Market-oriented new energy power supply and distribution system resource modeling method and system

Abstract

The invention relates to a market-oriented new energy power supply and distribution system resource modeling method and system, belongs to the field of new energy power, and provides a market-oriented new energy power supply and distribution system resource modeling method for solving the problem that an existing mode is difficult to meet the requirements of efficient operation and transaction optimization of a new energy power supply and distribution system. Comprising the steps of classifying resources according to resource types contained in the new energy power supply and distribution system based on characteristic parameters of the resources; establishing a mathematical model to which each resource type belongs; respectively establishing an association model to which each resource type belongs, wherein the association model is used for associating offers between the resources and the new energy power supply and distribution system; and based on the mathematical model and the correlation model, constructing a new energy power supply and distribution system scheduling model, realizing cooperative operation between new energy power generation resources and energy storage resources, further realizing optimized storage and release of energy, and realizing accurate modeling and efficient optimization management of various resources in the new energy power supply and distribution system.

Description

Technical Field

[0001] The present invention belongs to the field of new energy power, and specifically relates to a market-oriented new energy power supply and distribution system resource modeling method and system. Background Art

[0002] As an innovative model for integrating distributed energy resources, the new energy power supply and distribution system has emerged. It integrates numerous dispersed distributed energy sources, energy storage equipment and adjustable loads through advanced information technology and intelligent control methods, and realizes flexible response and optimized operation of the power system. In the participation of the new energy power supply and distribution system, accurate resource modeling is the key to achieving efficient operation and optimized transactions.

[0003] However, there are many problems in resource modeling in the new energy power supply and distribution system market. On the one hand, the existing resource modeling methods are often too simple and cannot fully and accurately reflect the characteristics and value of various resources. For example, for the modeling of new energy power generation resources, only the rated power output is considered, while the influence of natural factors such as light intensity and wind speed on its actual output power, as well as important parameters such as equipment conversion efficiency and failure rate are ignored. For energy storage equipment, only its capacity may be concerned, while the constraints of factors such as charging and discharging power, efficiency, and life on its actual operation effect are ignored. For adjustable loads, their elasticity coefficient, response time, reliability and other characteristics are often not fully considered, resulting in the inability to accurately predict and control their response behavior in actual operation. On the other hand, the existing resource modeling lacks in-depth consideration of the synergy and constraint relationship between resources. In the new energy power supply and distribution system, there are complex interactions between different types of resources. For example, energy storage equipment can smooth and backup the output power of new energy power generation resources to improve the stability and reliability of the system; adjustable loads can respond according to the needs of the power grid, adjust the peak and valley difference of the power grid, and realize demand-side management. However, current resource modeling methods often fail to fully consider these synergies, making it impossible to fully utilize the overall performance of the renewable energy power supply and distribution system. At the same time, there are various constraints between resources, such as the capacity limit of energy storage equipment and the response range of adjustable loads. If these constraints are not taken into account, they may cause problems in the operation of renewable energy power supply and distribution systems and affect the safety and stability of the power system.

[0004] In addition, market demand and price signals have an increasingly important impact on the operation and decision-making of renewable energy power supply and distribution systems. However, existing resource modeling methods are still insufficient in combining market demand and price signals, making it difficult to maximize the economic benefits of renewable energy power supply and distribution systems. For example, the output plan of renewable energy power generation resources, the charging and discharging strategy of energy storage equipment, and the response plan of adjustable loads cannot be adjusted in time according to changes in market prices, resulting in the renewable energy power supply and distribution system being in a passive position in market transactions and unable to give full play to its resource advantages and obtain the maximum economic benefits.

[0005] In summary, the current market-oriented resource modeling methods and systems for new energy power supply and distribution systems have many deficiencies and are difficult to meet the needs of efficient operation and optimized transactions of new energy power supply and distribution systems. Therefore, there is an urgent need for a more accurate and comprehensive resource modeling method and system that takes into account resource synergy and market factors to promote the development of new energy power supply and distribution systems, promote the consumption of new energy and the stable operation of power systems. Summary of the invention

[0006] In view of the problem that the existing methods are difficult to meet the needs of efficient operation and optimized trading of renewable energy power supply and distribution systems, a market-oriented resource modeling method and system for renewable energy power supply and distribution systems are provided to achieve accurate modeling and efficient optimized management of various resources in renewable energy power supply and distribution systems.

