An adjustable resource aggregation scheduling method and system for a distribution network

Abstract

The present invention relates to the technical field of resource scheduling, and discloses an adjustable resource aggregation scheduling method and system for a distribution network, including establishing a building thermal inertia model to obtain the thermal inertia values of each building; acquiring the load data and indoor temperature parameters of each building to determine the proportion of temperature-sensitive loads and adjustable loads in the building; establishing a load prediction model based on historical load data to obtain a load prediction result, and performing thermal inertia sensitivity correction on the load prediction result according to the proportion of temperature-sensitive loads and the time-of-use electricity price of the distribution network; calculating the charge and discharge priorities of each building according to the thermal inertia value, time-of-use electricity price, proportion of temperature-sensitive loads and adjustable loads, and generating an energy storage scheduling plan according to the charge and discharge priorities and the corrected load prediction result. The present invention significantly improves the overall demand response ability by quantifying the heterogeneity of building groups, dynamically predicting load demands and collaboratively optimizing charge and discharge strategies, and provides an efficient collaborative solution for smart grid and building energy management.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling, and in particular to a method and system for aggregate scheduling of adjustable resources in a distribution network. Background Art

[0002] In modern building energy management systems, the energy collaborative optimization of building clusters is increasingly valued. However, the current traditional distribution network dispatching technology still has great limitations in the collaborative optimization of multiple buildings. The core problem is that when responding to the peak and valley electricity price signals of the distribution network, each building in the building cluster often finds it difficult to form a unified dispatching strategy, resulting in low overall energy utilization efficiency and even exacerbating grid load fluctuations. The root cause of this lack of coordination lies in the significant heterogeneity within the building cluster.

[0003] On the one hand, due to the differences in thermal inertia of each building, their response speeds to temperature changes are different, which in turn affects their energy storage potential. On the other hand, the energy storage devices equipped in each building have different capacities and charging and discharging rates, which limits their flexibility in participating in grid dispatching. In addition, due to significant differences in the load characteristics of each building, such as peak load and load fluctuation frequency, their response requirements to electricity price signals vary. The combined effect of these factors leads to the fact that when the building group responds to the peak and valley electricity price signals of the distribution network, each building only responds independently according to its own situation, lacking global coordination, resulting in poor overall load regulation. Some buildings even concentrate energy storage during the low electricity price period, resulting in new load peaks, which in turn increases the burden on the power grid.

[0004] Due to the lack of coordination, the overall adjustable load response capability of the building group is far lower than the sum of the response capabilities of each building, and it cannot fully play its role in power grid peak regulation. This not only seriously restricts the efficiency improvement of the building energy management system, but also weakens the distribution network's control ability over the adjustable load. Therefore, how to overcome the heterogeneity within the building group and achieve effective coordinated scheduling has become a key issue that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an adjustable resource aggregation scheduling method and system for a distribution network, so as to overcome the heterogeneity within a building group, effectively improve the overall demand response capability of the building, and achieve effective collaborative scheduling.

[0006] In a first aspect, the present invention provides a method for aggregate scheduling of adjustable resources in a distribution network, the method comprising:

[0007] Obtain the temperature distribution parameters and building material parameters of each building in the building group, and establish a building thermal inertia model based on the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building;

[0008] Obtain the total load data, energy storage device data, and indoor temperature parameters of each building in the building group. According to the total load data, energy storage device data, indoor temperature parameters, and thermal inertia value, determine the proportion of temperature-sensitive load and adjustable load for each building;

[0009] Based on the historical load data of the building group, establish a load prediction model based on time scale to obtain the load prediction result. And according to the proportion of temperature-sensitive load and the time-of-use electricity price of the distribution network, perform thermal inertia sensitivity correction on the load prediction result to obtain the corrected load prediction result;

[0010] According to the thermal inertia value, time-of-use electricity price, proportion of temperature-sensitive load, and adjustable load, calculate the charge and discharge priority for each building. And according to the charge and discharge priority and the corrected load prediction result, generate an energy storage scheduling plan.

[0011] Further, the step of establishing a building thermal inertia model based on the temperature distribution parameter and building material parameter to obtain the thermal inertia value of each building includes:

[0012] Calculate the building heat dissipation coefficient according to the temperature distribution parameter of the building surface, the thermal conductivity of the building material, and the building wall thickness of each building;

[0013] Based on the building heat dissipation coefficient and the building surface area, use the heat balance equation to establish a building thermal inertia model to obtain the thermal inertia value of each building.

[0014] Further, the step of determining the proportion of temperature-sensitive load and adjustable load for each building according to the total load data, indoor temperature parameter, and thermal inertia value includes:

[0015] Calculate the temperature difference between the indoor target temperature and the indoor actual temperature according to the indoor temperature parameter, and calculate the temperature-sensitive load component according to the temperature difference and the thermal inertia value;

[0016] Determine the proportion of temperature-sensitive load for each building according to the ratio between the temperature-sensitive load component and the total load data;

[0017] Determine the actual charge and discharge rate of the energy storage device according to the rated power of the energy storage device of each building, and determine the device power upper limit according to the preset response time and the actual charge and discharge rate;

[0018] Obtain the current available capacity of the energy storage device, and determine the adjustable load according to the current available capacity and the device power upper limit.

[0019] Further, the temperature-sensitive load component is expressed by the following formula:

[0020]

[0021] Wherein, Q thermal represents the temperature-sensitive load component, represents the indoor target temperature, represents the indoor actual temperature, and H represents the heat inertia value;

[0022] The proportion of temperature-sensitive load is expressed by the following formula:

[0023]

[0024] Wherein, R thermal represents the proportion of temperature-sensitive load, and L total represents the total load data;

[0025] The adjustable load is expressed by the following formula:

[0026]

[0027] Wherein, △Q represents the adjustable load, represents the current available capacity of the energy storage device, and P rate represents the actual charge and discharge rate, represents the response time window.

[0028] Further, the step of establishing a load prediction model based on a time scale according to the historical load data of the building group and obtaining a load prediction result includes:

[0029] Dividing the historical load data of the building group according to the time scale to obtain interval load data, where the interval load data includes short-term load data, medium-term load data, and long-term load data;

[0030] According to each interval load data and the corresponding preset algorithm, establish sub-load prediction models at each time scale, and perform weighted summation on each sub-load prediction model according to the preset weights to obtain a load prediction model;

[0031] According to the load prediction model, obtain the load prediction result.

