Battery attenuation considering production and sales global value unified optimization method, device and system

Abstract

The application provides a kind of production and sale global value unified optimization method, device and system considering battery attenuation, belongs to new energy storage technology field.The multi-objective value function is constructed, to maximize the net income in system operation cycle as the goal, and deduct the battery cycle attenuation cost based on rain flow counting method;Establish a mixed integer programming model, the constraint conditions include equipment start-stop state, market transaction volume upper limit and SOC dynamic balance equation;MIP heuristic hybrid algorithm is used to solve the model by fusing decomposition coordination module and dynamic weight correction module, and the optimal charging and discharging strategy and equipment start-stop plan are output.When the battery health state is lower than the threshold, the attenuation penalty weight is improved.The application will solve the problem of short-term profit caused by ignoring long-term attenuation in the existing method, realize the collaborative optimization of economic benefit and equipment health state in the whole life cycle of battery, and improve the long-term economy and operation reliability of energy storage system participating in power transaction.

Description

Technical Field

[0001] This invention relates to the field of new energy storage technology, and in particular to a method, apparatus and system for unified optimization of the overall value of production and sales that takes into account battery degradation. Background Technology

[0002] In the context of energy transition, battery energy storage systems have become an important commercial application model for generating revenue through participation in electricity market transactions, such as peak-valley arbitrage or ancillary services. Most existing optimization methods focus on a single economic objective: maximizing the revenue from the charge-discharge price difference.

[0003] However, such methods have inherent drawbacks. Batteries have a finite cycle life, and frequent charge-discharge operations accelerate capacity decay, resulting in significant hidden replacement costs. Ignoring the degradation of the battery's State of Health (SOH), while maximizing short-term gains, may lead to a decline in the overall economics of the system over its entire lifecycle. Existing technologies, such as the optimal scalability decision model proposed in the "Journal of Energy Storage, 2020," only consider marginal contributions and fixed costs, failing to quantify battery cycle life degradation. Furthermore, some optimization methods that introduce multiple constraints, such as those in "IEEE Transactions on Sustainable Energy, 2021," also fail to fundamentally address the dynamic trade-off between lifespan degradation and market returns. This leads to short-term optimization strategies, failing to guarantee the economic viability and reliability of battery energy storage systems in long-term operation. Summary of the Invention

[0004] This application provides a method, apparatus, and system for unified optimization of the overall value of production and sales that takes into account battery degradation, in order to solve the problem of short-term revenue caused by neglecting long-term battery degradation in the prior art.

[0005] In a first aspect, this application provides a unified optimization method for the overall value of production and sales that takes into account battery degradation, the method comprising: Acquire real-time operating status data of the battery system and electricity market transaction data; A multi-objective value function is constructed with the goal of maximizing the total value of the system's operating cycle. The total value is the difference between the discharge revenue and the charging cost during the system's operating life cycle, minus the cycle degradation cost calculated based on the equivalent number of battery cycles. A hybrid integer programming model is established, which includes integer variables of equipment start-up and shutdown status and continuous variables of charging and discharging power. The model includes power balance constraints, dynamic balance constraints of state of charge, and market planned transaction volume constraints. The mixed integer programming model is solved using the MIP heuristic hybrid algorithm, and the optimal charging and discharging strategy and equipment start-up and shutdown plan are output.

[0006] Preferably, the formula for calculating the cycle decay cost is: ; in, For the cost of cyclic decay, The equivalent number of cycles within time period t is calculated based on the rainflow counting method, and k and α are the battery degradation coefficients calibrated experimentally.

[0007] Preferably, the cyclic decay cost incorporates a dynamic correction factor. The revised cost calculation formula is as follows: ; Wherein, the dynamic correction factor , The health status of the battery during time period t. As an adjustable parameter, when When it is below the preset threshold, The value is increased to enhance the weight of the decay penalty.

