Energy system base capacity configuration method and device, electronic equipment and storage medium

Abstract

The invention relates to an energy system base capacity configuration method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of energy supply sources, meeting a preset use condition, of a target base, and matching a plurality of energy supply control strategies based on the plurality of energy supply sources; performing energy supply modeling for each energy supply source to obtain an energy supply model of each energy supply source; and constructing a cost operation objective function of the target base by using the energy supply model, constructing a cost calculation model under each energy supply control strategy by combining the energy supply constraint of each energy supply source and the cost operation objective function, and calculating the corresponding energy supply cost by using the cost calculation model and the energy supply demand. And determining a capacity configuration scheme of a plurality of energy supply sources of the target base based on the energy supply cost. Therefore, the technical problem that the multi-energy system cannot achieve the optimal capacity configuration calculation, so that the operation economy of the multi-energy system is poor is solved.

Description

Technical Field

[0001] This application relates to the field of power generation or distribution system technology, and in particular to a method, apparatus, electronic device and storage medium for configuring base capacity of an energy system. Background Technology

[0002] With the transformation of the energy structure and the rapid development of renewable energy, building a new power system is an important measure to promote environmental protection. A new power system is based on the output characteristics and complementarity of multiple energy sources, comprehensively considering load balancing and wind and solar power consumption needs under different scenarios. It achieves goals such as load balancing, wind and solar power consumption, reducing the load on thermal power generation, and lowering the economic cost of combined power generation from multiple energy sources by rationally configuring and coordinating the output processes of various energy sources.

[0003] In related technologies, the calculation of the optimal capacity configuration for multi-energy systems is not achieved, resulting in poor economic efficiency in the operation of multi-energy systems, which needs to be improved. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for configuring the capacity of an energy system base, in order to solve the technical problem in the related art that the capacity configuration calculation for multi-energy systems cannot achieve the optimal result, resulting in poor economic efficiency in the operation of multi-energy systems.

[0005] The first aspect of this application provides a method for configuring the capacity of an energy system base, comprising the following steps: acquiring multiple energy sources that meet preset usage conditions for a target base, and matching multiple energy supply control strategies based on the multiple energy sources; performing energy supply modeling for each energy source to obtain an energy supply model for each energy source; constructing a cost operation objective function for the target base using the energy supply model, and constructing a cost calculation model under each energy supply control strategy by combining the energy supply constraints of each energy source and the cost operation objective function, and calculating the corresponding energy supply cost using the cost calculation model and the demand for electricity of the target base, and determining the capacity configuration scheme of the multiple energy sources for the target base based on the energy supply cost.

[0006] Based on the above technical means, the embodiments of this application can match multiple energy supply control strategies with multiple energy supply sources that meet the preset usage conditions of the target base. The cost operation objective function of the target base is constructed using the energy supply model of each energy supply source. By combining the energy supply constraints and cost operation objective function of each energy supply source, a cost calculation model under each energy supply control strategy is constructed. The corresponding energy supply cost is calculated using the cost calculation model. Based on the energy supply cost, the capacity configuration scheme of multiple energy supply sources of the target base is determined. Through the control strategy of multiple complementary energy sources, the balance between cost and power supply demand is achieved, and the economic efficiency of multi-energy system operation is improved.

[0007] Optionally, in one embodiment of this application, the step of matching multiple energy supply control strategies based on the multiple energy supply sources includes: determining whether there is at least one economical energy supply source among the multiple energy supply sources that meets a preset green energy standard; if there is at least one economical energy supply source, then using the at least one economical energy supply source as the primary energy supply source and matching the multiple energy supply control strategies.

[0008] Based on the above-mentioned technical means, the embodiments of this application can determine the energy supply control strategy according to the economic and environmental protection aspects of energy supply, so as to balance economic efficiency and power supply demand.

[0009] Optionally, in one embodiment of this application, the step of matching multiple power supply control strategies based on the multiple power supply sources includes: obtaining the current environmental parameters of the target base and the predicted environmental parameters within a preset time period; filtering out multiple power supply sources that meet preset availability conditions from the multiple power supply sources of the target base based on the current environmental parameters and the predicted environmental parameters, and matching the multiple power supply control strategies based on the multiple power supply sources to be configured.

