Park new energy analysis method, medium and computer equipment

Abstract

The invention provides an analysis method for park new energy, a medium and computer equipment. The analysis method comprises the steps that a new energy utilization rate model of the park is established, and new energy comprises at least one of wind power generation equipment and photovoltaic power generation equipment; the target function of the new energy utilization rate model is based on the time sequence generation power of a generator in a park in a predetermined time period, the time sequence charging and discharging power of an energy storage system in the park in the predetermined time period, and the rated power of the energy storage system and the generator; an economic cost function of the capacity of the power supply lines of the park and the cost calculation related to the energy storage system and the generator; solving the new energy utilization rate model through an optimization iterative algorithm; obtaining the time sequence actual generating capacity of the new energy in the preset time period from the solved result; calculating the actual total generating capacity of the new energy in the preset time period according to the time sequence actual generating capacity; and determining the initial utilization rate of the new energy according to the ratio of the actual total generating capacity to the available generating capacity of the new energy in the predetermined time period.

Description

Technical Field

[0001] This application relates to the field of new energy, and more specifically, to an analytical method, medium, and computer equipment for new energy in industrial parks. Background Technology

[0002] The current method for calculating the utilization rate of renewable energy in green electricity power supply projects in industrial parks mainly relies on grid connection to achieve absorption. Based on the boundary constraints or conditions of the project, the matching analysis of renewable energy power sources and loads is performed through typical load curves or production operation simulation calculations to calculate the renewable energy utilization rate.

[0003] Compared to conventional projects, green power supply projects in industrial parks are significantly constrained by load and grid boundary conditions. Such projects generally require that renewable energy be consumed within the corresponding load range of the park and cannot be transmitted to the grid.

[0004] The biggest shortcoming of current technologies lies in treating load as a static factor. Once its capacity, peak-to-valley difference, and other characteristics are determined, it is used as a fixed input for renewable energy utilization analysis. Some technologies may consider the uncertainty of load forecasting and include certain confidence intervals, using prediction error probabilities to improve the reliability of utilization analysis. However, most of these solutions are based on deterministic loads, neglecting the dynamic changes in load throughout the renewable energy project's lifecycle, including changes in characteristics and capacity. These changes will have a decisive impact on green power supply projects in industrial parks.

[0005] The lifecycle of new energy projects lasts for many years. In China, wind power projects typically last 20 years, and solar power projects typically last 25 years. While the power generation of a new energy project is largely predictable throughout its lifecycle, it's flawed to view the load it's tied to as a static parameter or to consider it as fluctuating around a deterministic parameter with only a probability of change. Over a 20 or 25-year period, the load situation will obviously change, even significantly, and this change will impact the utilization rate of the new energy project. Summary of the Invention

[0006] One of the purposes of this disclosure is to provide an analytical method that can dynamically calculate the utilization rate of new energy sources.

[0007] According to a first aspect of this disclosure, an analysis method for new energy sources in industrial parks is provided. The analysis method includes: establishing a new energy utilization rate model for the industrial park, wherein the new energy sources include at least one of wind power generation equipment and photovoltaic power generation equipment; the objective function of the new energy utilization rate model is an economic cost function calculated based on the time-series power generation of generators in the park during a predetermined time period, the time-series charging and discharging power of energy storage systems in the park during the predetermined time period, the rated power of energy storage systems and generators, the capacity of power supply lines in the park, and the costs associated with energy storage systems and generators; solving the new energy utilization rate model through an optimization iterative algorithm; obtaining the actual time-series power generation of new energy sources during the predetermined time period from the solution results; calculating the actual total power generation of new energy sources during the predetermined time period based on the actual time-series power generation; and determining the initial utilization rate of new energy sources based on the ratio of the actual total power generation to the power generation capacity of new energy sources during the predetermined time period.

[0008] Optionally, costs associated with energy storage systems and generators may include: the capital cost per additional unit capacity of each generator and energy storage system in the park, the unit capacity cost of the power supply line, the marginal cost of an additional unit power of the generator and energy storage system, the time-sequential start-up cost of the generator during a predetermined time period, and the time-sequential shutdown cost of the generator.

[0009] Optionally, the objective function of the new energy utilization model can be the sum of the following five items: the product of the generator's rated power and the capital cost per additional unit capacity of the generator; the product of the energy storage system's rated power and the capital cost per additional unit capacity of the energy storage system; the product of the power supply line's capacity and the cost per unit capacity of the power supply line; the product of the sum of the generator's time-series power generation during a predetermined time period and the marginal cost of increasing the generator's power per unit and the energy storage system's time-series charging and discharging power during a predetermined time period and the marginal cost of increasing the energy storage system's power per unit and the preset time weight; and the sum of the generator's time-series start-up cost and time-series shutdown cost during a predetermined time period.

[0010] Optionally, the constraints of the new energy utilization model may include generator constraints, energy storage system constraints, new energy constraints themselves, and load constraints related to new energy.

[0011] Optionally, the constraints of the new energy utilization model may also include at least one of the following: grid connection constraints, grid disconnection constraints, and power balance conditions at each bus of the power supply line.

