Power equipment operation simulation method, system, equipment and medium for wind, light and fire storage energy base

Abstract

The invention discloses a wind, light and fire storage energy base power equipment operation simulation method, system and device and a medium, and relates to the technical field of power simulation optimization, and the method comprises the steps: predicting the capacity information of a generator set through historical operation data in an energy base region and an artificial intelligence algorithm, judging an electric quantity consumption difference value in the energy base area based on the capacity information, and analyzing an electric quantity supply and demand balance state according to the electric quantity consumption difference value; generating a comprehensive income target function based on the electric quantity supply and demand balance state and the unit operation information, and establishing a wind-solar-thermal storage multi-target operation simulation model according to the comprehensive income target function; and operating the wind-light-fire-storage multi-target operation simulation model, performing segmentation processing on the operation simulation process according to the segmentation processing rule, and outputting a segmentation processing result to a human-computer interface by adopting a data bus technology. The operation simulation method is combined with the composition structure of the power grid, the consumption level of new energy power generation is improved, and the purpose of maximizing operation benefits is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power simulation optimization, and in particular to a method, system, equipment and medium for simulating the operation of power equipment in a wind, solar, thermal and energy storage base. Background Art

[0002] The proportion of clean energy in social energy consumption will continue to increase. Clean power systems based on clean energy generation such as wind power and photovoltaics will gradually replace high-carbon emission power systems based on traditional fossil energy generation. However, wind power and photovoltaic power generation are random and volatile, which poses challenges to the safe and reliable operation of future high-proportion clean energy power systems. In order to quantitatively describe the impact of flexible resources such as transmission channels, receiving power grids, thermal power and energy storage on energy bases, and to evaluate the economic benefits of system operation, it is necessary to carry out long-term operation simulation research.

[0003] However, current simulations mainly tend to be pre-construction planning simulations, and there are few studies and applications that combine actual production data, historical data and other aspects of data fusion to study and apply future production planning and production timing simulations. Due to the characteristics of fluctuations, randomness and intermittency of energy bases, in addition to coordinated optimization and control of real-time operation, it is increasingly important to carry out coordinated planning and timing production simulation of various energy sources in advance, especially the capacity planning in the overall complementary characteristics of wind, solar, thermal and storage, which directly affects the actual production and regulation capabilities. Therefore, it is necessary to carry out timing production simulation research of wind, solar, thermal and storage, combine the composition structure of the power grid, give play to regional resource endowments, improve the level of new energy power generation, maximize operating benefits, and improve the economic level.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In response to the problems in the related technology, the present invention proposes a method, system, equipment and medium for simulating the operation of power equipment in a wind, solar, thermal and energy storage base to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] According to a first aspect of the present invention, a method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base is provided, the operation simulation method comprising:

[0008] Use historical operating data and artificial intelligence algorithms in the energy base area to predict the capacity information of the generator set, determine the power consumption difference in the energy base area based on the capacity information, and analyze the power supply and demand balance status based on the power consumption difference;

[0009] Generate a comprehensive benefit objective function based on the power supply and demand balance status and unit operation information, and establish a wind, solar, thermal and storage multi-objective operation simulation model based on the comprehensive benefit objective function and operation constraints;

[0010] Run the multi-objective operation simulation model of wind, solar, thermal and energy storage, segment the operation simulation process according to the segment processing rules, and use data bus technology to output the segment processing results to the human-computer interface.

[0011] Preferably, using historical operation data in the energy base area and artificial intelligence algorithms to predict the capacity information of the generator set, judging the power consumption difference in the energy base area based on the capacity information, and analyzing the power supply and demand balance state according to the power consumption difference includes:

[0012] Collect historical meteorological data in the energy base area, and select photovoltaic influencing factors based on the historical meteorological data as input and combine them with artificial intelligence algorithms to obtain photovoltaic power prediction;

[0013] Obtain historical wind farm meteorological data in the energy base area, and select wind power influencing factors based on the historical wind farm meteorological data as input and combine them with artificial intelligence algorithms to obtain wind power forecasts;

[0014] Obtain historical load data in the energy base area, and use fitting algorithms to analyze the historical load data to obtain the power load capacity of the energy base area;

[0015] The capacity information of the generator set is obtained based on the predicted photovoltaic power, predicted wind power and power load capacity, and the power consumption difference within the energy base area is calculated based on the capacity information;

[0016] The power consumption difference is used to determine the load demand in the energy base area, and the load demand is compared with the power supply and demand balance target. The power supply and demand balance status is analyzed based on the comparison results.

[0017] Preferably, acquiring the capacity information of the generator set based on the predicted photovoltaic power, the predicted wind power and the power load capacity, and calculating the power consumption difference in the energy base area according to the capacity information includes:

[0018] Based on the photovoltaic power forecast and the wind power forecast, the photovoltaic power generation capacity and the wind power generation capacity are obtained respectively, and the power load capacity, the photovoltaic power generation capacity and the wind power generation capacity are used as the capacity information of the generator set;

[0019] The reserve capacity of the generator set is analyzed by using the capacity information to calculate the reserve capacity coefficient corresponding to the weather turning point change, and the thermal power generation capacity is analyzed based on the reserve capacity and thermal power generation capacity;

[0020] Constraints are set according to the installed capacity of thermal power generation, and the difference in electricity consumption within the energy base area is calculated based on the constraints and capacity information.

[0021] Preferably, the power supply and demand balance state includes two groups of situations: sufficient load demand and insufficient load demand;

[0022] When the power supply and demand balance state is that the load demand is sufficient, the generator set needs to operate and generate electricity at maximum capacity after the power supply and demand balance target is met;

[0023] When the power supply and demand balance state is that the load demand is insufficient, it is necessary to judge the load change state according to the photovoltaic power forecast, power load capacity, wind power forecast and spare capacity after the thermal power unit is operated.

