Multi-objective optimization modeling method and related devices for demand-side flexible resource participation

Abstract

The present invention discloses a multi-objective optimization modeling method and related devices involving demand-side flexible resources, which determine initial data including wind power generation, thermal power units, disordered charging of electric vehicles, distributed electric heating, and user-side energy storage; determine the objective function of the multi-objective optimization model, design the boundary conditions of the multi-objective optimization model based on the objective function and basic data, and construct a multi-objective optimization model; design conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the auxiliary service market; based on conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios, use the obtained initial data, adopt an improved particle swarm optimization algorithm to solve the multi-objective optimization model, and output the optimization solution results. The present invention optimizes the regulation capacity of the power grid, effectively reduces the wind abandonment rate and wind abandonment cost, improves the absorption capacity of new energy, and reduces the problem of power abandonment caused by transmission section blockage, which is of great significance to promoting the sustainable development of new energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid optimization operation, and specifically relates to a multi-objective optimization modeling method and related devices involving demand-side flexible resources. Background Art

[0002] With the global focus on reducing carbon emissions and improving energy efficiency, renewable energy is becoming increasingly important in the power system. However, the large-scale integration of renewable energy into the power grid has a significant impact on the economic efficiency of grid operation and the safe and stable operation of the power system.

[0003] First, the absorption of renewable energy has always been a major challenge plaguing the power system. Due to factors such as transmission congestion, large-scale curtailment of wind and solar power is common. In order to absorb a high proportion of renewable energy, the power system must incur higher costs. This not only includes the investment in transmission lines and energy storage facilities, but also the costs of adjusting and optimizing the power system during operation.

[0004] Secondly, the randomness, volatility, and intermittency of renewable energy generation pose challenges to the stable operation of the power system. The output power of renewable energy sources such as wind power and photovoltaic power generation is significantly affected by weather and climate conditions. After large-scale grid connection, these fluctuations may cause power system fluctuations, increasing the difficulty and instability of operation and scheduling.

[0005] Furthermore, with the development of smart grid and Internet of Things technologies, more and more flexible loads, such as air conditioners, electric heating, and electric vehicles, are being used on the demand side. The large and disorderly access of these devices not only increases the randomness of electricity consumption but can also put greater pressure on the grid during peak hours, impacting power quality and the safe and reliable operation of the grid.

[0006] In the face of these challenges, tapping into dispatchable demand-side flexible load resources and participating in the ancillary services market has become an important way to promote stable, secure, and efficient grid operation. Advanced information technology and control technologies can enable intelligent management and dispatch of these loads, thereby better balancing power supply and demand and improving the grid's adaptability and flexibility.

[0007] In short, the large-scale integration of renewable energy into the power grid poses new requirements and challenges to the power system. Through technological innovation and management optimization, these challenges can be effectively addressed, achieving efficient utilization of renewable energy and sustainable development of the power system. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned existing technologies and provide a multi-objective optimization modeling method and related devices with the participation of flexible resources on the demand side, so as to solve the technical problem of power abandonment caused by blockage of the transmission section and improve the absorption capacity of new energy.

[0009] The present invention adopts the following technical solutions:

[0010] A multi-objective optimization modeling method for demand-side flexible resource participation includes the following steps:

[0011] Determine the initial data including wind power generation, thermal power units, disorderly charging of electric vehicles, distributed electric heating and user-side energy storage;

[0012] Determine the objective function of the multi-objective optimization model, design the boundary conditions of the multi-objective optimization model based on the objective function and basic data, and construct the multi-objective optimization model;

[0013] Design conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market;

[0014] Based on the conventional thermal power regulation scenario and the typical demand-side flexible resource regulation scenario, the initial data obtained are used to adopt the improved particle swarm optimization algorithm to solve the multi-objective optimization model, obtain the regulation results of typical demand-side flexible resources and the consumption results of new energy, and realize the electricity consumption time regulation of demand-side flexible resources.

[0015] Preferably, the initial data is:

[0016] The power that a wind turbine can generate is expressed as the sum of the predicted output and the predicted deviation;

[0017] The neural network algorithm improved by particle swarm is used to predict the output of wind turbines;

[0018] Standard deviation based on forecast deviation , generated using the Latin hypercube sampling method n The predicted deviation under each scenario and the probability of the predicted deviation under each scenario are combined with the predicted output of the wind turbine to obtain the expected output of the wind turbine. ;

[0019] Input basic parameters such as thermal power unit capacity, maximum output, minimum output, upward ramp rate, downward ramp rate, etc.

[0020] Monte Carlo simulation is used to simulate the disorderly charging scenario of electric private cars;

[0021] Construct a relationship between the power consumption of distributed electric heating load and indoor and outdoor temperatures, input the power, equivalent thermal resistance, and equivalent heat capacity parameters of distributed electric heating equipment, and obtain a curve showing the change of the power of distributed electric heating users' heating equipment with outdoor temperature.

[0022] When not participating in the ancillary services market, the user-side energy storage device stores electricity at high power during off-peak periods and discharges it during peak periods, resulting in the output curve when the energy storage device is not involved in regulation.

[0023] Based on the other load curves on a certain working day, the power consumption of other loads in the area is simulated to obtain the curve of other load power consumption changing with time.

[0024] Preferably, t The power that the wind turbine can generate at any moment for:

[0025]

[0026] in, express t Predicted output of wind turbines at every moment; express t Wind power forecast deviation at each moment.

[0027] Preferably, the expected output of the wind turbine for:

[0028]

[0029] in, express t Predicted output of wind turbines at every moment; express t Moment The prediction deviation value in each scenario; Indicates the The probability of a scenario occurring.

[0030] Preferably, the relationship between the power consumption of the distributed electric heating load and the indoor and outdoor temperatures is as follows:

[0031]

[0032] in, express t Indoor temperature at all times; express t -1 moment indoor temperature; express t Distributed electric heating power at all times; Indicates the area of ​​decentralized electric heating user's room; Indicates the equivalent thermal resistance of electric heating equipment; express t Outdoor temperature at all times; Indicates the equivalent heat capacity of decentralized electric heating user's house; Indicates a time interval.

[0033] Preferably, the objective function of the multi-objective optimization model is:

[0034] Considering the minimum rate of renewable energy curtailment caused by section blockage, the first objective function is constructed as follows:

[0035]

[0036] in, express t The actual grid-connected power of the wind turbine at the moment, MW; express t Wind curtailment scenario switch variables at all times;

[0037] Considering the lowest cost of typical demand-side flexible resource aggregation participating in the ancillary service market, the second objective function is constructed as follows:

[0038]

[0039] in, Refers to the case where decentralized electric heating participates in the auxiliary service market t Total power at the moment; Refers to the case where electric vehicle charging load participates in the auxiliary service market t Total power at the moment; Refers to the case where user-side energy storage participates in the ancillary service market t Total power at the moment; Refers to decentralized electric heating without participating in the auxiliary service market t Total power at all times; Refers to the case where electric vehicle charging load does not participate in the auxiliary service market t Total power at all times; Refers to the case where user-side energy storage does not participate in the ancillary service market t Total power at all times; Refers to the duration of each scheduling period; refer to t Flexible resources on the demand side at every moment participate in regulating the average compensation price of ancillary services.

[0040] Preferably, the boundary conditions of the multi-objective optimization model include constraints on new energy power curtailment assessment indicators, power balance constraints, wind turbine output constraints, thermal power unit output constraints, thermal power unit ramping constraints, thermal power unit minimum start and stop time constraints, distributed electric heating user indoor temperature constraints, distributed electric heating load power constraints, electric vehicle charging power constraints, electric vehicle charging amount constraints, electric vehicle charging time constraints, user-side energy storage device power constraints, user-side energy storage device capacity constraints, user-side energy storage device storage amount timing constraints and user-side energy storage device storage amount constraints.

