New energy storage optimal configuration method

Abstract

The invention belongs to the technical field of new energy storage optimization, and particularly relates to a new energy storage optimization configuration method, which comprises the following steps of S01, collecting and analyzing system data; s02, determining an optimization target; s03, establishing an optimization model; s04, optimizing an algorithm and verifying; and S05, performing operation evaluation and dynamic adjustment. According to the new energy storage optimal configuration method, new energy power generation data and load data are accurately collected, characteristics of an energy storage system are meticulously analyzed, a reasonable energy ratio is determined, and a power balance target is realized by means of an optimization model and an algorithm; and it is ensured that the combined output power of the new energy power generation and energy storage system can stably match the load demand in real time. No matter in response to intermittency of solar energy and wind energy power generation or peak-valley change of load, the power shortage or excess condition can be effectively avoided, continuous and reliable power supply is provided for users, the power failure risk is reduced, the utilization efficiency of the whole energy system is improved, and sustainable energy development is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy energy storage optimization, and specifically relates to a new energy energy storage optimization configuration method. Background Technique

[0002] With the wide application of new energy, the power system is facing increasingly complex operation and management problems. These new energies are characterized by intermittency and instability, posing challenges to the stable operation of the power system. To address these challenges, energy storage technologies have been introduced into the power system to provide power balance and improve system stability and reliability.

[0003] Currently, new energies such as wind power and photovoltaic power have strong randomness and volatility. Their output is affected by various factors such as wind speed, wind direction, and cloud cover, making it difficult to accurately predict. This leads to unbalanced distribution of electric power and electricity in time and space, posing huge challenges to the power and electricity balance during specific periods. Especially in extreme weather conditions such as high temperature and cold snap, the power and electricity balance problem is prominent, which may lead to power shortages or surpluses. In view of this, a new energy energy storage optimization configuration method is proposed. Summary of the Invention

[0004] The main purpose of the present invention is to provide a new energy energy storage optimization configuration method that can solve the problems raised in the above background technique.

[0005] To achieve the above purpose, the new energy energy storage optimization configuration method proposed by the present invention is characterized by including the following steps:

[0006] S01. Collect and analyze system data;

[0007] S02. Determine the optimization goal;

[0008] S03. Establish an optimization model;

[0009] S04. Optimize the algorithm and verify;

[0010] S05. Run the evaluation and make dynamic adjustments;

[0011] Among them, when collecting and analyzing system data, it includes new energy power generation data collection, load data collection, energy storage system characteristic analysis, and energy ratio adjustment. The new energy power generation data collection includes solar power generation data collection and wind power generation data collection. For solar power generation, historical data of solar irradiance intensity at the location of the photovoltaic power station is accurately collected, and the sources cover long-term records of local weather stations and high-resolution satellite remote sensing information, detailed to the hourly and minute-by-minute changes. Combining the actual power and conversion efficiency specification parameters of the photovoltaic modules, the power generation power curve is deduced. For wind power generation, an anemometer is accurately installed at the hub height of the wind turbines in the wind farm to continuously collect real-time and historical data of wind speed and wind direction. At the same time, based on the detailed power curve provided by the wind turbine manufacturer, which accurately reflects the power generation efficiency at different wind speeds. The load data collection uses smart meters to widely collect various load data connected to the energy storage system, including industrial, commercial, and residential loads. The energy storage system characteristic analysis targets mainstream energy storage technologies, such as lithium-ion batteries, lead-acid batteries, flow batteries, compressed air energy storage, and flywheel energy storage, and comprehensively evaluates key performance parameters such as energy density, power density, charge and discharge efficiency, cycle life, and self-discharge rate. The energy ratio adjustment is through the analysis of the new energy power generation combination and the ratio adjustment based on the load characteristics.

[0012] Preferably, the new energy power generation combination analysis is based on the local energy power generation and resource conditions to evaluate the complementary potential of solar energy and wind energy. The ratio adjustment based on the load characteristics analyzes the time distribution of the load demand according to the collected load curve to ensure that the combined output power of the new energy power generation and the energy storage system can match the load demand in real time and stably.

[0013] Preferably, in step S02, determining the optimization objectives includes optimizing the technical performance objectives, optimizing the energy storage objectives, and optimizing the economic objectives.

