Building cluster load response decision-making device based on ASIC customized chip architecture

Abstract

The invention provides a building cluster load response decision-making device based on an ASIC customized chip architecture, which comprises an ASIC chip and a load response control algorithm, the ASIC chip integrates a plurality of communication interfaces for information transmission with various building loads and a low-power-consumption high-performance calculation unit, the load response control algorithm is built in the low-power-consumption high-performance calculation unit, and the load response control algorithm is built in the low-power-consumption high-performance calculation unit. The load response control algorithm comprises a single-chip centralized load response control algorithm, the single-chip centralized load response control algorithm adopts an equal incremental rate model, and the optimal power distribution is completed under the condition of considering the comfort level of each load user. The building lighting control system has the advantages that the ASIC chip is internally provided with the multi-channel control module, different lighting areas in a building can be independently adjusted, brightness control and on-off state management are supported, and the refined requirement of a complex environment is met; 2, the power distribution can be dynamically optimized according to the load data and the historical operation mode which are acquired in real time, and the response precision and the energy efficiency are improved;

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly to a building cluster load response decision-making device based on an ASIC customized chip architecture. Background Art

[0002] With the intensification of the global energy crisis and the increase in the proportion of renewable energy generation, the power system's supply-demand balance faces more and more challenges. The traditional power system mainly relies on the operation mode of "power generation following the load", but due to the intermittency and volatility of renewable energy, the pressure on the power system during peak load periods has increased significantly. As a load-side regulation strategy, demand response plays an important role in peak shaving and valley filling, reducing the operation cost of the power grid and improving energy utilization efficiency, and thus has gradually become a research hotspot in the field of smart grids.

[0003] Currently, the application of demand response technology in building clusters still faces the following problems: First, the real-time performance and accuracy of load regulation are insufficient, making it difficult to meet the dynamic power grid requirements; second, the characteristics of different building loads vary greatly, but the existing technologies lack flexible and efficient optimization methods to coordinate various load characteristics; third, the load response devices in building clusters have low flexibility in multi-region collaborative decision-making and cannot effectively achieve intelligent management and optimization of the load side. In addition, in large-scale deployment, traditional demand response devices are limited by hardware performance and are difficult to handle complex computing tasks, resulting in limited response efficiency and reliability.

[0004] Due to its high performance, low power consumption, and highly customized characteristics, ASIC chips have received extensive attention in the fields of smart grids and building energy efficiency management in recent years. Compared with general-purpose processing chips, ASIC chips can directly implement efficient logical operations through hardware circuits, significantly improving the computing efficiency while reducing the system power consumption and cost. Therefore, building load control devices based on ASIC chips, combined with distributed optimization algorithms and intelligent decision-making logics, are expected to achieve real-time and efficient load management in demand response scenarios. Summary of the Invention

[0005] To solve the problems of poor real-time performance of load response, insufficient accuracy, and weak multi-region collaborative optimization ability in the existing technology, the present invention provides a building cluster load response decision-making device based on an ASIC customized chip architecture, including an ASIC chip and a load response control algorithm. The ASIC chip integrates multiple communication interfaces for information transmission with various building loads and a low-power high-performance computing unit. The low-power high-performance computing unit incorporates the load response control algorithm, and the load response control algorithm includes a single-chip centralized load response control algorithm. The single-chip centralized load response control algorithm adopts an equal incremental rate model to complete optimal power distribution while considering the comfort of each load user.

[0006] As a further improvement of the present invention, the specific content of the single-chip centralized load response control algorithm is as follows:

[0007] By minimizing the loss of user comfort while meeting the total power of demand response regulation, the optimal response decision is made. The mathematical form of the problem is as follows:

[0008]

[0009] where N is the controllable load, c i represents the user comfort coefficient, p i represents the adjustment value of each load, i represents the decision-making device of each building cluster, P represents the total adjustment power, represents the upper bound of the load adjustment ability, represents the lower bound of the load adjustment ability;

[0010] The incremental rate γ corresponding to each load i is

[0011]

[0012] where is the partial derivative, and L is the Lagrangian function corresponding to the objective function;

[0013] When the incremental rate corresponding to each load is equal and is the maximum feasible incremental rate, the response power of each load is the optimal distribution. At this time, the maximum equal incremental rate can be obtained by solving the following optimization problem using linear programming:

[0014] max r

[0015] s.t.r=r i i=1,2,...,N

[0016] where r is a variable representing the incremental rate of the system, i.e., the value of the incremental rate at which each load is equal in the optimal solution. After obtaining the maximum incremental rate and substituting it into the definition of the incremental rate, the power to be adjusted for each load can be obtained.