[0041] Preferably, the assessment indicators for curtailed electricity generation from new energy sources are as follows:

[0042]

[0043] in, It represents the assessment indicator of the new energy power abandonment rate;

[0044] Power balance constraints:

[0045]

[0046] in, express t Moment j Output of thermal power units; express t Moment j Typhoon turbine output; express t Other loads in the power grid at all times;

[0047] Wind turbine output constraints

[0048]

[0049] in, Indicates the k Typhoon turbines in t Always make an effort; Indicates the k Typhoon turbines in t Always expect to predict output;

[0050] Output constraints of thermal power units:

[0051]

[0052] in, Indicates the j The minimum output of the thermal power unit; Indicates the j The maximum output of the thermal power units;

[0053] Thermal power unit ramp constraints:

[0054]

[0055]

[0056] in, Indicates the j Downward ramp rate of thermal power units; Indicates the j The upward climbing rate of the thermal power unit; express t- 1st moment j Output of thermal power units;

[0057] Minimum start and stop time constraints for thermal power units:

[0058]

[0059]

[0060] in, Indicates the j Continuous operation time of thermal power units; Indicates the j Minimum operating time of thermal power units, min; Indicates the j The outage time of the thermal power units; Indicates the j Minimum downtime of thermal power units;

[0061] Indoor temperature constraints for decentralized electric heating users:

[0062]

[0063] in, Indicates the maximum indoor comfortable temperature; express t Indoor temperature at all times; Indicates the minimum indoor comfortable temperature;

[0064] Distributed electric heating load power constraints:

[0065]

[0066] in, Indicates the i Taipower heating equipment t Operating power at all times; Indicates the i The upper limit of the operating power of Taipower heating equipment;

[0067] Electric vehicle charging power constraints:

[0068]

[0069] in, Indicates the i The minimum charging power allowed for electric vehicles; express t Moment i Charging power of electric vehicles; Indicates the i The maximum charging power allowed for an electric vehicle;

[0070] Electric vehicle charging capacity constraints:

[0071]

[0072] in, Indicates the i The amount of energy an electric car owner expects the battery to hold after charging. Indicates the i The amount of electricity stored in the battery of an electric vehicle when charging is complete; Indicates the i Measure the maximum storage capacity of electric vehicles;

[0073] Electric vehicle charging time constraints:

[0074]

[0075] in, Indicates the i The time when electric vehicles start charging; Indicates the i The time it takes for electric vehicles to arrive at the charging location; Indicates the i The time it takes for an electric vehicle to complete charging; Indicates the i Electric vehicle travel time;

[0076] Power constraints of user-side energy storage devices:

[0077]

[0078] in, express t Moment j Operating power of distributed energy storage devices; express t Moment j Maximum discharge power of each user-side energy storage device; express t Moment j Maximum charging power of each user-side energy storage device;

[0079] Capacity constraints of user-side energy storage devices:

[0080]

[0081] in, express t Moment j The amount of energy stored in the user-side energy storage device; Indicates the j The maximum storage capacity of the user-side energy storage device;

[0082] Timing constraints of energy storage capacity of user-side energy storage devices:

[0083]

[0084] Constraints on the storage capacity of user-side energy storage devices:

[0085]

[0086] in, Represents a scheduling cycle; Indicates user-side energy storage t The charging and discharging power at each moment; Indicates the duration of each scheduling period.

[0087] Preferably, conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios are as follows:

[0088] Without considering the participation of demand-side flexible resources in the ancillary services market, all loads will use electricity according to the original plan, and conventional traditional thermal power units will be used for peak load regulation to maximize the consumption of new energy;

[0089] Considering the aggregation of flexible resources on the demand side to participate in the ancillary service market, with the objective function of minimizing the cost of ancillary services and minimizing the amount of new energy curtailment caused by section blockage, decentralized electric heating, electric vehicle charging load, and user-side energy storage are aggregated and optimized for scheduling.

[0090] Preferably, a wind curtailment penalty is introduced into the objective function, the wind curtailment volume is converted into wind curtailment cost, and the wind curtailment cost is added to the peak load ancillary service cost to achieve the transformation from multiple objectives to a single objective, as follows:

[0091]

[0092] in, Indicates the unit wind abandonment penalty; represents the switch variable of the wind curtailment scenario at time t; Indicates the day-ahead forecast t Wind turbine output at all times; express t Wind turbine output at all times; Indicates the duration of each scheduling period; express t Total power of distributed electric heating load at all times; Indicates that the peak load is not involved. t Distributed electric heating load at all times; express t Total power of electric vehicle charging load at any moment; Indicates that the peak load is not involved. t Electric vehicle charging load at all times; express tTotal power of distributed energy storage load at the moment, positive value indicates charging, negative value indicates selling electricity to the grid; Indicates that the peak load is not involved. t Distributed energy storage load at all times; express t The compensation price that the load aggregator pays to users for participating in ancillary services.

[0093] Preferably, the improved particle swarm optimization algorithm is used to solve the model as follows:

[0094] S501, input original data;

[0095] S502, write an objective function according to relevant scenario settings;

[0096] S503, initialize the particle swarm and randomly generate an initial population that meets the constraints;

[0097] S504, continuously updating the individual best position and the global best position in the particle swarm, and updating the corresponding fitness value of the objective function;

[0098] S505: Determine whether the convergence condition is met. If so, stop the operation and output the optimization solution. If not, regenerate a new population and return to step S503 to continue the optimization.

[0099] Preferably, the original data includes configuration information, operating parameters, basic load data of distributed electric heating equipment, electric vehicles, and user-side energy storage, as well as relevant data of the power generation side units in the system.

[0100] Preferably, each particle in the particle swarm contains all decision variables of the demand solution - wind power output, thermal power output, electric vehicle charging power, distributed electric heating load power, and user energy storage charging and discharging power data at each moment within 24 moments.

[0101] In a second aspect, an embodiment of the present invention provides a multi-objective optimization modeling system with demand-side flexible resource participation, including:

[0102] The data module determines the initial data including wind power generation, thermal power units, disorderly charging of electric vehicles, distributed electric heating, and user-side energy storage;

[0103] Function module, determines the objective function of the multi-objective optimization model, designs the boundary conditions of the multi-objective optimization model according to the objective function and basic data, and constructs the multi-objective optimization model;

[0104] Scenario module, which designs conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market;

[0105] The output module, based on conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios, uses the obtained initial data and adopts an improved particle swarm optimization algorithm to solve the multi-objective optimization model, obtains the regulation results of typical demand-side flexible resources and the consumption results of new energy, and realizes the electricity consumption time adjustment of demand-side flexible resources.

[0106] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the multi-objective optimization modeling method involving demand-side flexible resources are implemented.

[0107] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the multi-objective optimization modeling method involving demand-side flexible resources.

[0108] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the multi-objective optimization modeling method involving flexible resources on the demand side are implemented.

[0109] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the multi-objective optimization modeling method involving demand-side flexible resources.

