Demand-side flexible resource participation multi-objective optimization modeling method and related device

Abstract

The invention discloses a demand side flexible resource participation multi-objective optimization modeling method and a related device. The method comprises the following steps: determining initial data including wind power generation, thermal power generating units, electric vehicle disordered charging, distributed electric heating and user side energy storage; an objective function of the multi-objective optimization model is determined, boundary conditions of the multi-objective optimization model are designed according to the objective function and the basic data, and the multi-objective optimization model is constructed; designing a conventional thermal power adjustment scene and a typical demand side flexible resource adjustment scene based on the auxiliary service market; based on a conventional thermal power adjustment scene and a typical demand side flexible resource adjustment scene, the obtained initial data are utilized, an improved particle swarm optimization algorithm is adopted to solve a multi-target optimization model, and an optimization solving result is output. According to the method, the regulation capability of the power grid is optimized, the wind curtailment rate and the wind curtailment cost are effectively reduced, the absorption capability of new energy is improved, the problem of power curtailment caused by blockage of a delivery section is reduced, and the method has important significance in promoting sustainable development of the 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 flexible resources on the demand side. 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 access of renewable energy to the power grid has a significant impact on the economic efficiency of power grid operation and the safe and stable operation of the power system.

[0003] First, the problem of absorbing new energy has always been a major problem that has plagued the power system. Due to reasons such as blockage of transmission sections, large-scale abandonment of wind and solar power of new energy occurs from time to time. In order to absorb a high proportion of renewable energy, the power system needs to pay a higher cost, which not only includes the investment in the construction of transmission lines and energy storage facilities, but also includes the cost of adjusting and optimizing the power system during operation.

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

[0005] In addition, with the development of smart grid and Internet of Things technology, more and more flexible loads such as air conditioners, electric heating, electric vehicles, etc. are being used on the demand side. The large-scale disorderly access of these devices not only increases the randomness of electricity consumption, but may also cause the power grid to bear greater pressure during peak hours, affecting the power quality and the safe and reliable operation of the power grid.

[0006] In the face of these challenges, tapping the dispatchable demand-side flexible load resources to participate in the regulation of the auxiliary service market has become an important way to promote the stable, safe and efficient operation of the power grid. Through advanced information technology and control technology, intelligent management and dispatch of these loads can be achieved, thereby better balancing power supply and demand and improving the adaptability and flexibility of the power grid.

[0007] In short, the large-scale access of new energy to the power grid has put forward new requirements and challenges to the power system. Through technological innovation and management optimization, these challenges can be effectively addressed, and the efficient use of new energy and the sustainable development of the power system can be achieved. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a multi-objective optimization modeling method and related devices involving flexible resources on the demand side in response to the deficiencies in the above-mentioned prior art, 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: A multi-objective optimization modeling method for demand-side flexible resource participation includes the following steps: Determine the initial data including wind power generation, thermal power units, disorderly charging of electric vehicles, decentralized 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 the conventional thermal power regulation scenario and the typical demand-side flexible resource regulation scenario, the initial data was used to solve the multi-objective optimization model using an improved particle swarm optimization algorithm to obtain the regulation results of typical demand-side flexible resources and the consumption results of new energy, thereby realizing the electricity consumption time regulation of demand-side flexible resources.

[0010] Preferably, the initial data is specifically: The power that a wind turbine can generate is expressed as the sum of the predicted output and the predicted deviation; The particle swarm-improved neural network algorithm is used to predict the output of wind turbines; Standard deviation based on forecast deviation , generated using the Latin hypercube sampling method n The prediction deviation under each scenario and the probability of prediction deviation under each scenario are combined with the predicted output of the wind turbine to obtain the expected output of the wind turbine. ; Input basic parameters such as thermal power unit capacity, maximum output, minimum output, upward climbing rate, downward climbing rate, etc. Monte Carlo simulation is used to simulate the disorderly charging scenario of private electric vehicles; Construct the relationship between the power consumption of decentralized electric heating load and the indoor and outdoor temperatures, input decentralized electric heating equipment power, equivalent thermal resistance, and equivalent thermal capacity parameters, and obtain the decentralized electric heating user heating equipment power versus outdoor temperature curve; When not participating in the ancillary service market, the user-side energy storage device stores electricity at high power during the off-peak period and discharges it during the peak period, obtaining the output curve when the energy storage device does not participate in regulation; Based on 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.

[0011] Preferably, t The power that the wind turbine can generate at any time for:

[0012] in, express t Predicted output of wind turbines at every moment; express t Wind power forecast deviation at each moment.

[0013] Preferably, the expected output of the wind turbine for:

[0014] in, express t Predicted output of wind turbines at every moment; express t Moment The prediction deviation value in each scenario; Indicates The probability of a scenario occurring.

[0015] Preferably, the relationship between the power consumption of the distributed electric heating load and the indoor and outdoor temperatures is as follows:

[0016] in, express t Indoor temperature at all times; express t -1 Indoor temperature at the moment; 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; It represents the equivalent heat capacity of decentralized electric heating user's house; Indicates a time interval.

