Multi-time scale light storage charging and discharging integrated energy management method, device, medium and product

Abstract

The invention discloses a multi-time-scale optical storage charging and discharging integrated energy management method and device, a medium and a product, and relates to the field of power scheduling, and the method comprises the steps: determining the earnings of an optical storage charging and discharging multifunctional integrated energy management system according to the day-ahead operation data; constructing an upper-layer control model; according to the upper layer control model, determining the load scheduling power of the zero-carbon park; power deviation is determined according to the real-time operation parameters and zero-carbon park load dispatching power, and a lower-layer control model is constructed; determining adjustment power according to the power deviation and a lower-layer control model; the load scheduling of the zero-carbon park is adjusted; and in combination with the real-time electricity price, the power utilization load, the charging pile load and the energy storage charging and discharging power of the zero-carbon park are adjusted and optimized in real time on the intra-day scale. According to the method, the time sequence coupling characteristics of the energy storage and the charging pile can be considered, so that the method better adapts to the time uncertainty of the user side load and the energy storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power scheduling, in particular to a multi-time scale light storage charging and discharging integrated energy management method, device, medium and product. BACKGROUND

[0002] In order to accelerate the green transformation of industrial and commercial electricity, many regions are building zero-carbon industrial parks. Among them, the "light storage charging and discharging" multifunctional integrated control system has become an important way to promote the low-carbonization of industrial and commercial electricity due to its advantages of multi-energy complementation and multi-element synergy. In the past five years, the scale of new energy power generation in China has continued to grow rapidly. By the end of 2024, China's new energy power generation capacity, including wind power, solar power and biomass power, reached 1.45 billion kilowatts, exceeding the installed capacity of thermal power for the first time. On the one hand, in industrial and commercial electricity, some provinces have implemented "five-stage" time-of-use electricity prices, further widening the peak-valley price difference of industrial and commercial electricity across the province. On the other hand, with the rapid growth of new energy vehicles and electric buses, the impact of charging pile electricity load on the power grid is becoming more and more serious. At the same time, there is a mismatch between the distribution of photovoltaic power generation and the distribution of electric vehicle (EV) charging demand. For example, in a certain province in May 2025, the average charging price of residential electric vehicles is 0.385 yuan / kWh during the charging low period, while the average charging price is 0.767 yuan / kWh during the peak and peak periods. In comparison, the electricity price during the low period is almost half of that during the peak period. In order to solve the above problems, a multi-time scale light storage charging and discharging integrated control system has emerged to participate in the power spot market for deep peak shaving. It consists of a distributed photovoltaic power generation system, an energy storage system, a charging pile, a local intelligent microgrid and a control unit to form a comprehensive energy system. Combined with relevant policies, it participates in the power spot market, realizes green electricity direct connection of zero-carbon park, user-side peak shifting and resource allocation.

[0003] Currently, many domestic zero-carbon parks arrange distributed photovoltaic panels and energy storage for grid connection. The current problems are as follows: (1) photovoltaic power generation load electricity and grid connection, energy storage peak-valley price arbitrage; (2) not achieving photovoltaic power generation and park load complementarity, electric buses charging disorderly; (3) not fully combining the matching between grid time-of-use electricity prices, new energy charging time scale and energy storage configuration capacity.

[0004] Based on the above problems, there is an urgent need to provide a multi-time scale characteristic matching control strategy considering the matching of photovoltaic output and park load considering the grid time-of-use electricity price. SUMMARY

[0005] The purpose of the present application is to provide a multi-time scale light storage charging and discharging integrated energy management method, device, medium and product, which can consider the time sequence coupling characteristics of energy storage and charging piles, and better adapt to the time uncertainty of user side load and energy storage.

[0006] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a multi-time scale light storage charging and discharging integrated energy management method, which is applied to a light storage charging and discharging multi-functional integrated energy management system in a light storage charging and discharging multi-functional integrated control system; the light storage charging and discharging multi-functional integrated control system further comprises: a distributed solar cell module array, a zero-carbon park load device, a lithium battery energy storage array, a distributed direct current super charging pile group, a group string type rectifier inverter, a power distribution cabinet, an alternating current transformer and a substation, a remote terminal unit of a power grid, and a communication control device; the multi-time scale light storage charging and discharging integrated energy management method comprises:

[0008] Obtaining operation data of the light storage charging and discharging multi-functional integrated control system in the day before; the operation data includes photovoltaic power generation power, zero-carbon park load power, energy storage adjustable capacity, and time-of-use electricity price of the power grid;

[0009] According to the operation data in the day before, determining the light storage charging and discharging multi-functional integrated energy management system benefit; the light storage charging and discharging multi-functional integrated energy management system benefit includes charging and discharging electricity selling benefit, zero-carbon park power purchase cost from the power grid, investment cost, and operation and maintenance cost;

