (t) and P bat (t) The calculation formulas are:

[0121] P pre (t) = P station (t)+P building (t)-P PV (t)

[0122] P bat (t) = P char (t)-P dis (t)

[0123] Based on this, the cost of purchasing electricity from the upper power grid for the integrated photovoltaic and storage system on the target day can be expressed as:

[0124]

[0125] The life cycle cost of energy storage refers to the costs invested in the entire life cycle of the energy storage system, including initial investment cost, operation and maintenance cost, replacement cost, recovery cost, charging cost, etc. The charging cost includes all costs incurred by charging the energy storage system from the upper power grid during its entire life cycle. For capacity-based energy storage scenarios, when quantifying the life cycle cost of energy storage in a specific period, the life cycle cost of electricity (LCOE) of energy storage can be used. bat Specifically, the target daily energy storage life cycle cost is the LCOE (Low Cost of Electricity). bat The target daily energy storage life cycle cost is calculated by multiplying the target daily energy storage processing power E0 by the following formula:

[0126] C ESS =LCOEbat E0

[0127] In the formula, LCOE bat It represents the cost per kilowatt-hour of energy storage throughout its entire life cycle, and E0 represents the target daily energy storage processing capacity.

[0128] Energy storage life cycle cost per kilowatt-hour (LCOE) bat , quantifies the cost of each unit of discharged electricity in the entire life cycle of the energy storage equipment, specifically refers to the cost invested in the entire life cycle of the energy storage system, which is allocated to the cumulative amount of electricity delivered in the entire life cycle of the energy storage. bat It is expressed by the following formula:

[0129]

[0130] Where C inv represents the initial investment cost of the energy storage system, C O&M represents the operation and maintenance cost of the energy storage system, C repl represents the energy storage system replacement cost, C rec Represents the residual value of energy storage equipment, E sum It is the total amount of electricity processed by the energy storage system throughout its life cycle. It should be noted that in actual applications, the cost per kilowatt-hour is generally a fixed value, so the target day energy storage life cycle cost depends on the amount of electricity processed on that day.

[0131] In the cost per kilowatt-hour, the initial investment cost of the energy storage system C inv It refers to the fixed capital invested once when building an energy storage system, including the costs of design, hardware, software, procurement, construction, etc. Therefore, the initial investment cost of the energy storage system is calculated using the following formula:

[0132] C inv =γ P P ESS +γ E E ESS

[0133] Where, γ P Represents the unit power investment cost of energy storage, P ESS is the rated power of energy storage, γ E represents the investment cost per unit capacity of energy storage, E ESS is the rated capacity of the energy storage;

[0134] Operation and maintenance costs (O&M costs) are the dynamic costs incurred to ensure the normal operation of energy storage equipment during its lifecycle, such as manpower, maintenance, and testing costs. They are usually measured on an annual basis. Specifically, the O&M costs of energy storage systems are calculated using the following formula:

[0135]

[0136] Where C OM represents the annual equipment operation and maintenance cost, ∈ O is the annual operation and maintenance cost per unit power of energy storage, ∈ M is the annual operation and maintenance cost coefficient per unit capacity of energy storage, r is the discount rate, m is the operation years of the energy storage system, including the years after battery replacement, and M represents the life cycle, that is, C O&M Indicates the total operation and maintenance cost of equipment with a life cycle of M years.

[0137] Energy storage system replacement cost C repl This means that the performance of energy storage batteries decreases with the increase in usage. When the battery performance cannot meet the requirements, the battery needs to be replaced to extend the service life of the energy storage power station. Specifically, the replacement cost of the energy storage system is calculated using the following formula:

[0138]

[0139] Where N repl represents the number of replacements, π repl is the replacement cost per unit capacity.

[0140] Energy storage equipment recovery residual value C rec It refers to the residual value recovery income generated by the energy storage system at the end of its service life. Specifically, the residual value of the energy storage equipment is calculated using the following formula:

[0141] C rec =σ rec R rec

[0142] Where σ rec R is the ratio of the residual value of the energy storage power station to the system cost, rec It is the system residual value of energy storage technology in a capacity-based scenario.

[0143] The total power consumption of the energy storage system over its entire life cycle is calculated using the following formula:

[0144] E sum =kDOPρτE ess

[0145] Where k represents the number of cycles of the energy storage system under the designed DOD, DOD is the depth of discharge (DOD) of the energy storage battery, ρ is the energy efficiency of the energy storage system, and τ is the equivalent capacity retention rate of the energy storage system per cycle.

