Dynamic carbon price coupled office building flexible load cooperative control method and system

Abstract

The invention discloses a dynamic carbon price coupled office building flexible load cooperative control method and system, and is applied to the technical field of intelligent calculation, and the method comprises the steps: constructing an equivalent carbon emission factor model of a target building according to the local photovoltaic power generation power consumption of the target building and the power grid purchase power consumption; the total electricity consumption in the equivalent carbon emission factor model is decomposed into indoor temperature power supply electric quantity, vehicle energy storage electric quantity, fixed energy storage electric quantity and other electric quantity; a thermal inertia model based on indoor temperature energy storage is constructed for the indoor temperature power supply electric quantity, and an energy storage model based on charging and discharging power is constructed for the vehicle energy storage electric quantity and the fixed energy storage electric quantity; and solving the equivalent carbon emission factor model by taking the minimum total generalized cost as a target to obtain control parameters of different time periods. According to the invention, the electricity price cost and the carbon emission cost are comprehensively considered to regulate and control the related data of the building, and the optimal balance point between the electricity price valley and the photovoltaic peak can be found according to the cost saving and carbon emission demands of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent carbon emission regulation, and particularly relates to an office building flexible load collaborative control method and system coupled with dynamic carbon price. BACKGROUND

[0002] With the promotion of the double carbon target, the low-carbon transformation of the building field is imminent. At present, the annual average carbon emission factor is used for calculating the carbon emission of buildings, which cannot reflect the real-time fluctuations of the carbon intensity of the power grid. Although the existing demand response technology mainly guides users to cut peaks and fill valleys according to time-of-use electricity prices to reduce electricity costs, the electricity price valley does not necessarily correspond to the carbon emission valley. For example, at night, thermal power is mostly used, and the carbon intensity is high; at noon, photovoltaic power is mostly used, and the carbon intensity is low.

[0003] The existing technical patents related to dynamic carbon mainly focus on the following research angles: (1) Focusing on the power generation side, a single target optimization of converting carbon tax into cost is proposed based on dynamic carbon, and a power system dispatching strategy and demand response method are proposed.

[0004] (2) For the electric vehicle charging station scenario, a multi-objective (load-operation cost-charging carbon emission) charging dispatching strategy is optimized.

[0005] (3) City-scale macro carbon emission prediction.

[0006] The above angles do not involve small-scale research at the building level, especially in the current stage of building low-carbon transformation with the superposition of photovoltaic, energy storage, and charging pile multiple energy forms. How to handle the two signals of electricity price and carbon factor that may have timing conflicts within a building system and collaboratively dispatch multiple heterogeneous loads is a problem that needs to be solved in the current technology.

[0007] In the prior art, the Chinese patent with the application number 202410356809.1 discloses a building comprehensive energy system double-layer optimization dispatching method considering virtual energy storage. It constructs a low-carbon building comprehensive energy system based on an energy hub containing wind, light, storage, and energy conversion devices, and comprehensively analyzes the system and the characteristics of each load to improve its demand response capability. Then, a double-layer optimization model containing an upper energy operator pricing layer and a lower building user optimization layer is proposed. The model considers building virtual energy storage and building user comfort indicators to improve system dispatching flexibility, and constructs a user comprehensive satisfaction index. Finally, the double-layer optimization model is solved to optimize the equipment output, demand response, and power purchase and sale plan of the building comprehensive energy system, and the optimal dispatching strategy is obtained. As can be seen from the above, although the building virtual energy storage is considered, the pricing strategy is only considered from the perspective of electricity price satisfaction, and the cost caused by carbon emission is ignored. SUMMARY

[0008] In order to at least overcome the above-mentioned deficiencies in the prior art, the purpose of the present application is to provide a dynamic carbon price coupled office building flexible load collaborative control method and system.