[0014] Preferably, for the optimization of the technical performance objectives, a fine model needs to be built to ensure that the combined output power of the new energy power generation system and the energy storage system accurately matches the load demand. For the optimization of the energy storage objectives, considering the intermittency of new energy power generation and the load demand characteristics, the required energy storage capacity of the energy storage system is accurately determined. At the same time, the emergency power supply duration under special working conditions such as extreme weather and equipment failures is considered. For example, it is required that the energy storage system can meet the power supply needs of critical loads for 2-3 days in the case of continuous absence of new energy power generation, so as to set the lower limit of the energy storage capacity. For the optimization of the economic objectives, a comprehensive cost function needs to be constructed, covering the full life cycle cost of the energy storage system, the construction and operation and maintenance costs of the new energy power generation system, avoiding power shortages or surpluses, providing continuous and reliable power supply for users, reducing the power outage risk, and reducing the overall expenses of system construction and operation.

[0015] Preferably, the establishment of the optimization model includes the establishment of a physical model and a mathematical optimization model. The physical model is constructed based on the physical principles of new energy power generation, energy storage systems, and loads to build a rigorous mathematical description. The mathematical optimization model is oriented by a determined optimization goal to establish a suitable mathematical optimization model.

[0016] Preferably, when constructing the mathematical description, the solar power generation formula needs to be added, incorporating actual attenuation factors such as component aging and dust shading to correct the calculation of power generation. For the state of charge update formula of the energy storage system, the influence of battery thermal management and charge-discharge rate on efficiency is considered to accurately simulate the change of the energy storage state.

[0017] Preferably, the solar power generation formula is: P = I * V * η;

[0018] Among them, P represents the output power of the solar panel; I represents the solar radiation intensity, which usually represents the light intensity of direct sunlight on the solar panel; V represents the voltage of the solar panel; η represents the conversion efficiency of the solar panel.

[0019] Preferably, the state of charge update formula of the energy storage system is: SOCt = (1 - d) * SOCt-1 + Pc,t * ηc / Es * Δt;

[0020] Among them, SOCt and SOCt-1 represent the state of charge at time t and time t-1 respectively; d represents the self-discharge rate of the battery; Pc,t represents the charging power of the battery in period t; ηc represents the charging efficiency of the battery; Es represents the rated capacity of the battery; Δt represents the time interval.

[0021] Preferably, the optimization algorithm and verification include the integration of traditional algorithms and the verification of the integrated algorithm.

[0022] Preferably, the integration of the traditional algorithms makes the genetic algorithm and the particle swarm optimization algorithm complement each other's advantages. In the early stage, the global search ability of the genetic algorithm is used to explore a wide solution space, and in the later stage, the fast convergence characteristic of the particle swarm optimization algorithm is used to focus on the optimal solution. The verification of the integrated algorithm is to solve the optimization model by using the selected algorithm and finely adjust the algorithm parameters.

[0023] The present invention provides a new energy energy storage optimization configuration method, which has the following beneficial effects:

[0024] (1) By accurately collecting new energy power generation data, load data, and carefully analyzing the characteristics of energy storage systems, this new energy storage optimization configuration method determines a reasonable energy ratio. Then, with the help of optimization models and algorithms, it achieves the power balance goal, ensuring that the combined output power of new energy power generation and energy storage systems can match the load demand in real time and stably. Whether dealing with the intermittency of solar and wind power generation or the peak-valley changes of the load, it can effectively avoid power shortages or surpluses, providing users with continuous and reliable power supply and reducing the risk of power outages.

[0025] (2) By comprehensively considering the initial investment, operation and maintenance, life cycle replacement and other costs of energy storage systems and new energy power generation systems during the optimization configuration process, this new energy storage optimization configuration method reduces the overall expenditure of system construction and operation. At the same time, through accurate market price prediction and strategy formulation, it maximizes the benefits of energy storage systems, thereby enhancing the economic return of new energy power systems.

