Virtual power plant economic dispatch method and system considering bilateral uncertainty of source and load

Abstract

The application provides a virtual power plant economic dispatch method and system considering source-load bilateral uncertainty, establishes a target function of maximizing benefits and minimizing wind and light curtailment rates, determines constraint conditions of the target function to ensure stable operation of the system, considers similarity of wind and light output scenes, establishes a scene screening model to delete similar scenes to overcome uncertainty of wind and light output, establishes an optimal operation model based on an IGDT theory to further overcome influence of load side uncertainty, and the obtained optimal operation model can further enrich a benefit approach of providing peak shaving services by the virtual power plant, and can be widely applied to a peak shaving auxiliary service field participated by the virtual power plant.

Description

Technical Field

[0001] This invention belongs to the field of economic dispatch of power systems, and specifically relates to a virtual power plant economic dispatch method and system that takes into account the uncertainties on both the source and load sides. Background Technology

[0002] As electricity market reforms deepen, the first batch of spot market pilot programs have officially commenced operations, and the second batch is gradually entering the trial operation phase. However, for other regions, peak-shaving ancillary services remain an effective means to ensure stable grid operation and promote the consumption of clean energy. Virtual power plants, by aggregating distributed clean energy and flexible resources, can achieve flexible output adjustments and rapid response, and thus have the capability to participate in the peak-shaving ancillary services market.

[0003] Virtual power plants can participate in peak shaving to gain peak-shaving benefits. This can be achieved by introducing flexible resources and controllable loads, thereby improving the overall flexibility of virtual power plants, with the introduction of renewable energy being a major trend. However, renewable energy output is more dependent on weather and natural conditions, exhibiting drawbacks such as intermittency and uncertainty. These uncertainties on the load side and in renewable energy output can lead to power plant instability. How to maximize trading benefits through reasonable economic dispatch, thereby improving the utilization rate of distributed energy and reducing the environmental impact of traditional power generation methods, is a pressing issue that needs to be addressed. Summary of the Invention

[0004] Based on this, the present invention provides a virtual power plant economic dispatch method and system that takes into account uncertainties on both the source and load sides. On the basis of considering the uncertainty of power output, it further reduces the impact of load-side uncertainty and enriches the revenue channels for virtual power plants to provide peak-shaving services.

[0005] This invention discloses an economic dispatch method for a virtual power plant that considers uncertainties on both the source and load sides. The unit units of the virtual power plant include at least gas turbines, wind and solar turbines, and power-to-gas turbines. The method includes:

[0006] Based on the unit units of the virtual power plant, establish an objective function that maximizes revenue and minimizes wind and solar curtailment rates, and determine the constraints of the objective function.

[0007] Establish a scenario screening model to screen the output scenarios of wind and solar power units and obtain the scenario screening results. Calculate the net income of wind and solar power units based on the scenario screening results and update the objective function based on the net income of wind and solar power units.

[0008] An optimized operation model based on IGDT theory is established. The optimized operation model is solved based on the updated objective function and constraints. The optimized operation model is used to guide the economic dispatch of virtual power plants. The uncertain parameters in the optimized operation model are set according to the load side.

[0009] Furthermore, the objective function is specifically expressed as:

[0010] f1 = max(r) vpp +r g )

[0011]

[0012] Where f1 represents the objective function for maximizing revenue, f2 represents the objective function for minimizing wind and solar curtailment rates, and r vpp r represents the revenue from selling electricity by a virtual power plant. g U represents the peak-shaving revenue of a virtual power plant. c U represents the actual amount of wind and solar power curtailment. t This indicates the total power generation of the wind and solar turbine units.

[0013] Furthermore, a scene selection model is established to overcome the uncertainty of wind and solar power output. The objective function is optimized based on the scene selection results, including:

[0014] The output scenarios of wind and solar turbine units were extracted using the Latin hypercube sampling method.

[0015] The extracted power output scenarios are reduced to obtain M power output scenarios;

[0016] The average wind and solar power output of M power output scenarios is calculated to obtain the wind and solar power output curves;

[0017] The net revenue of wind and solar power units is calculated based on the wind and solar power output curves as follows:

[0018]

[0019] in, and p represents the output power of the wind turbine and photovoltaic unit after overcoming uncertainties, respectively. vpp Indicates electricity price, c w c represents the levelized cost of electricity (LCOE) of a wind turbine. pv The unit of electricity (kWh) represents the cost per kilowatt-hour of a photovoltaic (PV) unit, and t represents time.

[0020] Furthermore, the extracted landscape output scenes are reduced to M landscape output scenes, including:

[0021] Step S1. Use scene distance measurement to reduce similar scenes, and calculate the average distance between any two scenes as follows:

[0022]

[0023] in, and Let X represent the average distances between scenes i and j, respectively. iw and X jw These represent the sample values ​​in scenarios i and j, respectively.