[0010] Building an upper control model with the maximum light storage charging and discharging multi-functional integrated energy management system benefit as an objective function, and with zero-carbon park power balance and power flow distribution constraints, photovoltaic output upper and lower limit constraints, energy storage operation output constraints, and charging pile charging power constraints as constraint conditions;

[0011] According to the upper control model, determining the zero-carbon park load scheduling power;

[0012] According to the real-time operation parameters and the zero-carbon park load scheduling power, determining the power deviation, and building a lower control model;

[0013] According to the power deviation and the lower control model, determining the adjustment power; and adjusting the zero-carbon park load scheduling; and combining the real-time electricity price, adjusting and optimizing the power of the zero-carbon park load, the charging pile load, and the energy storage charging and discharging in the intra-day scale.

[0014] Optionally, the upper control model specifically comprises:

[0015] Using the formula maxR=R-C grid -I C -OC determining a target function maxR;

[0016] wherein R is the charging and discharging electricity selling revenue, R = R PV +R BA +R EV +R Park , R PV is the grid-connected revenue of photovoltaic, R BA is the peak-valley arbitrage revenue of energy storage, R EV is the electricity selling revenue of EV charging pile, R Park is the electricity selling revenue of zero-carbon park load, C grid is the electricity purchasing cost of zero-carbon park from the grid, I C is the investment cost, I C =I PVC +I BAC +I EVC +I EMS , I PVC is the investment cost of photovoltaic, I BAC is the investment cost of lithium battery energy storage, I EVC is the investment cost of charging pile, I EMS is the cost of multi-functional integrated energy management system of photovoltaic, energy storage and charging, O C is the operation and maintenance cost, O C =O PVC +O BAC +O EVC +O EMS +O PL , O PVC is the operation and maintenance cost of photovoltaic power generation, O BAC is the operation and maintenance cost of lithium battery energy storage, O EVC is the operation and maintenance cost of charging pile, O EMS is the operation and maintenance cost of charging pile, O PL is the line loss;

[0017] determining the park power balance and power flow distribution constraints by using the formula ; wherein P i and P G,i are the active power and injected active power of the i-th branch of the zero-carbon park; P L,i and P loss,i are the active load and active power line loss of the i-th branch of the zero-carbon park; Q i and Q G,i are the reactive power and injected reactive power of the i-th branch; Q L,i and Q loss,i are the reactive load and reactive power line loss of the i-th branch of the zero-carbon park;

[0018] determining the park power balance and power flow distribution constraints by using the formula determining upper and lower limits of photovoltaic output; wherein, P PVS is the real-time output power of photovoltaic, is the maximum power of photovoltaic output, Q PVS is the reactive power of photovoltaic, is the power factor;

[0019] determining the operating output constraints of energy storage by using the formula ; wherein, are the minimum and maximum charging power of energy storage, respectively; are the minimum and maximum discharging power of energy storage, respectively; soc min , soc max are the minimum and maximum state of charge of energy storage, respectively;

[0020] determining the charging power constraints of charging piles by using the formula ; wherein P EV,i is the real-time output power of each charging pile, is the rated power of each charging pile.

[0021] Optionally, the zero-carbon park load scheduling power is determined according to the upper-layer control model, specifically including:

[0022] determining the zero-carbon park load scheduling power according to the upper-layer control model by using a genetic algorithm.

[0023] Optionally, the zero-carbon park load scheduling power is determined by using the formula P′ = P′ PVS + P′ online + P′ EVo + P′ bC + P′ bD + P′ Park + P′ EVo + P′ tools ; wherein,

[0024] P′ is the zero-carbon park load scheduling power; P′ PVS is the predicted power of photovoltaic output; P′ online is the predicted on-grid power of photovoltaic; P′ EVo is the predicted power purchased from the grid by charging piles; P′ bC is the predicted charging power of lithium battery energy storage; P′ bD is the predicted discharging power of lithium battery energy storage; P′ Park is the load adjustment power of the zero-carbon park; P′ EVo is the predicted power of the direct current super-charging pile of the zero-carbon park, P′ tools is the predicted line loss power.

[0025] Optionally, the step of determining the power deviation based on real-time operating parameters and the load scheduling power of the zero-carbon park, and constructing a lower-level control model, specifically includes:

[0026] Using the formula P = P PVS +P online +P EVo +P bC +P bD +P Park +P EVo +P tools Determine the real-time power P; where P PVS Photovoltaic power output; P online P represents the grid-connected power of photovoltaic power. EVo The power purchased from the grid for the charging pile; P bC Power for charging lithium battery energy storage; P bD P represents the energy storage and discharge power of a lithium battery. Park For zero-carbon park load power; P EVo For the DC supercharging pile power of the zero-carbon park, P tools This refers to line loss power;

[0027] The power deviation ΔP is determined using the formula ΔP = PP, where P is the load dispatch power of the zero-carbon park.