[0008] Preferably, the MIP heuristic hybrid algorithm includes: The decomposition and coordination module is configured to decompose the original optimization problem into an economic benefit subproblem and a battery life subproblem, and coordinate them using the Lagrange duality method. A dynamic weight correction module is configured to calculate economic weights based on real-time battery health status and adjust the weight allocation between economic benefits and lifespan degradation costs in the multi-objective value function accordingly; the formula for the economic weights is: ; in, As an economic weight.

[0009] Preferably, the MIP heuristic hybrid algorithm uses a genetic algorithm to process the integer variables of the device start / stop state, and the operation of the genetic algorithm includes: The start / stop status is encoded in binary, selected using a roulette wheel, and single-point crossover and bit-flip mutation operations are performed.

[0010] Preferably, the formula for the dynamic equilibrium constraint of the state of charge (SOC) is: ; in, and These are charging efficiency and discharging efficiency, respectively. and Let be the charging power and discharging power during time period t, respectively. It is constrained within a preset safety range.

[0011] Secondly, this application also provides a unified optimization device for the overall value of production and sales that takes into account battery degradation, the device comprising: The data acquisition module is configured to acquire real-time operating status data of the battery system and electricity market transaction data; A data storage module is connected to the data acquisition module and is configured to persistently store the acquired data. A data governance module, connected to the data storage module, is configured to perform data cleaning, outlier handling, and feature extraction. A function construction module, configured to construct a multi-objective value function with the objective of maximizing total value; A modeling module, configured to establish a hybrid integer programming model that includes integer variables of device start / stop status and continuous variables of charging / discharging power; The solution module is configured to use the MIP heuristic hybrid algorithm to solve the hybrid integer programming model and output the optimal charging and discharging strategy and equipment start-up and shutdown plan.

[0012] Thirdly, this application also provides a unified optimization system for the overall value of production and sales that takes into account battery degradation, the system comprising: A battery management system configured to monitor the battery's state of charge (SOC) and state of health (SOH) in real time and provide battery operating data; A power market data interface, configured to acquire real-time electricity price data from the power market; A unified optimization device for the overall value of production and sales that takes into account battery degradation, wherein the unified optimization device for the overall value of production and sales that takes into account battery degradation is configured to perform optimization calculations based on the battery operating data and real-time electricity price data; An execution unit is configured to receive the charging and discharging strategy and equipment start-up and shutdown plan issued by the unified optimization device for production and sales taking into account battery degradation, and control the battery energy storage system to perform the corresponding charging and discharging operations.

[0013] Preferably, the execution unit is further configured to: When the battery health status is detected to be lower than a preset threshold, an alarm signal is sent to the unified optimization device for the global value of production and sales that takes into account battery degradation, and the unified optimization device for the global value of production and sales that takes into account battery degradation is triggered to adjust the dynamic correction factor in the multi-objective value function.

[0014] Fourthly, this application also provides a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, it implements any of the above-described methods for unified optimization of production and sales value taking into account battery degradation.

[0015] As described above, this application provides a method, apparatus, and system for unified optimization of the overall value of production and sales, taking into account battery degradation. The method includes acquiring real-time operating status data and electricity market transaction data of the battery system; constructing a multi-objective value function with the goal of maximizing the total value of the system's operating cycle, where the total value is the difference between discharge revenue and charging cost over the system's operating lifespan, minus the cycle degradation cost calculated based on the equivalent number of battery cycles; establishing a hybrid integer programming model including integer variables of equipment start-up and shutdown states and continuous variables of charge and discharge power, where the model includes power balance constraints, dynamic state-of-charge balance constraints, and market planned transaction volume constraints; and using a MIP heuristic hybrid algorithm to solve the hybrid integer programming model, outputting the optimal charge-discharge strategy and equipment start-up and shutdown plan. This application solves the problem of short-term revenue reduction caused by neglecting long-term battery degradation in existing technologies. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This invention provides an overall architecture principle block diagram of a unified optimization system for production and sales value that takes into account battery degradation; Figure 2 A schematic diagram of a unified optimization device for production and sales value taking into account battery degradation, provided by the present invention. Figure 3 A flowchart of a unified optimization method for the overall value of production and sales that takes into account battery degradation, provided by the present invention; Figure 4 The timing diagram shows the solution process of the MIP-heuristic hybrid algorithm provided by this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0020] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0021] Figure 1 This invention provides an overall architecture principle block diagram of a unified optimization system for production and sales that takes into account battery degradation.