[0009] In a first aspect, the embodiments of the present application provide a dynamic carbon price coupled office building flexible load collaborative control method, comprising: constructing an equivalent carbon emission factor model of the target building according to local photovoltaic power generation electricity consumption and grid purchase electricity consumption of the target building; decomposing total electricity consumption in the equivalent carbon emission factor model into indoor temperature power supply electricity consumption, vehicle energy storage electricity consumption, fixed energy storage electricity consumption and other electricity consumption; constructing a thermal inertia model based on indoor temperature energy storage for the indoor temperature power supply electricity consumption, and constructing an energy storage model based on charging and discharging power for the vehicle energy storage electricity consumption and the fixed energy storage electricity consumption; solving the equivalent carbon emission factor model to obtain control parameters in different time periods, with the goal of minimizing total generalized cost; the total generalized cost is the sum of carbon emission cost and electricity price cost; the control parameters include target indoor temperature corresponding to the thermal inertia model and charging and discharging power corresponding to the energy storage model.

[0010] In a possible implementation manner, the equivalent carbon emission factor model adopts the following formula: In the formula, E eq (t) is an equivalent dynamic carbon emission factor, P grid (t) is grid purchase electricity consumption, E grid (t) is a grid dynamic carbon emission factor, P pv (t) is local photovoltaic power generation electricity consumption, P load (t) is total electricity consumption, P1(t) is temperature power supply electricity consumption, P2(t) is vehicle energy storage electricity consumption, P3(t) is fixed energy storage electricity consumption, and P4(t) is other electricity consumption.

[0011] In a possible implementation manner, the thermal inertia model adopts the following formula: In the formula, P1(t) is temperature power supply electricity consumption, C th is building heat capacity of an air conditioning heating area, η hvac is average equivalent air conditioning energy efficiency of the air conditioning heating area of the target building, T in (t) is indoor temperature at time t, T out (t) is outdoor temperature at time t, T in (t+1) is target indoor temperature at time t+1, and Δt is a time difference from time t to time t+1.gain (t) is the internal heat source power of the air conditioning heating area, K wall (t) is the equivalent thermal conductivity of the outer wall of the air conditioning heating area, C' th (t) is the building heat capacity of the air conditioning cooling area, η' hvac (t) is the average equivalent air conditioning energy efficiency of the air conditioning cooling area of the target building, Q' gain (t) is the internal heat source power of the air conditioning cooling area, K' wall (t) is the equivalent thermal conductivity of the outer wall of the air conditioning cooling area.

[0012] In a possible implementation, the energy storage model of the vehicle energy storage capacity adopts the following formula: In the formula, P2(t) is the vehicle energy storage capacity, P ev (t) is the vehicle charging pile charging power, E bat is the vehicle battery capacity, η ch is the vehicle charging pile charging efficiency, SOC ev (t out ) is the SOC value at the time when the vehicle finishes charging, SOC ev (t in ) is the SOC value at the time when the vehicle starts charging.

[0013] In a possible implementation, the energy storage model of the fixed energy storage capacity adopts the following formula: In the formula, P3(t) is the fixed energy storage capacity, P bat (t) is the charging and discharging power of the fixed energy storage, t'1 is the charging and discharging start time, and t'2 is the charging and discharging end time.

[0014] In a possible implementation, the objective function for solving the equivalent carbon emission factor model with the minimum total generalized cost as the target is as follows: In the formula, E eq (t) is the equivalent dynamic carbon emission factor, P grid (t) is the grid purchased electricity, C elec (t) is the time-of-use electricity price, μ is the carbon price conversion coefficient, and λ is the economic low-carbon preference coefficient.

[0015] In a possible implementation, the control parameters of different time periods are obtained as follows: The target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model are unknowns, the equivalent carbon emission factor model is solved by mixed integer linear programming with the minimum total generalized cost as the target, and the control parameters after a preset length of time are obtained. When the time reaches the preset duration, the relevant data of the target building are regulated by the control parameters calculated in the last round; The control parameter calculation after the preset duration is repeated, and the relevant data of the target building are regulated by the corresponding control parameters when the predetermined time is reached.

