Optimal collaborative configuration method and system for light storage transformer income of comprehensive energy station

Abstract

The invention provides a comprehensive energy station optical storage and transformation income optimal collaborative configuration method and system, and belongs to the technical field of optical storage and charging comprehensive energy stations. An electrical load curve and a photovoltaic environment are used as inputs, and a photovoltaic model selection model, an energy storage model selection model and a transformer model selection model are established; three groups of selectable configuration lists of photovoltaic power generation power, energy storage capacity, transformer capacity and specification are respectively given, and then based on energy storage business modes such as energy storage peak load shifting, new energy consumption and dynamic capacity increasing, the optimal light storage transformer income is used as an optimization objective function, and a Bayesian optimization parameter is used to improve a particle swarm optimization algorithm, so that the optimal light storage transformer income is obtained. A three-layer nested traversal search method is combined to construct a light storage transformer collaborative configuration model and system, the energy storage capacity and the energy storage strategy with the optimal light storage transformer income, the photovoltaic power generation power and the transformer specification and capacity are given, and a scientific decision basis is provided for site selection, design and operation of a comprehensive energy station.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic, storage and charging integrated energy stations, and specifically, relates to a method and system for optimal coordinated configuration of photovoltaic, storage and variable returns in an integrated energy station. Background Art

[0002] With the development of electric vehicles, the demand for charging market has surged. Many oil companies choose to make full use of existing gas stations and add charging services on their basis. This has formed a comprehensive energy station that integrates refueling, gas filling and charging. With the national carbon integration goals, more and more oil and gas stations are adding photovoltaic power generation to canopies and roofs to reduce energy consumption and add energy storage equipment to store surplus photovoltaic power.

[0003] As an important part of the new energy system, the construction and operation efficiency of the integrated energy station directly affects the optimal configuration of energy and economic benefits. The traditional integrated energy station design relies more on experience and judgment, lacks a systematic optimization method, and leads to the following problems in photovoltaic power generation, energy storage system and transformer configuration:

[0004] After data analysis, it was found that the following problems are common in such sites:

[0005] 1. Inaccurate prediction of photovoltaic power generation: Many construction or design units directly install photovoltaic power of corresponding area according to the size of the existing site; however, the integrated energy station is very different from the large-scale photovoltaic power station. The photovoltaic power generation is different in different time periods, different regions, different sunshine angles, and different shielding areas within a year. Therefore, the photovoltaic power generation cannot be calculated by multiplying the installed capacity provided by the photovoltaic manufacturer by a certain ratio, especially for urban stations with superior status.

[0006] 2. Unreasonable configuration of energy storage system: The selection of energy storage capacity is mostly based on experience, or the energy storage capacity is directly determined based on the difference between photovoltaic power generation and power consumption; the time-of-use electricity price and the self-use surplus of photovoltaic power generation are not fully considered.

[0007] 3. Excess or insufficient transformer capacity configuration: For power capacity expansion, the power capacity increase is determined only based on the number of charging piles and their power multiplied by a certain coefficient and the maximum power limit of a single transformer set by the local power department. Although the transformer itself has a certain overload bearing capacity and can cope with a certain amount of short-term overload operation, it can be combined with the energy storage discharge situation according to the time-sharing load and duration in the station. However, after analysis, the existing storage and discharge strategy for energy storage equipment is only based on low storage and high discharge according to the time-sharing electricity price, without comprehensively considering the relationship between transformer capacity and loss, photovoltaic power generation and load power consumption in each period, resulting in the energy storage equipment not fully realizing its value. Therefore, if the transformer is configured according to the number of charging piles and their power multiplied by a certain coefficient, it will not only cause over-investment, but also cause unnecessary power loss after operation.

[0008] 4. Lack of coordinated optimization: After installation, each equipment system operates independently, lacks an effective coordination mechanism, and fails to maximize the overall energy efficiency of the energy station as designed.

[0009] In summary, the existing technology has many shortcomings in terms of transformer capacity, photovoltaic power generation power, energy storage capacity configuration and energy storage strategy of integrated energy stations. It is necessary to find a reasonable balance between the three to achieve the goal of minimum investment and optimal economic benefits. Summary of the invention

[0010] To solve the above problems, the present invention proposes a method and system for optimal coordinated configuration of photovoltaic, energy storage and transformer benefits in an integrated energy station, which abandons the traditional method of independent engineering configuration of photovoltaic, energy storage and transformer. An advanced optimization algorithm is introduced, and the intelligent linkage and efficient coordination between photovoltaic, energy storage and transformer are realized through the optimal coordinated configuration method of photovoltaic, energy storage and transformer benefits. This method not only ensures the scientificity and rationality of the configuration parameters, but also enables the system to maximize the energy utilization and economic benefits of the system through precise calculation and simulation.