[0010] Based on the above technical means, the embodiments of this application can match the power supply control strategy with the local environment and the predicted environment, so as to avoid the problem that the actual strategy does not match the environment and thus affects the actual power supply capacity.

[0011] Optionally, in one embodiment of this application, the step of calculating the corresponding energy supply cost using the cost calculation model and the power demand of the target base includes: inputting the energy supply parameters of the energy source to be configured, the current environmental parameters, and the predicted environmental parameters into the corresponding cost calculation model; and calculating the energy supply cost under each energy supply control strategy, constrained by the multiple energy supply control strategies.

[0012] Based on the above technical means, the embodiments of this application can use the energy supply parameters and environmental information as calculation constraints to calculate the corresponding energy supply cost, so that the energy supply cost is more in line with the actual situation.

[0013] Optionally, in one embodiment of this application, the method further includes: obtaining the actual capacity configuration and actual cost of the target base; calculating the difference between the actual cost and the energy supply cost; and optimizing the current energy supply control strategy using the actual capacity configuration if the difference is greater than a preset deviation threshold.

[0014] Based on the above technical means, the embodiments of this application can optimize capacity configuration according to the difference between actual cost and computing cost, thereby realizing a self-learning optimization process.

[0015] A second aspect of this application provides an energy system base capacity configuration device, comprising: a matching module, configured to acquire multiple energy sources that meet preset usage conditions at a target base, and match multiple energy supply control strategies based on the multiple energy sources; a construction module, configured to perform energy supply modeling for each energy source to obtain an energy supply model for each energy source; and a configuration module, configured to construct a cost operation objective function for the target base using the energy supply model, and to construct a cost calculation model under each energy supply control strategy by combining the energy supply constraints of each energy source and the cost operation objective function, and to calculate the corresponding energy supply cost using the cost calculation model and the demand for electricity of the target base, and to determine the capacity configuration scheme of the multiple energy sources at the target base based on the energy supply cost.

[0016] Optionally, in one embodiment of this application, the matching module includes: a judgment unit, used to judge whether there is at least one economical energy source among the plurality of energy sources that meets the preset green energy standard; and a matching unit, used to match the plurality of energy supply control strategies with the at least one economical energy source as the main energy source when the at least one economical energy source exists.

[0017] Optionally, in one embodiment of this application, the matching module includes: an acquisition unit, configured to acquire the current environmental parameters of the target base and the predicted environmental parameters within a preset time period; and a filtering unit, configured to filter out multiple configurable energy sources that meet preset availability conditions from multiple energy sources in the target base based on the current environmental parameters and the predicted environmental parameters, and to match the multiple energy supply control strategies based on the multiple configurable energy sources.

[0018] Optionally, in one embodiment of this application, the configuration module includes: a first calculation unit, used to input the energy supply parameters of the energy source to be configured, the current environmental parameters, and the predicted environmental parameters into the corresponding cost calculation model; and a second calculation unit, used to calculate the energy supply cost under each energy supply control strategy, constrained by the plurality of energy supply control strategies.

[0019] Optionally, in one embodiment of this application, it further includes: an acquisition module, used to acquire the actual capacity configuration and actual cost of the target base; and a calculation module, used to calculate the difference between the actual cost and the energy supply cost, and, if the difference is greater than a preset deviation threshold, to optimize the current energy supply control strategy using the actual capacity configuration.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy system base capacity configuration method as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the energy system base capacity configuration method as described in the above embodiments.

[0022] A fifth aspect of this application provides a computer program product, including a computer program, which, when executed, is used to implement the above-described energy system base capacity configuration method.