[0110] Compared with the prior art, the present invention has at least the following beneficial effects:

[0111] A multi-objective optimization modeling method for demand-side flexible resource participation was developed. Distributed electric heating, electric vehicle charging loads, and user-side energy storage were selected as representative examples of demand-side flexible resources. The dual objectives of minimizing the amount of renewable energy curtailment due to section obstruction and minimizing the cost of aggregated demand-side flexible resources participating in the regulation of ancillary services were considered. Combined with multiple constraints, including curtailment rate assessment constraints and unit operation, a multi-objective optimization model for demand-side flexible resource participation in the regulation of ancillary services was constructed. An improved particle swarm optimization algorithm was used to solve the proposed model, outputting the regulation results for typical demand-side flexible resources and the results of renewable energy consumption. By adjusting the electricity consumption time of demand-side flexible resources, the proposed model effectively improved renewable energy consumption capacity and reduced the cost of curtailment caused by blocked renewable energy sections.

[0112] Furthermore, a neural network algorithm based on particle swarm optimization was combined with Latin hypercube to improve the accuracy of wind power forecasts. Furthermore, the power of electric heating loads was calculated by combining the relationship between electric heating equipment parameters and indoor and outdoor temperatures. Monte Carlo simulations were used to simulate the daily distribution of random electric vehicle charging loads. Furthermore, considering the participation of user-side energy storage in the electricity market, daily charge-discharge arbitrage was performed to analyze the time-varying power of user-side energy storage. The simulation algorithm provided basic parameters, improving the scientific nature of parameter design and laying the foundation for validating the model's effectiveness.

[0113] Furthermore, the dual objectives of minimizing the curtailment rate of renewable energy and minimizing the compensation costs of flexible regulation resources participating in the ancillary services market are proposed. By minimizing the curtailment rate of renewable energy, the utilization rate of renewable energy sources such as wind and solar energy can be increased, and the amount of electricity wasted due to section blockage can be reduced, thereby increasing the proportion of renewable energy in the power system. By guiding flexible regulation resources to participate in the peak-shaving ancillary services market and provide necessary regulation services, reliance on traditional generators can be reduced, lowering overall operating costs. This achieves a comprehensive optimization of system economics and environmental friendliness.

[0114] Furthermore, by setting upper and lower limits on the adjustable potential of flexible regulation resources, we ensure that while meeting grid regulation needs, the resource's inherent regulation capacity will not be exceeded. By setting constraints such as generator output and power balance, we ensure the integrity of the model.

[0115] Furthermore, the thermal power regulation scenario is taken as the basic scenario, and the typical demand-side flexibility resource regulation scenario is taken as the control scenario. By comparing the optimized scheduling results of the two scenarios, the renewable energy power curtailment situation and scheduling costs under different scenarios are analyzed, and the superiority of flexible resources participating in the regulation market is verified.

[0116] Furthermore, currently, energy storage optimization configuration problems are mostly solved using intelligent algorithms, such as genetic algorithms, whale optimization algorithms, simulated annealing algorithms, and particle swarm optimization algorithms. Compared with other algorithms, particle swarm optimization algorithms offer clear and easy-to-understand principles, fewer parameters, simple operation, ease of implementation, and high efficiency, but they are prone to getting stuck in local optimality. To enhance optimization effectiveness and improve the accuracy of results, an improved particle swarm optimization algorithm was selected as the solution for the proposed model. This improvement not only retains the previous global search capabilities but also increases search speed, while also minimizing the risk of premature convergence and getting stuck in local optimality.

[0117] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0118] In summary, the present invention uses a neural network algorithm based on particle swarm optimization to predict wind power generation, adopts a Latin hypercube sampling method to sample the wind power output prediction deviation, and generates a wind power expected prediction output curve; inputs the basic parameters of the thermal power unit; simulates the disordered charging scenario of electric vehicles based on the Monte Carlo method; obtains the electric heating load change curve according to the relationship between the distributed electric heating load and the outdoor temperature change; obtains the charging and discharging power curve of the user-side energy storage according to the "low storage and high generation" arbitrage behavior of the user-side energy storage; simulates the curve of the power consumption of other loads changing with time; constructs a model objective function: including a new energy consumption objective function and an economic objective function, so as to minimize the new energy power abandonment rate and the cost of regulating auxiliary services; designs model boundary conditions: including power abandonment rate assessment constraints, power balance constraints, new energy and thermal power unit output, ramping, start and stop time, and demand-side resource regulation potential constraints; designs conventional thermal power regulation scenarios and demand-side flexible resource aggregation participation regulation scenarios; and adopts an improved particle swarm optimization algorithm to solve the model. Through actual case analysis, the present invention optimizes the regulation capacity of the power grid, effectively reduces the wind curtailment rate and cost, improves the absorption capacity of new energy, and reduces the problem of power curtailment caused by blockage of the transmission section, which is of great significance to promoting the sustainable development of new energy.

[0119] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0120] Figure 1 This is a Monte Carlo simulation of a disorderly charging scenario for electric private cars in an embodiment of the present invention;

[0121] Figure 2 is the per-unit value of the wind power forecast within 24 time periods in the embodiment of the present invention;

[0122] Figure 3 is the wind power output and expected output per unit value in different scenarios in the embodiment of the present invention;

[0123] Figure 4 The disorderly charging load of the electric private car in the embodiment of the present invention;

[0124] Figure 5 The power of the distributed electric heating equipment varies with the outdoor temperature in the embodiment of the present invention;

[0125] Figure 6 The power consumption of other loads changes over time in the embodiment of the present invention;

[0126] Figure 7 is the objective function fitness curve in the embodiment of the present invention

[0127] Figure 8The output of thermal power units and wind turbines on the power generation side in scenario 1 in an embodiment of the present invention;

[0128] Figure 9 The load changes of electric vehicles, distributed electric heating, and user-side energy storage devices over time in scenario 2 in an embodiment of the present invention;

[0129] Figure 10 The output of wind turbines and thermal power plants changes over time in scenario 2 in an embodiment of the present invention;

[0130] Figure 11 The total load and total output of the system change over time in scenario 2 in an embodiment of the present invention;

[0131] Figure 12 A schematic diagram of a computer device provided in accordance with an embodiment of the present invention;

[0132] Figure 13 The block diagram of a chip provided according to one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0133] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0134] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0135] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0136] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0137] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0138] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0139] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0140] The present invention provides a multi-objective optimization modeling method involving flexible resources on the demand side. Demand side resources are a good adjustable resource. The current potential of power supply side regulation resources has entered a bottleneck period, and the existing regulation resources are difficult to meet the peak regulation needs of the power grid. When only the units on the power generation side are dispatched, it will lead to a large amount of wind power abandonment and a high wind abandonment rate, resulting in a waste of wind energy resources. Load side resources can participate in grid regulation by adjusting the power consumption size and time. There is no need for special transformation, and almost no transformation and operation costs are involved. It is a good adjustable resource. By fully tapping the dispatchable flexible load resources, optimizing the dispatch of decentralized adjustable loads, and reasonably arranging the power consumption time of adjustable loads, the system's ability to accept wind power can be further improved, which will help reduce wind power abandonment and increase the proportion of wind power absorption in the power grid.

[0141] The present invention provides a multi-objective optimization modeling method for demand-side flexible resource participation, comprising the following steps:

[0142] S1. Input initial data;

[0143] A particle swarm optimization neural network algorithm is used to predict regional wind power generation, generating a 24-period wind power output forecast curve. Latin hypercube sampling is then used to sample the wind power output forecast deviation and generate a wind power output expectation curve.