[0017] As a further improvement of the present invention, the control algorithm further includes a multi-chip adaptive distributed load response control algorithm. The multi-chip adaptive distributed load response control algorithm adopts a distributed equal incremental rate method to achieve collaborative optimization of each load control device in the building through distributed computing, automatically adjusts the load response weight, and preferentially adjusts the devices with low response costs.

[0018] As a further improvement of the present invention, the multi-chip distributed load response decision algorithm is specifically as follows:

[0019] Step 1: Each building cluster load response decision device i calculates its own current incremental rate

[0020] Step 2: Each building cluster load response decision device broadcasts its current own incremental rate to other building cluster load response decision devices;

[0021] Step 3: After receiving the incremental rate information sent by other building cluster load response decision devices, this building cluster load response decision device calculates the average value of its own current equal incremental rate and the incremental rates of all other building cluster load response decision devices collected, as the incremental rate of this building cluster load response decision device;

[0022] Step 4: Each building cluster load response decision device adjusts the power of each load within its control range according to its latest incremental rate.

[0023] As a further improvement of the present invention, the communication interface includes a lighting load control interface. The lighting load control interface is used to send control signals to the lighting load, dynamically adjust the brightness and switch state of the lighting load. The lighting load control interface has a multi-channel output function, supports independent zone control of multi-zone lighting loads, can obtain the current state of the lighting load, and can input the obtained current state of the lighting load into the building cluster load response decision device.

[0024] As a further improvement of the present invention, the communication interface further includes a HVAC load control interface. The HVAC load control interface is used to control the power of the HVAC system and obtain the current state of the HVAC load, and can input the obtained current state of the HVAC load into the building cluster load response decision device.

[0025] As a further improvement of the present invention, the data transmission format of the control signal of the HVAC load control interface is: HVAC load number, HVAC load response power, HVAC load switch flag bit; the data transmission format of the HVAC load operation status input by the HVAC load control interface is: HVAC load number, current power, controllable status flag bit.

[0026] As a further improvement of the present invention, the communication interface further includes a load response signal interface, and the load response signal interface is used to receive load response instructions from the superior control center and regularly send a power status briefing of the loads controlled by the current building cluster load response decision-making device to the superior control center.

[0027] As a further improvement of the present invention, the communication interface further includes an inter-device communication interface, and the inter-device communication interface is used to receive and send intermediate calculation results of the control algorithm; the inter-device communication interface supports the TCP / IP protocol and can network multiple building cluster load response decision-making devices through wired data transmission to meet the control requirements of a large number of controllable loads.

[0028] As a further improvement of the present invention, the ASIC chip is built-in with a data cache module and a hardware acceleration unit; the ASIC chip supports the real-time calculation function of the control algorithm and the maximum power consumption does not exceed 5W; the lighting load control interface is embedded with a fault detection unit, and the fault detection unit can monitor the abnormal conditions of lighting devices in real time and can avoid device damage through a hardware protection mechanism.

[0029] As a further improvement of the present invention, the ASIC chip includes an information security module, and the information security module is used to implement data encryption, identity authentication, and anti-tampering mechanisms to ensure the communication security between the building cluster load response decision-making device and the external system and between each building cluster load response decision-making device. The information security module uses the RSA encryption algorithm, and the encryption module is built-in with an information anomaly detection and response mechanism for identifying and blocking potential information security threats.

[0030] As a further improvement of the present invention, the building cluster load response decision-making device further includes a user interaction software system. The user interaction software system supports users to monitor the load response status of the building cluster in real time through a graphical interface. The user interaction software system can adjust the policy parameters. The graphical interface supports a variety of data visualization functions, including load distribution status, response efficiency, and energy consumption analysis. The graphical interface is implemented based on HTML and CSS.