[0026] (3) Based on an in-depth understanding of the local new energy resource endowment and combined with the time distribution of load demand, this new energy storage optimization configuration method reasonably plans the energy ratio, realizes the time matching of new energy power generation and electricity load, improves the local consumption capacity of new energy, avoids energy waste, gives full play to the complementary advantages between different new energy sources, and further improves the utilization efficiency of the entire energy system, promoting the development of sustainable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0028] Figure 1 It is the flowchart of the steps of the present invention;

[0029] Figure 2 It is the flowchart of data collection and analysis of the present invention;

[0030] Figure 3 It is the schematic diagram of energy ratio adjustment of the present invention;

[0031] Figure 4 It is the flowchart of determining the optimization goal of the present invention;

[0032] Figure 5 It is the flowchart of establishing the optimization model of the present invention.

[0033] The realization, functional characteristics and advantages of the object of the present invention will be further described in conjunction with the embodiments and with reference to the drawings. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Please refer to Figures 1 - 5 , the present invention proposes a new energy storage optimization configuration method, including the following steps:

[0036] S01. Collect and analyze system data;

[0037] S02. Determine the optimization goal;

[0038] S03. Establish an optimization model;

[0039] S04. Optimize the algorithm and verify;

[0040] S05. Run the evaluation and dynamically adjust;

[0041] In step S01 of the embodiment of the present invention, when collecting and analyzing system data, it includes new energy power generation data collection, load data collection, energy storage system characteristic analysis, and energy ratio adjustment. The new energy power generation data collection includes solar power generation data collection and wind power generation data collection. The load data collection uses smart meters to widely collect various load data connected to the energy storage system, including industrial, commercial, and residential loads. The energy storage system characteristic analysis targets mainstream energy storage technologies, such as lithium-ion batteries, lead-acid batteries, flow batteries, compressed air energy storage, and flywheel energy storage, and comprehensively evaluates key performance parameters such as energy density, power density, charge and discharge efficiency, cycle life, and self-discharge rate. The energy ratio adjustment is through new energy power generation portfolio analysis and ratio adjustment based on load characteristics.

[0042] Furthermore, for solar power generation, accurately collect historical data of solar irradiance intensity at the location of the photovoltaic power station, with the data source covering long-term records of local meteorological stations and high-resolution satellite remote sensing information, detailed to hourly and minute-by-minute changes, and combine with the actual power, conversion efficiency and other specification parameters of the photovoltaic modules to accurately calculate the power generation power curve; for wind power generation, accurately install an anemometer at the hub height of the wind turbines in the wind farm to continuously collect real-time and historical data of wind speed and wind direction, and at the same time, based on the detailed power curve provided by the wind turbine manufacturer, which accurately reflects the power generation efficiency at different wind speeds, accurately estimate the power generation amount.

[0043] Furthermore, according to the local energy generation and resource conditions, evaluate the complementary potential of solar and wind energy. For example, if a certain area has strong sunlight and low wind speed during the day, and relatively high wind speed at night, there is a certain complementarity in the power generation time of solar and wind energy. Initially, determine the proportion range of the two in the total power generation. Through the statistical analysis of historical meteorological data, calculate the correlation of the power generation power of the two in different seasons and different time periods. If the correlation is low, it means good complementarity, and the combined ratio can be appropriately increased; otherwise, it is necessary to adjust it carefully. At the same time, based on the collected load curve, analyze the time distribution of load demand. If industrial load is dominant and electricity consumption is concentrated during the day, give priority to ensuring the supply of new energy power generation during the day, and increase the proportion of new energy with high power generation efficiency during the day, such as solar energy; if the peak load of residential load is prominent at night, focus on configuring new energy that can generate electricity at night or reasonably allocate energy storage to ensure stable power supply at night, and flexibly fine-tune the ratio between new energy sources. Through the above methods, a reasonable energy ratio can be achieved, and then the power balance target can be realized by means of an optimization model and algorithm, ensuring that the combined output power of the new energy power generation and energy storage system can match the load demand in real time and stably. Whether it is to cope with the intermittency of solar and wind power generation or the peak-valley changes of the load, it can effectively avoid power shortages or surpluses, provide continuous and reliable power supply for users, reduce the risk of power outages, and at the same time, improve the local consumption capacity of new energy, avoid energy waste, give full play to the complementary advantages between different new energy sources, and thus improve the utilization efficiency of the entire energy system and promote the development of sustainable energy.