[0146] The photovoltaic life cycle cost covers multiple dimensions, among which initial investment cost, operation and maintenance, replacement and other costs account for a large proportion. Since the focus is on energy storage equipment, it can be considered that there is no photovoltaic replacement cost during the default energy storage operation cycle. The photovoltaic target day life cycle cost is calculated using the following formula:

[0147]

[0148] In the formula, LCOE PV The photovoltaic life cycle electricity cost is calculated using the following formula:

[0149]

[0150] Where C inv_PV represents the initial investment cost of photovoltaics, C O&M_PV Indicates the total operation and maintenance cost during the photovoltaic life cycle, E sum_PV represents the total power generation during the photovoltaic life cycle, where

[0151] C inv_PV =c inv_pv E PV

[0152]

[0153] Where c inv_pv is the initial investment cost per unit capacity of the photovoltaic power station, in yuan / W; E PV Indicates the rated capacity of the photovoltaic power station, in W; c O&M_pv It represents the annual unit operation and maintenance cost of the photovoltaic power station, in yuan / (W·year).

[0154] In an optional implementation, the constraints include: power balance constraints, demand management constraints, energy storage battery power constraints, demand response load constraints, response times and response time constraints, and energy storage battery state of charge constraints.

[0155] The power balance constraint is expressed as follows:

[0156]

[0157] P char (t)×P dis (t) = 0

[0158] P grid (t) = P pre (t)+P bat (t)

[0159]

[0160] Where, Represent the upper limit of the charging and discharging power of the energy storage device. grid (t) is the power exchanged between the photovoltaic and energy storage integrated system and the upper power grid at time t, is the maximum exchange power between the system and the upper grid at time t. They represent the upper and lower limits of the adjustable load of the charging pile respectively. They represent the upper and lower limits of the building's adjustable load respectively.

[0161] The demand management constraint includes the constraint on the target day's maximum load, which is expressed as follows:

[0162] P station (t)+P building (t)+P char (t)≤P MAX

[0163] Where, P MAX is the maximum load limit set, P station (t) represents the charging pile load, P building (t) represents the building load, P char (t) represents the energy storage charging power at time t.

[0164] The energy storage battery capacity constraint is determined based on the "low storage, high discharge" mechanism of energy storage. Therefore, the constraint can be expressed as follows:

[0165] 0≤E ini +E char (t)-E dis (t)≤E max

[0166] Where, E ini is the initial capacity of the energy storage battery on the target day, E char (t), E dis (t) are the charging and discharging capacity of the energy storage battery in period t, E max The upper limit of the energy storage battery capacity is taken here to ensure the safety of the energy storage system operation. ess .

[0167] Demand response generally requires that the average load after the response during the target response period does not exceed the baseline average, and the maximum load after the response does not exceed the baseline maximum. Therefore, during the demand response period, the demand response load constraint is expressed using the following formula:

[0168]

[0169]

[0170] In the formula, ave(·) represents the mean, and max(·) represents the maximum value. base,i (t) is the load baseline value corresponding to the i-th demand response in period t.

[0171] For the response number constraint, it is required that the number of demand responses on a single day on the target day cannot exceed the upper limit of the single-day demand response number. The response number constraint is expressed by the following formula:

[0172] N≤n max

[0173] Where N is the number of daily demand responses on the target day, n max The upper limit for the number of times you can participate in demand response in a single day.

[0174] Response time constraints include response time ΔT, total response time per day Specifically, the response time constraint is expressed using the following formula:

[0175] ΔT=T i,end -T i,start ≥T i,min

[0176]

[0177] Where, T i,min Indicates the lower limit of a single response time, T d,max Indicates the upper limit of the total response time in a single day.

[0178] Before determining the state of charge constraints of the energy storage battery, first determine the state of charge constraints of the system energy storage battery. The system energy storage battery state of charge calculation formula is as follows:

[0179]

[0180] Where, E res (t) indicates the remaining battery power, E ess Indicates the rated capacity of the battery.