[0031] The beneficial effects of the present invention are as follows: The present invention proposes a building cluster load response decision-making device based on an ASIC customized chip architecture. By integrating a lighting load control interface, a heating, ventilation, and air conditioning (HVAC) load control interface, and a network communication interface, and combining an adaptive distributed control algorithm and a network security communication control protocol, it realizes the dynamic regulation and optimized response of various loads within the building cluster. Compared with traditional technologies, the present invention has the following significant advantages: 1) Through the multi-channel control module built into the ASIC chip, different lighting areas within the building can be independently adjusted, supporting brightness control and switch state management to meet the refined requirements of complex environments; 2) Adopting a multi-chip adaptive distributed load response control algorithm, it can dynamically optimize power distribution according to the real-time collected load data and historical operation patterns, improving the response accuracy and energy efficiency; 3) Integrating a high-performance parallel computing architecture, it directly realizes fast load response calculations through chip hardware, ensuring the application reliability of the system in large-scale building clusters; 4) Supporting network security communication protocols to ensure the communication security between the load response device and external systems. Brief Description of the Drawings

[0032] Figure 1 It is a schematic block diagram of the principle of the high-performance parallel computing architecture adopted by the ASIC chip of the present invention;

[0033] Figure 2 It is a schematic diagram of the load operation state display interface in the user interaction software of the present invention;

[0034] Figure 3 It is a schematic diagram of the demand response statistical information display interface in the user interaction software of the present invention. Detailed Embodiment

[0035] A building cluster load response decision-making device based on an ASIC customized chip architecture provided by the present invention aims to solve the deficiencies of traditional building load response technologies in terms of real-time performance, accuracy, and flexibility. Traditional technologies usually rely on general-purpose processors or fixed logic controllers, making it difficult to meet the complex response requirements of diverse loads in building clusters. Especially in multi-region collaborative optimization scenarios, the response speed and adjustment efficiency are low, and there is a lack of precise control over load characteristics such as lighting and HVAC. In addition, the existing technologies lack sufficient security guarantees for network communication, resulting in limited overall system reliability. The device of the present invention, through the high-performance parallel computing architecture of the ASIC chip, integrates multiple communication interfaces, load response control algorithms, encryption modules, and a user interaction software system, significantly improving the real-time performance, accuracy, and collaborative optimization ability of building cluster load response.

[0036] The device of the present invention takes an ASIC chip as the core and integrates a lighting load control interface, a heating, ventilation, and air conditioning (HVAC) load control interface, a load response signal interface, an inter-device communication interface, and a user interaction software system. Among them, the lighting load control interface supports independent adjustment of multi-area lighting loads, can dynamically adjust the lighting brightness and switch status, and can obtain the current status of the lighting load in real time; the HVAC load control interface has the function of controlling the power of the HVAC system and obtaining the current status of the HVAC load, and inputting the obtained current status of the HVAC load into the building cluster load response decision-making device. The load response signal interface is used to receive load response instructions from superior control centers such as virtual power plants, load aggregators, or power dispatch centers, and regularly send a brief of the power status of the current load to the superior control center. The inter-device communication interface is used to receive and send intermediate calculation results of control algorithms. The inter-device communication interface supports the TCP / IP protocol and can network multiple building cluster load response decision-making devices through wired data transmission to achieve coordinated control of a large number of controllable loads. The user interaction software system supports users to monitor the load response status of the building cluster in real time through a graphical interface, adjust policy parameters, and provide data visualization functions such as load distribution status, response efficiency, and energy consumption analysis.

[0037] The building cluster load response decision-making device is deployed on an ASIC chip. The ASIC chip adopts a low-power and high-performance parallel computing architecture, and directly implements some software logic through hardware circuits to quickly process the response calculation tasks of various loads in the building cluster. The ASIC chip is built with a data cache module and a hardware acceleration unit, supports the real-time calculation function of control algorithms, and the maximum power consumption does not exceed 5W. The building cluster load response decision-making device is built with two load response control algorithms: a single-chip centralized load response control algorithm and a multi-chip adaptive distributed load response control algorithm. The single-chip centralized load response control algorithm is based on the equal incremental rate model and realizes optimal power distribution considering user comfort; the multi-chip adaptive distributed load response control algorithm supports multiple building cluster load response decision-making devices to perform distributed control through wired connections, dynamically adapt to newly added or removed devices in the building system, and achieve optimal power distribution for large-scale load responses. In addition, the building cluster load response decision-making device is built with an information security module, which uses the RSA encryption algorithm to implement data encryption, identity authentication, and anti-tampering mechanisms to ensure the communication security between the building cluster load response decision-making device and external systems and between various building cluster load response decision-making devices.