[0023] This application embodiment can match multiple energy supply control strategies with multiple energy sources that meet preset usage conditions at a target site. It constructs a cost operation objective function for the target site using the energy supply model of each energy source. Combining the energy supply constraints and cost operation objective function of each energy source, it constructs a cost calculation model under each energy supply control strategy. The cost calculation model is then used to calculate the corresponding energy supply cost. Based on the energy supply cost, a capacity configuration scheme for the multiple energy sources at the target site is determined. Through a multi-energy complementary control strategy, a balance between cost and power supply demand is achieved, improving the economic efficiency of multi-energy system operation. This solves the technical problem of poor economic efficiency in multi-energy systems due to the inability to achieve optimal capacity configuration calculations.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for configuring the capacity of an energy system base according to an embodiment of this application; Figure 2 This is a schematic diagram of an energy supply configuration according to an embodiment of this application; Figure 3 This is a flowchart of an energy system base capacity configuration method according to an embodiment of this application; Figure 4 This is a schematic diagram of the power generation output of a control strategy 1 according to an embodiment of this application; Figure 5 This is a schematic diagram of the power generation output of control strategy 2 according to an embodiment of this application; Figure 6 This is a schematic diagram of the power generation output of control strategy 3 according to an embodiment of this application; Figure 7This is a schematic diagram of the power generation output of control strategy 4 according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an energy system base capacity configuration device according to an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following description, with reference to the accompanying drawings, outlines an energy system base capacity configuration method, apparatus, electronic device, and storage medium according to embodiments of this application. Addressing the technical problem mentioned in the background art—namely, the inability to achieve optimal capacity configuration calculations for multi-energy systems, resulting in poor economic efficiency—this application provides an energy system base capacity configuration method. In this method, multiple energy supply control strategies are matched based on multiple energy sources meeting preset usage conditions at the target base. A cost operation objective function for the target base is constructed using the energy supply model of each energy source. Combining the energy supply constraints and cost operation objective function of each energy source, a cost calculation model is constructed under each energy supply control strategy. The corresponding energy supply cost is calculated using the cost calculation model. Based on the energy supply cost, a capacity configuration scheme for the multiple energy sources at the target base is determined. Through a multi-energy complementary control strategy, a balance between cost and power supply demand is achieved, improving the economic efficiency of multi-energy system operation. This solves the technical problem of poor economic efficiency caused by the inability to achieve optimal capacity configuration calculations for multi-energy systems.

[0028] Specifically, Figure 1 This is a flowchart illustrating an energy system base capacity configuration method provided in an embodiment of this application.

[0029] like Figure 1 As shown, the energy system base capacity configuration method includes the following steps: In step S101, multiple energy sources that meet the preset usage conditions of the target base are obtained, and multiple energy supply control strategies are matched based on the multiple energy sources.

[0030] In actual implementation, in addition to conventional energy sources such as thermal power generation, the embodiments of this application can determine whether there is an energy source that meets the corresponding requirements among various energy sources based on the environmental information of the target base, i.e. the power generation base to be configured, and the surrounding power supply demand. For example, when the target base is located in an area with strong winds, a wind power base station can be selected as the energy source, and when it is located in an area with abundant water resources, a hydropower base station can be selected as the energy source, etc.

[0031] Depending on the different energy sources, the embodiments of this application can match multiple energy supply control strategies in order to configure the capacity of the energy supply.

[0032] Optionally, in one embodiment of this application, multiple energy supply control strategies are matched based on multiple energy supply sources, including: determining whether there is at least one economical energy supply source among the multiple energy supply sources that meets the preset green energy standard; if there is at least one economical energy supply source, then the at least one economical energy supply source is used as the main energy supply source, and multiple energy supply control strategies are matched.

[0033] As one possible approach, embodiments of this application can determine whether one or more economically viable clean energy sources, such as wind power or photovoltaic power, exist among multiple energy sources that can be configured at the target site.

[0034] If wind power and photovoltaic power are available as energy sources, they can be used as the primary energy sources to improve economic performance and save costs.

[0035] Optionally, in one embodiment of this application, multiple power supply control strategies are matched based on multiple power supply sources, including: obtaining the current environmental parameters of the target base and the predicted environmental parameters within a preset time period; selecting multiple power supply sources that meet preset availability conditions from the multiple power supply sources of the target base based on the current environmental parameters and the predicted environmental parameters, and matching multiple power supply control strategies based on the multiple power supply sources to be configured.