[0032] Further, the step of establishing sub-load prediction models at each time scale according to each interval load data and the corresponding preset algorithm, and performing weighted summation on each sub-load prediction model according to the preset weights to obtain a load prediction model includes:

[0033] Establish a short-term sub-load prediction model according to the short-term load data and the autoregressive model;

[0034] Establish a medium-term sub-load prediction model according to the medium-term load data and the exponential smoothing model;

[0035] Establish a long-term sub-load prediction model according to the long-term load data and the grey model;

[0036] Based on a rolling time window, obtain the predicted values and actual values of each sub - load prediction model to get the total combined prediction error. The sub - load prediction models include a short - term sub - load prediction model, a medium - term sub - load prediction module, and a long - term sub - load prediction model;

[0037] Taking the minimization of the total combined prediction error as the objective function and the sum of the weights of each sub - load prediction model being equal to a threshold as the constraint condition, establish a weight optimization model;

[0038] Use the least - squares method to solve the weight optimization model to obtain the optimal weights of each sub - load prediction model;

[0039] Weighted - sum each sub - load prediction model according to the optimal weights to obtain a load prediction model.

[0040] Further, the step of correcting the load prediction result for thermal inertia sensitivity according to the proportion of temperature - sensitive load and the time - of - use electricity price of the distribution network to obtain the corrected load prediction result includes:

[0041] Determine the electricity price difference according to the time - of - use electricity price of the distribution network, and determine the electricity price fluctuation coefficient according to the ratio of the electricity price difference to the reference electricity price;

[0042] Determine the thermal inertia sensitivity correction coefficient according to the electricity price fluctuation coefficient and the proportion of temperature - sensitive load, and multiply the thermal inertia sensitivity correction coefficient by the load prediction result to obtain the corrected load prediction result;

[0043] Among them, the thermal inertia sensitivity correction coefficient is expressed by the following formula:

[0044]

[0045] In the formula, δ represents the thermal inertia sensitivity correction coefficient, μ represents the price elasticity coefficient, △P represents the electricity price difference, represents the reference electricity price, R thermal represents the proportion of temperature - sensitive load.

[0046] Further, the step of calculating the charging and discharging priorities of each building according to the thermal inertia value, time - of - use electricity price, proportion of temperature - sensitive load, and adjustable load includes:

[0047] Determine the electricity price difference according to the time - of - use electricity price of the distribution network, and determine the economic priority according to the product of the electricity price difference and the proportion of temperature - sensitive load;

[0048] Determine the response speed priority according to the thermal inertia value;

[0049] Determine the resource utilization priority according to the current available capacity of the energy storage device and the adjustable load;

[0050] Calculate the charging and discharging priority according to the economic priority, response speed priority, and resource utilization priority;

[0051] Among them, the charging and discharging priority is expressed by the following formula:

[0052]

[0053] In the formula, α represents the charging and discharging priority, △P represents the electricity price difference, H represents the thermal inertia value, R thermal represents the proportion of temperature-sensitive loads, △Q represents the adjustable load, represents the current available capacity.

[0054] Furthermore, the step of generating the energy storage scheduling plan according to the charging and discharging priority and the corrected load prediction result includes:

[0055] According to the corrected load prediction result, divide the load prediction period and determine the charging and discharging requirements for different periods;

[0056] Sort the charging and discharging priorities of each building according to different periods to obtain a sorted list corresponding to different periods;

[0057] Generate an energy storage scheduling plan for the building group based on the time-sharing charging and discharging power distribution according to the charging and discharging requirements and the sorted list corresponding to different periods.

[0058] In a second aspect, the present invention provides an adjustable resource aggregation scheduling system for a distribution network, and the system includes:

[0059] A load analysis module, configured to obtain the temperature distribution parameters and building material parameters of each building in the building group, and establish a building thermal inertia model according to the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building;

[0060] Obtain the total load data, energy storage device data, and indoor temperature parameters of each building in the building group, and determine the proportion of temperature-sensitive loads and adjustable loads of each building according to the total load data, energy storage device data, indoor temperature parameters, and thermal inertia value;

[0061] A load prediction module, configured to establish a load prediction model based on a time scale according to the historical load data of the building group to obtain a load prediction result, and perform thermal inertia sensitivity correction on the load prediction result according to the proportion of temperature-sensitive loads and the time-of-use electricity price of the distribution network to obtain a corrected load prediction result;

[0062] A collaborative scheduling module, configured to calculate the charging and discharging priority of each building according to the thermal inertia value, time-of-use electricity price, proportion of temperature-sensitive loads, and adjustable load, and generate an energy storage scheduling plan according to the charging and discharging priority and the corrected load prediction result.

[0063] The present invention provides a method and system for aggregating and scheduling adjustable resources in a distribution network. Based on the thermal inertia value and temperature-sensitive load decomposition of buildings, the present invention can improve the accuracy of load forecasting, providing data support for subsequent refined scheduling. Through a priority strategy based on electricity price difference and temperature-sensitive response, it can effectively reduce the energy consumption cost and improve the group scheduling benefit. At the same time, through a dual constraint mechanism of equipment power and capacity constraints, the safety of equipment and the power grid is ensured, and the reliability of the power grid is improved. Finally, based on a rolling optimization and real-time correction mechanism, it dynamically responds to environmental changes and enhances the robustness of the scheduling scheme.

[0064] By quantifying the heterogeneity of building groups, dynamically predicting load demands, and collaboratively optimizing charging and discharging strategies, the present invention significantly improves the overall demand response ability of building groups, providing a standardized and scalable efficient collaborative solution for building groups to participate in the demand response of a new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a schematic flow chart of the method for aggregating and scheduling adjustable resources in a distribution network according to an embodiment of the present invention;

[0066] Figure 2 is a schematic structural diagram of the system for aggregating and scheduling adjustable resources in a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Please refer to Figure 1 , a method for aggregating and scheduling adjustable resources in a distribution network proposed in the first embodiment of the present invention, includes steps S10 to S40:

[0069] Step S10, obtaining the temperature distribution parameters and building material parameters of each building in the building group, and establishing a building thermal inertia model based on the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building;

[0070] Step S20, obtaining the total load data, energy storage device data, and indoor temperature parameters of each building in the building group, and determining the proportion of temperature-sensitive load and adjustable load of each building according to the total load data, energy storage device data, indoor temperature parameters, and thermal inertia value;

[0071] Step S30: Based on the historical load data of the building cluster, establish a load forecasting model based on time scales to obtain the load forecasting results. Then, according to the proportion of temperature-sensitive loads and the time-of-use electricity price of the distribution network, perform a thermal inertia sensitivity correction on the load forecasting results to obtain the corrected load forecasting results.