[0011] The present invention is achieved through the following technical solutions:

[0012] An optimal coordinated configuration method for photovoltaic storage and transformation benefits in an integrated energy station:

[0013] The method specifically comprises the following steps:

[0014] Step 1, input power load curve and photovoltaic environment;

[0015] Step 2: Establish a photovoltaic selection model, an energy storage selection model, and a transformer selection model, and respectively give the photovoltaic power generation power, energy storage capacity, energy storage power, transformer capacity and specifications;

[0016] Step 3: Combine the three models in step 2 to select the photovoltaic range to determine the power generation cost per kilowatt-hour, initialize the energy storage strategy through the energy storage selection range, and select the dynamic capacity expansion range of the transformer;

[0017] Step 4: Taking the optimal benefit of PV-storage-variable as the optimization objective function, the particle swarm optimization algorithm is improved using Bayesian optimization parameters, and a three-layer nested traversal search method is used to construct a PV-storage-variable collaborative configuration model;

[0018] Step 5: Give the energy storage capacity and energy storage strategy, photovoltaic power generation power, transformer specifications and capacity under the optimal conditions of photovoltaic storage and transformation benefits.

[0019] Further, in step 1, the photovoltaic environment includes:

[0020] The local time-of-use electricity price of the energy station, the longitude and latitude of the energy station, the photovoltaic area that can be installed at the energy station, the photovoltaic shading conditions, the energy storage cost parameters and the photovoltaic power generation cost parameters.

[0021] Further, in step 2, the photovoltaic selection model determines the photovoltaic selection and power range based on the clear sky irradiance model and photovoltaic power generation simulation, combined with the power generation power model established by the maximum photovoltaic installation area and the power load curve on site;

[0022] Power generation = photovoltaic installation area × power generation per square meter;

[0023] P pv =S·E p

[0024] Where S is the photovoltaic installation area, E p The power generated per square meter;

[0025] Maximum power generation P pv,max The calculation formula is:

[0026] P pv,max =S max ·E p

[0027] Where: S max is the maximum photovoltaic installation area; E p The power generated per square meter;

[0028] Minimum power generation P pv,min The calculation formula is:

[0029] Select the local summer solstice irradiance curve, calculate the photovoltaic power generation according to the 24-hour load curve, and take the minimum power generation;

[0030]

[0031] Where k is the power coefficient, which is 0.75; P t,load is the load power at time t, where t is the period of stable photovoltaic power generation.

[0032] Further, in step 2, the energy storage capacity and the energy storage power range are set through the energy storage selection model;

[0033] The energy storage capacity is capable of being released during the peak electricity price and peak electricity price phases of time-of-use electricity prices, and can absorb the surplus of photovoltaic power generation for self-use;

[0034] The energy storage power must be sufficient to discharge during the discharge period and to charge the required amount of energy within the specified time;

[0035] The photovoltaic power generation model simulates the power generation curve as follows:

[0036] P maxch argr ≤|P t,load -P t,pv |

[0037] E c =2P maxch argr

[0038]

[0039] Where E c is the energy storage capacity, P maxch argr is the maximum charging and discharging power of energy storage, P t,pv is the power generated at time t,

[0040] t=j…k is the peak and peak electricity price period;

[0041] The energy storage capacity range requires calculation of the minimum and maximum capacity;

[0042] Calculate the minimum capacity:

[0043]

[0044] Calculate the maximum capacity:

[0045]

[0046] where γ max is the maximum capacity factor; γ min is the minimum capacity factor.

[0047] Further, in step 2, the capacity and specification of the transformer are determined through the transformer selection model;

[0048] The transformer capacity is:

[0049]

[0050] LC is the load factor; r is the transformer capacity range factor, which ranges from 0.75 to 0.9;

[0051] t is from 0 to 23 o'clock; is the power factor;

[0052] According to the transformer specifications, there are two transformer selection combinations:

[0053]

[0054] Among them, C i,trans is the transformer specification capacity, then:

[0055] Selection combination rule 1: transformers with the same specifications and minimum quantity;

[0056]

[0057] Selection combination rule 2: N+1 rule, N transformers have the same parameters, 1 transformer has the capacity corresponding to the surplus, and the number is the minimum;

[0058]

[0059] Transformer capacity / power total:

[0060] P min ≤nx i +mx k ≤P max .

