An energy community heat producer-consumer building distributed model predictive control method based on information privacy protection
By adopting privacy-preserving information exchange strategies and the DMPC method within the energy community, the problem of privacy leakage in information exchange between buildings was solved, the enthusiasm for thermal energy exchange and the utilization rate of solar energy were improved, and the efficient and safe operation of the energy community was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHEAST DIANLI UNIVERSITY
- Filing Date
- 2025-12-08
- Publication Date
- 2026-07-21
Smart Images
Figure CN121637824B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of privacy protection technology in the optimization and regulation of intelligent energy systems, and particularly relates to a distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection. Background Technology
[0002] Currently, optimization and control schemes for smart energy systems can be broadly categorized into Model Predictive Control (MPC), Decentralized Model Predictive Control (DPC), and Distributed Model Predictive Control (DMPC).
[0003] Model predictive control (MPC) is essentially a closed-loop control strategy based on a "predictive model." By continuously predicting the future state of the system and optimizing the control input, it achieves precise control of complex systems. Due to its robustness and ability to achieve good control results, it has been successfully applied to the optimization and regulation of energy communities in recent years.
[0004] Decentralized Model Predictive Control (MPC) is an improved strategy proposed for large-scale, distributed, or tightly coupled complex systems, based on the traditional centralized MPC. Its core logic is to decompose the entire complex system into several subsystems and configure a local MPC controller for each subsystem, thereby reducing the dimensionality of the controller and facilitating online rolling optimization.
[0005] Distributed model predictive control (DMPC) distributes the optimization problem of a large-scale interconnected system across several subsystems. These subsystems exchange and share information through a communication network, and each subsystem has its own independent controller. By solving their respective optimization problems through these controllers, the system's solution rate is improved.
[0006] As energy communities become increasingly complex, optimized regulation becomes more difficult. Simultaneously, with the widespread adoption of digital technologies, higher demands are being placed on information privacy protection within these communities, primarily manifested in the following aspects: (1) As the number of buildings in new energy communities gradually increases, there are problems such as long optimization and control time and large amount of calculation within the community. At the same time, buildings in new energy communities can interact with each other in terms of thermal energy, and it is necessary to share interactive information between buildings. Therefore, the optimization and control time and the coordination and communication between buildings need to be considered during the optimization and control process.
[0007] (2) In traditional distributed model predictive control, information privacy protection is not considered during the information exchange process of each subsystem in the communication network. When each subsystem shares state variables and control inputs and other privacy data, there is a risk of leakage of local sensitive information, which disrupts the interaction order within the energy community, damages the trust relationship between heat producers and consumers, weakens the enthusiasm of each subsystem to participate in coordinated scheduling, reduces or even stops heat energy interaction, and affects the energy community's level of new energy consumption. Summary of the Invention
[0008] To address the aforementioned technical problems, this invention proposes a distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection, thereby resolving the issues existing in the prior art.
[0009] To achieve the above objectives, this invention provides a distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection, comprising: S1. Based on an energy community consisting of multiple heat-producing and heat-consuming buildings interconnected by a thermal network, a state-space model is obtained by modeling the heat energy interaction relationships between each heat-producing and heat-consuming building and between them. S2. Based on the state-space model, construct an optimization problem with the goal of minimizing the total operating cost of the energy community, and determine the operating constraints of each device in the heat production and consumption building and the interaction constraints between buildings; S3. In each rolling optimization cycle, each heat-generating and consuming building, based on a preset privacy protection interaction strategy, iteratively exchanges desired heat power information with neighboring buildings to obtain the information required for collaborative optimization. S4. Each heat-generating and consuming building solves its corresponding local optimization control subproblem in parallel based on the current state of the state-space model, the information required for collaborative optimization, and the optimization problem, thereby obtaining its optimal control sequence in the future finite time domain. S5. Each heat-consuming building will send the control command corresponding to the current optimization moment in the optimal control sequence to the local physical execution device; S6. Update the state space model based on the system state after the control command is executed, and use it as the initial condition for the next optimization moment. Repeat steps S3 to S5 until the preset global optimization control cycle is completed.