[0072] Step S40: Calculate the charge-discharge priorities of each building according to the thermal inertia value, time-of-use electricity price, proportion of temperature-sensitive loads, and adjustable loads. Then, generate an energy storage scheduling plan based on the charge-discharge priorities and the corrected load forecasting results.

[0073] The present invention relates to a collaborative optimization scheduling method for adjustable resources in a building cluster for a distribution network, aiming to solve the problem of low efficiency in independently responding to grid electricity price signals due to differences in thermal inertia, energy storage device capacity / response speed, and load characteristics heterogeneity among current building clusters. Therefore, it is first necessary to analyze the thermal inertia of each building in the building cluster.

[0074] The thermal inertia of a building refers to a property exhibited by building materials or building structures in absorbing, storing, and releasing heat. Thermal inertia is related to the absorption, storage, and release of heat. For example, building materials with high thermal inertia can absorb and store a large amount of heat when the external temperature rises. When the external temperature drops, these heat-storing building materials will slowly release the stored heat, thereby maintaining the relative stability of the indoor temperature. Thermal inertia enables the building to have the ability to buffer temperature changes. It can delay the response speed of the indoor temperature to external temperature changes and reduce the amplitude of indoor temperature fluctuations, thus helping to reduce the heating and cooling energy consumption of the building.

[0075] Since the thermal inertia of buildings is not considered in the current collaborative scheduling process of building clusters participating in the power grid, in the scheduling method provided by the present invention, the thermal inertia of buildings is first calculated, and then the impact of thermal inertia on the load power is analyzed. In a preferred embodiment, the present invention calculates the thermal inertia values of each building in the building cluster using the following steps:

[0076] Calculate the building heat dissipation coefficient according to the temperature distribution parameters of the building surface, the thermal conductivity of building materials, and the building wall thickness of each building.

[0077] Based on the building heat dissipation coefficient and the building surface area, establish a building thermal inertia model using the heat balance equation to obtain the thermal inertia values of each building.

[0078] In this embodiment, the temperature distribution parameters of the outer surface of the building are collected by an infrared sensor array, the temperature change amount is determined according to the temperature distribution parameters, and the temperature change rate is obtained by dividing the temperature change amount by the unit time; then the thermal conductivity coefficients of the building materials of each building are read from the building material parameter library, and the building heat dissipation coefficient of each building is determined according to the temperature change amount, the thermal conductivity coefficient of the building materials, and the wall thickness of the building. The calculation formula is expressed as:

[0079]

[0080] In the formula, k represents the building heat dissipation coefficient, λ represents the thermal conductivity coefficient of the building materials, △T represents the temperature change amount, and d represents the wall thickness.

[0081] For the calculated building heat dissipation coefficient, combined with the surface area of the building exterior wall, a thermal inertia model of the building is constructed using the heat balance equation to determine the thermal inertia value of each building. Among them, the thermal inertia model can be expressed as:

[0082]

[0083] In the formula, H represents the thermal inertia value, A represents the building surface area, C p represents the specific heat capacity of the building materials.

[0084] In this embodiment, the heat storage capacity of each building is quantified, thereby providing accurate basic data for subsequent load modeling.

[0085] Based on the heat storage capacity of the building, the load characteristics of each building are analyzed below. The temperature-sensitive load is separated from the total load of the building, and at the same time, by accurately identifying the adjustable load, unnecessary scheduling is avoided, thereby improving the scheduling ability. Among them, the separation steps of the temperature-sensitive load and the specific calculation steps of the adjustable load include:

[0086] According to the indoor temperature parameters, calculate the temperature difference between the indoor target temperature and the indoor actual temperature, and calculate the temperature-sensitive load component according to the temperature difference and the thermal inertia value;

[0087] According to the ratio between the temperature-sensitive load component and the total load data, determine the proportion of the temperature-sensitive load of each building;

[0088] According to the rated power of the energy storage device of each building, determine the actual charge and discharge rate of the energy storage device, and determine the device power upper limit according to the preset response time and the actual charge and discharge rate;

[0089] Obtain the current available capacity of the energy storage device, and determine the adjustable load according to the current available capacity and the device power upper limit.

[0090] In this embodiment, the change in the cooling / heating load of the building is directly related to the building's thermal inertia value and the temperature difference between the inside and outside, which conforms to the heat balance equation. Therefore, by obtaining the actual indoor temperature and the target indoor temperature of the building, calculating the temperature difference between the two temperatures, and multiplying the temperature difference by the thermal inertia value calculated in the above steps, the temperature-sensitive load component can be separated from the total load. The formula is expressed as:

[0091]

[0092] In the formula, Q thermal represents the temperature-sensitive load component, represents the target indoor temperature, represents the actual indoor temperature, and H represents the thermal inertia value.

[0093] Then, obtain the total load data of the building, and divide the temperature-sensitive load component by the total load data to obtain the proportion of the temperature-sensitive load. The formula is expressed as:

[0094]

[0095] In the formula, R thermal represents the proportion of the temperature-sensitive load, and L total represents the total load data.

[0096] In this embodiment, in addition to separating the temperature-sensitive load component, the adjustable load is also identified. Among them, the adjustable load is used to quantify the maximum adjustable load capacity of the building energy storage device within a specific time window. The formula is expressed as:

[0097]

[0098] In the formula, △Q represents the adjustable load, represents the current available capacity of the energy storage device, P rate represents the actual charge / discharge rate, represents the response time window.

[0099] Specifically, the current available capacity of the energy storage device refers to the energy that has been stored but not released by the energy storage device at the current moment, that is, the remaining capacity of the energy storage device. Its value can be obtained by subtracting the used capacity from the rated capacity of the energy storage device; the actual charge-discharge rate refers to the maximum charge-discharge power under the current working conditions, and its value can be obtained by multiplying the rated power of the energy storage device by the device efficiency coefficient; the response time window refers to the time period during which the power grid requires the building to complete load regulation. This time window is determined by the dispatching instruction, and the corresponding response time window is different according to different scenarios and uses. For example, in the frequency modulation scenario, in order to quickly suppress the high-frequency fluctuations of the power grid, the time window issued by the power grid is 0.5 hours. When in the peak shaving and valley filling scenario, in order to reduce the peak-valley difference of the power grid, the issued time window is 2 hours. The product of the actual charge-discharge rate and the response time window represents the device power upper limit. Therefore, in this embodiment, the adjustable load is the minimum value of the current available capacity and the device power upper limit.

