Building low-carbon electricity management system and method based on modified augmented ε-constraint method

Abstract

The application discloses a building low-carbon power consumption management system and method based on a modified augmented epsilon-constraint method, which comprises the following steps: a data interaction module is used to collect basic power consumption data and input user preferences based on an interactive interface with the user; a model construction module is used to construct a building low-carbon power consumption double-objective optimization model according to the basic power consumption data; an optimization solving module is used to optimize and solve the building low-carbon power consumption double-objective optimization model by using a modified augmented epsilon-constraint solving algorithm, and generate a Pareto frontier under the condition that various building power consumption constraints are met; a preference selection module is used to select a personalized power consumption scheme for the user based on the user preferences according to the Pareto frontier; and a scheme execution module is used to execute the selected personalized power consumption scheme, adjust the power of the transferable load in different time periods by controlling the switch or power of the transferable load, and realize the cross-period coordination of the overall building power consumption load. The application provides a differentiated and adaptive power consumption scheme for a building main body.

Description

Technical Field

[0001] This invention relates to the fields of power system load management and building energy conservation and carbon reduction, specifically to a building low-carbon electricity management system and method based on the modified augmented ε-constraint method. Background Technology

[0002] With the deepening implementation of the "dual carbon" goals, the green and low-carbon transformation of the construction sector, as one of the main sources of carbon emissions, has gradually become a focus of attention. Statistics show that in 2024, carbon emissions from the operation of civil buildings nationwide reached 2.47 billion tons, accounting for 22.1% of the country's total energy consumption; among which, electricity accounted for as much as 66.8% of carbon emissions. At the same time, my country's building electrification level has continued to improve, exceeding 55% in 2024, with electricity replacing fossil fuels as the main energy source in building operations. Therefore, low-carbon electricity use is crucial for achieving carbon peaking in the construction sector and is key to reducing carbon emissions from building operations.

[0003] For the power system, with the increasing penetration rate of renewable energy sources such as wind power and photovoltaics, the demand for flexible load-side regulation capabilities is becoming increasingly urgent. Buildings, as important distributed resource access nodes and key participants in grid interaction, possess enormous potential for flexible regulation. Building flexible regulation is mainly achieved through guidance from electricity market price signals, reflected in the optimization of building electricity costs.

[0004] For buildings, the key issue is how to coordinate economic goals with low-carbon goals. Existing solutions mostly optimize for reducing individual electricity costs or carbon emissions. Other solutions simply weight the two, but the weights are often subjectively determined, lacking practical basis and difficult to apply to the different situations of various buildings.

[0005] Therefore, there is an urgent need for a building low-carbon electricity management solution that synergizes low-carbon and economic goals, provides buildings with diverse options as market players, and enables buildings to use electricity at low cost while maintaining low carbon emissions. Summary of the Invention

[0006] The technical problem this invention aims to solve is that existing solutions mostly optimize for either electricity cost or carbon emission reduction. Other solutions simply weight and average these two factors, with weights often subjectively determined, lacking practical basis and difficult to apply to the differentiated situations of different buildings. This invention provides a building low-carbon electricity management system and method based on the modified augmented ε-constraint method. It establishes a dual-objective optimization model that simultaneously considers economic and low-carbon objective functions, and proposes a solution method based on the modified augmented ε-constraint method. Compared to the conventional ε-constraint method, this invention's method can more accurately characterize the Pareto front of the dual-objective optimization in physics, providing differentiated and adaptable electricity solutions for building structures in practice.

[0007] This invention is achieved through the following technical solution:

[0008] In a first aspect, the present invention provides a building low-carbon electricity management system based on the modified augmented ε-constraint method, the system comprising:

[0009] The data interaction module is used to collect basic electricity consumption data and transmit the basic electricity consumption data to the model building module; and to allow users to input or select user preferences based on the interaction interface with the user.

[0010] The model building module is used to construct a dual-objective optimization model for low-carbon electricity consumption in buildings based on basic electricity consumption data. The dual-objective optimization model for low-carbon electricity consumption in buildings deeply integrates the economic objective function and the low-carbon objective function.

[0011] The optimization solution module is used to optimize the dual-objective optimization model of building low-carbon electricity consumption using the modified augmented ε-constraint solution algorithm, and generate the Pareto front under the condition of satisfying each building electricity consumption constraint.

[0012] The preference selection module is used to select a personalized power consumption plan for the user based on the Pareto frontier and user preferences.