[0010] Optionally, in step S1, the process of obtaining the state-space model includes: Based on the photothermal conversion efficiency, light-receiving area, and solar radiation of solar collectors in each heat-consuming building, a solar collector model is constructed. Based on the energy efficiency coefficient and power consumption of electric heat pumps in each heat-consuming building, an electric heat pump model is constructed. Based on the heat storage / release power, heat storage efficiency, heat release efficiency, heat dissipation coefficient, and heat storage capacity of the hot water storage tanks in each heat-consuming building, a hot water storage tank model is constructed. Based on the solar collector model, electric heat pump model, hot water storage tank model, heat load consumption power, and building interaction heat power model considering line losses, a power balance equation for heat-generating and heat-consuming buildings is constructed. Based on the power balance equation, the system's state variables and control inputs are defined, and the state-space model is derived.
[0011] Optionally, in S2, the optimization problem aimed at minimizing the total operating cost of the energy community is expressed by the objective function of minimizing the sum of the maintenance costs of solar collectors, the energy consumption costs of electric heat pumps, the operating costs of hot water storage tanks, and the interaction costs between buildings for all heat producers and consumers during the control period.
[0012] Optionally, the operational constraints in S2 include the output constraints of the solar collector, the heating constraints of the electric heat pump, the ramp-up constraints of the electric heat pump, the heat storage capacity constraints of the hot water storage tank, the maximum heat storage power constraints and the maximum heat release power constraints of the hot water storage tank, and the mutual exclusion constraints between the heat storage state and the heat release state of the hot water storage tank; the interaction constraints include the interaction heat power constraints between heat producers and consumers and buildings.
[0013] Optionally, the inter-building interactive heat power model considering line loss is expressed as follows: the actual received power transmitted from the heat-generating and consuming building to the building is equal to the transmitted power multiplied by a loss coefficient calculated based on the transmission line length and the percentage loss per unit length.
[0014] Optionally, in S3, the iterative exchange of desired heat power information between each heat-consuming building and its neighboring buildings based on a preset privacy-preserving interaction strategy includes: The sending heat-producing and consuming building divides the actual information of the expected interactive heat power to be exchanged and combines it with randomly generated parameters to form a vector to be transformed; the vector to be transformed is multiplied with an orthogonal transformation matrix of a preset order to generate false interactive information for transmission; the sending heat-producing and consuming building sends the false interactive information to the adjacent receiving heat-producing and consuming building. The receiving heat-consuming building determines the order of the orthogonal transformation matrix and the randomly generated parameters based on the received false interaction information, and performs a corresponding inverse transformation on the false interaction information according to the determined parameters to recover the true information of the expected interactive heat power of the sending heat-consuming building.
[0015] Optionally, during the iterative exchange process of S3, each heat-producing and consuming building forms an information set to be exchanged according to a preset aggregation rule during each information exchange. The information set includes its own expected interactive heat power real information and the expected interactive heat power real information of other buildings obtained during the information exchange process. After each information exchange, each heat-producing and consuming building updates its own information set to be exchanged according to the aggregation rule based on the information received and restored this time, for use in the next information exchange.
[0016] Optionally, in S3, the orthogonal transformation matrix used by the preset privacy protection interaction strategy is the Hadamard matrix.
[0017] Optionally, in S4, the parallel solution of the corresponding local optimization control subproblem includes: Each heat-generating and heat-consuming building uses the current state of the state space model as the initial state of the prediction time domain, and obtains the predicted values of light intensity and heat load in the future time domain. Based on the initial state, the predicted light intensity and heat load, the information required for collaborative optimization obtained from adjacent buildings, operational constraints and interaction constraints, a local optimization control subproblem is constructed with the goal of minimizing its own operating cost. The optimal control sequence is obtained by independently solving the local optimization control subproblem.
[0018] Optionally, before executing S1, initialization is also included: acquiring the initial state quantities of each heat-generating and heat-consuming building, the predicted data of external light intensity and heat load, and optimization parameters, and setting the total time and rolling optimization step size of the global optimization control cycle.
[0019] Compared with the prior art, the present invention has the following advantages and technical effects: This invention constructs an optimized operation framework for energy communities containing heat-producing and consumer buildings. It models heat-producing and consumer buildings containing various distributed resources within the energy community, and introduces operating conditions such as line losses and safety constraints, as well as market mechanisms, into the framework. This can fully mobilize the flexibility of heat-producing and consumer buildings and significantly improve the utilization rate of solar energy in the energy community.