[0181] To ensure that the energy storage battery is within the safe operating range at all times and to avoid damage to the battery caused by overcharging or over-discharging, it is necessary to statically constrain the SOC of the energy storage battery. The constraint model is as follows:

[0182] SOC min ≤SOC(t)≤SOC max

[0183] Where, SOC min and SOC max They are the minimum SOC value and the maximum SOC value allowed by the battery respectively.

[0184] At the same time, the SOC change state during the use of the energy storage battery can be calculated using the following formula:

[0185]

[0186] Where η char and η dis They are the charging and discharging efficiency of the energy storage battery respectively.

[0187] The battery can be charged up to the amount of electricity E in the corresponding period c (t) and the amount of energy that can be discharged downward E d (t) is calculated as follows:

[0188] E c (t) = [SOC max -SOC(t)]×E ESS

[0189] E d (t) = [SOC(t) - SOC min ]×E ESS

[0190] Therefore, the constraint model of the amount of electricity that can be charged upward and discharged downward in the corresponding period is as follows:

[0191]

[0192] At the end of the optimization cycle, the battery should reach a certain SOC level to ensure that a certain amount of power is retained for the next use. The battery SOC termination constraint model is as follows:

[0193] SOC(T)≥SOC(T) end

[0194] SOC(T) end Indicates the final SOC level that the battery should reach at the end of the target day.

[0195] Step S203: Solve the objective function according to the constraint conditions to obtain the energy storage device power scheduling strategy and battery state of charge configuration strategy for the integrated photovoltaic and storage system participating in the target scheduling period of demand response. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0196] The demand-response-based integrated PV-storage scheduling method provided by the present invention incorporates demand management into the economically optimal objective function, making the final optimized scheduling strategy more executable. Furthermore, the objective function incorporates the benefits, revenue, and costs of the integrated PV-storage system, encompassing multiple economic indicator dimensions. This allows the optimized objective function to more accurately and comprehensively reflect the economic benefits of the energy storage scheduling cycle. Furthermore, the constraints incorporate system power, demand management, energy storage battery charge, demand response load, number and duration of demand responses, and energy storage battery state of charge, further enhancing the feasibility and effectiveness of the scheduling strategy in practical application scenarios.

[0197] As a specific application example of the embodiment of the present invention, Figure 2 As shown in the figure, the demand-response-based integrated photovoltaic and storage scheduling method is implemented using the following process:

[0198] 1. Set up a microgrid system with an integrated photovoltaic and storage system, electric vehicle charging pile loads, and DC server loads in buildings.

[0199] 2. Obtain external data and internal parameter information for the integrated PV-storage microgrid on the target scheduling day. External data includes load and power forecast data for the target scheduling day, baseline load, time-of-use electricity price information, and demand response information. Internal parameter information includes energy storage device parameters, demand management upper limit, initial energy storage capacity and SOC for the target scheduling day, and cost per kilowatt-hour.

[0200] 3. Based on the characteristics of various components of the microgrid, construct actual constraints such as system power constraints, demand management constraints, energy storage battery power constraints, demand response load constraints, demand response number and duration constraints, and energy storage battery charge state constraints, focusing on unconventional constraints such as demand response and demand management.

[0201] 4. Comprehensively consider the economic benefits of photovoltaic self-generation and self-use, demand management, energy storage peak-valley arbitrage, the economic benefits of photovoltaic surplus grid access and the system's participation in demand response subsidies, as well as the cost of the microgrid purchasing electricity from the upper power grid, the target day energy storage life cycle cost, the target day photovoltaic life cycle cost, and integrate demand response scenarios and demand management to construct an optimized scheduling economic objective function.

[0202] 5. Based on the above constraints and objective functions, an economic optimization scheduling model for the integrated photovoltaic and storage microgrid system is constructed that is more suitable for actual operations in demand response scenarios. Finally, the economic optimization scheduling model of the system is solved, and an intelligent energy storage charging and discharging strategy is formulated for the integrated photovoltaic and storage microgrid participating in demand response scenarios.