[0038] Through the deep integration of hardware and software, the present invention realizes the efficient, safe and intelligent control of various loads in a building cluster. The load response decision-making device for the building cluster of the present invention not only improves the real-time performance and accuracy of load response, but also significantly improves the overall energy efficiency and intelligent level of the building cluster, providing an efficient solution for building energy consumption optimization and the development of smart grids. This load response decision-making device for the building cluster is widely applicable to the scenarios of intelligent building energy consumption management and modern smart grid demand response, and has important application value. The following is a detailed description of each part:

[0039] Hardware system:

[0040] 1. Lighting load control interface

[0041] The lighting load control interface can independently adjust the lighting loads in different areas, support brightness and switch state control, that is, dynamically adjust the brightness and switch state of the lighting load, and optimize the lighting load response strategy through linkage with the data of environmental sensors.

[0042] The lighting load control interface is responsible for sending and receiving signals to multiple lighting loads in the building cluster. To ensure system security, the lighting load control interface is embedded with a fault detection unit, which can real-time monitor abnormal conditions such as short circuits and overloads of lighting devices, and avoid device damage through a hardware protection mechanism. The lighting load control interface can ensure the safe and stable operation of the lighting system during the load adjustment process, while optimizing the response speed and reliability.

[0043] The target brightness of lighting brightness adjustment is represented by 8 bits, with a resolution of up to 256 levels, and the lighting brightness can be dynamically adjusted with high precision according to requirements. The lighting load control interface has a multi-channel output function, which can independently control the loads in multiple lighting areas in the building cluster, support brightness adjustment and switch control; the lighting load control interface supports up to 64 independent channel outputs, and each channel can be adjusted separately to meet the refined management requirements for lighting loads in different areas in complex building environments. The data transmission format of the control signal of the lighting load control interface is: lighting load number, set lighting load response power, set lighting load switch flag bit. The data transmission format of the lighting load information of the lighting load control interface is: lighting load number, current power, controllable state flag bit.

[0044] 2. HVAC load control interface

[0045] The HVAC load control interface utilizes the temperature adjustment logic embedded in the ASIC chip to control the start / stop state, temperature set value and wind speed adjustment of the HVAC system, meeting the distributed control requirements of HVAC equipment in multiple areas of the building. The device supports collaborative optimization among multiple control devices, and realizes fast response and global optimization in multiple building scenarios through distributed computing.

[0046] The HVAC load control interface realizes the dynamic adjustment of HVAC equipment through the embedded temperature adjustment logic, including start / stop, temperature setting, and wind speed adjustment. The temperature setting range is from 16°C to 30°C, and the wind speed supports three-level adjustment. This interface enables the device to access the ambient temperature data recorded by the HVAC equipment in real time and dynamically adjust the output power of the HVAC equipment in combination with the user settings.

[0047] The data transmission format of the control signal of the HVAC load control interface is: HVAC load number, set HVAC load response power, set HVAC load switch flag bit. The data transmission format of the operating state of the HVAC load input to the HVAC load control interface is: HVAC load number, current power, controllable state flag bit.

[0048] 3. Load response signal interface and device communication interface

[0049] These two types of interfaces respectively realize the communication functions with the superior compliance control center and other devices in a wired transmission manner through the TCP / IP protocol. Both of these two types of interfaces support data transmission rates above 100 Mbps, and the delay is controlled within 10 ms.

[0050] 4. Information security module

[0051] The building cluster load response decision device of the present invention includes an information security module. All network security parts are concentrated in the information security module to complete, and other modules and algorithms are no longer responsible for network security issues. This information security module realizes data encryption / decryption, identity authentication, and anti-tampering mechanisms through the encrypted RSA-256 algorithm to ensure the communication security between the device and the external system. This information security module can prevent data leakage and illegal attacks in a complex network environment. The ASIC chip has an internal anomaly detection and response mechanism, which can identify and block potential security threats, provide a stable and secure communication guarantee for the cross-network operation of the building cluster load response, and improve the system reliability.

[0052] Software system:

[0053] The building cluster load response decision device of the present invention adopts a single-chip centralized load response control algorithm. The single-chip centralized load response control algorithm adopts an equal incremental rate model to complete the optimal power distribution considering the comfort of each load user.

[0054] The building cluster load response decision device of the present invention adopts a multi-chip adaptive distributed load response control algorithm. By real-time collecting the operation data of the building cluster load, including the operation data of different loads such as lighting and HVAC, and combining with a multi-objective optimization model, a dynamic balance is achieved among energy consumption, user comfort, and response speed.