[0061] Furthermore, in step 4, a global search is nested in the order of the interval list of transformers, photovoltaics, and energy storage, and the benefits of photovoltaic storage and transformers are optimal. The core optimization adopts the improved particle swarm optimization algorithm;

[0062] First, the optimal energy storage benefit is calculated, and then the three-level nested traversal search of photovoltaic, storage and transformer is performed. Under the condition of given photovoltaic, storage and transformer capacity, the optimal energy storage benefit is obtained through the particle swarm algorithm, and the benefits of photovoltaic and transformer are superimposed at the same time.

[0063] The three-level nested PV-storage-transformer system traverses and searches for all PV-storage-transformer benefits. The optimal solution for PV-storage-transformer is the configuration of photovoltaics, energy storage, and transformers; that is, PV-storage-transformer benefit = energy storage benefit + photovoltaic benefit - transformer loss.

[0064] Furthermore, in step 4, photovoltaic income = photovoltaic direct consumption income + photovoltaic grid-connected income;

[0065] Transformer loss is active power loss, that is, the amount of power lost in 24 hours.

[0066] An optimal coordinated configuration system for photovoltaic storage and transformation benefits in integrated energy stations:

[0067] The collaborative configuration system includes: an input module, a computer-aided selection module, an intervention module, a coordination configuration module and an output module;

[0068] The input module is used to input the electric load curve and photovoltaic environment;

[0069] The computer-aided selection module is used to establish a photovoltaic selection model, an energy storage selection model and a transformer selection model, and respectively gives photovoltaic power generation power, energy storage capacity, energy storage power, transformer capacity and specifications;

[0070] The intervention module is based on three models, selects the photovoltaic range to determine the power generation cost per kilowatt-hour, initializes the energy storage strategy through the energy storage selection range, and selects the dynamic capacity increase range of the transformer;

[0071] The coordination configuration module takes the optimal benefit of PV-storage-variable as the optimization objective function, uses Bayesian optimization parameters to improve the particle swarm optimization algorithm, and combines it with a three-layer nested traversal search method to build a PV-storage-variable coordinated configuration model;

[0072] The output module provides the energy storage capacity and energy storage strategy, photovoltaic power generation power, transformer specifications and capacity under the optimal conditions of photovoltaic storage and transformation benefits.

[0073] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0074] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps of the above method are implemented.

[0075] Beneficial effects of the present invention

[0076] Compared with the prior art, the present invention has the following advantages:

[0077] 1) Improve the accuracy of energy forecasting

[0078] By taking into account the geographical location and environmental factors unique to the integrated energy station, the present invention improves the prediction accuracy of photovoltaic power generation, thereby ensuring a more reliable energy supply to the energy station.

[0079] 2) Optimize resource allocation

[0080] The present invention realizes the optimal configuration among photovoltaic power generation, energy storage and transformer through intelligent model and optimization algorithm, avoids waste of resources and improves the overall efficiency of the system.

[0081] 3) Reduce operating costs

[0082] Through precise configuration and optimized operation strategy, the present invention can reduce the construction and operation costs of energy stations, improve economic benefits, and make the project's return on investment higher.

[0083] 4) Improve energy utilization

[0084] The present invention effectively improves energy utilization and reduces energy waste by finely managing the storage and discharge strategies of the energy storage system.

[0085] 5) Enhanced adaptability and flexibility

[0086] The present invention is applicable to integrated energy stations of different sizes and types, has strong adaptability and flexibility, and can be effectively configured according to different needs and conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1The figure is a flow chart of the method of the present invention.

[0088] Figure 2 It is a computer-aided selection process and a computer intelligent collaborative configuration process.

[0089] Figure 3 It is the seasonal optimal power generation curve in the embodiment.

[0090] Figure 4 is the transformer capacity range coefficient of the present invention.

[0091] Figure 5 It is a schematic diagram of the computer intelligent collaborative configuration process of the present invention.

[0092] Figure 6 This is the energy storage optimization strategy of the present invention.

[0093] Figure 7 This is a parameter comparison table for the levelized energy storage cost. DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] Combination Figures 1 to 6 The experimental methods used in the following examples are conventional methods unless otherwise specified. The materials, reagents, methods and instruments used are conventional materials, reagents, methods and instruments in the art unless otherwise specified, and can be obtained by those skilled in the art through commercial channels.

[0096] A method for optimal coordinated configuration of photovoltaic, storage and transformation benefits in an integrated energy station, the method specifically comprising the following steps:

[0097] Step 1: Input the time-of-use electricity price, electricity load curve, site environment information (latitude and longitude, photovoltaic shielding conditions, photovoltaic area), energy storage cost parameters, and photovoltaic power generation cost parameters of the target station area from the computer;

[0098] The computer-aided selection process of steps 2-4 and the computer intelligent collaborative configuration process, such as Figure 2 As shown, the photovoltaic energy storage transformer models involved in these two processes fully consider the optimal operating range of major equipment in the station, such as electricity consumption, energy storage, and transformers. At the same time, they are also formulated on the principle of optimal use cycle and best efficiency and benefit.