[0013] The scheme execution module is used to execute the selected personalized electricity scheme and adjust the power of the transferable load at different time periods by controlling the switching or power of the transferable load, so as to realize cross-time coordination of the overall building electricity load.

[0014] Furthermore, the basic data includes electricity price, carbon factor, renewable energy generation capacity, state of charge of energy storage system, non-transferable load, transferable load and its transfer ratio limit parameters.

[0015] Furthermore, the dual-objective optimization model for low-carbon electricity consumption in buildings includes:

[0016] The economic objective function is the first optimization objective function for the electricity purchase cost of the building power grid.

[0017] The low-carbon objective function is the second optimization objective function for building indirect carbon emissions. The second optimization objective function is the minimum building electricity indirect carbon emissions calculated within a preset time interval, combining the nodal electricity carbon factor and the power output of the grid-connected nodes.

[0018] Furthermore, the nodal carbon factor refers to the amount of carbon dioxide emissions generated per kilowatt-hour of electricity consumed at a specific geographical location or node in a power system.

[0019] Furthermore, the expression for the economic objective function is:

[0020] ;

[0021] In the formula, C e The cost of electricity purchased from the grid by the building is expressed in yuan; T represents the total number of time periods optimized before the day, T=24; ΔT represents the time interval, which is 1 hour; P t λ represents the grid connection power of a building at time t, in kW; t This represents the electricity price at time t, expressed in yuan / kWh.

[0022] The expression for the low-carbon objective function is:

[0023] ;

[0024] In the formula, C c This indicates the indirect carbon emissions from building electricity consumption, expressed in kgCO2; μ t The nodal electrocarbon factor at time t represents the node where the building is located, in units of kgCO2 / kWh.

[0025] Furthermore, building power constraints include power balance constraints, transferable load transfer constraints, and energy storage system constraints;

[0026] The power balance constraint means that the sum of the renewable energy generation generated by the building's own rooftop photovoltaic system and the power purchased from the grid equals the load power.

[0027] Furthermore, the optimized solution module includes:

[0028] The problem rewriting unit is used to rewrite the optimization problem of the dual-objective optimization model for low-carbon electricity consumption in buildings into a simplified form;

[0029] An optimized solution unit is used to solve the simplified form by calling the embedded profit matrix algorithm and the modified augmented ε-constraint method, to obtain a series of Pareto solutions and the Pareto front they constitute.

[0030] Secondly, this invention provides a building low-carbon electricity management method based on the modified augmented ε-constraint method, the method comprising:

[0031] Obtain basic electricity consumption data; basic data includes electricity price, carbon factor, renewable energy generation capacity, energy storage system state of charge, non-transferable load, transferable load and its transfer ratio limit parameters;

[0032] Based on basic electricity consumption data, a dual-objective optimization model for low-carbon electricity consumption in buildings is constructed; the dual-objective optimization model for low-carbon electricity consumption in buildings deeply integrates the economic objective function and the low-carbon objective function;

[0033] The modified augmented ε-constraint solution algorithm is used to optimize the dual-objective optimization model of building low-carbon electricity consumption, and Pareto front is generated under the condition of satisfying the electricity consumption constraints of each building.

[0034] Based on the Pareto frontier, personalized electricity plans are selected for users based on their input preferences.

[0035] Based on the selected personalized power consumption plan, the personalized power consumption plan is executed, and the power of the transferable load is adjusted at different times by controlling the switching or power of the transferable load, so as to achieve cross-time coordination of the overall building power load.

[0036] Furthermore, the dual-objective optimization model for low-carbon electricity consumption in buildings includes:

[0037] The economic objective function is the first optimization objective function for the electricity purchase cost of the building power grid.

[0038] The low-carbon objective function is the second optimization objective function for building indirect carbon emissions. The second optimization objective function is the minimum building indirect carbon emissions from electricity consumption calculated within a preset time interval, combining the nodal carbon factor and the power output of the grid-connected nodes. The nodal carbon factor refers to the amount of carbon dioxide emissions generated per kilowatt-hour of electricity consumed at a certain geographical location or node in the power system.

[0039] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described building low-carbon electricity management method based on the modified augmented ε-constraint method.

[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0041] 1. This invention relates to a building low-carbon electricity management system and method based on the modified augmented ε-constraint method. The method of this invention fully considers the economic cost and indirect carbon emissions of building electricity consumption, and constructs a building low-carbon electricity optimization model that takes into account both economic and low-carbon goals. It solves the problem of existing building low-carbon electricity management methods that emphasize carbon reduction but neglect cost, and can take into account both the economic and environmental needs of users.