[0055] Both the single-chip centralized load response control algorithm and the multi-chip adaptive distributed load response control algorithm run in the low-power and high-performance computing unit of the ASIC chip. By collecting load operation data in real time, the response power is optimally allocated, and the optimization objectives include minimizing the load regulation error and maximizing user comfort.

[0056] 1. For the single-chip centralized load response control algorithm, assume the total regulation power is P, the chip can control N controllable loads, and the regulation value of each load is p i , the upper and lower bounds of the load regulation ability are respectively and The user comfort coefficient is c i . This algorithm makes an optimal response decision by minimizing the loss of user comfort while meeting the total power of the demand response regulation. The mathematical form of the problem is as follows:

[0057]

[0058] The incremental rate γ corresponding to each load i is the partial derivative of the Lagrangian function L corresponding to the objective function with respect to its adjustment power p i , that is:

[0059]

[0060] where is the partial derivative.

[0061] When the incremental rate corresponding to each load is equal and is the maximum feasible incremental rate, the response power of each load is the optimal allocation. At this time, the maximum equal incremental rate can be obtained by solving the following optimization problem using linear programming:

[0062] max r

[0063] s.t.r=r i i=1,2,...,N

[0064] where r is a variable representing the system equal incremental rate, that is, the value of the equal incremental rate of each load in the optimal solution. After obtaining the maximum equal incremental rate, substituting it into the definition of the incremental rate, the adjustment power of each load can be obtained.

[0065] 2. The multi-chip adaptive distributed load response control algorithm adopts the distributed equal incremental rate method. This control algorithm supports the hot-swap function and can dynamically adapt to other building cluster load response decision-making devices newly added or removed within the building system. The multi-chip adaptive distributed load response control algorithm realizes the collaborative optimization of each load control device in the building through distributed computing, automatically adjusts the load response weight, and preferentially adjusts the devices with low response costs. Its hot-swap function ensures that there is no need to shut down the system during system upgrade or adjustment, improving the flexibility and adaptability of the building cluster load response device. The problem solved by the distributed equal incremental rate is the same as that of the centralized control method, but the solution process is different. The method for solving the distributed equal incremental rate is as follows: First, each device i calculates its own current incremental rate

[0066] Then, through the communication interface between devices, it broadcasts its current incremental rate to other building cluster load response decision-making devices. After receiving the incremental rate information sent by other building cluster load response decision-making devices, this building cluster load response decision-making device calculates the average value of its own current equal incremental rate and all the incremental rates collected from other building cluster load response decision-making devices as the next incremental rate of this building cluster load response decision-making device. For example, for device j, the incremental rate at time t + 1 is Finally, each device of the present invention adjusts the power of each load within its control range according to its latest incremental rate.

[0067] Both the single-chip centralized load response control algorithm and the multi-chip adaptive distributed load response control algorithm support multi-objective optimization. Considering economy, response speed, and user comfort comprehensively, they achieve the balance of different load management goals through dynamic weight adjustment. The algorithm has high efficiency and real-time performance, can adapt to the complex and changeable load response requirements in the building cluster, and improve the accuracy and energy efficiency of load regulation.

[0068] 3. The ASIC chip adopts a high-performance parallel computing architecture, directly implements software logic through hardware circuits, and can quickly process the response calculation tasks of various loads in the building cluster. The ASIC chip supports real-time computing function, with a built-in data cache module and a hardware acceleration unit, greatly improving the data processing efficiency. The maximum power consumption of the ASIC chip does not exceed 5W. This architecture reduces the system power consumption while ensuring the response speed, and is suitable for the complex load response scenarios of large-scale building complexes to achieve precise and efficient energy management. Through the high-performance parallel computing architecture of the ASIC chip in the present invention, combined with the adaptive distributed control algorithm, it realizes the dynamic optimization regulation of the building cluster load, supports the collaborative response between multiple regions and multiple devices, and comprehensively improves the demand response effect and the building energy efficiency level.

[0069] Figure 1The ASIC chip of the present invention adopts a high-performance parallel computing architecture, with an overall power consumption lower than 5W. It realizes software logic functions through hardware circuits, significantly improving the computing efficiency and response speed. The ASIC chip integrates a lighting load control interface, a heating, ventilation, and air conditioning (HVAC) load control interface, and a communication module to ensure that the device can efficiently adapt to various application scenarios.