[0100] In this embodiment, through capacity constraint, it is ensured that the dispatching amount does not exceed the current available capacity of the energy storage device, avoiding equipment shutdown caused by over-discharge. Through power constraint, it is prevented from exceeding the device power upper limit, avoiding efficiency decline or hardware damage caused by overload. This embodiment determines the actual adjustable ability through a dual constraint mechanism, thereby ensuring the feasibility of the subsequent dispatching strategy.

[0101] Based on the above steps, based on the historical load data of the building group, a load prediction model of the building group is established. Among them, the load prediction model can be constructed based on neural networks or data fitting methods. However, in actual application scenarios, the fluctuation characteristics of load data at different time scales are different. Therefore, in order to improve the accuracy of the prediction results, in a preferred embodiment, the present invention adopts a time-sharing prediction method. By performing time-sharing prediction on the load in different time periods, a combined prediction result is obtained. The specific steps include:

[0102] The historical load data of the building group is divided according to the time scale to obtain interval load data, and the interval load data includes short-term load data, medium-term load data, and long-term load data;

[0103] According to each interval load data and the corresponding preset algorithm, a sub-load prediction model at each time scale is established, and the sub-load prediction models are weighted and summed according to the preset weights to obtain a load prediction model;

[0104] According to the load prediction model, a load prediction result is obtained.

[0105] In this embodiment, first, based on different time scales, the historical load data is divided. Preferably, the time scales are set as daily scale, weekly scale, and monthly scale. According to different time scales, the clustering analysis method is used to classify the historical load data respectively, so as to obtain short-term load data, medium-term load data, and long-term load data, which respectively correspond to daily-scale load data, weekly-scale load data, and monthly-scale load data.

[0106] The load fluctuation characteristics reflected by the load data under different time scales are also different. For example, the intraday fluctuation is mainly manifested as two peaks in the morning and evening, the weekly fluctuation reflects the load difference between weekdays and weekends, and the monthly fluctuation reflects the seasonal change. Therefore, in this embodiment, for the load data under different time scales, different prediction algorithms are used to establish sub-load prediction models. Specifically, an autoregressive model is used to model the short-term load data, an exponential smoothing model is used to smooth the medium-term load data, and a grey model is used to handle the data sparsity of the long-term load data, so as to obtain sub-load prediction models under different time scales. It should be noted that the modeling algorithms used in this embodiment are only preferred and not specifically limited. Other algorithms can also be used to establish sub-load prediction models, which are not overly limited here. The specific modeling process can refer to the modeling steps of conventional modeling algorithms and will not be elaborated here one by one.

[0107] Based on the sub-load prediction models under different time scales, weighted summation is performed according to the weights of different models, so as to obtain a load prediction model after combining different time scales, where the weights of each sub-model can be determined by preset values.

[0108] In a preferred embodiment, in order to improve the accuracy of the load prediction model, the present invention provides a method for determining weights based on prediction errors. In this embodiment, first, based on a rolling time window, the prediction errors of each sub-load prediction model are determined. Specifically, the window length and rolling step of the rolling time window are set, that is, a fixed time span (such as the past 7 days, 24 hours, etc.) is selected, and each time the window moves forward by one time unit (such as 1 day or 1 hour), and the data within the window is updated. Then, according to the actual load value sequence and the prediction value sequences of each sub-model within the time window, the prediction errors of each sub-model are calculated. Here, the squared error is used to characterize the prediction error, and an error matrix is constructed.

[0109] Taking the weights of each sub - load prediction model as unknowns, the predicted values of each sub - load prediction model are weighted and summed to obtain a combined predicted value. Then, with the minimization of the prediction error between the actual load value and the combined load value, that is, the minimization of the square error as the objective function, and the sum of the weights of each sub - load prediction model being equal to 1 as the constraint condition, a weight optimization model is constructed. Finally, the least - squares method is used to solve the weight optimization model, that is, the Lagrange multiplier method is adopted. A Lagrange multiplier is introduced into the objective function to construct a Lagrange function, and derivatives are taken to obtain a system of equations. Finally, the linear system of equations is solved through an error matrix to obtain the optimal weights of each sub - load prediction model.

[0110] After determining the optimal weights of each sub - load prediction model based on the above method, the load prediction model can be obtained by weighted - summing each sub - load prediction model based on the weight values. Further, the weight values in this embodiment can be dynamically adjusted according to the latest error to adapt to the change of the load and ensure the accuracy of the load prediction model.

[0111] In a preferred embodiment, in order to further improve the accuracy of the load prediction result, the present invention also provides a method for correcting the thermal inertia sensitivity of the load prediction result. The specific correction steps are as follows:

[0112] According to the time - of - use electricity price of the distribution network, determine the electricity price difference, and determine the electricity price fluctuation coefficient according to the ratio of the electricity price difference to the reference electricity price;

[0113] According to the electricity price fluctuation coefficient and the proportion of temperature - sensitive loads, determine the thermal inertia sensitivity correction coefficient, and multiply the thermal inertia sensitivity correction coefficient by the load prediction result to obtain the corrected load prediction result.

[0114] Based on the above description, it can be seen that the thermal inertia of the building and the fluctuation of the grid electricity price will also affect the load demand of the building. In the above - mentioned embodiment, the load prediction model only considers historical load data and does not consider the influence of electricity price signals and thermal inertia values on the building load, resulting in the prediction not reflecting the dynamic influence of the building's thermal characteristics. Therefore, in this embodiment, the fluctuation of the electricity price and the building's thermal inertia are taken into account in the load prediction.