[0099] Step 2, computer-aided selection process: establish photovoltaic selection model, energy storage selection model and transformer selection model, and give three sets of optional configuration lists: photovoltaic power generation power range, energy storage capacity and power range, transformer capacity and specifications;

[0100] (1) The photovoltaic model is based on the clear sky irradiance model and photovoltaic power generation simulation, and is combined with the power generation model established by the maximum photovoltaic installation area on site and the power load curve to determine the photovoltaic selection and power range.

[0101] In an embodiment, Figure 3 The photovoltaic selection model can be based on the photovoltaic power generation prediction physical model described in patent CN117374956A, using the clear sky irradiance model and photovoltaic power generation simulation as the basis, and then combining the power generation model established by the maximum photovoltaic installation area and the power load curve on site to determine the photovoltaic selection and power range, expand the optimal power generation curve on specific days of the winter solstice, summer solstice, vernal equinox, and autumnal equinox, and the optimal power generation curve without obstruction, establish a photovoltaic power generation simulation model, and output a photovoltaic optional list. The main contents of the list include: power generation, optimal power generation curve and power generation cost; (wherein, given the photovoltaic power, the power generation curve on a sunny day in the summer solstice is the optimal power generation curve)

[0102] Power generation = photovoltaic installation area × power generation per square meter;

[0103] P pv =S·E p

[0104] Where S is the photovoltaic installation area, E p The power generated per square meter;

[0105] In the embodiment, the installation orientation and angle are mainly based on photovoltaic power generation consumption. For example, if the power load is mostly in the afternoon, the installation orientation is biased to the west.

[0106] Photovoltaic selection model and power range

[0107] Maximum power generation P pv,max The maximum area that can be installed with photovoltaic panels is taken as the power generation function rate.

[0108] P pv,max =S max ·E p

[0109] Where: S max is the maximum photovoltaic installation area; E p The power generated per square meter;

[0110] The surplus power generation during the peak electricity price period according to the time-of-use electricity price is equal to the electricity load during the evening peak period.

[0111] According to the load curve, photovoltaic self-consumption is taken as the benchmark, that is, the photovoltaic power generation curve is smaller than the load curve.

[0112] Minimum power generation P pv,min The calculation formula is:

[0113] P pv,min =Min(f(t,P t,load ,H t )),P t,pv <P t,load

[0114] Where: Min(f(t,P t,load ,H t )) is the photovoltaic power demand function, input the load and irradiance at a certain time and moment; select the irradiance curve of the local equinox, calculate the photovoltaic power generation power according to the 24-hour load curve, and take the minimum power generation power.

[0115] H t is the irradiance at time t, and the calculation method is based on the irradiance calculation method described in patent CN117374956A.

[0116] P t,pv It is given based on the photovoltaic power generation prediction physical model described in patent CN117374956A.

[0117] In other embodiments, the minimum power generation simplified method may be used:

[0118] Select the local summer solstice irradiance curve, calculate the photovoltaic power generation according to the 24-hour load curve, and take the minimum power generation.

[0119]

[0120] Where k is the power coefficient, which is 0.75, and t is the period of stable photovoltaic power generation.

[0121] (2) Energy storage selection rate model

[0122] Energy storage capacity and power:

[0123] Energy storage capacity refers to the amount of energy that can be released during the peak electricity price and peak electricity price phases of time-of-use electricity prices. In addition, it can also absorb the surplus of photovoltaic power generation for self-use. At the same time, the energy storage power can be discharged during the discharge period, that is, the amount of energy required for charging within the specified time. Among them, according to the photovoltaic power generation model described in patent CN117374956A, the power generation curve is simulated

[0124] P maxch argr ≤|P t,load -P t,pv |

[0125] E c =2P maxch argr

[0126]

[0127] Where E c is the energy storage capacity, P maxch argr is the maximum charging and discharging power of energy storage,

[0128] t = j…k is the peak and peak electricity price period (discharge demand);

[0129] The capacity range requires calculation of the minimum and maximum capacity;

[0130] Calculate the minimum capacity:

[0131]

[0132] Calculate the maximum capacity:

[0133]

[0134] in:

[0135] γ max is the maximum capacity factor, and the recommended value is 0.8 to 0.9;

[0136] γ min It is the minimum capacity factor, and the recommended value is 0.8 to 0.9.

