Power system production simulation method and system considering source-load interaction

Abstract

The invention provides a power system production simulation method and system considering source-load interaction, and the method comprises the steps: carrying out the modeling of uncertain factors, and enabling the uncertain factors to comprise load, wind power and photovoltaic; constructing a flexible load external characteristic model; the method specifically comprises the following steps: respectively constructing an external characteristic model of an air conditioner and an external characteristic model of electric hydrogen production; integrating the flexible load external characteristic model into power system production simulation to construct a production simulation model; the method comprises the following steps: firstly, constructing a target function considering operation cost, bad operation penalty and flexible load adjustment cost; then constructing a simulation constraint condition; solving the production simulation model to complete power system production simulation; based on the method, the invention also provides a power system production simulation system considering source-load interaction. According to the method, multi-dimensional uncertainty modeling is established, an external characteristic model of distributed controllable resources is constructed, and the external characteristic model is integrated into power system production simulation, so that power and electric quantity balance with optimal economical efficiency is realized, and the accuracy and reliability of a simulation result are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of novel power system simulation, and in particular relates to a power system production simulation method and system taking source-load interaction into consideration. Background Art

[0002] With the continuous increase in the proportion of new energy access, the structure and operation mode of the power system are undergoing profound changes. In the future, the power system will face increasingly complex components, diverse external environmental influences, and more multidimensional volatility and uncertainty. In particular, the rapid development of distributed resources has gradually played an important role in the power system. Distributed resources, including distributed power sources, energy storage systems, and flexible adjustable loads (such as air conditioning, hydrogen production, and electric vehicles), have realized the dynamic scheduling and efficient utilization of resources, greatly improving the stability and flexibility of power system operation. However, with the increase in the number of connected distributed resources, the power system's demand for their optimized scheduling has become more urgent.

[0003] In traditional power system production simulation, only relatively fixed generation, transmission and load side parameters are often considered. However, the generation characteristics of new energy sources such as wind power and photovoltaics show obvious volatility and randomness, and the load demand also changes with environmental and economic factors. The accumulation of these uncertainties makes it difficult for a single production simulation model to meet the requirements of accuracy and precision. In addition, the addition of flexible loads further increases the complexity of the system. Distributed resources have unique external characteristics, such as uncertainty, time series operation characteristics and nonlinear dynamic response. Traditional simulation tools are difficult to accurately characterize their performance under different scheduling conditions. At the same time, facing massive heterogeneous distributed controllable resources, existing production simulation methods are difficult to solve efficiently, especially after superimposing multidimensional uncertainty factors in the power system, traditional models and algorithms often face problems of high computational complexity and poor convergence. In the current power system simulation research, as the uncertainty characteristics of new energy sources such as wind power and photovoltaics and the complex dynamic characteristics of flexible loads gradually attract attention, the limitations of traditional simulation methods are becoming more and more prominent. A single simulation model is difficult to simultaneously capture multidimensional uncertainty factors such as wind power, photovoltaics and loads, as well as the time series response characteristics of flexible loads. Existing production simulation research often ignores new distributed controllable elements, and there is a lack of efficient methods and tools for solving production simulation of power systems containing distributed resources on the market. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a power system production simulation method and system taking into account source-load interaction, so as to achieve the economic optimal balance of electric power and improve the accuracy and reliability of simulation results.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A power system production simulation method considering source-load interaction comprises the following steps:

[0007] Modeling uncertain factors, including load, wind power and photovoltaic power; discretizing the data of the modeling of uncertain factors;

[0008] Constructing a flexible load external characteristic model; specifically, constructing an air conditioning external characteristic model and an electric hydrogen production external characteristic model respectively;

[0009] The flexible load external characteristic model is integrated into the power system production simulation to construct a production simulation model; first, an objective function is constructed that considers operating costs, penalties for poor operation, and flexible load regulation costs; then simulation constraints are constructed; finally, the production simulation model is solved to complete the power system production simulation; the simulation constraints include power balance constraints, backup constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air-conditioning clusters and electric hydrogen production clusters.

[0010] A power system production simulation system considering source-load interaction, comprising: a first modeling module, a second modeling module and a simulation module;

[0011] The first modeling module is used to model uncertainty factors, where the uncertainty factors include load, wind power and photovoltaic power; and discretize the data of the uncertainty factor modeling;

[0012] The second modeling module is used to construct a flexible load external characteristic model; specifically, to construct an external characteristic model of air conditioning and an external characteristic model of electric hydrogen production respectively;

[0013] The simulation module is used to integrate the flexible load external characteristic model into the power system production simulation to construct a production simulation model; first, an objective function that considers operating costs, poor operation penalties and flexible load regulation costs is constructed; then simulation constraints are constructed; finally, the production simulation model is solved to complete the power system production simulation; the simulation constraints include power balance constraints, standby constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air-conditioning clusters and electric hydrogen production clusters.

[0014] The effects provided in the content of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0015] The present invention proposes a method and system for simulating the production of a power system considering source-load interaction, the method comprising the following steps: modeling uncertain factors, the uncertain factors include load, wind power and photovoltaics; discretizing the data for modeling the uncertain factors; constructing a flexible load external characteristic model; specifically, respectively constructing an external characteristic model of air conditioning and an external characteristic model of electric hydrogen production; integrating the flexible load external characteristic model into the power system production simulation to construct a production simulation model; first constructing an objective function that considers operating costs, penalties for poor operation and flexible load regulation costs; then constructing simulation constraints; finally solving the production simulation model to complete the power system production simulation; the simulation constraints include power balance constraints, standby constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air conditioning clusters and electric hydrogen production clusters. Based on a method for simulating the production of a power system considering source-load interaction, a power system production simulation system considering source-load interaction is also proposed. Aiming at the uncertainties of wind power, photovoltaic power and load, the present invention establishes multi-dimensional uncertainty modeling, constructs the external characteristic model of distributed controllable resources, and integrates it into the power system production simulation to achieve the most economical power balance and improve the accuracy and reliability of the simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a power system production simulation method considering source-load interaction proposed in Example 1 of the present invention;

[0017] Figure 2 The multidimensional uncertainty model construction process proposed in Example 1 of the present invention;

[0018] Figure 3 This is the process of constructing a typical flexible load external characteristic model proposed in Example 1 of the present invention;

[0019] Figure 4 The construction process of the power system production simulation method considering distributed resource scheduling proposed in Example 1 of the present invention;

[0020] Figure 5 This is a typical load curve proposed in Example 1 of the present invention;

[0021] Figure 6 This is a typical wind power output curve proposed in Example 1 of the present invention;

[0022] Figure 7 This is a typical photovoltaic output curve proposed in Example 1 of the present invention;

[0023] Figure 8 The air conditioning load baseline proposed in Example 1 of the present invention;

[0024] Fig. 9The load baseline of hydrogen production by electricity proposed in Example 1 of the present invention;

[0025] Fig.10 The system power balance curve proposed in Example 1 of the present invention;

[0026] Fig.11 The resource adjustment curves proposed in Example 1 of the present invention;

[0027] Fig.12 This is a schematic diagram of a power system production simulation system considering source-load interaction proposed in Example 2 of the present invention. DETAILED DESCRIPTION

[0028] Example 1

[0029] Embodiment 1 of the present invention proposes a method for simulating power system production taking into account source-load interaction, which is used to solve the technical problems existing in power system production simulation in the prior art. The present invention establishes multidimensional uncertainty modeling for the uncertainty of wind power, photovoltaic power and load, constructs an external characteristic model of distributed controllable resources, and integrates it into the power system production simulation to achieve the most economically optimal power balance and improve the accuracy and reliability of the simulation results.

