A blockchain-based distributed energy storage device collaborative optimization system and method

Abstract

The application relates to a kind of distributed energy storage equipment collaborative optimization system and method based on blockchain, the system includes data acquisition and pretreatment module, data analysis module, collaborative optimization module and blockchain interaction module.Data acquisition and pretreatment module real-time acquisition distributed energy storage equipment operating parameter, power market information and load data, and pretreatment is carried out;Data analysis module constructs the constraint model of energy storage equipment participating in auxiliary service, whole cycle economy evaluation system, in combination with the probabilistic income of energy storage equipment participating in auxiliary service is calculated by monte carlo method;Collaborative optimization module solves optimal scheduling strategy by using improved particle swarm algorithm;Blockchain interaction module implements information interaction, and executes scheduling strategy, income distribution and record storage based on smart contract.Meanwhile, the application also discloses the method using the system.The application realizes the collaborative optimization scheduling of distributed energy storage equipment participating in auxiliary service, credible information interaction and reasonable income distribution.

Description

Technical Field

[0001] This invention relates to the field of distributed energy storage equipment technology, and in particular to a blockchain-based collaborative optimization system and method for distributed energy storage equipment. Background Technology

[0002] With the increasing penetration rate of new energy power generation, the demand for flexible adjustment resources in the power system is becoming increasingly urgent. Distributed energy storage devices, due to their fast response speed and flexible deployment, have become an important resource for participating in power system ancillary services. However, distributed energy storage devices have small capacity and are scattered, and when they participate in ancillary services alone, they have limited adjustment capabilities and insufficient economic efficiency. Therefore, they need to be aggregated and managed to achieve large-scale application.

[0003] Existing blockchain-based distributed energy storage device collaborative optimization systems and methods suffer from the following problems: difficulty in constructing constraint models for energy storage devices participating in ancillary services; difficulty in analyzing the full life-cycle cost of energy storage devices, cost savings of conventional units, and benefits from participating in ancillary services across multiple scenarios; difficulty in using Monte Carlo methods to simulate market price fluctuations and analyze probabilistic returns; difficulty in using improved particle swarm optimization algorithms to solve for optimal scheduling strategies; and difficulty in using blockchain to achieve information interaction and scheduling execution with aggregated service providers and the electricity market based on smart contracts. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a blockchain-based distributed energy storage device collaborative optimization system for achieving accurate and efficient scheduling.

[0005] Another technical problem to be solved by the present invention is to provide a method for collaborative optimization of distributed energy storage devices using this system.

[0006] To address the aforementioned problems, the present invention provides a blockchain-based distributed energy storage device collaborative optimization system, characterized in that: the system comprises a data acquisition and preprocessing module, a data analysis module, a collaborative optimization module, and a blockchain interaction module; wherein:

[0007] The data acquisition and preprocessing module collects the operating parameters of the distributed energy storage device, electricity market information, and load data in real time, and performs preprocessing.

[0008] The data analysis module constructs a constraint model for energy storage equipment to participate in ancillary services; based on this constraint model, it calculates the full life cycle cost of energy storage equipment, the cost savings of conventional units, and the multi-scenario benefits of energy storage equipment participating in ancillary services; and it uses the Monte Carlo method to simulate market price fluctuations and calculate the probabilistic benefits of energy storage equipment participating in frequency regulation, peak shaving, voltage regulation, and backup services.

[0009] The collaborative optimization module uses an improved particle swarm optimization algorithm to solve the optimal scheduling strategy and outputs a single-service or multi-service collaborative scheme.

[0010] The blockchain interaction module implements information exchange between distributed energy storage devices and aggregate service providers, and between aggregate service providers and the electricity market, and executes scheduling strategies, revenue distribution and record storage based on smart contracts.

[0011] The preprocessing includes data cleaning to remove outliers, standardizing and unifying data formats, and aligning units and time series.

[0012] The constraint model refers to the physical constraints and service coordination constraints of energy storage devices; the physical constraints include upper and lower limits of energy storage state of charge, limits of energy storage charging and discharging power, fast charging and discharging constraints, depth of discharge constraints, dead zone constraints, and energy storage cycle life decay constraints; the service coordination constraints include compatibility constraints, timing coordination constraints, and remaining available time constraints for energy storage to participate in multiple ancillary services.

[0013] The total life cycle cost refers to the sum of the initial investment and replacement costs, operation and maintenance costs, and disposal costs.

[0014] The cost savings of conventional units refer to the value of coal consumption saved by conventional units throughout the entire lifecycle after the energy storage units are connected.

