Ground source heat pump and building energy storage collaborative operation optimization method

Abstract

The invention belongs to the technical field of energy system optimization control, and particularly discloses a ground source heat pump and building energy storage collaborative operation optimization method, which comprises the following steps: based on historical operation data and seasonal climate prediction, setting a predicted energy supply sequence in a preset time period through multi-factor fusion; executing predicted energy supply and collecting operation data in real time, and calculating the deviation between the operation data and the predicted energy supply in each dimension to generate each deviation adjustment item; when any deviation adjustment item exceeds a preset threshold value, a correction scheme containing a ground source heat pump correction energy supply sequence and an energy storage charging and discharging correction sequence is generated through re-optimization by taking the current data as an initial state; and finally, based on the deviation degree corresponding to each correction operation, dynamically generating a cross-device priority sequence and sequentially executing the cross-device priority sequence. By constructing a closed-loop control system of prediction, monitoring, correction, sorting and execution, active maintenance of soil heat balance and collaborative optimization operation of the ground source heat pump and energy storage are achieved.

Description

Technical Field

[0001] This invention belongs to the field of energy system optimization and control technology, and relates to an optimization method for the coordinated operation of ground source heat pumps and building energy storage. Background Technology

[0002] Ground source heat pump systems extract heat energy from the soil to provide heating and cooling for buildings, offering advantages such as energy conservation and environmental protection. However, long-term operation can easily lead to an imbalance in the soil temperature field, causing heat accumulation or depletion, reducing system efficiency, and even causing failure. Therefore, building energy storage systems are typically introduced to operate in conjunction with the system, utilizing their energy charging and discharging characteristics to regulate the ground source heat pump's operating conditions, smoothing peak flows and filling valleys, and helping to maintain soil thermal balance. The synergistic optimization of ground source heat pumps and energy storage involves multi-timescale decisions: the annual scale needs to consider the long-term impact of seasonal climate on soil thermal balance; the day-ahead scale needs to combine load and electricity prices to formulate energy supply plans; and the real-time scale needs to dynamically adjust based on operating conditions. The challenge lies in achieving synergy between the two under different constraints, balancing load demand and soil thermal balance.

[0003] Currently, the control methods for the coordinated operation of ground source heat pumps and energy storage in the industry mainly rely on multi-timescale optimization scheduling strategies. For example, Chinese invention patent CN116883196A discloses a method, system, and equipment for the coordinated operation of building energy sources, loads, and storage using ground source heat pumps. This solution is based on micro-source, load, and energy storage data from the building energy center and formulates scheduling plans through two timescales: a day-ahead optimization model and an intraday optimization model. It aims to solve the optimal decision-making problem under uncertainty and rapid changes in electricity prices, and achieve comprehensive utilization of multiple energy sources and overall economic operation.

[0004] However, the above-mentioned existing technologies have the following obvious limitations: First, the above scheme takes economic optimization as the scheduling objective. Its optimization model includes economic factors such as load, electricity price and micro-source start-up and shutdown. Soil temperature is not included as a state variable in monitoring and optimization. It ignores the changes in soil temperature field caused by long-term operation of ground source heat pumps, and therefore cannot perceive and respond to such changes, resulting in a decrease in efficiency during long-term operation.

[0005] Second, the above scheme adopts two-level optimization: day-ahead and intraday. Although intraday optimization introduces real-time electricity prices, it is a planned adjustment based on a preset time scale. It does not perform closed-loop correction based on real-time operating data, and therefore cannot calculate deviations and dynamically adjust instructions in real time when there are sudden load changes or expected equipment status deviations. At the same time, the above scheme does not prioritize the correction operations of ground source heat pumps and energy storage. When multiple instructions are executed concurrently, there is a lack of execution order decision mechanism, which leads to equipment response conflicts.

[0006] Therefore, there is an urgent need for an optimization method that can take soil thermal balance as the core objective, has the ability to perceive and dynamically correct deviations in real time, and can realize the priority of coordinated execution of ground source heat pumps and energy storage, in order to solve the above-mentioned technical problems. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background technology, an optimization method for the coordinated operation of ground source heat pump and building energy storage is proposed.

[0008] The objective of this invention can be achieved through the following technical solution: This invention provides a method for optimizing the coordinated operation of ground source heat pumps and building energy storage, including: based on historical operating data and seasonal climate prediction, with the goal of maintaining soil thermal balance, setting a predicted energy supply sequence under a preset time period.

[0009] The system executes the predicted energy supply sequence at the current time point, and collects real-time energy supply values ​​from the ground source heat pump, soil heat distribution data, and the real-time state of charge of building energy storage to calculate the deviation values ​​between the actual operating data and the predicted energy supply values ​​in each dimension, and generates each deviation adjustment item.

[0010] If any deviation adjustment item exceeds a preset threshold, the corrected energy supply scheme will be re-optimized and generated based on the current actual operating data as the initial state. The corrected energy supply scheme includes a corrected energy supply sequence for the ground source heat pump and a corrected charging and discharging sequence for building energy storage.