[0036] In some embodiments, current environmental parameters of the target site, such as real-time wind speed, light intensity, temperature, precipitation, and water level, can be collected and combined with the current environmental parameters and the historical environmental parameters of the target site to make environmental predictions, so as to obtain wind speed, light intensity, temperature, rainfall forecasts, expected load changes, etc. for future periods (such as the next 24 hours, 48 ​​hours, a quarter, or a year).

[0037] Furthermore, embodiments of this application can select energy sources that can operate normally, reliably, and on demand under current and predicted conditions from all possible energy sources (wind power, photovoltaic, thermal power, reservoirs, pumped storage, backup diesel generators, etc.).

[0038] For example, based on physical feasibility screening, it is necessary to check whether the wind speed is within the wind turbine start-up and shutdown range, whether the sunlight reaches the photovoltaic start-up threshold, whether the water level of the reservoir is within the safe / usable range, whether the water level of the pumped storage reservoir allows for pumping / power generation, and whether the fuel is sufficient.

[0039] Maintenance / status constraint filtering: whether the equipment is under maintenance or has any fault alarms.

[0040] Environmental / policy constraints are considered, such as whether emission limits are met and whether it is during a period when thermal power generation is prohibited.

[0041] Furthermore, tasks can be dynamically allocated, operating points set, and coordination methods set for multiple available energy sources to meet load demands and optimize specific objectives (such as lowest cost, lowest carbon emissions, highest renewable energy utilization rate, and best system stability).

[0042] The energy supply configuration strategy may include: output allocation, power distribution among various power sources; energy storage scheduling, such as charging or discharging, pumped storage or power generation; operation mode switching, such as whether to start / stop certain units; and economic scheduling, such as how to combine different cost power sources to minimize the total cost under constraints.

[0043] In step S102, energy supply modeling is performed for each energy source to obtain the energy supply model for each energy source.

[0044] Photovoltaic power generation modeling: Photovoltaic power output: , in, Rated power, For time t The actual light intensity at that time k For power temperature coefficient, For time t Operating temperature at that time This refers to the temperature of the photovoltaic cell under standard operating conditions (as specified by the manufacturer).

[0045] Wind power generation modeling: Wind power output: , in, For output power, air density, The area swept by the fan blades. This refers to wind speed.

[0046] Battery charge and discharge modeling: Relationship between battery voltage and current: , , in, For battery power, This represents the total potential of the battery. This represents the total resistance of the battery.

[0047] Modeling of pumped storage power generation: Pumped storage unit power output: , in, For the output power of pumped storage units, For fluid density, For traffic, It is the acceleration due to gravity. For water level, For unit efficiency.

[0048] Thermal power generating unit power generation modeling: , , in, For the steam drum pressure, This refers to the output power of thermal power units. For the steam regulating valve opening, For the fuel regulating valve opening, For the water regulating valve opening, For a certain moment.

[0049] In step S103, the cost operation objective function of the target base is constructed using the energy supply model. By combining the energy supply constraints and cost operation objective function of each energy supply source, a cost calculation model is constructed under each energy supply control strategy. The corresponding energy supply cost is calculated using the cost calculation model and the demand for electricity supply of the target base. Based on the energy supply cost, the capacity configuration scheme of multiple energy supply sources of the target base is determined.

[0050] In actual implementation, the embodiments of this application can combine the energy supply model to construct the cost operation objective function of the target base, so as to combine the energy supply constraints and cost operation objective function of each energy supply source to construct the cost calculation model under each energy supply control strategy. Then, the demand for electricity supply and the energy supply control strategy are used as constraints to calculate the cost under each energy supply control strategy, so as to obtain the energy supply control strategy with the lowest cost for capacity configuration.

[0051] The embodiments of this application can take the minimum annual investment cost as the objective function, which includes: initial investment cost, operating cost and maintenance cost.

[0052] , in, To minimize the annual investment cost of the system, The initial investment cost of the system, For the daily operating costs of the system, For system maintenance costs, This refers to the system's operating lifespan.

[0053] 1) Initial investment cost , in, , , , , These are the unit power input costs for wind turbines, batteries, pumped storage, thermal power units, and photovoltaic units, respectively.