[0203] As a specific application embodiment of the present invention, when the demand response-based integrated photovoltaic and storage scheduling method is applied, based on the relevant data and parameter information of a certain actual large-scale user photovoltaic and storage integrated microgrid, the response day of the actual demand response announcement released in the summer is selected as the target scheduling day, and the economic optimization scheduling strategy of energy storage is designed for the microgrid system with electric vehicle charging pile load, DC server load in the building, and photovoltaic and storage integration. Using the Gurobi optimizer, the relevant forecast data, electricity price information, and parameter information are input into the model, and the average load of the first five days of the same type (working days in this embodiment) is taken as the baseline load. The corresponding objective function and constraint conditions are set, and the 24-hour energy storage optimization scheduling results of the target day are obtained as shown in Table 1 and Figure 3 As shown, in Figure 3In the figure, the horizontal axis represents the 24 time periods (24 hours) of the target day (scheduling cycle), which respectively indicate the number of the corresponding time period and may be without units; the vertical axis represents the charging / discharging power of the energy storage in the corresponding time period (positive values ​​indicate charging, negative values ​​indicate discharging), in kW·h.

[0204] Table 1 Charging and discharging strategies for energy storage devices

[0205]

[0206]

[0207] According to the energy storage dispatch strategy in Table 1, the self-generated and self-consumed PV power on the target dispatch day is 150.55 kWh. Based on the monthly time-of-use electricity price, the benefit from self-generated PV power is 162.7 yuan. Through demand management, compared to a maximum load limit of 40 kW, at a base electricity price of 40.8 yuan / kW, the microgrid system can reduce its base electricity bill by 720.528 yuan, or an average daily reduction of 24.0176 yuan. The microgrid's energy storage equipment reduced its electricity purchase costs by 141.9599 yuan on the target day through the "low storage, high discharge" mechanism. Therefore, the total benefit of the integrated PV and storage microgrid on the target dispatch day is 328.6775 yuan.

[0208] In terms of revenue on the target dispatch day, all PV power is supplied to the park, resulting in zero PV grid-connected electricity and corresponding zero PV grid-connected revenue. During the demand response period (period 15), the integrated PV-storage system, compared to the baseline, assumes no other loads are adjusted, and only energy storage is used to adjust loads. The microgrid supplies power to the park through energy storage discharge. Based on a demand response compensation unit price of 2.4 yuan / kW, this yields a demand response subsidy of 3.264 yuan. The total revenue for the integrated PV-storage microgrid on the target dispatch day is 3.264 yuan.

[0209] In terms of the target dispatch day cost, the electricity purchase fee within the microgrid includes the microgrid basic electricity fee and the electricity fee per kilowatt-hour, which are 30.3824 and 48.8641 yuan respectively. The target day energy storage discharge is 235.93kWh. Calculated at a storage electricity cost of 0.333 yuan / kWh, the corresponding target day energy storage life cycle cost is 78.5647 yuan. The target day photovoltaic power generation is 360.9kWh. Calculated at a photovoltaic electricity cost of 0.277 yuan / kWh, the corresponding target day photovoltaic power generation life cycle cost is 99.9693 yuan (of which the electricity used for self-generation and self-use in the integrated photovoltaic and storage microgrid is 150.55kWh, corresponding to a target day life cycle cost of 41.7024 yuan). Therefore, the total cost of the integrated photovoltaic and storage microgrid on the target dispatch day is 257.7805 yuan. Furthermore, the total economic utility of the integrated photovoltaic and storage microgrid on the target dispatch day is 74.161 yuan.

[0210] The demand-response-based integrated photovoltaic and storage scheduling method provided in this embodiment further demonstrates the energy storage scheduling results in scenarios that consider demand management and participation in demand response, in addition to the conventional "off-peak charging, peak discharging" scheduling strategy for the photovoltaic and storage integrated microgrid. Furthermore, by measuring the overall utility of the system, the economic feasibility of the proposed demand-response-based integrated photovoltaic and storage scheduling method is demonstrated.

[0211] This embodiment also provides a demand-responsive integrated photovoltaic and energy storage scheduling device, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described are omitted. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0212] This embodiment provides a demand-responsive integrated photovoltaic and storage scheduling device. Figure 4 As shown, including:

[0213] Parameter acquisition module 41, used to obtain parameter information of the photovoltaic integrated storage system within the target scheduling period, the parameter information including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information;

[0214] Condition and function building module 42, used to build the constraint conditions and economic optimization objective function of the photovoltaic storage integrated system during operation based on parameter information;

[0215] The scheduling strategy determination module 43 is used to solve the objective function according to the constraint conditions to obtain the energy storage device power scheduling strategy and battery state of charge configuration strategy of the integrated photovoltaic and energy storage system within the target scheduling period participating in demand response.