[0044] In step S02 of the embodiment of the present invention, determining the optimization objectives includes optimizing technical performance objectives, optimizing energy storage objectives, and optimizing economic objectives. When optimizing technical performance objectives, a fine model needs to be built to ensure that the combined output power of the new energy power generation system and the energy storage system accurately matches the load demand; when optimizing energy storage objectives, it is necessary to combine the intermittency of new energy power generation and the characteristics of load demand to accurately determine the energy storage capacity required by the energy storage system, and at the same time consider the emergency power supply duration under special working conditions such as extreme weather and equipment failures. For example, it is required that the energy storage system can meet the power supply demand of critical loads for 2-3 days in the case of continuous new energy power generation interruption, so as to set the lower limit of the energy storage capacity; when optimizing economic objectives, a comprehensive cost function is constructed, covering the full life cycle cost of the energy storage system and the construction and operation and maintenance costs of the new energy power generation system, and a cost-effective solution is selected through cost-benefit analysis.

[0045] In step S03 of the embodiment of the present invention, establishing an optimization model includes establishing a physical model and a mathematical optimization model. Among them, when establishing the physical model, based on the physical principles of new energy power generation, energy storage systems, and loads, a rigorous mathematical description is constructed. For example, for the solar power generation formula, actual attenuation factors such as component aging and dust shielding are incorporated to correct the calculation of power generation. For the state of charge update formula of the energy storage system, the influence of battery thermal management and charge and discharge rate on efficiency is considered to accurately simulate the change of the energy storage state. The mathematical optimization model is oriented by the determined optimization goal, and a suitable mathematical optimization model is established. For complex actual scenarios, a mixed integer nonlinear programming model is mostly used.

[0046] Among them, the solar power generation formula is: P = I * V * η;

[0047] P represents the output power of the solar panel; I represents the solar radiation intensity, which usually represents the light intensity directly irradiated by the sun on the solar panel; V represents the voltage of the solar panel; η represents the conversion efficiency of the solar panel.

[0048] The state of charge update formula of the energy storage system is: SOCt = (1 - d) * SOCt-1 + Pc,t * ηc / Es * Δt;

[0049] SOCt and SOCt-1 represent the state of charge at time t and time t - 1 respectively; d represents the self-discharge rate of the battery; Pc,t represents the charging power of the battery in time period t; ηc represents the charging efficiency of the battery; Es represents the rated capacity of the battery; Δt represents the time interval.

[0050] In step S04 of the embodiment of the present invention, the optimization algorithm and verification include the fusion of traditional algorithms and the verification of the fused algorithm. The advantages of the genetic algorithm and the particle swarm optimization algorithm are complementary. In the early stage, the global search ability of the genetic algorithm is used to explore a wide solution space, and in the later stage, the fast convergence characteristic of the particle swarm optimization algorithm is used to focus on the optimal solution, improving the efficiency and accuracy of solving complex nonlinear optimization models. And the selected algorithm is used to solve the optimization model, and the algorithm parameters are finely adjusted, such as the crossover probability and mutation probability of the genetic algorithm, and the inertia weight of the particle swarm optimization algorithm, to ensure the stable and efficient operation of the algorithm.

[0051] Furthermore, the big data analysis platform is used to continuously monitor core indicators such as power output, energy storage SOC, and operating costs, and compare them item by item with the optimization objectives. Once a deviation is found, such as the actual ratio of new energy power generation not matching the expectation and affecting power supply stability, or the energy storage cost exceeding the budget due to premature battery aging, the dynamic adjustment mechanism is immediately activated. Through measures such as remotely adjusting the energy storage charge and discharge strategy, scheduling equipment maintenance and repair, and re-optimizing the configuration when necessary, the long-term efficient operation of the system is ensured, and the overall expenses of system construction and operation are reduced. At the same time, through accurate market price prediction and strategy formulation, the maximization of the energy storage system's revenue is achieved, thereby enhancing the economic return of the new energy power system.