[0070] 4. User Interaction Software System

[0071] The building cluster load response decision-making device of the present invention includes a user interaction software system, which supports users to monitor the load response status of the building cluster in real time through a graphical interface and adjust the policy parameters. The user interaction module provides real-time monitoring and load adjustment functions through the graphical interface. The interface is implemented based on HTML (HyperText Markup Language) and CSS (Cascading Style Sheets), and this interface supports a variety of data visualization functions, including load distribution status, response efficiency, and energy consumption analysis, facilitating users to intuitively understand the system operation. In addition, through this software system, the private key of the RSA encryption module can also be set to enhance the operability and user experience of the system. For the schematic diagram of the building cluster load operation status display interface, refer to Figure 2 . The schematic diagram of the display interface for statistical information such as demand response efficiency and energy consumption is Figure 3 .

[0072] A building cluster load response decision-making device based on an ASIC customized chip architecture proposed by the present invention realizes dynamic optimization adjustment and real-time response to various loads in the building cluster by designing a highly integrated ASIC chip, combining multi-functional interfaces, an adaptive distributed control algorithm, and a high-performance hardware architecture. The core of the present invention is the modular design of the ASIC chip and the hardware implementation of intelligent algorithms, enabling the device to have efficient, accurate, and reliable load response capabilities in complex building environments.

[0073] The following describes the building cluster load response decision-making device of the present invention in more detail through actual examples and drawings.

[0074] Example 1

[0075] Two devices A and B proposed by the present invention are installed in the building cluster. Devices A and B have previously agreed on the public key as

[0076] "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA7A+TU8x7B8506Y4SL9pbiy / 1STX8M8dpvE34pWRJyfHEb3+wLiEKu3bb3OI1scUQRfx5E5tg+KGZuV7Wo24ejcx2mqnjB5c99n3Yu+1inry8kxL1tJcXq4nsCzE6RzzxvrKr / xXobp79DCOIwqg9tgXMvUIaF5UTR1tMiaxmvLygVe5VHEKPVDNTigmmAy7u3d4oy7TIpmPE1O1gZhUckHoSn729cLkx / WSTaFf1eLGAwHrweqAKSjpgh9qmeZLN6lc84lMGumxGGg8RGTSiR6ZZ / S3IYkrP0+w4Gtvf4+Vu5Uqe7Xuw41eOyJFp4CbHYkZBl1 / Ew9vqFNNGJdOjdQIDAQAB", the private key is

[0077]

[0078] In this example, Device A encrypts the value 0.5 using the public key. After encryption, the text becomes

[0079] "AolhEkN0STMRAfHBoeaRMvaB46256lsHix67LiwgxovDNF4VDlZLXG1WWYIT / Fdu8ybQyKpK5jLzT / mr3nOMUG6D46oElS9OriDa1RaSaukhsimh65 / / qMPjHiUtYgvvGxtzQYlu2uK21E7Z4lRFuv3v+BfKzu60eA9wR9qjF7xCM60Ui+MhdzNkmVB+yudS544SAGTvpDeXAaXenGXfBOhoUzOZ7Amu8+myF2rCw6BxjyorA2G4a5DikIAV7ItMeDAGT / G7xDwLQEtpcxKczP+aayMwjPOnK5kTaryIMqNUAproO0gYS9MwiLKkUb1ae934GsUla9opCD8niLr+Yw==". Device B decodes the encrypted text using the private key and obtains the incremental rate corresponding to the current optimal solution in Device A as the value 0.5.

[0080] Embodiment 2

[0081] An architectural cluster load response decision-making device proposed by the present invention is installed in an office building. The device is used to adjust the lighting and HVAC loads in the building to meet the demand response instructions issued by the power dispatching center. The total load of the office building includes two groups of lighting loads and two HVAC systems. According to the instructions of the power dispatching center, the building needs to reduce the power by 5 kW within 10 minutes while minimizing the impact on user comfort and lighting requirements. The control device of the present invention calculates the power reduction plan for each device by real-time monitoring of the load operation data and combining with the adaptive distributed control algorithm, and realizes the goal through control instructions.