[0017] Preferably, the objective function of the multi-objective optimization model is specifically: Considering the minimum rate of new energy abandonment caused by section blocking, the first objective function is constructed as follows:

[0018] in, express t The actual grid-connected power of the wind turbine at the moment, MW; express t The switch variable of wind curtailment scenario at any moment; 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:

[0019] in, Refers to decentralized electric heating participating 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.

[0020] Preferably, the boundary conditions of the multi-objective optimization model include constraints on new energy power abandonment 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.

[0021] Preferably, the assessment indicators for new energy curtailment are as follows:

[0022] in, It represents the assessment index of the power abandonment rate of new energy; Power balance constraints:

[0023] in, express t Moment j Output of thermal power units; express t Moment j Typhoon generator output; express t Other loads in the power grid at all times; Wind turbine output constraints

[0024] in, Indicates k Typhoon turbines in t Always contribute; Indicates k Typhoon turbines in t Always expect to predict output; Output constraints of thermal power units:

[0025] in, Indicates j The minimum output of the thermal power unit; Indicates j The maximum output of the thermal power units; Thermal power unit climbing constraints:

[0026]

[0027] in, Indicates j Downward ramp rate of thermal power units; Indicates 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:

[0028]

[0029] in, Indicates j Continuous operation time of thermal power units; Indicates j Minimum operating time of thermal power units, min; Indicates j The shutdown time of the thermal power units; Indicates j Minimum downtime of thermal power units; Indoor temperature constraints for decentralized electric heating users:

[0030] in, Indicates the maximum indoor comfortable temperature; express tIndoor temperature at all times; Indicates the minimum indoor comfortable temperature; Distributed electric heating load power constraints:

[0031] in, Indicates i Taipower heating equipment t Operating power at all times; Indicates i The upper limit of the operating power of Taipower heating equipment; Electric vehicle charging power constraints:

[0032] in, Indicates i The minimum charging power allowed for an electric vehicle; express t Moment i Charging power of electric vehicles; Indicates i The maximum charging power allowed for an electric vehicle; Electric vehicle charging capacity constraints:

[0033] in, Indicates i The amount of energy that an electric car owner expects the battery to hold after charging; Indicates i The amount of electricity stored in the battery of an electric vehicle when charging is complete; Indicates i Measure the maximum storage capacity of electric vehicles; Electric vehicle charging time constraints:

[0034] in, Indicates i The time when the electric vehicle starts charging; Indicates i The time it takes for an electric vehicle to arrive at the charging location; Indicates i The time when electric vehicles finish charging; Indicates i Electric vehicle travel time; Power constraints of energy storage devices on the user side:

[0035] in, express tMoment j Operating power of distributed energy storage devices; express t Moment j Maximum discharge power of a user-side energy storage device; express t Moment j Maximum charging power of a user-side energy storage device; Capacity constraints of energy storage devices on the user side:

[0036] in, express t Moment j The amount of energy storage capacity of the user-side energy storage device; Indicates j The maximum storage capacity of the user-side energy storage device; Timing constraints of energy storage capacity of user-side energy storage device:

[0037] Constraints on the amount of energy stored in the user-side energy storage device:

[0038] 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.

[0039] Preferably, conventional thermal power regulation scenarios and typical demand-side flexible resource regulation scenarios are specifically: Without considering the participation of demand-side flexible resources in the ancillary service market, all loads use electricity according to the original plan, and conventional traditional thermal power units are used to adjust the peak load to promote the consumption of new energy as much as possible; Considering the aggregation of flexible resources on the demand side to participate in the ancillary service market, with the lowest ancillary service cost and the minimum amount of new energy abandonment caused by section blockage as the objective function, decentralized electric heating, electric vehicle charging load and user-side energy storage are aggregated and optimized for scheduling.

[0040] Preferably, a wind abandonment penalty is introduced into the objective function, the wind abandonment volume is converted into wind abandonment cost, and the wind abandonment cost is added to the peak load auxiliary service cost to realize the transformation from multiple objectives to a single objective, as follows:

[0041] in, Indicates the unit wind abandonment penalty; represents the switch variable of the wind curtailment scenario at time t; Indicates the forecast of the day before 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 it does not participate in peak load regulation t Distributed electric heating load at all times; express t Total power of electric vehicle charging load at all times; Indicates that it does not participate in peak load regulation t The electric vehicle charging load at all times; express t Total power of distributed energy storage load at the moment, positive value indicates charging, negative value indicates selling electricity to the grid; Indicates that it does not participate in peak load regulation t Distributed energy storage load at all times; express t The compensation price that load aggregators pay to users for participating in ancillary services.

[0042] Preferably, the improved particle swarm optimization algorithm is used to solve the model as follows: S501, input original data; S502, writing an objective function according to relevant scenario settings; S503, initializing the particle swarm, and randomly generating an initial population that satisfies the constraint conditions; 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.

[0043] 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 units in the system.

[0044] Preferably, each particle in the particle swarm contains all decision variables required for the 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.

[0045] In a second aspect, an embodiment of the present invention provides a multi-objective optimization modeling system involving demand-side flexible resources, including: 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; Function module, which 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; 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, 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.