[0115] First, according to the time - of - use electricity price of the grid, determine the electricity price difference between peak and valley periods, and determine the electricity price fluctuation coefficient according to the ratio between the electricity price difference and the reference electricity price. The reference electricity price refers to the non - time - of - use electricity price. Then, according to the electricity price fluctuation coefficient and the proportion of temperature - sensitive loads, determine the thermal inertia sensitivity correction coefficient:

[0116]

[0117] In the formula, δ represents the thermal inertia sensitivity correction coefficient, μ represents the price elasticity coefficient, whose range is between 0.2 and 0.5, △P represents the electricity price difference, represents the benchmark electricity price, R thermal represents the proportion of temperature-sensitive load.

[0118] Based on the above thermal inertia sensitivity correction coefficient, the load forecasting result is corrected, and the corrected load forecasting result is:

[0119]

[0120] In the formula, L` represents the corrected load forecasting result, and L represents the load forecasting result before correction.

[0121] In the correction logic of this embodiment, the higher the thermal inertia value of the building, the greater the impact of electricity price fluctuations on load forecasting. Through the correction method in this embodiment, the electricity price fluctuations and the thermal inertia of the building are taken into account in load forecasting, further improving the accuracy of load forecasting.

[0122] After obtaining the load forecasting results of the building group, a energy storage scheduling plan can be generated based on the load demands predicted for each building. In the present invention, through the aggregated scheduling of adjustable resources in the distribution network, the scheduling cost is effectively reduced and the scheduling efficiency is improved. When performing resource scheduling, in order to further optimize resource allocation and avoid inefficient scheduling, in a preferred embodiment, the present invention also provides a method for scheduling adjustable aggregated resources based on the charge and discharge priority of the building, wherein the steps for determining the charge and discharge priority include:

[0123] Determine the economic priority according to the time-of-use electricity price of the distribution network;

[0124] Determine the temperature change response priority according to the thermal inertia value and the proportion of temperature-sensitive load;

[0125] Determine the resource utilization priority according to the current available capacity and adjustable load of the energy storage device;

[0126] Calculate the charge and discharge priority according to the economic priority, temperature change response priority and resource utilization priority.

[0127] In this embodiment, the charge-discharge priority dynamically evaluates each building from multiple dimensions such as economy, temperature change response speed, and resource utilization rate to determine the charge-discharge priority of each building. Specifically, the economy of scheduling is characterized by the electricity price difference, that is, the peak-valley electricity price difference. The electricity price difference and the priority should be positively correlated, that is, the larger the electricity price difference, the higher the adjustment benefit, and the higher the priority should be; the sensitivity of the building to temperature changes is reflected by the proportion of temperature-sensitive loads. The proportion of temperature-sensitive loads and the priority are positively correlated, that is, the higher the proportion of temperature-sensitive loads, the stronger the load adjustability of the building, and the higher the priority; the speed of the building's response to temperature changes is reflected by the thermal inertia. Thermal inertia and priority are negatively correlated, that is, the larger the thermal inertia, the slower the building's response speed to temperature. To avoid scheduling delays, the priority should be reduced; the scheduling potential of the building is represented by the adjustable load of the energy storage device. The adjustable load and the priority are positively correlated. The larger the adjustable load, the greater the scheduling potential, and the higher the priority; the maximum adjustable amount of the building is limited by the current available capacity of the energy storage device, that is, the remaining capacity of the energy storage device. Therefore, the current available capacity and the priority should be negatively correlated, that is, the smaller the remaining capacity, the lower the scheduling margin. To prevent capacity depletion, the priority should be reduced.

[0128] Based on the above correlation analysis, it can be seen that scheduling during high electricity price difference periods is significantly beneficial, and discharging should be arranged first. At the same time, for buildings with a high proportion of temperature-sensitive loads, their load fluctuations are strongly correlated with electricity price signals, and costs can be significantly reduced through adjustment. Therefore, in this embodiment, the product of the electricity price difference and the proportion of temperature-sensitive loads is used as the economic priority, and the coordination logic of the electricity price difference and the proportion of temperature-sensitive loads is used to improve the economy of scheduling. The thermal inertia value is used as the response speed priority. When the high thermal inertia value is greater than 120, for example, due to the large thermal inertia of the building, the response speed is slow, and it takes longer to reach the target temperature. Therefore, its priority should be reduced to avoid delays affecting the real-time regulation of the power grid. Finally, the ratio of the adjustable load to the current available capacity is used as the resource utilization priority. The adjustable load is used as the numerator. The larger the current adjustable capacity, the higher the scheduling flexibility. The current available capacity is used as the denominator. The smaller the remaining capacity, the fewer the remaining resources after scheduling, and careful allocation is required. Using the ratio of the two as the resource utilization priority can dynamically balance the current available resources and the long-term scheduling potential to prevent local resource depletion.

[0129] Combining the above priorities, in this embodiment, the charge-discharge priority can be expressed as:

[0130]

[0131] In the formula, α represents the charge-discharge priority, △P represents the electricity price difference, H represents the thermal inertia value, R thermal represents the proportion of temperature-sensitive loads, △Q represents the adjustable load, represents the current available capacity. In addition, when When it is less than 10% of the rated capacity, the forced degradation priority is adopted to avoid overcharging and over-discharging.

[0132] In this embodiment, the charge-discharge priority formula realizes the refined scheduling of the energy storage resources of the building group through the coupling of parameters in three dimensions: economic weight, thermal inertia constraint, and resource utilization rate balance. The dynamic adjustment of the priority is driven by the real-time electricity price signal and the load prediction data, which can effectively adapt to the changes in the power grid demand. The response delay is avoided through the thermal inertia value constraint, and overcharging / over-discharging is prevented through the remaining capacity constraint, providing a safety guarantee for the stability of charge and discharge. By combining the priority of the building with the energy storage scheduling in this embodiment, group collaborative optimization can be achieved, thereby improving the group benefits of resource scheduling.

[0133] After obtaining the charge-discharge priorities of each building and the load prediction results of the building group based on the above steps, a distributed algorithm can be used to coordinate the charge-discharge timing to generate an energy storage scheduling method. The specific steps include:

[0134] According to the corrected load prediction results, divide the load prediction period and determine the charge-discharge demands in different periods;

[0135] Sort the charge-discharge priorities of each building according to different periods to obtain a sorted list corresponding to different periods;

[0136] Generate an energy storage scheduling plan for the building group based on the charge-discharge demands and the sorted list corresponding to different periods.