[0012] Optionally, generator constraints may include the generator's time-series power output being greater than or equal to the product of the generator's time-series availability lower limit and the generator's rated power, and less than or equal to the product of the generator's time-series availability upper limit and the generator's rated power; energy storage system constraints may include the energy storage system's time-series charge / discharge power being less than the energy storage system's rated power; new energy self-constraints may include the new energy utilization rate being greater than or equal to a preset lower limit and the new energy penetration rate being greater than or equal to a preset threshold, where the new energy penetration rate represents the total actual electricity generated by new energy within a predetermined time period divided by the total load electricity of the park within the predetermined time period; load constraints related to new energy may include the total electricity consumption of loads related to new energy being less than or equal to a preset electricity consumption; grid connection constraints may include the total grid connection electricity within a predetermined time period being less than or equal to a preset grid connection constraint value; grid disconnection constraints may include the total grid disconnection electricity within a predetermined time period being less than or equal to a preset grid disconnection constraint value; and power balance conditions at each bus of the power supply line may include the time-series load at each bus being equal to the difference between the sum of the time-series power output of the generator on the corresponding bus and the charge / discharge power of the energy storage system on the corresponding bus and the time-series power flow on the corresponding line.

[0013] Optionally, the analysis method may also include: calculating the utilization rate of new energy under different natural peak-valley difference rates, where the natural peak-valley difference rate represents the ratio of the difference between the daily load peak and load valley values ​​of the park to the load peak value.

[0014] Optionally, the analysis method may also include: calculating the utilization rate of new energy sources under different load adjustability, where load adjustability represents the proportion of the park's load that can be dynamically adjusted in terms of electricity consumption.

[0015] Optionally, the analysis method may also include: determining the maximum percentage of load loss allowed at different stages of the entire life cycle of the new energy source based on the initial utilization rate and a predetermined minimum utilization rate.

[0016] Optionally, the analysis method may also include determining the maximum time required for the load to be restored to its initial level at different time periods in order to maintain the minimum utilization rate of the renewable energy.

[0017] According to a second aspect of this disclosure, a computer-readable storage medium is provided that stores a program or instructions that, when executed by a processor, cause the processor to perform the analysis method described above.

[0018] According to a third aspect of this disclosure, a computer device is provided, the computer device including a memory and a processor, the memory storing a program or instructions that, when executed by the processor, cause the processor to perform the above-described analysis method.

[0019] The analysis method according to embodiments of this disclosure can be used to guide the matching of new energy power sources with loads.

[0020] The analysis method according to the embodiments of this disclosure matches the load scale and characteristics based on the expected development scale of new energy sources, thereby assisting in load screening.

[0021] The analysis method according to the embodiments of this disclosure can match the scale of new energy development according to the load conditions, so as to achieve the expected level of new energy utilization. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the analysis method of the first embodiment of this disclosure.

[0023] Figure 2 This is a schematic diagram of a model for a campus power supply system, illustrating an embodiment of the present disclosure.

[0024] Figure 3 This is a flowchart illustrating the analysis method of the second embodiment of this disclosure.

[0025] Figure 4 This is a flowchart illustrating the analysis method of the third embodiment of this disclosure.

[0026] Figure 5 This is a graph showing the variation of renewable energy utilization rate with load peak-valley difference rate in embodiments of this disclosure.

[0027] Figure 6 This is a graph showing the variation of renewable energy utilization rate with load adjustability according to embodiments of this disclosure. Detailed Implementation

[0028] The following detailed description is provided to aid in obtaining a full understanding of the methods, apparatus, and / or systems described herein. However, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein; equivalent substitutions or changes may be made, except for operations that must occur or be performed in a specific order. Furthermore, for clarity and conciseness, descriptions of content well-known in the art will be omitted or simplified.

[0029] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0030] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals described in previous embodiments that reappear in later embodiments may be omitted. Furthermore, technical features described in different or the same embodiments can be combined in any way, as long as the combined embodiment or technical solution is complete and can solve the technical problems of this application or achieve the technical effects described or not described in this disclosure but which can be determined based on the complete technical solution described above. The terminology used in this disclosure is briefly described below.

[0031] New energy utilization rate: The ratio of actual new energy power generation to the available new energy power generation during a predetermined period (e.g., the whole year).

[0032] New energy penetration rate: The total actual electricity generated by new energy sources during a predetermined period (e.g., the whole year) divided by the total annual load.

[0033] Natural peak-to-valley ratio: The ratio of peak-to-valley difference to the highest load within a statistical time interval.

[0034] Load adjustability: The load can dynamically adjust the proportion of electricity consumption based on external signals (such as grid requirements, changes in new energy power generation, etc.).

[0035] Green electricity: Using specific power generation equipment such as wind turbines and solar photovoltaic cells, renewable energy sources such as wind and solar energy are converted into electrical energy. The electricity generated in this way produces little or no harmful emissions (such as nitric oxide, nitrogen dioxide, carbon dioxide, sulfur dioxide, etc.) during the power generation process and does not require the consumption of fossil fuels.

[0036] Green electricity industrial park: refers to an industrial park that uses a certain proportion of green or new energy electricity as an advantage to promote the circular transformation and low-carbon transformation of the park's production.