[0024] Preferably, generating a comprehensive benefit objective function based on the power supply and demand balance state and the unit operation information, and establishing a wind, solar, thermal and storage multi-objective operation simulation model according to the comprehensive benefit objective function and the operation constraint conditions includes:

[0025] When the power supply and demand balance state is in the state of insufficient load demand, the revenue and cost information of the generator set is obtained, and a revenue function is established based on the revenue and cost information of the generator set. The revenue function is converted into a unified objective function based on the entropy weight method to generate a comprehensive revenue objective function;

[0026] Establish the operating constraints of the generator set based on the comprehensive benefit objective function, and use the operating constraints of the generator set as the constraints of the operation simulation;

[0027] Based on the comprehensive benefit objective function and constraint variables, a simulation model framework is selected to establish a multi-objective operation simulation model of wind, solar, thermal and energy storage.

[0028] Preferably, obtaining the revenue and expense information of the generator set, establishing a revenue function according to the revenue and expense information of the generator set, and converting the revenue function into a unified objective function based on the entropy weight method to generate a comprehensive revenue objective function includes:

[0029] Obtain the actual output information of photovoltaic and wind power equipment at any time, and generate the wind power and photovoltaic power generation revenue function in combination with the time-of-use electricity price information at the corresponding time;

[0030] The energy storage life cycle cost allocation coefficient is obtained based on the investment cost and energy storage capacity of the generator set, and the energy storage operation profit function is generated by combining the energy storage charging and discharging power information of the generator set with the time-of-use electricity price;

[0031] Obtain the fuel cost and ramp cost of thermal power equipment in the generator set, and generate a thermal power operation profit function based on the fuel cost and ramp cost;

[0032] The entropy weight method is used to generate weight information values ​​to convert the wind power and photovoltaic power generation profit functions, energy storage operation profit functions and thermal power operation profit functions into a unified objective function to obtain a comprehensive profit objective function.

[0033] Preferably, the expression of wind power and photovoltaic power generation revenue function is:

[0034]

[0035] In the formula, f1 represents the income of wind power and photovoltaic power generation, T represents the cycle period, t represents the time t, η1 represents the time-of-use electricity price, η2 represents the green carbon income factor, and They represent the actual output of wind power and photovoltaic power generation at time t respectively.

[0036] Preferably, the expression of the energy storage operation benefit function is:

[0037]

[0038] Where f2 represents the energy storage operation benefit, ψ s represents the energy storage device set of the generator set, T represents the cycle period, t represents the time t, and They represent the energy storage charging and discharging power of the generator set at time t, η1 represents the time-of-use electricity price, δ represents the energy storage life cycle cost sharing coefficient, and They represent the energy storage charging and discharging efficiency of the generator set at time t respectively, and s represents the sth energy storage device.

[0039] Preferably, the expression of the thermal power operation profit function is:

[0040]

[0041] In the formula, f2 represents the operating income of thermal power, T represents the cycle period, t represents the time t, η1 represents the time-of-use electricity price, represents the output power of thermal power unit i at time t, η3 represents the carbon emission cost factor, f represents the fuel cost, and l represents the ramp cost.

[0042] Preferably, the entropy weight method is used to generate weight information values ​​to convert the wind power and photovoltaic power generation revenue function, the energy storage operation revenue function and the thermal power operation revenue function into a unified objective function, and the comprehensive revenue objective function is obtained, including:

[0043] The information entropy of factors is obtained based on the entropy weight method, and the information utility value is calculated according to the information entropy of factors. The weight values ​​corresponding to the wind power and photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function are obtained using the information utility value.

[0044] Based on the weight value, the wind power and photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function are converted into a unified objective function, and the unified objective function is used as the comprehensive profit objective function.

[0045] Preferably, establishing the operating constraints of the generator set based on the comprehensive benefit objective function, and using the operating constraints of the generator set as the constraints of the operation simulation includes:

[0046] Based on the energy storage charging and discharging power and charging and discharging efficiency of the generator set, the power balance constraint condition without considering the network loss is defined, and the inequality constraints of the wind, solar, thermal and storage multi-objective operation simulation model are set according to the running and stopping status of the generator set and the set constraint information;

[0047] Based on the planned grid-connected power values ​​of photovoltaic and wind power equipment at any time, grid-connected constraints of photovoltaic and wind power are defined respectively, and grid section constraints are set according to the power limit values;

[0048] The power balance constraints, inequality constraints, grid-connected constraints and grid section constraints are combined as generator set operation constraints, and the generator set operation constraints are used as operation simulation constraints.

[0049] Preferably, running the wind, solar, thermal and storage multi-objective operation simulation model, performing segmented processing on the operation simulation process according to the segmented processing rules, and outputting the segmented processing results to the human-machine interface using the data bus technology includes:

[0050] The wind, solar, thermal and storage multi-objective operation simulation model is run based on the operation simulation constraints, and the seasonal characteristics of wind, solar, thermal and storage are used to classify the data indicators of the operation simulation solution process according to the season;

[0051] The similarity of the operation simulation results in the same season is calculated based on the classification results, and the scenarios are extracted using the similarity results as the power operation simulation results of the seasonal energy base;

[0052] The operation simulation information is extracted from the power operation simulation results of the seasonal energy base according to the division standard of days, and the operation simulation information is output to the human-machine interface via the data bus using the data bus technology.