[0024] Step S2. Remove the nearest sample from the scene dataset and calculate the distance-probability value S between scenes i and j. ij =p j s ij , where p j s represents the probability of scenario j occurring. ij This represents the distance between scenes i and j;

[0025] Step S3. Calculate the distance-probability value between scene i and all scenes, and delete the scene j with the smallest distance-probability value. d ;

[0026] Step S4. Update the probability of sample i appearing as follows: p i This indicates the probability of the scene occurring before the scene update. The scene j that is deleted d The probability of occurrence;

[0027] Step S5. Repeat steps S1 to S4 until the number of output scenarios is reduced to M.

[0028] Furthermore, an optimization model based on IGDT theory is established, and the optimization model is solved according to the updated objective function, including:

[0029] The optimization operation model based on IGDT theory is established as follows:

[0030]

[0031]

[0032] Where Y represents the uncertain parameter, d represents the decision variable, F(Y,d) represents the objective function, H(Y,d) and G(Y,d) represent the equality constraint and inequality constraint, respectively, and α represents the variation of the uncertain parameter, α≥0. This indicates that the range of the uncertain parameter Y deviating from the predicted value does not exceed [a certain value]. F0 represents the objective value of determining the model, β a and β s Indicating the degree of deviation from the predicted value, the robust decision value ensures that the expected value does not exceed (1+β) for any disturbance within the decision-maker's acceptable range. a For any chance decision value, there exists at least one Y within an acceptable range such that the expected value does not exceed (1-β). s )F0;

[0033] Substitute the predicted values ​​of the following uncertain parameters into the above optimized operating model for solution.

[0034]

[0035]

[0036]

[0037]

[0038] Among them, y b1 and y b2 This represents the optimal value obtained from the deterministic model;

[0039] Set the range of variation of the uncertain parameter as The subscript 'l' indicates the load;

[0040] The optimized operating model considering the above uncertain parameters simplifies to the following form:

[0041]

[0042]

[0043]

[0044]

[0045] A pessimistic decision-making model based on risk mitigation is constructed according to a robust decision-making strategy, with the actual load demand set as the minimum value of the predicted output deviation. Set the deviation β of the actual output disturbance on the objective function value. i If i = 1, 2, then the maximum pessimistic values ​​for each objective are (1-β1)y. b1 , (1+β2)y b2 The robust decision-making strategy in the above optimized operating model is expressed as:

[0046]

[0047] An optimistic decision-making model based on opportunity pursuit is constructed according to the opportunity decision-making strategy, and the actual load demand is set as the maximum value of the predicted output deviation. The opportunity decision-making strategy in the above optimized operating model is then expressed as:

[0048]

[0049] Furthermore, the constraints include system power balance constraints, gas turbine constraints, wind turbine output constraints, photovoltaic turbine output constraints, and system reserve capacity constraints.

[0050] Furthermore, the system power balance constraint is as follows:

[0051]

[0052] Among them, g i,g g represents the active power output of gas turbine unit i. j,w G represents the active power output of wind turbine j. m,pv Let m represent the active power output of the photovoltaic unit, and D represent the load demand of the power system.

[0053] Furthermore, gas turbine unit constraints include gas turbine unit output constraints, gas turbine unit ramping constraints, and gas turbine unit start-up and shutdown constraints;

[0054] The output constraint of the gas turbine unit is:

[0055]

[0056] in, This represents the maximum dispatchable output of gas turbine unit i. This represents the minimum dispatchable output of gas turbine unit i;

[0057] The ramping constraint for gas turbine units is:

[0058]

[0059] in, and This represents the power increase / decrease constraint of gas turbine unit i;

[0060] The start-stop constraints for gas turbine units are:

[0061] (T i on (t-1)-MT i on (u) i (t-1)-u i (t)≥0

[0062] (T i off (t-1)-MT i off (u) i (t)-u i (t-1))≥0

[0063] Among them, T i on (t-1) represents the operating time of gas turbine unit i at time t-1, MT i on T represents the shortest operating time of gas turbine unit i. i off (t-1) represents the downtime of gas turbine unit i at time t-1, MT ioff denoted as the shortest downtime of gas turbine unit i, and u represents the start-up / shutdown parameters.

[0064] Furthermore, the output constraint of the wind turbine is as follows:

[0065]

[0066] in, This represents the maximum dispatchable output of wind turbine j. This represents the minimum dispatchable output of wind turbine j.

[0067] Furthermore, the output constraint of the photovoltaic unit is as follows:

[0068]

[0069] in, This represents the maximum dispatchable output of photovoltaic unit m. This represents the minimum dispatchable output of photovoltaic unit m.

[0070] Furthermore, the system reserve capacity constraint is as follows:

[0071]

[0072] Where D(t) is the system load demand at time t, R(t) is the system reserve demand at time t, l represents the system line loss rate, θ represents the unit self-consumption rate, and g max (t) represents the maximum unit output at time t.