[0042] 2. This invention relates to a building low-carbon electricity management system and method based on the modified augmented ε-constraint method. The invention proposes a modified augmented ε-constraint method to solve the dual-objective optimization model for building low-carbon electricity consumption. On one hand, this method integrates economic and low-carbon objectives, yielding a series of Pareto solutions for users to choose freely according to their preferences. This avoids the problem of existing solution methods using uniform weights and ignoring users' subjective intentions, maximizing user benefits while fully respecting users' differentiated needs. On the other hand, in the modification stage, this method further searches for some unclear Pareto fronts, obtaining more representative Pareto solutions, thus providing users with more choices.

[0043] 3. The present invention is a building low-carbon electricity management system and method based on the modified augmented ε-constraint method. The economic and low-carbon objectives designed in this invention are deeply coupled in terms of objective function, constraint conditions, solution process, result correction and other aspects, which deeply couple electricity consumption scheme, electricity cost and electricity carbon emissions. In the modeling and solution process, the variables influence, constrain and restrain each other, which fundamentally realizes the synergistic optimization of the two.

[0044] 4. The present invention relates to a building low-carbon electricity management system and method based on the modified augmented ε-constraint method, which realizes the full-process management of data input, model construction, optimization solution, preference selection and control execution. The system integrates elements such as data, model, algorithm and control, and realizes user preference selection to meet the differentiated needs of users, which can significantly improve the level of building low-carbon electricity management. Attached Figure Description

[0045] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a structural block diagram of the building low-carbon electricity management system based on the modified augmented ε-constraint method of the present invention;

[0047] Figure 2 This is a flowchart of the building low-carbon electricity management method based on the modified augmented ε-constraint method of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0049] The dual-objective optimization model for low-carbon electricity consumption in buildings established in this invention deeply integrates the economic objective function and the low-carbon objective function, as specifically reflected below:

[0050] 1. In constructing the objective function, carbon emissions from building electricity consumption are determined through... (C) c Indicates indirect carbon emissions from building electricity consumption, in μ t P represents the nodal electrocarbon factor at time t of the node where the building is located, ΔT represents the time interval, and P represents the nodal electrocarbon factor at time t. t The power output of the building at the grid node at time t is obtained by quantitative solution and is used as the second optimization objective function in the dual-objective optimization model for low-carbon electricity consumption in buildings. Together with the economic objective function, it serves as the objective function of the dual-objective optimization model for low-carbon electricity consumption in buildings, reflecting the full attention paid to carbon emission reduction in the optimization process.

[0051] 2. In solving the dual-objective optimization model for low-carbon electricity consumption in buildings, dictionary optimization is first used to obtain its payoff matrix, reflecting the value ranges of both the economic and low-carbon objectives. These two objectives together constitute the Pareto front search region. Then, ε-constraints are introduced to limit the low-carbon objective... Transformed into constraints, this scheme addresses semi-fixed low-carbon targets. At the same time optimize economic goals By deeply integrating economic and low-carbon goals to form a single-objective optimization model, Pareto solutions can be accurately retrieved from the payoff matrix, thereby finding a relatively clear Pareto front.

[0052] 3. In the stage of revising the Pareto frontier, for areas where the Pareto frontier change trend is unclear, the ε-constraint method is further used for retrieval. This can help find more solutions where slight changes in low-carbon targets lead to significant improvements in economic targets, and provide buildings with more electricity consumption options.

[0053] 4. Building electricity consumption constraints are closely linked to economic and low-carbon goals: due to building power balance constraints. It can be seen that the electricity purchased by the building power grid, P t With the building's electrical load P load,t Closely related, while the building grid purchase of electricity P t This is directly reflected in the economic objective function. and low-carbon objective function In the middle. Furthermore, the building's internal electrical load P load,t This is closely related to building electricity strategies because buildings adjust their own electricity usage plans, which involves adjusting the transferability of loads, a crucial component of electricity load. Therefore, the constraints tightly link economic goals, low-carbon goals, and electricity usage plans.