[0015] The multi-scenario revenue of energy storage equipment participating in ancillary services refers to the total revenue obtained by calculating the revenue of four types of services—frequency regulation, peak shaving, voltage regulation, and standby—in turn, and then weighting and summing the revenues based on the compatibility coefficients between different services. Frequency regulation is compensated based on regulation capacity and mileage; peak shaving is compensated based on deep peak shaving electricity subsidies; voltage regulation is rewarded based on reactive power regulation and voltage qualification rate; and standby is calculated based on capacity and energy costs.

[0016] The probabilistic return is obtained as follows:

[0017] (1) Determine the probability distribution of market price fluctuations and fit probability models for frequency regulation mileage price, peak shaving electricity price, and reserve capacity price based on historical data;

[0018] (2) The Monte Carlo method is used to generate multiple sets of random price samples. The effective samples are then selected by combining the power constraints and compatibility coefficients of each service in the constraint model.

[0019] (3) For each group of valid samples, calculate the revenue of the corresponding service according to the revenue formula of frequency modulation, peak shaving, and reserve.

[0020] (4) Summarize the returns of all samples, statistically analyze their probability distribution characteristics, and output the expected return and confidence interval as the probabilistic return result.

[0021] The optimal scheduling strategy is obtained using the following method:

[0022] ① Initialize the particle swarm, using the energy storage charging and discharging power of each time period as the particle dimension, randomly generate initial positions and velocities, and incorporate them into the constraints;

[0023] ② Introduce chaotic search to improve population diversity, assign weights to particles according to fitness, and randomly mutate the best individual during iteration;

[0024] ③ Construct a fitness function with the goal of maximizing net profit over the entire lifecycle, integrating full-cycle costs, cost savings from conventional units, and benefits from multiple scenarios;

[0025] ④ Through particle iterative optimization, output a single-service scheduling strategy or a multi-service collaborative scheme based on compatibility constraints when the constraints are met.

[0026] The blockchain interaction module is implemented in the following manner:

[0027] i. Real-time synchronization of operating parameters, constraint status, and service participation capabilities of each energy storage device through blockchain nodes;

[0028] ii. Upload the execution results of the scheduling strategy, obtain market prices and instructions, verify the authenticity of the data based on smart contracts, and interact with the electricity market;

[0029] iii. Based on the scheme output by the collaborative optimization module, the smart contract automatically triggers device charging and discharging commands;

[0030] iv. Distribute ancillary service revenue according to a preset algorithm, and store transaction records and operational data on the blockchain to ensure immutability.

[0031] A method for collaborative optimization of distributed energy storage devices using the system described above includes the following steps:

[0032] S1: Real-time acquisition of operating parameters of distributed energy storage devices, electricity market information and load data, and preprocessing.

[0033] S2: Construct a constraint model for energy storage equipment to participate in ancillary services; calculate the full life cycle cost of energy storage equipment, the cost savings of conventional units, and the multi-scenario benefits of participating in ancillary services based on the constraint model; use the Monte Carlo method to simulate market price fluctuations and calculate the probabilistic benefits of energy storage equipment participating in frequency regulation, peak shaving, voltage regulation, and backup services;

[0034] S3: Use an improved particle swarm optimization algorithm to solve the optimal scheduling strategy and output a single-service or multi-service collaborative solution.

[0035] S4: Information exchange between distributed energy storage devices and aggregation service providers, and between aggregation service providers and the electricity market, based on smart contracts to execute scheduling strategies, distribute revenue, and record and store evidence.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] 1. This invention provides a foundation for precise scheduling by constructing a model that includes multi-dimensional constraints such as energy storage state of charge, charging and discharging power, cycle life decay, and multi-service compatibility.

[0038] 2. This invention overcomes the difficulty in comprehensively analyzing economic efficiency by establishing a calculation system for full life cycle cost (including initial investment, operation and maintenance, scrapping, and cost savings of conventional units) and multi-scenario benefits (including frequency regulation, peak shaving, etc.), thereby improving the accuracy of benefit assessment.

[0039] 3. This invention simulates market price fluctuations and calculates probabilistic returns using the Monte Carlo method, and combines it with an improved particle swarm optimization algorithm to solve the optimal scheduling strategy, thus solving the problem of multi-service collaborative optimization and achieving efficient scheduling of single or multiple services.

[0040] 4. This invention uses a blockchain interaction module and smart contracts to achieve trusted information exchange, automatic scheduling, and revenue distribution storage between distributed energy storage and aggregation service providers and the electricity market. Attached Figure Description

[0041] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0042] Figure 1 This is a system module diagram of the present invention.