[0053] According to a second aspect of the present invention, a wind, solar, thermal and energy storage base power equipment operation simulation system is provided, and the operation simulation system comprises:

[0054] The supply and demand balance status analysis module is used to use the historical operation data in the energy base area and the artificial intelligence algorithm to predict the capacity information of the generator set, determine the power consumption difference in the energy base area based on the capacity information, and analyze the power supply and demand balance status according to the power consumption difference;

[0055] The operation simulation model building module is used to generate a comprehensive benefit objective function based on the power supply and demand balance state and the unit operation information, and establish a wind, solar, thermal and storage multi-objective operation simulation model based on the comprehensive benefit objective function and operation constraints;

[0056] The operation result analysis output module is used to run the wind, solar, thermal and energy storage multi-objective operation simulation model, segment the operation simulation process according to the segment processing rules, and use data bus technology to output the segment processing results to the human-computer interface.

[0057] According to a third aspect of the present invention, a computer device is provided.

[0058] Preferably, the computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0059] According to a fourth aspect of the present invention, a computer readable storage medium is provided.

[0060] Preferably, a computer program is stored on the computer-readable storage medium, and the computer program implements the steps of the above method when executed by a processor.

[0061] The beneficial effects of the present invention are:

[0062] 1. The operation simulation method proposed in the present invention is based on the characteristics of the energy base of the new power system, taking into account the operation constraints of cross-regional DC transmission, the section constraints of the receiving power grid, the annual receiving power grid load and other power exchanges, the output of renewable energy such as wind and solar, the flexibility of conventional power supply ramping and starting, etc., combined with the composition structure of the power grid, giving full play to the regional resource endowment, improving the level of consumption of new energy power generation, maximizing the operation benefits, and improving the economic level.

[0063] 2. The present invention takes into account the seasonal characteristics of resources and power grids, and considers the operation simulation optimization under different resource conditions. In the solution process, the long-term operation optimization process is decoupled into multiple periodic units, and parallel computing is used to improve the efficiency of computational simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 It is a flow chart of a method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base according to an embodiment of the present invention;

[0066] Figure 2 This is a principle block diagram of a wind, solar, thermal and energy storage base power equipment operation simulation system according to an embodiment of the present invention;

[0067] Figure 3 It is an implementation flow chart of a method for simulating the operation of power equipment in a wind, solar, thermal and storage energy base according to an embodiment of the present invention;

[0068] Figure 4 This is a calculation example structure diagram of a method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base according to an embodiment of the present invention;

[0069] Figure 5 is a schematic diagram of a simulation operation and human-machine display structure according to an embodiment of the present invention;

[0070] Figure 6 is a unit output curve diagram after simulation operation according to an embodiment of the present invention;

[0071] Figure 7 is a schematic diagram of the structure of a computer device according to an embodiment of the present invention.

[0072] In the figure:

[0073] 1. Supply and demand balance status analysis module; 2. Operation simulation model construction module; 3. Operation result analysis output module. DETAILED DESCRIPTION

[0074] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0075] According to an embodiment of the present invention, a method, system, device and medium for simulating the operation of power equipment in a wind, solar, thermal and energy storage base are provided.

[0076] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the wind, solar, thermal and storage energy base power equipment operation simulation method according to an embodiment of the present invention, the operation simulation method includes:

[0077] Step S1, using the historical operation data in the energy base area and the artificial intelligence algorithm to predict the capacity information of the generator set, judging the power consumption difference in the energy base area based on the capacity information, and analyzing the power supply and demand balance state according to the power consumption difference.

[0078] In this embodiment, when using historical operating data in the energy base area and an artificial intelligence algorithm to predict the capacity information of the generator set, judging the power consumption difference in the energy base area based on the capacity information, and analyzing the power supply and demand balance state according to the power consumption difference, historical meteorological data in the energy base area can be collected, and photovoltaic influencing factors are selected as input based on the historical meteorological data to obtain photovoltaic power prediction in combination with the artificial intelligence algorithm; historical wind farm meteorological data in the energy base area are obtained, and wind power influencing factors are selected as input based on the historical wind farm meteorological data to obtain wind power prediction in combination with the artificial intelligence algorithm; historical load data in the energy base area are obtained, and the historical load data are analyzed using a fitting algorithm to obtain the power load capacity of the energy base area; capacity information of the generator set is obtained based on the photovoltaic power prediction, wind power prediction and power load capacity, and the power consumption difference in the energy base area is calculated according to the capacity information; load demand in the energy base area is judged by using the power consumption difference, and the load demand is compared with the power supply and demand balance target, and the power supply and demand balance state is analyzed based on the comparison result.

[0079] In this embodiment, when the capacity information of the generator set is obtained based on the photovoltaic power forecast, the wind power forecast and the power load capacity, and the power consumption difference in the energy base area is calculated according to the capacity information, the photovoltaic power generation capacity and the wind power generation capacity can be obtained respectively based on the photovoltaic power forecast and the wind power forecast, and the power load capacity, the photovoltaic power generation capacity and the wind power generation capacity are used as the capacity information of the generator set; the spare capacity coefficient corresponding to the meteorological turning point is calculated using the capacity information to analyze the spare capacity of the generator set, and the thermal power generation installed capacity is analyzed based on the spare capacity and the thermal power generation capacity; constraints are set according to the thermal power generation installed capacity, and the power consumption difference in the energy base area is calculated based on the constraints and the capacity information.

[0080] It should be explained that the main goal of the power equipment operation simulation method for wind, solar, thermal and storage energy bases proposed in this example is to establish a two-layer optimization model. The first-layer model meets the power supply and demand balance target of the power grid while maintaining an appropriate amount of flexible resources, and the second-layer model considers the optimal comprehensive system benefits.