[0137] Energy storage capacity selection list: select the optional capacity list based on the capacity range, and the constraint is that the capacity of the selected energy storage devices is consistent.

[0138] (3) Transformer selection and capacity setting model

[0139] like Figure 4 As shown, the transformer capacity

[0140]

[0141] Where: LC is the load factor, usually defaulted to 0.85;

[0142] t is from 0 to 23 o'clock; is the power factor, the default value is 0.9;

[0143] r is the transformer capacity range coefficient, which takes a value of 0.75 to 0.9. The load factor is calculated as the ratio of the maximum load to the maximum instantaneous load. According to the characteristics of the integrated energy station, the actual measured 5-minute instantaneous load is statistically analyzed, and the load factor basically presents a normal distribution, and the coefficient is selected between 0.75 and 0.90.

[0144] It is worth noting that transformers are usually designed with a certain overload capacity in mind so that they can withstand excess power loads for a short period of time under special circumstances. This overload capacity is limited because long-term overloads can cause the equipment to heat up, which in turn damages the insulation material of the transformer and may even cause safety accidents; the instantaneous overload capacity of the transformer should usually not exceed 30% (in winter) or 15% (in summer) of the rated load, and the duration of the overload should be strictly controlled.

[0145] According to international standards and actual operating experience, there are multiple levels of overload capacity, such as:

[0146] - Grade I means no less than 100% rated current and can operate continuously;

[0147] -II level means no less than 110% rated current and can run continuously for 2 hours (corresponding to overload 121%);

[0148] -Level III is not less than 150% of the rated current and can run continuously for 30 minutes;

[0149] According to the transformer specifications, there are two transformer selection combinations:

[0150]

[0151] Among them, C i,trans is the transformer specification capacity, then:

[0152] Selection combination rule 1: transformers with the same specifications and minimum quantity;

[0153]

[0154] Selection combination rule 2: N+1 rule, N transformers have the same parameters, 1 transformer has the capacity corresponding to the surplus, and the number is the minimum;

[0155]

[0156] For example, transformer capacity list:

[0157] x=[500,630,800,1000,1250,1600,2000]

[0158] Transformer capacity / power total:

[0159] P min ≤nx i +mx k ≤P max

[0160] Step 3, professional intervention process: Based on energy storage business models such as peak shaving and valley filling, new energy consumption and dynamic capacity expansion, electrical professionals manually select the photovoltaic range to determine the power generation cost per kilowatt-hour by combining the three models in step 2, initialize the energy storage strategy through the energy storage selection range, and select the dynamic capacity expansion range of the transformer; (the purpose of this manual intervention process is to narrow the selection screening range and improve calculation efficiency)

[0161] Step 4: Through computer intelligent collaborative configuration, taking the optimal benefit of PV-storage-variable as the optimization objective function, using Bayesian optimization parameters to improve the particle swarm optimization algorithm, and combining it with a three-layer nested traversal search method, a PV-storage-variable collaborative configuration model is constructed; Figure 5 As shown;

[0162] The transformer capacity, energy storage capacity and power, and photovoltaic power generation power range list calculated by the above models are calculated in one step through a three-level nested traversal search method and energy storage optimization strategy combined with two-level optimization methods.

[0163] A global search is performed in the order of the interval list of transformers, photovoltaics, and energy storage. The photovoltaic storage transformer benefits are optimal. The core optimization uses an improved particle swarm optimization algorithm. First, the optimal energy storage benefits are calculated. Secondly, the photovoltaic storage transformer three-level nested traversal search is performed. Under the condition of a given photovoltaic storage transformer capacity, the optimal energy storage benefits are obtained through the particle swarm algorithm. At the same time, the photovoltaic and transformer benefits are superimposed. The photovoltaic storage transformer three-level nested traversal search of all photovoltaic storage transformer benefits, the optimal solution of photovoltaic storage transformer is the photovoltaic, energy storage, and transformer configuration.

[0164] Among them, the improved particle swarm algorithm is realized by dynamically modifying and updating the boundary condition values. When the three-level nested traversal search of the photovoltaic energy storage and transformer is performed, the boundary condition values ​​are updated according to the energy storage capacity and power, and the load power each time the search is performed.