[0007] The technical solution adopted by the present invention is as follows: A market-oriented new energy power supply and distribution system resource modeling method, comprising the steps of: S1, based on the characteristic parameters of resources, classify the resources according to the resource types contained in the new energy power supply and distribution system; S2, according to the classification of resources, establish mathematical models for each resource type; S3, according to the classification of resources, establish the association model of each resource type, so as to associate the resources with the quotation of the new energy power supply and distribution system; S4, based on the mathematical model constructed in step S2 and the association model constructed in step S3, a new energy power supply and distribution system scheduling model is constructed to achieve coordinated operation between new energy power generation resources and energy storage resources, thereby achieving optimized storage and release of energy.

[0008] Furthermore, the specific process of step S1 is: dividing the resources in the new energy power supply and distribution system into three categories: new energy power generation resources, energy storage resources and load resources.

[0009] Furthermore, in step S1, the new energy power generation resources include photovoltaic power generation and wind power generation; the energy storage resources include battery energy storage; and the adjustable loads include industrial loads, commercial loads, and residential loads. For example, solar power generation needs to consider the impact of factors such as the type of photovoltaic panels, installation angle, and geographical location on power generation efficiency; wind power generation needs to consider the impact of factors such as the model of wind turbines and wind farm layout on power generation.

[0010] Furthermore, the specific process of step S2 is as follows: For renewable energy power generation resources, a dynamic model is established based on the physical characteristics and operating environment of renewable energy power generation resources; For energy storage resources, establish an energy storage model that takes into account charging and discharging efficiency and capacity limitations; For load resources, a load model is established based on user behavior and electricity demand patterns summarized from historical data.

[0011] Furthermore, the new energy power generation resources include solar power generation and wind power generation. The modeling of the new energy power generation resources is as follows: The output of photovoltaic generators is mainly affected by light intensity and temperature. Its operating characteristics are: ; In the formula, express The output power of the photovoltaic generator set at the moment, Indicates the rated output power of the photovoltaic generator set. express The light intensity received by the photovoltaic generator set at the moment, It indicates the light intensity of the photovoltaic generator under standard conditions. express The actual operating temperature of the photovoltaic generator set at the moment, Indicates the rated operating temperature of the photovoltaic generator set. It is the temperature coefficient of the photovoltaic generator set. N-type single crystal photovoltaic modules generally take , P-type monocrystalline and P-type multicrystalline photovoltaic modules generally take .

[0012] Photovoltaic generators are devices that use solar energy to generate electricity. Semiconductors form potential differences and photocurrents through the photovoltaic effect, converting solar energy directly into electrical energy.

[0013] Wind turbines generate electricity by converting the kinetic energy of wind, and their output power is mainly affected by wind speed. Corresponding to different wind speed ranges, wind turbines have four different working characteristics.

[0014] When the wind speed is less than the cut-in wind speed, the wind turbine does not work and the output is 0.

[0015] When the wind speed is greater than the cut-in wind speed and less than the rated wind speed, the output power of the wind turbine increases with the increase of wind speed.

[0016] When the wind speed is greater than the rated wind speed and less than the cut-out wind speed, the output power of the wind turbine generator set is the rated power.

[0017] When the wind speed is greater than the cut-out wind speed, the wind turbine generator set will not work to protect the unit and the output is 0.

[0018] The operating characteristics of a wind turbine are expressed by the following equation.

[0019] ; In the formula, express The output power of the wind turbine at time express The wind speed at the time, , Indicates the cut-in wind speed and cut-out wind speed of the wind turbine. Indicates the rated wind speed of the wind turbine. Indicates the rated power of the wind turbine.

[0020] Since the height at which the weather station measures the wind speed is inconsistent with the height of the wind turbine, the actual wind speed of the wind turbine needs to be converted according to the following formula.

[0021] ; In the formula, Indicates that the wind turbine generator system The wind speed at the moment, Indicates the height of the wind turbine axis relative to the ground. It indicates the height of the location where the weather station measures the wind speed relative to the ground. express The wind speed measured at the weather station at the moment, is the surface roughness.

[0022] Furthermore, the energy storage device is modeled as follows: The power of the battery is usually expressed by the state of charge (SOC). The change process of SOC can be expressed by the following formula.

[0023] ; In the formula, , Respectively indicate the battery The SOC at the moment and the previous moment, , They represent the charging and discharging power of the battery at the previous moment respectively. , Respectively represent the battery charging and discharging efficiency, Indicates the rated capacity of the battery.

[0024] The SOC state needs to meet the following constraints: .

[0025] Furthermore, for load resources, a load model is established based on user behavior and electricity demand patterns summarized from historical data.

[0026] The basic load is determined by statistically analyzing the average load data over a long period of time. The average values ​​are calculated for different time periods, including weekdays and weekends, and represented by a piecewise function.

[0027] ; in The corresponding time on weekdays The average base load, It is the weekend corresponding time The average basic load of , they can all be fitted into specific function expressions using statistical analysis methods based on historical data.