[0043] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0044] like Figure 1 As shown, a blockchain-based distributed energy storage device collaborative optimization system includes a data acquisition and preprocessing module 1, a data analysis module 2, a collaborative optimization module 3, and a blockchain interaction module 4. Wherein:

[0045] The data acquisition and preprocessing module 1 collects real-time operating parameters of distributed energy storage devices (such as State of Charge (SOC), charge / discharge power, and cycle count), electricity market information (such as time-of-use pricing, ancillary service subsidy prices, and market trading rules), and load data (such as regional electricity / heat / cooling load curves), and performs preprocessing. Preprocessing includes data cleaning to remove outliers, standardizing and unifying data formats, and aligning units and time series (synchronizing collected data to the same time granularity, such as 15 minutes / time), providing a high-quality data foundation for subsequent analysis.

[0046] Data analysis module 2 constructs a constraint model for energy storage equipment participating in ancillary services; based on this constraint model, it calculates the full life cycle cost of energy storage equipment, the cost savings of conventional units, and the multi-scenario benefits of energy storage equipment participating in ancillary services; and it uses the Monte Carlo method to simulate market price fluctuations and calculate the probabilistic benefits of energy storage equipment participating in frequency regulation, peak shaving, voltage regulation, and backup services.

[0047] The constraint model refers to the physical constraints and service coordination constraints of energy storage devices. Physical constraints include upper and lower limits of the energy storage's state of charge, limits on energy storage charging and discharging power, fast charging and discharging constraints, depth of discharge constraints, dead zone constraints, and energy storage cycle life decay constraints. Service coordination constraints include compatibility constraints, timing coordination constraints, and remaining available time constraints for energy storage participating in various ancillary services. Details are as follows:

[0048] Energy storage state of charge (SOC) constraint: S min ≤SOC(t)≤S max , of which S min Typically 10%~20%, S max Typically, it is 80% to 90%;

[0049] Charge / discharge power constraint: 0 ≤ PC ≤ PC max , 0≤PD≤PD max (PC) max PD max (80%~100% of the equipment's rated power).

[0050] Fast charge and discharge constraints: power limiting curves based on SOC (e.g., allowing high-power charging at low SOC and only allowing low-power charging at high SOC to avoid exacerbating polarization).

[0051] Depth of Discharge (DOD) constraint: DOD ≤ 80% (deep cycle energy storage) or 25% (shallow cycle energy storage), extending lifespan;

[0052] Dead zone constraint: During frequency modulation, the frequency deviation must exceed the dead zone threshold, such as Δf ≥ 0.033 Hz, before a response is received, reducing frequent actions;

[0053] Cycle lifetime decay constraint:

[0054]

[0055] Where: δ soc,av The average SOC value for a single cycle; Depth of charge / discharge; For specific and δ soc,av The number of cycles; ΔC ES (t) represents a specific time period before t. and δ soc,avThe amount of energy storage capacity decay under certain conditions;

[0056] Service collaboration compatibility constraints: For example, the compatibility coefficient between frequency regulation and voltage regulation is 0, so they cannot participate simultaneously; the compatibility coefficient between frequency regulation and standby capacity is 0.5, so they can collaborate to a limited extent.

[0057] Timing coordination constraints: Classify the discharge frequency and time according to service type, such as type 1: regular discharge, type 2: random occasional discharge, type 3: random frequent discharge, and prioritize scheduling high-priority services;

[0058] Remaining available time constraints: For example, if the frequency modulation service has only 3% remaining available time and the standby capacity reaches 98%, low-priority services can only participate in the remaining time after the high-priority service ends.

[0059] The total life cycle cost is calculated by adding up the initial investment and replacement costs, operation and maintenance costs, and disposal costs. Specifically:

[0060] Initial investment and replacement cost are used to calculate the total investment cost of energy storage equipment from initial purchase to battery replacement throughout its lifespan. This is applicable to economic assessments during the planning phase and cost allocation during the operational phase. The initial investment and replacement cost are calculated using the formula obtained from the data acquisition and processing module. The formula for initial investment and replacement cost is:

[0061]

[0062] In the formula: C inv The initial investment and total replacement cost are expressed in yuan; C PCS Cost per unit power, expressed in yuan / kW, refers to the unit power cost of the energy storage converter PCS, with industry data suggesting a range of 2000-3000 yuan / kW; P rated The rated power of the energy storage is expressed in kW, and is determined based on project requirements, such as 500kW; C bat Cost per unit capacity, expressed in yuan / kWh, refers to the manufacturing cost per unit capacity of the battery itself. The reference value for lithium batteries is 1.2~1.8 yuan / Wh (i.e., 1200~1800 yuan / kWh, e.g., 1500 yuan / kWh); E rated The rated capacity of the energy storage is expressed in kWh, such as 2000kWh, which is a 500kW energy storage system with a 4-hour runtime; θ is the discount rate, expressed in %, with a reference industry benchmark rate of return of 5% to 8%; ρ is the replacement frequency index; T is the design life of the energy storage device, expressed in years, usually taken as 20 years; qn is the number of battery replacements within the life cycle of the energy storage device. The design life of a lithium battery is about 10 years, and the battery cycle life is about 3000 cycles. If there are 150 cycles per year, then it needs to be replaced once every 20 years.