[0144] Input the basic parameters of the thermal power unit, including maximum output, minimum output, and ramp rate;

[0145] The Monte Carlo method is used to simulate the disorderly charging scenario of electric vehicles, and the load power curves of disorderly charging of electric vehicles in 24 periods are obtained;

[0146] According to the relationship between the distributed electric heating load and the outdoor temperature change and the parameter values ​​of the distributed electric heating equipment, the curve of the distributed electric heating load changing with the outdoor temperature in 24 periods is obtained;

[0147] Considering that behind-the-meter energy storage generally achieves arbitrage through "low storage, high generation" when not participating in the ancillary services market, the charging and discharging power curves of behind-the-meter energy storage for 24 time periods are obtained;

[0148] Based on the other load curves on a certain working day, the power consumption of other loads in the area is simulated to obtain the curve of other load power consumption changing with time.

[0149] S101. Consider that the power that a wind turbine can generate is expressed as the sum of the predicted output and the predicted deviation;

[0150] The calculation formula is as follows:

[0151]

[0152] in, express t The power that the wind turbine can generate at any moment, MW; express t Predicted output of wind turbines at the moment, MW; express t Wind power forecast deviation at the moment, MW. Considering that the wind power forecast output fluctuates around the actual output, the forecast deviation should fluctuate around 0. Therefore, the forecast deviation is set to obey the expectation of 0 and the variance is The normal distribution of Standard Deviation It can be obtained by fitting the normal distribution based on the prediction deviation obtained in the historical prediction process.

[0153] S102, using a neural network algorithm improved by particle swarm optimization to predict the output of wind turbines;

[0154] S103. Standard deviation based on forecast deviation , generated using the Latin hypercube sampling methodn The predicted deviation under each scenario and the probability of the predicted deviation under each scenario, combined with the predicted output of the wind turbine, can obtain the expected output of the wind turbine;

[0155] The calculation formula is as follows:

[0156]

[0157] in, represents the expected output of the wind turbine, MW; express t Moment The prediction deviation value in each scenario, MW; Indicates the The probability of a scenario occurring.

[0158] S104. Input basic parameters such as thermal power unit capacity, maximum output, minimum output, upward ramp rate, and downward ramp rate;

[0159] S105, using Monte Carlo simulation of disorderly charging of electric private cars, the process is as follows Figure 1 As shown;

[0160] S106. Considering that the power consumption of the distributed electric heating load is closely related to the indoor and outdoor temperatures, a relationship between the power consumption of the distributed electric heating load and the indoor and outdoor temperatures is constructed. Parameters such as the power, equivalent thermal resistance, and equivalent thermal capacity of the distributed electric heating equipment are input to obtain a curve showing the change in the power of the distributed electric heating user's heating equipment versus the outdoor temperature.

[0161] The relationship between the power consumption of distributed electric heating load and indoor and outdoor temperatures is as follows:

[0162]

[0163] S107. Considering that all user-side energy storage devices can only perform charge-discharge arbitrage once a day, when not participating in the ancillary services market, user-side energy storage devices store electricity at high power during off-peak periods and discharge during peak periods, achieving "low storage, high generation." This yields the output curve when the energy storage device is not involved in regulation.

[0164] S108. Based on other load curves on a certain working day, simulate the power consumption of other loads in the area to obtain a curve of the power consumption of other loads changing with time.

[0165] S2, construct the model objective function;

[0166] Two objective functions are considered, including the new energy consumption objective function and the economic objective function. The new energy consumption objective function refers to minimizing the new energy curtailment rate caused by section blockage in the region; the economic objective function refers to minimizing the cost of typical demand-side flexible resources participating in the ancillary service market.

[0167] S201, considering the minimum rate of new energy curtailment caused by section obstruction, construct the first objective function;

[0168] The first objective function is as follows:

[0169]

[0170] in, express t The actual grid-connected power of the wind turbine at the moment, MW; express t The switch variable of the wind curtailment scenario at any moment.

[0171] S202. Considering the lowest cost of typical demand-side flexible resource aggregation participating in the ancillary service market, construct the second objective function.

[0172] The second objective function is as follows:

[0173]

[0174] in, Refers to the case where decentralized electric heating participates in the auxiliary service market t Total power at the moment, MW; Refers to the case where electric vehicle charging load participates in the auxiliary service market t Total power at the moment, MW; Refers to the case where user-side energy storage participates in the ancillary service market t Total power at the moment, in MW. Positive values ​​indicate charging, while negative values ​​indicate selling power to the grid. Refers to decentralized electric heating without participating in the auxiliary service market t Total power at the moment, MW; Refers to the case where electric vehicle charging load does not participate in the auxiliary service market t Total power at the moment, MW; Refers to the case where user-side energy storage does not participate in the ancillary service market t Total power at the moment, MW, positive value indicates charging, and load indicates discharging; Refers to the duration of each scheduling period, h ; refer to t Average compensation price for the participation of demand-side flexible resources in regulating ancillary services, RMB / MWh.

[0175] S3. Design model boundary conditions based on the objective function and basic data;

[0176] Various constraints are taken into consideration, including new energy power curtailment assessment index constraints, power balance constraints, wind turbine output constraints, thermal power unit output constraints, thermal power unit ramping constraints, thermal power unit minimum start and stop time constraints, distributed electric heating user indoor temperature constraints, distributed electric heating load power constraints, electric vehicle charging power constraints, electric vehicle charging amount constraints, electric vehicle charging time constraints, user-side energy storage device power constraints, user-side energy storage device capacity constraints, user-side energy storage device storage amount timing constraints and user-side energy storage device storage amount constraints.

[0177] 1) Constraints on assessment indicators for curtailed new energy power:

[0178] The curve of new energy curtailment caused by section obstruction in the region is as follows:

[0179]

[0180] in, Indicates the abandoned power of renewable energy.

[0181] The calculation formula for the new energy curtailment rate at different times is obtained as shown below:

[0182]

[0183] use represents the assessment index of new energy power abandonment rate, then:

[0184]

[0185] Finally, the constraints on the assessment indicators for curtailed new energy power are as follows:

[0186]

[0187] in, It represents the assessment indicator of new energy power curtailment rate.

[0188] 2) Power balance constraints:

[0189] At any time, the power system must maintain equal supply and demand, that is, power balance, the formula is as follows:

[0190]

[0191] in, express t Moment j Output of thermal power units, MW; express t Moment jTyphoon turbine generator output, MW; express t Other loads in the grid at time t, MW. When demand-side flexible resources participate in the ancillary service market, other loads are assumed to remain unchanged.

[0192] 3) Wind turbine output constraints

[0193] The wind turbine output level should not exceed its predicted level, subject to the following constraints:

[0194]

[0195] in, Indicates the k Typhoon turbines in t Time output, MW; Indicates the k Typhoon turbines in t The expected output at any given moment, MW.

[0196] 4) Thermal power unit output constraints

[0197] Thermal power units have maximum and minimum output constraints during their output process. The constraints are as follows:

[0198]

[0199] in, Indicates the j Minimum output of thermal power units, MW; Indicates the j Maximum output of thermal power units, MW.

[0200] 5) Thermal power unit ramp constraints

[0201] The rate at which a thermal power unit changes its load is limited by its ramping capability, where the load increase constraints are as follows:

[0202]

[0203] The load reduction constraints are as follows:

[0204]

[0205] in, Indicates the j Downward ramp rate of thermal power units, MW / min; Indicates the j Ramp-up rate of thermal power units, MW / min; express t- 1st moment j Output of thermal power units, MW.

[0206] 6) Minimum start and stop time constraints for thermal power units

[0207] Thermal power units must meet their minimum start and stop time constraints during operation. The constraints are as follows:

[0208]

[0209]

[0210] in, Indicates the j Continuous operation time of thermal power units, min; Indicates the j Minimum operating time of thermal power units, min; Indicates the j The downtime of thermal power units, min; Indicates the j Minimum shutdown time of thermal power units, min.