[0011] Based on the degree of deviation of the deviation adjustment item corresponding to each correction operation in the corrected power supply scheme, a priority order of correction operations is dynamically generated, and the corresponding correction operations are executed in sequence according to the priority order.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention takes maintaining soil thermal balance as the optimization goal, collects soil heat distribution data in real time, and extracts reference soil temperature value based on historical stable operating state when generating deviation adjustment item, thus constructing an active monitoring and deviation sensing mechanism for soil thermal state, thereby realizing timely correction when soil temperature deviates from the reference value, avoiding soil heat accumulation or heat depletion caused by long-term operation.

[0013] (2) This invention collects the energy supply value of the ground source heat pump, soil heat distribution data and energy storage charge status in real time, calculates the deviation value between the actual operating data and the expected energy supply value in each dimension to generate deviation adjustment items, and automatically triggers correction when any deviation item exceeds the preset threshold, so as to adjust the operation strategy in a timely manner when uncertain disturbances such as sudden load changes, equipment output fluctuations or sudden changes in climate conditions occur.

[0014] (3) This invention adopts a multi-factor fusion method that calculates the predicted energy supply sequence by multiplying the historical benchmark energy supply sequence, the normalized energy supply change rate and the climate influence coefficient obtained based on fuzzy reasoning, and sets a preset time period. This achieves a comprehensive consideration of historical patterns, dynamic characteristics and external environment, and improves the adaptability of the predicted energy supply sequence.

[0015] (4) This invention calculates the comprehensive correction demand index for each period based on the deviation between the ground source heat pump correction energy supply sequence and the load forecast sequence, and dynamically judges the charging and discharging demand type in combination with the electricity price level, thereby determining the charging and discharging correction sequence of building energy storage and allocating energy storage capacity according to the correction priority, thereby improving energy utilization efficiency.

[0016] (5) The present invention calculates the comprehensive priority index of each correction operation based on the deviation degree and optimization weight of the deviation adjustment item corresponding to each correction operation, and dynamically generates the cross-device priority ranking of ground source heat pump power supply correction operation and energy storage charging and discharging correction operation, which solves the response conflict problem when multiple instructions are concurrent and avoids the equipment oscillation problem caused by improper instruction execution order. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the connections between the steps of the method of the present invention.

[0019] Figure 2 A detailed flowchart illustrating the steps involved in setting the predicted energy supply sequence under a preset time period for this invention.

[0020] Figure 3 This is a schematic diagram showing the connection steps of the modified energy supply scheme generation method of the present invention. Detailed Implementation

[0021] 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.

[0022] This invention achieves coordinated and optimized operation of ground source heat pumps and building energy storage by constructing a closed-loop control system that includes preset planning, real-time monitoring, deviation triggering, correction generation, dynamic sorting, and collaborative execution. Specifically, the method first sets a predicted energy supply sequence for a preset time period based on historical operating data and climate prediction; then, it collects the energy supply value of the ground source heat pump, soil heat distribution, and energy storage state of charge in real time, calculates deviations in each dimension, and generates deviation adjustment items; when any deviation item exceeds a preset threshold, it re-optimizes and generates a corrected energy supply scheme based on the current actual operating data as the initial state; finally, it dynamically generates a priority order based on the degree of deviation corresponding to each correction operation and executes them sequentially. This solves the problems of existing technologies lacking active monitoring of soil heat balance, inability to dynamically correct deviations in real time, and lack of cross-device collaborative execution mechanisms.

[0023] Please see Figure 1 As shown, the present invention provides an optimization method for the coordinated operation of ground source heat pump and building energy storage, which includes the following steps S1 to S4.

[0024] S1. Set the predicted energy supply sequence under the preset time period.

[0025] Step S1 aims to establish a predicted energy supply sequence for a preset time period, based on historical operational data and seasonal climate forecasts, with the goal of maintaining soil thermal balance. This provides an expected benchmark for subsequent real-time monitoring and adjustments. Please refer to [link / reference]. Figure 2 As shown, the specific steps include: S1-1, obtaining historical energy supply sequences and calculating the baseline energy supply sequence.

[0026] Retrieve historical energy supply sequences from the historical operation database, starting from the current time point and within a preset time period (e.g., the next 24 hours or the next 7 days). The historical energy supply sequence refers to the actual energy supply data records of the ground source heat pump within multiple time periods of the same length in the past.

[0027] The average of multiple historical energy supply sequences obtained is calculated at the same point in time to obtain the baseline energy supply value at each time point under a preset time period. These values ​​are then arranged in chronological order to form a baseline energy supply sequence. This baseline energy supply sequence represents the average energy supply level based on historical operating patterns.

[0028] S1-2. Determine the rate of change of energy supply at each time point and perform normalization processing.

[0029] The energy supply values ​​at each time point are extracted from the historical energy supply sequence, and time series analysis is performed on them based on the chronological order of the energy supply sequence. The time series analysis can be performed by calculating the change in energy supply at adjacent time points using the difference method, dividing the change by the time interval to obtain the rate of change in energy supply at each time point.

[0030] To eliminate the influence of different dimensions, the calculated energy supply change rate is normalized. The normalization process can adopt the minimum-maximum normalization method, which maps the current change rate to the interval [0, 1] based on the statistical range of historical energy supply change rates, to obtain the normalized energy supply change rate at each time point.