[0063] Operation and maintenance costs are used to calculate the daily operation and maintenance expenses throughout the entire life cycle of energy storage equipment, including equipment inspections, battery maintenance, and software upgrades. Data obtained from the data acquisition and processing module, including unit power operation and maintenance cost, unit capacity operation and maintenance cost, and annual energy storage charge and discharge volume, are substituted into the formula to calculate the operation and maintenance cost. The operation and maintenance cost formula is:

[0064]

[0065] In the formula: C OM Total cost of operation and maintenance throughout the entire lifecycle; C POM The unit power operation and maintenance cost; C EOM W(t) represents the unit capacity operation and maintenance cost; W(t) represents the annual charge and discharge capacity of energy storage; t is the number of monitoring periods; i represents the unit.

[0066] The end-of-life disposal cost is used to calculate the dismantling, recycling, and environmental treatment costs of energy storage equipment at the end of its lifespan. Data obtained from the data acquisition and processing module, including end-of-life cost per unit power, end-of-life cost per unit capacity, and the number of disposal cycles, are substituted into the formula to calculate the total end-of-life disposal cost. The formula for the end-of-life disposal cost is:

[0067]

[0068] In the formula: C scr It is the total cost of the entire lifecycle disposal; C Pscr Cost per unit of power to be scrapped; C Escr is the cost per unit capacity to be scrapped; w is the index of the number of scrapping processes.

[0069] The cost savings of conventional generating units refer to the value of coal consumption saved by conventional generating units over the entire lifecycle after the integration of energy storage units. Calculating the value of coal consumption savings for conventional generating units involves converting the reduced power generation of conventional generating units into coal cost savings, directly reflecting the improvement in the economic efficiency of conventional unit operation due to energy storage. In other words, calculating the total reduction in power generation by conventional generating units quantifies the reduction in power generation by conventional generating units, primarily thermal power units, due to the participation of energy storage equipment in ancillary services such as frequency regulation and peak shaving, reflecting the effect of energy storage on alleviating the pressure on conventional generating units. Various data obtained from the data acquisition and processing module, including conventional unit coal consumption levels, current coal prices, output of conventional generating units before and after energy storage integration, are substituted into the formula to calculate the value of coal consumption saved by conventional generating units after the integration of energy storage units over the entire lifecycle, i.e., the cost savings of conventional generating units. The formula for the cost savings of conventional generating units is:

[0070] Y C = MP C C;

[0071]

[0072] In the formula: Y C This refers to the cost savings of conventional generating units, specifically the coal consumption savings of conventional generating units throughout their entire lifecycle after the energy storage unit is connected; P C Indicates the coal consumption level of conventional generating units; C represents the current coal price; M is the total power generation reduction of conventional generating units participating in ancillary services due to energy storage integration throughout the entire lifecycle; P G0 (t) represents the output of the conventional unit when no energy storage is connected; P B (t) represents the output of the conventional unit when energy storage is connected; Δt is the calculation time interval.

[0073] The multi-scenario revenue from energy storage equipment participating in ancillary services refers to the total revenue obtained by calculating the revenue from four types of services—frequency regulation, peak shaving, voltage regulation, and reserve—separately, and then weighting the revenues together with the compatibility coefficients between different services. Frequency regulation revenue is calculated based on the frequency regulation ancillary service fee C per transaction day. tp Compensation; peak shaving is based on the daily operating peak shaving cost C. yf Subsidies; voltage regulation is rewarded based on reactive power regulation and voltage qualification rate; standby is calculated based on capacity and energy costs.

[0074] Subsidies Y for energy storage participating in ancillary services s Calculation formula:

[0075]

[0076] In the formula: P b (i) represents the real-time energy storage power of unit i; P PFR Subsidies for the power output of energy storage units participating in ancillary services.

[0077] The frequency regulation ancillary service fee is calculated based on the magnitude of its contribution to power grid frequency regulation. The frequency regulation ancillary service fee for each trading day is calculated using the following formula:

[0078]

[0079] In the formula: C tp C. Frequency regulation ancillary service fees for the unit being assessed on the trading day; f The fixed electricity price for frequency regulation power; Q f Frequency regulation electricity volume for the unit of assessment on the trading day; D tp The frequency regulation compliance rate of the unit being assessed during the trading day; T tp The average peak-shaving coefficient for the unit being assessed on a trading day; P maxi Adjust the upper limit for registered AGCs for AGC unit i; P mini Adjust the lower limit for registered AGC for AGC unit i;

[0080] The daily peak-shaving cost for assessment unit j can be calculated using the following formula:

[0081]

[0082] In the formula: C yf C0 represents the peak-shaving cost for the trading day; C0 represents the peak-shaving unit price; T k T represents the operating time of the unit during the peak shaving period of k; yf Let k be the average peak-shaving coefficient during the peak-shaving period; P hjk The maximum adjustable output declared by assessment unit j during the peak-shaving period of operation k; P 1jk The minimum technical output declared by assessment unit j during the peak-shaving period of k; 48 is the total number of peak-shaving periods calculated based on the peak-shaving costs of the trading day;

[0083] For generating units providing auxiliary pressure regulation services, their pressure regulation qualification rate is calculated monthly, and the pressure regulation cost can be calculated using the following formula:

[0084]

[0085] In the formula: C ty Monthly voltage regulation auxiliary service fee for power plants; Q y1 The voltage regulation reward and penalty electricity for power plants; Q y2 This is the compensation amount for the reduced active power generation of power plant units due to voltage regulation during leading-phase operation; Q y3 Cp represents the compensation electricity generated by the power plant units due to the reduced active power generation caused by increased reactive power generation and voltage regulation; Cp represents the unit price of the active power generation bonus for voltage regulation; m represents the total number of power plant units; T M The total number of time periods used to calculate the monthly voltage regulation ancillary service fee for power plants; Q i (t) represents the reactive power absorbed by unit i during the leading phase operation in the TMS. A negative value indicates that reactive power is absorbed during the leading phase operation. Cq is the bonus unit price for the reactive power absorbed by the unit during the leading phase operation.

[0086] Reserve revenue is calculated based on reserve capacity and actual energy usage. The usage fee formula for month γ is as follows:

[0087]

[0088] In the formula: C Um The usage fee for month γ; R γ P represents the spare capacity delivered by the unit during the γth month. Uγ The usage price of the unit in month γ.

[0089] The data obtained from the data acquisition and processing module includes parameters such as real-time energy storage power, frequency regulation electricity quota price, peak shaving unit price, and unit voltage regulation bonus active power quota price. These parameters are then substituted into formulas to calculate the revenue from four types of services: frequency regulation, peak shaving, voltage regulation, and reserve. To ensure the grid frequency remains at 50Hz, generating units (excluding AGC units) are divided into four categories based on their frequency regulation tasks. Units within the same power plant undertaking the same type of frequency regulation task are designated as a single assessment unit to strengthen performance management. These four frequency regulation assessment units are: the first, second, and third frequency regulation units, and the load monitoring unit. Their frequency regulation ancillary service fees are calculated based on their contribution to grid frequency regulation. The frequency regulation ancillary service fee for each trading day is calculated using the formula.

[0090] Frequency modulation power Q of various assessment units f The calculation method is as follows. The first, second, and third frequency regulation units and the load monitoring unit, when... And f l Exceeding 50.0±Δf N At that time, Q f for: .

[0091] When the AGC function is put into use And f l When ≠50.0; or during the period when ACC function is off, f l When the frequency exceeds 50.0Hz ± 0.1Hz, Q f for: .

[0092] In the formula: Δf N To assess the responsible frequency range of the unit, the first frequency modulation unit is 0.02 Hz, the second frequency modulation unit is 0.05 Hz, the third frequency modulation unit is 0.08 Hz, and the load monitoring unit is 0.1 Hz; The actual output of the assessment unit j in the l-th minute, as collected by EMS; The actual output of the EMS-collected assessment unit j at minute (l+1); f l The power grid frequency collected by EMS in the 1st minute; The power grid frequency of the assessment unit j collected by EMS at the (l+1)th minute; The power grid frequency of the assessment unit j collected by EMS in the l-th minute.

[0093] FM pass rate D tp Calculate using the following formula:

[0094] In the formula: T m1 For the power grid frequency exceeding the limit by 50.0 ± Δf cThe cumulative time, in minutes; T m2 For the power grid frequency exceeding the limit by 50.0 ± Δf c During this period, the time during which the assessment unit has no capacity to adjust is measured in minutes; T m3 For the power grid frequency exceeding the limit by 50.0 ± Δf c During this period, the assessment unit had the capability to adjust, but the actual adjustment rate of output was less than the specified value ΔP for output adjustment. N Time, in minutes; Δf c The frequency deviation for the assessment unit is 0.05Hz for the first and second frequency modulation units, 0.08Hz for the third frequency modulation unit, and 0.1Hz for the load monitoring unit.