[0211] 7) Indoor temperature constraints for decentralized electric heating users

[0212] When regulating the distributed electric heating load, it should be ensured that the user's comfort is not affected, that is, the user's indoor temperature is kept within the comfortable range, with the following constraints:

[0213]

[0214] in, Indicates the maximum indoor comfortable temperature, ℃; express t Indoor temperature at the moment, ℃; Indicates the minimum comfortable indoor temperature, ℃.

[0215] 8) Distributed electric heating load power constraints

[0216] When regulating the distributed electric heating load, the operating power should not exceed its maximum allowable value. That is, for any electric heating equipment under regulation, its operating power constraint is as follows:

[0217]

[0218] in, Indicates the i Taipower heating equipment t Operating power at the moment, kW; Indicates the i The upper limit of operating power of Taipower heating equipment, kW.

[0219] 9) Electric vehicle charging power constraints

[0220] When an electric vehicle is charging, its charging power should be within the maximum and minimum allowed charging power range. That is, for any electric vehicle, its charging power constraint is as follows:

[0221]

[0222] in, Indicates the i The minimum charging power allowed for an electric vehicle, kW; express t Moment i Charging power of electric vehicles, kW; Indicates the i The maximum charging power allowed for an electric vehicle, kW.

[0223] 10) Electric vehicle charging capacity constraints

[0224] After the electric vehicle participates in the dispatch, its battery storage capacity should meet the minimum expectations of the owner, with the following constraints:

[0225]

[0226] in, Indicates the i The amount of energy that the owner of an electric vehicle expects to store in the battery after charging, in kWh; Indicates the i The amount of electricity stored in the battery of an electric vehicle at the end of charging, kWh; Indicates the i Measure the maximum storage capacity of electric vehicles, kWh.

[0227] 11) Electric vehicle charging time constraints

[0228] When scheduling electric vehicle loads, the normal use of the vehicle owner should not be delayed. That is, the charging start time should be later than the time when the user arrives at the charging station, and the charging end time should be earlier than the time when the user next travels. The specific constraints are as follows:

[0229]

[0230] in, Indicates the i The time when electric vehicles start charging; Indicates the i The time it takes for electric vehicles to arrive at the charging location; Indicates the i The time it takes for an electric vehicle to complete charging; Indicates the i Electric vehicle travel time.

[0231] 12) Power constraints of user-side energy storage devices

[0232] For user-side energy storage devices, the power cannot exceed its maximum power, whether charging or discharging, subject to the following constraints:

[0233]

[0234] in, express t Moment j Operating power of distributed energy storage devices, MW; express t Moment j Maximum discharge power of a user-side energy storage device, MW; express t Moment j Maximum charging power of a user-side energy storage device, MW.

[0235] 13) Capacity constraints of user-side energy storage devices

[0236] The amount of energy stored in the user-side energy storage device cannot exceed its maximum storage capacity, subject to the following constraints:

[0237]

[0238] in, express t Moment j The amount of energy stored in the user-side energy storage device, MWh; Indicates the j The maximum storage capacity of the user-side energy storage device, MWh.

[0239] 14) Timing constraints of energy storage capacity of user-side energy storage devices

[0240] The user-side energy storage device must first store electricity during the off-peak period before discharging it during the peak period. The amount of energy stored at any given moment must meet the following conditions, with the following constraints:

[0241]

[0242] 15) Constraints on the storage capacity of user-side energy storage devices

[0243] After each scheduling cycle, the storage capacity of any user-side energy storage device should be the same as the initial state. Here, the initial state storage capacity is set to 0, and the constraints are as follows:

[0244]

[0245] S4, scene design;

[0246] Based on the ancillary service market, two scenarios are designed, including a conventional thermal power regulation scenario and a typical demand-side flexible resource regulation scenario.

[0247] S401: Demand-side flexible resources will not be considered for participation in the ancillary services market. All loads will be used according to the original plan, and only conventional thermal power units will be used for peak load regulation to maximize the consumption of new energy and reduce the curtailment of new energy due to section blockage.

[0248] S402: Considering the aggregation of demand-side flexible resources to participate in the ancillary services market, with the objective function of minimizing the cost of regulating ancillary services and minimizing the amount of new energy curtailment due to section blockage, decentralized electric heating, electric vehicle charging loads, and user-side energy storage are aggregated and optimized. A wind curtailment penalty is introduced here to allow the objective function to be summed, as shown below:

[0249]

[0250] in, Indicates the unit wind curtailment penalty value, RMB / kWh.

[0251] S5. Optimization solution.

[0252] The improved particle swarm optimization algorithm is used to solve the model. The specific steps are as follows:

[0253] S501. Input relevant raw data, including configuration information and operating parameters of distributed electric heating equipment, electric vehicles, and user-side energy storage, base load data, and relevant data on the power generation side of the system, such as thermal power unit operating parameters and wind turbine output forecast data;

[0254] S502. Write an objective function according to relevant scenario settings;

[0255] S503: Initialize the particle swarm and randomly generate an initial population that satisfies the constraints. Each particle in the particle swarm represents a possible potential solution. That is, each particle contains all the decision variables that need to be solved in this paper—wind power output, thermal power output, electric vehicle charging power, distributed electric heating load power, and user energy storage charging and discharging power data at each moment within 24 moments.

[0256] S504, continuously updating the individual best position and the global best position in the particle swarm, and updating the corresponding fitness value of the objective function;

[0257] S505: Determine whether the convergence condition is met. If so, stop the operation and output the optimization solution. If not, regenerate a new population and return to step S503 to continue the optimization.

[0258] In another embodiment of the present invention, a multi-objective optimization modeling system is provided, which can be used to implement the above-mentioned multi-objective optimization modeling method involving flexible resources on the demand side. Specifically, the multi-objective optimization modeling system includes a data module, a function module, a scenario module and an output module.

[0259] Among them, the data module determines the initial data including wind power generation, thermal power units, disorderly charging of electric vehicles, distributed electric heating and user-side energy storage;

[0260] Function module, determines the objective function of the multi-objective optimization model, designs the boundary conditions of the multi-objective optimization model according to the objective function and basic data, and constructs the multi-objective optimization model;

[0261] Scenario module, which designs conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market;

[0262] The output module, based on conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios, uses the obtained initial data and an improved particle swarm optimization algorithm to solve the multi-objective optimization model and output the optimization solution results.

[0263] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the multi-objective optimization modeling method for demand-side flexible resource participation, including:

[0264] Determine the initial data including wind power generation, thermal power units, disordered charging of electric vehicles, distributed electric heating and user-side energy storage; determine the objective function of the multi-objective optimization model, design the boundary conditions of the multi-objective optimization model according to the objective function and basic data, and construct the multi-objective optimization model; design conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market; based on conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios, use the obtained initial data, adopt the improved particle swarm optimization algorithm to solve the multi-objective optimization model, and output the optimization solution results.

[0265] See also Figure 12 The terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable by the processor 61. When executed by the processor 61, the computer program 63 implements the method for calculating the fluid composition in the reservoir stimulation wellbore of the embodiment. To avoid repetition, a detailed description is omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the multi-objective optimization modeling system of the embodiment. To avoid repetition, a detailed description is omitted here.

[0266] The computer device 60 may be a desktop computer, a notebook computer, a PDA, a cloud server, or other computing devices. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. It will be understood by those skilled in the art that Figure 12 This is merely an example of the computer device 60 and does not constitute a limitation of the computer device 60 . The computer device 60 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0267] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0268] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 60.