[0030] Figure 1 A flow chart of a power system production simulation method considering source-load interaction proposed in Example 1 of the present invention;

[0031] In step 1, uncertainty factor modeling is performed, and the uncertainty factors include load, wind power and photovoltaic power; the data for uncertainty factor modeling is discretized;

[0032] The specific process of modeling uncertainty factors includes: obtaining the annual daily load curve of the entire system in the simulation area, and using the annual daily load curve of the entire system as a baseline to construct a load timing uncertainty model; obtaining the output curve of the wind turbine set, and using the output curve of the wind turbine set as a baseline to construct a wind turbine timing uncertainty model; obtaining the output curve of the photovoltaic set, and using the output curve of the photovoltaic set as a baseline to construct a photovoltaic set timing uncertainty model.

[0033] Figure 2 The multidimensional uncertainty model construction process proposed in Example 1 of the present invention;

[0034] In step 1.1, the annual load curve and the daily maximum load curve in the simulation area are obtained, and the load values ​​in the daily load curve are normalized based on the daily maximum load curve to obtain the standardized daily load curves of various loads;

[0035] in, is the standardized daily load curve of the rth type of load; is the power value of the original daily load curve of the rth type of load at time t; is the daily maximum load curve of the rth type of load;

[0036] The load scales are:

[0037] Among them, β r is the proportion of the rth type of load; P L is the total load scale of the simulation area;

[0038] Multiply and superimpose the maximum values ​​of various loads with the annual daily load curve, and use formula (3) to synthesize the annual daily load curve of the entire system during the simulation period:

[0039]

[0040] in, is the load size of the system at time t, Ω L Load category collection;

[0041] Using the system annual daily load curve as the baseline, a load time series uncertainty model is constructed to describe the uncertainty changes in the simulation period. The load time series uncertainty model is:

[0042]

[0043] is the load forecast scale considering uncertain factors; L is the load forecast standard deviation of the annual daily load curve, γ% is the percentage of load fluctuation in the load forecast output.

[0044] In step 1.2, the wind turbine output curve is obtained, and the process of using the wind turbine output curve as a baseline to construct a wind turbine timing uncertainty model includes:

[0045] Normalize the wind speed value in the wind speed curve: V t,nor =V t / V max , t=1...8760; (5)

[0046] Among them, V t is the wind speed of the original wind speed curve at time t; V max is the annual maximum value of the wind speed curve; V t,nor is the normalized wind speed curve;

[0047] The annual maximum wind speed in the simulated area is investigated and multiplied with the annual wind speed curve and superimposed. The annual daily wind speed curve of the entire system in the simulation period is synthesized using formula (6), and the following can be obtained: Vt =V max ·V t,nor ; (6)

[0048] The cut-in wind speed, cut-out wind speed, rated wind speed and rated power data of typical wind turbines are investigated, and the output curve of the wind turbine output is constructed based on the MPPT curve of the wind turbine. The output curve of the wind turbine output is:

[0049]

[0050] in, Represents the predicted value of the wind turbine power output at time t; V in is the cut-in wind speed; V out is the cut-off wind speed; V N is the rated wind speed; is the rated power of the wind turbine, The value of is the total installed capacity of wind turbines in the node;

[0051] The random uncertainty model of wind turbines is constructed, and the prediction error of wind turbines is calculated using normal distribution. The output of wind turbines considering wind power uncertainty is:

[0052]

[0053] in, The predicted size of wind turbines considering uncertain factors; Δε w is the wind power prediction error;

[0054]

[0055] Among them, σ w is the predicted standard deviation of the wind turbine, which is proportional to the predicted output of the wind turbine, that is, α% is the percentage of wind power output fluctuation in the predicted wind power output.

[0056] In step 1.3, the solar energy resource data of the area is investigated to form a typical light variation curve; the typical photovoltaic unit data is investigated to form a photovoltaic unit output curve based on the light variation curve; the output curve is used as the base value to simulate the random photovoltaic output during the production simulation operation period.

[0057] The process of obtaining the output curve of the photovoltaic unit and constructing the photovoltaic unit timing uncertainty model based on the output curve of the photovoltaic unit as the baseline includes: normalizing the light intensity in the light curve to obtain the standardized

[0058] Light Curve: R t,nor =R t / R max, t=1...8760; (10)

[0059] Among them, R t,nor is the normalized illumination curve; R t is the value of the original illumination curve at time t;

[0060] The annual maximum sunlight in the investigated simulation area is multiplied and superimposed with the annual sunlight curve, and the annual sunlight curve of the whole system in the simulation period is synthesized using the following formula: R t =R max ·R t,nor ; (11)

[0061] Investigate the rated power and rated light intensity of typical photovoltaic units, and construct the output curve of photovoltaic units. The output curve of photovoltaic units is:

[0062]

[0063] in, Represents the predicted value of the power output of the photovoltaic unit at time t; R N Represents the rated light intensity of the photovoltaic unit; is the rated power of the photovoltaic unit. The value is the total installed capacity of PV units in the node;

[0064] The random uncertainty model of photovoltaic units is constructed, and the prediction error of wind turbine units is calculated using normal distribution. The output of wind turbine units considering wind power uncertainty is:

[0065]

[0066] in, To predict the size of the photovoltaic group considering uncertain factors; Δε p is the output prediction error of the photovoltaic unit, and the probability distribution density of the prediction error is;

[0067]

[0068] σ p is the predicted standard deviation of the photovoltaic unit, which is proportional to the predicted output of the photovoltaic unit, that is, β% is the percentage of PV output fluctuation in the predicted PV output.

[0069] In step 1.4, the continuous probability distribution function is discretely sampled as follows:

[0070]

[0071] Among them, P(·) is the probability of the corresponding parameter; They are the discrete values ​​of load, wind power prediction error and photovoltaic prediction error respectively; h is the discrete step length.

[0072] In step 2, the flexible load external characteristic model is constructed; specifically, the external characteristic model of air conditioning and the external characteristic model of hydrogen production are constructed respectively;

[0073] Starting from the specific physical characteristics and resource properties of typical flexible loads, the present invention constructs the external characteristic curves of air conditioning and electric hydrogen production resources on an hourly time scale for power and electricity balance to describe the above-mentioned resources with different characteristics. Figure 3 This is a typical flexible load external characteristic model construction process proposed in Example 1 of the present invention; the specific process includes two parts: external characteristic curve investigation and external characteristic model construction.

[0074] In step 2.1, we first investigate the historical weather forecast data of the simulation area to obtain the outdoor temperature curve under the typical operation scenario, which is used as the input parameter for the air conditioning power baseline and conditional space calculation; we investigate the hydrogen consumption of typical hydrogen production loads to form a hydrogen consumption curve of typical hydrogen production loads, which is used as the calculation input parameter for the hydrogen production power baseline.