[0037] This disclosure addresses green power supply systems in industrial parks that are deeply integrated with load, taking into account the impact of load changes throughout the entire lifecycle, and employing a dynamic calculation method to calculate the utilization rate of new energy projects throughout their entire lifecycle. Preferred embodiments of this disclosure are described below with reference to the accompanying drawings.

[0038] Figure 1 This is a flowchart illustrating the analysis method of the first embodiment of this disclosure. Figure 2 This is a schematic diagram of a model for a campus power supply system, illustrating an embodiment of the present disclosure.

[0039] Reference Figure 1 The analysis method according to the first embodiment of the present disclosure may include steps S110, S120, S130, S140 and S150.

[0040] In step S110, a model for the utilization rate of new energy sources in the park is established.

[0041] The park's power supply system can be as follows Figure 2 As shown, the first generator g1,1 and the first energy storage system h 1,1 The first load d1 is connected to bus 1, which can be connected to other devices (e.g., transformers) via transmission line (or power supply line) f1, and can be connected to bus 2 via transmission line f2. Bus 2 can be connected to other devices (e.g., transformers) via transmission line f3. The second generator g... 2,1 Second energy storage system h 2,1 The second load d2 is connected to bus 2. Bus 1 and bus 2 are electrically connected via transmission line f2. Bus 1 and bus 2 can be connected to other networks via transmission lines f1 and f3, thereby increasing system stability and enhancing the ability to respond to emergencies. For example, if one bus fails, the other bus can continue to supply power.

[0042] Bus 1 and Bus 2 are connected via transmission line f2, forming a small interconnected power grid. However, this is merely an example, and the number of generators, energy storage systems, loads, buses, transmission lines, etc., is not specifically limited. As an example, new energy sources or new energy equipment may include at least one of wind power generation equipment and photovoltaic power generation equipment. The generators here may include wind turbines, photovoltaic generators, thermal power generators (e.g., gas turbine generators), etc.

[0043] The aforementioned renewable energy utilization rate model can be a mathematical model used to quantify the utilization rate of renewable energy sources (such as solar and wind power) within green energy parks. This model can encompass multiple elements, including source, grid, load, and storage. After establishing the renewable energy utilization rate model, this disclosure allows for time-series-based production and operation simulations. Iterative optimization methods are employed to optimize the output allocation of generator units during economic dispatch, thereby reducing power generation costs. Based on the generator output allocation, dynamic calculations of the renewable energy utilization rate can be performed.

[0044] As an example, the objective function of a renewable energy utilization model can be a function that minimizes economic costs, which may include the capital costs of relevant components of the power supply system and the operating costs of the generators. However, this disclosure is not limited to this and other cost factors may also be considered.

[0045] As an example, the objective function of a renewable energy utilization model can be an economic cost function calculated based on the time-series power generation of generators in the park over a predetermined period (e.g., half a year, a year) (e.g., power generation per hour of each day), the time-series charging and discharging power of the energy storage system in the park over the predetermined period, the rated power of the energy storage system and generators, the capacity of the park's power supply lines, and the costs associated with the energy storage system and generators. Here, the time-series related to power can refer to the actual power generation of the relevant components per hour, per day, or per month within the predetermined period (e.g., a year), depending on the design purpose of the renewable energy utilization model.

[0046] As an example, costs associated with energy storage systems and generators include: the capital cost of each additional unit capacity in the generators and energy storage systems in the park, the unit capacity cost of the power supply lines, the marginal cost of each additional unit power in the generators and energy storage systems, the time-sequential start-up cost and time-sequential shutdown cost of the generators during predetermined time periods, and so on.

[0047] The capital cost per unit increase in generator capacity refers to the investment cost required to add one unit of capacity beyond the generator's rated power. This cost typically includes the cost of purchasing new equipment, installation costs, commissioning costs, and other related infrastructure construction costs. For example, if an existing wind turbine has a rated power of 100kW, the one-time investment required to expand it to 150kW is part of this capital cost. The capital cost per unit increase in energy storage system capacity is similar to that of generator capacity and will not be elaborated further here.

[0048] The unit capacity cost of a transmission or power supply line can refer to the construction and maintenance costs required to increase the capacity of a power supply line by one unit (usually a kilowatt or megawatt).

[0049] The marginal cost of generators and energy storage systems can refer to the cost incurred for each additional unit of power output or storage capacity beyond the existing rated power of the generator or energy storage capacity.

[0050] The time-series start-up cost and time-series shutdown cost of a generator within a predetermined time period refer to the one-time expenses required for each generator to switch from a shutdown state to an operating state (start-up cost) or from an operating state to a shutdown state (shutdown cost) within a predetermined time period. These costs are typically related to the type and technical characteristics of the generator and play a crucial role in power system planning and operation. The generators mentioned here can include the various generators described above.