[0269] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is about to be output.

[0270] See also Figure 13 The terminal device is a chip. The chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 622 may be configured to execute the computer program to implement the aforementioned multi-objective optimization modeling method for demand-side flexible resource participation.

[0271] In addition, the chip 600 may further include a power supply component 626 and a communication component 650. The power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, such as wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0272] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0273] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the multi-objective optimization modeling method for demand-side flexible resource participation in the above embodiment; the processor may load and execute the following steps:

[0274] Determine the initial data including wind power generation, thermal power units, disordered charging of electric vehicles, distributed electric heating and user-side energy storage; determine the objective function of the multi-objective optimization model, design the boundary conditions of the multi-objective optimization model according to the objective function and basic data, and construct the multi-objective optimization model; design conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market; based on conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios, use the obtained initial data, adopt the improved particle swarm optimization algorithm to solve the multi-objective optimization model, and output the optimization solution results.

[0275] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0276] Example

[0277] Enter initial data.

[0278] The neural network optimized by particle swarm algorithm is used to predict the wind power output of a typical day in winter at a wind farm, and the wind power output results for 24 periods are obtained as follows: Figure 2 As shown in Figure 2, the parameters of the neural network optimized by the particle swarm optimization algorithm are set as follows: the learning factors c1 and c2 of the particle swarm optimization algorithm are set to 1.49, the number of iterations is 200, and the population size is 50; the number of BP neural network training times is 10,000, and the learning rate is 0.01.

[0279] According to the historical forecast results, the forecast deviation obtained in the forecast process is fitted with normal distribution and obeys N (0, 3.06 2 Based on this prediction deviation, the Latin hypercube sampling method is used to generate wind power output prediction deviations under five random scenarios and the probability of each scenario occurring, as shown in Table 1:

[0280] Table 1 Probability of wind turbine output change scenario set

[0281]

[0282] The expected output of the wind turbine is obtained according to the following formula:

[0283]

[0284] The wind power output and expected output per unit value under different scenarios are as follows: Figure 3 shown.

[0285] The capacity of thermal power units in the area is 300MW, with a minimum output of 120MW and a maximum output of 300MW. The downward ramp rate is 6MW / min and the upward ramp rate is 6MW / min.

[0286] Taking a normal working day as an example, electric vehicles can be charged twice a day, one during the daytime working hours (8 am to 5 pm) and the other during the break time (6 pm to 7 am the next day). For most families, charging once a day can meet daily needs, so it is assumed that all vehicles are only charged once a day after get off work. Assume that 2,000 electric private cars are aggregated to participate in the auxiliary service market. The battery capacity of each car is 60kWh, the rated regular charging power is 6.6kW, and the initial state of charge follows a normal distribution. N (0.5, 0.12), the charging start time follows a normal distribution N (18.50, 0.8 2 ). According to user needs, electric vehicles need to stop charging before 7 am the next day. Monte Carlo simulation method is used to simulate the disorderly charging scenario of users, and the result shows that the electric private car does not need to be charged. Figure 4 shown.

[0287] The relationship between the power consumption of distributed electric heating load and indoor and outdoor temperatures is shown in the following formula:

[0288]

[0289] Assume that the load aggregator can aggregate 5,000 decentralized electric heating users, and the heating area of ​​a single user is 60m 2 ;Household electric heating equipment power is 5kW;Equivalent thermal resistance ; Equivalent heat capacity B = The user's comfortable indoor temperature range in winter is 17-25°C. Assume that the user's indoor temperature is kept constant at 20°C before accepting load aggregator regulation. Taking the outdoor average temperature of -10°C as an example, the power of the heating equipment of the decentralized electric heating user changes with the outdoor temperature as shown below: Figure 5 shown.

[0290] On the user side, energy storage is primarily used in conjunction with distributed power sources for industrial, commercial, and residential applications, or as a standalone energy storage power station, to meet the user's own electricity needs and capitalize on peak-valley price arbitrage. For ease of analysis, this article assumes that user-side energy storage is used solely for peak-valley price arbitrage. Assume that the load aggregator can control 20 distributed energy storage devices, and that each user-side energy storage device has a maximum storage capacity of 2.5 MWh and a maximum charge and discharge power of 1 MW within the state of charge (SOC) limits (for simplicity, losses during the charge and discharge process are ignored). When not participating in regulation, user-side energy storage devices store electricity at high power during off-peak periods and discharge it during peak periods, achieving "low storage, high generation."

[0291] Based on other load curves on a certain working day, the power consumption of other loads in the area is simulated to obtain the changes in power consumption of other loads over time, such as Figure 6 shown.

[0292] With reference to the on-grid electricity prices of wind power in various regions, the penalty for wind curtailment is taken as 0.54 yuan / kWh; with reference to the peak-shaving price of interruptible load in the peak-shaving ancillary service market, the average compensation unit price for demand-side flexible resources participating in the regulation of the ancillary service market is taken as 0.30 yuan / kWh.

[0293] The peak, valley and normal periods of winter electricity consumption for residents divided by the power grid in this region and the corresponding electricity prices are shown in Table 2.

[0294] Table 2 Peak, flat and valley periods of the power grid and corresponding electricity prices

[0295]

[0296] The improved particle swarm optimization algorithm is used to solve the proposed model. The particle swarm size is set to 100, the number of iterations is set to 300, the cognitive learning factor c1 is set to 1.5, and the social learning factor c2 is set to 2.0.

[0297] After 300 iterations, the optimization results tend to be stable, and the fitness curve is as follows Figure 7 shown.

[0298] In the scenario where thermal power units participate in the regulation auxiliary service market, flexible resources on the demand side are not dispatched. Only conventional peak-shaving methods are considered to change the output of thermal power units to promote the consumption of new energy and reduce the abandonment of new energy due to section blockage. The output of thermal power units and wind turbines on the power generation side is as follows: Figure 8 shown.

[0299] When only thermal power units participate in regulation, they adjust their output to maximize wind power consumption. However, due to unit operating conditions, when wind power output is high and the thermal power units have reached their minimum output, the thermal power units are unable to adjust and are forced to reduce wind power output, resulting in wind curtailment. In this scenario, the mathematical expectation of wind turbine curtailment is 249.46 MWh, the curtailment rate is 18.38%, and the curtailment cost is 124,700 yuan. This shows that the regulation potential of generator-side units is limited. When only generator-side units participate in regulation, the resulting wind curtailment is high, the curtailment rate is high, and wind energy resources are wasted.

[0300] Scenario 2 considers distributed electric heating load, electric vehicle charging load, and user-side energy storage participating in the regulation auxiliary service market, and obtains the time-varying changes of three types of demand-side flexible resources as follows: Figure 9 shown.

[0301] After participating in regulation, all three demand-side flexible resources altered their electricity loads to maximize wind power consumption. Electric vehicles shifted all their charging loads to the nighttime off-peak period, starting at 11:00 PM and stopping before 7:00 AM the following day. Total charging remained unchanged, increasing off-peak charging by 62.67 MWh. Distributed electric heating loads altered their heating power, increasing off-peak power consumption by 1.47 MWh while maintaining user comfort levels. During the other 17 periods, heating power was kept within a relatively small range compared to the unregulated power consumption at the same time, ensuring minimal fluctuations in total distributed electric heating power consumption throughout the regulation period. Compared to the unregulated scenario, distributed energy storage devices under regulation shifted some charging capacity to the later off-peak period, better smoothing wind power fluctuations and facilitating wind power consumption during the latter part of the off-peak period. During the entire regulation period, the three types of demand-side flexible resources provided a total of 118.96 MWh of regulated power.