[0165] The load curve used by the optimal collaborative traversal search algorithm model for the photovoltaic energy storage and transformation benefits is a superimposed load curve, that is, the energy supply load on the power supply side of the power grid, the power load and the photovoltaic power generation simulation are superimposed;

[0166] P t,net =P t,load -P t,pv

[0167] The particle swarm algorithm is used to optimize the energy storage charging and discharging strategy. The algorithm randomly gives the 24-hour energy storage charging and discharging power. The power rules are as follows:

[0168]

[0169] P maxch argr ≤P t,ch arge

[0170] P maxch argr ≤Pt,pvch arge

[0171] P maxch argr ≤P t,disch arge

[0172] (1) The energy storage strategy optimization is as follows: Figure 6 As shown, the mainstream energy storage business model on the market is fully combined with the energy storage cost per kilowatt-hour, energy storage capacity and power, and the optimal model of revenue to determine the energy storage selection and setting capacity and power range.

[0173] There are currently seven mainstream business models on the market. The present invention does not consider demand-side response and spot electricity trading. It mainly focuses on peak-valley arbitrage and new energy consumption, supplemented by distribution capacity expansion and peak shaving and valley filling, with the optimization goal of optimizing energy storage benefits.

[0174] (2) Solar-storage-transformer income = energy storage income + photovoltaic income - transformer loss;

[0175] The optimal calculation formula for the photovoltaic energy storage conversion benefit is:

[0176]

[0177] in:

[0178] P t,disch arge is the discharge power at time t, price t is the electricity price at time t, P t,ch arge is the charging power at time t,

[0179] pprice is the average cost per kilowatt-hour,

[0180]

[0181] pvprice is the cost of photovoltaic power generation, P t,pvload The amount of electricity consumed by photovoltaic power generation.

[0182] P t,over =P t,load -αC trans The transformer is overloaded with electricity (customer loss);

[0183] k is the transformer overload penalty coefficient, α is the transformer overload capacity coefficient,

[0184] The total capacity of transformer specifications;

[0185] C i,trans is the transformer specification capacity;

[0186] Bill ch arge Bill is the charging fee (yuan / kWh); trans is the capacity electricity fee (yuan / kVA);

[0187] Rewards for increasing transformer capacity

[0188] ∑violation is the cumulative penalty for violations during the energy storage optimization process;

[0189] q is the transformer capacity increase reward coefficient; β is the transformer penalty proportional coefficient;

[0190] Cos t,trans is the average transformer cost, η is the average transformer cost penalty coefficient;

[0191] is the transformer loss;

[0192] λ is other penalty coefficient, ρ is other penalty benefit;

[0193] P t,Load is the load power at time t, P t,pv is the power generated at time t, P pv is the photovoltaic power generation power.

[0194] (3) Perform photovoltaic power generation simulation based on the content of step 2 and calculate the power generation revenue;

[0195] Photovoltaic income = direct photovoltaic consumption income + photovoltaic grid-connected income

[0196]

[0197] in:

[0198] Direct self-consumption income from photovoltaic power generation

[0199] Photovoltaic grid-connected income

[0200] netprice is the on-grid electricity price;

[0201] (4) Photovoltaic electricity cost model;

[0202] Referring to NB / T 10394-2020 photovoltaic power generation system efficiency specification, the levelized cost of electricity indicator calculation formula is improved as follows:

[0203]

[0204] Where: i is the discount rate (%), n is the number of years the system has been in operation, N is the evaluation period of the photovoltaic power generation system,

[0205] I0 is the static initial investment of the photovoltaic system, I t VAT deduction for the project,

[0206] VR is the residual value of the photovoltaic system, M n is the operating cost in the nth year (including maintenance, materials, labor, etc.),

[0207] Y n Annual online electricity consumption;

[0208] Note: In the embodiment, since taxes are not taken into account in electricity prices and revenues, the value-added tax portion is removed here.

[0209] (5) Energy storage cost and benefit model

[0210] The levelized energy storage cost adopts the LCOS (Levelized Cost of Storage) in the original text of the U.S. Department of Energy's energy storage technology tender, that is, the levelized energy storage cost.

[0211]

[0212] Then there is energy storage benefit:

[0213]

[0214] (6) Transformer capacity electricity charges and losses

[0215] Transformer capacity electricity fee: Bill trans = (monthly) capacity electricity fee / 30 / 24

[0216] Transformer active power loss (loss): Based on the active power loss calculation formula in the electrician's manual: ΔP=P0+β 2 ×PK

[0217] Where: P0 is no-load loss (kw), β is load rate, PK is rated load loss (kw);

[0218] Note: The embodiment herein adopts a transformer design of specification 2, and is provided with N transformers, N-1 of which are the same, and 1 transformer is different.

[0219] Transformer power loss in 24 hours:

[0220]

[0221] Step 5: Give the photovoltaic power generation power, energy storage capacity and energy storage strategy, energy storage power, transformer specifications and capacity under the optimal conditions of photovoltaic storage and transformation benefits.