[0031] S1-3. Obtain meteorological forecast data and generate climate prediction parameters.

[0032] It acquires official weather forecast data released by meteorological departments for a preset time period, including hourly temperature, humidity, and solar radiation intensity. Simultaneously, based on historical meteorological statistics, it generates concurrent climate statistical characteristics for the same date type as the preset time period, such as historical average temperature, average humidity, and average solar radiation for the same period.

[0033] The official meteorological forecast data is fused and corrected with the corresponding climate statistical characteristics to generate climate prediction parameters. The fusion and correction can employ a weighted average method, with weights dynamically determined based on forecast accuracy and historical statistical confidence levels. The final climate prediction parameters include at least hourly temperature, humidity, and solar radiation intensity.

[0034] S1-4. Calculate the climate impact coefficient based on fuzzy inference.

[0035] To achieve refined modeling of the impact of climate factors on energy supply, this step constructs and applies a multi-input, single-output fuzzy inference system. The inputs to this system are the climate prediction parameters generated in steps S1-3, specifically including hourly temperature, relative humidity, and solar radiation intensity; the output is the climate impact coefficient at each time point, whose value range is usually set to [0.5, 1.5], representing the adjustment range of climate factors relative to the baseline operating conditions.

[0036] The construction of the fuzzy inference system includes the following sub-steps: S1-4-1, defining the fuzzy sets and membership functions of the input and output variables.

[0037] Based on historical meteorological data and corresponding system operation data, the universe of discourse for each input and output variable is partitioned, and fuzzy sets are defined. For example, the universe of discourse for temperature is [-20℃, 40℃], which is divided into three fuzzy sets: {low, medium, high}. The membership function is a Gaussian function, and the parameters are determined by statistically analyzing the mean and variance of historical data. The universe of discourse for humidity is [0%, 100%], which is divided into two fuzzy sets: {low, high}. The membership function is a trapezoidal function. The universe of discourse for solar radiation is [0W / m², 1000W / m²], which is divided into three fuzzy sets: {weak, medium, strong}. The membership function is a triangular function.

[0038] The universe of discourse for the output climate impact coefficient is [0.5, 1.5], which is divided into five fuzzy sets: {very small, small, medium, large, very large}. The membership function adopts a symmetrical triangular function so that each fuzzy set uniformly covers the entire universe of discourse.

[0039] S1-4-2. Establish a fuzzy reasoning rule base.

[0040] By combining domain expert knowledge and association rule mining of historical operational data, a fuzzy rule base is established. Rule mining can employ the Apriori algorithm or decision tree method to extract the correlation between the actual energy supply deviation from the baseline of climate parameter combinations from historical data and transform it into fuzzy rules. For example: Rule 1: If the temperature is high and the solar radiation is strong, the climate influence coefficient is large; Rule 2: If the temperature is low and the solar radiation is weak, the climate influence coefficient is small; Rule 3: If the temperature is moderate and the humidity is high, the climate influence coefficient is moderate.

[0041] S1-4-3, Determine the methods for reasoning and defuzzification.

[0042] The inference process employs the Mamdani minimum operation algorithm, where the excitation intensity of each rule is taken as the minimum value of the membership degrees of the antecedents. Then, the conclusions of each rule are truncated to obtain the output fuzzy set. Finally, the output fuzzy sets of all rules are superimposed (the union is taken) to obtain a comprehensive output fuzzy set.

[0043] Defuzzification employs the centroid method, which calculates the precise value corresponding to the centroid of the synthesized output fuzzy set, and uses this value as the time point. Climate impact coefficient The formula for calculating the center of gravity is: ,in This is the membership function for the comprehensive output of the fuzzy set.

[0044] S1-5, Product calculation generates predicted energy supply sequence.

[0045] The baseline energy supply sequence obtained in step S1-1, the normalized energy supply change rate at each time point obtained in step S1-2, and the climate influence coefficient at each time point obtained in step S1-4 are multiplied to calculate the energy supply value at each time point under the preset time period. After arranging them in chronological order, the predicted energy supply sequence under the preset time period is obtained.

[0046] In one specific embodiment, time point Energy supply value The formula for calculating the product can be expressed as: In the formula for The baseline energy supply value at a given time point. For time points Normalized rate of change of energy supply.

[0047] S2, Real-time monitoring and deviation adjustment item generation.

[0048] Step S2 aims to execute the predicted energy supply sequence at the current time point and calculate the deviation between the actual operation and the predicted energy supply value by collecting multi-dimensional operating data in real time, generating various deviation adjustment items. Specifically, it includes the following steps: S2-1, Execute the predicted energy supply value and collect operating data in real time.

[0049] The expected energy supply value corresponding to the current time point in the predicted energy supply sequence generated in step S1 is used to control the ground source heat pump to supply energy according to the plan. At the same time, the real-time energy supply value of the ground source heat pump, the heat distribution data at the buried pipe soil, and the real-time state of charge of the building energy storage are collected in real time through the sensor system as actual operation data.

[0050] S2-2, Obtain reference parameters for each dimension.