[0028] Assumptions For the main electrical equipment Type of electrical equipment, assuming its power is , the function of the device startup probability changing with time is (This probability function can be obtained by analyzing the proportion of the number of times the device is turned on at different times in the historical data to the total number of times, etc.) Then the impact of this part of the equipment on the load It can be expressed as:

[0029] According to the differences in people's electricity consumption habits at different times of the day, a function related to time is used to characterize the impact of this difference on the load. Trigonometric functions are used to represent peak and valley conditions. Suppose the time difference impact function is :

[0030] in Indicates the fluctuation range (which can be determined by analyzing the difference between peak and trough load in historical data, etc.), Related to the cycle (such as the cycle parameters corresponding to 24 hours a day), is the initial phase (determined according to the starting time of the specific peak electricity consumption, etc.), C is a constant term, which can be understood as the average time period difference benchmark value. Similarly, these parameters are obtained through historical data fitting or statistical analysis.

[0031] Due to the influence of environmental factors such as temperature in different seasons, the electricity demand is also different. Let the seasonal influence function be , can be expressed by a piecewise function combined with the average load differences in different seasons, such as: ; in, , , , They are the influencing factors of the four seasons on the load, spring, summer, autumn and winter.

[0032] The final complete load model It is the expression of adding the base load and the variable load part: ; Furthermore, according to the classification of resources, a quotation model is established as follows: New energy power generation quotation model: ; In the formula, yes The quotation of renewable energy power generation at all times; is the basic cost coefficient (including fixed cost allocation such as equipment depreciation and operation and maintenance); is the cost coefficient associated with the generated power; yes The renewable energy power generation capacity at the moment; is the market volatility adjustment factor; It is the power adjustment caused by the expected change in market price (which can be obtained based on market analysis and forecasting models).

[0033] According to the price signal of the electricity market and the operating status of the system, the output of renewable energy power generation and energy storage equipment is coordinated. When the price of the electricity market is high, the energy storage equipment is called upon to discharge first to increase the economic benefits of the system; when the price of the electricity market is low, renewable energy power generation is used to charge the energy storage equipment to reduce the operating cost. At the same time, the life loss and maintenance cost of the energy storage equipment are considered, and the number and depth of charge and discharge are reasonably arranged to extend the service life of the energy storage equipment.

[0034] Energy storage system discharge quotation model: ; In the formula, yes Energy storage discharge quotation at all times; is the basic cost coefficient of discharge; is the cost coefficient related to the discharge power; yes Discharge power at the moment; is a cost factor related to the state of charge (taking into account the impact of remaining capacity on discharge capacity and value).

[0035] Energy storage system charging quotation model: ; In the formula, yes Energy storage charging quotes at all times, is the basic cost coefficient of charging (including equipment depreciation, operation and maintenance, etc.); is the cost coefficient related to charging power; yes Charging power at the moment; is a cost factor related to the state of charge (taking into account the impact on battery life, etc.).

[0036] Consider the intermittent and volatile nature of renewable energy generation, as well as the charging and discharging characteristics of energy storage equipment, and formulate a charging and discharging strategy for the energy storage system. Coordinate the output of renewable energy generation and energy storage equipment based on power market price signals and system operating conditions. At the same time, consider the life loss and maintenance costs of energy storage equipment, reasonably arrange the number and depth of charging and discharging, and extend the service life of energy storage equipment.

[0037] Residential load demand model (based on cluster analysis and time series): ; In the formula, yes Resident load demand at every moment; It is The residential electricity consumption patterns (obtained through cluster analysis) are The load of time, is its weight coefficient; is the number of categories of residential electricity consumption patterns; is the residential load adjustment factor caused by changes in market prices; It is the impact of expected changes in market prices on load.

[0038] According to the load change trend and the output of renewable energy power generation, power supply plans and load regulation strategies are formulated in advance. By real-time monitoring of the actual data of load and renewable energy power generation, the prediction model is corrected and optimized to improve the matching accuracy.

[0039] Establish a load response incentive mechanism to encourage users to actively adjust their electricity usage behavior based on electricity market price signals and system operation requirements, improve system flexibility and reliability, reduce peak-to-valley differences, and optimize power resource allocation.

[0040] Furthermore, according to market demand and price signals, a scheduling model for the new energy power supply and distribution system is established with the objective function of maximizing the economic benefits of the new energy power supply and distribution system, which is expressed as: ; Indicates that the system is The sum of the power generation income, energy storage discharge income and power sales income to the load at the moment. is the electricity selling price of the virtual power plant, It is the price at which the virtual power plant sells electricity to the load. Indicates that the system is The sum of the power generation cost and energy storage charging and discharging cost at each moment. By accumulating the benefits and costs at each moment and maximizing this accumulated value, the economic benefits of the new energy power supply and distribution system can be maximized within a given time period.