[0054] 2) Daily operating costs , in, , , , , The operating times are respectively for wind turbines, batteries, pumped storage, thermal power units, and photovoltaic units. These are the operating cost coefficients for wind turbines, batteries, pumped storage, thermal power units, and photovoltaic units, respectively.

[0055] 3) Maintenance costs , in, These are the maintenance coefficients for wind turbines, batteries, pumped storage units, thermal power units, and photovoltaic units, respectively.

[0056] Among them, the constraints of thermal power units are: The output constraint of a thermal power unit is shown in the following formula: , in, , These represent the minimum and maximum output of the thermal power unit, respectively.

[0057] Constraints on wind power and photovoltaic units: The output constraints of wind power and photovoltaic units are as follows: , , in, , These represent the maximum output of wind and solar power, respectively.

[0058] Battery constraints: The battery's charge constraint is shown in the following formula: , in, , These represent the minimum and maximum remaining battery capacity, respectively. This represents the actual charge level of the battery.

[0059] Constraints of pumped storage units: The output constraint of a pumped storage unit is shown in the following formula: , in, , These represent the minimum and maximum output of the pumped storage unit, respectively.

[0060] Optionally, in one embodiment of this application, the corresponding energy supply cost is calculated using a cost calculation model and the required power supply of the target base, including: inputting the energy supply parameters of the energy source to be configured, the current environmental parameters, and the predicted environmental parameters into the corresponding cost calculation model; and calculating the energy supply cost under each energy supply control strategy as a constraint.

[0061] In practical applications, the embodiments of this application can input the energy supply parameters to be configured, such as the fan blade area and unit efficiency, the current environmental parameters, such as real-time wind force and real-time light intensity, and the predicted environmental parameters for a certain period of time in the future, into the cost calculation model, so as to use the energy supply control strategy as a constraint and calculate the energy supply cost under each energy supply control strategy.

[0062] Optionally, in one embodiment of this application, the method further includes: obtaining the actual capacity configuration and actual cost of the target base; calculating the difference between the actual cost and the energy supply cost; and optimizing the current energy supply control strategy using the actual capacity configuration if the difference is greater than a preset deviation threshold.

[0063] After executing the corresponding capacity configuration, the embodiments of this application can record the actual cost, and then determine whether the deviation between the actual cost and the calculated energy supply cost is too large. If the deviation is too large, the current energy supply control strategy is optimized to continuously improve the economy of the configuration in actual use.

[0064] Combination Figures 2 to 8 As shown, the working principle of the energy system base capacity configuration method of this application is explained in detail with multiple embodiments.

[0065] like Figure 2As shown, the embodiments of this application can establish a smart energy system base, i.e., a target base, which includes thermal power generation, photovoltaic power generation, wind power generation, batteries and pumped storage. Mathematical models of each part are built, and then the optimal capacity configuration of each generator unit in the target base is obtained by solving the objective function using the cross-particle swarm optimization algorithm.

[0066] like Figure 3 As shown, embodiments of this application may include the following steps: Step S301: Obtain the basic parameters of the wind turbine, photovoltaic power station, battery, pumped storage unit, and thermal power unit. Then, use these basic parameters to construct models for each of the wind turbine, photovoltaic power station, battery, pumped storage unit, and thermal power unit.

[0067] Furthermore, embodiments of this application can be used to construct power supply control strategies.

[0068] The energy supply control strategy primarily regulates the operation of wind power, photovoltaic power, batteries, pumped storage, and thermal power. Based on real-time weather changes and grid load conditions, the output power of each power generation unit is rationally allocated. While wind and photovoltaic power are green energy sources with low generation costs, their inherent instability necessitates supplementary support from other power generation units. This smart energy system's operation control strategy employs different priority settings. Wind power and photovoltaic power are prioritized first, followed by batteries, pumped storage, and thermal power, which are then ordered according to their respective priorities, resulting in different control strategies.

[0069] Control Strategy 1: First, wind power generation and photovoltaic power generation, then pumped storage power generation, then battery power generation, and finally thermal power generation.