[0016] In a second aspect, the application further provides an office building flexible load collaborative control system coupled with dynamic carbon price, comprising: A modeling unit configured to construct an equivalent carbon emission factor model of a target building according to local photovoltaic power generation electricity consumption and grid purchased electricity consumption of the target building; A decomposition unit configured to decompose total electricity consumption in the equivalent carbon emission factor model into indoor temperature power supply electricity consumption, vehicle energy storage electricity consumption, fixed energy storage electricity consumption and other electricity consumption; A sub-modeling unit configured to construct a thermal inertia model based on indoor temperature energy storage for the indoor temperature power supply electricity consumption, and construct an energy storage model based on charging and discharging power for the vehicle energy storage electricity consumption and the fixed energy storage electricity consumption; A solving unit configured to solve the equivalent carbon emission factor model to obtain control parameters in different time periods, with the objective of minimizing total generalized cost; the total generalized cost is the sum of carbon emission cost and electricity price cost; the control parameters include target indoor temperature corresponding to the thermal inertia model and charging and discharging power corresponding to the energy storage model.

[0017] In a possible implementation, the equivalent carbon emission factor model adopts the following formula: In the formula, E eq (t) is an equivalent dynamic carbon emission factor, P grid (t) is grid purchased electricity consumption, E grid (t) is a grid dynamic carbon emission factor, P pv (t) is local photovoltaic power generation electricity consumption, P load (t) is total electricity consumption, P1(t) is temperature power supply electricity consumption, P2(t) is vehicle energy storage electricity consumption, P3(t) is fixed energy storage electricity consumption, and P4(t) is other electricity consumption.

[0018] In a possible implementation, the solving unit is further configured to: Solve the equivalent carbon emission factor model by mixed integer linear programming, with the objective of minimizing total generalized cost, and with the target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model as unknowns, to obtain control parameters after a preset duration; When the time reaches the preset duration, the relevant data of the target building is regulated by the control parameter calculated in the last round. The control parameter calculation after the preset duration is repeated, and the relevant data of the target building is regulated by the corresponding control parameter when the predetermined time is reached.

[0019] Compared with the prior art, the present application has the following advantages and beneficial effects: The dynamic carbon price coupled office building flexible load collaborative control method and system comprehensively considers the regulation of the relevant data of the building by the electricity price cost and the carbon emission cost, and can find the best balance point between the electricity price low valley and the photovoltaic peak according to the cost saving and carbon emission demand of the user. BRIEF DESCRIPTION OF DRAWINGS

[0020] The drawings described herein are used to provide further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings: Figure 1 The figure is a schematic diagram of the steps of the embodiment of the present application; Figure 2 The figure is a schematic diagram of the power load time course of the embodiment of the present application; Figure 3 The figure is a schematic diagram of the flow of the embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description, and do not serve to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowchart shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can not be implemented in sequence, and the steps without logical contextual relationship can be reversed in sequence or implemented simultaneously. In addition, one or more other operations can be added to the flowchart or one or more operations can be removed from the flowchart under the guidance of the content of the present application.

[0022] In addition, the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] Please refer to Figure 1 The flowchart of the dynamic carbon price coupled office building flexible load collaborative control method provided by the embodiments of the present application is further described as follows: the dynamic carbon price coupled office building flexible load collaborative control method can specifically include the contents described in the following steps S1-S4.

[0024] S1: constructing an equivalent carbon emission factor model of the target building according to the local photovoltaic power generation electricity consumption and the grid purchase electricity consumption of the target building; S2: decomposing the total electricity consumption in the equivalent carbon emission factor model into indoor temperature power supply electricity consumption, vehicle energy storage electricity consumption, fixed energy storage electricity consumption and other electricity consumption; S3: constructing a thermal inertia model based on indoor temperature energy storage for the indoor temperature power supply electricity consumption, and constructing an energy storage model based on charging and discharging power for the vehicle energy storage electricity consumption and the fixed energy storage electricity consumption; S4: solving the equivalent carbon emission factor model to obtain control parameters in different time periods, with the goal of minimizing the total generalized cost; the total generalized cost is the sum of carbon emission cost and electricity price cost; the control parameters include target indoor temperature corresponding to the thermal inertia model and charging and discharging power corresponding to the energy storage model.