[0081] Its primary goal is to maintain a balance between power supply and demand in the system. The main strategies adopted are as follows:

[0082] Collect historical meteorological data in the area of ​​wind, solar, thermal and storage energy bases, including wind resource data and sunshine resource data in recent years, and use AI algorithms or similar day algorithms to predict power to obtain output values. The photovoltaic power prediction algorithm takes important influencing factors such as irradiance, temperature, humidity, and power generation as input, uses data from a period of time as a training sample set, and uses other meteorological data as input to output photovoltaic power prediction data, or divides meteorological types into sunny, cloudy, overcast, rainy and snowy weather, and classifies historical data in the sample set to obtain daily power generation curves under typical weather conditions. Then, compare the meteorological data in the test set with the typical weather, and use the daily power generation with the highest similarity as the prediction curve to obtain photovoltaic power prediction data.

[0083] The wind power prediction algorithm takes important influencing factors such as wind speed, wind direction, air pressure, and power generation as input, uses data over a period of time as a training sample set, uses other meteorological data as input, and outputs wind power prediction data. At the same time, it collects regional load historical data and uses AI algorithms or fitting algorithms to perform load prediction to obtain demand capacity. The load prediction algorithm here uses temperature, humidity, weekday types, holidays, and load power as input, uses data over a period of time as a training sample set, uses other meteorological data as input, and outputs load prediction data to obtain power load capacity.

[0084] In order to maintain the balance between electricity supply and demand, it is necessary to balance the electricity consumption in the region or reduce the power transmission outside the region. The calculation formula for the difference in electricity consumption in the energy base area is:

[0085] min f0=|P load -P pv -P w -P t |;

[0086] Where f0 represents the difference in power consumption within the energy base area, P load Indicates load capacity, P pv Represents photovoltaic power generation capacity, P w represents wind power generation capacity, P t Indicates thermal power generation capacity.

[0087] The constraint condition is to maintain the necessary thermal power flexibility resources to cope with the power support under transitional meteorological conditions, where: P t +P b ≤P t.N ;P b =αP pv +βP w +γP load +P other ;

[0088] And Pb represents the spare capacity, P t.N represents the installed capacity of thermal power generation, P other represents other reserve capacity, α represents the reserve capacity coefficient of photovoltaic power generation during meteorological turning point changes, β represents the reserve capacity coefficient of wind power generation during meteorological turning point changes, and γ represents the flexible reserve capacity coefficient during load changes.

[0089] Specifically, when there is sufficient load demand or sufficient transmission channels, priority is given to meeting the electricity supply and demand balance target and maximizing unit power generation.

[0090] When the load demand is insufficient or the transmission channel is blocked, priority should be given to maintaining the operation of an appropriate number of thermal power units to provide the necessary flexibility resources, considering the output value P according to the optical power prediction. pv α times, the demand capacity P obtained by load forecasting load α times, wind power forecast output value P w β times and retain the thermal power flexibility resource capacity P b The sum is calculated to cope with the load changes and random fluctuations of wind and solar resources, and then the optimal operation is solved according to the comprehensive benefit target.

[0091] Step S2: Generate a comprehensive benefit objective function based on the electricity supply and demand balance state and the unit operation information, and establish a wind, solar, thermal and energy storage multi-objective operation simulation model based on the comprehensive benefit objective function and the operation constraints.

[0092] In this embodiment, when a comprehensive benefit objective function is generated based on the electricity supply and demand balance state and the unit operation information, and a wind, solar, thermal and storage multi-objective operation simulation model is established according to the comprehensive benefit objective function and the operation constraints, the income and cost information of the generator set can be obtained when the electricity supply and demand balance state is in a state where the load demand is insufficient, and a benefit function is established according to the income and cost information of the generator set, and the benefit function is converted into a unified objective function based on the entropy weight method to generate a comprehensive benefit objective function; the generator set operation constraints are established based on the comprehensive benefit objective function, and the generator set operation constraints are used as the constraints of the operation simulation; the simulation model framework is selected based on the comprehensive benefit objective function and the constraint variables, and a wind, solar, thermal and storage multi-objective operation simulation model is established.

[0093] In this embodiment, when obtaining the revenue and cost information of the generator set, establishing a revenue function based on the revenue and cost information of the generator set, and converting the revenue function into a unified objective function based on the entropy weight method to generate a comprehensive revenue objective function, the actual output information of photovoltaic and wind power equipment at any time can be obtained, and the wind power and photovoltaic power generation revenue function can be generated in combination with the time-of-use electricity price information at the corresponding time; the energy storage life cycle cost sharing coefficient is obtained based on the investment cost and energy storage capacity of the generator set, and the energy storage operation revenue function is generated in combination with the energy storage charging and discharging power information of the generator set and the time-of-use electricity price; the fuel cost and climbing cost of the thermal power equipment in the generator set are obtained, and the thermal power operation revenue function is generated based on the fuel cost and the climbing cost; the entropy weight method is used to generate weight information values ​​to convert the wind power and photovoltaic power generation revenue function, the energy storage operation revenue function and the thermal power operation revenue function into a unified objective function to obtain a comprehensive revenue objective function.

[0094] In this embodiment, when the entropy weight method is used to generate weight information values ​​to convert the wind power photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function into a unified objective function to obtain a comprehensive profit objective function, the factor information entropy can be obtained based on the entropy weight method, and the information utility value can be calculated based on the factor information entropy, and the weight values ​​corresponding to the wind power photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function can be obtained using the information utility value; based on the weight value, the wind power photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function are converted into a unified objective function, and the unified objective function is used as the comprehensive profit objective function.

[0095] In this embodiment, when establishing the operating constraints of the generator set based on the comprehensive benefit objective function and using the operating constraints of the generator set as the constraints of the operation simulation, the power balance constraints under the condition of not considering network losses can be defined based on the charging and discharging power and charging and discharging efficiency of the energy storage of the generator set, and the inequality constraints of the wind, solar, thermal and storage multi-objective operation simulation model are set according to the operation and stop status of the generator set and the set constraint information; based on the planned grid-connected power values ​​of photovoltaic and wind power equipment at any time, the grid-connected constraints of photovoltaic and wind power are defined respectively, and the grid section constraints are set according to the limited power value; the power balance constraints, inequality constraints, grid-connected constraints and grid section constraints are combined as the operating constraints of the generator set, and the operating constraints of the generator set are used as the constraints of the operation simulation.