[0041] A market-oriented new energy power supply and distribution system resource modeling system, comprising: A resource classification module is used to classify resources based on characteristic parameters of resources and according to the resource types contained in the new energy power supply and distribution system; The resource modeling module is used to establish mathematical models for each resource type according to the resource classification; The association modeling module is used to establish association models for each resource type according to the resource classification, and is used to associate the resources with the quotations of the new energy power supply and distribution system; The optimization decision module is used to construct a new energy power supply and distribution system scheduling model based on the mathematical model constructed by the resource modeling module and the association model constructed by the association modeling module, so as to enable the coordinated operation of new energy power generation resources and energy storage resources, thereby realizing the optimized storage and release of energy.

[0042] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention can accurately reflect the characteristics and value of various resources in the new energy power supply and distribution system, and provide strong support for the optimized operation and market transactions of the system. Through accurate resource modeling and association modeling, it is possible to grasp the production, storage and consumption of energy in the system in real time, reasonably arrange the scheduling and use of resources, and improve energy utilization efficiency and system operation reliability.

[0043] (2) Considering the synergy and constraint relationship between resources, the overall performance and reliability of the new energy power supply and distribution system are improved. The coordinated operation of new energy power generation and energy storage resources can effectively smooth the fluctuations of energy output and reduce the impact on the power grid; the interactive response of load resources and the system can achieve demand-side management and optimize the allocation of power resources. At the same time, the safe operation constraint model ensures the stable operation of the system under various working conditions and reduces the risk of system failure.

[0044] (3) By establishing a new energy power supply and distribution system dispatch model, the economic benefits of the new energy power supply and distribution system can be maximized in the market environment. According to market demand and price signals, reasonable strategies for power generation, energy storage and load regulation can be formulated to improve the profitability of the system and promote the sustainable development of the new energy industry. At the same time, the optimization decision-making model can adapt to market changes, adjust the operation strategy in a timely manner, provide scientific decision-making basis for system operators, and enhance the system's advantages in market competition. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a typical load curve for a certain area.

[0046] Figure 2 Output curve diagram of the new energy power supply and distribution system before and after optimization. DETAILED DESCRIPTION

[0047] The present invention will be further described and illustrated below in conjunction with the accompanying drawings and specific implementation methods.

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] The present application will use the terms commonly used by those skilled in the art to describe the various aspects of the illustrative embodiments, so that the essence of their work is conveyed to other technicians in the field. However, it is obvious to those skilled in the art that some alternative embodiments can be put into practice using parts of the described aspects. For the purpose of explanation, in order to provide a thorough understanding of the illustrative embodiments, specific numbers, materials and configurations are described. However, it is obvious to those skilled in the art that alternative embodiments can be implemented without specific details. In other cases, in order not to confuse the illustrative embodiments, some well-known features are omitted or simplified.

[0050] In a preferred embodiment of the present invention, a market-oriented new energy power supply and distribution system resource modeling method is provided, which includes the following steps: S1, based on the characteristic parameters of resources, classify resources according to the resource types contained in the new energy power supply and distribution system; divide the resources in the new energy power supply and distribution system into three categories: new energy power generation resources, energy storage resources and load resources; the new energy power generation resources include photovoltaic power generation and wind power generation; the energy storage resources include battery energy storage; the adjustable loads include industrial loads, commercial loads and residential loads. For example, solar power generation needs to consider the impact of factors such as the type of photovoltaic panels, installation angle, and geographical location on power generation efficiency; wind power generation needs to consider the impact of factors such as the model of wind turbines and wind field layout on power generation. S2, according to the classification of resources, establish mathematical models for each resource type; For renewable energy power generation resources, a dynamic model is established based on the physical characteristics and operating environment of renewable energy power generation resources; The output of photovoltaic generators is mainly affected by light intensity and temperature. Its operating characteristics are: ; In the formula, express The output power of the photovoltaic generator set at the moment, Indicates the rated output power of the photovoltaic generator set. express The light intensity received by the photovoltaic generator set at the moment, It indicates the light intensity of the photovoltaic generator under standard conditions. express The actual operating temperature of the photovoltaic generator set at the moment, Indicates the rated operating temperature of the photovoltaic generator set. It is the temperature coefficient of the photovoltaic generator set. N-type single crystal photovoltaic modules generally take , P-type single crystal and P-type multi-crystalline photovoltaic modules generally take .