[0051] The objective function of a renewable energy utilization model can be the sum of the following five items: the product of the generator's rated power and the capital cost per additional unit capacity; the product of the energy storage system's rated power and the capital cost per additional unit capacity; the product of the power line's capacity and the cost per unit capacity; the product of the generator's time-series power generation over a predetermined time period and the marginal cost per additional unit power, and the energy storage system's time-series charging / discharging power over a predetermined time period and the marginal cost per additional unit power, multiplied by a preset time weight; and the sum of the generator's time-series start-up cost and time-series shutdown cost over a predetermined time period. However, this is merely an example; the objective function of a renewable energy utilization model can also consider other cost factors.

[0052] As an example, the objective function f of the new energy utilization rate model can be...

[0053]

[0054] In equation (1), n∈N={0,...|N|-1} represents the number of buses, which, as mentioned above, can be 2; t∈T={0,...|T|-1} represents the time series, which, if taken as a whole year, can be 24×365=8760; l∈L={0,...|L|-1} represents the sequence number of the transmission line, where L is the total number of transmission lines; s∈S={0,...|S|-1} represents the different types of generators or energy storage systems marked on each bus; c n，s This represents the capital cost of increasing the rated power of a generator or energy storage unit by 1MW. This represents the rated power of the energy storage system s on bus n; c represents the rated power of generator s on bus n; l This represents the unit capacity cost of a transmission line; F l ω represents the capacity of the transmission line l; t The weight representing time t, for example, measured in hours, ω t =1, measured in 15 minutes, ω t =1 / 4; o n，s This represents the marginal cost of adding 1MW to the engine or energy storage system; g n，s，t h represents the generating power of engine s on scheduling bus n at time t; n，s，t This represents the charging and discharging power of the energy storage system s on the time-t dispatch bus n; suc n，s，t This represents the start-up cost of the combined generator at time t; sdc n，s，t This represents the shutdown cost of the combined generator when it is shut down at time t. The above method of dividing generators, energy storage systems, and loads according to the bus is merely an example, and this disclosure is not limited thereto.

[0055] The constraints of the renewable energy utilization model can include generator constraints, energy storage system constraints, renewable energy constraints themselves, and load constraints related to renewable energy. The load constraints related to renewable energy can be associated with the time-series power output of the generator.

[0056] Generator constraints may include the generator’s time-series power output being greater than or equal to the product of the generator’s time-series availability lower limit and the generator’s rated power, and less than or equal to the product of the generator’s time-series availability upper limit and the generator’s rated power.

[0057] Specifically, each generator has a scheduling variable used to identify the bus and a specific generator on the bus, which follows these constraints:

[0058]

[0059] In equation (2), This represents the upper limit of generator availability per unit of rated power on bus n at time t; This represents the lower limit of generator availability per unit of rated power on bus n at time t; This represents the rated power of generator s on bus n.

[0060] Alternatively, a time-series binary state variable u can be introduced. n，s，t ∈{0,1}, which indicates whether the generator / line is operating (1) or not operating (0) during the time period t. The limitation of the generator / line output can be expressed as:

[0061]

[0062] The meanings of the variables in equation (3) are as described above, and will not be repeated here.

[0063] As an example, constraints on energy storage systems may include that the time-series charge and discharge power of the energy storage system is less than the rated power of the energy storage system.

[0064] Specifically, the constraints of the energy storage system can be shown in equations (4) and (5) below.

[0065]

[0066] And charging state constraints:

[0067]

[0068] In equation (5), r n，s The nominal power hours of an energy storage system at full charge; SOC n，s，t This indicates the amount of energy stored.

[0069] The constraints on new energy sources may include the utilization rate of new energy sources being greater than or equal to a preset lower limit and the penetration rate of new energy sources being greater than or equal to a preset threshold. The penetration rate of new energy sources can be represented by dividing the total actual electricity generated by new energy sources within a predetermined time period by the total load electricity of the park within the predetermined time period.

[0070] Load constraints related to new energy sources may include: the total electricity consumption of loads related to new energy sources is less than or equal to a preset electricity consumption. These load constraints are closely related to the rated power of the generator or the time-series power generation of the generator, and can also be reflected through power flow constraints.

[0071] As an example, the constraints of a renewable energy utilization model may also include at least one of the following: grid connection constraints, grid disconnection constraints, and power balance conditions at each bus of the power supply line. Grid connection constraints refer to the maximum and minimum limits allowed for electricity to flow from generating equipment into the grid in the power system. These constraints are designed to ensure the stability and security of the power system, while also taking into account the grid's carrying capacity.

[0072] Internet access constraints may include a total electricity consumption within a predetermined time period that is less than or equal to a preset internet access constraint value. As an example, the analysis method of this disclosure is preferably applicable to green energy parks that cannot access the internet, i.e., the preset internet access constraint value is zero. Internet disconnection constraints may include a total electricity consumption within a predetermined time period that is less than or equal to a preset disconnection constraint value, which may be greater than or equal to zero.

[0073] In addition, network access and disconnection constraints can also be imposed based on the sum of the electricity consumption for both online and offline transactions. For example, total network access and disconnection constraints: the sum of the electricity consumption for online and offline transactions throughout the year is less than or equal to the total network access and disconnection constraints set by the user; network access and disconnection difference constraints: the difference between the electricity consumption for online and offline transactions throughout the year is less than or equal to the network access and disconnection difference constraints set by the user.