[0020] This invention addresses the privacy concerns of information exchange in distributed optimization and control by proposing a privacy-preserving information exchange method. This method transmits information hidden within publicly available, ordinary media, ensuring building information security while enhancing the enthusiasm for thermal interaction within the energy community, thus providing a reliable solution for protecting the privacy of information exchange within the energy community.
[0021] This invention employs the DMPC method to optimize and control the energy community of buildings containing heat producers and consumers. It has a good fit with information interaction strategies and has good reliability and scalability for solving the optimization and control problem of large-scale heat producers and consumers buildings, which can meet the needs of practical engineering. Attached Figure Description
[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the energy community operation framework for buildings containing heat producers and consumers, according to an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating an information interaction method that takes privacy protection into account in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the DMPC optimization and control process according to an embodiment of the present invention. Figure 4 This is a graph showing the optimization and control results of Example 1 of the present invention. Figure 5 This is a graph showing the optimization and control results of Example 2 of the present invention. Figure 6 This is a graph showing the optimization and control results of Example 3 in this embodiment of the invention. Figure 7 This is a curve comparing real and false information in an embodiment of the present invention. Figure 8 This is a schematic diagram illustrating the optimization time for different heat-consuming building sizes in an embodiment of the present invention. Detailed Implementation
[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0024] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0025] Example 1 This embodiment provides a distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection, including: (a) The operational framework for energy communities including buildings housing heat producers and consumers; This invention considers an energy community composed of heat-producing and heat-consuming buildings. Within the energy community, these buildings can interact through a thermal network to consume excess heat generated within the community. Its operational framework is as follows: Figure 1 As shown. Each heat-consuming building contains a hot water storage tank, solar collectors, electric heat pumps, and flexible heat loads. Each heat-consuming building also contains a DMPC controller to control the flexible resources within the heat-consuming building.
[0026] In the energy community, each heat-consuming building converts solar energy into heat energy through solar collectors. After meeting its own heat load requirements, excess heat energy is stored in a hot water tank or exchanged with other heat-consuming buildings. When the heat generated by the solar collectors cannot meet its own heat load requirements, an electric heat pump can compensate for the heat shortfall. The internal controller of each heat-consuming building exchanges information through a communication network to obtain the expected power information of other heat-consuming buildings and formulates corresponding control strategies based on its own renewable energy output and load conditions.
[0027] (ii) Building modeling for heat producers and consumers in energy communities; To accurately describe the heat balance relationship within heat-producing and heat-consuming buildings, a model is constructed for heat-producing and heat-consuming buildings containing various distributed resources within the energy community. Considering line losses caused by heat interaction, the energy conversion and storage characteristics of solar collectors, electric heat pumps, and hot water storage tanks are formulated as the basis for subsequent optimization and control.
[0028] (1) Solar collector model; Solar collectors typically consist of a collection system, a circulation system, a heat storage system, and a heat dissipation system. The energy balance equation for a solar collector is expressed as follows: (1) In the formula, for Buildings that are constantly producing heat The thermal power output of the solar collector; The light-to-thermal conversion efficiency of a solar collector; The light-receiving area of a solar collector; for Moment Solar Collector The average amount of direct solar radiation.
[0029] (2) Electric heat pump model; Electric heat pumps are controlled supplemental heat sources for buildings that generate and consume heat, converting electrical energy into heat energy. The expression for the heat generation power of an electric heat pump is: (2) In the formula, for Buildings that are constantly producing heat The heat output power of an electric heat pump; The heat production efficiency of an electric heat pump, i.e. The coefficient is closely related to the heat source temperature and the outlet water temperature of the electric heat pump, and is usually taken as 3-5. for Momentary heat pump The electrical power consumed.
[0030] (3) Hot water storage tank model; The energy storage element in a heat distribution network system is typically a hot water storage tank; The operating status of a hot water storage tank is related to its heat storage capacity, heat storage / release power, and current heat storage level. The energy equation can be expressed as: (3) In the formula, and They are respectively Time and Hot water storage tank Internally stored thermal energy; The heat dissipation coefficient of the thermal storage system; and They are respectively Buildings that are constantly producing heat The heat storage and heat release capacity of the hot water storage tank; and These are the system's heat storage and heat release efficiencies, respectively.