[0073] This invention also provides a virtual power plant economic dispatch system that takes into account uncertainties on both the source and load sides. The virtual power plant includes at least gas turbine units, wind and solar turbine units, and power-to-gas turbine units. The system includes:

[0074] The objective function construction module is used to establish an objective function that maximizes revenue and minimizes wind and solar curtailment rates, and to determine the constraints of the objective function.

[0075] The scene selection module is used to establish a scene selection model to select the output scenes of wind and solar turbines, obtain scene selection results, calculate the net income of wind and solar turbines based on the scene selection results, and update the objective function based on the net income of wind and solar turbines.

[0076] The operation model construction module is used to establish an optimized operation model based on IGDT theory. It solves the optimized operation model based on the updated objective function and constraints. The optimized operation model is used to guide the economic dispatch of virtual power plants. The uncertain parameters in the optimized operation model are set according to the load side.

[0077] In addition, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described virtual power plant economic dispatch method taking into account uncertainties on both the source and load sides.

[0078] The present invention provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described virtual power plant economic dispatch method taking into account uncertainties on both the source and load sides.

[0079] The present invention has the following beneficial effects:

[0080] This invention provides a virtual power plant economic dispatch method and system that takes into account uncertainties on both the source and load sides. Starting from the market-based trading of virtual power plants, it considers the functional transfer of peak-shaving ancillary services of each generating unit in the virtual power plant, and takes into account the uncertainties on the source and load sides. It establishes an operation model for the economic dispatch of virtual power plants, further enriching the path for virtual power plants to participate in market transactions. It can be widely applied in the field of virtual power plants participating in peak-shaving ancillary service market transactions. Attached Figure Description

[0081] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0082] Figure 1 This is a schematic diagram of a virtual power plant structure provided in an embodiment of the present invention;

[0083] Figure 2 An optional execution flow for a virtual power plant economic dispatch method considering uncertainties on both the source and load sides, provided in an embodiment of the present invention;

[0084] Figure 3 This is a schematic diagram of the structure of a virtual power plant economic dispatch system that takes into account the uncertainties on both the source and load sides, provided in an embodiment of the present invention.

[0085] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0086] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] See Figure 1 It illustrates the structure of a virtual power plant participating in peak-shaving ancillary services. The power plant components include gas turbine units (shown as an example), wind turbine units, photovoltaic units, and power-to-gas conversion equipment. Figure 1 It also shows the power flow and natural gas flow of each power plant component.

[0088] Combination Figure 2 This illustrates an optional execution flow of a virtual power plant economic dispatch method considering both source and load uncertainties provided by an embodiment of the present invention, including:

[0089] Step S11. Establish an objective function that maximizes revenue and minimizes wind and solar curtailment rates, and determine the constraints of the objective function.

[0090] Specifically, the components of the virtual power plant participating in peak-shaving ancillary services mainly include gas turbine units, wind turbine units, and photovoltaic units. Based on the physical models and revenue models of each power output unit, the objective function for maximizing the system's revenue can be further determined.

[0091] Because power-to-gas (EPG) equipment can convert electrical energy into natural gas or hydrogen, and store the resulting gas in natural gas pipelines or storage facilities, it can be used for conversion and storage during peak renewable energy output periods and for power supply during power shortages, thereby improving the system's renewable energy absorption capacity. Introducing EPG equipment into a virtual power plant enables efficient natural gas recycling. EPG technology can convert unused electrical energy into methane for storage, and then convert it back into electricity via gas turbines when necessary, playing a positive role in clean energy generation and absorption, and transforming system energy coupling from unidirectional to bidirectional. Therefore, in further embodiments, the objective function for maximizing benefits can also consider the benefits brought by EPG equipment.

[0092] Step S12. Establish a scenario screening model to screen the output scenarios of wind and solar power units and obtain the scenario screening results. Calculate the net income of wind and solar power units based on the scenario screening results and update the objective function based on the net income of wind and solar power units.

[0093] Specifically, the scene selection is mainly to overcome the uncertainty of wind and solar power output, that is, to include the uncertainty on the source side as a factor in the optimization operation model. Specifically, the Latin hypercube sampling method can be used to perform stratified sampling of the cumulative probability curve to obtain sample data, and similar scenes can be deleted to overcome the uncertainty of wind and solar power output.

[0094] Step S13. Establish an optimized operation model based on IGDT theory. Solve the optimized operation model based on the updated objective function and constraints. The optimized operation model is used to guide the economic dispatch of the virtual power plant. The uncertain parameters in the optimized operation model are set according to the load side.

[0095] IGDT (Information Gap Decision Theory) is a non-probabilistic, non-fuzzy uncertainty risk management method. This method does not require knowledge of the probability distribution and fluctuation range of uncertain parameters. It includes two strategies: risk aversion and risk preference. The former indicates that the decision-maker resists risk and fears loss, acting as a risk-averse individual; the latter indicates that the decision-maker views risk as an opportunity to gain more benefits, acting as a risk seeker. Based on the objective function updated by the aforementioned steps, an optimized operation model based on IGDT can be established. This optimized operation model ensures that the components of the virtual power plant operate stably around the objective function and constraints.