[0052] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made under the inventive concept of the present invention by using the content of the specification and drawings of the present invention, or directly / indirectly applied in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the configuration of new energy energy storage, characterized in that, It includes the following steps: S01. Collect and analyze system data; S02. Determine the optimization objectives; S03. Establish an optimization model; S04. Optimize the algorithm and verify; S05. Run the evaluation and make dynamic adjustments; Among them, when collecting and analyzing system data, it includes the collection of new energy power generation data, the collection of load data, the analysis of the characteristics of energy storage systems, and the adjustment of energy ratios. The collection of new energy power generation data includes the collection of solar power generation data and wind power generation data. The collection of load data uses smart meters to widely collect various types of load data connected to the energy storage system, including industrial, commercial, and residential loads. The analysis of the characteristics of energy storage systems targets mainstream energy storage technologies, such as lithium-ion batteries, lead-acid batteries, flow batteries, compressed air energy storage, and flywheel energy storage, and comprehensively evaluates key performance parameters such as energy density, power density, charge-discharge efficiency, cycle life, and self-discharge rate. The adjustment of energy ratios is through the analysis of the new energy power generation combination and the ratio adjustment based on the load characteristics.

2. The method for optimizing the configuration of a new energy energy storage according to claim 1, wherein The analysis of the new energy power generation combination is based on the local energy generation and resource conditions to evaluate the complementary potential of solar energy and wind energy. The ratio adjustment based on the load characteristics analyzes the time distribution of load demand according to the collected load curve.

3. A new energy energy storage optimization configuration method according to claim 1, characterized in that In step S02, determining the optimization objectives includes optimizing technical performance objectives, optimizing energy storage objectives, and optimizing economic objectives.

4. The method for optimizing the configuration of a new energy energy storage according to claim 3, characterized in that, The optimization of technical performance objectives requires building a refined model to ensure that the coordinated output power of the new energy power generation system and the energy storage system precisely matches the load demand. The optimization of energy storage objectives needs to combine the intermittency of new energy power generation and the characteristics of load demand to accurately determine the energy storage capacity required by the energy storage system. The optimization of economic objectives requires constructing a comprehensive cost function covering the full life cycle cost of the energy storage system and the construction and operation and maintenance costs of the new energy power generation system.

5. A method for optimizing the configuration of a new energy energy storage according to claim 1, characterized in that The establishment of the optimization model includes establishing a physical model and a mathematical optimization model. The establishment of the physical model constructs a rigorous mathematical description based on the physical principles of new energy power generation, energy storage systems, and loads. The mathematical optimization model is oriented by the determined optimization objectives to establish a suitable mathematical optimization model.

6. The method for optimizing the configuration of a new energy energy storage according to claim 5, characterized in that When constructing the mathematical description, the solar power generation power formula needs to be added, incorporating actual attenuation factors such as component aging and dust shielding to correct the calculation of power generation. The state of charge update formula of the energy storage system considers the influence of battery thermal management and charge-discharge rate on efficiency to accurately simulate the change of the energy storage state.

7. A new energy energy storage optimization configuration method according to claim 6, characterized in that, The solar power generation power formula is: P = I * V * η; Among them, P represents the output power of the solar panel; I represents the solar radiation intensity, which usually represents the light intensity of the sun directly hitting the solar panel; V represents the voltage of the solar panel; η represents the conversion efficiency of the solar panel.

8. A new energy energy storage optimization configuration method according to claim 7, characterized in that The state of charge update formula of the energy storage system is: SOCt = (1 - d) * SOCt-1 + Pc,t * ηc / Es * Δt; Among them, SOCt and SOCt-1 represent the state of charge at time t and time t-1 respectively; d represents the self-discharge rate of the battery; Pc,t represents the charging power of the battery in time period t; ηc represents the charging efficiency of the battery; Es represents the rated capacity of the battery; Δt represents the time interval.

9. A new energy energy storage optimization configuration method according to claim 1, characterized in that The optimization algorithm and verification include the integration of traditional algorithms and the verification of the integrated algorithm.

10. A method for optimizing the configuration of a new energy energy storage according to claim 9, characterized in that, The integration of the traditional algorithms makes the genetic algorithm and the particle swarm optimization algorithm complement each other's advantages. In the early stage, the global search ability of the genetic algorithm is used to explore a wide solution space, and in the later stage, the fast convergence characteristic of the particle swarm optimization algorithm is used to focus on the optimal solution. The verification of the integrated algorithm is carried out by using the selected algorithm to solve the optimization model and finely adjusting the algorithm parameters.

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