[0051] From the predicted energy supply sequence under the preset time period generated in step S1, obtain the reference energy supply value corresponding to the current time point as the expected energy supply value in the energy supply dimension.

[0052] Based on historical operational databases, soil heat values ​​and state of charge (SOC) values ​​corresponding to the ground source heat pump under stable operating conditions are extracted. Stable operating conditions refer to periods where the ground source heat pump operates continuously for more than a preset duration and the rate of change in power supply is below a preset threshold. The extracted soil heat values ​​and SOC values ​​are sorted, and their median values ​​are used as reference soil temperature and reference SOC values. Using median values ​​instead of average values ​​effectively avoids the impact of extreme outliers on the reference baseline.

[0053] S2-3. Calculate the initial deviation values ​​for each dimension.

[0054] Calculate the initial deviation between the actual operating data and the corresponding reference parameters for each monitoring dimension: For the energy supply dimension, calculate the difference between the actual energy supply and the expected energy supply obtained from the predicted energy supply sequence.

[0055] For the soil temperature dimension, the difference between the actual soil temperature and the reference soil temperature value is calculated.

[0056] For the energy storage state of charge dimension, the difference between the actual state of charge and the reference state of charge value is calculated.

[0057] Optionally, allowable fluctuation ranges can be preset for both soil temperature and energy storage state of charge. If the actual value exceeds this range, the distance beyond the boundary is calculated as the initial deviation value; if it is within the range, the initial deviation value is 0. The allowable fluctuation range can be preset based on the statistical distribution of historical operating data or engineering experience. For example, the allowable fluctuation range for soil temperature is ±2℃ from the reference value, and the allowable fluctuation range for state of charge is ±10% from the reference value.

[0058] S2-4. Normalization process generates deviation adjustment terms.

[0059] The initial deviation values ​​for each monitoring dimension are normalized to generate the deviation degree for each monitoring dimension. The normalization process can employ a normalization method based on a preset threshold, where the initial deviation value is divided by the preset threshold for that dimension to obtain the dimensionless deviation degree.

[0060] The deviation of each monitoring dimension is used as a deviation adjustment item for subsequent correction triggering. The larger the value of the deviation adjustment item, the more serious the deviation of the actual operating state of that dimension from the expected benchmark.

[0061] Preferably, the preset thresholds for each deviation adjustment item can be set based on actual engineering needs and statistical analysis results of historical operating data. Specifically, for energy supply deviation, soil temperature deviation, and energy storage state of charge deviation, deviation data from the system during historical stable operating periods can be extracted, their mean and standard deviation can be calculated, and the sum or difference of the mean and several times the standard deviation can be used as the initial threshold. The thresholds can also be engineered based on the maximum allowable fluctuation range of the system. For example, the allowable deviation range for energy supply can be set to ±5% of the rated energy supply, the allowable fluctuation range for soil temperature can be set to ±2℃ of the reference soil temperature value, and the allowable fluctuation range for state of charge can be set to ±10% of the reference state of charge value. In addition, to avoid the problem of insufficient adaptability of fixed thresholds under different operating scenarios, the thresholds can be set as dynamic adjustment values ​​that automatically switch according to scenario characteristics such as seasonal type, day type, or operating period. The specific rules for the above dynamic adjustment can be flexibly set by those skilled in the art according to actual application needs.

[0062] S3, Generating a modified energy supply scheme.

[0063] Step S3 aims to re-optimize and generate a corrected energy supply scheme, using the current actual operating data as the initial state, when any deviation adjustment item exceeds a preset threshold. This includes corrected energy supply sequences for ground source heat pumps and corrected charging / discharging sequences for building energy storage. Please refer to [link to relevant documentation]. Figure 3 As shown, the specific steps include: S3-1, generation of the ground source heat pump modified energy supply sequence.

[0064] S3-1-1, Determine the deviation optimization weights.

[0065] Based on the current degree of deviation of each deviation adjustment item generated in step S2, the deviation optimization weight corresponding to each deviation item is determined. The greater the degree of deviation, the higher the corresponding deviation optimization weight, reflecting the priority given to correcting severe deviations.

[0066] Specifically, multiple deviation severity intervals are preset, each corresponding to a different weight determination rule. These deviation severity intervals include at least a mild deviation interval, a moderate deviation interval, and a severe deviation interval. Based on the current deviation severity of each deviation adjustment item, its corresponding deviation severity interval is determined, and the initial weight for each deviation item is calculated using the weight determination rule corresponding to that interval. Finally, the initial weights are normalized so that the sum of the deviation optimization weights for each deviation item is 1, yielding the final deviation optimization weights.

[0067] S3-1-2, Load and Electricity Price Forecasting.

[0068] Based on current operational data, the building load and time-of-use electricity price are predicted for a future preset time period, resulting in a load prediction sequence and an electricity price prediction sequence.

[0069] The load forecasting process includes: obtaining the historical average load for the same date type (weekday, rest day, holiday) as the forecast date from historical operational data, and determining the baseline load sequence for a preset future period; and correcting the baseline load sequence using a temperature difference-driven model based on hourly weather forecast data to obtain the load forecast sequence. The temperature difference-driven model is based on building thermodynamics principles and uses the difference between the outdoor air temperature and the baseline temperature as the basis for load correction.