[0051] Photovoltaic generators are devices that use solar energy to generate electricity. Semiconductors form potential differences and photocurrents through the photovoltaic effect, converting solar energy directly into electrical energy.

[0052] Wind turbines generate electricity by converting the kinetic energy of wind, and their output power is mainly affected by wind speed. Corresponding to different wind speed ranges, wind turbines have four different working characteristics.

[0053] When the wind speed is less than the cut-in wind speed, the wind turbine does not work and the output is 0.

[0054] When the wind speed is greater than the cut-in wind speed and less than the rated wind speed, the output power of the wind turbine increases with the increase of wind speed.

[0055] When the wind speed is greater than the rated wind speed and less than the cut-out wind speed, the output power of the wind turbine generator set is the rated power.

[0056] When the wind speed is greater than the cut-out wind speed, the wind turbine generator set will not work to protect the unit and the output is 0.

[0057] The operating characteristics of a wind turbine are expressed by the following equation.

[0058] ; In the formula, express The output power of the wind turbine at time express The wind speed at the time, , Indicates the cut-in wind speed and cut-out wind speed of the wind turbine. Indicates the rated wind speed of the wind turbine. Indicates the rated power of the wind turbine.

[0059] Since the height at which the weather station measures the wind speed is inconsistent with the height of the wind turbine, the actual wind speed of the wind turbine needs to be converted according to the following formula.

[0060] ; In the formula, Indicates that the wind turbine generator system The wind speed at the moment, Indicates the height of the wind turbine axis relative to the ground. It indicates the height of the location where the weather station measures the wind speed relative to the ground. express The wind speed measured at the weather station at the moment, is the surface roughness.

[0061] For energy storage resources, establish an energy storage model that takes into account charging and discharging efficiency and capacity limitations; There are two common energy storage methods in power grids: physical energy storage and chemical energy storage. Physical energy storage methods mainly include pumped storage and compressed air storage; chemical energy storage methods mainly include battery storage, capacitor storage, hydrogen storage, etc. Battery storage is currently the most widely used energy storage method with the most mature engineering technology. Currently, batteries can be divided into lithium-ion batteries, lead-acid batteries, flow batteries, sodium-sulfur batteries, etc.

[0062] The amount of electricity in a battery is often expressed in terms of the state of charge (SOC). The change in SOC can be expressed by the following formula.

[0063] ; In the formula, , Respectively indicate the battery The SOC at the moment and the previous moment, , They represent the charging and discharging power of the battery at the previous moment respectively. , Respectively represent the battery charging and discharging efficiency, Indicates the rated capacity of the battery; The SOC state needs to meet the following constraints: .

[0064] For load resources, a load model is established based on user behavior and electricity demand patterns summarized from historical data.

[0065] The basic load is determined by statistically analyzing the average load data over a long period of time. The average values ​​are calculated for different time periods, including weekdays and weekends, and represented by a piecewise function.

[0066] ; in The corresponding time on weekdays The average base load, It is the weekend corresponding time The average basic load of , they can all be fitted into specific function expressions using statistical analysis methods based on historical data.

[0067] Assumptions For the main electrical equipment Type of electrical equipment, assuming its power is , the function of the device startup probability changing with time is (This probability function can be obtained by analyzing the proportion of the number of times the device is turned on at different times in the historical data to the total number of times, etc.) Then the impact of this part of the equipment on the load It can be expressed as:

[0068] According to the differences in people's electricity consumption habits at different times of the day, a function related to time is used to characterize the impact of this difference on the load. Trigonometric functions are used to represent peak and valley conditions. Suppose the time difference impact function is :

[0069] in Indicates the fluctuation range (which can be determined by analyzing the difference between peak and trough load in historical data, etc.), Related to the cycle (such as the cycle parameters corresponding to 24 hours a day), is the initial phase (determined according to the specific starting time of the peak power consumption, etc.), It is a constant term, which can be understood as the average time period difference benchmark value. Similarly, these parameters are obtained through historical data fitting or statistical analysis.

[0070] Due to the influence of environmental factors such as temperature in different seasons, the electricity demand is also different. Let the seasonal influence function be , can be expressed by a piecewise function combined with the average load differences in different seasons, such as: ; in, , , , They are the influencing factors of the four seasons on the load, spring, summer, autumn and winter.

[0071] The final complete load model It is the expression of adding the base load and the variable load part: .

[0072] The typical load curve for the whole year at the specific experimental location of the model is shown in Figure 1 .