[0096] In a specific embodiment, the objective function in step S10 is specifically expressed as:

[0097] f1 = max(r) vpp +r g )

[0098]

[0099] Where f1 represents the objective function for maximizing revenue, f2 represents the objective function for minimizing wind and solar curtailment rates, and r vpp r represents the revenue from selling electricity by a virtual power plant. g U represents the peak-shaving revenue of a virtual power plant. c U represents the actual amount of wind and solar power curtailment. t This indicates the total power generation of the wind and solar turbine units.

[0100] When maximizing the revenue of a virtual power plant in this embodiment of the invention, the revenue from gas turbine units, wind and solar turbine units, and power-to-gas turbine units is mainly considered. The revenue calculation for these three units is as follows:

[0101] (1) Gas turbine unit

[0102] For a gas turbine unit, its output model can be expressed as:

[0103] g i,g (t)=F GT (t)·η G (t)·HHV

[0104] In the formula, F GT (t) represents the natural gas consumption of gas turbine unit i during time period t, g i,g(t) represents the electrical energy provided by the gas turbine unit during time period t, η G (t) represents the power generation efficiency of the gas turbine unit during time period t, and HHV is the higher calorific value of natural gas, which can be taken as 36 MJ / m³ in a further embodiment. 3 .

[0105] The carbon dioxide emission formula for gas turbine units is as follows:

[0106] e i,g (t)=F GT (t)·e G

[0107] Among them, e i,g (t) represents the carbon dioxide emissions of the gas turbine unit, e G This refers to the unit carbon dioxide emissions of the gas turbine unit.

[0108] The revenue of gas turbine units includes compensation for peak-shaving ancillary services and revenue from power generation; therefore, its net revenue model can be expressed as:

[0109]

[0110]

[0111] Where, r G G represents the net revenue of the gas turbine unit. i,g (t) represents the amount of electricity generated by the wind turbine that is diverted from the gas turbine during time period t, P a (t) represents the ancillary service price during time period t, c g This represents the total cost of gas. Indicates the operation and maintenance cost, δ g For gas prices, This indicates the amount of natural gas used by the gas turbine unit from the gas storage tank.

[0112] (2) Wind and solar turbines

[0113] Wind and solar power systems generally include wind turbines and solar photovoltaic units. Wind turbines rely on natural wind for power output, and natural wind has a high degree of randomness. The output power of wind turbines fluctuates with wind speed. Using the Weibull distribution to simulate natural wind speed, the probability density function is as follows:

[0114]

[0115] Where v represents natural wind speed, φ and These represent the shape and scale parameters of the distribution function, respectively.

[0116] When the wind speed is within the acceptable range of the wind turbine, the turbine power increases with the increase of wind speed. However, if it exceeds the acceptable range, i.e., the wind speed is too low or too high, the wind turbine will not start to avoid damage to the machine. Therefore, the functional relationship between the wind turbine output and the wind speed is as follows:

[0117]

[0118] Among them, g j,w (t) represents the available output of wind turbine j at time t, g r The rated output power of the wind turbine, v i,w and v o,w These represent the cut-in and cut-out wind speeds, respectively, v r,w Let v(t) be the rated wind speed, and v(t) be the actual wind speed at time t.

[0119] The output curve of a photovoltaic (PV) unit generally follows a Beta distribution:

[0120]

[0121] Where α and β represent the shape parameters of the Beta distribution, and θ is the irradiance correlation coefficient. The parameters of Beta are calculated by introducing the mean and standard deviation of the irradiance as follows:

[0122]

[0123]

[0124] Where μ and δ represent the mean and normal distribution values ​​of solar radiation, respectively.

[0125] Integral calculation of the above Beta distribution:

[0126]

[0127] Where, θ c and θ d These represent the upper and lower limits of solar irradiance θ, respectively.

[0128] The output model of a photovoltaic unit can be expressed as:

[0129] g m,pv (t)=η PV ×S PV ×θ t

[0130] Where, η PV For the power efficiency of photovoltaic units, S PV Let θ be the total area of ​​the photovoltaic unit. t This refers to the amount of sunlight received by the photovoltaic unit.

[0131] The net income model for wind and solar power units can be expressed as follows:

[0132]

[0133] Where, r wpv p represents the net income of wind and solar power units. vpp Indicates electricity price, c w c represents the levelized cost of electricity (LCOE) of a wind turbine. pv This indicates the cost per kilowatt-hour of a photovoltaic (PV) unit.