[0028] Optionally, determining the adjustment power based on the power deviation and the lower-level control model specifically includes:

[0029] Using formula p AJ =P″ Park +P″ EVo +P″ bC +P″ bD Determine the adjustment power p AJ ;

[0030] Among them, P″ Park Adjusting power for load in zero-carbon industrial parks; P″ EVo Adjusting the power of DC supercharging piles in the zero-carbon park; P″ bC Adjusting charging power for lithium battery energy storage; P″ bD Adjust the discharge power for lithium battery energy storage.

[0031] Secondly, this application provides a multi-timescale integrated photovoltaic-storage-charge-discharge energy management device, the multi-timescale integrated photovoltaic-storage-charge-discharge energy management device comprising:

[0032] The day-ahead data acquisition module is used to acquire the day-ahead operating data of the integrated photovoltaic-storage-charging-discharging control system; the operating data includes photovoltaic power generation, zero-carbon park load power, adjustable energy storage capacity, and grid time-of-use electricity price.

[0033] A benefit determination module is configured to determine a benefit of the photovoltaic-storage-charging-discharging multifunctional integrated energy management system according to the operation data of the previous day, wherein the benefit of the photovoltaic-storage-charging-discharging multifunctional integrated energy management system includes a charging-discharging electricity selling benefit, a zero-carbon park power grid electricity purchasing cost, an investment cost and an operation and maintenance cost.

[0034] An upper control model determination module is configured to construct an upper control model with a maximum benefit of the photovoltaic-storage-charging-discharging multifunctional integrated energy management system as an objective function, and with a zero-carbon park power balance and power flow distribution constraint, a photovoltaic output upper and lower limit constraint, a storage operation output constraint and a charging pile charging power constraint as constraint conditions.

[0035] A power scheduling module is configured to determine a zero-carbon park load scheduling power according to the upper control model.

[0036] A lower control model construction module is configured to determine a power deviation according to real-time operation parameters and the zero-carbon park load scheduling power, and to construct a lower control model.

[0037] An optimization module is configured to determine an adjusted power according to the power deviation and the lower control model, to adjust the zero-carbon park load scheduling, and to combine a real-time electricity price to perform real-time adjustment and optimization on the zero-carbon park electricity load, the charging pile load and the storage charging-discharging power in an intraday scale.

[0038] In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the multi-time scale photovoltaic-storage-charging-discharging integrated energy management method.

[0039] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program executable by a processor to implement the multi-time scale photovoltaic-storage-charging-discharging integrated energy management method.

[0040] In a fifth aspect, a computer program product is provided, which includes a computer program executable by a processor to implement the multi-time scale photovoltaic-storage-charging-discharging integrated energy management method.

[0041] According to the embodiments provided in the present application, the following technical effects are achieved:

[0042] The application provides a multi-time-scale light storage charging and discharging integrated energy management method, device, medium and product, which can consider the time sequence coupling characteristics of energy storage and charging piles, better adapt to the time uncertainty of user side load and energy storage, and perform double-layer target control architecture through an upper layer control model and a lower layer control model. The upper layer control model obtains a whole-day light storage charging and discharging scheduling plan and maximization of the overall benefits of the light storage charging and discharging multifunctional integrated energy management system in a day-ahead prediction scale; and the lower layer control model is a power error adjustment, which adjusts and optimizes the power of the power load, the charging pile load and the energy storage charging and discharging in the park in an intraday scale in combination with a real-time electricity price. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments 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 any creative effort.

[0044] Figure 1 It is an embodiment of the present application to provide a multi-time-scale light storage charging and discharging integrated energy management method flowchart.

[0045] Figure 2 It is a light storage charging and discharging multifunctional integrated control system structure schematic diagram.

[0046] Figure 3 It is an upper layer control model and a lower layer control model solving flowchart.

[0047] Figure 4 It is a multi-time-scale characteristic matching control strategy schematic diagram of photovoltaic output and zero-carbon park load. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort are within the protection scope of the present application.

[0049] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail in combination with the drawings and specific embodiments.