[0073] S3, according to the classification of resources, establish the association model of each resource type, so as to associate the resources with the quotation of the new energy power supply and distribution system; Considering the intermittent and volatile nature of renewable energy generation, as well as the charging and discharging characteristics of energy storage equipment, formulate a charging and discharging strategy for the energy storage system. When the power generated by renewable energy is greater than the load demand, the excess energy is stored in the energy storage equipment; when the power generated by renewable energy is insufficient, the energy storage equipment releases energy to supplement the power supply. By optimizing the charging and discharging strategy, the energy utilization efficiency is improved, the phenomenon of wind and solar power abandonment is reduced, and the stable operation of the system is guaranteed.

[0074] New energy power generation quotation model (taking photovoltaic power generation as an example):

[0075] In the formula, yes The price of photovoltaic power generation at all times; is the basic cost coefficient (including fixed cost allocation such as equipment depreciation and operation and maintenance); is the cost coefficient associated with the generated power; yes Photovoltaic power generation at the moment; is the market volatility adjustment factor; It is the power adjustment caused by the expected change in market price (which can be obtained based on market analysis and forecasting models).

[0076] According to the price signal of the electricity market and the operating status of the system, the output of renewable energy power generation and energy storage equipment is coordinated. When the price of the electricity market is high, the energy storage equipment is called upon to discharge first to increase the economic benefits of the system; when the price of the electricity market is low, renewable energy power generation is used to charge the energy storage equipment to reduce the operating cost. At the same time, the life loss and maintenance cost of the energy storage equipment are considered, and the number and depth of charge and discharge are reasonably arranged to extend the service life of the energy storage equipment.

[0077] Energy storage system discharge quotation model:

[0078] In the formula, yes Energy storage discharge quotation at all times; is the basic cost coefficient of discharge; is the cost coefficient related to the discharge power; yes Discharge power at the moment; is a cost factor related to the state of charge (taking into account the impact of remaining capacity on discharge capacity and value).

[0079] Energy storage system charging quotation model:

[0080] In the formula, yes Energy storage charging quotes at all times, is the basic cost coefficient of charging (including equipment depreciation, operation and maintenance, etc.); is the cost coefficient related to charging power; yes Charging power at the moment; is a cost factor related to the state of charge (taking into account the impact on battery life, etc.).

[0081] Establish an interactive response model between load resources and new energy power supply and distribution systems to achieve intelligent load regulation and demand-side management.

[0082] Establish a matching model between load demand forecast and renewable energy power generation forecast, and formulate power supply plans and load regulation strategies in advance according to the load change trend and renewable energy power generation output. By real-time monitoring of actual data of load and renewable energy power generation, the forecast model can be corrected and optimized to improve matching accuracy.

[0083] Establish a load response incentive mechanism to encourage users to actively adjust their electricity usage behavior based on electricity market price signals and system operation requirements, improve system flexibility and reliability, reduce peak-to-valley differences, and optimize power resource allocation.

[0084] Residential load demand model (based on cluster analysis and time series): ; In the formula, yes Resident load demand at every moment; It is The residential electricity consumption patterns (obtained through cluster analysis) are The load of time, is its weight coefficient; is the number of categories of residential electricity consumption patterns; is the residential load adjustment factor caused by changes in market prices; It is the impact of expected changes in market prices on load.

[0085] S4, based on the mathematical model constructed in step S2 and the association model constructed in step S3, a new energy power supply and distribution system scheduling model is constructed to enable the coordinated operation of new energy power generation resources and energy storage resources, thereby realizing the optimized storage and release of energy.

[0086] Based on the objective function of maximizing the economic benefits of the new energy power supply and distribution system, a scheduling model for the new energy power supply and distribution system is established, which can be expressed as: ; Indicates that the system is The sum of power generation income, energy storage discharge income and power sales income to loads at the moment; is the electricity selling price of the virtual power plant, It is the price at which the virtual power plant sells electricity to the load. Indicates that the system is The sum of the power generation cost and energy storage charging and discharging cost at the moment.

[0087] Gurobi, CPLEX and other optimization solvers can be used to solve the objective function and obtain the optimal operation strategy of various resources in the renewable energy power supply and distribution system.

[0088] Example 2 A market-oriented resource modeling system for a new energy power supply and distribution system comprises: a resource classification module for classifying resources according to resource types contained in the new energy power supply and distribution system based on characteristic parameters of the resources; The resource modeling module is used to establish mathematical models for each resource type according to the resource classification; The association modeling module is used to establish association models for each resource type according to the resource classification, and is used to associate the resources with the quotations of the new energy power supply and distribution system; The optimization decision module is used to construct a new energy power supply and distribution system scheduling model based on the mathematical model constructed by the resource modeling module and the association model constructed by the association modeling module, so as to enable the coordinated operation of new energy power generation resources and energy storage resources, thereby realizing the optimized storage and release of energy.