[0222] Table 1 Transformer data

[0223]

[0224] Table 2 Photovoltaic initial parameter input

[0225]

[0226] There are three ways to install photovoltaic panels: roof bracket, ground bracket, and backboard installation.

[0227] Table 3 Energy storage input data

[0228]

[0229]

[0230] The device system of the present invention uses the Python development language and is implemented based on tools such as sklearn, pvlib, and pandas.

[0231] An optimal coordinated configuration system for photovoltaic storage and transformation benefits in integrated energy stations:

[0232] The collaborative configuration system includes: an input module, a computer-aided selection module, an intervention module, a coordination configuration module and an output module;

[0233] The input module is used to input the electric load curve and photovoltaic environment;

[0234] The computer-aided selection module is used to establish a photovoltaic selection model, an energy storage selection model and a transformer selection model, and respectively gives photovoltaic power generation power, energy storage capacity, energy storage power, transformer capacity and specifications;

[0235] The intervention module is based on three models, selects the photovoltaic range to determine the power generation cost per kilowatt-hour, initializes the energy storage strategy through the energy storage selection range, and selects the dynamic capacity increase range of the transformer;

[0236] The coordination configuration module takes the optimal benefit of PV-storage-variable as the optimization objective function, uses Bayesian optimization parameters to improve the particle swarm optimization algorithm, and combines it with a three-layer nested traversal search method to build a PV-storage-variable coordinated configuration model;

[0237] The output module provides the energy storage capacity and energy storage strategy, photovoltaic power generation power, transformer specifications and capacity under the optimal conditions of photovoltaic storage and transformation benefits.

[0238] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0239] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps of the above method are implemented.

[0240] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory, ROM, a programmable read-only memory, PROM, an erasable programmable read-only memory, EPROM, an electrically erasable programmable read-only memory, EEPROM, or a flash memory. The volatile memory may be a random access memory, RAM, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory static RAM, SRAM, dynamic random access memory dynamic RAM, DRAM, synchronous dynamic random access memory synchronous DRAM, SDRAM, double data rate synchronous dynamic random access memory double data rate SDRAM, DDR SDRAM, enhanced synchronous dynamic random access memory enhanced SDRAM, ESDRAM, synchronous link dynamic random access memory synchlink DRAM, SLDRAM, and direct memory bus random access memory direct rambus RAM, DR RAM. It should be noted that memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0241] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center through a wired method such as coaxial cable, optical fiber, digital subscriber line digital subscriber line, DSL or wireless such as infrared, wireless, microwave, etc. to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium such as a floppy disk, a hard disk, a tape, an optical medium such as a high-density digital video disc digital video disc, DVD, or a semiconductor medium such as a solid state hard disk solid state disc, SSD, etc.

[0242] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0243] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined to perform. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0244] The above is a detailed introduction to the optimal coordinated configuration method and system for the photovoltaic storage and transformation benefits of an integrated energy station proposed in the present invention, and the principles and implementation methods of the present invention are explained. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in the field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for optimal coordinated configuration of photovoltaic, storage and transformation benefits in an integrated energy station, characterized by: The method specifically comprises the following steps: Step 1, input power load curve and photovoltaic environment; Step 2: Establish a photovoltaic selection model, an energy storage selection model, and a transformer selection model, and respectively give the photovoltaic power generation power, energy storage capacity, energy storage power, transformer capacity and specifications; Step 3: Combine the three models in step 2 to select the photovoltaic range to determine the power generation cost per kilowatt-hour, initialize the energy storage strategy through the energy storage selection range, and select the dynamic capacity expansion range of the transformer; Step 4: Taking the optimal benefit of PV-storage-variable as the optimization objective function, the particle swarm optimization algorithm is improved using Bayesian optimization parameters, and a three-layer nested traversal search method is used to construct a PV-storage-variable collaborative configuration model; Step 5: Give the energy storage capacity and energy storage strategy, photovoltaic power generation power, transformer specifications and capacity under the optimal conditions of photovoltaic storage and transformation benefits.

2. The collaborative configuration method according to claim 1, characterized in that: In step 1, the photovoltaic environment includes: The local time-of-use electricity price of the energy station, the longitude and latitude of the energy station, the photovoltaic area that can be installed at the energy station, the photovoltaic shading conditions, the energy storage cost parameters and the photovoltaic power generation cost parameters.