[0075] In step 2.2, the aggregate model of the air conditioner is constructed, including: state transfer equation, power upper and lower boundaries, and SOC state constraints; the state transfer equation is:

[0076] soc AC (t+1)=Asoc AC (t)+BΔP(t)+C; (18)

[0077] Among them, A is the first state transfer coefficient of the air conditioner, B is the second state transfer coefficient of the air conditioner, and C is the third state transfer coefficient of the air conditioner;

[0078] soc AC (t+1) is the equivalent SOC size of the air conditioner at time t+1; soc AC (t) is the equivalent SOC of the air conditioner at time t; ΔP(t) is the adjustable power value of the air conditioner;

[0079]

[0080] Among them, α j is the first state transfer parameter of the air conditioning unit; β j is the second state transfer parameter of the air conditioner unit; γ j is the third state transition parameter of the air conditioner unit; n is the total number of air conditioner aggregates;

[0081]

[0082] Among them, C j represents equivalent heat capacity (kJ / ℃); R j represents equivalent thermal resistance (℃ / kW); η represents refrigeration energy efficiency coefficient; T jmin Represents the minimum indoor temperature to meet user comfort; T jmax Represents the maximum indoor temperature to meet user comfort; T jset is the set temperature of the jth air conditioner;

[0083] Construct upper and lower power constraints that describe the power output limits of the air conditioner:

[0084] P Smin ≤ΔP S (t)+P Sbaseline ≤P Smax ; (twenty one)

[0085] Where ΔP S (t) is the output of the air conditioning aggregate at time t; P Sbaseline is the power baseline of the air conditioning aggregate; P Smax is the upper power limit; P Smin is the lower power boundary;

[0086]

[0087] Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner;

[0088] Construct the SOC state constraints that describe the upper and lower boundaries of the air conditioner equivalent SOC: 0≤soc AC (t)≤1; (23).

[0089] In step 2.3, the external characteristic model of hydrogen production is constructed with the hydrogen consumption curve as input, and then the power change base value and adjustment space curve of hydrogen production in a typical time period are formed. The equivalent SOC is used to describe the hydrogen storage capacity of the hydrogen storage tank of the hydrogen production equipment. The constructed model includes the state transfer equation, hydrogen consumption power constraint and hydrogen storage tank state constraint.

[0090] Construct the state transition equation to describe the SOC state transition of hydrogen production:

[0091]

[0092] Among them, SOC HST (t) is the current SOC of hydrogen produced by electricity at time t; V HSTThe maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; is the gas storage capacity of the gas storage equipment in period t; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; α e,t is the electrical efficiency, is the efficiency of hydrogen production by electrolysis; Q HHV is the calorific value of hydrogen, which is 1.43×10 8 Fixed and unchanging; is the operating power of the electric hydrogen production equipment at time t;

[0093] Construct a hydrogen power constraint that describes the power limit when the hydrogen production is actually running:

[0094]

[0095] in, Maximum hydrogen power;

[0096] Construct a hydrogen storage tank state constraint that describes the capacity limit of the hydrogen storage tank:

[0097]

[0098] in, The upper and lower limits of the hydrogen storage capacity are specified for the hydrogen storage tank respectively.

[0099] In step 3, the flexible load external characteristic model is integrated into the power system production simulation to construct a production simulation model; first, an objective function considering operation cost, bad operation penalty and flexible load regulation cost is constructed; then simulation constraints are constructed; finally, the production simulation model is solved to complete the power system production simulation; the simulation constraints include power balance constraints, backup constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air conditioning clusters and electric hydrogen clusters. Figure 4 The construction process of the power system production simulation method considering distributed resource scheduling proposed in Example 1 of the present invention;

[0100] The objective function is:

[0101]

[0102] in, is the coal consumption cost of thermal power units; is the carbon emission cost of thermal power units; f r (r + ,r- ) is the standby cost, f UC (v,w) is the start-stop cost; f p (p LS ,p VRE ) is the power abandonment penalty and load shedding penalty; f VPP (p VPP ) is the distributed resource adjustment cost; p G Represents the output of thermal power unit; r + 、r - Represents positive and negative reserve sizes respectively; v represents the open state flag; w represents the closed state flag; p LS ,p VRE Respectively represent the load shedding size and power abandonment size; p VPP Represents the output of the virtual power plant;

[0103] Considering the fuel consumption during the normal operation of thermal power units, the coal consumption cost is constructed:

[0104]

[0105] Among them, λ coal is the unit coal price; Ω g For thermal power generation units; is the active power output of the kth thermal power unit at time t; f fuel (·) represents the coal consumption characteristic function of thermal power units;

[0106] Considering the CO2 emission from thermal power units, the carbon emission cost is:

[0107]

[0108] in, is the carbon dioxide emission cost per unit of thermal power generation;

[0109] Considering the economic loss of thermal power units due to providing backup, the cost of building backup is:

[0110]

[0111] in, is the unit positive reserve cost of the kth thermal power unit; is the unit negative reserve cost of the kth thermal power unit; is the positive reserve size of the kth thermal power unit at time t; is the negative reserve size of the kth thermal power unit at time t;

[0112] Considering the economic losses caused by the start and stop of the unit, the start and stop costs are constructed:

[0113]

[0114] in, is the single startup cost of the kth thermal power unit; is the single shutdown cost of the kth thermal power unit; is the startup variable of the kth thermal power unit at time t. The variable is 1, indicating that the unit has a startup event state; is the shutdown variable of the kth thermal power unit at time t. The variable is 1, indicating that the unit has a shutdown event;

[0115] Considering the poor operation of the unit, load shedding penalty and power abandonment penalty are constructed:

[0116]

[0117] Among them, Ω l is the set of load nodes; is the penalty cost per unit load shedding power; is the load removed at node i at time t; Ω VRE It is a collection of VRE power generation units; It is the operating cost or penalty cost of unit power curtailment; is the abandoned power of the unit at node i at time t;

[0118] Considering the adjustment cost of flexible load, the adjustment cost of flexible load is constructed:

[0119]

[0120] Among them, Ω VPP is a collection of resource aggregates; is the regulation cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

[0121] Construct production simulation constraints that take into account security constraints, network constraints, equipment characteristics constraints, etc., mainly including: power balance constraints, backup constraints, line transmission capacity constraints, thermal power unit characteristics constraints, energy storage unit characteristics constraints, wind and solar unit characteristics constraints, and air conditioning clusters and electric hydrogen cluster characteristics constraints. Specifically: Considering the power balance satisfied by the normal operation of the power system, the power balance constraint is constructed as follows:

[0122]

[0123] in, The thermal power output of node i in each time period; The output of iVRE units at each time node; Energy storage output for node i in each time period; Contribute to the resource aggregate of node i in each time period; is the line load power; is the load shedding power;

[0124] Considering that the normal operation of the unit should provide backup for the system, the backup constraint is constructed as follows:

[0125]

[0126] in, is the positive reserve and negative reserve size of the thermal power unit at node i at time t; L + Positive reserve requirement factor reserved for load; L - Negative reserve requirement factor reserved for load; W + The positive reserve requirement factor reserved for the VRE unit output; W - Negative reserve requirement factor reserved for VRE unit output;

[0127] Considering the maximum transmission capacity in line operation, the line transmission capacity constraint is constructed as:

[0128]

[0129] in, Real-time flow of the line, is the minimum power flow and is the maximum power flow of the line; the characteristic constraints of thermal power units include active output constraints considering standby output, ramp constraints and minimum start-stop time constraints; the active output constraints are:

[0130]

[0131] in, Indicates that the kth thermal power unit is in operation at time t, which is a 0-1 variable; They are the maximum output ratio and the minimum output ratio of thermal power units respectively; Represents the impact capacity of the kth thermal power unit;

[0132] The climbing rate constraint is:

[0133] in, is the maximum climbing rate of the unit; is the maximum descending and climbing rate of the unit; Represents the active output of the kth thermal power unit at time k+1;

[0134] The minimum start and stop time constraints are:

[0135]

[0136] T ON is the minimum startup time of thermal power units; T OFF The minimum stop time of the thermal power unit;

[0137] The energy storage unit characteristic constraints include state of charge constraints, state of charge change constraints and charge and discharge power constraints; among which, the state of charge constraint is: min,ES C ES ≤E t ≤λ max,ES C ES ; (40)

[0138] Among them, E t is the total energy stored in the battery; max,ES is the maximum ratio allowed by the battery; min,ES The minimum ratio allowed by the battery; C ES is the maximum capacity of the battery;

[0139] The state of charge change constraint is:

[0140]

[0141] Among them, p ESS,t is the electric power of the battery in period t; δ is the numerical self-consumption rate; η ch ,η dis E is the charging efficiency and discharging efficiency respectively; (t-Δt) represents the capacity of the energy storage unit at the previous moment; Δt represents the simulation step length;

[0142] The charge and discharge power constraints are: -p dis,max ≤p ch,t ≤p ch,max ; (42)

[0143] Among them, p dis,max Maximum power for charging the battery; p ch,max is the maximum power of battery discharge; p ch,t Represents the charging and discharging power of the energy storage unit;

[0144] The characteristic constraints of wind and solar power units are:

[0145] in, is the installed capacity of wind and solar power at node i; is the value of the normalized maximum output curve of wind and solar power at node i at time t;

[0146] The equipment constraints of the air conditioning cluster include: state transition constraints, adjustment power constraints, and state upper and lower limit constraints; among which, the state transition constraints are:

[0147] The adjustment power constraint is:

[0148] The state upper and lower limit constraints are:

[0149] in, The regulating power of the air conditioner; is the baseline power of the air conditioner; is the upper power limit of the air conditioner; is the lower power boundary of the air conditioner;

[0150] The characteristic constraints of the electric hydrogen production cluster include state transition constraints, adjustment power constraints, and state upper and lower limit constraints; among which, the state transition constraints are:

[0151]

[0152] in, The gas volume of hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last solution time of hydrogen production by electricity; SCR is the self-consumption rate of the hydrogen storage tank; is the efficiency of hydrogen production by electrolysis; α e,t is electrical efficiency; is the output power of the hydrogen production system; is the utilization rate of hydrogen entering the gas tank; is the total amount of gas output by the gas storage device during period t; is the utilization rate of hydrogen output tank; V HST is the maximum volume of hydrogen that can be stored in the hydrogen storage tank at rated pressure and temperature; Δt is the simulation step length;

[0153] The adjustment power constraint is:

[0154] in, is the output power of the hydrogen production system; is the maximum output power of the hydrogen production system;

[0155] The state upper and lower limit constraints are:

[0156] The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively; Specify the hydrogen storage capacity for the hydrogen storage tank.

[0157] A rolling call method with a daily cycle is adopted to run the source-grid-load-storage collaborative model to perform short-term process simulation, generate system operation status data under the planning scheme, and calculate the system operation status at each moment of the day; the operation data such as the unit start and stop status and energy storage charge status at the last moment of the previous day will be used as the initial state of the next day, and the operation results will be connected in series day by day to finally form a complete 8760 time series operation data.

[0158] The present invention is suitable for large-scale power system production simulation containing distributed controllable resources. Through the coordinated work of three parts, namely, multi-dimensional uncertainty modeling, construction of distributed resource external characteristic model and production simulation method considering distributed resource scheduling, the safe and economic operation of the power system is realized. The first part models the uncertainty of wind power, photovoltaic and load changes based on historical data to obtain a predicted fluctuation curve. Specifically, it includes: fitting the maximum load and typical load curves to form a typical load change curve in the region, simulating random loads; investigating regional wind resources and typical wind turbine data to form a wind turbine output curve, simulating random wind power output; investigating solar energy resources and typical photovoltaic unit data to form a photovoltaic unit output curve, simulating random photovoltaic output. The second part constructs an external characteristic model of air conditioning and electric hydrogen flexible loads based on the physical characteristics and resource characteristics of typical flexible loads, and forms a power change base value and an adjustment space curve. The specific process includes: investigating historical weather data to obtain an outdoor temperature curve and construct an air conditioning external characteristic model; investigating the hydrogen consumption of electric hydrogen loads, constructing an electric hydrogen external characteristic model, using equivalent SOC to describe the hydrogen storage capacity of the hydrogen storage tank, and forming a state transfer equation and constraint conditions. The third part integrates the above external characteristic model into the power system production simulation, constructs a production simulation model with the goal of economic optimization, takes into account operating costs, penalties for poor operation and the cost of distributed resource regulation, establishes power balance constraints, backup constraints, line transmission capacity constraints, equipment characteristic constraints and characteristic constraints of each resource, and ensures that the power system operates effectively while meeting the requirements of safety and economy.

[0159] The power system production simulation method considering source-load interaction proposed in Example 1 of the present invention can optimize the operation strategy of the power system, improve energy utilization efficiency, reduce operation costs, and ensure the safe and stable operation of the power system.

[0160] In order to fully illustrate the process implemented in Example 1 of the present invention, a simplified power grid system in a certain place is taken as an example to verify the effect of the method of the present invention. The system has a total of 90 nodes, 200 lines, 57.9GW thermal power plants, 30.225GW wind power plants, 65.34GW photovoltaic power plants, and a total of 2GW energy storage stations, and includes two voltage levels of 500kV and 100kV. The maximum initial load is 84.8GW; in addition, the system is equipped with a total of 25.14GW of flexible adjustable loads, including 9.58GW of air-conditioning loads and 15.56GW of electric hydrogen production loads. Figure 5 This is a typical load curve proposed in Example 1 of the present invention.

[0161] By investigating the installation data of various power sources, energy storage and lines of the system, we can form a table of operating parameters of each device of this coefficient. The parameters of some thermal power units, wind and solar units, energy storage units and lines of this system are shown in Tables 1 to 4.

[0162] Table 1: Equipment and parameters of some thermal power plants

[0163]

[0164]

[0165] Table 2: Installation locations and parameters of some wind and solar generator sets in the system

[0166]

[0167] Table 3: System energy storage unit configuration and parameters

[0168]

[0169] Table 4 Installation location and parameters of some lines in the system

[0170]

[0171]

[0172] Further considering the uncertainty factors of wind and solar units, the typical wind and solar curve of the system can be formed as follows: Figure 6-Figure 7 As shown; Figure 6 This is a typical wind power output curve proposed in Example 1 of the present invention; Figure 7 This is a typical photovoltaic output curve proposed in Example 1 of the present invention;

[0173] Investigate typical outdoor temperature change curves and hydrogen consumption curves, build external characteristic models of air conditioning and electric hydrogen production, and form a typical operating baseline for flexible adjustable loads, such as Figure 8-Figure 9 As shown, Figure 8 The air conditioning load baseline proposed in Example 1 of the present invention; Fig. 9The load baseline of hydrogen production by electricity proposed in Example 1 of the present invention;

[0174] Four typical days were selected as research cases in the simulation, where typical days 1 and 4 represent scenarios with small loads and large wind and solar power generation, and typical days 2 and 3 correspond to scenarios with large loads and small wind and solar power generation.