[0096] It needs to be explained that the objective function should be to take the optimal comprehensive benefit of the power equipment in the energy base as the objective function, and consider the operating constraints of cross-regional DC transmission, the section constraints of the receiving power grid, the load of the receiving power grid throughout the year and the power exchange between other provinces, the output of renewable energy such as wind and solar power, and the flexibility of conventional power sources in ramping and starting. At the same time, the coordinated operation of multiple energy sources such as coal, gas, wind, light, and storage in the energy quarter, unit maintenance, operation mode, dynamic limit, etc., simulate the operation of power equipment for 8760 hours a year.

[0097] Specifically, the objective function includes the wind power and photovoltaic power generation revenue function, the energy storage operation revenue function and the thermal power operation revenue function, among which:

[0098] The expression of wind power and photovoltaic power generation revenue function (wind power and photovoltaic green carbon revenue model) is:

[0099]

[0100] In the formula, f1 represents the income of wind power and photovoltaic power generation (the combined output income of wind power and photovoltaic power generation), T represents the cycle period, t represents the time t, η1 represents the time-of-use electricity price, η2 represents the green carbon income factor, and They represent the actual output of wind power and photovoltaic power generation at time t respectively.

[0101] The expression of the energy storage operation benefit function (considering the energy storage operation benefit cost of the entire life cycle) is:

[0102]

[0103] Where f2 represents the energy storage operation benefit, ψ s represents the energy storage device set of the generator set, T represents the cycle period, t represents the time t, and They represent the energy storage charging and discharging power of the generator set at time t, η1 represents the time-of-use electricity price, δ represents the energy storage life cycle cost sharing coefficient, δ = investment cost / (charge and discharge times*capacity), and They represent the energy storage charging and discharging efficiency of the generator set at time t respectively, and s represents the sth energy storage device.

[0104] The expression of thermal power operation revenue function (thermal power operation revenue cost, mainly the electricity revenue minus the fuel cost and ramp cost) is:

[0105]

[0106] In the formula, f2 represents the operating income of thermal power, T represents the cycle period, t represents the time t, η1 represents the time-of-use electricity price, represents the output power of thermal power unit i at time t, η3 represents the carbon emission cost factor, f represents the fuel cost, and l represents the ramp cost.

[0107] And through the entropy weight method, multiple different optimization objectives are transformed into a unified objective function. Specifically, the above objective functions are combined to generate a total objective function, and the maximum value of the function is calculated, as follows:

[0108] maxF=w1f1+w2f3+w3f3;

[0109] w1+w2+w3=1;

[0110] The entropy weight method is used to solve w1, w2, and w3, and the process is as follows:

[0111]

[0112] d j =1-e j ;

[0113]

[0114] In the formula, w j is the weight of the jth factor, m is the total number of factors, d j is the information utility value, which reflects the information utility of the jth factor, e j is the information entropy of the jth factor, when p hj = 0, p hj lnp hj =0; p hj is the weight of the hth object on the jth factor; x hj is the original data of the hth object on the jth factor, x' hj is the standardized data, min(x j ) and max(x j ) are the minimum and maximum values ​​of the j-th factor, respectively.

[0115] The operation constraints are to consider the operation constraint characteristics of inter-regional DC transmission, the section constraints of the receiving power grid, the annual receiving power grid load and other inter-provincial power exchanges, the output of renewable energy such as wind and solar, and the flexibility of conventional power supply ramping and starting and stopping, including:

[0116] (1) System power balance constraint: Without considering the system network loss, the sum of the output of wind power, photovoltaic power, thermal power and energy storage system is equal to the real-time load, specifically:

[0117]

[0118] In the formula, is the output power of thermal power unit i at time t; and are the actual outputs of wind power and photovoltaic power generation at time t, p c,t With p d,t are the charging power and discharging power of the energy storage system respectively; η c , η d are the charging efficiency and discharging efficiency of the energy storage system respectively; is the load at time t.

[0119] (2) Inequality constraints of the long-term operation simulation model of power equipment (multi-objective operation simulation model of wind, solar, thermal and energy storage):

[0120] A1·(P,U,S,D)≤d1

[0121]

[0122] Inequality constraints of the long-term operation simulation model of the power system, including unit output, unit ramping, unit operating state conversion, unit on / off time and unit standby; S g,t With D g,t represents the running and stopping state transformation of the unit at time t, A1 represents the coefficient matrix of the corresponding constraint variables, d1 represents the constant column vector, Represents the unit 0-1 variable operating state constraints.

[0123] (3) Photovoltaic grid-connected power constraints:

[0124]

[0125] In the formula, represents the maximum planned grid-connected power value of photovoltaic o in period t, p pvo,j Indicates the planned grid-connected power value of photovoltaic o in period t.

[0126] (4) The wind power grid-connected power is not greater than the maximum output constraint of the wind farm:

[0127]

[0128] In the formula, represents the maximum planned grid-connected power value of wind power b in period t, p wb,t It represents the planned grid-connected power value of wind power b in period t.

[0129] (5) Section constraints: You can specify any number of lines to form a transmission section and specify its section limit value:

[0130]

[0131] In the formula, represents the maximum planned power value of section c in period t, p dmc,t Indicates the maximum power value of section c in period t.