[0089] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

[0090] Application Examples The present invention uses Python to write the method described in the present invention, calls the GUROBI mathematical optimizer to solve the model, and demonstrates the implementation effect based on case data.

[0091] Operating environment: 12thGenIntel(R) Core(TM) i5-125003.00GHz, 16GB memory, MicrosoftWindows10X64 GUROBI10.0.2 Implementation results: The simulation experiment was conducted under the IEEE-33 node network. This application example is based on the photovoltaic unit output forecast and the load forecast of non-adjustable resources. The maximum output power of the energy storage is 50kW and the rated capacity of the energy storage is 300kWh. The maximum power purchase power in the retail power market is 10kW.

[0092] A summer working day was selected as the test day. During the day, there was plenty of sunshine and high temperatures. At the same time, the power grid in the area was expected to have a peak in electricity consumption in the evening. Based on the results of resource modeling and association modeling, the new energy power supply and distribution system made the following operation decisions: The solar photovoltaic power generation system generates electricity at full capacity during the day, and the power generation is expected to meet part of the load demand and have a surplus. According to the collaborative operation model, the excess electricity will be stored in the battery energy storage system, which will charge at an appropriate power based on its SOC model and charging and discharging strategy while ensuring battery life and safety.

[0093] The wind power generation system starts generating electricity when the wind speed reaches the start-up conditions based on real-time wind speed monitoring data, and predicts the generated power based on its power model. Since the wind speed is relatively stable during the day, wind power generation can provide a certain stable power output, which can meet the load demand together with photovoltaic power generation.

[0094] In terms of load, through the remote control function of smart meters and user notifications, residents are encouraged to reduce unnecessary electricity consumption during peak hours, such as turning off some electrical equipment or adjusting electricity usage time. At the same time, real-time electricity consumption information and electricity price preferential policy tips are provided to residents to guide them to use electricity reasonably.

[0095] When the peak of power consumption in the power grid comes, the new energy power supply and distribution system will adjust its operation according to the optimization decision results: As the power of solar photovoltaic power generation gradually decreases, the battery energy storage system begins to release electricity according to the pre-established discharge strategy to fill the power supply gap and meet the load demand. At the same time, the discharge power is adjusted according to the real-time price information of the electricity market to obtain the maximum economic benefit.

[0096] The wind power generation system continues to operate, adjusting the power generation in real time according to the wind speed changes. If the wind power generation is insufficient, the system will give priority to the battery energy storage system to ensure the stability of power supply.

[0097] During peak hours, the power consumption is relatively reduced due to the active response of residents and the control of smart meters. At the same time, the system monitors and analyzes residents' power consumption data in real time, reminds and adjusts individual areas with overloaded power, and ensures the safety and stability of residents' power consumption.

[0098] like Figure 2 As shown, the output power of the new energy power supply and distribution system before and after optimization by the modeling method of the present invention is compared. The output power variance of the optimized new energy power supply and distribution system is reduced by 36.7%.

[0099] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A market-oriented new energy power supply and distribution system resource modeling method, characterized in that: Includes steps: S1, based on the characteristic parameters of resources, classify the resources according to the resource types contained in the new energy power supply and distribution system; S2, according to the classification of resources, establish mathematical models for each resource type; S3, according to the classification of resources, establish the association model of each resource type, so as to associate the resources with the quotation of the new energy power supply and distribution system; S4, based on the mathematical model constructed in step S2 and the association model constructed in step S3, a new energy power supply and distribution system scheduling model is constructed to enable the coordinated operation of new energy power generation resources and energy storage resources, thereby achieving optimized storage and release of energy.

2. The market-oriented new energy power supply and distribution system resource modeling method according to claim 1 is characterized in that: The specific process of step S1 is: dividing the resources in the new energy power supply and distribution system into three categories: new energy power generation resources, energy storage resources and load resources. The new energy power generation resources include photovoltaic power generation and wind power generation; the energy storage resources include battery energy storage; the adjustable loads include industrial loads, commercial loads and residential loads.

3. The market-oriented new energy power supply and distribution system resource modeling method according to claim 2 is characterized in that: The specific process of step S2 is as follows: For renewable energy power generation resources, a dynamic model is established based on the physical characteristics and operating environment of renewable energy power generation resources; For energy storage resources, establish an energy storage model that takes into account charging and discharging efficiency and capacity limitations; For load resources, a load model is established based on user behavior and electricity demand patterns summarized from historical data.