[0082]

[0083] In this example, after the control device of the present invention is started, it first collects the voltage, current, and power data in the building in real time through the data collection module. According to the real-time power data, the maximum adjustable power of lighting load 1 is 0.5 kW, the maximum adjustable power of lighting load 2 is 0.2 kW, the maximum adjustable power of HVAC system 1 is 10.5 kW, the maximum adjustable power of HVAC system 2 is 11.5 kW, and the total adjustable power is 22.7 kW. Then, the control device uses the embedded adaptive distributed control algorithm to calculate the power reduction allocation for each load while considering minimizing the negative impact on user comfort. The results are as follows:

[0084] Load type Distributed response power (kW) Lighting load 1 0 Lighting load 2 0 HVAC load 1 5 HVAC load 2 0

[0085] According to the calculation results, the control device sends a PWM adjustment signal to the controllable load device through the lighting load control interface to keep the brightness of lighting loads 1 and 2 unchanged, and sends a control signal to the HVAC system through the HVAC load 1 control interface to reduce the wind speed of HVAC load 1 to medium speed and appropriately adjust the set temperature to reduce power consumption.

[0086] During the power reduction process, the control device monitors the effect of load adjustment in real time and feeds back the reduction results to the power dispatching center through the network communication module. The final data shows that at the 8th second, the target power reduction successfully reaches 5 kW, meeting the requirements of demand response. At the same time, the impact on user comfort in the building is controlled at the lowest level, and the lighting brightness and indoor temperature remain within an acceptable range.

[0087] Embodiment 3

[0088] In an office park with two buildings, two control devices A and B of the present invention are installed respectively, and each control device is responsible for managing the controllable loads in its respective building. The load conditions and related parameters of the two buildings are as follows:

[0089]

[0090] The dispatching center issues a demand response instruction, requiring the two buildings to collaboratively reduce the total power by 3 kW within 15 minutes. The control devices need to share data through the communication module, allocate the respective load response ratios through the distributed optimization algorithm, and complete the adjustment tasks.

[0091] In this example, to optimize the reduction allocation, control devices A and B collaboratively calculate the reduction targets through the distributed optimization algorithm. According to the proportion of adjustable power of each building, the reduction target for Building 1 is allocated as 2.5 kW, and the reduction target for Building 2 is allocated as 0.5 kW. Subsequently, each control device optimally allocates the loads in its building. The specific allocation results are as follows:

[0092]

[0093] According to the allocation result, the lighting load 1 and the HVAC load 2 of Building 1 remain unchanged, and the HVAC load 2 is reduced to operate at 8.1 kW, with a total reduction power of 2.5 kW. The lighting load 2 and the HVAC load 3 of Building 2 operate normally, and the lighting load 3 is reduced to operate at 6.0 kW, with a total reduction power of 0.5 kW. After the reduction instruction is issued, the two buildings together reduce 3 kW of power. Through the distributed optimization algorithm, each control device achieves efficient cooperation without the need for centralized calculation. The allocation result not only meets the demand response goal but also minimizes the impact on user comfort and normal power consumption.

[0094] The present invention has the following remarkable advantages: 1. Based on the hardware architecture of the ASIC chip, it realizes the rapid processing of complex load response tasks through high-performance parallel computing. Compared with traditional general-purpose processors, the computing efficiency is significantly improved, and at the same time, the system power consumption is reduced; 2. Based on the adaptive distributed control algorithm, combined with real-time data acquisition and historical operation modes, it dynamically optimizes the load response strategy and can adapt to the complex and changeable building cluster environment; 3. The lighting and HVAC load control interfaces support independent adjustment of multiple regions and multiple devices. Combined with the fault detection and protection mechanism, it improves the security and stability of the system; 4. It supports the network security communication control protocol and combines the RSA encryption algorithm to ensure the cross-region data communication security of the building cluster load response devices; 5. The user interaction interface provides an intuitive graphical interface, supports real-time monitoring of the load response status and adjustment of strategy parameters, and improves the user operation experience.

[0095] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. An architectural cluster load response decision-making device based on an ASIC customized chip architecture, characterized in that: It includes an ASIC chip and a load response control algorithm. The ASIC chip integrates multiple communication interfaces for information transmission with various building loads and a low-power high-performance computing unit. The load response control algorithm is built into the low-power high-performance computing unit. The load response control algorithm includes a single-chip centralized load response control algorithm, and the single-chip centralized load response control algorithm adopts an equal incremental rate model to complete optimal power distribution while considering the comfort of each load user.