[0022] like Figure 1 As shown, the system mainly includes a Battery Management System (BMS), an electricity market data interface, optimization devices, and execution units. To clearly illustrate the data flow and functional division, this architecture diagram can be divided into three layers: a data acquisition layer, a model building and solving layer, and a strategy execution layer.

[0023] Data Acquisition Layer 101: Contains the Battery Management System (BMS) and the electricity market data interface. The BMS is responsible for real-time monitoring of key parameters of the battery clusters, such as State of Charge (SOC), State of Health (SOH), voltage, and temperature. The electricity market data interface obtains predicted or real-time electricity prices for future periods from the electricity market operator. This layer is responsible for uploading all raw data to the model building and solution layer.

[0024] Model building and solution layer 102: This is the core computing unit of the system. It receives raw data from the data acquisition layer and executes the core optimization algorithm of this invention.

[0025] Strategy Execution Layer 103: Contains execution units and battery energy storage systems. The execution unit receives the charging and discharging strategies issued by the optimization device and controls the energy storage converter PCS to complete the specific charging and discharging operations.

[0026] To describe its internal structure more accurately, Figure 2 This is a schematic diagram of a unified optimization device for production and sales that takes into account battery degradation, provided by the present invention.

[0027] like Figure 2 As shown, the optimization device comprises the following four core modules, and the direction of data flow between these modules is clearly indicated by the arrows in the diagram: Data storage module and data governance module 201: Receives raw data from BMS and market interface, and is responsible for data cleaning, verification, feature extraction and persistent storage, providing high-quality and reliable input data for subsequent modules.

[0028] Function Construction Module 202: Connected to the Data Storage and Governance Module, it constructs a multi-objective value function based on the preprocessed data, specifically including calculating the dynamic correction factor. Equivalent number of loops And form the objective function: .

[0029] Modeling Module 203: Used to build mixed-integer programming (MIP) models and define decision variables. , , It also sets constraints such as power balance, SOC dynamic balance, and market trading volume.

[0030] in, For the cost of cyclic decay, For the discharge electricity price, The electricity price for charging.

[0031] Solver Module 204: Used to run the MIP heuristic hybrid algorithm to solve the model and output the optimal charging and discharging strategy. , and equipment start-up and shutdown plan The results are then sent to the execution unit.

[0032] Figure 3 The flowchart illustrates a unified optimization method for the overall value of production and sales, taking into account battery degradation, provided by this invention. Figure 3 As shown, this method is executed within 15 minutes in each optimization cycle, and specifically includes the following steps: Step 301: Data Acquisition and Step 302: Data Preprocessing.

[0033] The data storage and management module of the optimization device obtains real-time SOC and SOH data of the battery from the BMS, and obtains electricity price information from the electricity market data interface, and performs validity verification.

[0034] Step 303: Construct a multi-objective value function.

[0035] The function building module first calculates the dynamic correction factor based on the battery's current SOH value. Subsequently, the equivalent number of cycles was calculated using the rainflow counting method. Finally, a value function for time period t is constructed, with the global objective of maximizing the total value. The value function for time period t: ; in, Let be the net revenue value function of battery energy storage in a specific time period t. The equivalent number of cycles within time period t is calculated based on the rainflow counting method, and k and α are the battery degradation coefficients calibrated experimentally.

[0036] In this invention, the rainflow counting method is used to accurately count the equivalent cycle number of the battery under complex charge and discharge conditions. The specific application process is as follows: Data preparation: Obtain time series data of battery state of charge (SOC) for an optimization period, such as 24 hours: SOC(1), SOC(2), ..., SOC(T). This series is usually collected by a BMS at fixed time intervals, such as 15 minutes.