[0175] On typical days 1 and 4, due to the high output of wind power and photovoltaic power generation and low load, the system can reliably meet the load demand. At this time, wind and solar resources become the main power source, while thermal power units are only in standby mode and maintain a small amount of operation. However, due to the excess output of wind and solar resources, the system faces a certain degree of absorption pressure, and some electricity is abandoned because it cannot be connected to the power grid. In order to reduce the economic losses caused by power abandonment in the system, the load of hydrogen production is actively adjusted during periods of high photovoltaic output to increase electricity demand, thereby effectively improving the utilization rate of wind and solar resources and alleviating the problem of power abandonment;

[0176] On typical days 2 and 3, due to the low wind power output, the system faced the challenge of insufficient power supply capacity during the peak load period. At this time, the thermal power unit became the main power supply support for the system, providing the necessary peak capacity. At the same time, photovoltaic power generation played an important role during the peak load period at noon, ensuring the reliability of power supply during this period. However, during the evening peak period, the system was short of power supply. At this time, the air conditioning load and the electric hydrogen production load actively reduced the power consumption according to the system demand, avoiding the occurrence of certain load shedding and ensuring the stable operation of the system.

[0177] The simulation results of four typical days show that the air conditioning load has a low ability to participate in regulation, and mainly improves the overall economy of the system by reducing electricity demand. However, the electric hydrogen load shows stronger flexibility, with a longer energy transfer time span, and can flexibly respond to system needs at different times, thus significantly enhancing the adaptability to wind and solar fluctuations and the system's regulation potential. Fig.10 The system power balance curve proposed in Example 1 of the present invention; Fig.11 The resource adjustment curves proposed in Example 1 of the present invention;

[0178] Example 2

[0179] Based on the power system production simulation method considering source-load interaction proposed in Example 1 of the present invention, Example 2 of the present invention further proposes a power system production simulation system considering source-load interaction. Fig.12 A schematic diagram of a power system production simulation system considering source-load interaction proposed in Embodiment 2 of the present invention, the system comprising: a first modeling module, a second modeling module and a simulation module;

[0180] The first modeling module is used to model uncertainty factors, where the uncertainty factors include load, wind power and photovoltaic power; and discretize the data of the uncertainty factor modeling;

[0181] The second modeling module is used to construct the flexible load external characteristic model; specifically, to construct the external characteristic model of air conditioning and the external characteristic model of electric hydrogen production respectively;

[0182] The simulation module is used to integrate the flexible load external characteristic model into the power system production simulation to construct a production simulation model; first, an objective function is constructed that takes into account operating costs, penalties for poor operation, and flexible load regulation costs; then simulation constraints are constructed; finally, the production simulation model is solved to complete the power system production simulation; the simulation constraints include power balance constraints, standby constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air-conditioning clusters and electric hydrogen production clusters.

[0183] In the first modeling module, the specific process of modeling uncertainty factors includes: obtaining the annual daily load curve of the entire system in the simulation area, and using the annual daily load curve of the entire system as a baseline to construct a load timing uncertainty model; obtaining the output curve of the wind turbine group, and using the output curve of the wind turbine group as a baseline to construct a wind turbine timing uncertainty model; obtaining the output curve of the photovoltaic group, and using the output curve of the photovoltaic group as a baseline to construct a photovoltaic group timing uncertainty model.

[0184] The process of obtaining the annual daily load curve of the whole system in the simulation area and using the annual daily load curve of the whole system as a baseline to construct the load time series uncertainty model includes:

[0185] The annual load curve and the daily maximum load curve in the simulation area are obtained, and the load values ​​in the daily load curve are normalized based on the daily maximum load curve to obtain the standardized daily load curves for various loads;

[0186] in, is the standardized daily load curve of the rth type of load; is the power value of the original daily load curve of the rth type of load at time t; is the daily maximum load curve of the rth type of load;

[0187] The load scales are:

[0188] Among them, β r is the proportion of the rth type of load; P L is the total load scale of the simulation area;

[0189] The annual daily load curve of the whole system is:

[0190]

[0191] in, is the load size of the system at time t, Ω L Load category collection;

[0192] The load timing uncertainty model is:

[0193]

[0194] in, is the load forecast scale considering uncertain factors; L is the load forecast standard deviation of the annual daily load curve;

[0195] The process of obtaining the wind turbine output curve and using the wind turbine output curve as a baseline to construct a wind turbine timing uncertainty model includes:

[0196] Normalize the wind speed value in the wind speed curve: V t,nor =V t / V max , t=1...8760; (5)

[0197] Among them, V t is the wind speed of the original wind speed curve at time t; V max is the annual maximum value of the wind speed curve; V t,nor is the normalized wind speed curve; so we can get: V t =V max ·V t,nor ; (6)

[0198] The wind turbine output curve is:

[0199]

[0200] in, Represents the predicted value of the wind turbine power output at time t; V in is the cut-in wind speed; V out is the cut-off wind speed; V N is the rated wind speed; is the rated power of the wind turbine;

[0201] The uncertain timing model of wind turbines is:

[0202]

[0203] in, The predicted size of wind turbines considering uncertain factors; Δε w is the wind power prediction error;

[0204]

[0205] Among them, σ w is the predicted standard deviation of the wind turbine;

[0206] The process of obtaining the photovoltaic unit output curve and constructing the photovoltaic unit timing uncertainty model with the photovoltaic unit output curve as the baseline includes: normalizing the light intensity in the light curve to obtain a standardized light curve: R t,nor =R t / R max , t=1...8760; (10)

[0207] Among them, R t,nor is the normalized illumination curve; R t is the value of the original illumination curve at time t;

[0208] So we can get: R t =R max ·R t,nor ; (11)

[0209] The output curve of photovoltaic unit is:

[0210]

[0211] in, Represents the predicted value of the power output of the photovoltaic unit at time t; R N Represents the rated light intensity of the photovoltaic unit; is the rated power of the photovoltaic unit;

[0212] The timing uncertainty model of photovoltaic units is:

[0213]

[0214] in, To predict the size of the photovoltaic group considering uncertain factors; Δε p is the output prediction error of the photovoltaic unit;

[0215]

[0216] Among them, σ p is the predicted standard deviation of the PV unit.

[0217] The process of discretizing data to model uncertainty includes:

[0218]

[0219]

[0220] Among them, P(·) is the probability of the corresponding parameter; They are the discrete values ​​of load, wind power prediction error and photovoltaic prediction error respectively; h is the discrete step length.

[0221] In the second modeling module, the process of constructing the external characteristic model of the air conditioner includes: constructing an aggregate model of the air conditioner, specifically including: state transfer equations, upper and lower power boundaries, and SOC state constraints;

[0222] Among them, the state transfer equation is: AC (t+1)=Asoc AC (t)+BΔP(t)+C; (18)

[0223] Among them, A is the first state transfer coefficient of the air conditioner, B is the second state transfer coefficient of the air conditioner, and C is the third state transfer coefficient of the air conditioner;

[0224] soc AC (t+1) is the equivalent SOC size of the air conditioner at time t+1; soc AC (t) is the equivalent SOC of the air conditioner at time t; ΔP(t) is the adjustable power value of the air conditioner;

[0225]

[0226] Among them, α j is the first state transfer parameter of the air conditioning unit; β j is the second state transfer parameter of the air conditioner unit; γ j is the third state transition parameter of the air conditioner unit; n is the total number of air conditioner aggregates;

[0227]

[0228] Among them, C j Represents equivalent heat capacity; R j represents the equivalent thermal resistance; η represents the refrigeration energy efficiency coefficient; T jmin Represents the minimum indoor temperature to meet user comfort; T jmax Represents the maximum indoor temperature to meet user comfort; T jset is the set temperature of the jth air conditioner;

[0229] Construct upper and lower power constraints that describe the power output limits of the air conditioner:

[0230] P Smin ≤ΔP S (t)+P Sbaseline ≤P Smax ; (twenty one)

[0231] Where ΔP S (t) is the output of the air conditioning aggregate at time t; P Sbaseline is the power baseline of the air conditioning aggregate; P Smax is the upper power limit; P Smin is the lower power boundary;

[0232]

[0233] Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner;

[0234] Construct the SOC state constraints that describe the upper and lower boundaries of the air conditioner equivalent SOC: 0≤soc AC (t)≤1;(23).