[0050] In an exemplary embodiment, as shown in Figure 1 A multi-time-scale light storage charging and discharging integrated energy management method is provided, which is applied to Figure 2The light storage charging and discharging multifunctional integrated energy management system in the light storage charging and discharging multifunctional integrated control system; the light storage charging and discharging multifunctional integrated control system further comprises: a distributed solar cell component array, a zero-carbon park load device, a lithium battery energy storage array, a distributed direct-current super charging pile group, a group string rectifier inverter, a power distribution cabinet, an alternating current transformer and a substation, a remote terminal unit (RTU) of a power grid and a communication control device; wherein the distributed solar cell component array, the zero-carbon park load device, the lithium battery energy storage array, the distributed direct-current super charging pile group, the group string rectifier inverter, the power distribution cabinet, the alternating current transformer and the substation are primary electrical systems; the remote terminal unit of the power grid, the “light storage charging and discharging” multifunctional integrated energy management system and the communication control device are secondary control systems; the method comprises the following S101 to S106. Wherein:

[0051] S101, obtaining operation data of the light storage charging and discharging multifunctional integrated control system in the previous day; the operation data includes photovoltaic power generation power, zero-carbon park load power, energy storage adjustable capacity and power grid time-of-use electricity price;

[0052] S102, determining the “light storage charging and discharging” multifunctional integrated energy management system benefit according to the operation data in the previous day; the “light storage charging and discharging” multifunctional integrated energy management system benefit includes charging and discharging electricity sales benefit, zero-carbon park power purchase cost from the power grid, investment cost and operation and maintenance cost;

[0053] S103, constructing an upper control model with the maximum “light storage charging and discharging” multifunctional integrated energy management system benefit as an objective function, and with zero-carbon park power balance, power flow distribution constraints, photovoltaic output upper and lower limit constraints, energy storage operation output constraints and charging pile charging power constraints as constraint conditions;

[0054] The target function maxR is determined by the formula maxR=R-C grid -I C -O C ; wherein, R is the charging and discharging electricity sales benefit, C grid is the zero-carbon park power purchase cost from the power grid, I C is the investment cost, and O C is the operation and maintenance cost;

[0055] Wherein, R=R PV +R BA +R EV +R Park , R PV is the grid-connected benefit of photovoltaic, R BA is the energy storage peak-valley arbitrage benefit, R EV is the electricity sales benefit of the EV charging pile, and R Park is the electricity use benefit of the zero-carbon park load.

[0056] The grid-connected benefit of photovoltaics is determined by the formula

[0057] In the formula, T on is the photovoltaic grid-connected power duration, P online is the photovoltaic grid-connected power, and p es is the photovoltaic grid-connected price.

[0058] The peak-valley arbitrage benefit of energy storage is determined by the formula

[0059] In the formula, T beos is the lithium battery energy storage grid-connected duration, P bDo is the lithium battery energy storage grid-connected power, and p ebos is the photovoltaic grid-connected price; T beip is the lithium battery energy storage purchase of park photovoltaic power duration, P bCi is the lithium battery energy storage purchase of photovoltaic charging power, and p ebis is the photovoltaic power generation and sale price; T beop is the lithium battery energy storage grid purchase power duration, P bCo is the lithium battery energy storage grid purchase power, and p beop is the grid purchase time-of-use price.

[0060] The selling benefit of EV charging piles is determined by the formula

[0061] In the formula, T evos is the charging pile grid selling power duration, P EVs is the charging pile grid selling power, and p eos is the charging pile grid selling price; T evop is the charging pile purchase of park photovoltaic power duration, P EVo is the charging pile purchase of park photovoltaic power, and p eop is the charging pile purchase of park photovoltaic price; T evop is the charging pile grid purchase power duration, P EVo is the charging pile grid purchase power, and p eop is the charging pile grid purchase time-of-use price.

[0062] The electricity load benefit of park electricity is determined by the formula

[0063] In the formula, T parkeip is the zero-carbon park green electricity duration, P Park is the zero-carbon park green electricity power, and p eip is the zero-carbon park purchase green electricity price.

[0064] The investment cost is I​​​​C Mainly consists of 4 parts, respectively, photovoltaic investment cost, lithium battery energy storage investment cost, charging pile investment cost and a set of energy management system cost, namely I C For:

[0065] I C = I PVC + I BAC + I EVC + I EMS ;

[0066] In the formula, I PVC is the investment cost of photovoltaic, I BAC is the investment cost of lithium battery energy storage, I EVC is the investment cost of charging pile, I EMS is the cost of a set of "light storage charging and discharging" multifunctional energy management system.

[0067] I PVCP = P PVS *p PVI ;

[0068] In the formula, P PVS is the power of photovoltaic panel of the whole park, p PVI is the unit power photovoltaic panel price.

[0069] I BAC = P BAC *p BAC ;

[0070] In the formula, P BAC is the rated power of lithium battery energy storage of the park, p BAC is the unit power lithium battery energy storage price.

[0071] I PVCP = P EVS *p EVI ;

[0072] In the formula, P EVS is the rated power of direct current super charging pile of the park, p EVI is the unit power charging pile price.

[0073] The operation and maintenance cost of the system is O C , mainly including photovoltaic power generation operation and maintenance, lithium battery energy storage operation and maintenance, charging pile operation and maintenance, energy management system operation and maintenance and line loss.