[0037] Sequence reconstruction: The original time-series SOC data is converted into a load-time series with time as the horizontal axis and SOC as the vertical axis, and minor fluctuation noise is removed to highlight the main charge-discharge cycles.

[0038] Cyclic extraction: Identify all peaks (local maxima) and valleys (local minima) in the sequence.

[0039] Based on the "four-point method" rule of rainflow counting, complete closed hysteresis loops are identified from the reconstructed load sequence. Each hysteresis loop corresponds to an independent charge-discharge half-cycle or full cycle. For example, a process of discharging from 50% SOC to 30% and then recharging back to 55% can be identified as a cycle with a range of 25%.

[0040] Equivalent Cycle Count Calculation: For each identified cycle, its cycle range is substituted into an equivalent model, such as the commonly used Ah-throughput model or semi-empirical model, to calculate the loss to battery life caused by that cycle, and then converted into the equivalent cycle count under standard charge-discharge cycles, such as 0%~100% SOC. The formula can be expressed as: And perform weighted accumulation on some loops.

[0041] in, The range of the i-th iteration.

[0042] By using the rainflow counting method, this invention can quantify the irregular and random actual charge-discharge load spectrum into a unified and calculable fatigue damage index, namely the equivalent number of cycles. This allows for accurate calculation of cycle decay costs. Laying the foundation.

[0043] In addition, the battery degradation coefficient k and Calibration requires battery cycle aging tests. The specific procedures and data sources are as follows: Experimental Design: Sample selection: Select battery cells or modules of the same model and batch as the target energy storage system as experimental samples.

[0044] Test conditions: Design a series of charge and discharge cycle test conditions with different depths of DoD, such as constant depth cycle test with DoD=20%, 40%, 60%, and 80%, and variable depth cycle test simulating actual market trading.

[0045] Monitoring parameters: During the cycling process, key performance parameters of the battery, such as capacity, internal resistance, voltage, and temperature, are continuously monitored and recorded.

[0046] Data acquisition and processing: Data source: The core data comes from battery capacity degradation trajectories collected under the aforementioned controlled laboratory environment. Standard capacity calibration is performed on the experimental batteries periodically, such as every 100 cycles, to obtain their current maximum usable capacity. And calculate its health status: .

[0047] in, This is the initial capacity.

[0048] Data fitting: Using the cumulative equivalent cycle number obtained by processing the experimental load spectrum through rainflow counting as the independent variable N, and the capacity decay or impedance increase of the battery as the dependent variable, a power-law model was used. Nonlinear regression fitting was performed on the experimental data.

[0049] in, This is the capacity decay amount. This represents the equivalent number of iterations.

[0050] Coefficient Determination: Through optimization algorithms such as the least squares method, the coefficients that best characterize the degradation characteristics of this battery model are obtained by fitting. proportionality coefficient and Power exponent, usually This reflects the nonlinearity of the decay. This indicates that battery degradation accelerates with increasing cycle count.

[0051] This calibration process ensures the consistency between the degradation cost model and the actual battery aging characteristics, and is the quantitative basis of this invention.

[0052] Step 304: Establish a mixed integer programming (MIP) model.

[0053] The modeling module models the optimization problem as a MIP model and defines decision variables. , It also sets constraints such as power balance, SOC dynamic balance, and market trading volume.

[0054] Decision variables: including continuous variable charging power and discharge power Its value range is [0, ... Integer variable device start / stop status The value can be 0 or 1 (1 indicates running, 0 indicates stopping).

[0055] Constraints: Power and logic constraints: ≤ ; ≤ This constraint ensures that the equipment is in a stopped state ( When (=0), both charging and discharging power are zero.

[0056] in, This is the rated maximum charge / discharge power.

[0057] SOC dynamic equilibrium constraints: ; in and These represent charging and discharging efficiencies, respectively. To optimize the time period length. Meanwhile, It must always be kept within the preset safety range (e.g., [0.2, 0.9]).