[0216] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0217] The embodiment of the present invention also provides a computer device having the above Figure 5 The demand-response-based integrated photovoltaic and storage scheduling device shown.

[0218] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0219] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0220] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0221] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created based on the use of a computer device for displaying a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0222] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0223] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0224] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0225] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0226] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A demand-response-based integrated photovoltaic and storage scheduling method, characterized in that: The method comprises: Obtaining parameter information of the integrated photovoltaic and photovoltaic system within the target scheduling period, the parameter information including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information; Based on the parameter information, the constraint conditions of the photovoltaic and storage integrated system during operation and the objective function for optimal economic performance are constructed; Solving the objective function according to the constraint conditions to obtain a power scheduling strategy for energy storage devices and a battery state of charge configuration strategy for the integrated photovoltaic and energy storage system within a target scheduling period participating in demand response; The objective function is determined based on the difference between the total cost, total revenue, and total benefit of the photovoltaic and storage integrated system within the target scheduling period; the total cost of the photovoltaic and storage integrated system within the target scheduling period is determined by the sum of the cost of electricity purchased by the photovoltaic and storage integrated system from the upper power grid, the target day energy storage life cycle cost, and the target day photovoltaic life cycle cost; The total benefit of the integrated photovoltaic and energy storage system within the target scheduling period is determined by the sum of the benefits of photovoltaic self-use, the demand management benefits generated by system-wide demand management, and the peak-valley arbitrage benefits of energy storage. The photovoltaic self-generation and self-use benefits are calculated using the following formula: P load (t)=P station (t)+P building (t)+P char (t) Where, represents the self-use benefit of photovoltaic power generation, λ(t) represents the time-of-use electricity price at time t, and P PV (t) represents the photovoltaic power at time t, P load (t) represents the sum of all types of loads in the system at time t, P station (t) represents the load of the charging pile connected to the photovoltaic storage integrated system at time t, P building (t) represents the building load connected to the photovoltaic storage integrated system at time t, P char (t) represents the energy storage charging load at time t, and T represents the target scheduling period; The demand management benefits generated by demand management within the system are calculated using the following formula: C DM_0 =p base ×P m C DM =p base ×max{P spike ,P peak ,P shoulder ,P valley } ΔC DM =(C DM0 -C DM ) / D Where C DM_0 It represents the historical highest basic electricity fee calculated based on the maximum load limit demand of the system, p base Indicates the basic electricity price, P m is the maximum load limit of the system, C DM Indicates the basic electricity fee calculated according to actual demand when performing demand management, P spike 、P peak 、P shoulder 、P valley Respectively represent the actual value of demand during the peak, flat, and valley periods within the calculation cycle, ΔC DM is the average daily demand management benefit after demand management is allocated in the corresponding month, and D represents the number of days in the corresponding month; The energy storage peak-valley arbitrage benefit is calculated using the following formula: Where, ΔC bat represents the peak-valley arbitrage benefit of energy storage, P dis (t) represents the energy storage discharge power at time t, P char (t) represents the energy storage charging power at time t; The total revenue of the integrated photovoltaic and storage system within the target dispatch period is determined by the sum of the revenue from the photovoltaic surplus grid connection and the subsidy revenue from the system's participation in demand response. The photovoltaic surplus grid-connected income is calculated using the following formula: Where, sale (t) is the photovoltaic grid price at time t; The system's demand response subsidy income is calculated using the following formula: Where C DR represents the subsidy income of the system participating in demand response, i represents the i-th demand response participated by the system, n represents the number of demand response participated in on a single day on the target day, T i,start 、T i,end are the start and end time of the i-th demand response respectively; λ DR,i (t i ) represents the demand response compensation unit price corresponding to the i-th demand response at time t, P DR,i (t i ) represents the effective response amount of the corresponding period of the i-th demand response; P DR,i (t i ) is calculated using the following formula: P DR,i (t i )=P base,i (t i )-P load (t i ) Where, P base,i (t i ) represents the response baseline, P load (t i ) represents t i The sum of all types of loads in the system during a period of time.