[0074] O C = O PVC + O BAC + O EVC + O EMS + O PL ;

[0075] In the formula, O PVC For the operation and maintenance costs of photovoltaic power generation, O BAC Lithium battery energy storage operation and maintenance costs, O EVC To reduce the operation and maintenance costs of charging stations, O EMS To reduce the operation and maintenance costs of charging stations, O PL For line losses, determine them using the following formulas.

[0076] O PVC =P PVS *p PVO ;

[0077] O BAC =P BAS *p BAO ;

[0078] O EVC =P EVS *p EVO ;

[0079] O PL =P tools *p eop ;

[0080] In the formula, p PVI The price of operation and maintenance per unit power of photovoltaic panels, p BAO The price of operation and maintenance of lithium battery energy storage per unit power, p EVO The unit power charging pile operation and maintenance cost, P tools p represents the line loss power. eop This refers to the time-of-use pricing for electricity on the power grid.

[0081] Using formula Determine the power balance and power flow distribution constraints for the industrial park; where P i and P G,i P represents the active power and injected active power of the i-th branch in the zero-carbon park, respectively; L,i and P loss,i Let Q be the active load and active power line loss of the i-th branch in the zero-carbon park, respectively; i and Q G,i Let Q be the reactive power of the i-th branch and the injected reactive power, respectively; L,i and Q loss,i These represent the reactive load and reactive power line loss of the i-th branch in the zero-carbon park, respectively.

[0082] Using formula Determine the upper and lower limits of photovoltaic power output constraints; where P PVS Real-time photovoltaic power output Q represents the maximum power output of the photovoltaic system. PVS The reactive power of photovoltaics, For power factor;

[0083] Determine the energy storage operation output constraint by using the formula ; wherein, are the minimum charging power and the maximum charging power of the energy storage, respectively; are the minimum discharging power and the maximum discharging power of the energy storage, respectively; soc min , soc max are the minimum state of charge and the maximum state of charge of the energy storage, respectively;

[0084] Determine the charging pile charging power constraint by using the formula ; wherein P EV,i is the real-time output power of each charging pile, is the rated power of each charging pile.

[0085] S104, determine the zero-carbon park load scheduling power according to the upper-layer control model;

[0086] Determine the zero-carbon park load scheduling power by using the formula P' = P' PVS + P' online + P' EVo + P' bC + P' bD + P' Park + P' EVo + P' tools

[0087] ; wherein, P' is the zero-carbon park load scheduling power; P' PVS is the photovoltaic output prediction power; P' online is the photovoltaic predicted on-grid power; P' EVo is the predicted power purchase from the grid by the charging pile; P' bC is the predicted charging power of the lithium battery energy storage; P' bD is the predicted discharging power of the lithium battery energy storage; P' Park is the zero-carbon park load adjustment power; P' EVo is the predicted power of the direct current super charging pile of the zero-carbon park, P' tools is the line predicted loss power.

[0088] S105, determine the power deviation according to the real-time operation parameters and the zero-carbon park load scheduling power, and build a lower-layer control model;

[0089] Determine the power deviation by using the formula P = P PVS + P online + P EVo + P bC + P bD + P Park + P EVo + P tools ​Determine the real-time power P; where P PVS Photovoltaic power output; P online P represents the grid-connected power of photovoltaic power. EVo The power purchased from the grid for the charging pile; P bC Power for charging lithium battery energy storage; P bD P represents the energy storage and discharge power of a lithium battery. Park For zero-carbon park load power; P EVo For the DC supercharging pile power of the zero-carbon park, P tools This refers to line loss power;

[0090] The power deviation ΔP is determined using the formula ΔP = PP′, where P′ is the load dispatch power of the zero-carbon park.

[0091] S106 determines the adjustment power based on power deviation and lower-level control model; adjusts the load scheduling of the zero-carbon park accordingly; and, in conjunction with real-time electricity prices, adjusts and optimizes the electricity load, charging pile load, and energy storage charging and discharging power of the zero-carbon park in real time on a daily scale (15 minutes).

[0092] Using formula p AJ =P″ Park +P″ EVo +P″ bC +P″ bD Determine the adjustment power p AJ ;

[0093] Among them, P″ Park Adjusting power for load in zero-carbon industrial parks; P″ EVo Adjusting the power of DC supercharging piles in the zero-carbon park; P″ bC Adjusting charging power for lithium battery energy storage; P″ bD Adjust the discharge power for lithium battery energy storage.