[0235] The process of constructing the external characteristic model of hydrogen production by electricity includes: constructing the state transfer equation, hydrogen power constraint and hydrogen storage tank state constraint;

[0236] Construct the state transition equation to describe the SOC state transition of hydrogen production:

[0237]

[0238] Among them, SOC HST (t) is the current SOC of hydrogen produced by electricity at time t; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; is the gas storage capacity of the gas storage equipment in period t; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; α e,t is the electrical efficiency, is the efficiency of hydrogen production by electrolysis; Q HHV is the calorific value of hydrogen; is the operating power of the electric hydrogen production equipment at time t;

[0239] Construct a hydrogen power constraint that describes the power limit when the hydrogen production is actually running:

[0240]

[0241] in, Maximum hydrogen power;

[0242] Construct a hydrogen storage tank state constraint that describes the capacity limit of the hydrogen storage tank:

[0243]

[0244] in, The upper and lower limits of the hydrogen storage capacity are specified for the hydrogen storage tank respectively.

[0245] In the simulation module, the objective function is:

[0246]

[0247] in, is the coal consumption cost of thermal power units; is the carbon emission cost of thermal power units; f r (r + ,r - ) is the standby cost, f UC (v,w) is the start-stop cost; f p (p LS ,p VRE ) is the power abandonment penalty and load shedding penalty; f VPP (p VPP ) is the distributed resource adjustment cost; p G Represents the output of thermal power unit; r + 、r - Represents positive and negative reserve sizes respectively; v represents the open state flag; w represents the closed state flag; p LS ,p VRE Respectively represent the load shedding size and power abandonment size; p VPP Represents the output of the virtual power plant;

[0248]

[0249] Among them, λ coal is the unit coal price; Ω g For thermal power generation units; is the active power output of the kth thermal power unit at time t; f fuel (·) represents the coal consumption characteristic function of thermal power units;

[0250]

[0251] in, is the carbon dioxide emission cost per unit of thermal power generation;

[0252]

[0253] in, is the unit positive reserve cost of the kth thermal power unit; is the unit negative reserve cost of the kth thermal power unit; is the positive reserve size of the kth thermal power unit at time t; is the negative reserve size of the kth thermal power unit at time t;

[0254]

[0255] in, is the single startup cost of the kth thermal power unit; is the single shutdown cost of the kth thermal power unit; is the startup variable of the kth thermal power unit at time t; is the shutdown variable of the kth thermal power unit at time t;

[0256]

[0257] Among them, Ω l is the set of load nodes; is the penalty cost per unit load shedding power; is the load removed at node i at time t; Ω VRE It is a collection of VRE power generation units; It is the operating cost or penalty cost of unit power curtailment; is the abandoned power of the unit at node i at time t;

[0258]

[0259] Among them, Ω VPP is a collection of resource aggregates; is the regulation cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

[0260] The power balance constraint is:

[0261]

[0262] in, The thermal power output of node i in each time period; The output of iVRE units at each time node; Energy storage output for node i in each time period; Contribute to the resource aggregate of node i in each time period; is the line load power; is the load shedding power.

[0263] The alternative constraints are:

[0264]

[0265] in, is the positive reserve and negative reserve size of the thermal power unit at node i at time t; L + Positive reserve requirement factor reserved for load; L - Negative reserve requirement factor reserved for load; W + The positive reserve requirement factor reserved for the VRE unit output; W - Negative reserve requirement factor reserved for VRE unit output.

[0266] The line transmission capacity constraint is:

[0267] in, Real-time flow of the line, is the minimum power flow and is the maximum flow of the line.

[0268] The characteristic constraints of thermal power units include active output constraints considering standby output, ramp constraints, and minimum start-stop time constraints; the active output constraints are:

[0269]

[0270] in, Indicates that the kth thermal power unit is in operation at time t; They are the maximum output ratio and the minimum output ratio of thermal power units respectively; Represents the impact capacity of the kth thermal power unit;

[0271] The climbing rate constraint is:

[0272] in, is the maximum climbing rate of the unit; is the maximum descending and climbing rate of the unit; Represents the active output of the kth thermal power unit at time k+1;

[0273] The minimum start and stop time constraints are:

[0274]

[0275] T ON is the minimum startup time of thermal power units; T OFF It is the minimum stop time of thermal power units.

[0276] The characteristic constraints of the energy storage unit include state of charge constraints, state of charge change constraints and charge and discharge power constraints; among which, the state of charge constraint is: min,ES C ES ≤E t ≤λ max,ES C ES ; (40)

[0277] Among them, E t is the total energy stored in the battery; max,ES is the maximum ratio allowed by the battery; min,ES The minimum ratio allowed by the battery; C ES The maximum capacity of the battery.

[0278] The state of charge change constraint is:

[0279]

[0280] Among them, p ESS,t is the electric power of the battery in period t; δ is the numerical self-consumption rate; η ch ,η dis E is the charging efficiency and discharging efficiency respectively; (t-Δt) Represents the capacity of the energy storage unit at the previous moment; Δt represents the simulation step size.

[0281] The charge and discharge power constraints are: -p dis,max ≤p ch,t ≤p ch,max ; (42)

[0282] Among them, p dis,max Maximum power for charging the battery; p ch,max is the maximum power of battery discharge; p ch,t Represents the charging and discharging power of the energy storage unit.

[0283] The characteristic constraints of wind and solar power units are:

[0284] in, is the installed capacity of wind and solar power at node i; is the value of the normalized maximum output curve of wind and solar power at node i at time t.

[0285] The equipment constraints of the air conditioning cluster include: state transition constraints, adjustment power constraints, and state upper and lower limit constraints; among which, the state transition constraints are:

[0286] The adjustment power constraint is:

[0287] The state upper and lower limit constraints are:

[0288] in, The regulating power of the air conditioner; is the baseline power of the air conditioner; is the upper power limit of the air conditioner; It is the lower power limit of the air conditioner.

[0289] The characteristic constraints of the electric hydrogen production cluster include state transition constraints, adjustment power constraints, and state upper and lower limit constraints; among which, the state transition constraints are:

[0290]

[0291] in, The gas volume of hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last solution time of hydrogen production by electricity; SCR is the self-consumption rate of the hydrogen storage tank; is the efficiency of hydrogen production by electrolysis; α e,t is electrical efficiency; is the output power of the hydrogen production system; is the utilization rate of hydrogen entering the gas tank; is the total amount of gas output by the gas storage device during period t; is the utilization rate of hydrogen output tank; V HST is the maximum volume of hydrogen that can be stored in the hydrogen storage tank at rated pressure and temperature; Δt is the simulation step size.

[0292] The adjustment power constraint is:

[0293] in, is the output power of the hydrogen production system; is the maximum output power of the hydrogen production system.

[0294] The state upper and lower limit constraints are:

[0295] The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively; Specify the hydrogen storage capacity for the hydrogen storage tank.