[0074] In addition, the power balance condition at each bus of the power supply line includes the difference between the time-series load on each bus and the sum of the time-series generating power of the generator on the corresponding bus and the charging and discharging power of the energy storage system on the corresponding bus, and the time-series power flow on the corresponding line.

[0075] For a power flow controllable link, each component has an optimization variable that satisfies equation (6).

[0076] |f l,t |≤F l (6)

[0077] In equation (6), f l，t This represents the power flow in transmission line l at time t.

[0078] Furthermore, power balance is guaranteed at each bus n at each time t, that is, equation (7) is satisfied.

[0079]

[0080] In equation (7), d n，s，t This represents the load s on bus n at time t. The total load on all buses can be determined based on load size, time-series load, etc.

[0081] The meanings of the variables in equations (1) to (7) can be referenced from each other, and the same variables have the same meaning.

[0082] The constraints of green power supply projects in industrial parks are relatively complex. Key considerations include whether the power grid only handles fluctuations in the natural peak-to-valley range of the load, and whether supplying power to the park's loads should not increase the grid's peak-shaving pressure. Are there minimum requirements for the utilization and penetration rates of renewable energy sources? Are there minimum requirements for the total electricity consumption of the load? Are there typical load curves? What are the characteristics of these load curves? Can renewable energy sources only supply power to newly added loads? As an example, the renewable energy sources disclosed in this paper can supply power only to newly added loads, and are strongly tied to this requirement.

[0083] In step S120, the renewable energy utilization model is solved using an optimization iterative algorithm. That is, under all constraints, the optimal solution is found through the optimization iterative algorithm, so that the objective function (e.g., minimizing economic cost) reaches its optimum.

[0084] Here, optimization iterative algorithms are a process of repeatedly improving a solution through iterative steps, with the aim of finding the optimal solution or a near-optimal solution. These algorithms can handle complex nonlinear problems and are suitable for finding various configurations that optimize the objective function. The types of optimization iterative algorithms are not specifically limited; in addition to linear programming, they can also utilize genetic algorithms, particle swarm optimization, nonlinear programming, simulated annealing, and other algorithms.

[0085] In step S130, the actual power generation of new energy sources during the predetermined time period is obtained from the solution results.

[0086] For example, the solution results may include the time-series power generation of generators and the time-series charge and discharge power of energy storage systems within a preset time period (e.g., a whole year). The time-series power generation of generators can be obtained from the above results. The time-series power generation of generators here may include the time-series power generation of new energy generators, or it may include the time-series power generation of various thermal power generators, etc.

[0087] In other words, after solving the problem using an optimized iterative algorithm, the actual power generation of new energy sources within a predetermined time period can be extracted from the results (the power generation can be calculated by combining the corresponding time series). Here, the predetermined time period can be days, months, years, etc.

[0088] In step S140, the actual total power generation of new energy sources over a predetermined time period is calculated based on the actual power generation over time. As an example, all the actual power generation over time calculated based on the power generation over time in step S130 can be added together to obtain the total power generation of new energy sources over the predetermined time period. This accumulation process helps to comprehensively understand the performance of new energy sources throughout the entire time period.

[0089] In step S150, the initial utilization rate of new energy is determined based on the ratio of the actual total power generation to the power generation of new energy within a predetermined time period.

[0090] The potential power generation of a new energy source over a predetermined period (e.g., the whole year) (i.e., the theoretical maximum power generation that the new energy source can produce under given conditions) can be predicted based on historical data or estimated based on collected basic data. For example, the annual potential power generation of a new energy source can be determined based on collected meteorological data (sunlight, wind speed) and equipment performance parameters, using historical data analysis trends or physical models for prediction. However, this is just an example, and the methods for determining the annual potential power generation of a new energy source are not limited to this. As an example, the utilization rate of a new energy source can include at least one of wind power utilization rate, photovoltaic utilization rate, and new energy utilization rate (including the utilization rates of multiple new energy sources such as wind power and photovoltaics). In calculation, the corresponding utilization rate can be obtained by using the ratio of the actual power generation to the potential power generation.

[0091] Therefore, the initial utilization rate of new energy sources can be calculated by dividing the actual total power generation within a predetermined time period by the available power generation within that time period. This initial utilization rate can then be used to assess the effectiveness and efficiency of new energy facilities.

[0092] Figure 3 This is a flowchart illustrating the analysis method of the second embodiment of this disclosure. Figure 4 This is a flowchart illustrating the analysis method of the third embodiment of this disclosure. Figure 5 This is a graph showing the variation of renewable energy utilization rate with load peak-valley difference rate according to embodiments of this disclosure. Figure 6 This is a graph showing the variation of renewable energy utilization rate with load adjustability according to embodiments of this disclosure.

[0093] Reference Figure 3In addition to steps S110, S120, S130, S140, and S150, the analysis method according to the embodiments of this disclosure may also include step S160: calculating the new energy utilization rate under different natural peak-valley difference rates. As an example, the natural peak-valley difference rate here can represent the ratio of the difference between the daily load peak value and the load valley value of the park to the load peak value.