[0132] (6) Inter-section interconnection line: Given the upper and lower limits of power transmission (including reverse transmission) of the section interconnection line, the power planning curve of the interconnection line is given:

[0133]

[0134] In the formula, and They represent the upper and lower limits of the power value of section c in time period t, p lxc,t Indicates the maximum power value of section c in period t.

[0135] (7) The planned power of DC transmission must meet the following constraints:

[0136]

[0137] It is a daily operation continuity constraint, requiring that the planned DC transmission power at the beginning and end of the day be equal, so that the inter-day transmission power remains continuous and power switching is reduced.

[0138]

[0139] In the formula, T represents the period, which is 24, and u t is a 0-1 variable, u t =1 means adjusting the DC channel transmission power during time period t, u t =0 means that the DC channel transmission power is not adjusted during period t. represents the planned power of DC transmission in time period t, and M represents the adjustable capacity of the DC channel.

[0140] It is the transmission power switching constraint. When u t =0, the planned DC transmission power in period t should be kept unchanged.

[0141] Step S3, run the wind, solar, thermal and energy storage multi-objective operation simulation model, perform segmented processing on the operation simulation process according to the segmented processing rules, and output the segmented processing results to the human-computer interface using data bus technology.

[0142] In this embodiment, when running the wind, solar, thermal and storage multi-objective operation simulation model, the operation simulation process is segmented according to the segmentation processing rules, and the segmentation processing results are output to the human-computer interface using the data bus technology. The wind, solar, thermal and storage multi-objective operation simulation model can be run based on the operation simulation constraints, and the operation simulation solution process can be classified according to the season data indicators using the seasonal characteristics of wind, solar, thermal and storage; the similarity of the operation simulation results in the same season is calculated based on the classification results, and the similarity results are used to extract scenarios as seasonal energy base power operation simulation results; the operation simulation information is extracted from the seasonal energy base power operation simulation results according to the division standard in days, and the operation simulation information is output to the human-computer interface via the data bus using the data bus technology.

[0143] It should be explained that according to the seasonal characteristics of wind, solar, thermal and storage, the simulation solution process is divided into seasonal segments and the data indicators are classified according to the season. The similarity of multiple samples within a season is calculated, including parameter data such as irradiance, wind speed, temperature, wind, solar, thermal output, holiday type, etc., and the vector r is used. a With e a To represent different samples, samples with high similarity are extracted and reduced to representative scenes. The similarity calculation formula using Euclidean distance is as follows:

[0144]

[0145] In the formula, r a With e a Represents two sample vectors, n represents the number of sample vectors, and d(r,e) represents the similarity between samples.

[0146] Then, the whole year is further decomposed and the simulation operation process is solved on a daily basis. The representative samples in the quarter are extracted on a daily basis using the similarity method mentioned above. A new sample set is formed by filling the representative samples, and simulation calculations are performed. The results of similar samples are replaced by the representative sample results. The simulation results are output through the human-computer interface using data bus technology. After the simulation operation calculation is completed, the data is sent to the data bus using data bus technology. The human-computer interface obtains the data from the data bus and displays it in a variety of ways, such as tables, pie charts, bar charts, etc.

[0147] like Figures 3 to 6 As shown, in specific applications, this embodiment should be implemented according to the process of establishing a model, inputting parameters, then decoupling processing and running optimization simulation, and finally outputting the simulation optimization results.

[0148] Referring to the load characteristics of the receiving area, the load is lower at midnight in summer, and shows a trend of continuous increase starting in the morning and finally falling back. Therefore, the fitted power transmission curve adopts a single-step trend. The winter load shows two peaks in the morning and evening, falls at noon, and is the lowest at both ends of midnight. The fitted power transmission curve is saddle-shaped. Wind power output often shows the characteristics of "large in winter and small in summer", and photovoltaic output is "large in summer and small in winter", which makes wind and solar output have natural complementary characteristics. The seasonal characteristics of power coal prices are mainly manifested in the trend of rising prices during the peak period of electricity demand in summer and the heating period in winter. This trend is also affected by many factors such as coal supply stability, inventory levels, policy regulation and environmental protection requirements.

[0149] Combination Figure 3 and Figure 4 The basic process steps of this embodiment are described, and the specific implementation process is as follows:

[0150] Step 1: Establish the model and input parameters, with the optimal system comprehensive benefit as the objective function, taking into account the constraints of cross-regional DC transmission operation, receiving grid section constraints, annual receiving grid load and other inter-provincial power exchange, wind and solar power and other renewable energy output, and conventional power supply ramp-up and start-stop flexibility, and considering the coordinated operation of multiple energy sources such as coal, gas, wind, light, and storage in the energy quarter, unit maintenance, operation mode, dynamic limit, etc., combined with actual application conditions, and flexibly adopt effective data.

[0151] Step 2. Under different seasonal conditions, adjust the weight factors of multiple objective functions, set the weight factors through manual experience, set the wind, solar, energy storage, and thermal power coefficients to 0.5, 0.3, and 0.2 respectively in the season when coal prices are high, and give full play to the potential of renewable energy power generation. In other seasons, set the wind, solar, energy storage, and thermal power coefficients to 0.4, 0.3, and 0.3 respectively. You can also use actual data to calculate and adjust each weight factor through the objective entropy weight method.

[0152] Step 3: During the 8760-hour operation simulation, the periodic characteristics of power supply and load are taken into consideration and calculated on a daily basis. The long-span operation simulation is converted into a process that can be calculated in parallel to improve the calculation efficiency.

[0153] Step 4: Output operation simulation information, including wind, solar and thermal output curves, energy storage charging power curves, load transmission curves and other result information, adjust the strategy according to the optimization results, and provide guidance for the optimization operation plan of the new energy base.