[0296] The process of solving the production simulation model includes: using a time-series production simulation solution method based on daily rolling solution to complete the power system production simulation.

[0297] Embodiment 2 of the present invention provides an electric power system production simulation system that takes into account source-load interaction. Aiming at the uncertainties of wind power, photovoltaic power and load, multi-dimensional uncertainty modeling is established, and an external characteristic model of distributed controllable resources is constructed. The model is integrated into the electric power system production simulation to achieve the most economically optimal power balance and improve the accuracy and reliability of the simulation results.

[0298] The description of the relevant parts of an electric power system production simulation system considering source-load interaction provided in Example 2 of the present application can be found in the detailed description of the corresponding parts of an electric power system production simulation method considering source-load interaction provided in Example 1 of the present application, and will not be repeated here.

[0299] Although the above describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. On the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.

Claims

1. A method for simulating power system production considering source-load interaction, characterized in that: The following steps are involved: Modeling uncertain factors, including load, wind power and photovoltaics; Discretize the data to model the uncertainty factors; Constructing a flexible load external characteristic model; specifically, constructing an air conditioning external characteristic model and an electric hydrogen production external characteristic model respectively; The flexible load external characteristic model is integrated into the power system production simulation to construct a production simulation model; first, an objective function is constructed that considers operating costs, penalties for poor operation, and flexible load regulation costs; then simulation constraints are constructed; finally, the production simulation model is solved to complete the power system production simulation; the simulation constraints include power balance constraints, backup constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air-conditioning clusters and electric hydrogen production clusters.

2. A method for simulating power system production considering source-load interaction according to claim 1, characterized in that: The specific process of modeling uncertainty factors includes: Obtaining an annual daily load curve of the entire system in the simulation area, and using the annual daily load curve of the entire system as a baseline to construct a load time series uncertainty model; Obtaining a wind turbine output curve, and using the wind turbine output curve as a baseline to construct a wind turbine timing uncertainty model; The photovoltaic unit output curve is obtained, and the photovoltaic unit timing uncertainty model is constructed by taking the photovoltaic unit output curve as a baseline.

3. A method for simulating power system production considering source-load interaction according to claim 2, characterized in that: The process of obtaining the annual daily load curve of the whole system in the simulation area and using the annual daily load curve of the whole system as a baseline to construct the load time series uncertainty model includes: The annual load curve and the daily maximum load curve in the simulation area are obtained, and the load values ​​in the daily load curve are normalized based on the daily maximum load curve to obtain the standardized daily load curves for various loads; in, is the standardized daily load curve of the rth type of load; is the power value of the original daily load curve of the rth type of load at time t; is the daily maximum load curve of the rth type of load; The load scales are: Among them, β r is the proportion of the rth type of load; P L is the total load scale of the simulation area; The annual daily load curve of the whole system is: in, is the load size of the system at time t, Ω L Load category collection; The load timing uncertainty model is: in, is the load forecast scale considering uncertain factors; L is the load forecast standard deviation of the annual daily load curve; The process of obtaining the wind turbine output curve and using the wind turbine output curve as a baseline to construct a wind turbine timing uncertainty model includes: Normalize the wind speed values ​​in the wind speed curve: V t,nor =V t / V max ,t=1...8760; (5) Among them, V t is the wind speed of the original wind speed curve at time t; V max is the annual maximum value of the wind speed curve; V t,nor is the normalized wind speed curve; So we can get: V t =V max V t,nor ; (6) The wind turbine output curve is: in, Represents the predicted value of the wind turbine power output at time t; V in is the cut-in wind speed; V out is the cut-off wind speed; V N is the rated wind speed; is the rated power of the wind turbine; The uncertain timing model of wind turbines is: in, The predicted size of wind turbines considering uncertain factors; Δε w is the wind power prediction error; Among them, σ w is the predicted standard deviation of the wind turbine; The process of obtaining the photovoltaic unit output curve and constructing the photovoltaic unit timing uncertainty model using the photovoltaic unit output curve as a baseline includes: Normalize the light intensity in the light curve to get the standardized light curve: R t,nor =R t / R max ,t=1...8760; (10) Among them, R t,nor is the normalized illumination curve; R t is the value of the original illumination curve at time t; So we can get: R t =R max ·R t,nor ; (11) The output curve of photovoltaic unit is: in, Represents the predicted value of the power output of the photovoltaic unit at time t; R N Represents the rated light intensity of the photovoltaic unit; is the rated power of the photovoltaic unit; The timing uncertainty model of photovoltaic units is: in, To predict the size of the photovoltaic group considering uncertain factors; Δε p is the output prediction error of the photovoltaic unit; Among them, σ p is the predicted standard deviation of the PV unit.

4. A method for simulating power system production considering source-load interaction according to claim 3, characterized in that: The process of discretizing the data for modeling the uncertainty factors includes: Among them, P(·) is the probability of the corresponding parameter; They are the discrete values ​​of load, wind power prediction error and photovoltaic prediction error respectively; h is the discrete step length.

5. The method for simulating power system production considering source-load interaction according to claim 1, characterized in that: The process of building the external characteristic model of the air conditioner includes: Construct an aggregate model of the air conditioner, including: state transition equations, power upper and lower boundaries, and SOC state constraints; Among them, the state transfer equation is: soc AC (t+1)=Asoc AC (t)+BΔP(t)+C; (18) Among them, A is the first state transfer coefficient of the air conditioner, B is the second state transfer coefficient of the air conditioner, and C is the third state transfer coefficient of the air conditioner; soc AC (t+1) is the equivalent SOC size of the air conditioner at time t+1; soc AC (t) is the equivalent SOC of the air conditioner at time t; ΔP(t) is the adjustable power value of the air conditioner; Among them, α j is the first state transfer parameter of the air conditioner unit; β j is the second state transfer parameter of the air conditioner unit; γ j is the third state transition parameter of the air conditioner unit; n is the total number of air conditioner aggregates; Among them, C j Represents equivalent heat capacity; R j represents the equivalent thermal resistance; η represents the refrigeration energy efficiency coefficient; T jmin Represents the minimum indoor temperature to meet user comfort; T jmax Represents the maximum indoor temperature to meet user comfort; T jset is the set temperature of the jth air conditioner; Construct upper and lower power constraints that describe the power output limits of the air conditioner: P Smin ≤ΔP S (t)+P Sbaseline ≤P Smax ; (21) Among them, ΔP S (t) is the output of the air conditioning aggregate at time t; P Sbaseline is the power baseline of the air conditioning aggregate; P Smax is the upper power limit; P Smin is the lower power boundary; Among them, P jbaseline (t) is the power baseline of the jth air conditioner at time t; P jmax is the upper limit of the operating power of the jth air conditioner; P jmin is the lower limit of the operating power of the jth air conditioner; Construct the SOC state constraints that describe the upper and lower boundaries of the air conditioner equivalent SOC: 0≤soc AC (t)≤1; (23)。 6. A method for simulating power system production considering source-load interaction according to claim 5, characterized in that: The process of constructing the external characteristic model of hydrogen production by electricity includes: constructing the state transfer equation, hydrogen power constraint and hydrogen storage tank state constraint; Construct the state transition equation to describe the SOC state transition of hydrogen production: Among them, SOC HST (t) is the current SOC of hydrogen produced by electricity at time t; V HST The maximum volume of hydrogen that can be stored in a hydrogen storage tank at rated pressure and temperature; is the gas storage capacity of the gas storage equipment in period t; is the total amount of gas output by the gas storage device during period t; SCR is the self-consumption rate of the hydrogen storage tank; is the utilization rate of hydrogen entering the gas tank; is the utilization rate of hydrogen output tank; α e,t is the electrical efficiency, is the efficiency of hydrogen production by electrolysis; Q HHV is the calorific value of hydrogen; is the operating power of the electric hydrogen production equipment at time t; Construct a hydrogen power constraint that describes the power limit when the hydrogen production is actually running: in, Maximum hydrogen power; Construct a hydrogen storage tank state constraint that describes the capacity limit of the hydrogen storage tank: in, The upper and lower limits of the hydrogen storage capacity are specified for the hydrogen storage tank respectively.