[0094] It should be noted that green power supply projects in industrial parks often require that the peak-to-valley difference rate of electricity received from the public power grid not exceed the natural peak-to-valley difference rate of the newly added load. This requirement directly limits the utilization rate of renewable energy. The larger the natural peak-to-valley difference rate, the greater the range of load fluctuations, which is conducive to the absorption of renewable energy.

[0095] For example, depending on industry characteristics, commercial and service industries typically consume large amounts of electricity during daytime business hours and significantly less at night, resulting in a higher natural peak-to-valley difference rate, ranging from 30% to 50%. Industrial manufacturing users have diverse electricity consumption patterns. Some factories with continuous production (such as chemical and steel plants) may operate 24 hours a day, resulting in a relatively smaller natural peak-to-valley difference rate, between 10% and 20%. However, for enterprises with shift work or specific production cycles, the natural peak-to-valley difference rate may be even higher, reaching 20% ​​to 40%. Public utilities such as schools and hospitals have relatively stable electricity consumption, resulting in a relatively low natural peak-to-valley difference rate, generally not exceeding 20%.

[0096] The following describes a specific example of this disclosure. Due to the unique load characteristics of the green power supply project in the park, a new energy utilization rate model can be established based on the modeling method described above, with reference to the following boundaries.

[0097] (1) The green power supply project in the industrial park is based on the energy demand of the new load in the same industrial park, and the corresponding scale of new energy is allocated to market-oriented new energy projects. All the electricity generated by the new energy is consumed by the new load in the industrial park.

[0098] (2) The total annual electricity consumption of the newly added load shall not be less than 500 million kWh.

[0099] (3) In principle, green power supply projects in the park should be equipped with energy storage devices of no less than 15% (4 hours) of new energy scale.

[0100] (4) Ensure that the peak-valley difference rate of the electricity received from the public power grid is not higher than the natural peak-valley difference rate of the newly added load.

[0101] In addition, wind power can be selected as the analysis object for new energy sources, with wind power resources selected as 100MW and full-load operating hours of 3300h. Energy storage configuration is 15MW*4h.

[0102] The load is selected according to the following boundaries:

[0103] (5) The natural peak-valley difference rate of the load is 22.5%, the adjustability is 50%, and the equivalent utilization hours are not less than 7500h.

[0104] (6) Power generation is not permitted to be transmitted to the grid. Referring to the grid boundary, the peak-to-valley difference rate of power generation to the grid shall not exceed 22.5%.

[0105] The time-series power generation of wind power is obtained through iterative optimization, and the utilization rate of new energy (i.e., the initial utilization rate of wind power) is calculated, as shown in the table below.

[0106] Table 1. Utilization rate of renewable energy under different load scales

[0107] Load scale New energy utilization rate 98MW / 735 million kWh 80.00％ 118MW / 885 million kWh 85.54％ 144MW / 1.08 billion kWh 91.16％

[0108] Under the same boundary conditions, with the same scale of wind power, the utilization rate of new energy will increase as the load scale expands. To achieve a 90% utilization rate of new energy, with a natural peak-to-valley load difference of 22.5%, the load scale and new energy planning need to be roughly 1.5:1.

[0109] For details, please refer to Figure 5 Taking a load of 144MW / 1.08 billion kWh as an example, this paper analyzes the impact of the natural peak-valley difference rate of the load on the wind power utilization rate. Figure 5 In the graph, the vertical axis represents the utilization rate of new energy sources. It can be seen that as the peak-valley load difference increases, the wind power utilization rate significantly improves. Therefore, it is recommended to select loads with large natural peak-valley differences whenever possible when searching for loads.

[0110] Reference Figure 3 The analysis method according to the embodiments of this disclosure may further include step S170: calculating the utilization rate of new energy under different load adjustability, where load adjustability represents the proportion of the park's load that can be dynamically adjusted in terms of electricity consumption.

[0111] Based on the above analysis, when the adjustability ranges from 0% to 70%, the change in wind power utilization rate can be calculated when the adjustability changes (the peak-valley difference rate remains unchanged at 22.5%).

[0112] Taking a load of 144MW / 1.08 billion kWh as an example, this paper analyzes the impact of load adjustability changes on wind power utilization. Figure 6 The vertical axis represents wind power utilization, and the horizontal axis represents load adjustability. Figure 6 It can be seen that as load adjustability increases, utilization rate increases significantly. However, when adjustability increases to a certain level (40%), wind power utilization rate no longer changes. At this point, peak-valley difference rate becomes the main constraint, and the amount of electricity supplied to the grid no longer changes, resulting in wind power utilization rate no longer increasing.

[0113] Reference Figure 4 The analytical method according to this disclosure may further include step S180: determining the maximum allowable percentage of load loss at different times throughout the entire life cycle of the new energy source based on the initial utilization rate and a predetermined minimum utilization rate.

[0114] Taking a 20-year wind power cycle as an example, this paper analyzes the maximum allowable load loss ratio in different years to ensure the minimum wind power utilization rate (66.61%) of the project under different initial loads in the above case.