[0031] (4) A heat generation and consumption model for buildings that considers line losses; In heat-consuming buildings, heat energy flows through various components such as the heat collection system, heat storage system, and heat load, using water as the medium. The power balance of a heat-consuming building can be described as follows: (4) In the formula, for Buildings that are constantly producing heat The heat power consumed by the load; for Buildings that are constantly producing heat From the heat production consumer building Interactive thermal power.
[0032] The expression for the interaction power between buildings with different heat consumption characteristics, taking into account line losses, is as follows: (5) In the formula, Percentage of loss per kilometer of line; The length of the transmission line; for Buildings that are constantly producing heat To heat production and consumption buildings The power of interaction.
[0033] The state-space equations for the heat production and consumption buildings in the energy community are then obtained as follows: (6) (iii) Information interaction that takes into account information privacy protection; During information exchange, external eavesdroppers may steal information for illicit economic gain, leading to information leakage, damaging trust between heat-consuming buildings, and reducing or even halting heat energy exchange. To address this issue, this embodiment proposes a privacy-protected information exchange method, enabling reliable information exchange between each heat-consuming building while ensuring information privacy.
[0034] (1) Information exchange methods that take privacy protection into account; To achieve privacy-preserving information exchange between heat-generating consumer buildings under eavesdropping attacks, this embodiment employs a privacy-preserving information exchange method to transmit the desired interaction power information between buildings, such as... Figure 2 As shown.
[0035] To prevent external eavesdroppers from stealing information from the heat production and consumption buildings In the The true information of the expected interactive heat power of the next transfer. The information is hidden using the Hadamard transformation. To ensure matching the order of the Hadamard matrix, the real information is... Equal portions.
[0036] (10) In the formula, For true information conduct The real vector after equal division; For heat production consumption buildings No. A positive integer randomly generated during the transmission of this information.
[0037] The real information after equal division pass The Hadamard transform is used for hiding the information. Based on the properties of orthogonal transforms, the original information is evenly distributed into pseudo-random information, which is expressed as: (11) In the formula, This is false information; for A Hadamard matrix of order 1.
[0038] Hot production consumption building False information about the expected interactive heat power Transmitted to adjacent heat-consuming buildings Hot production and consumption buildings By analyzing false information The order of the matrix is then used to obtain random numbers. And based on the Hadamard inverse transform formula (12), the heat production and consumption buildings are reconstructed. The real information Ultimately, the heat production and consumption of buildings were obtained. The true information conveyed .
[0039] (12) (13) In the formula, .
[0040] To achieve the sharing of expected heat power information among heat-producing and heat-consuming buildings within the energy community, during the information exchange process, heat-producing and heat-consuming buildings... In addition to transmitting its own expected heat power, it should also include information on the expected heat power of its neighbors. (Heat production and consumption building) The actual expected thermal power information for the next transmission is updated using the following formula.
[0041] (14) Repeating the above process, as the number of information exchanges increases, the heat production and consumption of buildings... While taking into account the protection of user information privacy, it is possible to obtain The expected heat output of other heat-consuming buildings within the Time Energy Community.
[0042] (iv) Energy community regulation strategies for buildings containing heat producers and consumers; With the overall economic optimization of the energy community as the control objective, and based on the aforementioned information interaction strategy that considers information privacy protection and the DMPC algorithm, this paper optimizes the control of an interconnected energy community with multiple heat producers and consumers, which has state and control input constraints. The DMPC optimization control process is as follows: Figure 3 As shown.
[0043] (1) Objective function; For an energy community containing buildings that generate and consume heat, the optimization objective is to meet the energy demand of the entire energy community while reducing the total cost of operation. The objective function can be expressed as: (15) (16) In the formula: The total operating cost of an energy-efficient building community that includes heat production and consumption; To optimize the start time of regulation; The total number of time periods in a complete control cycle; where , , and They are respectively Buildings that are constantly producing heat Maintenance costs of solar collectors, energy consumption costs of electric heat pumps, operating costs of hot water storage tanks, and building interaction costs for heat producers and consumers; Maintenance costs for solar collectors; Time-of-use pricing; This is the depreciation cost coefficient for hot water storage tanks; The price of heat generated and consumed by buildings interacts with each other.