[0046] 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 the processor implements the steps of the multi-objective optimization modeling method involving flexible resources on the demand side when executing the computer program.

[0047] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the multi-objective optimization modeling method involving flexible resources on the demand side.

[0048] 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 the processor implements the steps of the multi-objective optimization modeling method involving flexible resources on the demand side when executing the computer program.

[0049] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the multi-objective optimization modeling method involving flexible resources on the demand side.

[0050] Compared with the prior art, the present invention has at least the following beneficial effects: A multi-objective optimization modeling method for the participation of demand-side flexible resources is proposed. Distributed electric heating, electric vehicle charging load, and user-side energy storage are selected as typical representatives of demand-side flexible resources. The dual objectives of minimizing the amount of new energy abandonment caused by section blockage and minimizing the cost of the aggregated demand-side flexible resources participating in the regulation of the auxiliary service market are considered. Combined with multiple constraints such as abandonment rate assessment constraints and unit operation, a multi-objective optimization model for the participation of demand-side flexible resources in the regulation of the auxiliary service market is constructed. The proposed model is solved using an improved particle swarm algorithm to output the regulation results of typical demand-side flexible resources and the consumption results of new energy. The proposed model effectively improves the consumption capacity of new energy and reduces the abandonment cost caused by the obstruction of new energy sections by adjusting the electricity consumption time of demand-side flexible resources.

[0051] Furthermore, the neural network algorithm based on particle swarm improvement is combined with Latin hypercube to improve the accuracy of wind power prediction. At the same time, the power of electric heating load is given by combining the relationship between electric heating equipment parameters and indoor and outdoor temperatures. Monteraro is used to simulate the distribution of electric vehicle disorderly charging load within a day, and considering the user-side energy storage participating in the electric energy market, charging and discharging arbitrage behavior is carried out once a day to give the change of user-side energy storage charging and discharging power over time. The basic parameters are given by the simulation algorithm, which improves the scientific nature of parameter design and lays the foundation for verifying the effectiveness of the model.

[0052] Furthermore, the dual goals of minimizing the curtailment rate of new energy and minimizing the compensation cost of flexible regulation resources participating in the auxiliary service market are proposed. By minimizing the curtailment rate of new energy, improving the utilization rate of new energy such as wind energy and solar energy, and reducing the power resources wasted due to section blockage, the proportion of new energy in the power system can be increased. By guiding flexible regulation resources to participate in the peak-shaving auxiliary service market and providing necessary regulation services, the dependence on traditional power generation units can be reduced and the overall operating costs can be reduced. The comprehensive optimization of system economy and environmental friendliness is achieved.

[0053] Furthermore, by setting the upper and lower limits of the adjustable potential of the flexible regulation resources, it is ensured that while meeting the grid regulation needs, the regulation capacity of the resources themselves will not be exceeded. By setting constraints such as generator output constraints and power balance, the integrity of the model construction is ensured.

[0054] Furthermore, the thermal power regulation scenario is taken as the basic scenario, and the typical demand-side flexible resource regulation scenario is taken as the control scenario. By comparing the optimized scheduling results of the two scenarios, the renewable energy power abandonment situation and scheduling costs under different scenarios are analyzed, and the superiority of flexible resources participating in the regulation market is verified.

[0055] Furthermore, currently, intelligent algorithms are mostly used to solve the problem of energy storage optimization configuration, such as genetic algorithms, whale optimization algorithms, simulated annealing algorithms, particle swarm optimization algorithms, etc. Compared with other algorithms, the particle swarm optimization algorithm has clear and easy-to-understand principles, fewer parameters, simple operation, easy implementation and high efficiency, but it is easy to fall into local optimality. In order to improve the optimization effect and the accuracy of the results, the improved particle swarm optimization algorithm is selected as the solution algorithm for the proposed model. After the improvement, the algorithm not only retains the global search capability of the early stage, but also improves the search speed, while also avoiding the situation of premature convergence and falling into local optimality as much as possible.

[0056] 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.

[0057] In summary, the present invention predicts wind power generation using a neural network algorithm optimized by a particle swarm, samples the wind power output prediction deviation using a Latin hypercube sampling method, and generates an expected predicted wind power output curve; inputs the basic parameters of a thermal power unit; simulates the disordered charging scenario of electric vehicles based on the Monte Carlo method; obtains the electric heating load change curve based on 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 based on 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 over time; constructs a model objective function: including a new energy consumption objective function and an economic objective function to minimize the new energy abandonment rate and the cost of regulating auxiliary services; designs model boundary conditions: including 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 and participation regulation scenarios; and uses 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 abandonment rate and wind abandonment cost, improves the new energy absorption capacity, and reduces the power abandonment problem caused by transmission section blockage, which is of great significance to promoting the sustainable development of new energy.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a Monte Carlo simulation process for the disorderly charging scenario of electric private cars in an embodiment of the present invention; Figure 2 is the wind force prediction output per unit value in 24 time periods in the embodiment of the present invention; Figure 3 is the wind power output and expected output per unit value in different scenarios in the embodiment of the present invention; Figure 4The disorderly charging load of the electric private car in the embodiment of the present invention; Figure 5 The power of the decentralized electric heating equipment varies with the outdoor temperature in the embodiment of the present invention; Figure 6 The power consumption of other loads varies with time in the embodiment of the present invention; Figure 7 is the objective function fitness curve in the embodiment of the present invention Figure 8 The output of thermal power units and wind power units on the power generation side in scenario 1 in an embodiment of the present invention; Fig. 9 The load changes over time of electric vehicles, decentralized electric heating, and user-side energy storage devices in scenario 2 in an embodiment of the present invention; Fig.10 The output of the wind turbine and the thermal power unit changes over time in scenario 2 in the embodiment of the present invention; Fig.11 The total load and total output of the system change over time in scenario 2 in the embodiment of the present invention; Fig.12 A schematic diagram of a computer device provided by an embodiment of the present invention; Fig.13 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of 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.