3. The collaborative configuration method according to claim 2, characterized in that: In step 2, the photovoltaic selection model determines the photovoltaic selection and power range based on the clear sky irradiance model and photovoltaic power generation simulation, combined with the power generation model established by the maximum photovoltaic installation area and the power load curve on site; Power generation = photovoltaic installation area × power generation per square meter; P pv =S·E p Where S is the photovoltaic installation area, E p The power generated per square meter; Maximum power generation P pv，max The calculation formula is: P pv，max =S max ·E p Where: S max is the maximum photovoltaic installation area; E p The power generated per square meter; Minimum power generation P pv，min The calculation formula is: Select the local summer solstice irradiance curve, calculate the photovoltaic power generation according to the 24-hour load curve, and take the minimum power generation; Where k is the power coefficient, which is 0.75; P t，load is the load power at time t; t is the period of stable photovoltaic power generation.

4. The collaborative configuration method according to claim 3, characterized in that: In step 2, the energy storage capacity and energy storage power range are set through the energy storage selection model; The energy storage capacity is capable of being released during the peak electricity price and peak electricity price phases of time-of-use electricity prices, and can absorb the surplus of photovoltaic power generation for self-use; The energy storage power must be sufficient to discharge during the discharge period and to charge the required amount of energy within the specified time; The photovoltaic power generation model simulates the power generation curve as follows: P maxch argr ≤|P t，load -P t，pv | AND c =2P maxch argr Where E c is the energy storage capacity, P maxch argr is the maximum charging and discharging power of energy storage, P t，pv is the power generated at time t, t=j…k is the peak and peak electricity price period; The energy storage capacity range requires calculation of the minimum and maximum capacity; Calculate the minimum capacity: Calculate the maximum capacity: where γ max is the maximum capacity factor; γ min is the minimum capacity factor.

5. The collaborative configuration method according to claim 4, characterized in that: In step 2, the transformer capacity and specifications are determined through the transformer selection model; The transformer capacity is: LC is the load factor; r is the transformer capacity range factor, which ranges from 0.75 to 0.9; t is from 0 to 23 o'clock; is the power factor; There are two transformer selection combinations according to transformer specifications: Among them, C i，trans is the transformer specification capacity, then: Selection combination rule 1: transformers with the same specifications and minimum quantity; Selection combination rule 2: N+1 rule, N transformers have the same parameters, 1 transformer has the capacity corresponding to the surplus, and the number is the minimum; Transformer capacity / power total: P min ≤nx i +mx k ≤P max 。 6. The collaborative configuration method according to claim 5, characterized in that: In step 4, a global search is nested in the order of the interval list of transformers, photovoltaics, and energy storage, and the benefits of photovoltaic storage and transformer are optimal. The core optimization adopts the improved particle swarm optimization algorithm; First, the optimal energy storage benefit is calculated, and then the three-level nested traversal search of photovoltaic, storage and transformer is performed. Under the condition of given photovoltaic, storage and transformer capacity, the optimal energy storage benefit is obtained through the particle swarm algorithm, and the benefits of photovoltaic and transformer are superimposed at the same time. The three-level nested PV-storage-transformer system traverses and searches for all PV-storage-transformer benefits. The optimal solution for PV-storage-transformer is the configuration of photovoltaics, energy storage, and transformers; that is, PV-storage-transformer benefit = energy storage benefit + photovoltaic benefit - transformer loss.

7. The collaborative configuration method according to claim 6, characterized in that: In step 4, photovoltaic income = photovoltaic direct consumption income + photovoltaic grid-connected income; Transformer loss is active power loss, that is, the amount of power lost in 24 hours.

8. A collaborative configuration system for executing the optimal collaborative configuration method for photovoltaic, storage and transformation benefits of an integrated energy station according to any one of claims 1 to 7, characterized in that: The collaborative configuration system includes: an input module, a computer-aided selection module, an intervention module, a coordination configuration module and an output module; The input module is used to input the electric load curve and photovoltaic environment; The computer-aided selection module is used to establish a photovoltaic selection model, an energy storage selection model and a transformer selection model, and respectively gives photovoltaic power generation power, energy storage capacity, energy storage power, transformer capacity and specifications; The intervention module is based on three models, selects the photovoltaic range to determine the power generation cost per kilowatt-hour, initializes the energy storage strategy through the energy storage selection range, and selects the dynamic capacity increase range of the transformer; The coordination configuration module takes the optimal benefit of PV-storage-variable as the optimization objective function, uses Bayesian optimization parameters to improve the particle swarm optimization algorithm, and combines it with a three-layer nested traversal search method to build a PV-storage-variable coordinated configuration model; The output module provides the energy storage capacity and energy storage strategy, photovoltaic power generation power, transformer specifications and capacity under the optimal conditions of photovoltaic storage and transformation benefits.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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