[0095] The scheduling objective, i.e., maximizing operational revenue, of energy storage devices participating in ancillary services within a regional integrated energy system, constrained by compatibility requirements, is expressed by the formula:

[0096] S1= Y s + Y c + S pva - C inv - C OM - C scr

[0097] S2= Y s + Y c +2S pva - C inv - C OM - C scr

[0098] In the formula: S1 represents the revenue from a single cycle of peak-valley arbitrage plus ancillary service system; S2 represents the revenue from multiple cycles of peak-valley arbitrage plus ancillary service system; Y s Subsidies received for participating in ancillary services for energy storage; Y c Cost savings for conventional units; S pva To profit from peak-valley arbitrage; C inv C represents the initial investment and replacement costs; OM For operation and maintenance costs; C scr This represents the cost of disposal after decommissioning. S1 and S2 in both equations reach their maximum values. In addition, compatibility constraints when energy storage participates in different ancillary services should be considered. This should be reflected in the benefits of participating in ancillary services in the formulas as follows:

[0099] S1 = γY s + Y c + S pva - C inv - C OM - C scr

[0100] S2 = γYs + Y c +2S pva - C inv - C OM - C scr

[0101] Wherein: γ is the ancillary service compatibility coefficient under the compatibility constraints shown. The compatibility coefficients for different services are as follows: γ = 0.5 when participating in both frequency regulation and reserve capacity, and γ = 0 when participating in both frequency regulation and voltage regulation.

[0102] This invention employs the Monte Carlo method, fitting a price fluctuation probability distribution (such as a normal distribution) to historical market price data to generate multiple sets of random price samples. A constraint model is then used to select effective samples, and the probabilistic returns and confidence intervals for each service are calculated. The probabilistic returns are obtained as follows:

[0103] (1) Determine the probability distribution of market price fluctuations and fit probability models for frequency regulation mileage price, peak shaving electricity price, and reserve capacity price based on historical data;

[0104] (2) The Monte Carlo method is used to generate multiple sets of random price samples. The effective samples are then selected by combining the power constraints and compatibility coefficients of each service in the constraint model.

[0105] (3) For each group of valid samples, calculate the revenue of the corresponding service according to the revenue formula of frequency modulation, peak shaving, and reserve.

[0106] (4) Summarize the returns of all samples, statistically analyze their probability distribution characteristics, and output the expected return and confidence interval as the probabilistic return result.

[0107] Specifically, based on historical data, such as the frequency regulation mileage price and peak regulation electricity price over the past three years, a price probability model is fitted, such as a normal distribution N(μ,σ). 2 ), where μ is the mean and σ is the standard deviation. A Monte Carlo method is used to generate 1000–10000 random price samples. Valid samples are selected based on constraints such as charging / discharging power limits and compatibility constraints (e.g., price fluctuations within a reasonable range and service synergy). For valid samples, the revenue from services such as frequency modulation and peak shaving is calculated using formulas. The revenues of all samples are summarized, and the expected revenue (mean) and confidence interval (e.g., a 95% confidence interval) are calculated as probabilistic revenue results, reflecting the risk of revenue under market fluctuations.

[0108] The collaborative optimization module 3 uses an improved particle swarm optimization algorithm to solve for the optimal scheduling strategy and outputs a single-service or multi-service collaborative scheme. The optimal scheduling strategy is obtained as follows:

[0109] ① Initialize the particle swarm, using the energy storage charging and discharging power of each time period as the particle dimension, randomly generate initial positions and velocities, and incorporate them into the constraints;

[0110] ② Introduce chaotic search to improve population diversity, assign weights to particles according to fitness, and randomly mutate the best individual during iteration;

[0111] ③ Construct a fitness function with the goal of maximizing net profit over the entire lifecycle, integrating full-cycle costs, cost savings from conventional units, and benefits from multiple scenarios;

[0112] ④ Through particle iterative optimization, output a single-service scheduling strategy or a multi-service collaborative scheme based on compatibility constraints when the constraints are met.