[0062] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0063] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0064] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the 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.

[0065] 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 determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0066] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0067] The present invention provides a multi-objective optimization modeling method involving flexible resources on the demand side, and the demand side resources are a good adjustable resource. At present, the potential of regulating resources on the power supply side has entered a bottleneck period, and the existing regulating resources are difficult to meet the peak-shaving 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 and time, without the need for special transformation, and almost no transformation and operation costs. 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 consumption in the power grid.

[0068] The present invention provides a multi-objective optimization modeling method for demand-side flexible resource participation, comprising the following steps: S1. Input initial data; The wind power generation in the region is predicted based on the neural network algorithm of particle swarm optimization, and the wind power output prediction curve of 24 time periods is obtained. Then the Latin hypercube sampling method is used to sample the wind power output prediction deviation to generate the wind power prediction output expectation curve. Input the basic parameters of the thermal power unit, including maximum output, minimum output, and ramp rate; 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; 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 curves of the distributed electric heating load changing with the outdoor temperature in 24 periods are obtained; Considering that user-side energy storage generally arbitrages through "low storage and high generation" when not participating in the regulation of the ancillary service market, the charging and discharging power curves of user-side energy storage in 24 time periods are obtained; Based on 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.

[0069] S101, considering that the power that can be generated by the wind turbine generator set is expressed as the sum of the predicted output and the predicted deviation; The calculation formula is as follows:

[0070] 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.

[0071] S102, using a neural network algorithm improved by particle swarm to predict the output of wind turbines; S103, Standard deviation based on prediction deviation , generated using the Latin hypercube sampling method nThe prediction deviation in each scenario and the probability of occurrence of the prediction deviation in each scenario, combined with the predicted output of the wind turbine, can obtain the expected output of the wind turbine. The calculation formula is as follows:

[0072] in, represents the expected output of the wind turbine, MW; express t Moment The prediction deviation value in each scenario, MW; Indicates The probability of a scenario occurring.

[0073] S104. Input basic parameters such as capacity, maximum output, minimum output, upward ramp rate, downward ramp rate, etc. of the thermal power unit; S105, Monte Carlo simulation of disorderly charging of electric private cars, the process is as follows Figure 1 As shown; 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, and the power, equivalent thermal resistance, equivalent thermal capacity and other parameters of the distributed electric heating equipment are input to obtain a curve of the power of the distributed electric heating user's heating equipment changing with the outdoor temperature; The relationship between the power consumption of distributed electric heating load and the indoor and outdoor temperatures is as follows:

[0074] S107. Considering that all user-side energy storage devices can only perform charge-discharge arbitrage once a day, when not participating in the ancillary service market, the user-side energy storage device stores electricity at high power during the off-peak period and discharges during the peak period, achieving "low storage and high generation", and obtaining the output curve when the energy storage device does not participate in regulation; 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.

[0075] S2, construct model objective function; Two objective functions are considered, including the new energy consumption objective function and the economic objective function. Among them, the new energy consumption objective function refers to the minimum new energy abandonment rate caused by section blockage in the region; the economic objective function refers to the lowest cost of typical demand-side flexible resources participating in the ancillary service market.

[0076] S201, considering the minimum rate of new energy abandonment caused by section blocking, construct the first objective function; The first objective function is as follows:

[0077] in, express t The actual grid-connected power of the wind turbine at the moment, MW; express t The switch variable of wind curtailment scenario at any time.

[0078] S202. Considering the lowest cost of typical demand-side flexible resource aggregation participating in the ancillary service market, construct the second objective function.

[0079] The second objective function is as follows:

[0080] in, Refers to decentralized electric heating participating 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, MW, positive value indicates charging, negative value indicates 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 The average compensation price for the participation of flexible resources on the demand side in regulating ancillary services at each moment, RMB / MWh.

[0081] S3. Design model boundary conditions based on objective function and basic data; Various constraints are considered, including constraints on new energy power abandonment 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.

[0082] 1) Constraints on assessment indicators for abandoned new energy power: The new energy curtailment curve caused by section blockage in the region is as follows:

[0083] in, Indicates the abandoned power of renewable energy.