[0070] The electricity price forecasting process includes: extracting typical daily electricity price curves of the same date type as the forecast date based on historical time-of-use electricity price data, as a benchmark electricity price sequence; and performing a coupling correction on the benchmark electricity price sequence based on the ratio of the load forecast sequence obtained in this step to the historical benchmark load sequence for the same period, to obtain the electricity price forecast sequence. The coupling correction is based on the fundamental economic law that higher load leads to higher electricity prices.

[0071] S3-1-3, Correct and optimize to generate corrected energy supply values.

[0072] This step uses the current actual operating data as the initial state and the deviation optimization weights determined in step S3-1-1. ( The weighting coefficients (corresponding to the three dimensions of energy supply, soil temperature, and state of charge, respectively) are used for multi-objective optimization, combined with the load prediction sequence obtained in step S3-1-2. and electricity price forecast series And system operation constraints, for the remaining time points within the preset time period The corrected energy supply value is optimized by solving the solution. The optimization objective is to minimize the combined weighted sum of operating costs, soil thermal deviation, and energy storage state of charge deviation while meeting load demand.

[0073] The multi-objective optimization model is established as follows: (1) Decision variables.

[0074] Ground source heat pumps in Power supply during a given time period; , Energy storage The charging and discharging power during a given period are non-negative and cannot be positive simultaneously.

[0075] (2) Objective function.

[0076] Minimize overall cost , In the formula: Operating electricity cost (yuan). The duration is in hours (h). The cumulative absolute deviation (°C) of soil temperature from a reference value reflects the degree of soil thermal imbalance. The reference soil temperature value. It can be estimated from historical heat exchange accumulation using a soil thermal response model; It is the cumulative absolute deviation between the energy storage's state of charge and a reference value, reflecting the degree to which the energy storage deviates from the ideal state. This is the reference state of charge value.

[0077] Weighting coefficient , , The result is dynamically determined by step S3-1-1 based on the current degree of deviation, which corresponds to the result after the deviation optimization weight normalization.

[0078] (3) Constraints.

[0079] Power balance constraints: , That is, the sum of the energy supplied by the ground source heat pump and the net discharge of the energy storage must meet the building load.

[0080] Ground source heat pump output constraints: (Climb rate constraint).

[0081] Energy storage charging and discharging power constraints: ,and (Cannot be charged and discharged simultaneously).

[0082] Energy storage state of charge constraints: , , .in , These represent charge and discharge efficiencies, This refers to the rated capacity of the energy storage.

[0083] Initial state constraints: , That is, the current measured value is used as the boundary condition for optimization.

[0084] (4) Solution algorithm.

[0085] The above model is a linear or mixed-integer linear programming problem (when introducing mutually exclusive 0-1 variables for charging and discharging), which can be solved using mature commercial solvers or open-source solvers (such as CBC). For nonlinear terms (such as absolute values), linearization can be achieved by introducing auxiliary variables and inequality constraints. The optimal corrected energy supply values ​​for each future time period are obtained after solving the problem. and the corresponding energy storage charging and discharging plan , .Will Arranged in chronological order, this is the corrected energy supply sequence for a ground source heat pump.

[0086] If the optimization problem has no feasible solution, the emergency response procedure is activated: prioritize ensuring load supply, cut off non-essential loads or call backup energy according to the preset priority, and issue an alarm signal at the same time.

[0087] S3-2 Determination of the building energy storage charging and discharging correction sequence.

[0088] S3-2-1. Calculate and sort the comprehensive adjusted demand index.

[0089] Obtain the load forecast sequence for the future preset time period obtained in step S3-1-2, and calculate the deviation between it and the ground source heat pump corrected energy supply sequence obtained in step S3-1-3 to obtain the difference between the ground source heat pump energy supply and load demand for each time period. Positive values ​​indicate insufficient energy supply, while negative values ​​indicate excess energy supply.

[0090] Based on the aforementioned difference and considering factors such as electricity price levels and energy storage status, a comprehensive correction demand index is calculated for each time period. This comprehensive correction demand index is used to quantify the urgency of energy storage charging and discharging corrections during each time period.

[0091] The calculation method is as follows: Let the time period be... The comprehensive revised demand index is The value is determined by the degree of supply-demand imbalance, electricity price level, and the current state of charge of energy storage. The specific calculation formula is as follows: In the formula, The difference between the energy supplied by the ground source heat pump and the load demand. The maximum value in the load forecast sequence. For time period Electricity price, The maximum value in the electricity price forecast series. The state of charge at the start of time period t (if no current value is available, the latest measured value will be used). To allow an upper limit for the state of charge, , and The preset weighting coefficients satisfy... The higher the index, the more important it is to prioritize energy storage for correction during that period.

[0092] The time points within the future preset period are sorted from largest to smallest according to the comprehensive correction demand index to obtain the correction priority sequence. The time period with the larger comprehensive correction demand index is allocated energy storage capacity first.

[0093] S3-2-2, Initialize energy storage parameters.