[0113] This invention uses the energy storage charging and discharging power at different times as the particle dimension. For example, a 24-hour scheduling corresponds to 24 dimensions. Initial particle positions, power values, and velocities are randomly generated, incorporating constraints such as charging and discharging power and SOC. Chaotic search is introduced, using Z... n+1 = u*Z n *(1-Z n Generate chaotic variables to optimize the initial population diversity; where Z n Z is the chaotic variable in the nth iteration, with a value range of [0,1]; n+1 The chaotic variable is generated through the iterative formula in the (n+1)th iteration; u is the chaotic system parameter, usually set to 4 to ensure the system is in a chaotic state; the chaotic variable is used to optimize the initial population distribution, improve population diversity, and prevent the algorithm from getting stuck in local optima. Weights are allocated according to the net fitness gain of particles, with elite particles receiving higher weights to accelerate optimization; a neighborhood search is introduced for the optimal individual: Y´ = Y + R. b *(2*r⁴- 1), avoiding local optima. Y is the position of the current optimal individual, i.e., the charge / discharge power combination under a certain scheduling strategy; Y´ is the position of the mutated individual; R b The neighborhood search radius is determined in the b-th iteration and dynamically adjusted with the iteration count, such as decreasing from large to small, to balance global search and local optimization. r4 is a random number in the range [0,1] used to generate random mutation directions. Small-amplitude random mutations are introduced into the optimal individual during iteration to prevent the algorithm from stagnating at local optima and prematurely converging. The fitness function is constructed with the objective of maximizing the net profit (total profit - total cost) over the entire cycle, i.e., f = Y. s + Y c + S pva - C inv - C OM - C scr In the formula, Y s For subsidies received by energy storage participating in ancillary services, Y c To save costs for conventional units, S pva To profit from peak-valley arbitrage, C inv For initial investment and replacement costs, C OM For operation and maintenance costs, Cscr Cost of scrapping. After meeting the iteration limit, such as 400 iterations, output a single-service scheduling strategy (e.g., only participating in frequency regulation) or a multi-service collaborative solution (e.g., frequency regulation + peak shaving, which must meet compatibility constraints).

[0114] Blockchain interaction module 4 implements information exchange between distributed energy storage devices and aggregation service providers, and between aggregation service providers and the electricity market. It executes scheduling strategies, revenue distribution, and record storage based on smart contracts. Distributed energy storage devices, aggregation service providers, and the electricity market act as blockchain nodes, synchronizing device operating parameters, constraint status, and service capabilities in real time. Specifically, distributed energy storage devices upload real-time operating data such as SOC and power to the blockchain nodes via the Internet of Things (IoT), while aggregation service providers and the electricity market synchronize market prices, scheduling instructions, and other information, achieving full network data transparency. The authenticity of the data is verified based on smart contracts (e.g., confirming the SOC data is tamper-proof through device digital signatures), then the scheduling strategy execution results are uploaded, and market instructions are obtained. Market instructions are transmitted encrypted to ensure integrity. Based on the scheme output by collaborative optimization module 3, the smart contract automatically triggers charging and discharging instructions (e.g., charging during off-peak hours and discharging during peak hours to participate in peak shaving) and provides real-time feedback on the execution results. Revenue for each participant is calculated according to preset rules (e.g., device contribution ratio), and automatically transferred via smart contracts. Transaction records, operating data, and revenue distribution results are stored on the blockchain to ensure immutability and traceability. The specific implementation method is as follows:

[0115] i. Real-time synchronization of operating parameters, constraint status, and service participation capabilities of each energy storage device through blockchain nodes;

[0116] ii. Upload the execution results of the scheduling strategy, obtain market prices and instructions, verify the authenticity of the data based on smart contracts, and interact with the electricity market;

[0117] iii. Based on the scheme output by the collaborative optimization module 3, the smart contract automatically triggers device charging and discharging commands;

[0118] iv. Distribute ancillary service revenue according to a preset algorithm, and store transaction records and operational data on the blockchain to ensure immutability.

[0119] like Figure 2 As shown, a method for collaborative optimization of distributed energy storage devices using this system includes the following steps:

[0120] S1: Real-time acquisition of operating parameters of distributed energy storage devices, electricity market information and load data, and preprocessing.

[0121] S2: Construct a constraint model for energy storage equipment to participate in ancillary services; calculate the full life cycle cost of energy storage equipment, the cost savings of conventional units, and the multi-scenario benefits of participating in ancillary services based on the constraint model; use the Monte Carlo method to simulate market price fluctuations and calculate the probabilistic benefits of energy storage equipment participating in frequency regulation, peak shaving, voltage regulation, and backup services;

[0122] S3: Use an improved particle swarm optimization algorithm to solve the optimal scheduling strategy and output a single-service or multi-service collaborative solution.

[0123] S4: Information exchange between distributed energy storage devices and aggregation service providers, and between aggregation service providers and the electricity market, based on smart contracts to execute scheduling strategies, distribute revenue, and record and store evidence.