[0058] Market constraints: + ≤ This ensures that the trading volume in a single period does not exceed the market rule limit.

[0059] in, For the electricity traded.

[0060] Step 305: Solve the model using the MIP heuristic algorithm.

[0061] Figure 4 This is a timing diagram illustrating the solution process of the MIP heuristic hybrid algorithm provided by this invention. Figure 4 As shown, the algorithm is implemented by the solver module, which involves the collaborative work of two core modules: Decomposition and Coordination Module: This module decomposes the complex MIP model into relatively simpler economic subproblems (maximizing revenue) and lifetime subproblems (minimizing decay costs). Coordination is achieved using the Lagrange dual method, iteratively updating the dual variables, as shown in the formula: ; This allows the solutions to the two subproblems to gradually approach the optimal solution to the original problem, where, This is the new value used in the (k+1)th iteration after calculation. The value of the Lagrange multiplier at the kth iteration. This is the step size at the k-th iteration, also known as the learning rate.

[0062] Dynamic weight correction module: Calculates economic weights based on real-time collected SOH(t). .

[0063] This weight is used to adjust the objective function in each iteration: .

[0064] This allows the optimization algorithm to automatically prioritize protecting battery life when battery health declines.

[0065] Subsequently, a genetic algorithm was used to process the integer variables. During implementation, for The algorithm performs binary encoding and efficiently searches the solution space for the optimal start-stop plan through operations such as roulette wheel selection, single-point crossover, and bit-flip mutation. Finally, the algorithm outputs the optimal charging and discharging power command. , and equipment start-up and shutdown plan .

[0066] Step 306: Strategy Output and Execution.

[0067] The optimization device sends the optimal strategy obtained from the solution to the execution unit. After parsing the instructions, the execution unit controls the PCS to perform the corresponding charging and discharging operations. At the same time, the system continuously monitors the battery status. If the state of equilibrium (SOH) decreases rapidly, an alarm is triggered and fed back to the optimization device to adjust the β(t) parameter, forming a closed-loop optimization.

[0068] The present invention, through the above specific embodiments, particularly through the device, Figure 2 The coordinated operation of the internal modules enables the synergistic optimization of economic benefits and health status throughout the battery's entire life cycle.

[0069] This embodiment has the following advantages: The method, apparatus, and system for unified optimization of the overall production and sales value considering battery degradation provided in this embodiment solve the problem of short-term benefits caused by neglecting long-term degradation in existing optimization methods by quantifying battery life degradation as a dynamic cost and integrating it into economic objectives. Compared with optimization methods that focus on a single economic objective, in a preferred embodiment, this invention can achieve synergistic optimization of economic benefits and equipment health status throughout the entire battery life cycle. Simulation verification shows that it can improve long-term benefits by 10%-15% and effectively reduce the battery degradation rate by more than 20%, significantly enhancing the long-term economic efficiency and reliability of energy storage system operation.

[0070] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the discussion in some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the contents of this disclosure, thereby enabling those skilled in the art to better utilize the embodiments.

Claims

1. A unified optimization method for the overall value of production and sales, taking into account battery degradation, characterized in that, Includes the following steps: Acquire real-time operating status data of the battery system and electricity market transaction data; A multi-objective value function is constructed with the goal of maximizing the total value of the system's operating cycle; the total value is the difference between the discharge revenue and the charging cost within the system's operating cycle, minus the cycle degradation cost calculated based on the equivalent number of battery cycles; A hybrid integer programming model is established, which includes integer variables of equipment start-up and shutdown states and continuous variables of charging and discharging power; the model includes power balance constraints, dynamic balance constraints of state of charge, and market planned transaction volume constraints. The MIP heuristic hybrid algorithm is used to solve the hybrid integer programming model, and the optimal charging and discharging strategy and equipment start-up and shutdown plan are output. The formula for calculating the cycle decay cost is as follows: ； in, For the cost of cyclic decay, The equivalent number of cycles within time period t is calculated based on the rainflow counting method, and k and α are the battery degradation coefficients calibrated experimentally. The cyclic decay cost is adjusted using a dynamic correction factor, and the adjusted cost calculation formula is as follows: ； in, The dynamic correction factor, the dynamic correction factor , The health status of the battery during time period t. It is an adjustable parameter; when When it is below the preset threshold, The value is increased to enhance the weight of the decay penalty.