[0094] Obtain the rated energy storage capacity of the building's energy storage at the current point in time from the building's real-time state of charge. Permissible state of charge limit and upper limit Based on this, the initial remaining adjustable capacity is calculated: This represents the total capacity of the energy storage system currently available for charge / discharge regulation. Simultaneously, the preset maximum number of iterations is obtained, which serves as the termination condition for subsequent state-of-charge iteration verification.

[0095] S3-2-3, Determine the charging and discharging demand type for each time period.

[0096] Following the correction priority determined in step S3-2-1, traverse each time period in descending order to determine the charging and discharging demand type for each time period: if the difference between the ground source heat pump energy supply and the load demand is... If the value is less than the negative supply-demand balance threshold, indicating a significant oversupply of energy, then the period is considered a period of charging demand.

[0097] If the difference between the energy supplied by the ground source heat pump and the load demand is greater than the supply and demand balance judgment threshold, that is, the energy supply is significantly insufficient, then the period is determined to be the period of discharge demand.

[0098] If the absolute value of the difference between the energy supply from the ground source heat pump and the load demand is less than or equal to the supply-demand balance threshold, i.e. the supply and demand are basically balanced, then the demand is determined according to the electricity price level: when the electricity price during this period is lower than the preset electricity price threshold, it is determined as charging demand (using low-price charging); when it is higher than the preset electricity price threshold, it is determined as discharging demand (using high-price discharging).

[0099] S3-2-4. Calculate the correction amount for each time period and set the charging and discharging power.

[0100] Obtain the maximum charge / discharge power of the building energy storage at the current time from the real-time state of charge. Based on this, the maximum correction amount that can be allocated to each time period is calculated. : ,in This represents the length of the time period.

[0101] Determine the adjustment amount for each time period based on the demand type and the maximum allocable adjustment amount. Charging demand periods: That is, take the smaller value between the maximum allocatable correction amount and the excess energy supply amount.

[0102] Discharge demand period: That is, take the smaller value between the maximum allocatable correction amount and the insufficient energy supply amount.

[0103] Set the charging and discharging power according to the correction amount: Charging demand period: , Discharge demand period: , .

[0104] The remaining adjustable capacity is adjusted by subtracting the initial adjustable capacity from the correction amount to update the remaining adjustable capacity. Allocation for subsequent time periods is stopped when the remaining adjustable capacity is less than or equal to 0.

[0105] S3-2-5, Charge State Deduction and Verification.

[0106] The state of charge at the end of each time period is calculated recursively in chronological order: In the formula, For charging efficiency, This refers to the discharge efficiency.

[0107] Verify whether the state of charge at each time period is within the allowable range, i.e., whether it meets the requirements. .like If the charging power exceeds the limit, the charging power for the corresponding time period will be reduced; if If the discharge power is below the lower limit, the discharge power for the corresponding period will be reduced.

[0108] If the state of charge (SOC) changes during a certain period due to power adjustment, the SOC for all subsequent periods must be recalculated based on the adjusted charge / discharge power, and verified again. This recalculation and verification process is repeated until the SOC for all periods meets the allowable range constraints, or the preset maximum number of iterations is reached. The final charge / discharge power sequence is the corrected sequence that meets the requirements for safe operation of energy storage. If there are still periods that do not meet the constraints after reaching the preset maximum number of iterations, the feasible solution closest to the allowable range is adopted, and an alarm message is generated.

[0109] S3-2-6, Generate the charge / discharge correction sequence.

[0110] The verified charge and discharge power at each time point and Arranged chronologically, the corrected sequence of charge and discharge for building energy storage is obtained.

[0111] It should be noted that during the optimization of the ground source heat pump power supply sequence in step S3-1-3 or the recursive verification of the energy storage state of charge in step S3-2-5, situations may arise where the optimization solution fails or there is no feasible solution. For example, this may occur when the adjustable capacity of the energy storage is exhausted and the load demand still cannot be met, or when the ground source heat pump has reached its maximum power supply but there is still a power shortage. In this case, the system will trigger an emergency protection mode: First, based on the importance of the building load, non-essential loads will be cut off according to the preset load priority order to ensure the supply of critical loads (such as medical facilities, data centers, etc.); second, if the system is equipped with a backup heat source (such as an electric boiler, gas boiler, etc.), the backup heat source will be activated as needed to supplement the power shortage; simultaneously, an alarm message will be generated and pushed to the monitoring platform to remind maintenance personnel to intervene manually. The execution priority of the above emergency protection strategy is higher than that of the regular correction operation, and after the emergency is lifted, the system automatically returns to the regular monitoring and correction mode.

[0112] S4. Execution of dynamic priority sorting and correction operations.

[0113] Step S4 aims to dynamically generate a priority ranking of correction operations based on the degree of deviation of the deviation adjustment items corresponding to each correction operation in the energy supply scheme, and execute the corresponding correction operations sequentially according to the priority ranking. Specifically, it includes the following steps: S4-1, determining the association between correction operations and deviation adjustment items.