[0154] Through the description of this embodiment and the introduction and analysis of the specific implementation process, it can be seen that the method for rapid operation simulation of power equipment in a wind, solar, thermal and storage energy base proposed in this embodiment is effective, versatile and reasonable, and is helpful to further improve the comprehensive benefits of the operation of the new energy base. At the same time, this embodiment takes into account the operating constraints of cross-regional DC transmission, the cross-section constraints of the receiving power grid, the load of the receiving power grid throughout the year and the power exchange between other provinces, the output of renewable energy such as wind and solar, and the flexibility of conventional power sources in ramping and starting. Combined with the composition structure of the power grid, it gives play to the regional resource endowment, improves the level of absorption of new energy power generation, maximizes operating benefits, and improves the economic level. It also takes into account the seasonal characteristics of resources and power grids, and considers the operation simulation optimization under different resource conditions. In the solution process of the present invention, the long-term operation optimization process is decoupled into multiple periodic units, and the computing efficiency is improved through parallel computing.

[0155] like Figure 2 According to another embodiment of the present invention, there is also provided a wind, solar, thermal and energy storage base power equipment operation simulation system, the operation simulation system comprising:

[0156] The supply and demand balance state analysis module 1 is used to use the historical operation data in the energy base area and the artificial intelligence algorithm to predict the capacity information of the generator set, determine the power consumption difference in the energy base area based on the capacity information, and analyze the power supply and demand balance state according to the power consumption difference;

[0157] Operation simulation model building module 2 is used to generate a comprehensive benefit objective function based on the power supply and demand balance state and unit operation information, and establish a wind, solar, thermal and storage multi-objective operation simulation model based on the comprehensive benefit objective function and operation constraints;

[0158] The operation result analysis output module 3 is used to operate the wind, solar, thermal and energy storage multi-objective operation simulation model, segment the operation simulation process according to the segment processing rules, and output the segment processing results to the human-computer interface using data bus technology.

[0159] Figure 7 An embodiment of a computer device of the present invention is shown. The computer device may be a server, and the computer device includes a processor, a memory, and a network interface connected via a system bus. 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 database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0160] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0161] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0162] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0163] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base, characterized in that: The operation simulation method includes: Use historical operating data and artificial intelligence algorithms in the energy base area to predict the capacity information of the generator set, determine the power consumption difference in the energy base area based on the capacity information, and analyze the power supply and demand balance status based on the power consumption difference; Generate a comprehensive benefit objective function based on the power supply and demand balance status and unit operation information, and establish a wind, solar, thermal and storage multi-objective operation simulation model based on the comprehensive benefit objective function and operation constraints; Run the multi-objective operation simulation model of wind, solar, thermal and energy storage, segment the operation simulation process according to the segment processing rules, and use data bus technology to output the segment processing results to the human-computer interface.

2. The method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base according to claim 1 is characterized in that: The method of using historical operation data in the energy base area and artificial intelligence algorithms to predict the capacity information of the generator set, judging the power consumption difference in the energy base area based on the capacity information, and analyzing the power supply and demand balance state according to the power consumption difference includes: Collect historical meteorological data in the energy base area, and select photovoltaic influencing factors based on the historical meteorological data as input and combine them with artificial intelligence algorithms to obtain photovoltaic power prediction; Obtain historical wind farm meteorological data in the energy base area, and select wind power influencing factors based on the historical wind farm meteorological data as input and combine them with artificial intelligence algorithms to obtain wind power forecasts; Obtain historical load data in the energy base area, and use fitting algorithms to analyze the historical load data to obtain the power load capacity of the energy base area; The capacity information of the generator set is obtained based on the predicted photovoltaic power, predicted wind power and power load capacity, and the power consumption difference within the energy base area is calculated based on the capacity information; The power consumption difference is used to determine the load demand in the energy base area, and the load demand is compared with the power supply and demand balance target. The power supply and demand balance status is analyzed based on the comparison results.

3. The method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base according to claim 2 is characterized in that: The method of obtaining the capacity information of the generator set based on the photovoltaic power forecast, the wind power forecast and the power load capacity, and calculating the power consumption difference in the energy base area according to the capacity information includes: Based on the photovoltaic power forecast and the wind power forecast, the photovoltaic power generation capacity and the wind power generation capacity are obtained respectively, and the power load capacity, the photovoltaic power generation capacity and the wind power generation capacity are used as the capacity information of the generator set; The reserve capacity of the generator set is analyzed by using the capacity information to calculate the reserve capacity coefficient corresponding to the weather turning point change, and the thermal power generation capacity is analyzed based on the reserve capacity and thermal power generation capacity; Constraints are set according to the installed capacity of thermal power generation, and the difference in electricity consumption within the energy base area is calculated based on the constraints and capacity information.

4. A method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base according to claim 3, characterized in that: The power supply and demand balance state includes two groups of situations: sufficient load demand and insufficient load demand; When the power supply and demand balance state is that the load demand is sufficient, the generator set needs to operate and generate electricity at maximum capacity after the power supply and demand balance target is met; When the power supply and demand balance state is that the load demand is insufficient, it is necessary to judge the load change state according to the photovoltaic power forecast, power load capacity, wind power forecast and spare capacity after the thermal power unit is operated.

5. The method for simulating the operation of power equipment in a wind, solar, thermal and energy storage base according to claim 4 is characterized in that: The generating of a comprehensive benefit objective function based on the power supply and demand balance state and the unit operation information, and establishing a wind, solar, thermal and storage multi-objective operation simulation model according to the comprehensive benefit objective function and the operation constraint conditions include: When the power supply and demand balance state is in the state of insufficient load demand, the revenue and cost information of the generator set is obtained, and a revenue function is established based on the revenue and cost information of the generator set. The revenue function is converted into a unified objective function based on the entropy weight method to generate a comprehensive revenue objective function; Establish the operating constraints of the generator set based on the comprehensive benefit objective function, and use the operating constraints of the generator set as the constraints of the operation simulation; Based on the comprehensive benefit objective function and constraint variables, a simulation model framework is selected to establish a multi-objective operation simulation model of wind, solar, thermal and energy storage.