[0094] like Figure 3 As shown, the upper-level control model is first solved, and the number of iterations is initialized. The predicted photovoltaic power generation and the load power of the zero-carbon park are determined using day-ahead data. The data is then clustered and decomposed and input into the upper-level control model. Combined with the grid day-ahead clearing price, the electricity sales and purchase costs of the zero-carbon park are calculated, and the predicted load dispatch power of the zero-carbon park is determined. A genetic algorithm is used to iterate and maximize the upper-level revenue objective. The lower-level control system utilizes the capacity configuration and real-time parameters received by the integrated photovoltaic-storage-charging-discharging system to optimize power within its allowable range, while ensuring the constraints of line power output. The error deviation between the predicted dispatch power and the zero-carbon park is assessed, and the power output of the lower-level control model is corrected. This involves adjusting the grid-connected power, energy storage charging and discharging power, and charging pile power sales and purchases of the zero-carbon park. Finally, the load dispatch power of the entire zero-carbon park is adjusted.

[0095] Figure 4 The overall control strategy for matching the multi-time scale characteristics of photovoltaic output and park load is divided into upper and lower control models. The upper control model is for maximum profit, and combines the day-ahead operation data (photovoltaic output prediction curve, day-ahead clearing segmented price of the power grid, energy storage planning capacity, and park load power prediction) to obtain the day-ahead power consumption plan of the zero-carbon park through genetic algorithm solution. The charging and discharging power and time point of the energy storage are built in combination with the cycle number of the lithium battery. The power and charging time node of the new energy bus and car are also built. The lower objective function is real-time park power adjustment, and combines four kinds of real-time operation data, photovoltaic output power, power grid segmented time price, energy storage adjustable capacity, and zero-carbon park load power to perform rolling adjustment, change the grid-connected power of the zero-carbon park and the charging and discharging power and time length of the energy storage, and finally realize power adjustment at the day-ahead, hourly, and minute levels;

[0096] When the photovoltaic grid-connected power selling price of the zero-carbon park is lower than the power grid power purchasing price, the photovoltaic output is large at noon, and the power is sold in the power spot market. In combination with the segmented time price policy of the power grid and the energy storage capacity, part of the power is preferentially charged to the energy storage at the time of the deep valley of the low valley of power consumption and at the time of zero price. The remaining power supplies the park load and the charging pile load. If the power is insufficient, the power is purchased from the power grid. At the peak time, the photovoltaic output is reduced, and the energy storage capacity preferentially supplies high-income power consumption loads, especially EV charging pile loads and zero-carbon park industrial power. At the flat price time, the electric bus is charged. In the whole "photovoltaic-energy storage-charging and discharging" integrated energy management system, the multi-time scale characteristics of photovoltaic output and park load are matched. In the target solution process, the genetic algorithm is used to obtain the "photovoltaic-energy storage-charging and discharging" integrated energy management power optimization scheduling and maximum profit, to meet the zero-carbon park load demand and new energy vehicle power compensation demand, and to realize the sustainable extension of the energy storage peak shaving time by 4 hours.

[0097] Based on the same inventive concept, the embodiments of the present application also provide a multi-time scale photovoltaic-energy storage-charging and discharging integrated energy management device for implementing the above-mentioned multi-time scale photovoltaic-energy storage-charging and discharging integrated energy management method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more multi-time scale photovoltaic-energy storage-charging and discharging integrated energy management device embodiments provided below can be referred to the limitations of the multi-time scale photovoltaic-energy storage-charging and discharging integrated energy management method in the above text, which will not be described here again.

[0098] In one exemplary embodiment, a multi-time scale photovoltaic-energy storage-charging and discharging integrated energy management device is provided, comprising:

[0099] The previous data acquisition module is used for acquiring previous operation data of the light storage charging and discharging multifunctional integrated control system; the operation data includes photovoltaic power generation power, zero-carbon park load power, energy storage adjustable capacity and time-of-use electricity price of the power grid;

[0100] The income determination module is used for determining the light storage charging and discharging multifunctional integrated energy management system income according to the previous operation data; the light storage charging and discharging multifunctional integrated energy management system income includes charging and discharging electricity selling income, zero-carbon park power purchase cost from the power grid, investment cost and operation and maintenance cost;

[0101] The upper control model determination module is used for constructing an upper control model with the maximum light storage charging and discharging multifunctional integrated energy management system income as an objective function, and with zero-carbon park power balance, power flow distribution constraint, photovoltaic output upper and lower limit constraint, energy storage operation output constraint and charging pile charging power constraint as constraint conditions;

[0102] The power scheduling module is used for determining the zero-carbon park load scheduling power according to the upper control model;

[0103] The lower control model construction module is used for determining a power deviation according to real-time operation parameters and the zero-carbon park load scheduling power, and constructing a lower control model;

[0104] The optimization module is used for determining an adjustment power according to the power deviation and the lower control model, adjusting the zero-carbon park load scheduling, and adjusting and optimizing the power of the zero-carbon park, the charging pile load and the energy storage charging and discharging power in real time on an intraday scale in combination with a real-time electricity price.

[0105] In an exemplary embodiment, a computer device can be provided, which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a multi-time scale light storage charging and discharging integrated energy management method.