[0114] For each correction operation in the modified energy supply scheme (including energy supply correction operations for ground source heat pumps and charge / discharge correction operations for building energy storage), identify the associated deviation adjustment terms. For example, a ground source heat pump energy supply correction operation may be associated with an energy supply deviation term and a soil temperature deviation term, while an energy storage charge / discharge correction operation may be associated with an energy storage state of charge deviation term and an energy supply deviation term.

[0115] S4-2, Obtain the degree of deviation and the deviation optimization weight.

[0116] Obtain the current deviation level of each associated deviation adjustment item, and the corresponding deviation optimization weight determined in step S3-1-1.

[0117] S4-3, Calculate the comprehensive priority index.

[0118] For each correction operation, a comprehensive priority index is calculated based on the degree of deviation and the corresponding deviation optimization weight of each associated deviation adjustment item. The comprehensive priority index can be calculated using a weighted summation method, that is, summing the products of the degree of deviation of each associated deviation item and its corresponding deviation optimization weight. If a correction operation has no associated deviation adjustment items, its comprehensive priority index is 0, or it is the preset minimum priority value. Such correction operations will be executed sequentially according to a preset default order after all correction operations with positive priority have been executed.

[0119] S4-4. Generate a priority sort and execute it sequentially.

[0120] Sort all correction operations in descending order of their overall priority index to obtain the correction operation priority ranking. The higher the overall priority index, the more severe the corresponding deviation or the higher the urgency of the correction, and the higher the priority should be executed.

[0121] According to the priority order, the corresponding correction operations are executed in sequence to achieve coordinated and optimized operation of ground source heat pump and building energy storage.

[0122] After the correction operation is performed, the expected parameters monitored subsequently are updated to the corresponding values ​​in the corrected energy supply scheme. That is, the corrected energy supply sequence of the ground source heat pump and the corrected energy storage charging and discharging sequence of the building are used as the new expected benchmarks for the next round of real-time monitoring and deviation calculation, forming a complete closed-loop control process.

[0123] In actual operation, to avoid repeated system corrections caused by frequent fluctuations in the deviation adjustment term near the threshold due to environmental disturbances or measurement noise, an anti-chatter mechanism can be introduced into the correction trigger logic. For example, a minimum correction time interval can be set, meaning that after any correction operation is completed, a preset time window (e.g., 15 minutes) must be waited before a new correction process can be triggered again, ensuring that the system has enough time to respond to the previous correction command and tend to stabilize.

[0124] This invention, through steps S1 to S4, constructs a complete optimized control process for the coordinated operation of ground source heat pumps and building energy storage. By systematically integrating ideal plan settings, real-time deviation monitoring, dynamic correction optimization, and cross-device priority ranking, it provides quantitative basis and coordinated control methods for the operational decisions of building energy systems.

[0125] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0126] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0128] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0129] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimized method for the coordinated operation of ground source heat pumps and building energy storage, characterized in that: The method includes: Based on historical operational data and seasonal climate forecasts, and with the goal of maintaining soil thermal balance, a predicted energy supply sequence is set for a preset time period. The system executes the predicted energy supply sequence at the current time point and collects the real-time energy supply value of the ground source heat pump, soil heat distribution data, and the real-time state of charge of building energy storage in real time to calculate the deviation values ​​between the actual operating data and the expected energy supply value in each dimension and generate each deviation adjustment item. If any deviation adjustment item exceeds the preset threshold, the corrected energy supply scheme will be re-optimized and generated based on the current actual operating data as the initial state; the corrected energy supply scheme includes the corrected energy supply sequence of the ground source heat pump and the corrected charging and discharging sequence of building energy storage. Based on the degree of deviation of the deviation adjustment item corresponding to each correction operation in the corrected power supply scheme, a priority order of correction operations is dynamically generated, and the corresponding correction operations are executed in sequence according to the priority order.

2. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 1, characterized in that: The predicted energy supply sequence under the preset time period includes: Obtain historical energy supply sequences from historical operational data, starting from the current time point and within a preset time period; The average value of the historical energy supply sequence is calculated to obtain the benchmark energy supply sequence under a preset time period. The energy supply values ​​at each time point are obtained from the historical energy supply sequence, and time series analysis is performed on the energy supply sequence based on the chronological order to determine the rate of change of energy supply at each time point. The energy supply change rate is normalized to obtain the normalized energy supply change rate at each time point. Obtain official weather forecast data released by meteorological departments within a preset time period, and combine it with historical meteorological statistics to generate climate prediction parameters; Climate prediction parameters are input into a pre-established fuzzy inference rule base to obtain the climate impact coefficients at each time point; The predicted energy supply sequence for the preset time period is obtained by multiplying the baseline energy supply sequence, the normalized rate of change of energy supply at each time point, and the climate influence coefficient.

3. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 2, characterized in that: The calculation of the climate impact coefficient at each time point includes: Obtain official weather forecast data released by meteorological departments for a preset time period; Based on historical meteorological statistics, generate climate statistical characteristics for the same period; The official meteorological forecast data is fused and corrected with the corresponding climate statistical characteristics to generate climate prediction parameters; Climate prediction parameters are input into a pre-established fuzzy inference rule base for fuzzification and inference, and the inference results are defuzzified to obtain the climate impact coefficients at each time point.

4. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 1, characterized in that: The generation of each deviation adjustment item includes: Obtain the reference energy supply value corresponding to the current time point from the predicted energy supply sequence under the preset time period; Based on historical operating data, the soil heat values ​​and state of charge values ​​corresponding to the ground source heat pump under stable operating conditions are extracted. The soil heat values ​​and state of charge values ​​are sorted and the median value is taken as the reference soil temperature value and reference state of charge value. Calculate the initial deviation between the actual operating data and the corresponding reference parameters for each monitoring dimension; The initial deviation values ​​of each monitoring dimension are normalized to generate the deviation degree of each monitoring dimension, which is then used as the deviation adjustment item.

5. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 1, characterized in that: The generation of the corrected energy supply sequence for the ground source heat pump includes: Based on the current degree of deviation of each deviation adjustment item, determine the deviation optimization weight corresponding to each deviation item; Based on current actual operating data, predict the building load and time-of-use electricity price within a preset period in the future to obtain the load prediction sequence and the electricity price prediction sequence; Using the aforementioned deviation optimization weight as a weighting coefficient for the optimization objective, and combining the load forecast sequence, electricity price forecast sequence, and the current deviation degree of each deviation adjustment item, the energy supply value for the remaining time points within the preset time period is corrected and optimized to generate the corrected energy supply value corresponding to each time point. Arrange the corrected energy supply values ​​at each time point in chronological order to obtain the corrected energy supply sequence of the ground source heat pump.

6. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 5, characterized in that: The determination of the deviation optimization weights corresponding to each deviation item includes: Multiple deviation degree ranges are preset, each range corresponding to a different weight determination rule; the deviation degree ranges include at least a slight deviation range, a moderate deviation range, and a severe deviation range; Based on the current degree of deviation of each deviation adjustment item, determine the deviation degree range to which it belongs; Using the weight determination rule corresponding to the interval, the initial weights corresponding to each deviation term are calculated. The initial weights are normalized to obtain the deviation optimization weights corresponding to each deviation term.

7. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 5, characterized in that: The forecasts for the load forecast sequence and the electricity price forecast sequence include: Obtain the average load of the same date type from historical operation data to determine the baseline load sequence for the future preset period; Based on hourly meteorological forecast data, the baseline load sequence is corrected using a temperature difference-driven model to obtain the load forecast sequence; Based on historical time-of-use electricity price data, typical daily electricity price curves of the same date type as the forecast date are extracted as benchmark electricity price series; The benchmark electricity price sequence is coupled and corrected based on the ratio of the load forecast sequence to the benchmark load sequence to obtain the electricity price forecast sequence.

8. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 1, characterized in that: The determination of the charge / discharge correction sequence for building energy storage includes: Obtain the load forecast sequence for the future preset time period, and calculate the difference between the ground source heat pump energy supply and load demand for each time period by comparing the deviation with the ground source heat pump corrected energy supply sequence. Based on this, calculate the comprehensive corrected demand index for each time period. The correction priority sequence is obtained by sorting the time points within the future preset period according to the comprehensive correction demand index from largest to smallest. The rated capacity, allowable lower and upper limits of charge of the building's energy storage at the current time point are obtained from the real-time state of charge of the building's energy storage, and the remaining adjustable capacity is calculated accordingly. Iterate through each time period in descending order of correction priority and determine the charging and discharging demand type for each time period. The maximum charging and discharging power of the building energy storage at the current time point is obtained from the real-time state of charge of the building energy storage, and the maximum correction amount that can be allocated in each time period is calculated accordingly. Based on the demand type and the maximum allocable correction amount, determine the correction amount for each time period, and set the charging and discharging power according to the correction amount; The state of charge at the end of each time period is calculated recursively according to the time sequence, and it is verified whether the state of charge of each time period is within the upper and lower limits of the allowable state of charge. If it exceeds the upper limit, the charging power of the corresponding time period is reduced; if it is below the lower limit, the discharging power of the corresponding time period is reduced. The charging and discharging power at each time point after verification is arranged in chronological order to obtain the corrected charging and discharging sequence of building energy storage.

9. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 8, characterized in that: The types of charging and discharging demand determined for each time period include: If the difference between the energy supplied by the ground source heat pump and the load demand is less than the negative supply and demand balance judgment threshold, then the period is determined to be the charging demand period. If the difference between the energy supplied by the ground source heat pump and the load demand is greater than the supply and demand balance judgment threshold, then the period is determined to be the discharge demand period. If the absolute value of the difference between the energy supply from the ground source heat pump and the load demand is less than or equal to the supply-demand balance judgment threshold, then when the electricity price during that period is lower than the preset electricity price threshold, it is judged as a charging demand period, and when it is higher than the preset electricity price threshold, it is judged as a discharging demand period.

10. The method for optimizing the coordinated operation of ground source heat pumps and building energy storage according to claim 1, characterized in that: The priority ordering of the dynamically generated correction operations includes: For each correction operation, identify the associated deviation adjustment items and obtain the current deviation level and corresponding deviation optimization weight of each deviation adjustment item; Calculate the overall priority index for each correction operation; Sort all correction operations in descending order of their overall priority index to obtain the correction operation priority ranking.

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