[0084] The calculation formula for the new energy abandonment rate at different times is obtained as shown below:

[0085] use It represents the assessment index of the new energy abandonment rate, then:

[0086] Finally, the constraints of the assessment indicators for new energy power abandonment are as follows:

[0087] in, It represents the assessment indicator of the new energy power abandonment rate.

[0088] 2) Power balance constraints: At any time, the power system must maintain equal supply and demand, that is, power balance, the formula is as follows:

[0089] in, express t Moment j Output of thermal power units, MW; express t Moment j Typhoon generator output, MW; express t Other loads in the grid at time, MW. When demand-side flexible resources participate in the ancillary service market, other loads are assumed to remain unchanged.

[0090] 3) Wind turbine output constraints The wind turbine output level should not exceed its predicted level, and the constraints are as follows:

[0091] in, Indicates k Typhoon turbines in t Always make efforts, MW; Indicates k Typhoon turbines in t Expected output at any time, MW.

[0092] 4) Output constraints of thermal power units Thermal power units have maximum and minimum output constraints during the output process. The constraints are as follows:

[0093] in, Indicates j Minimum output of thermal power units, MW; Indicates j Maximum output of thermal power units, MW.

[0094] 5) Thermal power unit climbing constraints The rate at which thermal power units change the load is limited by their ramping capability, where the load increase constraints are as follows:

[0095] The load reduction constraints are as follows:

[0096] in, Indicates j Downward ramp rate of thermal power units, MW / min; Indicates j The upward climbing rate of the thermal power unit, MW / min; express t- 1st moment j Output of thermal power units, MW.

[0097] 6) Minimum start and stop time constraints for thermal power units During operation, thermal power units should meet their minimum start and stop time constraints, which are as follows:

[0098]

[0099] in, Indicates j Continuous operation time of thermal power units, min; Indicates j Minimum operating time of thermal power units, min; Indicates j The shutdown time of each thermal power unit, min; Indicates j Minimum shutdown time of thermal power units, min.

[0100] 7) Indoor temperature constraints for decentralized electric heating users When regulating the decentralized 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, and the constraints are as follows:

[0101] in, Indicates the maximum indoor comfortable temperature, ℃; express t Indoor temperature at the moment, ℃; Indicates the minimum indoor comfortable temperature, ℃.

[0102] 8) Distributed electric heating load power constraints When regulating the distributed electric heating load, the operating power should not exceed its maximum allowable value. That is, for any electric heating equipment that is regulated, its operating power constraint is as follows:

[0103] in, Indicates i Taipower heating equipment t Operating power at the moment, kW; Indicates i The upper limit of operating power of Taipower heating equipment, kW.

[0104] 9) Electric vehicle charging power constraints When an electric vehicle is charged, 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:

[0105] in, Indicates i The minimum charging power allowed for an electric vehicle, kW; express t Moment i Charging power of electric vehicles, kW; Indicates i The maximum charging power allowed for an electric vehicle, kW.

[0106] 10) Constraints on electric vehicle charging capacity After the electric vehicle has participated in the dispatch, its battery storage capacity should meet the minimum expectations of the owner, with the following constraints:

[0107] in, Indicates i The amount of electricity that the owner of an electric car expects to store in the battery after charging, kWh; Indicates i The amount of electricity stored in the battery of an electric vehicle when charging is complete, kWh; Indicates i Measure the maximum storage capacity of electric vehicles, kWh.

[0108] 11) Electric vehicle charging time constraints 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 goes out next time. The specific constraints are as follows:

[0109] in, Indicates i The time when the electric vehicle starts charging; Indicates i The time it takes for an electric vehicle to arrive at the charging location; Indicates i The time when electric vehicles finish charging; Indicates i Electric car travel time.

[0110] 12) Power constraints of energy storage devices on the user side For the user-side energy storage device, whether charging or discharging, the power cannot exceed its maximum power, and the constraints are as follows:

[0111] 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.

[0112] 13) Capacity constraints of energy storage devices on the user side The amount of energy stored in the user-side energy storage device cannot be greater than its maximum storage capacity, subject to the following constraints:

[0113] in, express t Moment j The amount of energy storage capacity of the user-side energy storage device, MWh; Indicates j The maximum storage capacity of the user-side energy storage device, MWh.

[0114] 14) Timing constraints of energy storage capacity of user-side energy storage devices The user-side energy storage device must first store electricity during the valley period before discharging it during the peak period. The amount of energy stored at any given moment must meet the following conditions, and the constraints are as follows:

[0115] 15) Constraints on the storage capacity of user-side energy storage devices 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: .

[0117] S4, scene design; Based on the ancillary service market, two scenarios are designed, including the conventional thermal power regulation scenario and the typical demand-side flexible resource regulation scenario.

[0118] S401. The participation of demand-side flexible resources in the ancillary service market is not considered. All loads use electricity according to the original plan, and only conventional traditional thermal power units are used to adjust the peak to promote the consumption of new energy and reduce the abandonment of new energy due to section blockage. S402. 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 regulating ancillary services and minimizing the amount of new energy abandoned due to section blockage, the decentralized electric heating, electric vehicle charging load, and user-side energy storage are aggregated and optimized for scheduling. The wind abandonment penalty is introduced here so that the objective function can be summed up, as shown below:

[0119] in, Indicates the unit wind curtailment penalty value, RMB / kWh.