[0070] Control Strategy 2: First, wind power generation and photovoltaic power generation, then battery power generation, then pumped storage power generation, and finally thermal power generation.

[0071] Control Strategy 3: First, wind power generation and photovoltaic power generation, then thermal power generation, then pumped storage power generation, and finally battery power generation.

[0072] Control Strategy 4: First, wind power and photovoltaic power generation, then thermal power generation, then battery power generation, and finally pumped storage power generation.

[0073] Step S302: Perform power forecasting to estimate the power generation from wind power, photovoltaic power, and pumped storage, as well as the grid load demand. Utilize the model and environmental forecasts from step S301 to perform power forecasting and obtain the power supply demand.

[0074] Step S303: Construct the cost objective function: The minimum annual input cost is the cost objective function. Based on the constraints, the objective function is solved using an optimization algorithm. In this embodiment, a cross-particle swarm optimization algorithm can be used for the objective function. The cross-search employs a dual search mechanism of horizontal and vertical cross-search, performing different cross-operations in two directions to obtain new particles. Their fitness values ​​are calculated and compared with the previous optimal solution. If the fitness value is higher than the previous optimal solution, the next step is performed; otherwise, the particle is discarded.

[0075] Step S304: Under the condition of meeting the grid load, the optimal capacity configuration of thermal power units, wind power units, photovoltaic units, batteries and pumped storage units can be obtained, so that the target base can achieve the most economical operating state.

[0076] Combination Figures 4-7 The following is a detailed description using one embodiment.

[0077] Taking a large-scale power base as the research object, the typical daily power grid load demand is 50,000 MW. The unit power input costs of wind turbines, batteries, pumped storage, thermal power units, and photovoltaic units are 5,000, 2,500, 1,500, 2,000, and 4,000 yuan, respectively. The operating cost coefficients of wind turbines, batteries, pumped storage, thermal power units, and photovoltaic units are 0.6, 1.7, 0.5, 2, and 0.4, respectively. The maintenance coefficients of wind turbines, batteries, pumped storage units, thermal power units, and photovoltaic units are 1.5, 3.2, 1.2, 3, and 1.1, respectively. The cross-particle swarm optimization algorithm is used to solve the problem.

[0078] Control Strategy 1: Wind power, photovoltaic power > pumped storage power > battery power > thermal power A smart energy system based on wind, solar, thermal, energy storage, and pumped hydro storage operates as follows, meeting the grid load requirements: Figure 4 As shown in Table 1, the power supply type, installed capacity, and annual investment cost are as follows.

[0079] Table 1

[0080] Control Strategy 2: Wind power, photovoltaic power > battery power > pumped storage power > thermal power A smart energy system based on wind, solar, thermal, energy storage, and pumped hydro storage operates as follows, meeting the grid load requirements: Figure 5 As shown in Table 2, the power supply type, installed capacity, and annual investment cost are as follows.

[0081] Table 2

[0082] Control Strategy 3: Wind power, photovoltaic power > thermal power > pumped storage power > battery power A smart energy system based on wind, solar, thermal, energy storage, and pumped hydro storage operates as follows, meeting the grid load requirements: Figure 6 As shown in Table 3, the power supply type, installed capacity, and annual investment cost are as follows.

[0083] Table 3

[0084] Control Strategy 4: Wind power, photovoltaic power > thermal power > battery power > pumped storage power A smart energy system based on wind, solar, thermal, energy storage, and pumped hydro storage operates as follows, meeting the grid load requirements: Figure 7 As shown in Table 4, the power supply type, installed capacity, and annual investment cost are as follows.

[0085] Table 4

[0086] By optimizing under different control strategies and objective functions, the annual investment costs are: Control Strategy 1: 53.79 million yuan; Control Strategy 2: 64.89 million yuan; Control Strategy 3: 67.84 million yuan; and Control Strategy 4: 69.8 million yuan. Among the four control strategies, Control Strategy 1 has the lowest annual investment cost while still meeting the electricity load requirements.

[0087] In summary, the target base based on wind-solar-thermal-storage-pumped hydropower in this application embodiment achieves the effect of multi-energy complementarity. By adopting different operation control strategies and using the cross-particle swarm optimization algorithm to solve the objective function, the optimal installed capacity configuration is obtained, making the target base operation more economical.