4. The market-oriented new energy power supply and distribution system resource modeling method according to claim 3 is characterized in that: The new energy power generation resources include solar power generation and wind power generation, and the established dynamic model includes: Output operation characteristic model of photovoltaic generator set: ； In the formula, express The output power of the photovoltaic generator set at the moment, Indicates the rated output power of the photovoltaic generator set. express The light intensity received by the photovoltaic generator set at the moment, It indicates the light intensity of the photovoltaic generator under standard conditions. express The actual operating temperature of the photovoltaic generator set at the moment, Indicates the rated operating temperature of the photovoltaic generator set. is the temperature coefficient of the photovoltaic generator set; Wind turbine operating characteristic model: When the wind speed is less than the cut-in wind speed, the wind turbine generator set does not work and the output is 0; When the wind speed is greater than the cut-in wind speed and less than the rated wind speed, the output power of the wind turbine increases with the increase of wind speed; When the wind speed is greater than the rated wind speed and less than the cut-out wind speed, the output power of the wind turbine generator set is the rated power; When the wind speed is greater than the cut-out wind speed, the wind turbine generator set will not work to protect the unit and the output is 0.

5. The market-oriented new energy power supply and distribution system resource modeling method according to claim 3 is characterized in that: The energy storage model includes: The battery power is usually expressed by the state of charge SOC: ； In the formula, , Respectively indicate the battery The SOC at the moment and the previous moment, , They represent the charging and discharging power of the battery at the previous moment respectively. , Respectively represent the battery charging and discharging efficiency, Indicates the rated capacity of the battery; The SOC state satisfies the following constraints: 。 6. The market-oriented new energy power supply and distribution system resource modeling method according to claim 3 is characterized in that: The load model is: ； in, is the load model, As the basic load part, The impact of the equipment on the load, is the time period difference impact function; seasonal impact function ; Base load part expression: ； in The corresponding time on weekdays The average base load, It is the weekend corresponding time The average basic load of both can be fitted into specific function expressions using statistical analysis methods based on historical data; Impact of equipment on load Expressed as: ； in, The types of main electrical equipment are: For the j The power of electrical equipment, is the function of the probability of device opening changing over time; The time period difference impact function is: : ； in, Indicates the fluctuation range, is the period parameter, is the initial phase, C is a constant term; Seasonal influence function It is expressed as: ； in, , , , They are the influencing factors of the four seasons on the load, spring, summer, autumn and winter.

7. The market-oriented new energy power supply and distribution system resource modeling method according to claim 1 is characterized in that: The specific process of step S3 includes: New energy power generation quotation model: ； In the formula, yes The quotation of renewable energy power generation at all times; is the basic cost coefficient; is the cost coefficient associated with the generated power; yes The renewable energy power generation capacity at the moment; is the market volatility adjustment factor; is the amount of power adjustment caused by expected changes in market prices; Energy storage system discharge quotation model: ； In the formula, yes Energy storage discharge quotation at all times; is the basic cost coefficient of discharge; is the cost coefficient related to the discharge power; yes Discharge power at the moment; is the cost coefficient related to the state of charge; Energy storage system charging quotation model: ； In the formula, yes Energy storage charging quotes at all times, is the basic cost coefficient of charging; is the cost coefficient related to charging power; yes Charging power at the moment; is the cost coefficient related to the state of charge; Residential load demand model: ； In the formula, yes Resident load demand at every moment; It is Residential electricity consumption patterns The load of time, is its weight coefficient; is the number of categories of residential electricity consumption patterns; is the residential load adjustment factor caused by changes in market prices; It is the impact of expected changes in market prices on load.

8. The market-oriented new energy power supply and distribution system resource modeling method according to claim 7 is characterized in that: Based on the objective function of maximizing the economic benefits of the new energy power supply and distribution system, a scheduling model for the new energy power supply and distribution system is established, which can be expressed as: ； Indicates that the system is The sum of power generation income, energy storage discharge income and power sales income to loads at the moment; is the electricity selling price of the virtual power plant, is the price at which the virtual power plant sells electricity to the load; Indicates that the system is The sum of the power generation cost and energy storage charging and discharging cost at the moment.

9. A market-oriented new energy power supply and distribution system resource modeling system, characterized in that: include: A resource classification module is used to classify resources based on the characteristic parameters of the resources and according to the resource types contained in the new energy power supply and distribution system; The resource modeling module is used to establish mathematical models for each resource type according to the resource classification; The association modeling module is used to establish association models for each resource type according to the resource classification, and is used to associate the resources with the quotations of the new energy power supply and distribution system; The optimization decision module is used to construct a new energy power supply and distribution system scheduling model based on the mathematical model constructed by the resource modeling module and the association model constructed by the association modeling module, so as to enable the coordinated operation of new energy power generation resources and energy storage resources, thereby realizing the optimized storage and release of energy.

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