[0120] S5. Optimization solution.

[0121] The improved particle swarm optimization algorithm is used to solve the model. The specific steps are as follows: S501. Input relevant original data, including 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, such as operating parameters of thermal power units and output forecast data of wind power units; S502, write an objective function according to relevant scenario settings; S503, initialize the particle swarm, randomly generate the initial population that meets the constraint conditions, where 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; 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.

[0122] In yet another embodiment of the present invention, a multi-objective optimization modeling system is provided, which can be used to implement the 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.

[0123] 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; Function module, which 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; 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 and output the optimization solution results.

[0124] In another embodiment of the present invention, a terminal device is provided, the terminal device including a processor and a memory, 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., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the multi-objective optimization modeling method for the participation of flexible resources on the demand side, including: Determine the initial data including wind power generation, thermal power units, disordered charging of electric vehicles, decentralized 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.

[0125] See also Fig.12 , the terminal device is a computer device, and 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 on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the multi-objective optimization modeling system in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0126] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Fig.12This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It 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.

[0127] 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. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0128] 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.

[0129] 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 to be output.

[0130] See also Fig.13 , the terminal device is a chip, and 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. In addition, the processor 622 may be configured to execute the computer program to execute the multi-objective optimization modeling method for demand-side flexible resource participation.

[0131] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein 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, for example, 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.

[0132] In another embodiment of the present invention, the present invention also 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 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, and 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-VolatileMemory), such as at least one disk memory.

[0133] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the multi-objective optimization modeling method for demand-side flexible resource participation in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: Determine the initial data including wind power generation, thermal power units, disordered charging of electric vehicles, decentralized 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.

[0134] 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, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually 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 in 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.

[0135] Example Enter initial data.

[0136] 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 of 24 periods are obtained as follows: Figure 2 As shown in the figure, the neural network parameters 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 10000, and the learning rate is 0.01.

[0137] 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 5 random scenarios and the probability of occurrence of each scenario, as shown in Table 1: Table 1 Probability of wind turbine output change scenario set

[0138] The expected output of the wind turbine is obtained according to the following formula:

[0139] The wind power output and expected output per unit value under different scenarios are as follows Figure 3 shown.

[0140] 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.

[0141] Taking a normal working day as an example, electric vehicles can be charged twice a day, during the daytime working hours (8 am to 5 pm) and during the rest time after get off work (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 is that the electric private car does not need to charge load. Figure 4 shown.

[0142] The relationship between the power consumption of distributed electric heating load and the indoor and outdoor temperatures is shown in the following formula:

[0143] 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 indoor comfortable temperature range in winter is 17~25℃. Assume that the user's indoor temperature is kept constant at 20℃ before accepting the load aggregator's regulation. Taking the weather with an average outdoor temperature of -10℃ as an example, the power of the heating equipment of the decentralized electric heating user changes with the outdoor temperature. Figure 5 shown.

[0144] Energy storage on the user side is mainly used in conjunction with distributed power sources such as industrial, commercial, and household power sources, or as an independent energy storage power station to meet the user's own electricity demand and peak-valley price arbitrage. For the sake of analysis, it is assumed here that user-side energy storage is only used for peak-valley price arbitrage. Assume that the load aggregator can regulate 20 distributed energy storage devices, and the maximum storage capacity of each user-side energy storage device within the charge state limit range is 2.5 MWh, and the maximum charging and discharging power is 1 MW (to simplify the calculation, the loss of the charging and discharging process is ignored here). When not involved in regulation, the user-side energy storage device stores electricity at high power during the low-power consumption period and discharges during the peak power consumption period, achieving "low storage and high generation".

[0145] 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.

[0146] With reference to the on-grid electricity prices of wind power in various regions, the penalty for wind abandonment is taken as 0.54 yuan / kWh; with reference to the peak-shaving price of interruptible loads in the peak-shaving ancillary service market, the average compensation unit price for flexible resources on the demand side to participate in the regulation of the ancillary service market is taken as 0.30 yuan / kWh.

[0147] The peak, valley and normal periods of winter electricity consumption for residents divided by the power grid in the region and the corresponding electricity prices are shown in Table 2.

[0148] Table 2 Peak, flat and valley periods of the power grid and corresponding electricity prices

[0149] 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.

[0150] After 300 iterations, the optimization results tend to be stable, and the fitness curve is as follows: Figure 7 shown.

[0151] In the scenario where thermal power units participate in the regulation of the auxiliary service market, no flexible resources on the demand side are dispatched. Only the output of thermal power units is changed through conventional peak-shaving methods to promote the consumption of new energy and reduce the abandonment of new energy caused by section blockage. The output of thermal power units and wind turbines on the power generation side is as follows: Figure 8 shown.