[0025] When the embodiments of the present application are implemented, an equivalent carbon emission factor model needs to be constructed first. This model is used to represent the carbon emission factor of the target building as a whole in the state of coexistence of local photovoltaic power generation electricity consumption and grid purchase electricity consumption. The actual situation of this model is described in Figure 2 The figure is a timing comparison schematic diagram of electricity price signal, dynamic carbon emission factor signal and load in a typical summer working day. The dashed line in the figure represents the step time-of-use electricity price, and the solid line represents the dynamic carbon emission factor. As can be seen, in the period of 12:00-14:00, the reverse characteristics (signal conflict area) of "high electricity price and low carbon emission" are presented, and in the night period of 0:00-6:00, the characteristics of "low electricity price and high carbon emission" are presented. The traditional strategy based only on electricity price will cut off the load at this time. The purpose of the present application is to solve this timing misalignment problem by introducing photovoltaic consumption.

[0026] In the embodiment of the present application, the equivalent carbon emission factor model is inversely proportional to the total power consumption, and the total power consumption is divided in this case. The indoor temperature power supply, vehicle energy storage power and fixed energy storage power can be used for power adjustment, while other power such as lighting power supply cannot be adjusted. The main purpose of the embodiment of the present application is to adjust the indoor temperature power supply, vehicle energy storage power and fixed energy storage power in different time periods to control the power balance.

[0027] In the embodiment of the present application, the indoor temperature power supply needs to consider the thermal inertia of the whole building, so a thermal inertia model that can represent the thermal energy storage of the building needs to be constructed, and a storage model of the vehicle energy storage power and the fixed energy storage power also needs to be constructed. Since the thermal inertia model is constructed, the thermal energy storage capacity of the building can be considered comprehensively, so when the optimal solution is solved, a result that pre-cooling or pre-heating starts in the low-carbon period can be generated, thereby reducing the air conditioning energy consumption in the high-carbon period, and improving the emission reduction effect without increasing the hardware cost. In the embodiment of the present application, the vehicle energy storage power and the fixed energy storage power are storage models based on the charging and discharging power. When the optimal solution of these factors is solved, the charging power can be increased in the local photovoltaic large power generation period, thereby reducing the light rejection rate and reducing the indirect carbon emission of the building.

[0028] In the embodiment of the present application, when the optimal solution is solved, the total generalized cost needs to be considered comprehensively. The total generalized cost is the sum of the carbon emission cost and the electricity price cost. The sum process can adopt weighted sum to adapt to different application scenarios. For example, if it is necessary to reduce carbon emission, the weight of carbon emission is increased, and if it is necessary to reduce power consumption, the weight of the electricity price cost is increased. The objective function constructed by minimizing the total generalized cost can solve the equivalent carbon emission factor model, and then the control parameters that need to be controlled are obtained, including the indoor temperature and the charging and discharging power corresponding to the energy storage model, and then a round of control is performed. After the control is completed, the next round of control can be performed to realize the control cycle.

[0029] In a possible implementation manner, the equivalent carbon emission factor model adopts the following formula: In the formula, E eq (t) is the equivalent dynamic carbon emission factor, P grid (t) is the grid purchased power consumption, E grid (t) is the grid dynamic carbon emission factor, P pv (t) is the local photovoltaic power generation power consumption, P load(t) is total power consumption, P1(t) is temperature power consumption, P2(t) is vehicle energy storage power consumption, P3(t) is fixed energy storage power consumption, and P4(t) is other power consumption.

[0030] In the implementation of the embodiments of the present application, all (t) appearing represents a time sequence function, wherein the grid purchased power consumption corresponds to a corresponding grid dynamic carbon emission factor, and the carbon emission corresponding to the local photovoltaic power consumption is 0. It should be understood that when the parameters after the preset actual are calculated, the corresponding power consumption can be obtained through historical data, and the embodiments of the present application are not limited.