[0134] Considering the uncertainty in the output of wind and solar power units, a scenario selection model is established to overcome the uncertainty in wind and solar power output. The specific process includes:

[0135] Step S121. Use scene distance measurement to reduce similar scenes, and calculate the average distance between any two scenes as follows:

[0136]

[0137] in, and Let X represent the average distance between scenes i and j, respectively. iw and X jw These represent the sample values ​​for scenarios i and j, respectively.

[0138] Step S122. Remove the nearest sample from the scene dataset and calculate the distance-probability value S between scenes i and j. ij =p j s ij , where p j s represents the probability of scenario j occurring. ij This represents the distance between scenes i and j.

[0139] Step S123. Calculate the distance-probability value between scene i and all scenes, and delete the scene j with the smallest distance-probability value. d .

[0140] Step S124. Update the probability of sample i appearing as follows: p i This indicates the probability of the scene occurring before the scene update. The scene j that is deleted d The probability of occurrence.

[0141] Step S125. Repeat steps S121 to S124 until the number of landscape output scenes is reduced to M.

[0142] After overcoming the uncertainty of power output, the net revenue model of wind and solar power units can be expressed as:

[0143]

[0144] in, and p represents the output power of the wind turbine and photovoltaic unit after overcoming uncertainties, respectively. vpp Indicates electricity price, c w c represents the levelized cost of electricity (LCOE) of a wind turbine. pv The unit of electricity (kWh) represents the cost per kilowatt-hour of a photovoltaic (PV) unit, and t represents time.

[0145] (3) Electric-to-gas generator unit

[0146] The power-to-gas technology utilizes surplus electricity during off-peak hours to electrolyze water and generate methane through a methanation process. Its operating principle is as follows:

[0147] Q pg,t =P pg,t η pg

[0148]

[0149] Among them, Q pg,t To determine the amount of gas generated through electro-gas conversion, P pg,t η is the electricity consumption for converting electricity to gas. pg Indicates electrical conversion efficiency. This indicates the amount of electricity generated by the gas turbine unit using natural gas supplied from the gas storage tank. η represents the amount of natural gas used from the gas storage tank when the gas turbine unit is in operation. MT This indicates the gas-fired power generation efficiency of the gas turbine unit.

[0150] Gas storage tanks are used to store converted natural gas and allocate it rationally based on price. During peak load periods, the stored gas can be used for power generation, or it can be sold to the natural gas network. The flow of natural gas in the storage tank can be represented as follows:

[0151]

[0152] Among them, Q GST,t Let be the amount of gas stored in the gas storage tank at time t. This represents the initial gas storage capacity. This indicates the amount of natural gas stored after the electricity-to-gas conversion. This represents the amount of natural gas input from the gas storage tank to the gas turbine unit at time t.

[0153] At the same time, the gas storage tank is designed to prevent simultaneous gas storage and release operations, as follows:

[0154]

[0155] Based on the above construction of the physical model and revenue model for each power output unit, the part of the objective function related to revenue maximization can be expressed as:

[0156] f1 = max(r) vpp +r g ) = max(r' wpv +r G -P pg,t ·p vpp )

[0157] For the established objective function, the constraints to ensure stable system operation include system power balance constraints, gas turbine constraints, wind turbine output constraints, photovoltaic turbine output constraints, and system reserve capacity constraints.

[0158] The system power balance constraint is:

[0159]

[0160] Among them, g i,g g represents the active power output of gas turbine unit i. j,w G represents the active power output of wind turbine j. m,pv denoted by m, D represents the active power output of the photovoltaic unit, D represents the load demand of the power system, NG represents the number of gas turbine units in the virtual power plant, NW represents the number of wind turbine units in the virtual power plant, and NPV represents the number of photovoltaic units in the virtual power plant.

[0161] Gas turbine unit constraints include gas turbine unit output constraints, gas turbine unit ramping constraints, and gas turbine unit start-stop constraints.

[0162] The output constraint of the gas turbine unit is:

[0163]

[0164] in, This represents the maximum dispatchable output of gas turbine unit i. This represents the minimum dispatchable output of gas turbine unit i;

[0165] The ramping constraint for gas turbine units is:

[0166]

[0167] in, and This represents the power increase / decrease constraint of gas turbine unit i;

[0168] The start-stop constraints for gas turbine units are:

[0169] (T i on (t-1)-MT ion (u) i (t-1)-u i (t)≥0

[0170] (T i off (t-1)-MT i off (u) i (t)-u i (t-1))≥0

[0171] Among them, T i on (t-1) represents the operating time of gas turbine unit i at time t-1, MT i on T represents the shortest operating time of gas turbine unit i. i off (t-1) represents the downtime of gas turbine unit i at time t-1, MT i off denoted as the shortest downtime of gas turbine unit i, and u represents the start-up / shutdown parameters.

[0172] The output constraint of the wind turbine is:

[0173]

[0174] in, This represents the maximum dispatchable output of wind turbine j. This represents the minimum dispatchable output of wind turbine j.