[0044] (2) Constraints; In addition to the power balance equation constraints mentioned above, the following constraints also apply: 1) Output constraints of solar collectors: (17) In the formula, This represents the maximum output of the solar collector.
[0045] 2) Heating constraints and ramp-up constraints of electric heat pumps: (18) (19) In the formula, and These are the upper and lower limits of the heating power of the electric heat pump, respectively. , These represent the downward and upward ramp rate limits of the electric heat pump, respectively.
[0046] 3) The controllability and storage capacity constraints of the hot water storage tank: (20) (twenty one) (twenty two) In the formula, For hot water storage tank Minimum heat storage; For hot water storage tank Maximum heat storage capacity; typically, a thermal storage system will not be in both heat storage and heat release states simultaneously. for The heat storage and release status of the thermal energy storage system during a given time period is a 0-1 variable, where 1 represents the system storing thermal energy and 0 represents the system releasing thermal energy. and Describe the hot water storage tank respectively Maximum heat storage and heat release power.
[0047] 4) Heat generation and consumption interaction between buildings: Buildings that generate and consume heat can sell excess heat to other buildings when they have a heat surplus, or purchase heat when they have a heat shortage. To prevent malicious arbitrage between buildings and to avoid damage to heating pipes from heat exchange, the heat generation capacity of buildings should vary within a defined range: (twenty three) In the formula, and For heat production consumption buildings The minimum and maximum values of the heat power exchanged between buildings and other heat-producing and heat-consuming buildings.
[0048] (3) DMPC control strategy; Step 1: Initialization, obtain the state variables of the system in the initial stage. As the initial value for rolling optimization, it predicts the future disturbance information, illumination intensity, and user load increment in the time domain.
[0049] Step 2: Each heat-consuming building sends and receives power data from other heat-consuming buildings through information exchange strategies that consider privacy protection, as defined in equations (10)-(14). .
[0050] Step 3: Model the energy community of buildings with heat producers and consumers, as shown in Equation (6), solve the optimization problem (15), and perform parallel calculations of the optimization problems of each subsystem to obtain the optimal control sequence and state variables. Only the first value of the optimal control sequence is applied to the privacy-conscious DMPC control system.
[0051] Step 4: Determine Is the current time the final time of the system's set time period? If not, scroll to... At any given moment, return to step 1 and repeat the above steps until all time periods are output. The regulatory plan.
[0052] This invention focuses on energy communities containing heat-generating and heat-consuming buildings. Considering load demand, fluctuations in solar intensity, and line losses, a model of these buildings is constructed. A DMPC (Digital Dynamics Control) strategy, taking into account information privacy protection, is used to optimize and control the model, thereby achieving safe, efficient, and economical operation of the energy community.
[0053] To verify the effectiveness of the proposed scheme, a 33-node heat distribution network energy community was simulated and analyzed on the Matlab simulation platform. The energy community contains 20 heat production and consumption buildings equipped with solar collectors, electric heat pumps and hot water storage tanks, and 13 general buildings equipped only with hot water storage tanks.
[0054] Analysis of the Results of the Heat Production and Consumption Building Regulation Strategy: Using a typical winter day as a background, the following three simulation scenarios are set up for verification: Example 1: Within the energy community, there is no heat exchange; heat producers and consumers independently plan their energy consumption and storage charging / discharging schedules. The optimized control result curve for Example 1 is shown below. Figure 4 As shown.
[0055] Example 2: Thermal energy interaction occurs within an energy community. Line losses are considered, but information privacy is not taken into account. Each heat producer / consumer building independently schedules its energy consumption and storage charging / discharging plans. The optimized control result curve for Example 2 is shown below. Figure 5 As shown.
[0056] Example 3: Thermal energy interaction occurs within an energy community. Considering line losses and information privacy protection, the privacy-considering DMPC control strategy proposed in this paper is used for control optimization. The optimization control result curves of Example 2 are shown below. Figure 6 As shown.