[0031] In a possible implementation, the thermal inertia model adopts the following formula: In the formula, P1(t) is temperature power consumption, C th is the building heat capacity of the air conditioning heating area, η hvac is the average equivalent air conditioning energy efficiency of the air conditioning heating area of the target building, T in (t) is the indoor temperature at time t, T out (t) is the outdoor temperature at time t, T in (t+1) is the target indoor temperature at time t+1, Δt is the time difference from time t to time t+1, Q gain (t) is the internal heat source power of the air conditioning heating area, K wall is the equivalent thermal conductivity of the outer wall of the air conditioning heating area, C' th is the building heat capacity of the air conditioning cooling area, η' hvac is the average equivalent air conditioning energy efficiency of the air conditioning cooling area of the target building, Q' gain (t) is the internal heat source power of the air conditioning cooling area, K' wall is the equivalent thermal conductivity of the outer wall of the air conditioning cooling area.

[0032] In the implementation of the embodiment of the present application, the temperature power supply power is divided into two parts, namely the heating part and the refrigeration part, because for the same target building, there may be heating and refrigeration regions at the same time, for example, the office region needs to be heated in winter, and the computer room region needs to be refrigerated. The processes of part of heat generation and dissipation will be different in these two regions, so they need to be calculated separately and combined. The heating region is divided into three parts. The first part is used to represent the influence of air conditioning work on the heating region, which needs to consider the building heat capacity and indoor temperature in the region, and the target indoor temperature is the temperature expected to be reached after a preset time Δt. Since the target indoor temperature will be solved as an unknown in subsequent calculations, in some cases, the result of its calculation will deviate from the temperature required in the season, so the target indoor temperature also needs to be set with a corresponding range, which needs to be within the range of human comfort. The second part of the heating region is used to represent the temperature rise caused by internal heat sources, which generally include lighting heat sources, equipment heat sources, human body heat sources, etc., which can fully represent the additional heat sources in the region. The third part of the heating region is used to represent the influence of external wall heat dissipation, which is positively related to the equivalent thermal conductivity of the external wall and related to the outdoor temperature, which can be obtained from the meteorological department. Similarly, the three parts of the refrigeration region are similar to those of the heating region.

[0033] In a possible implementation, the energy storage model of the vehicle energy storage power adopts the following formula: In the formula, P2(t) is the vehicle energy storage power, P ev (t) is the vehicle charging pile charging power, E bat is the vehicle battery capacity, η ch is the vehicle charging pile charging efficiency, SOC ev (t out ) is the SOC value at the time when the vehicle finishes charging, SOC ev (t in ) is the SOC value at the time when the vehicle starts charging.

[0034] In the implementation of the embodiment of the present application, in the energy storage model of the vehicle energy storage power, since the vehicle charging pile charging power needs to be optimized as an unknown, the actual possible power needs to be expected first, which is directly related to the SOC data of the vehicle. When the SOC data is introduced for calculation, the battery attenuation factor represented by the SOC data can also be effectively utilized.

[0035] In a possible implementation, the energy storage model of the fixed energy storage power adopts the following formula: In the formula, P3(t) is the fixed energy storage power, P bat(t) is the fixed energy storage charging and discharging power, t'1 is the charging and discharging start time, and t'2 is the charging and discharging end time.

[0036] In the implementation of the embodiments of the present application, unlike the vehicle energy storage power, the fixed energy storage power is locally arranged and known, so the corresponding power can be directly obtained for subsequent calculation.

[0037] In a possible implementation, a target function for solving the equivalent carbon emission factor model with the minimum total generalized cost as the target is: In the formula, E eq (t) is the equivalent dynamic carbon emission factor, P grid (t) is the grid purchase power, C elec (t) is the time-of-use electricity price, μ is the carbon price conversion coefficient, and λ is the economic low-carbon preference coefficient.

[0038] In the implementation of the embodiments of the present application, the target function with the minimum total generalized cost as the target is introduced; the target is to minimize the final economic cost J; it should be understood that the target function is a total value, that is, the expected value in multiple periods is superimposed, generally, one period is selected as 15 min to 60 min, and the total value is selected as the total value in 24 hours or 12 hours, that is, t is the length of a calculation period, and T is the length of a total period. For the first term, it represents the electricity price cost brought by grid purchase, and the second term represents the carbon emission cost brought by grid purchase. By introducing the economic low-carbon preference coefficient, the relationship between the two can be conditioned, and generally, the economic low-carbon preference coefficient is taken as 0.5 when balance is considered.