[0302] The output results of wind turbines and thermal power plants in scenario 2 are as follows: Figure 10 shown.

[0303] from Figure 10 As can be seen, after the three demand-side flexible resources were included in the regulation, thermal power generation decreased during the nighttime off-peak period, while wind turbine generation significantly increased. Compared to the same time period in Scenario 1, thermal power generation decreased by 21.83MWh during the off-peak period, while wind turbine generation increased by 85.93MW. Over the entire dispatch cycle, thermal power generation decreased by 90.78MWh, while wind turbine generation increased by 90.60MWh.

[0304] During the entire regulation process of Scenario 2, the power generation side and the load side must always maintain power balance. The total load and total output of the system change over time in Scenario 2 are as follows: Figure 11 shown.

[0305] In Scenario 2, the mathematical expectation of wind turbine curtailment is 158.86MWh, the curtailment rate is 11.71%, and the curtailment cost is 79,400 yuan. Compared with Scenario 1, where thermal power units participate in regulation, the mathematical expectation of wind curtailment in Scenario 2 is reduced by 90.60MWh, the curtailment rate is reduced by 6.67%, and the curtailment cost is reduced by 45,300 yuan. This shows that the aggregation of demand-side flexible resources in the regulation ancillary service market can further improve the system's ability to accept wind power by rationally scheduling demand-side resource usage, helping to reduce wind power curtailment and increase the proportion of wind power absorbed by the grid.

[0306] In summary, the present invention provides a multi-objective optimization modeling method and related devices for demand-side flexible resource participation, which use a particle swarm optimization neural network algorithm to predict wind power generation, adopt a Latin hypercube sampling method to sample the wind power output prediction deviation, and generate a wind power expected prediction output curve; input the basic parameters of the thermal power unit; simulate the disordered charging scenario of electric vehicles based on the Monte Carlo method; obtain the electric heating load change curve according to the relationship between the distributed electric heating load and the outdoor temperature change; obtain the charging and discharging power curve of the user-side energy storage according to the "low storage and high generation" arbitrage behavior of the user-side energy storage; simulate the curve of the power consumption of other loads changing with time; construct a model objective function: including the new energy consumption objective function and the economic objective function, so as to minimize the new energy power abandonment rate and the regulation auxiliary service cost; design the model boundary conditions: including the power abandonment rate assessment constraint, power balance constraint, the output of new energy and thermal power units, climbing, start and stop time, and the demand-side resource regulation potential; design a conventional thermal power regulation scenario and a demand-side flexible resource aggregation participation regulation scenario; and use an improved particle swarm optimization algorithm to solve the model. Through actual case analysis, the present invention optimizes the regulation capacity of the power grid, effectively reduces the wind curtailment rate and cost, improves the absorption capacity of new energy, and reduces the problem of power curtailment caused by blockage of the transmission section, which is of great significance to promoting the sustainable development of new energy.

[0307] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0308] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0309] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0310] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0311] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0312] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0313] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0314] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0315] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0316] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0317] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A multi-objective optimization modeling method for demand-side flexible resource participation, characterized in that: The following steps are involved: Determine the initial data including wind power generation, thermal power units, disorderly charging of electric vehicles, distributed electric heating and user-side energy storage. The initial data are as follows: The power that a wind turbine can generate is expressed as the sum of the predicted output and the predicted deviation; The neural network algorithm improved by particle swarm is used to predict the output of wind turbines; Standard deviation based on forecast deviation , generated using the Latin hypercube sampling method n The predicted deviation under each scenario and the probability of the predicted deviation under each scenario are combined with the predicted output of the wind turbine to obtain the expected output of the wind turbine. ; Input the capacity, maximum output, minimum output, upward ramp rate, and downward ramp rate of the thermal power unit; Monte Carlo simulation is used to simulate the disorderly charging scenario of electric private cars; Construct a relationship between the power consumption of distributed electric heating load and indoor and outdoor temperatures, input the power, equivalent thermal resistance, and equivalent heat capacity parameters of distributed electric heating equipment, and obtain a curve showing the change of the power of distributed electric heating users' heating equipment with outdoor temperature. When not participating in the ancillary services market, the user-side energy storage device stores electricity at high power during off-peak periods and discharges it during peak periods, resulting in the output curve when the energy storage device is not involved in regulation. Based on the other load curves on a certain working day, simulate the power consumption of other loads in the area to obtain the curve of other load power consumption changing with time; Determine the objective function of the multi-objective optimization model, design the boundary conditions of the multi-objective optimization model based on the objective function and initial data, and construct the multi-objective optimization model. The objective function of the multi-objective optimization model is specifically: Considering the minimum rate of renewable energy curtailment caused by section blockage, the first objective function is constructed as follows: in, express t The actual grid-connected power of the wind turbine at that moment; express t Wind curtailment scenario switch variables at all times; Considering the lowest cost of typical demand-side flexible resource aggregation participating in the ancillary service market, the second objective function is constructed as follows: in, Refers to the case where decentralized electric heating participates in the auxiliary service market t Total power at the moment; Refers to the case where electric vehicle charging load participates in the auxiliary service market t Total power at the moment; Refers to the case where user-side energy storage participates in the ancillary service market t Total power at the moment; Refers to decentralized electric heating without participating in the auxiliary service market t Total power at all times; Refers to the case where electric vehicle charging load does not participate in the auxiliary service market t Total power at all times; Refers to the case where user-side energy storage does not participate in the ancillary service market t Total power at all times; Refers to the duration of each scheduling period; refer to t The average compensation price of auxiliary services is adjusted by flexible resources on the demand side at all times. for t The power that the wind turbine can generate at any moment; Design conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market; Based on the conventional thermal power regulation scenario and the typical demand-side flexible resource regulation scenario, the initial data obtained are used to adopt the improved particle swarm optimization algorithm to solve the multi-objective optimization model, obtain the regulation results of typical demand-side flexible resources and the consumption results of new energy, and realize the electricity consumption time regulation of demand-side flexible resources.

2. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: t The power that the wind turbine can generate at any moment for: in, express t Predicted output of wind turbines at every moment; express t Wind power forecast deviation at each moment.

3. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: Expected output of wind turbines for: in, express t Predicted output of wind turbines at every moment; express t Moment The prediction deviation value in each scenario; Indicates the The probability of a scenario occurring.

4. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: The relationship between the power consumption of distributed electric heating load and indoor and outdoor temperatures is as follows: in, express t Indoor temperature at all times; express t -1 moment indoor temperature; express t Distributed electric heating power at all times; Indicates the area of ​​decentralized electric heating user's room; Indicates the equivalent thermal resistance of electric heating equipment; express t Outdoor temperature at all times; Indicates the equivalent heat capacity of decentralized electric heating user's house; Indicates a time interval.

5. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: The boundary conditions of the multi-objective optimization model include constraints on new energy power curtailment assessment indicators, power balance constraints, wind turbine output constraints, thermal power unit output constraints, thermal power unit climbing constraints, thermal power unit minimum start and stop time constraints, distributed electric heating user indoor temperature constraints, distributed electric heating load power constraints, electric vehicle charging power constraints, electric vehicle charging amount constraints, electric vehicle charging time constraints, user-side energy storage device power constraints, user-side energy storage device capacity constraints, user-side energy storage device storage amount timing constraints and user-side energy storage device storage amount constraints.

6. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 5 is characterized in that: Constraints on assessment indicators for curtailed new energy power: in, It represents the assessment indicator of the new energy power abandonment rate; Power balance constraints: in, express t Moment j Output of thermal power units; express t Moment j The actual grid-connected power of the typhoon turbines; express t Other loads in the power grid at all times; Wind turbine output constraints: in, Indicates the k Typhoon turbines in t The actual grid-connected power output at any moment; Indicates the k Typhoon turbines in t The power that can be generated at any time; Output constraints of thermal power units: in, Indicates the j The minimum output of the thermal power unit; Indicates the j The maximum output of the thermal power units; Thermal power unit ramp constraints: in, Indicates the j Downward ramp rate of thermal power units; Indicates the j The upward climbing rate of the thermal power unit; express t- 1st moment j Output of thermal power units; Minimum start and stop time constraints for thermal power units: in, Indicates the j Continuous operation time of thermal power units; Indicates the j Minimum operating time of thermal power units, min; Indicates the j The outage time of the thermal power units; Indicates the j Minimum downtime of thermal power units; Indoor temperature constraints for decentralized electric heating users: in, Indicates the maximum indoor comfortable temperature; express t Indoor temperature at all times; Indicates the minimum indoor comfortable temperature; Distributed electric heating load power constraints: in, Indicates the i Taipower heating equipment t Operating power at all times; Indicates the i The upper limit of the operating power of Taipower heating equipment; Electric vehicle charging power constraints: in, Indicates the i The minimum charging power allowed for electric vehicles; express t Moment i Charging power of electric vehicles; Indicates the i The maximum charging power allowed for an electric vehicle; Electric vehicle charging capacity constraints: in, Indicates the i The amount of energy an electric car owner expects the battery to hold after charging. Indicates the i The amount of electricity stored in the battery of an electric vehicle when charging is complete; Indicates the i Measure the maximum storage capacity of electric vehicles; Electric vehicle charging time constraints: in, Indicates the i The time when electric vehicles start charging; Indicates the i The time it takes for electric vehicles to arrive at the charging location; Indicates the i The time it takes for an electric vehicle to complete charging; Indicates the i Electric vehicle travel time; Power constraints of user-side energy storage devices: in, express t Moment j Operating power of distributed energy storage devices; express t Moment j Maximum discharge power of each user-side energy storage device; express t Moment j Maximum charging power of each user-side energy storage device; Capacity constraints of user-side energy storage devices: in, express t Moment j The amount of energy stored in the user-side energy storage device; Indicates the j The maximum storage capacity of the user-side energy storage device; Timing constraints of energy storage capacity of user-side energy storage devices: Constraints on the storage capacity of user-side energy storage devices: in, Represents a scheduling cycle; Indicates user-side energy storage t The charging and discharging power at each moment; Indicates the duration of each scheduling period.

7. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: Conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios are as follows: Without considering the participation of demand-side flexible resources in the ancillary services market, all loads will use electricity according to the original plan, and conventional traditional thermal power units will be used for peak load regulation to maximize the consumption of new energy; Considering the aggregation of flexible resources on the demand side to participate in the ancillary service market, with the objective function of minimizing the cost of ancillary services and minimizing the amount of new energy curtailment caused by section blockage, decentralized electric heating, electric vehicle charging load, and user-side energy storage are aggregated and optimized for scheduling.

8. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 7 is characterized in that: Introducing a wind curtailment penalty into the objective function, converting the amount of wind curtailment into wind curtailment cost, and adding the wind curtailment cost to the ancillary service market cost to achieve the transformation from multiple objectives to a single objective, as follows: in, Indicates the unit wind abandonment penalty.

9. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: The improved particle swarm optimization algorithm is used to solve the multi-objective optimization model as follows: S501, input original data; S502, write an objective function according to relevant scenario settings; S503, initialize the particle swarm and randomly generate an initial population that meets the constraints; S504, continuously updating the individual best position and the global best position in the particle swarm, and updating the corresponding fitness value of the objective function; S505: Determine whether the convergence condition is met. If so, stop the operation and output the optimization solution. If not, regenerate a new population and return to step S503 to continue the optimization.

10. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 9 is characterized in that: The original data includes configuration information, operating parameters, basic load data of distributed electric heating equipment, electric vehicles, and user-side energy storage, as well as relevant data of the power generation units in the system.

11. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 9 is characterized in that: Each particle in the particle swarm contains all the decision variables of the demand solution - wind power output, thermal power output, electric vehicle charging power, distributed electric heating load power, and user energy storage charging and discharging power data at each moment within 24 moments.

12. A multi-objective optimization modeling system involving flexible resources on the demand side, characterized in that: include: The data module determines the initial data including wind power generation, thermal power units, disordered charging of electric vehicles, distributed electric heating, and user-side energy storage. The initial data is as follows: The power that a wind turbine can generate is expressed as the sum of the predicted output and the predicted deviation; The neural network algorithm improved by particle swarm is used to predict the output of wind turbines; Standard deviation based on forecast deviation , generated using the Latin hypercube sampling method n The predicted deviation under each scenario and the probability of the predicted deviation under each scenario are combined with the predicted output of the wind turbine to obtain the expected output of the wind turbine. ; Input the capacity, maximum output, minimum output, upward ramp rate, and downward ramp rate of the thermal power unit; Monte Carlo simulation is used to simulate the disorderly charging scenario of electric private cars; Construct a relationship between the power consumption of distributed electric heating load and indoor and outdoor temperatures, input the power, equivalent thermal resistance, and equivalent heat capacity parameters of distributed electric heating equipment, and obtain a curve showing the change of the power of distributed electric heating users' heating equipment with outdoor temperature. When not participating in the ancillary services market, the user-side energy storage device stores electricity at high power during off-peak periods and discharges it during peak periods, resulting in the output curve when the energy storage device is not involved in regulation. Based on the other load curves on a certain working day, simulate the power consumption of other loads in the area to obtain the curve of other load power consumption changing with time; Function module, determines the objective function of the multi-objective optimization model, designs the boundary conditions of the multi-objective optimization model based on the objective function and initial data, and constructs the multi-objective optimization model. The objective function of the multi-objective optimization model is specifically: Considering the minimum rate of renewable energy curtailment caused by section blockage, the first objective function is constructed as follows: in, express t The actual grid-connected power of the wind turbine at that moment; express t Wind curtailment scenario switch variables at all times; Considering the lowest cost of typical demand-side flexible resource aggregation participating in the ancillary service market, the second objective function is constructed as follows: in, Refers to the case where decentralized electric heating participates in the auxiliary service market t Total power at the moment; Refers to the case where electric vehicle charging load participates in the auxiliary service market t Total power at the moment; Refers to the case where user-side energy storage participates in the ancillary service market t Total power at the moment; Refers to decentralized electric heating without participating in the auxiliary service market t Total power at all times; Refers to the case where electric vehicle charging load does not participate in the auxiliary service market t Total power at all times; Refers to the case where user-side energy storage does not participate in the ancillary service market t Total power at all times; Refers to the duration of each scheduling period; refer to t The average compensation price of auxiliary services is adjusted by flexible resources on the demand side at all times. for t The power that the wind turbine can generate at any moment; Scenario module, which designs conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios based on the ancillary service market; The output module, based on conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios, uses the obtained initial data and adopts an improved particle swarm optimization algorithm to solve the multi-objective optimization model, obtains the regulation results of typical demand-side flexible resources and the consumption results of new energy, and realizes the electricity consumption time adjustment of demand-side flexible resources.

13. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 11.

14. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method according to any one of claims 1 to 11.

15. A chip, characterized in that: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 13.

16. An electronic device, characterized in that: Comprising the chip as claimed in claim 15.

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