[0057] In Example 1, due to the impact of time-of-use pricing, heat-consuming buildings shift their load to off-peak hours by participating in demand response between 0:00 and 4:00, purchasing more low-priced electricity to charge their hot water storage tanks as backup while meeting their own heat load. This reduces the amount of electricity purchased during peak hours by 38.98%, but the utilization rate of solar energy is only 48.72%.
[0058] Example 2 considers heat exchange within the energy community. Between 10:00 and 17:00, since the heat load demand of the heat producers and consumers' buildings is much less than the heat energy generated by the solar collectors, Example 2 uses heat sharing within the energy community to exchange surplus heat energy with other heat producers and consumers or users, thus increasing its solar energy utilization rate by 21.60% compared to Example 1.
[0059] Example 3, building upon Example 2, considers information privacy protection during the thermal interaction process. Simulation results show that Example 3 increases the overall interactive thermal power by 46.57% compared to Example 2. Between 10:00 and 17:00, due to the consideration of information privacy protection, thermal energy interaction within the energy community becomes more frequent. Example 3 fully utilizes solar energy through localized thermal energy interaction within the energy community, achieving a 24.01% increase in solar energy utilization compared to Example 2. This demonstrates that considering information privacy protection increases the enthusiasm of heat producers and consumers in participating in internal thermal interactions, promoting the absorption of solar energy within the energy community.
[0060] Information privacy protection analysis: Figure 7 This diagram compares real and false information regarding the expected interactive heat power transmitted between buildings and heat producers / consumers. Since all elements in the false information matrix are identical and the actual values of false information are much larger than those of real information, this embodiment only compares one element in the false information matrix with the real information, scaling it down to the same order of magnitude as the real information for comparison. This diagram only compares the correspondence between real and false information; therefore, unit information is omitted.
[0061] Depend on Figure 7 It is evident that the false expected heat power information obtained by the Hadamard transform has no correlation with the true information. The false expected power information depends on the random numbers generated each time the heat-consuming building transmits information, and there is no simple positive or negative correlation between it and the true expected heat power information. Therefore, even if an external eavesdropper obtains the false information transmitted by the heat-consuming building, they cannot deduce the true expected heat power information.
[0062] Depend on Figure 8 It can be seen that as the number of heat-consuming buildings within the energy community increases, the optimization time exhibits an approximately positive correlation with the number of heat-consuming buildings. Under the same scale of heat-consuming buildings, the optimization time decreases with the increase in the number of information interactions, indicating that the algorithm has good scalability and can be applied to large-scale heat-consuming building optimization in practical engineering.
[0063] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection, characterized in that, Includes the following steps: S1. Based on an energy community consisting of multiple heat-producing and heat-consuming buildings interconnected by a thermal network, a state-space model is obtained by modeling the heat energy interaction relationships between each heat-producing and heat-consuming building and between them. S2. Based on the state-space model, construct an optimization problem with the goal of minimizing the total operating cost of the energy community, and determine the operating constraints of each device in the heat production and consumption building and the interaction constraints between buildings; S3. In each rolling optimization cycle, each heat-generating and consuming building, based on a preset privacy protection interaction strategy, iteratively exchanges desired heat power information with neighboring buildings to obtain the information required for collaborative optimization. In step S3, each heat-consuming building iteratively exchanges desired heat power information with neighboring buildings based on a preset privacy-preserving interaction strategy, including: The sending heat-producing and consuming building divides the actual information of the expected interactive heat power to be exchanged and combines it with randomly generated parameters to form a vector to be transformed; the vector to be transformed is multiplied with an orthogonal transformation matrix of a preset order to generate false interactive information for transmission; the sending heat-producing and consuming building sends the false interactive information to the adjacent receiving heat-producing and consuming building. The receiving heat-consuming building determines the order of the orthogonal transformation matrix and the randomly generated parameters based on the received false interaction information, and performs a corresponding inverse transformation on the false interaction information according to the determined parameters to recover the true information of the expected interactive heat power of the sending heat-consuming building. S4. Each heat-generating and consuming building solves its corresponding local optimization control subproblem in parallel based on the current state of the state-space model, the information required for collaborative optimization, and the optimization problem, thereby obtaining its optimal control sequence in the future finite time domain. S5. Each heat-consuming building will send the control command corresponding to the current optimization moment in the optimal control sequence to the local physical execution device; S6. Update the state space model based on the system state after the control command is executed, and use it as the initial condition for the next optimization moment. Repeat steps S3 to S5 until the preset global optimization control cycle is completed.