[0175] The output constraint of the photovoltaic unit is:

[0176]

[0177] in, This represents the maximum dispatchable output of photovoltaic unit m. This represents the minimum dispatchable output of photovoltaic unit m.

[0178] The system's reserve capacity constraint is:

[0179]

[0180] Where D(t) is the system load demand at time t, R(t) is the system reserve demand at time t, l represents the system line loss rate, θ represents the unit self-consumption rate, and g max (t) represents the maximum unit output at time t.

[0181] Specifically, step S13 involves constructing an optimization model based on IGDT theory and solving the optimization model based on the updated objective function and constraints, which includes the following process:

[0182] The theoretical model of IGDT considering uncertainty mainly includes three elements: deterministic model (maximum / minimum model), uncertainty model, and performance requirements.

[0183] If we define the optimization objective F as a minimization function, then the general expression for the uncertainty model is as follows:

[0184]

[0185] Where Y represents the uncertain parameter, d represents the decision variable, F(Y,d) represents the objective function, and H(Y,d) and G(Y,d) represent the equality constraint and the inequality constraint, respectively.

[0186] Uncertain parameter Y around the predicted value Fluctuations can be expressed as:

[0187]

[0188] Where α represents the magnitude of the variation of the uncertain parameter, α≥0, This indicates that the range of the uncertain parameter Y deviating from the predicted value does not exceed [a certain value].

[0189] Therefore, for the operation of virtual power plant units, based on the two decision-making directions in IGDT theory and considering the uncertainty on the load side, an optimized operation model that considers risk aversion and risk preference can be established as follows:

[0190]

[0191]

[0192] F0 represents the objective value of determining the model, β a and β s Indicating the degree of deviation from the predicted value, the robust decision value ensures that the expected value does not exceed (1+β) for any disturbance within the decision-maker's acceptable range. a For any chance decision value, there exists at least one Y within an acceptable range such that the expected value does not exceed (1-β). s )F0.

[0193] Substitute the predicted values ​​of the following uncertain parameters into the above optimized operating model for solution.

[0194]

[0195]

[0196]

[0197]

[0198] Among them, y b1 and y b2 This represents the optimized value obtained from the deterministic model.

[0199] The range of variation for the uncertain parameter is set as follows:

[0200]

[0201] In this context, the subscript 1 indicates the load.

[0202] The optimized operating model considering the above uncertain parameters simplifies to the following form:

[0203]

[0204]

[0205]

[0206]

[0207] A pessimistic decision-making model based on risk mitigation is constructed according to a robust decision-making strategy, with the actual load demand set as the minimum value of the predicted output deviation. Set the deviation β of the actual output disturbance on the objective function value. i If i = 1, 2, then the maximum pessimistic values ​​for each objective are (1-β1)y. b1 , (1+β2)y b2 The risk avoidance strategy in the above optimized operating model is expressed as:

[0208]

[0209] An optimistic decision-making model based on opportunity pursuit is constructed according to the opportunity decision-making strategy, and the actual load demand is set as the maximum value of the predicted output deviation. The risk preference strategy in the above optimized operating model is then expressed as:

[0210]

[0211] like Figure 3 As shown, this embodiment of the invention also provides a virtual power plant economic dispatch system 300 that takes into account uncertainties on both the source and load sides, including:

[0212] The objective function construction module 310 is used to establish an objective function that maximizes revenue and minimizes wind and solar curtailment rates, and to determine the constraints of the objective function.

[0213] The scene selection module 320 is used to establish a scene selection model to select the output scenes of wind and solar power units, obtain scene selection results, calculate the net income of wind and solar power units based on the scene selection results, and update the objective function based on the net income of wind and solar power units.

[0214] The operation model construction module 330 is used to establish an optimized operation model based on IGDT theory. It solves the optimized operation model based on the updated objective function and constraints. The optimized operation model is used to guide the economic dispatch of the virtual power plant. The uncertain parameters in the optimized operation model are set according to the load side.

[0215] The specific implementation logic of the above modules can be found in the relevant introduction of the virtual power plant economic dispatch method that takes into account the uncertainties on both the source and load sides, and will not be repeated here.

[0216] The virtual power plant economic dispatch system 300, which takes into account the uncertainties on both the source and load sides, provided in this application embodiment, can be applied to electronic devices. Figure 4 The hardware structure block diagram of the electronic device is shown. Its hardware structure may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0217] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0218] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0219] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0220] The memory stores a program, which the processor can call. The program is used to implement the various processing steps in the aforementioned virtual power plant economic dispatch scheme that takes into account the uncertainties on both the source and load sides.