6. A method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 5, characterized in that: The obtaining of the revenue and expense information of the generator set, establishing a revenue function according to the revenue and expense information of the generator set, and converting the revenue function into a unified objective function based on the entropy weight method to generate a comprehensive revenue objective function includes: Obtain the actual output information of photovoltaic and wind power equipment at any time, and generate the wind power and photovoltaic power generation revenue function in combination with the time-of-use electricity price information at the corresponding time; The energy storage life cycle cost allocation coefficient is obtained based on the investment cost and energy storage capacity of the generator set, and the energy storage operation profit function is generated by combining the energy storage charging and discharging power information of the generator set with the time-of-use electricity price; Obtain the fuel cost and ramp cost of thermal power equipment in the generator set, and generate a thermal power operation profit function based on the fuel cost and ramp cost; The entropy weight method is used to generate weight information values ​​to convert the wind power and photovoltaic power generation profit functions, energy storage operation profit functions and thermal power operation profit functions into a unified objective function to obtain a comprehensive profit objective function.

7. A method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 6, characterized in that: The expression of the wind power and photovoltaic power generation revenue function is: In the formula, f1 represents the income of wind power and photovoltaic power generation, T represents the cycle period, t represents the time t, η1 represents the time-of-use electricity price, η2 represents the green carbon income factor, and They represent the actual output of wind power and photovoltaic power generation at time t respectively.

8. The method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 7 is characterized in that: The expression of the energy storage operation benefit function is: Where f2 represents the energy storage operation benefit, ψ s represents the energy storage device set of the generator set, T represents the cycle period, t represents the time t, and They represent the energy storage charging and discharging power of the generator set at time t, η1 represents the time-of-use electricity price, δ represents the energy storage life cycle cost sharing coefficient, and They represent the energy storage charging and discharging efficiency of the generator set at time t respectively, and s represents the sth energy storage device.

9. A method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 8, characterized in that: The expression of the thermal power operation profit function is: In the formula, f2 represents the operating income of thermal power, T represents the cycle period, t represents the time t, η1 represents the time-of-use electricity price, represents the output power of thermal power unit i at time t, η3 represents the carbon emission cost factor, f represents the fuel cost, and l represents the ramp cost.

10. A method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 9, characterized in that: The entropy weight method is used to generate weight information values ​​to convert the wind power and photovoltaic power generation revenue function, the energy storage operation revenue function and the thermal power operation revenue function into a unified objective function, and the comprehensive revenue objective function is obtained, including: The information entropy of factors is obtained based on the entropy weight method, and the information utility value is calculated according to the information entropy of factors. The weight values ​​corresponding to the wind power and photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function are obtained using the information utility value. Based on the weight value, the wind power and photovoltaic power generation profit function, the energy storage operation profit function and the thermal power operation profit function are converted into a unified objective function, and the unified objective function is used as the comprehensive profit objective function.

11. A method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 10, characterized in that: The generating set operation constraint conditions are established based on the comprehensive benefit objective function, and the generating set operation constraint conditions are used as the operation simulation constraint conditions, including: Based on the energy storage charging and discharging power and charging and discharging efficiency of the generator set, the power balance constraint condition without considering the network loss is defined, and the inequality constraints of the wind, solar, thermal and storage multi-objective operation simulation model are set according to the running and stopping status of the generator set and the set constraint information; Based on the planned grid-connected power values ​​of photovoltaic and wind power equipment at any time, grid-connected constraints of photovoltaic and wind power are defined respectively, and grid section constraints are set according to the power limit values; The power balance constraints, inequality constraints, grid-connected constraints and grid section constraints are combined as generator set operation constraints, and the generator set operation constraints are used as operation simulation constraints.

12. A method for simulating the operation of electric power equipment in a wind, solar, thermal and energy storage base according to claim 11, characterized in that: The operation of the wind, solar, thermal and storage multi-objective operation simulation model, segmenting the operation simulation process according to the segmentation processing rules, and outputting the segmentation processing results to the human-machine interface using the data bus technology includes: The wind, solar, thermal and storage multi-objective operation simulation model is run based on the operation simulation constraints, and the seasonal characteristics of wind, solar, thermal and storage are used to classify the data indicators of the operation simulation solution process according to the season; The similarity of the operation simulation results in the same season is calculated based on the classification results, and the scenarios are extracted using the similarity results as the power operation simulation results of the seasonal energy base; The operation simulation information is extracted from the power operation simulation results of the seasonal energy base according to the division standard of days, and the operation simulation information is output to the human-machine interface via the data bus using the data bus technology.

13. A wind, solar, thermal and storage energy base power equipment operation simulation system, used to implement the wind, solar, thermal and storage energy base power equipment operation simulation method according to any one of claims 1 to 12, characterized in that: The operation simulation system includes: The supply and demand balance status analysis module is used to use the historical operation data in the energy base area and the artificial intelligence algorithm to predict the capacity information of the generator set, determine the power consumption difference in the energy base area based on the capacity information, and analyze the power supply and demand balance status according to the power consumption difference; The operation simulation model building module is used to generate a comprehensive benefit objective function based on the power supply and demand balance state and unit operation information, and establish a wind, solar, thermal and storage multi-objective operation simulation model based on the comprehensive benefit objective function and operation constraints; The operation result analysis output module is used to run the wind, solar, thermal and energy storage multi-objective operation simulation model, segment the operation simulation process according to the segment processing rules, and use data bus technology to output the segment processing results to the human-computer interface.

14. A computer 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 12 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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