2. The energy community heat production and consumption building distributed model predictive control method based on information privacy protection according to claim 1, characterized in that, In S1, the process of obtaining the state-space model includes: Based on the photothermal conversion efficiency, light-receiving area, and solar radiation of solar collectors in each heat-consuming building, a solar collector model is constructed. Based on the energy efficiency coefficient and power consumption of electric heat pumps in each heat-consuming building, an electric heat pump model is constructed. Based on the heat storage / release power, heat storage efficiency, heat release efficiency, heat dissipation coefficient, and heat storage capacity of the hot water storage tanks in each heat-consuming building, a hot water storage tank model is constructed. Based on the solar collector model, electric heat pump model, hot water storage tank model, heat load consumption power, and building interaction heat power model considering line losses, a power balance equation for heat-generating and heat-consuming buildings is constructed. Based on the power balance equation, the system's state variables and control inputs are defined, and the state-space model is derived.
3. The energy community heat production and consumption building distributed model predictive control method based on information privacy protection according to claim 1, characterized in that, In S2, the optimization problem aimed at minimizing the total operating cost of the energy community is expressed by the objective function of minimizing the sum of the maintenance costs of solar collectors, the energy consumption costs of electric heat pumps, the operating costs of hot water storage tanks, and the interaction costs between buildings for all heat producers and consumers during the control period.
4. The energy community heat production and consumption building distributed model predictive control method based on information privacy protection according to claim 3, characterized in that, The operational constraints in S2 include the output constraints of the solar collector, the heating constraints of the electric heat pump, the ramping constraints of the electric heat pump, the heat storage capacity constraints of the hot water storage tank, the maximum heat storage power constraints and the maximum heat release power constraints of the hot water storage tank, and the mutual exclusion constraints between the heat storage state and the heat release state of the hot water storage tank; the interaction constraints include the interaction heat power constraints between heat producers and consumers in buildings.
5. The energy community heat production and consumption building distributed model predictive control method based on information privacy protection according to claim 2, characterized in that, The inter-building heat power model considering line loss is expressed as follows: the actual received power transmitted from the heat-generating and heat-consuming building to the building is equal to the transmitted power multiplied by a loss coefficient calculated based on the transmission line length and the percentage loss per unit length.
6. The distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection as described in claim 1, characterized in that, During the iterative exchange process of S3, each heat production and consumption building forms a set of information to be exchanged according to the preset aggregation rules during each information exchange. The information set includes its own expected interactive heat power real information and the expected interactive heat power real information of other buildings obtained during the information exchange process; After each information exchange, each heat-producing and consumer building updates its own set of information to be exchanged according to the aggregation rules based on the information received and restored in this exchange, for use in the next information exchange.
7. The energy community heat production and consumption building distributed model predictive control method based on information privacy protection according to claim 6, characterized in that, In S3, the orthogonal transformation matrix used by the preset privacy protection interaction strategy is the Hadamard matrix.
8. The distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection as described in claim 1, characterized in that, In S4, the parallel solution of the corresponding local optimization control subproblem includes: Each heat-generating and heat-consuming building uses the current state of the state space model as the initial state of the prediction time domain, and obtains the predicted values of light intensity and heat load in the future time domain. Based on the initial state, the predicted light intensity and heat load, the information required for collaborative optimization obtained from adjacent buildings, operational constraints and interaction constraints, a local optimization control subproblem is constructed with the goal of minimizing its own operating cost. The optimal control sequence is obtained by independently solving the local optimization control subproblem.
9. The distributed model predictive control method for energy community heat production and consumption buildings based on information privacy protection as described in claim 1, characterized in that, Before executing S1, initialization is also included: acquiring the initial state quantities of each heat-generating and heat-consuming building, the predicted data of external light intensity and heat load, and optimization parameters, and setting the total time and rolling optimization step size of the global optimization control cycle.