2. The method for unified optimization of the overall production and sales value considering battery degradation according to claim 1, characterized in that, The MIP heuristic hybrid algorithm includes: The decomposition and coordination module is configured to decompose the original optimization problem into an economic benefit subproblem and a battery life subproblem, and coordinate them using the Lagrange duality method; the original optimization problem is an optimization problem with a single economic objective. A dynamic weight correction module is configured to calculate economic weights based on real-time battery health status and adjust the weight allocation between economic benefits and lifespan degradation costs in the multi-objective value function accordingly; the formula for the economic weights is: ； in, As an economic weight.

3. The method for unified optimization of the overall production and sales value considering battery degradation according to claim 2, characterized in that, The MIP heuristic hybrid algorithm uses a genetic algorithm to process the integer variables of the device's start / stop status. The operation of the genetic algorithm includes: The start / stop status is encoded in binary, selected using a roulette wheel, and single-point crossover and bit-flip mutation operations are performed.

4. The method for unified optimization of the overall production and sales value considering battery degradation according to claim 1, characterized in that, The formula for the dynamic equilibrium constraint of the state of charge is: ； in, In a charged state, and These are charging efficiency and discharging efficiency, respectively. and Let be the charging power and discharging power during time period t, respectively. It is constrained within a preset safety range.

5. A unified optimization device for the overall value of production and sales, taking into account battery degradation, characterized in that, The device is applicable to the unified optimization method for production and sales value taking into account battery degradation as described in any one of claims 1 to 4, and the device comprises: The data acquisition module is configured to acquire real-time operating status data of the battery system and electricity market transaction data; A data storage module is connected to the data acquisition module and is configured to persistently store the acquired data. A data governance module, connected to the data storage module, is configured to perform data cleaning, outlier handling, and feature extraction. A function construction module, configured to construct a multi-objective value function with the objective of maximizing total value; A modeling module, configured to establish a hybrid integer programming model that includes integer variables of device start / stop status and continuous variables of charging / discharging power; The solution module is configured to use the MIP heuristic hybrid algorithm to solve the hybrid integer programming model and output the optimal charging and discharging strategy and equipment start-up and shutdown plan.

6. A unified optimization system for the overall value of production and sales, taking into account battery degradation, characterized in that, The system is applicable to the unified optimization device for production and sales considering battery degradation as described in claim 5, and the system includes: A battery management system configured to monitor the battery's state of charge and health in real time and provide battery operating data; A power market data interface, configured to acquire real-time electricity price data from the power market; A unified optimization device for the overall value of production and sales that takes into account battery degradation, wherein the unified optimization device for the overall value of production and sales that takes into account battery degradation is configured to perform optimization calculations based on the battery operating data and real-time electricity price data; An execution unit is configured to receive the charging and discharging strategy and equipment start-up and shutdown plan issued by the unified optimization device for production and sales taking into account battery degradation, and control the battery energy storage system to perform the corresponding charging and discharging operations.

7. A unified optimization system for overall production and sales value taking into account battery degradation, as described in claim 6, is characterized in that... The execution unit is further configured to: When the battery health status is detected to be lower than a preset threshold, an alarm signal is sent to the unified optimization device for the global value of production and sales that takes into account battery degradation, and the unified optimization device for the global value of production and sales that takes into account battery degradation is triggered to adjust the dynamic correction factor in the multi-objective value function.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the production and sales global value unified optimization method that takes into account battery degradation, as described in any one of claims 1 to 4.

Images