[0115] Table 1 Maximum Allowable Load Reduction Ratio for Different Years

[0116]

[0117] As an example, the current total load and wind power installed capacity can be determined in advance, and then the maximum allowable load reduction percentage per year can be calculated while ensuring a minimum utilization rate. The table above shows the maximum allowable load reduction percentage (which will not be restored) per year (including before the project is put into operation) under the premise of ensuring a wind power utilization rate of 66.61% (the bottom line).

[0118] Before the project is put into operation, the minimum utilization rate of wind power is consistent at 66.61%. However, due to the different load capacities, the load loss ratio will vary.

[0119] For a load of 98MW / 735 million kWh, in the worst-case scenario, the project is allowed to reduce all load from the 15th year onwards, while still ensuring a minimum utilization rate of 66.61% for wind power. Similarly, when the load is 118MW / 885 million kWh, the project is allowed to reduce all load from the 14th year onwards. When the load is 144MW / 1.08 billion kWh, the project is allowed to reduce all load from the 12th year onwards.

[0120] This method allows for the assessment of load changes and potential lifespan, enabling the calculation of whether a project can guarantee a minimum utilization rate without subsequent load replenishment.

[0121] Reference Figure 4 The analysis method according to the embodiments of this disclosure may further include step S190, determining the maximum time required for the load to be restored to the initial level in order to maintain the minimum utilization rate of new energy sources in different time periods.

[0122] We analyze load reduction scenarios of 20%, 40%, and 60% under various initial load conditions, and determine the measures required to ensure the minimum wind power utilization rate (66.61%) when load reduction occurs in each year of the project's lifecycle. The analysis focuses on the scenario with an initial load of 98MW / 735 million kWh as an example.

[0123] Table 2 shows the load situation with an initial load of 98MW / 735 million kWh and a 20% load reduction.

[0124]

[0125] Table 3 shows the load situation with an initial load of 98MW / 735 million kWh and a 40% load reduction.

[0126]

[0127] Table 4 shows the load situation with an initial load of 98MW / 735 million kWh and a 60% load reduction.

[0128]

[0129] Referring to Tables 3 to 5, the first column shows the load reduction at the end of each year (0 indicates load reduction even without operation). In the table data, the horizontal rows correspond to the wind power utilization rate for each year, with the horizontal arrow indicating that the wind power utilization rate is consistent with the previous data. The last row indicates no load reduction, meaning the utilization rate remains the same as the initial year.

[0130] When the initial load is 98MW / 735 million kWh, the initial wind power utilization rate is 80%. The wind power utilization rates corresponding to load reductions of 20%, 40%, and 60% are 73.50%, 65.30%, and 52.30%, respectively, and the corresponding loads are 78.4MW, 58.8MW, and 39.2MW.

[0131] Taking Table 5 as an example, the arrow in the first column (data 5) indicates that from the start of the project to the fifth year, the load remains constant, and the annual wind power utilization rate is 80%. From the sixth year onwards, a 60% load reduction occurs, and the wind power utilization rate drops to 52.30%. To ensure the project's minimum utilization rate, this situation can only continue until the end of the 16th year at most. From the 17th year onwards, the load must be restored to the initial level, i.e., the wind power utilization rate becomes 80%, until the project ends. It can be seen that even if a load reduction occurs from the 8th year onwards, and the load cannot be restored in subsequent years, the overall minimum utilization rate can still be achieved. This situation is more common in the tables showing load reductions of 20% and 40%.

[0132] Based on the above analysis, we can provide a reference timeframe for load recovery when load loss occurs during project operation, in order to ensure the minimum utilization rate of wind power.

[0133] The control method according to the exemplary embodiments of this disclosure can rely entirely on the operation of computer programs or instructions to achieve the corresponding functions. That is, each device corresponds to each step in the functional architecture of the computer program, so that the entire system is called through a special software package (e.g., a lib library) to achieve the corresponding functions.

[0134] This disclosure provides a computer-readable storage medium that stores a program or instructions that, when executed by a processor, cause the processor to perform an analysis method. The computer-readable storage medium includes non-transitory computer-readable storage media, such as magnetic media like floppy disks and magnetic tapes, optical media (including optical disc (CD) ROMs and DVD ROMs), magneto-optical media like flexible optical discs, hardware devices such as ROMs, RAMs, and flash memory designed for storing and executing program instructions. The program instructions include language code executable by a computer using an interpreter and machine language code generated by a compiler.

[0135] This disclosure provides a computer device including a memory and a processor, wherein the memory stores programs or instructions that, when executed by the processor, cause the processor to perform the above-described analysis method.

[0136] The analysis method according to the embodiments of this disclosure can dynamically calculate the utilization rate of new energy sources.

[0137] The analysis method according to embodiments of this disclosure can be used to guide the matching of new energy power sources with loads.

[0138] The analysis method according to the embodiments of this disclosure matches the load scale and characteristics based on the expected development scale of new energy sources, thereby assisting in load screening.

[0139] The analysis method according to the embodiments of this disclosure can match the scale of new energy development according to the load conditions, so as to achieve the expected level of new energy utilization.

[0140] The specific embodiments of this disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments can be combined, modified and improved without departing from the principles and spirit of this disclosure as defined by the claims and their equivalents. Such combinations, modifications and improvements should also be within the protection scope of this disclosure.