[0106] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0107] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0108] In an exemplary embodiment, a computer program product is provided, comprising a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0109] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0110] It can be understood by those skilled in the art that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0111] The database involved in each embodiment provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each embodiment provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0112] In the present application, all actions of obtaining signals, information or data are performed in compliance with the corresponding data protection regulations and policies of the country where the device is located, and on the premise of obtaining authorization from the owner of the corresponding device.

[0113] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0114] The principles and implementation modes of the present application are described by applying specific examples herein, and the above descriptions of the embodiments are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation modes and application ranges can be changed according to the idea of the present application. In summary, the content of the present application should not be understood as a limitation.

Claims

1. A multi-timescale integrated energy management method for photovoltaic storage and charging / discharging, characterized in that, An integrated energy management system for photovoltaic-energy storage-charging-discharge multifunctional integrated control system is applied to the photovoltaic-energy storage-charging-discharge multifunctional integrated control system; the integrated photovoltaic-energy storage-charging-discharge multifunctional integrated control system further includes: distributed solar cell module array, zero-carbon park load equipment, lithium battery energy storage array, distributed DC supercharging pile group, string rectifier inverter, distribution cabinet, AC transformer and substation, remote terminal unit of power grid, and communication control device; the multi-timescale photovoltaic-energy storage-charging-discharge integrated energy management method includes: Acquire the day-ahead operating data of the integrated photovoltaic-storage-charging-discharging control system; the operating data includes photovoltaic power generation, zero-carbon park load power, adjustable energy storage capacity, and time-of-use electricity price. Based on recent operational data, the revenue of the integrated photovoltaic-storage-charging-discharging energy management system is determined. The revenue of the integrated photovoltaic-storage-charging-discharging energy management system includes: revenue from selling electricity during charging and discharging, electricity purchase costs from the grid for the zero-carbon park, investment costs, and operation and maintenance costs. Construct an upper-level control model with the objective function of maximizing the benefits of a multi-functional integrated energy management system for photovoltaic storage and charging, and with constraints such as power and power flow distribution for zero-carbon park electricity consumption balance, upper and lower limits of photovoltaic output, energy storage operation output constraints, and charging power constraints of charging piles. Based on the upper-level control model, determine the load scheduling power of the zero-carbon park; The power deviation is determined based on real-time operating parameters and load scheduling power of the zero-carbon park, and a lower-level control model is constructed. The adjustment power is determined based on the power deviation and the lower-level control model; the load scheduling of the zero-carbon park is adjusted accordingly; and the electricity load, charging pile load and energy storage charging and discharging power of the zero-carbon park are adjusted and optimized in real time on an intraday scale in conjunction with the real-time electricity price.

2. The multi-timescale integrated photovoltaic-storage-charge-discharge energy management method according to claim 1, characterized in that, The upper-level control model specifically includes: Using the formula maxR = RC grid -I C -O C Determine the objective function maxR; Where R is the revenue from charging and discharging electricity sales, R = R PV +R BA +R EV +R Park R PV For the grid-connected revenue of photovoltaic power, R BA For energy storage peak-valley arbitrage profits, R EV For the electricity sales revenue of EV charging stations, R Park For the electricity load and revenue of the zero-carbon industrial park, C grid For the cost of purchasing electricity from the grid for zero-carbon industrial parks, I C For investment costs, I C =I PVC +I BAC +I EVC +I EMS I PVC For photovoltaic investment costs, I BAC For the investment cost of lithium battery energy storage, I EVC For the investment cost of charging piles, I EMS For the cost of a multi-functional integrated energy management system for photovoltaic storage, charging, and discharging, O C For maintenance costs, O C =O PVC +O BAC +O EVC +O EMS +O PL O PVC For the operation and maintenance costs of photovoltaic power generation, O BAC To reduce the operation and maintenance costs of lithium battery energy storage, O EVC To reduce the operation and maintenance costs of charging stations, O EMS To reduce the operation and maintenance costs of charging stations, O PL For line loss; Using formula Determine the power balance and power flow distribution constraints for the industrial park; where P i and P G,i P represents the active power and injected active power of the i-th branch in the zero-carbon park, respectively; L,i and P loss,i Let Q be the active load and active power line loss of the i-th branch in the zero-carbon park, respectively; i and Q G,i Let Q be the reactive power of the i-th branch and the injected reactive power, respectively; L,i and Q loss,i These represent the reactive load and reactive power line loss of the i-th branch in the zero-carbon park, respectively. Using formula Determine the upper and lower limits of photovoltaic power output constraints; where P PVS Real-time photovoltaic power output Q represents the maximum power output of the photovoltaic system. PVS The reactive power of photovoltaics, Power factor; Using formula Determine the output constraints for energy storage operation; among which, These are the minimum and maximum charging power for energy storage, respectively. These represent the minimum and maximum discharge power of the energy storage, respectively; SOC min , soc max These represent the minimum and maximum states of charge of energy storage, respectively. Using formula Determine the charging power constraints for the charging pile; where P EV,i Real-time output power for each charging station. The rated power of each charging station.