[0039] In a possible implementation, the control parameters of different time periods are obtained by: The target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model are unknowns, the equivalent carbon emission factor model is solved by mixed integer linear programming with the minimum total generalized cost as the target, and the control parameters after a preset time length are obtained; When the time reaches the preset time length, the related data of the target building are regulated and controlled by the control parameters calculated in the last round; The control parameter calculation after a preset time length is repeated, and the related data of the target building are regulated and controlled by the corresponding control parameters when a predetermined time is reached.

[0040] In the implementation of the embodiments of the present application, the unknowns can be solved by the optimal solution. Although the embodiments of the present application use mixed integer linear programming to solve, other methods can also be used to solve, which should be regarded as equivalent to the embodiments of the present application. The optimal solution of multiple unknowns under the constraint condition by mixed integer linear programming is a mature existing technology, and the embodiments of the present application do not make more limitations. After obtaining the control parameters, the same type of parameters of the target building can be adjusted according to the control parameters at the next time point, and the control parameters in the next period need to be adjusted, so as to realize the cycle control.

[0041] For example, please refer to Figure 3 Taking an office building in the southeast region as an example, in summer weekdays, the electricity price implements two-part time-of-use electricity price for industry and commerce (peak electricity price from 12:00 to 14:00). The grid carbon factor presents a "duck curve" (the carbon factor is lowest from 12:00 to 14:00 due to photovoltaic access). The preference coefficient λ is set to 0.5 (taking into account cost and carbon). After calculating the optimization, in the period from 13:00 to 14:00, although the electricity price is high, the carbon factor is extremely low and there is local photovoltaic, the current indoor temperature is 26.5℃, and the outdoor temperature is 36℃, the target temperature obtained by optimization is 25℃, at this time, it is equivalent to pre-cooling and storing cold energy using photovoltaic power; At the same time, the vehicle charging power reaches the highest in this period, and the fixed energy storage charging power reaches the highest. In the next period, from 14:00 to 15:00, the target temperature obtained by optimization is 26℃, and the vehicle charging power and the fixed energy storage charging power are reduced. Again, in the period from 19:00 to 20:00, it belongs to the peak electricity price and high carbon period, and the target temperature obtained by optimization is 27.5℃, at this time, the stored cold energy is used to maintain the indoor temperature; At the same time, the vehicle charging power reaches the lowest in this period, and the fixed energy storage begins to discharge, and the discharge power reaches the highest.

[0042] Based on the same inventive concept, the present application also provides an office building flexible load collaborative control system coupled with dynamic carbon price, comprising: A modeling unit configured to construct an equivalent carbon emission factor model of the target building according to local photovoltaic power generation electricity consumption and grid purchased electricity consumption of the target building; A decomposition unit configured to decompose total electricity consumption in the equivalent carbon emission factor model into indoor temperature power consumption, vehicle energy storage power consumption, fixed energy storage power consumption, and other power consumption; A sub-modeling unit configured to construct a thermal inertia model based on indoor temperature energy storage for the indoor temperature power consumption, and construct an energy storage model based on charging and discharging power for the vehicle energy storage power consumption and the fixed energy storage power consumption; The solving unit is configured to solve the equivalent carbon emission factor model to obtain control parameters of different time periods, with the minimum total generalized cost as the target, wherein the total generalized cost is the sum of carbon emission cost and electricity price cost, and the control parameters include a target indoor temperature corresponding to the thermal inertia model and charging and discharging power corresponding to the energy storage model.