Claims

1. A blockchain-based distributed energy storage device collaborative optimization system, characterized in that: The system includes a data acquisition and preprocessing module (1), a data analysis module (2), a collaborative optimization module (3), and a blockchain interaction module (4); among which: The data acquisition and preprocessing module (1) acquires the operating parameters, electricity market information and load data of the distributed energy storage device in real time and performs preprocessing. The data analysis module (2) constructs a constraint model for energy storage equipment participating in ancillary services; based on this constraint model, it calculates the full life-cycle cost of energy storage equipment, the cost savings of conventional units, and the multi-scenario benefits of energy storage equipment participating in ancillary services; and uses the Monte Carlo method to simulate market price fluctuations and calculate the probabilistic benefits of energy storage equipment participating in frequency regulation, peak shaving, voltage regulation, and standby services; the constraint model refers to the physical constraints and service coordination constraints of energy storage equipment; the physical constraints include upper and lower limits of energy storage state of charge, limits of energy storage charging and discharging power, fast charging and discharging constraints, depth of discharge constraints, and dead zone constraints. Energy storage cycle life decay constraint; the service coordination constraint includes compatibility constraints, timing coordination constraints, and remaining available time constraints for energy storage participating in multiple ancillary services; the conventional unit cost saving refers to the value of coal consumption saved by the conventional unit throughout the entire cycle after the energy storage unit is connected; the multi-scenario benefits of energy storage equipment participating in ancillary services refer to the total benefits obtained by calculating the benefits of four types of services—frequency regulation, peak regulation, voltage regulation, and standby—and then weighting and summing them together with the compatibility coefficients between different services; frequency regulation is compensated based on regulation capacity and mileage; peak regulation is subsidized based on deep peak regulation electricity; voltage regulation is rewarded based on reactive power regulation and voltage qualification rate; standby is calculated based on capacity and energy cost. The collaborative optimization module (3) uses an improved particle swarm optimization algorithm to solve for the optimal scheduling strategy and outputs a single-service or multi-service collaborative scheme; the optimal scheduling strategy is obtained by the following method: ① Initialize the particle swarm, using the energy storage charging and discharging power of each time period as the particle dimension, randomly generate initial positions and velocities, and incorporate them into the constraints; ② Introduce chaotic search to improve population diversity, assign weights to particles according to fitness, and randomly mutate the best individual during iteration; ③ Construct a fitness function with the goal of maximizing net profit over the entire lifecycle, integrating full-cycle costs, cost savings from conventional units, and benefits from multiple scenarios; ④ Through particle iterative optimization, output a single-service scheduling strategy or a multi-service collaborative scheme based on compatibility constraints when the constraints are met; The blockchain interaction module (4) implements information interaction between distributed energy storage devices and aggregation service providers, and between aggregation service providers and the electricity market, and executes scheduling strategies, revenue distribution, and record storage based on smart contracts; the blockchain interaction module (4) is implemented in the following manner: i. Real-time synchronization of operating parameters, constraint status, and service participation capabilities of each energy storage device through blockchain nodes; ii. Upload the execution results of the scheduling strategy, obtain market prices and instructions, verify the authenticity of the data based on smart contracts, and interact with the electricity market; iii. Based on the scheme output by the collaborative optimization module (3), the smart contract automatically triggers the device charging and discharging command; iv. Distribute ancillary service revenue according to a preset algorithm, and store transaction records and operational data on the blockchain to ensure immutability.

2. The blockchain-based distributed energy storage device collaborative optimization system as described in claim 1, characterized in that: The preprocessing includes data cleaning to remove outliers, standardizing and unifying data formats, and aligning units and time series.

3. The blockchain-based distributed energy storage device collaborative optimization system as described in claim 1, characterized in that: The total life cycle cost refers to the sum of the initial investment and replacement costs, operation and maintenance costs, and disposal costs.

4. The blockchain-based distributed energy storage device collaborative optimization system as described in claim 1, characterized in that: The probabilistic return is obtained as follows: (1) Determine the probability distribution of market price fluctuations and fit probability models for frequency regulation mileage price, peak shaving electricity price, and reserve capacity price based on historical data; (2) The Monte Carlo method is used to generate multiple sets of random price samples. The effective samples are then selected by combining the power constraints and compatibility coefficients of each service in the constraint model. (3) For each group of valid samples, calculate the revenue of the corresponding service according to the revenue formula of frequency modulation, peak shaving, and reserve. (4) Summarize the returns of all samples and statistically analyze their probability distribution characteristics. Output the expected return and confidence interval as the probabilistic return result.

5. A method for collaborative optimization of distributed energy storage devices using the system described in any one of claims 1 to 4, comprising the following steps: S1: Real-time acquisition of operating parameters of distributed energy storage devices, electricity market information and load data, and preprocessing. S2: Construct a constraint model for energy storage equipment to participate in ancillary services; calculate the full life cycle cost of energy storage equipment, the cost savings of conventional units, and the multi-scenario benefits of participating in ancillary services based on the constraint model; use the Monte Carlo method to simulate market price fluctuations and calculate the probabilistic benefits of energy storage equipment participating in frequency regulation, peak shaving, voltage regulation, and backup services; S3: Use an improved particle swarm optimization algorithm to solve the optimal scheduling strategy and output a single-service or multi-service collaborative solution. S4: Information exchange between distributed energy storage devices and aggregation service providers, and between aggregation service providers and the electricity market, based on smart contracts to execute scheduling strategies, distribute revenue, and record and store evidence.

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