[0137] In this embodiment, first, according to the load prediction results, the charge-discharge periods are divided into peak periods (high load), valley periods (low load), and normal periods. For example, when the load is greater than 800 kW, it is a peak period; when the load is less than 400 kW, it is a valley period; and the rest are normal periods. Then, based on different periods, the charge-discharge demands are determined. For example, in the peak period, give priority to discharging to reduce the power grid load; in the valley period, give priority to charging to utilize low-price electric energy; and in the normal period, flexible adjustment is carried out to maintain the energy storage state.

[0138] After the period division and demand determination, dynamic sorting of the priorities by period is carried out, that is, based on the priority matrix composed of the priorities of each building, the priorities of all buildings in each period are extracted, and then the priorities are sorted by period. The sorting rule is: in the peak period, arrange in descending order of priority, and the high-priority buildings discharge first; in the valley period, arrange in ascending order of priority, and the low-priority buildings charge first.

[0139] Then, based on the sorted list, adjustable load, and real-time constraints, charge and discharge power allocation is performed. The allocation principle is as follows: during peak periods, discharge power is allocated from high to low until the grid load reduction target is met or the adjustable capacity of the building is exhausted; during valley periods, charging power is allocated from low to high until the energy storage capacity is full or the low-price period ends.

[0140] Based on the above allocation principle, the steps of the power allocation algorithm adopted in this embodiment are as follows: First, determine the requirements for different periods: during peak periods, the load needs to be reduced, and the reduction value is the predicted load minus the grid safety threshold; during valley periods, charging is required, and the chargeable capacity is equal to the charging power that the grid can withstand.

[0141] Then, power allocation is performed building by building according to the sorted list. When performing discharge power allocation during peak periods, first determine the current total reduction amount and the corresponding priority ranking. According to the priority ranking sequence, calculate the available power of each building. The available power is the smaller value between the adjustable load of the building and the upper limit of the charge and discharge rate. For example, if the adjustable load of the building is 250kW and the upper limit of the charge and discharge rate is 200kW, then its available power is 200kW. Then determine the current remaining amount to be allocated, which is the difference obtained by subtracting the total allocated amount from the total reduction amount, and take the smaller value between the remaining amount to be allocated and the available power as the allocated amount for this building. Accumulate this allocation to the total allocated amount and record the discharge power of this building during the current period. Finally, determine whether the discharge terminates, that is, if the total allocated amount is greater than or equal to the total reduction amount, immediately terminate the allocation process.

[0142] When performing charging power allocation during valley periods, first determine the current chargeable capacity and the corresponding priority ranking. According to the priority ranking sequence, calculate the chargeable amount of each building. The chargeable amount is the difference obtained by subtracting the current available capacity from the rated capacity of the building. For example, if the rated capacity of the building is 2000kW and the current available capacity (i.e., the remaining capacity) is 800kW, then its chargeable amount is 1200kW. Then determine the available power of the building. The real-time charging power of the building is restricted by two factors. One is the restriction of the rated charging rate, and the other is the constraint of the time window, that is, the ratio between the chargeable amount and the valley period duration. Take the smaller value of the two as the available power of the building. For example, if the chargeable amount is 1200kW and the valley period duration is 4 hours, then the theoretical power upper limit is 1200 / 4 = 300kW, but the rated power of the equipment is 200kW, so the actual available power is 200kW. Then, according to the remaining allocable power of the current grid (chargeable capacity minus total allocated amount), determine the actual allocated power of the building. The actual allocated power takes the smaller value between the available power and the remaining allocable power. Finally, update and record the total allocated amount, the charging power of the building, and the remaining capacity of the building, and determine whether the termination condition is reached. If the total allocated amount is greater than the chargeable capacity, immediately terminate the allocation process.

[0143] Finally, based on the above power allocation, a time-sharing charging and discharging power allocation table is generated, and the power allocation table is rolled optimized and corrected in real time according to real-time electricity price fluctuations, load forecast errors, equipment status changes, etc., so as to realize the coordinated energy storage scheduling of the building group.

[0144] The power allocation algorithm of this embodiment, during the discharge scheduling process, prioritizes high-priority buildings for scheduling, achieves maximum benefits, calculates the remaining amount to be allocated in real time, flexibly adjusts the allocated power of a single building, strictly follows the power and capacity constraints of the equipment, avoids the risk of overload, and stops allocation immediately after the goal is achieved, effectively reducing invalid operations, thereby meeting the needs of the power grid while taking into account the safety and economy of building operation. During the charging scheduling process, charging is maximized during the valley period with low electricity prices, effectively reducing energy costs, giving priority to filling buildings with low remaining capacity, improving the utilization rate of energy storage devices, calculating the remaining allocable power in real time, flexibly adjusting the allocation of a single building, strictly following the power and capacity constraints, preventing equipment overload or capacity overflow, and providing safety boundary guarantees, so that charging tasks can be completed efficiently and sufficient energy reserves can be provided for peak demand response. The energy storage scheduling scheme provided in this embodiment realizes a closed loop of the entire process from priority calculation to power allocation, thereby ensuring the efficiency and reliability of the coordinated scheduling of building groups.

[0145] This embodiment provides a method for aggregate scheduling of adjustable resources in a distribution network. The present invention improves the accuracy of load forecasting based on the thermal inertia of buildings and temperature-sensitive load decomposition, provides data support for subsequent refined scheduling, and effectively reduces energy costs and improves group scheduling benefits through priority strategies based on electricity price differences and temperature-sensitive responses. At the same time, through the dual constraint mechanism of equipment power and capacity constraints, the safety of equipment and power grids is guaranteed, overload risks are avoided, and the reliability of power grids is improved. Finally, based on rolling optimization and real-time correction mechanisms, it dynamically responds to environmental changes and improves the robustness of scheduling schemes. The present invention significantly improves the overall demand response capability of building groups by quantifying the heterogeneity of building groups, dynamically predicting load demand, and collaboratively optimizing charging and discharging strategies, and provides a standardized, scalable, and efficient collaborative solution for building groups to participate in the demand response of new power systems.

[0146] See also Figure 2 Based on the same inventive concept, a second embodiment of the present invention proposes an adjustable resource aggregation scheduling system for a distribution network, comprising:

[0147] The load analysis module 10 is used to obtain the temperature distribution parameters and building material parameters of each building in the building group, and to establish a building thermal inertia model based on the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building;

[0148] Obtain the total load data, energy storage device data, and indoor temperature parameters of each building in the building group, and determine the proportion of temperature-sensitive load and adjustable load of each building according to the total load data, energy storage device data, indoor temperature parameters, and thermal inertia value.