[0088] The energy system base capacity configuration method proposed in this application can match multiple energy supply control strategies with multiple energy sources that meet preset usage conditions at the target base. It constructs a cost operation objective function for the target base using the energy supply model of each energy source. Combining the energy supply constraints and cost operation objective function of each energy source, it constructs a cost calculation model under each energy supply control strategy. The cost calculation model is then used to calculate the corresponding energy supply cost. Based on the energy supply cost, a capacity configuration scheme for the multiple energy sources at the target base is determined. Through a multi-energy complementary control strategy, a balance between cost and power supply demand is achieved, improving the economic efficiency of multi-energy system operation. This solves the technical problem of poor economic efficiency in multi-energy system operation due to the inability to achieve optimal capacity configuration calculations.

[0089] Next, the energy system base capacity configuration device according to the embodiments of this application is described with reference to the accompanying drawings.

[0090] Figure 8 This is a block diagram of an energy system base capacity configuration device according to an embodiment of this application.

[0091] like Figure 8 As shown, the energy system base capacity configuration device 10 includes: a matching module 100, a construction module 200, and a configuration module 300.

[0092] Specifically, the matching module 100 is used to acquire multiple energy sources that meet preset usage conditions at the target base, and to match multiple energy supply control strategies based on the multiple energy sources.

[0093] Module 200 is used to model the energy supply for each energy source, resulting in an energy supply model for each energy source.

[0094] The configuration module 300 is used to construct the cost operation objective function of the target base using the energy supply model. It combines the energy supply constraints and cost operation objective function of each energy supply source to construct the cost calculation model under each energy supply control strategy. It uses the cost calculation model and the demand power supply of the target base to calculate the corresponding energy supply cost, and determines the capacity configuration scheme of multiple energy supply sources of the target base based on the energy supply cost.

[0095] Optionally, in one embodiment of this application, the matching module 100 includes a judgment unit and a matching unit.

[0096] The judgment unit is used to determine whether there is at least one economical energy source among the multiple energy sources that meets the preset green energy standard.

[0097] The matching unit is used to match multiple energy supply control strategies, with at least one economical energy source as the primary energy source, when at least one economical energy source is available.

[0098] Optionally, in one embodiment of this application, the matching module 100 includes an acquisition unit and a filtering unit.

[0099] The acquisition unit is used to acquire the current environmental parameters of the target base and the predicted environmental parameters within a preset time period.

[0100] The screening unit is used to select multiple energy sources that meet the preset availability conditions from multiple energy sources at the target site based on the current environmental parameters and predicted environmental parameters, and to match multiple energy supply control strategies based on the multiple energy sources to be configured.

[0101] Optionally, in one embodiment of this application, the configuration module 300 includes: a first computing unit and a second computing unit.

[0102] The first calculation unit is used to input the energy supply parameters of the energy source to be configured, the current environmental parameters, and the predicted environmental parameters into the corresponding cost calculation model.

[0103] The second calculation unit is used to calculate the energy supply cost under each energy supply control strategy, subject to multiple energy supply control strategies.

[0104] Optionally, in one embodiment of this application, the energy system base capacity configuration device 10 further includes an acquisition module and a calculation module.

[0105] The acquisition module is used to obtain the actual capacity configuration and actual cost of the target base.

[0106] The calculation module is used to calculate the difference between the actual cost and the energy supply cost. If the difference is greater than the preset deviation threshold, the current energy supply control strategy is optimized using the actual capacity configuration.

[0107] It should be noted that the foregoing explanation of the embodiment of the energy system base capacity configuration method also applies to the energy system base capacity configuration device of this embodiment, and will not be repeated here.