[0221] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0222] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0223] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. The above embodiments are only used to illustrate the present invention, and the structure, connection method and manufacturing process of each component can be varied. All equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

[0225] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A virtual power plant economic dispatch method considering uncertainties on both the source and load sides, characterized in that, The virtual power plant's generating units include at least gas turbine units, wind and solar turbine units, and power-to-gas turbine units; the method includes: Based on the unit units of the virtual power plant, establish an objective function that maximizes revenue and minimizes wind and solar curtailment rates, and determine the constraints of the objective function. A scenario screening model is established to screen the output scenarios of the wind and solar power units to obtain scenario screening results. The net income of the wind and solar power units is calculated based on the scenario screening results, and the objective function is updated based on the net income of the wind and solar power units. An optimized operation model based on IGDT theory is established. The optimized operation model is solved based on the updated objective function and the constraints. The optimized operation model is used to guide the economic dispatch of virtual power plants. The uncertain parameters in the optimized operation model are set according to the load side. The process of establishing a scenario screening model to screen the output scenarios of wind and solar turbines and obtaining scenario screening results, and calculating the net revenue of the wind and solar turbines based on the scenario screening results, includes: The output scenarios of wind and solar turbine units were extracted using the Latin hypercube sampling method. The extracted power output scenarios are reduced to obtain M power output scenarios; The average wind and solar power output of the M power output scenarios is calculated to obtain the wind and solar power output curves; The net revenue of the wind and solar power units is calculated based on the aforementioned wind and solar power output curves as follows: in, and These represent the output power of the wind turbine and photovoltaic unit after overcoming uncertainties, respectively. Indicates electricity price, This indicates the cost per kilowatt-hour of a wind turbine. This indicates the levelized cost of electricity (LCOE) of a photovoltaic (PV) generator. Indicates time; The establishment of the optimization operation model based on IGDT theory, and the solution of the optimization operation model based on the updated objective function and the constraints, includes: The optimization operation model based on IGDT theory is established as follows: in, Indicates an uncertain parameter. Represents decision variables, Describe the objective function. and Let these represent equality constraints and inequality constraints, respectively. Indicates the magnitude of variation of an uncertain parameter. , Indicates uncertain parameters The deviation from the predicted value shall not exceed , This indicates that the optimization objective value of the model has been determined. and Indicating the degree of deviation from the predicted value, the robust decision value ensures that the expected value does not exceed the predicted value under any disturbance within the decision-maker's acceptable range. For opportunity decision values, there exists at least one. Within acceptable limits, the expected value should not exceed ; Substitute the predicted values ​​of the following uncertain parameters into the optimization model based on IGDT theory for solution. in, and This represents the optimal value obtained from the deterministic model; Set the range of variation of the uncertain parameter as subscript Indicates load; The optimized operating model considering the uncertain parameters simplifies to the following form: A pessimistic decision-making model based on risk mitigation is constructed according to a robust decision-making strategy, with the actual load demand set as the minimum value of the predicted output deviation. Set the deviation of the actual output disturbance from the objective function value. The maximum pessimistic values ​​for each objective are respectively , The robust decision-making strategy of the optimized operating model is expressed as: An optimistic decision-making model based on opportunity pursuit is constructed according to the opportunity decision-making strategy, and the actual load demand is set as the maximum value of the predicted output deviation. The opportunity decision-making strategy of the optimized operating model is then expressed as: 。 2. The method according to claim 1, characterized in that, The objective function is specifically expressed as follows: in, This represents the objective function that maximizes profit. The objective function represents the minimum curtailment rate of wind and solar power. This represents the revenue from the sale of electricity by the virtual power plant. This represents the peak-shaving revenue of a virtual power plant. This represents the actual amount of wind and solar power curtailment. This indicates the total power generation of the wind and solar turbine units.

3. The economic scheduling method according to claim 1, characterized in that, The process of reducing the extracted power output scenarios to obtain M power output scenarios includes: Step S1. Use scene distance measurement to reduce similar scenes, and calculate the average distance between any two scenes as follows: in, and Representing the scene respectively and average distance, and Representing the scene respectively and The sample values ​​below; Step S2. Remove the nearest sample from the scene dataset and calculate the scene. and Distance-probability value ,in, Representing a scene The probability of occurrence Representing a scene and The distance between them; Step S3. Calculate the scenario Distance-probability values ​​with all scenes, then delete the scene with the smallest distance-probability value. ; Step S4. Update the sample The probability of occurrence is , This indicates the probability of the scene occurring before the scene update. Indicates a deleted scene The probability of occurrence; Step S5. Repeat steps S1 to S4 until the number of output scenarios is reduced to M.

4. The economic scheduling method according to claim 1, characterized in that, The constraints include system power balance constraints, gas turbine constraints, wind turbine output constraints, photovoltaic turbine output constraints, and system reserve capacity constraints.

5. The economic scheduling method according to claim 4, characterized in that, The system power balance constraint is: in, Indicates gas turbine unit Those who have made contributions Indicates wind turbine Those who have made contributions Indicates photovoltaic unit Those who have made contributions This represents the load demand value of the power system.