7. A method for simulating power system production considering source-load interaction according to claim 6, characterized in that: The objective function is: in, is the coal consumption cost of thermal power units; is the carbon emission cost of thermal power units; f r (r + ,r - ) is the standby cost, f UC (v,w) is the start-stop cost; f p (p LS ,p VRE ) is the power abandonment penalty and load shedding penalty; f VPP (p VPP ) is the distributed resource adjustment cost; p G Represents the output of thermal power unit; r + 、r - Represents positive and negative reserve sizes respectively; v represents the open state flag; w represents the closed state flag; p LS ,p VRE Respectively represent the load shedding size and power abandonment size; p VPP Represents the output of the virtual power plant; Among them, λ coal is the unit coal price; Ω g For thermal power generation units; is the active power output of the kth thermal power unit at time t; f fuel (·) represents the coal consumption characteristic function of thermal power units; in, is the carbon dioxide emission cost per unit of thermal power generation; in, is the unit positive reserve cost of the kth thermal power unit; is the unit negative reserve cost of the kth thermal power unit; is the positive reserve size of the kth thermal power unit at time t; is the negative reserve size of the kth thermal power unit at time t; in, is the single startup cost of the kth thermal power unit; is the single shutdown cost of the kth thermal power unit; is the startup variable of the kth thermal power unit at time t; is the shutdown variable of the kth thermal power unit at time t; Among them, Ω l is the set of load nodes; is the penalty cost per unit load shedding power; is the load removed at node i at time t; Ω VRE It is a collection of VRE power generation units; It is the operating cost or penalty cost of unit power curtailment; is the abandoned power of the unit at node i at time t; Among them, Ω VPP is a collection of resource aggregates; is the regulation cost per unit power change of the resource aggregate; is the power change of the resource aggregate at node i at time t.

8. A method for simulating power system production considering source-load interaction according to claim 7, characterized in that: The power balance constraint is: in, The thermal power output of node i in each time period; The output of iVRE units at each time node; Energy storage output for node i in each time period; Contribute to the resource aggregate of node i in each time period; is the line load power; is the load shedding power; The alternative constraints are: in, is the positive reserve and negative reserve size of the thermal power unit at node i at time t; L + Positive reserve requirement factor reserved for load; L - Negative reserve requirement factor reserved for load; W + The positive reserve requirement factor reserved for the VRE unit output; W - Negative reserve requirement factor reserved for VRE unit output; The line transmission capacity constraint is: in, Real-time flow of the line, is the minimum power flow and is the maximum power flow of the line; The characteristic constraints of thermal power units include active output constraints considering standby output, ramp constraints, and minimum start-stop time constraints; the active output constraints are: in, Indicates that the kth thermal power unit is in operation at time t; They are the maximum output ratio and the minimum output ratio of thermal power units respectively; Represents the impact capacity of the kth thermal power unit; The climbing rate constraint is: in, is the maximum climbing rate of the unit; is the maximum descending and climbing rate of the unit; Represents the active output of the kth thermal power unit at time k+1; The minimum start and stop time constraints are: T ON is the minimum startup time of thermal power units; T OFF The minimum stop time of the thermal power unit; The characteristic constraints of energy storage units include state of charge constraints, state of charge change constraints and charge and discharge power constraints; among which, the state of charge constraints are: l min,ES C ES ≤E t ≤λ max,ES C ES (40) Among them, E t is the total energy stored in the battery; max,ES is the maximum ratio allowed by the battery; min,ES The minimum ratio allowed by the battery; C ES is the maximum capacity of the battery; The state of charge change constraint is: Among them, p ESS,t is the electric power of the battery in period t; δ is the numerical self-consumption rate; η ch ,η dis E is the charging efficiency and discharging efficiency respectively; (t-Δt) represents the capacity of the energy storage unit at the previous moment; Δt represents the simulation step length; The charge and discharge power constraints are: -p dis,max ≤p ch,t ≤p ch,max ; (42) Among them, p dis,max Maximum power for charging the battery; p ch,max is the maximum power of battery discharge; p ch,t Represents the charging and discharging power of the energy storage unit; The characteristic constraints of wind and solar power units are: in, is the installed capacity of wind and solar power at node i; is the value of the normalized maximum output curve of wind and solar power at node i at time t; The equipment constraints of the air conditioning cluster include: state transition constraints, adjustment power constraints, and state upper and lower limit constraints; among which, the state transition constraints are: The adjustment power constraint is: The state upper and lower limit constraints are: in, The regulating power of the air conditioner; is the baseline power of the air conditioner; is the upper power limit of the air conditioner; is the lower power boundary of the air conditioner; The characteristic constraints of the electric hydrogen production cluster include state transition constraints, adjustment power constraints, and state upper and lower limit constraints; among which, the state transition constraints are: in, The gas volume of hydrogen storage tank at time t+1 for hydrogen production by electricity; is the gas volume of the hydrogen storage tank at the last solution time of hydrogen production by electricity; SCR is the self-consumption rate of the hydrogen storage tank; is the efficiency of hydrogen production by electrolysis; α e,t is electrical efficiency; is the output power of the hydrogen production system; is the utilization rate of hydrogen entering the gas tank; is the total amount of gas output by the gas storage device during period t; is the utilization rate of hydrogen output tank; V HST is the maximum volume of hydrogen that can be stored in the hydrogen storage tank at rated pressure and temperature; Δt is the simulation step length; The adjustment power constraint is: in, is the output power of the hydrogen production system; is the maximum output power of the hydrogen production system; The state upper and lower limit constraints are: The upper and lower limits of hydrogen storage capacity are specified for hydrogen storage tanks respectively; Specify the hydrogen storage capacity for the hydrogen storage tank.

9. The method for simulating power system production considering source-load interaction according to claim 1, characterized in that: The process of solving the production simulation model includes: using a time-series production simulation solution method based on daily rolling solution to complete the power system production simulation.

10. A power system production simulation system considering source-load interaction, characterized in that: include: a first modeling module, a second modeling module, and a simulation module; The first modeling module is used to model uncertain factors, and the uncertain factors include load, wind power and photovoltaic; Discretize the data to model the uncertainty factors; The second modeling module is used to construct a flexible load external characteristic model; specifically, to construct an external characteristic model of air conditioning and an external characteristic model of electric hydrogen production respectively; The simulation module is used to integrate the flexible load external characteristic model into the power system production simulation to construct a production simulation model; first, an objective function that considers operating costs, poor operation penalties and flexible load regulation costs is constructed; then simulation constraints are constructed; finally, the production simulation model is solved to complete the power system production simulation; the simulation constraints include power balance constraints, standby constraints, line transmission capacity constraints, thermal power unit characteristic constraints, energy storage unit characteristic constraints, wind and solar unit characteristic constraints, and characteristic constraints of air-conditioning clusters and electric hydrogen production clusters.

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