[0108] The energy system base capacity configuration device proposed in this application can match multiple energy supply control strategies with multiple energy sources that meet preset usage conditions at the target base. It constructs a cost operation objective function for the target base using the energy supply model of each energy source. Combining the energy supply constraints and cost operation objective function of each energy source, it constructs a cost calculation model under each energy supply control strategy. The cost calculation model is then used to calculate the corresponding energy supply cost. Based on the energy supply cost, a capacity configuration scheme for the multiple energy sources at the target base is determined. Through a multi-energy complementary control strategy, a balance between cost and power supply demand is achieved, improving the economic efficiency of multi-energy system operation. This solves the technical problem of poor economic efficiency in multi-energy system operation due to the inability to achieve optimal capacity configuration calculations.

[0109] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0110] When the processor 902 executes the program, it implements the energy system base capacity configuration method provided in the above embodiments.

[0111] Furthermore, electronic devices also include: Communication interface 903 is used for communication between memory 901 and processor 902.

[0112] The memory 901 is used to store computer programs that can run on the processor 902.

[0113] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0114] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0115] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0116] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0117] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described energy system base capacity configuration method.

[0118] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the energy system base capacity configuration method provided in this embodiment of the invention.

[0119] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0122] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0123] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0124] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0126] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for configuring the capacity of an energy system base, characterized in that, Includes the following steps: Acquire multiple energy sources that meet preset usage conditions at the target base, and match multiple energy supply control strategies based on the multiple energy sources; A power supply model is created for each energy source to obtain the power supply model for each energy source. The cost operation objective function of the target base is constructed using the energy supply model. By combining the energy supply constraints of each energy source with the cost operation objective function, a cost calculation model is constructed under each energy supply control strategy. The corresponding energy supply cost is calculated using the cost calculation model and the demand for electricity of the target base. Based on the energy supply cost, the capacity configuration scheme of multiple energy sources of the target base is determined.

2. The method according to claim 1, characterized in that, The matching of multiple energy sources with multiple energy control strategies includes: Determine whether there is at least one economical energy source among the plurality of energy sources that meets the preset green energy standard; If at least one economical energy source exists, then the at least one economical energy source is used as the primary energy source, and the multiple energy supply control strategies are matched accordingly.

3. The method according to claim 1, characterized in that, The matching of multiple energy sources with multiple energy control strategies includes: Obtain the current environmental parameters and the predicted environmental parameters within a preset time period of the target base; Based on the current environmental parameters and the predicted environmental parameters, multiple energy sources that meet the preset availability conditions are selected from the multiple energy sources at the target base, and multiple energy supply control strategies are matched based on the multiple energy sources to be configured.

4. The method according to claim 3, characterized in that, The calculation of the corresponding energy supply cost using the cost calculation model and the power demand of the target base includes: Input the energy supply parameters to be configured, the current environmental parameters, and the predicted environmental parameters into the corresponding cost calculation model; With the multiple energy supply control strategies as constraints, the energy supply cost under each energy supply control strategy is calculated.

5. The method according to claim 1, characterized in that, Also includes: Obtain the actual capacity configuration and actual cost of the target base; Calculate the difference between the actual cost and the energy supply cost. If the difference is greater than a preset deviation threshold, optimize the current energy supply control strategy using the actual capacity configuration.

6. An energy system base capacity configuration device, characterized in that, include: The matching module is used to acquire multiple energy sources that meet preset usage conditions at the target base, and to match multiple energy supply control strategies based on the multiple energy sources. A construction module is used to model the energy supply for each energy source, thereby obtaining the energy supply model for each energy source; The configuration module is used to construct the cost operation objective function of the target base using the energy supply model, and to construct the cost calculation model under each energy supply control strategy by combining the energy supply constraints of each energy supply source and the cost operation objective function. The module is used to calculate the corresponding energy supply cost using the cost calculation model and the demand power supply of the target base, and to determine the capacity configuration scheme of multiple energy supply sources of the target base based on the energy supply cost.

7. The apparatus according to claim 6, characterized in that, The matching module includes: The judgment unit is used to determine whether there is at least one economical energy source among the plurality of energy sources that meets the preset green energy standard; The matching unit is used to match the multiple energy supply control strategies, with the at least one economical energy source as the primary energy source, when the at least one economical energy source is available.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the energy system base capacity configuration method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the energy system base capacity configuration method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the energy system base capacity configuration method as described in any one of claims 1-5.

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