3. The multi-timescale integrated photovoltaic-storage-charge-discharge energy management method according to claim 1, characterized in that, The determination of load scheduling power for the zero-carbon industrial park based on the upper-level control model specifically includes: Based on the upper-level control model, a genetic algorithm is used to determine the load scheduling power of the zero-carbon park.

4. The multi-timescale integrated photovoltaic-storage-charge-discharge energy management method according to claim 1 or 3, characterized in that, Using the formula P′=P′ PVS +P′ online +P′ EVo +P′ bC +P′ bD +P′ Park +P′ EVo +P′ tools Determine the load dispatching capacity of the zero-carbon industrial park; Where P′ represents the load dispatch power of the zero-carbon park; P′ PVS Predicted power output for photovoltaic power generation; P′ online Predicted grid-connected power for photovoltaic power; P′ EVo Predict the amount of electricity to be purchased from the grid for charging piles; P′ bC Predicting charging power for lithium battery energy storage; P′ bD Predicting discharge power for lithium battery energy storage; P′ Park Adjusting power for load in zero-carbon industrial parks; P′ EVo Predicted power of DC supercharging piles in zero-carbon parks, P′ tools Predict power loss for the line.

5. The multi-timescale integrated photovoltaic-storage-charge-discharge energy management method according to claim 1, characterized in that, The process of determining the power deviation based on real-time operating parameters and the load scheduling power of the zero-carbon park, and constructing a lower-level control model, specifically includes: Using the formula P = P PVS +P online +P EVo +P bC +P bD +P Park +P EVo +P tools Determine the real-time power P; where P PVS Photovoltaic power output; P online P represents the grid-connected power of photovoltaic power. EVo The power purchased from the grid for the charging pile; P bC Power for charging lithium battery energy storage; P bD P is the energy storage discharge power of the lithium battery; Park For zero-carbon park load power; P EVo For the DC supercharging pile power of the zero-carbon park, P tools This refers to line loss power; The power deviation ΔP is determined using the formula ΔP = PP′, where P′ is the load dispatch power of the zero-carbon park.

6. The multi-timescale integrated photovoltaic-storage-charge-discharge energy management method according to claim 1, characterized in that, The process of determining the adjustment power based on the power deviation and the lower-level control model specifically includes: Using formula p AJ =P″ Park +P″ EVo +P″ bC +P″ bD Determine the adjustment power p AJ ; Among them, P″ Park Adjusting power for load in zero-carbon industrial parks; P″ EVo Adjusting the power of DC supercharging piles in the zero-carbon park; P″ bC Adjusting charging power for lithium battery energy storage; P″ bD Adjust the discharge power for lithium battery energy storage.

7. A multi-timescale integrated photovoltaic-storage-charging-discharging energy management device, characterized in that, The multi-timescale integrated photovoltaic energy storage and charging / discharging energy management device includes: The day-ahead data acquisition module is used to acquire the day-ahead operating data of the integrated photovoltaic-storage-charging-discharging control system; the operating data includes photovoltaic power generation, zero-carbon park load power, adjustable energy storage capacity, and grid time-of-use electricity price. The revenue determination module is used to determine the revenue of the photovoltaic-storage-charging-discharging multifunctional integrated energy management system based on the current day's operating data. The revenue of the photovoltaic-storage-charging-discharging multifunctional integrated energy management system includes: revenue from selling electricity during charging and discharging, the cost of purchasing electricity from the grid in the zero-carbon park, investment costs, and operation and maintenance costs. The upper-level control model determination module is used to construct an upper-level control model with the objective function of maximizing the benefits of the multi-functional integrated energy management system for photovoltaic storage and charging, and with constraints such as the power and power flow distribution of the zero-carbon park's power consumption balance, upper and lower limits of photovoltaic output, energy storage operation output constraints, and charging power constraints of charging piles. The power scheduling module is used to determine the load scheduling power of the zero-carbon park based on the upper-level control model. The lower-level control model construction module is used to determine the power deviation based on real-time operating parameters and the load scheduling power of the zero-carbon park, and to construct the lower-level control model. The optimization module is used to determine the adjustment power based on the power deviation and the lower-level control model; adjust the load scheduling of the zero-carbon park accordingly; and combine the real-time electricity price to adjust and optimize the electricity load, charging pile load and energy storage charging and discharging power of the zero-carbon park on an intraday scale.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-timescale integrated photovoltaic-storage-charge-discharge energy management method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-timescale integrated energy management method for photovoltaic storage and charging / discharging as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-timescale integrated energy management method for photovoltaic storage and charging / discharging as described in any one of claims 1-6.

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