[0043] In a possible implementation, the equivalent carbon emission factor model adopts the following formula: In the formula, E eq (t) is an equivalent dynamic carbon emission factor, P grid (t) is grid electricity purchase, E grid (t) is a grid dynamic carbon emission factor, P pv (t) is local photovoltaic power generation, P load (t) is total electricity consumption, P1(t) is temperature power consumption, P2(t) is vehicle energy storage power consumption, P3(t) is fixed energy storage power consumption, and P4(t) is other power consumption.

[0044] In a possible implementation, the solving unit is further configured to: solve the equivalent carbon emission factor model through mixed integer linear programming, with the minimum total generalized cost as the target, and with the target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model as unknowns, to obtain control parameters after a preset time period; when the time reaches the preset time period, control relevant data of the target building through the control parameters calculated in the last round; repeat the control parameter calculation after the preset time period, and control relevant data of the target building through the corresponding control parameters when a predetermined time is reached.

[0045] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person 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 the present application.

[0046] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are merely illustrative, for example, the division of the units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can also be electrical, mechanical or other form of connection.

[0047] The units described as separate components can or can not be physically separate, and it is obvious to those skilled in the art that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0048] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or software functional unit.

[0049] The integrated unit, if realized in the form of software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art, or the whole or part of the technical solutions can be embodied in the form of software product, which is stored in a storage medium and includes a plurality of instructions for making a computer device (which can be a personal computer, a server, or a grid device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and various program code storage media.

[0050] The above detailed description of the specific embodiments of the present application has been given to understand the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for coordinated control of flexible loads in office buildings based on dynamic carbon price coupling, characterized in that, include: Construct an equivalent carbon emission factor model for the target building based on its local photovoltaic power generation and grid-purchased electricity consumption. The total electricity consumption in the equivalent carbon emission factor model is decomposed into indoor temperature power supply electricity, vehicle energy storage electricity, stationary energy storage electricity and other electricity; A thermal inertia model based on indoor temperature is constructed for the power supply at the indoor temperature, and an energy storage model based on charging and discharging power is constructed for the energy storage of the vehicle and the energy storage of the stationary energy. The equivalent carbon emission factor model is solved with the goal of minimizing the total generalized cost to obtain control parameters for different time periods; the total generalized cost is the sum of carbon emission cost and electricity price cost. The control parameters include the target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model.

2. The method for coordinated control of flexible loads in office buildings based on dynamic carbon price coupling according to claim 1, characterized in that, The equivalent carbon emission factor model uses the following formula: In the formula, E eq (t) is the equivalent dynamic carbon emission factor, P grid (t) represents the electricity purchased by the power grid, E grid (t) represents the dynamic carbon emission factor of the power grid, P pv (t) represents the local photovoltaic power generation consumption, P load P1(t) represents the total electricity consumption, P2(t) represents the electricity supplied by temperature-controlled power supply, P3(t) represents the electricity stored in the vehicle, P4(t) represents the electricity stored in the stationary energy storage, and P5(t) represents other electricity consumption.

3. The method for coordinated control of flexible loads in office buildings based on dynamic carbon price coupling according to claim 1, characterized in that, The thermal inertia model adopts the following formula: In the formula, P1(t) represents the electrical charge supplied to the temperature source, and C... th η is the building heat capacity of the air-conditioned heating area. hvac T represents the average equivalent air conditioning energy efficiency of the air-conditioned heating zone of the target building. in (t) represents the indoor temperature at time t, T out (t) represents the outdoor temperature at time t, T in (t+1) represents the target indoor temperature at time t+1, Δt represents the time difference between time t and time t+1, and Q gain (t) represents the internal heat source power of the air-conditioned heating zone, K. wall C' is the equivalent thermal conductivity of the exterior wall of the air-conditioned heating area. th For the building heat capacity of the air-conditioned cooling area, η' hvac Q' is the average equivalent air conditioning energy efficiency of the air-conditioned cooling area of ​​the target building. gain (t) represents the internal heat source power of the air-conditioned cooling zone, K' wall The equivalent thermal conductivity of the exterior wall of the air-conditioned cooling zone.