[0149] The load prediction module 20 is used to establish a load prediction model based on the time scale according to the historical load data of the building group, obtain the load prediction result, and perform thermal inertia sensitivity correction on the load prediction result according to the proportion of temperature-sensitive load and the time-of-use electricity price of the distribution network to obtain the corrected load prediction result.

[0150] The collaborative scheduling module 30 is used to calculate the charge and discharge priorities of each building according to the thermal inertia value, time-of-use electricity price, proportion of temperature-sensitive load, and adjustable load, and generate an energy storage scheduling plan according to the charge and discharge priorities and the corrected load prediction result.

[0151] The technical features and technical effects of the adjustable resource aggregation scheduling system of the distribution network proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be repeated here. Each module in the above adjustable resource aggregation scheduling system of the distribution network can be implemented in whole or in part by software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0152] In summary, an embodiment of the present invention proposes a method and system for aggregate scheduling of adjustable resources in a distribution network. The method obtains temperature distribution parameters and building material parameters of each building in a building group, and establishes a building thermal inertia model based on the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building; obtains total load data, energy storage device data and indoor temperature parameters of each building in the building group, and determines the temperature-sensitive load proportion and adjustable load of each building based on the total load data, energy storage device data, indoor temperature parameters and thermal inertia value; establishes a load forecasting model based on a time scale based on the historical load data of the building group to obtain a load forecasting result, and performs thermal inertia sensitivity correction on the load forecasting result based on the temperature-sensitive load proportion and the time-of-use electricity price of the distribution network to obtain a corrected load forecasting result; calculates the charging and discharging priority of each building based on the thermal inertia value, time-of-use electricity price, temperature-sensitive load proportion and adjustable load, and generates an energy storage scheduling plan based on the charging and discharging priority and the corrected load forecasting result. The present invention improves the accuracy of load forecasting based on the thermal inertia value and temperature-sensitive load decomposition of buildings, provides data support for subsequent refined scheduling, and effectively reduces energy costs and improves group scheduling benefits through priority strategies based on electricity price differences and temperature-sensitive responses. At the same time, through the dual constraint mechanism of equipment power and capacity constraints, it ensures the safety of equipment and power grids, avoids overload risks, and improves the reliability of the power grid. Finally, based on rolling optimization and real-time correction mechanisms, it dynamically responds to environmental changes and improves the robustness of scheduling schemes. The present invention significantly improves the overall demand response capability of building groups by quantifying the heterogeneity of building groups, dynamically predicting load demand, and collaboratively optimizing charging and discharging strategies, and provides a standardized, scalable, and efficient collaborative solution for building groups to participate in the demand response of new power systems.

[0153] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The above-described embodiments merely represent several preferred embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A method for aggregate scheduling of adjustable resources in a distribution network, characterized in that: include: Obtain the temperature distribution parameters and building material parameters of each building in the building group, and establish a building thermal inertia model based on the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building; Obtain the total load data, energy storage device data and indoor temperature parameters of each building in the building group, and determine the temperature-sensitive load proportion and adjustable load of each building based on the total load data, energy storage device data, indoor temperature parameters and thermal inertia value; According to the historical load data of the building group, a time-scale-based load forecasting model is established to obtain the load forecasting results. According to the proportion of temperature-sensitive loads and the time-of-use electricity price of the distribution network, the load forecasting results are corrected for thermal inertia sensitivity to obtain the corrected load forecasting results. Calculate the charging and discharging priority of each building based on thermal inertia, time-of-use electricity price, temperature-sensitive load ratio and adjustable load, and generate an energy storage scheduling plan based on the charging and discharging priority and the corrected load forecast results; The step of determining the temperature sensitive load proportion and adjustable load of each building according to the total load data, indoor temperature parameters and thermal inertia value includes: According to the indoor temperature parameters, the temperature difference between the indoor target temperature and the indoor actual temperature is calculated, and the temperature sensitive load component is calculated according to the temperature difference and the thermal inertia value; Determine the proportion of temperature-sensitive loads of each building based on the ratio between the temperature-sensitive load component and the total load data; Determine the actual charge and discharge rate of the energy storage device according to the rated power of the energy storage device of each building, and determine the upper limit of the device power according to the preset response time and the actual charge and discharge rate; Obtain the current available capacity of the energy storage device, and determine the adjustable load based on the current available capacity and the upper power limit of the device; The step of performing thermal inertia sensitivity correction on the load forecast result according to the proportion of temperature sensitive loads and the time-of-use electricity price of the distribution network to obtain the corrected load forecast result includes: Determine the price difference based on the time-of-use electricity price of the distribution network, and determine the price fluctuation coefficient based on the ratio of the price difference to the benchmark electricity price; According to the electricity price fluctuation coefficient and the proportion of temperature-sensitive load, the thermal inertia sensitivity correction coefficient is determined, and the thermal inertia sensitivity correction coefficient is multiplied by the load forecast result to obtain the corrected load forecast result; Among them, the thermal inertia sensitivity correction coefficient is expressed by the following formula: In the formula, δ represents the thermal inertia sensitivity correction coefficient, μ represents the price elasticity coefficient, △P represents the electricity price difference, represents the base electricity price, R thermal Indicates the proportion of temperature sensitive load; The step of calculating the charging and discharging priority of each building according to the thermal inertia value, time-of-use electricity price, temperature-sensitive load proportion and adjustable load includes: Determine the price difference based on the time-of-use electricity price of the distribution network, and determine the economic priority based on the product of the price difference and the proportion of temperature-sensitive loads; Determine the response speed priority based on the thermal inertia value; Determine resource utilization priorities based on the current available capacity and adjustable load of the energy storage device; Calculate charging and discharging priorities based on economic priorities, response speed priorities, and resource utilization priorities; Among them, the following formula is used to express the charging and discharging priority: In the formula, α represents the charge and discharge priority, △P represents the electricity price difference, H represents the thermal inertia value, R thermal represents the proportion of temperature sensitive load, △Q represents the adjustable load, Indicates the current available capacity.

2. The adjustable resource aggregation scheduling method of the distribution network according to claim 1 is characterized in that: The step of establishing a building thermal inertia model according to the temperature distribution parameters and the building material parameters to obtain the thermal inertia value of each building comprises: Calculate the building heat dissipation coefficient based on the temperature distribution parameters of the building surface, the thermal conductivity of the building materials and the thickness of the building walls of each building; According to the building heat dissipation coefficient and building surface area, the building thermal inertia model is established using the heat balance equation to obtain the thermal inertia value of each building.