[0152] When only thermal power units participate in regulation, thermal power units try to promote wind power consumption by changing their own output. However, due to the limitations of the unit's operating conditions, when the wind power output is high and the thermal power units have reached the minimum output, the thermal power units can no longer make adjustments and can only reduce the output of the wind power units, resulting in wind power abandonment. In this scenario, the mathematical expectation of wind power abandonment by wind turbines is 249.46MWh, the wind abandonment rate is 18.38%, and the wind abandonment cost is 124,700 yuan. It can be seen that the regulation potential of the units on the power generation side is limited. When only the units on the power generation side participate in regulation, 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.

[0153] 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 changes of three types of demand-side flexible resources over time as follows: Fig. 9 shown.

[0154] After participating in the regulation, the three demand-side flexible resources all changed their own power loads to try their best to promote the consumption of wind power. Electric vehicles will transfer all charging loads to the low-peak period at night, start charging at 11 pm, and stop charging before 7 am the next day. The total charging amount remains unchanged, and the low-peak charging power is increased by 62.67MWh. Under the premise of not exceeding the user's indoor temperature comfort range, the distributed electric heating load changes its own heating power, increasing the low-peak power consumption by 1.47MWh. In the other 17 time periods, the heating power is kept within a small difference range with the power consumption at the same time in the unregulated situation as much as possible, so that the total power consumption of distributed electric heating does not change significantly during the entire regulation cycle. Compared with the unregulated situation, the distributed energy storage device in the regulation situation transfers part of the charging space to the late valley period to better smooth the fluctuation of wind power output and promote the consumption of wind power in the late valley period. During the entire regulation period, the three types of demand-side flexible resources provided a total of 118.96MWh of regulation power.

[0155] The output results of wind turbines and thermal power plants in scenario 2 are as follows: Fig.10 shown.

[0156] from Fig.10 It can be seen that after the three demand-side flexible resources participated in the regulation, the output of thermal power units decreased during the nighttime off-peak period, and the grid-connected power of wind power units increased significantly; compared with the same time period in scenario 1, the thermal power units reduced the grid-connected power by 21.83MWh during the off-peak period, while the wind power units increased the grid-connected power by 85.93MW during the nighttime off-peak period. During the entire dispatch cycle, the thermal power units reduced the grid-connected power by 90.78MWh, and the wind power units increased the power generation by 90.60MWh.

[0157] 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: Fig.11 shown.

[0158] In scenario 2, the mathematical expectation of wind turbine abandonment is 158.86MWh, the abandonment rate is 11.71%, and the abandonment cost is 79,400 yuan; compared with the thermal power units participating in the regulation in scenario 1, the mathematical expectation of abandoned wind power in scenario 2 is reduced by 90.60MWh, the abandonment rate is reduced by 6.67%, and the abandonment cost is reduced by 45,300 yuan. This shows that the aggregation of flexible resources on the demand side to participate in the regulation of the auxiliary service market can further improve the system's acceptance of wind power by reasonably arranging the power consumption time of demand-side resources, which will help reduce wind power abandonment and increase the proportion of wind power consumption in the power grid.

[0159] In summary, the present invention provides a multi-objective optimization modeling method and related devices for the participation of flexible resources on the demand side, which predicts wind power generation using a neural network algorithm optimized by a particle swarm, samples the wind power output prediction deviation using a Latin hypercube sampling method, and generates an expected predicted wind power 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 based on 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 based on 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 to minimize the new energy abandonment rate and the cost of regulating auxiliary services; designs model boundary conditions: including abandonment rate assessment constraints, power balance constraints, new energy and thermal power unit output, climbing, start and stop time, and demand-side resource regulation potential constraints; designs conventional thermal power regulation scenarios and demand-side flexible resource aggregation and participation regulation scenarios; and uses 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 abandonment rate and wind abandonment cost, improves the new energy absorption capacity, and reduces the power abandonment problem caused by transmission section blockage, which is of great significance to promoting the sustainable development of new energy.

[0160] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be 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 in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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, which will not be repeated here.

[0161] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0163] 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0166] 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 processes 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. 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, U disk, mobile hard disk, 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.

[0167] 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 the 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 generate 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.

[0168] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0170] The above contents are 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, decentralized 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 the conventional thermal power regulation scenario and the typical demand-side flexible resource regulation scenario, the initial data was used to solve the multi-objective optimization model using an improved particle swarm optimization algorithm to obtain the regulation results of typical demand-side flexible resources and the consumption results of new energy, thereby realizing 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: 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 particle swarm-improved neural network algorithm is used to predict the output of wind turbines; Standard deviation based on forecast deviation , generated using the Latin hypercube sampling method n The prediction deviation under each scenario and the probability of prediction deviation under each scenario are combined with the predicted output of the wind turbine to obtain the expected output of the wind turbine. ; Input basic parameters such as thermal power unit capacity, maximum output, minimum output, upward climbing rate, downward climbing rate, etc. Monte Carlo simulation is used to simulate the disorderly charging scenario of private electric vehicles; Construct the relationship between the power consumption of decentralized electric heating load and the indoor and outdoor temperatures, input decentralized electric heating equipment power, equivalent thermal resistance, and equivalent thermal capacity parameters, and obtain the decentralized electric heating user heating equipment power versus outdoor temperature curve; When not participating in the ancillary service market, the user-side energy storage device stores electricity at high power during the off-peak period and discharges it during the peak period, obtaining the output curve when the energy storage device does not participate in regulation; Based on 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.