6. The economic scheduling method according to claim 4, characterized in that, The constraints on the gas turbine unit include gas turbine unit output constraints, gas turbine unit ramping constraints, and gas turbine unit start-stop constraints. The output constraint of the gas turbine unit is: in, Indicates gas turbine unit The maximum dispatchable output, Indicates gas turbine unit The minimum schedulable output; The ramp-up constraint for the gas turbine unit is: in, and Indicates gas turbine unit Power increase / decrease constraints; The start-stop constraints for the gas turbine unit are: in, Indicates gas turbine unit exist The runtime of each moment. Indicates gas turbine unit The shortest running time, Indicates gas turbine unit exist The downtime at specific times, For gas turbine units The shortest downtime, where u represents the start / stop parameter.

7. The economic scheduling method according to claim 4, characterized in that, The output constraint of the wind turbine is: in, Indicates wind turbine The maximum dispatchable output, Indicates wind turbine The minimum schedulable output.

8. The economic scheduling method according to claim 4, characterized in that, The output constraint of the photovoltaic unit is: in, Indicates photovoltaic unit The maximum dispatchable output, Indicates photovoltaic unit The minimum schedulable output.

9. The economic scheduling method according to claim 4, characterized in that, The system's backup capacity constraint is: in, For the system Constant load demand, For the system in The need for backup at all times Indicates the system's line loss rate. This indicates the unit's self-consumption rate. Indicates that the unit is Maximum unit output at any given time.

10. A virtual power plant economic dispatch system considering uncertainties on both the source and load sides, characterized in that, The virtual power plant's generating units include at least gas turbine units, wind and solar turbine units, and power-to-gas turbine units. The system includes: The objective function construction module is used to establish an objective function that maximizes revenue and minimizes wind and solar curtailment rates based on the unit units of the virtual power plant, and to determine the constraints of the objective function. The scene selection module is used to establish a scene selection model to select the output scenes of the wind and solar turbines to obtain scene selection results, calculate the net income of the wind and solar turbines based on the scene selection results, and update the objective function based on the net income of the wind and solar turbines. The operation model construction module is used to establish an optimized operation model based on IGDT theory. The optimized operation model is solved based on the updated objective function and the constraints. The optimized operation model is used to guide the economic dispatch of virtual power plants. The uncertain parameters in the optimized operation model are set according to the load side. The process of establishing a scenario screening model to screen the output scenarios of wind and solar turbines and obtaining scenario screening results, and calculating the net revenue of the wind and solar turbines based on the scenario screening results, includes: The output scenarios of wind and solar turbine units were extracted using the Latin hypercube sampling method. The extracted power output scenarios are reduced to obtain M power output scenarios; The average wind and solar power output of the M power output scenarios is calculated to obtain the wind and solar power output curves; The net revenue of the wind and solar power units is calculated based on the aforementioned wind and solar power output curves as follows: in, and These represent the output power of the wind turbine and photovoltaic unit after overcoming uncertainties, respectively. Indicates electricity price, This indicates the cost per kilowatt-hour of a wind turbine. This indicates the levelized cost of electricity (LCOE) of a photovoltaic (PV) generator. Indicates time; The establishment of the optimization operation model based on IGDT theory, and the solution of the optimization operation model based on the updated objective function and the constraints, includes: The optimization operation model based on IGDT theory is established as follows: in, Indicates an uncertain parameter. Represents decision variables, Describe the objective function. and Let these represent equality constraints and inequality constraints, respectively. Indicates the magnitude of variation of an uncertain parameter. , Indicates uncertain parameters The deviation from the predicted value shall not exceed , This indicates that the optimization objective value of the model has been determined. and Indicating the degree of deviation from the predicted value, the robust decision value ensures that the expected value does not exceed the predicted value under any disturbance within the decision-maker's acceptable range. For opportunity decision values, there exists at least one. Within acceptable limits, the expected value should not exceed ; Substitute the predicted values ​​of the following uncertain parameters into the optimization model based on IGDT theory for solution. in, and This represents the optimal value obtained from the deterministic model; Set the range of variation of the uncertain parameter as subscript Indicates load; The optimized operating model considering the uncertain parameters simplifies to the following form: A pessimistic decision-making model based on risk mitigation is constructed according to a robust decision-making strategy, with the actual load demand set as the minimum value of the predicted output deviation. Set the deviation of the actual output disturbance from the objective function value. The maximum pessimistic values ​​for each objective are respectively , The robust decision-making strategy of the optimized operating model is expressed as: An optimistic decision-making model based on opportunity pursuit is constructed according to the opportunity decision-making strategy, and the actual load demand is set as the maximum value of the predicted output deviation. The opportunity decision-making strategy of the optimized operating model is then expressed as: 。 11. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the virtual power plant economic dispatch method considering both source and load uncertainties as described in any one of claims 1 to 9.

12. A readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the virtual power plant economic dispatch method that takes into account the uncertainties on both the source and load sides as described in any one of claims 1 to 9.

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