3. The adjustable resource aggregation scheduling method of the distribution network according to claim 1 is characterized in that: The temperature sensitive load component is expressed using the following formula: In the formula, Q thermal represents the temperature-sensitive load component, Indicates the indoor target temperature, Indicates the actual indoor temperature, and H indicates the thermal inertia value; The following formula is used to express the proportion of temperature sensitive load: In the formula, R thermal Indicates the proportion of temperature sensitive load, L total Indicates total load data; The adjustable load is expressed by the following formula: In the formula, △Q represents the adjustable load, Indicates the current available capacity of the energy storage device, P rate Indicates the actual charge and discharge rate, Represents the response time window.

4. The adjustable resource aggregation scheduling method of the distribution network according to claim 1 is characterized in that: The step of establishing a load forecasting model based on a time scale according to the historical load data of the building group to obtain the load forecasting result comprises: Divide the historical load data of the building group according to the time scale to obtain interval load data, which includes short-term load data, medium-term load data and long-term load data; According to the load data of each interval and the corresponding preset algorithm, a sub-load forecasting model at each time scale is established, and the sub-load forecasting model is weighted and summed according to the preset weights to obtain the load forecasting model; According to the load forecasting model, the load forecasting result is obtained.

5. The adjustable resource aggregation scheduling method of the distribution network according to claim 4 is characterized in that: The steps of establishing sub-load forecasting models at various time scales according to the load data of each interval and the corresponding preset algorithm, and performing weighted summation on each sub-load forecasting model according to preset weights to obtain the load forecasting model include: According to the short-term load data and autoregressive model, a short-term sub-load forecasting model is established; According to the medium-term load data and exponential smoothing model, a medium-term sub-load forecasting model is established; According to the long-term load data and grey model, a long-term sub-load forecasting model is established; Based on the rolling time window, the predicted value and actual value of each sub-load forecasting model are obtained to obtain the combined forecasting total error. The sub-load forecasting model includes a short-term sub-load forecasting model, a medium-term sub-load forecasting module and a long-term sub-load forecasting model. Taking the minimization of the total error of combined forecasting as the objective function and the sum of the weights of each sub-load forecasting model equal to the threshold as the constraint condition, a weight optimization model is established; The least square method is used to solve the weight optimization model to obtain the optimal weight of each sub-load forecasting model; The load forecasting model is obtained by weighted summing up each sub-load forecasting model according to the optimal weight.

6. The adjustable resource aggregation scheduling method of the distribution network according to claim 1 is characterized in that: The step of generating an energy storage scheduling plan according to the charging and discharging priority and the corrected load forecast result includes: According to the revised load forecast results, the load forecast period is divided and the charging and discharging requirements in different periods are determined; According to different time periods, the charging and discharging priorities of each building are sorted to obtain the sorting lists corresponding to different time periods; According to the charging and discharging demands and sorting lists corresponding to different time periods, an energy storage scheduling plan for the building group based on time-sharing charging and discharging power allocation is generated.

7. An adjustable resource aggregation dispatching system for a distribution network, characterized in that: include: The load analysis module is used to obtain the temperature distribution parameters and building material parameters of each building in the building group, and to establish a building thermal inertia model based on the temperature distribution parameters and building material parameters to obtain the thermal inertia value of each building; Obtain the total load data, energy storage device data and indoor temperature parameters of each building in the building group, and determine the temperature-sensitive load proportion and adjustable load of each building based on the total load data, energy storage device data, indoor temperature parameters and thermal inertia value; include: According to the indoor temperature parameters, the temperature difference between the indoor target temperature and the indoor actual temperature is calculated, and the temperature sensitive load component is calculated according to the temperature difference and the thermal inertia value; Determine the proportion of temperature-sensitive loads of each building based on the ratio between the temperature-sensitive load component and the total load data; Determine the actual charge and discharge rate of the energy storage device according to the rated power of the energy storage device of each building, and determine the upper limit of the device power according to the preset response time and the actual charge and discharge rate; Obtain the current available capacity of the energy storage device, and determine the adjustable load based on the current available capacity and the upper power limit of the device; The load forecasting module is used to establish a time-scale-based load forecasting model based on the historical load data of the building group to obtain the load forecasting results, and to perform thermal inertia sensitivity correction on the load forecasting results based on the proportion of temperature-sensitive loads and the time-of-use electricity price of the distribution network to obtain the corrected load forecasting results; including: Determine the price difference based on the time-of-use electricity price of the distribution network, and determine the price fluctuation coefficient based on the ratio of the price difference to the benchmark electricity price; According to the electricity price fluctuation coefficient and the proportion of temperature-sensitive load, the thermal inertia sensitivity correction coefficient is determined, and the thermal inertia sensitivity correction coefficient is multiplied by the load forecast result to obtain the corrected load forecast result; Among them, the thermal inertia sensitivity correction coefficient is expressed by the following formula: In the formula, δ represents the thermal inertia sensitivity correction coefficient, μ represents the price elasticity coefficient, △P represents the electricity price difference, represents the base electricity price, R thermal Indicates the proportion of temperature sensitive load; The collaborative scheduling module is used to calculate the charging and discharging priority of each building according to the thermal inertia value, time-of-use electricity price, temperature-sensitive load ratio and adjustable load, and generate the energy storage scheduling plan according to the charging and discharging priority and the corrected load forecast results; The step of calculating the charging and discharging priority of each building according to the thermal inertia value, time-of-use electricity price, temperature-sensitive load proportion and adjustable load includes: Determine the price difference based on the time-of-use electricity price of the distribution network, and determine the economic priority based on the product of the price difference and the proportion of temperature-sensitive loads; Determine the response speed priority based on the thermal inertia value; Determine resource utilization priorities based on the current available capacity and adjustable load of the energy storage device; Calculate charging and discharging priorities based on economic priorities, response speed priorities, and resource utilization priorities; Among them, the following formula is used to express the charging and discharging priority: In the formula, α represents the charge and discharge priority, △P represents the electricity price difference, H represents the thermal inertia value, R thermal represents the proportion of temperature sensitive load, △Q represents the adjustable load, Indicates the current available capacity.

Images