4. The method for coordinated control of flexible loads in office buildings based on dynamic carbon price coupling according to claim 1, characterized in that, The energy storage model for vehicle energy storage capacity is as follows: In the formula, P2(t) represents the energy stored in the vehicle, P ev (t) represents the charging power of the vehicle charging station, E bat For vehicle battery capacity, η ch For vehicle charging station charging efficiency, SOC ev (t out The State of Charge (SOC) value is the value at the moment the vehicle finishes charging. ev (t in The SOC value is the value at which the vehicle begins charging.

5. The method for coordinated control of flexible loads in office buildings based on dynamic carbon valence coupling according to claim 1, characterized in that, The energy storage model with a fixed energy storage capacity adopts the following formula: In the formula, P3(t) is the fixed energy storage capacity, P bat (t) represents the charging and discharging power of the fixed energy storage, t'1 represents the start time of charging and discharging, and t'2 represents the end time of charging and discharging.

6. The method for coordinated control of flexible loads in office buildings with dynamic carbon valence coupling according to claim 1, characterized in that, The objective function for solving the equivalent carbon emission factor model with the goal of minimizing the total generalized cost is: In the formula, E eq (t) is the equivalent dynamic carbon emission factor, P grid (t) represents the electricity purchased from the power grid, C elec (t) represents the time-of-use electricity price, μ represents the carbon price conversion coefficient, and λ represents the low-carbon economic preference coefficient.

7. The method for coordinated control of flexible loads in office buildings with dynamic carbon valence coupling according to claim 1, characterized in that, Obtaining control parameters for different time periods includes: Using the target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model as unknowns, and with the goal of minimizing the total generalized cost, the equivalent carbon emission factor model is solved by mixed integer linear programming to obtain the control parameters after a preset time. When the preset duration is reached, the relevant data of the target building are adjusted using the control parameters calculated in the previous round; The control parameters are repeatedly calculated after a preset duration, and the relevant data of the target building are adjusted according to the corresponding control parameters when the predetermined time is reached.

8. A flexible load coordination control system for office buildings with dynamic carbon price coupling, characterized in that, include: The modeling unit is configured to construct an equivalent carbon emission factor model of the target building based on the target building's local photovoltaic power generation and grid-purchased power consumption; The decomposition unit is configured to decompose the total electricity consumption in the equivalent carbon emission factor model into indoor temperature power supply electricity, vehicle energy storage electricity, stationary energy storage electricity and other electricity; The sub-modeling unit is configured to build a thermal inertia model based on indoor temperature for the power supply at indoor temperature, and to build an energy storage model based on charging and discharging power for the energy stored in the vehicle and the energy stored at fixed location. The solution unit is configured to solve the equivalent carbon emission factor model with the objective of minimizing the total generalized cost, and obtain control parameters for different time periods; the total generalized cost is the sum of carbon emission cost and electricity price cost; The control parameters include the target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model.

9. The flexible load coordination control system for office buildings with dynamic carbon valence coupling according to claim 1, characterized in that, The equivalent carbon emission factor model uses the following formula: In the formula, E eq (t) is the equivalent dynamic carbon emission factor, P grid (t) represents the electricity purchased by the power grid, E grid (t) represents the dynamic carbon emission factor of the power grid, P pv (t) represents the local photovoltaic power generation consumption, P load P1(t) represents the total electricity consumption, P2(t) represents the electricity supplied by temperature-controlled power supply, P3(t) represents the electricity stored in the vehicle, P4(t) represents the electricity stored in the stationary energy storage, and P5(t) represents other electricity consumption.

10. The flexible load coordination control system for office buildings with dynamic carbon valence coupling according to claim 1, characterized in that, The solving unit is further configured to: Using the target indoor temperature corresponding to the thermal inertia model and the charging and discharging power corresponding to the energy storage model as unknowns, and with the goal of minimizing the total generalized cost, the equivalent carbon emission factor model is solved by mixed integer linear programming to obtain the control parameters after a preset time. When the preset duration is reached, the relevant data of the target building are adjusted using the control parameters calculated in the previous round; The control parameters are repeatedly calculated after a preset duration, and the relevant data of the target building are adjusted according to the corresponding control parameters when the predetermined time is reached.

Images