2. The method according to claim 1, characterized in that The cost of purchasing electricity from the upper power grid for the photovoltaic and storage integrated system is calculated using the following formula: Where C buy represents the cost of the photovoltaic and storage integrated system purchasing electricity from the upper power grid, C DM Indicates the basic electricity fee calculated according to actual demand when performing demand management, P pre (t) is the net load forecast value of the day before the t period, P bat (t) is the charge and discharge power of the energy storage system during period t; The target daily energy storage life cycle cost is calculated using the following formula: C ESS =LCOE bat E0 In the formula, LCOE bat represents the energy storage life cycle electricity cost, E0 represents the target daily energy storage processing power, LCOE bat It is expressed by the following formula: Where C inv represents the initial investment cost of the energy storage system, C O&M represents the operation and maintenance cost of the energy storage system, C repl represents the energy storage system replacement cost, C rec Represents the residual value of energy storage equipment, E sum The total amount of electricity processed during the entire life cycle of the energy storage system; The initial investment cost of the energy storage system is calculated using the following formula: C inv =c P P ESS +g E E ESS Where, γ P Represents the unit power investment cost of energy storage, P ESS is the rated power of energy storage, γ E represents the investment cost per unit capacity of energy storage, E ESS is the rated capacity of the energy storage; The operation and maintenance costs of the energy storage system are calculated using the following formula: Where,∈ O is the annual operation and maintenance cost per unit power of energy storage, ∈ M is the annual operation and maintenance cost per unit capacity of energy storage, r is the discount rate, m is the number of years the energy storage system is in operation, including the year after battery replacement, and M represents the life cycle; The replacement cost of the energy storage system is calculated using the following formula: Where N repl represents the number of replacements, π repl is the replacement cost per unit capacity; The residual value of energy storage equipment is calculated using the following formula: C rec =s rec R rec Where σ rec R is the ratio of the residual value of the energy storage power station to the system cost, rec It is the system residual value of energy storage technology in a capacity scenario; The total power consumption of the energy storage system over its entire life cycle is calculated using the following formula: AND sum =kDODρτE ESS Where k represents the number of cycles of the energy storage system under the designed DOD, DOD is the depth of discharge (DOD) of the energy storage battery, ρ is the energy efficiency of the energy storage system, and τ is the equivalent capacity retention rate of the energy storage system per cycle. The photovoltaic life cycle cost on the target day is calculated using the following formula: In the formula, LCOE PV Indicates the cost of electricity for the entire photovoltaic life cycle; The cost of electricity for the entire photovoltaic life cycle is calculated using the following formula: Where C inv_PV represents the initial investment cost of photovoltaics, C O&M_PV Indicates the total operation and maintenance cost during the photovoltaic life cycle, E sum_PV represents the total power generation during the photovoltaic life cycle, where C inv_PV =c inv_pv E PV Where c inv_pv is the initial investment cost per unit capacity of the photovoltaic power station, in yuan / W; E PV Indicates the rated capacity of the photovoltaic power station, in W; c O&M_pv It represents the annual unit operation and maintenance cost of the photovoltaic power station, in yuan / (W·year).

3. The method according to claim 1, characterized in that The constraints include: power balance constraints, demand management constraints, energy storage battery power constraints, demand response load constraints, response times and response time constraints, and energy storage battery state of charge constraints.

4. The method according to claim 3, characterized in that The demand management constraint is expressed as follows: P station (t)+P building (t)+P char (t)≤P MAX Where, P MAX is the maximum load limit set, P station (t) represents the charging pile load, P building (t) represents the building load, P char (t) represents the energy storage charging power at time t.

5. A demand-responsive integrated photovoltaic and storage scheduling device, characterized in that: The photovoltaic storage integrated scheduling method based on demand response according to any one of claims 1 to 4 is adopted, wherein the device comprises: A parameter acquisition module is used to obtain parameter information of the photovoltaic integrated system within the target scheduling period, including load and power information, electricity price information, energy storage and photovoltaic parameter information, and demand response information; A condition and function building module, used to build the constraint conditions of the photovoltaic storage integrated system during operation and the objective function for economic optimization based on the parameter information; The scheduling strategy determination module is used to solve the objective function according to the constraint conditions to obtain the energy storage device power scheduling strategy and battery state of charge configuration strategy of the integrated photovoltaic and storage system within the target scheduling period participating in demand response.

6. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the demand-responsive photovoltaic integrated scheduling method according to any one of claims 1 to 4 by executing the computer instructions.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the demand-response-based photovoltaic and storage integrated scheduling method according to any one of claims 1 to 4.

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