2. The building cluster load response decision-making device according to claim 1, wherein The specific content of the single-chip centralized load response control algorithm is as follows: By minimizing the loss of user comfort under the condition of meeting the total power of demand response regulation, the optimal response decision is made. The mathematical form of the problem is as follows: Among them, N is the controllable load, c i represents the user comfort coefficient, p i represents the adjustment value of each load, i represents the decision-making device of each building cluster, and P represents the total adjustment power. represents the upper bound of the load adjustment ability, represents the lower bound of the load adjustment ability; Incremental rate γ corresponding to each load i is Among them, is the partial derivative, and L is the Lagrangian function corresponding to the objective function; When the incremental rate corresponding to each load is equal and is the maximum feasible incremental rate, the response power of each load is the optimal distribution. At this time, the maximum equal incremental rate can be obtained by solving the following optimization problem using linear programming: max r s.t. r = r i i = 1, 2,..., N where r represents the system equal incremental rate, that is, the value of the equal incremental rate of each load in the optimal solution. After obtaining the maximum equal incremental rate, substituting it into the definition of the incremental rate, the power to be adjusted for each load can be obtained.

3. The building cluster load response decision-making device according to claim 1, characterized in that: The control algorithm also includes a multi-chip adaptive distributed load response control algorithm. The multi-chip adaptive distributed load response control algorithm adopts a distributed equal incremental rate method to achieve collaborative optimization of each load control device in the building through distributed computing, automatically adjust the load response weight, and preferentially adjust the devices with low response cost.

4. The building cluster load response decision-making device according to claim 3, wherein: The specific content of the multi-chip distributed load response decision algorithm is as follows: Step 1: Each building cluster load response decision-making device i calculates its own current incremental rate Step 2: Each building cluster load response decision device broadcasts its current own incremental rate to other building cluster load response decision devices; Step 3: After receiving the incremental rate information sent by other building cluster load response decision devices, this building cluster load response decision device calculates the average value of its own current equal incremental rate and the incremental rates of all other building cluster load response decision devices it has collected as the incremental rate of this building cluster load response decision device; Step 4: Each building cluster load response decision device adjusts the power of each load within its control range according to its latest incremental rate.

5. The building cluster load response decision-making device according to claim 1, wherein: The communication interface includes a lighting load control interface. The lighting load control interface is used to send control signals to the lighting load, dynamically adjust the brightness and switch state of the lighting load. The lighting load control interface has a multi-channel output function, supports the zoning independent control of multi-region lighting loads, can obtain the current state of the lighting load, and can input the obtained current state of the lighting load into the building cluster load response decision device.

6. The building cluster load response decision-making device according to claim 1, wherein: The communication interface also includes a heating, ventilation, and air conditioning (HVAC) load control interface. The HVAC load control interface is used to control the power of the HVAC system and obtain the current state of the HVAC load, and can input the obtained current state of the HVAC load into the building cluster load response decision device; The data transmission format of the HVAC load control interface control signal is: HVAC load number, HVAC load response power, HVAC load switch flag bit; the data transmission format of the HVAC load operation status input by the HVAC load control interface is: HVAC load number, current power, controllable status flag bit.

7. The building cluster load response decision-making device according to claim 1, characterized in that: The communication interface further includes a load response signal interface, which is used to receive load response instructions from the superior control center and regularly send the power status briefing of the loads controlled by the current building cluster load response decision-making device to the superior control center; The communication interface further includes an inter-device communication interface, which is used to receive and send intermediate calculation results of the control algorithm; the inter-device communication interface supports the TCP / IP protocol and can network multiple building cluster load response decision-making devices through wired data transmission to meet the control requirements of a large number of controllable loads.

8. The building cluster load response decision-making device according to claim 5, wherein: The ASIC chip is built-in with a data cache module and a hardware acceleration unit; the ASIC chip supports the real-time calculation function of the control algorithm, and the maximum power consumption does not exceed 5W; the lighting load control interface is embedded with a fault detection unit, which can monitor the abnormal conditions of lighting devices in real time and avoid equipment damage through a hardware protection mechanism.

9. The building cluster load response decision-making device according to claim 1, wherein: The ASIC chip includes an information security module, which is used to implement data encryption, identity authentication, and anti-tampering mechanisms to ensure the communication security between the building cluster load response decision-making device and the external system and between building cluster load response decision-making devices. The information security module uses the RSA encryption algorithm, and the encryption module is built-in with an information anomaly detection and response mechanism to identify and block potential information security threats.

10. The building cluster load response decision-making device according to claim 1, characterized in that: The building cluster load response decision-making device further includes a user interaction software system, which supports users to monitor the load response status of the building cluster in real time through a graphical interface. The user interaction software system can adjust the policy parameters. The graphical interface supports multiple data visualization functions, including load distribution status, response efficiency, and energy consumption analysis. The graphical interface is implemented based on HTML and CSS.

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