Claims

1. An analytical method for new energy sources in industrial parks, characterized in that, The analytical method includes: A new energy utilization rate model is established for the park, wherein the new energy includes at least one of wind power generation equipment and photovoltaic power generation equipment. The objective function of the new energy utilization rate model is an economic cost function calculated based on the time-series power generation of the generator in the park during a predetermined time period, the time-series charging and discharging power of the energy storage system in the park during the predetermined time period, the rated power of the energy storage system and the generator, the capacity of the power supply line of the park, and the costs associated with the energy storage system and the generator. The new energy utilization rate model is solved by optimizing the iterative algorithm; The actual time-series power generation of the new energy source during the predetermined time period is obtained from the solution results. Calculate the actual total power generation of the new energy source during the predetermined time period based on the actual power generation in the time sequence; The initial utilization rate of the new energy source is determined based on the ratio of the actual total power generation to the power generation capacity of the new energy source during the predetermined time period.

2. The analytical method for new energy sources in industrial parks according to claim 1, characterized in that, The costs associated with the energy storage system and the generator include: the capital cost of each additional unit capacity of the generator and the energy storage system in the park; the unit capacity cost of the power supply line; the marginal cost of each additional unit power of the generator and the energy storage system; and the time-sequential start-up cost and time-sequential shutdown cost of the generator during the predetermined time period.

3. The analytical method for new energy sources in industrial parks according to claim 2, characterized in that, The objective function of the new energy utilization rate model is the sum of the following five terms: The product of the generator's rated power and the capital cost per additional unit capacity of the generator; The product of the rated power of the energy storage system and the capital cost per additional unit capacity of the energy storage system; The product of the capacity of the power supply line and the unit capacity cost of the power supply line; The product of the time-series power generation of the generator in the predetermined time period and the marginal cost of increasing the power per unit of the generator, and the product of the time-series charging and discharging power of the energy storage system in the predetermined time period and the marginal cost of increasing the power per unit of the energy storage system, multiplied by a preset time weight. The generator's time-sequential start-up cost and time-sequential shutdown cost during the predetermined time period.

4. The analytical method for new energy sources in industrial parks according to claim 1, characterized in that, The constraints of the new energy utilization rate model include generator constraints, energy storage system constraints, new energy constraints themselves, and load constraints related to new energy.

5. The analytical method for new energy sources in industrial parks according to claim 4, characterized in that, The constraints of the new energy utilization model also include at least one of the following: grid connection constraints, grid disconnection constraints, and power balance conditions at each bus of the power supply line.

6. The analytical method for new energy sources in industrial parks according to claim 5, characterized in that, The generator constraints include that the engine's time-series power output is greater than or equal to the product of the generator's time-series availability lower limit and the generator's rated power, and less than or equal to the product of the generator's time-series availability upper limit and the generator's rated power. The constraints of the energy storage system include that the time-series charge and discharge power of the energy storage system is less than the rated power of the energy storage system. The constraints on the new energy source itself include the utilization rate of the new energy source being greater than or equal to a preset lower limit and the penetration rate of the new energy source being greater than or equal to a preset threshold. The penetration rate of the new energy source is represented by the total actual electricity generated by the new energy source during the predetermined time period divided by the total load electricity of the park during the predetermined time period. The load constraints related to new energy sources include: the total electricity consumption of loads related to new energy sources is less than or equal to the preset electricity consumption. The internet access constraint includes the total internet usage during the predetermined time period being less than or equal to a preset internet access constraint value. The offline constraint condition includes that the total offline electricity volume within the predetermined time period is less than or equal to a preset offline constraint value. The power balance condition at each bus of the power supply line includes the difference between the time-series load on each bus and the sum of the time-series generating power of the generator on the corresponding bus and the charging and discharging power of the energy storage system on the corresponding bus, and the time-series power flow on the corresponding line.

7. The analytical method for new energy sources in industrial parks according to claim 1, characterized in that, The analysis method further includes: calculating the new energy utilization rate under different natural peak-valley difference rates, wherein the natural peak-valley difference rate represents the ratio of the difference between the daily load peak and load valley of the park to the load peak.

8. The analytical method for new energy sources in industrial parks according to claim 1 or 7, characterized in that, The analysis method further includes: calculating the utilization rate of new energy sources under different load adjustability, wherein the load adjustability represents the proportion of the park's load that can be dynamically adjusted to meet electricity consumption.

9. The analytical method for new energy sources in industrial parks according to claim 1, characterized in that, The analysis method further includes: determining the maximum allowable percentage of load loss at different times throughout the entire life cycle of the new energy source based on the initial utilization rate and the predetermined minimum utilization rate.

10. The analytical method for new energy sources in industrial parks according to claim 9, characterized in that, The analysis method also includes determining the maximum time required for the load to recover to the initial level in order to maintain the minimum utilization rate of the new energy source during the different time periods.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, cause the processor to perform the analysis method according to any one of claims 1 to 10.

12. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing programs or instructions that, when executed by the processor, cause the processor to perform the analysis method according to any one of claims 1 to 10.

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