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

4. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 2 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 probability of a scenario occurring.

5. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 2 is characterized in that: The relationship between the power consumption of distributed electric heating load and the indoor and outdoor temperatures is as follows: in, express t Indoor temperature at all times; express t -1 Indoor temperature at the moment; 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; It represents the equivalent heat capacity of decentralized electric heating user's house; Indicates a time interval.

6. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 1 is characterized in that: The objective function of the multi-objective optimization model is as follows: Considering the minimum rate of new energy abandonment caused by section blocking, the first objective function is constructed as follows: in, express t The actual grid-connected power of the wind turbine at the moment, MW; express t The switch variable of wind curtailment scenario at any moment; 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 decentralized electric heating participating 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.

7. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 6 is characterized in that: The boundary conditions of the multi-objective optimization model include constraints on new energy power abandonment 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.

8. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 7 is characterized in that: Constraints on assessment indicators for abandoned electricity of new energy sources: in, It represents the assessment index of the power abandonment rate of new energy; Power balance constraints: in, express t Moment j Output of thermal power units; express t Moment j Typhoon generator output; express t Other loads in the power grid at all times; Wind turbine output constraints in, Indicates k Typhoon turbines in t Always contribute; Indicates k Typhoon turbines in t Always expect to predict output; Output constraints of thermal power units: in, Indicates j The minimum output of the thermal power unit; Indicates j The maximum output of the thermal power units; Thermal power unit climbing constraints: in, Indicates j Downward ramp rate of thermal power units; Indicates 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 j Continuous operation time of thermal power units; Indicates j Minimum operating time of thermal power units, min; Indicates j The shutdown time of the thermal power units; Indicates 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 i Taipower heating equipment t Operating power at all times; Indicates i The upper limit of the operating power of Taipower heating equipment; Electric vehicle charging power constraints: in, Indicates i The minimum charging power allowed for an electric vehicle; express t Moment i Charging power of electric vehicles; Indicates i The maximum charging power allowed for an electric vehicle; Electric vehicle charging capacity constraints: in, Indicates i The amount of energy that an electric car owner expects the battery to hold after charging; Indicates i The amount of electricity stored in the battery of an electric vehicle when charging is complete; Indicates i Measure the maximum storage capacity of electric vehicles; Electric vehicle charging time constraints: in, Indicates i The time when the electric vehicle starts charging; Indicates i The time it takes for an electric vehicle to arrive at the charging location; Indicates i The time when electric vehicles finish charging; Indicates i Electric vehicle travel time; Power constraints of energy storage devices on the user side: in, express t Moment j Operating power of distributed energy storage devices; express t Moment j Maximum discharge power of a user-side energy storage device; express t Moment j Maximum charging power of a user-side energy storage device; Capacity constraints of energy storage devices on the user side: in, express t Moment j The amount of energy storage capacity of the user-side energy storage device; Indicates j The maximum storage capacity of the user-side energy storage device; Timing constraints of energy storage capacity of user-side energy storage device: Constraints on the amount of energy stored in the user-side energy storage device: 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.

9. 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 service market, all loads use electricity according to the original plan, and conventional traditional thermal power units are used to adjust the peak load to promote the consumption of new energy as much as possible; Considering the aggregation of flexible resources on the demand side to participate in the ancillary service market, with the lowest ancillary service cost and the minimum amount of new energy abandonment caused by section blockage as the objective function, decentralized electric heating, electric vehicle charging load and user-side energy storage are aggregated and optimized for scheduling.

10. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 9 is characterized in that: The wind abandonment penalty is introduced into the objective function, the wind abandonment volume is converted into the wind abandonment cost, and the wind abandonment cost is added to the peak load auxiliary service cost to realize the transformation from multiple objectives to a single objective, as follows: in, Indicates the unit wind abandonment penalty; represents the switch variable of the wind curtailment scenario at time t; Indicates the forecast of the day before 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 it does not participate in peak load regulation t Distributed electric heating load at all times; express t Total power of electric vehicle charging load at all times; Indicates that it does not participate in peak load regulation t The electric vehicle charging load at all times; express t Total power of distributed energy storage load at the moment, positive value indicates charging, negative value indicates selling electricity to the grid; Indicates that it does not participate in peak load regulation t Distributed energy storage load at all times; express t The compensation price that load aggregators pay to users for participating in ancillary services.

11. 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 model as follows: S501, input original data; S502, writing an objective function according to relevant scenario settings; S503, initializing the particle swarm, and randomly generating an initial population that satisfies the constraint conditions; 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.

12. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 11 is characterized in that: The original data includes the 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.

13. The multi-objective optimization modeling method for demand-side flexible resource participation according to claim 11 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.

14. 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, disorderly charging of electric vehicles, distributed electric heating and user-side energy storage; Function module, which 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; 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, 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.

15. 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 13.

16. 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 of any one of claims 1 to 13.

17. 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.

18. An electronic device, characterized in that: Comprising the chip as claimed in claim 17.

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