[0054] In summary, the dual-objective optimization model for low-carbon building electricity consumption established in this invention fully considers both the economic and low-carbon objectives of building electricity consumption. In the process of establishing constraints and solving the dual-objective optimization, the two objectives are deeply integrated. More solutions are obtained through the modified augmented ε-constraint method, avoiding the phenomenon of favoring one over the other in traditional optimization, and achieving overall coordination between economic and low-carbon objectives.

[0055] Example 1

[0056] like Figure 1 As shown, the present invention relates to a building low-carbon electricity management system based on the modified augmented ε-constraint method. The system includes:

[0057] The data interaction module is used to collect basic electricity consumption data and transmit the basic electricity consumption data to the model building module; and to allow users to input or select user preferences based on the interaction interface with the user.

[0058] The model building module, directly connected to the data interaction module, is used to construct a dual-objective optimization model for low-carbon electricity consumption in buildings based on basic electricity consumption data. This dual-objective optimization model deeply integrates the economic objective function and the low-carbon objective function.

[0059] The optimization solution module is directly connected to the model building module. It is used to optimize the dual-objective optimization model of building low-carbon electricity consumption using the modified augmented ε-constraint solution algorithm and generate the Pareto front under the condition of satisfying each building electricity consumption constraint.

[0060] The preference selection module is directly connected to the optimization solution module and the data interaction module. The optimization solution module transmits a series of energy consumption schemes represented by a series of Pareto solutions to this module, and selects a personalized power consumption scheme for the user based on the user's preferences. At the same time, this module also transmits a series of energy consumption schemes to the data interaction module for the user to choose from. If the user makes a manual selection, this module will re-analyze the user's preferences and adaptively adjust the parameters.

[0061] The scheme execution module is directly connected to the preference selection module. It is used to execute the personalized electricity scheme according to the selected personalized electricity scheme, and adjust the power of the transferable load at different time periods by controlling the switching or power of the transferable load, so as to realize the cross-time coordination of the overall building electricity load.

[0062] In this embodiment, the data interaction module uses smart sensors and data acquisition terminals to collect basic data, including electricity price, carbon factor, renewable energy generation capacity, state of charge of energy storage system, non-transferable load, transferable load and its transfer ratio limit parameters. It also provides an interface for user interaction, allowing users to manually modify data or input user preferences during selection.

[0063] In this embodiment, the model building module, optimization solution module, and preference selection module can all be implemented using an industrial computer, while the solution execution module can be implemented using a PLC controller.

[0064] In this embodiment, a built-in dual-objective optimization model for low-carbon building electricity consumption is preprocessed to obtain a standard model. The dual-objective optimization model for low-carbon building electricity consumption considers the economic objective minC. e Low carbon target minC c The dual-objective optimization model specifically includes:

[0065] (1) The economic objective function is the first optimization objective function for the electricity purchase cost of the building power grid; the expression of the economic objective function is:

[0066] ;

[0067] In the formula, C e The cost of electricity purchased from the grid by the building is expressed in yuan; T represents the total number of time periods optimized before the day, T=24; ΔT represents the time interval, which is 1 hour; P t λ represents the grid connection power of a building at time t, in kW; t This represents the electricity price at time t, expressed in yuan / kWh.

[0068] (2) The low-carbon objective function is the second optimization objective function for building indirect carbon emissions. This second optimization objective function is the minimum indirect carbon emissions from building electricity consumption calculated within a preset time interval, combining the nodal carbon factor and the downstream power of the connected grid node. The nodal carbon factor refers to the amount of carbon dioxide emissions generated per kilowatt-hour consumed at a specific geographical location or node in the power system. The expression for the low-carbon objective function is:

[0069] ;

[0070] In the formula, C c This indicates the indirect carbon emissions from building electricity consumption, expressed in kgCO2; μ t The nodal electrocarbon factor at time t represents the node where the building is located, in units of kgCO2 / kWh.

[0071] (3) Building power consumption constraints include power balance constraints, transferable load transfer constraints, and energy storage system constraints, etc.

[0072] First, the power balance constraint states that the sum of renewable energy generation from the building's rooftop solar panels and the electricity purchased from the grid must equal the load power. The expression for the power balance constraint is:

[0073] ;

[0074] In the formula, P new,t P represents the power generation capacity of renewable energy sources such as rooftop photovoltaics in the building at time t, in kW; s,t P represents the charging and discharging power of the building energy storage system at time t. s,t >0 indicates discharge, P s,t <0 indicates charging; P load,t This represents the total electrical load of the building at time t, divided into transferable load P. load-tr,t and non-transferable load P load-im,t The units are all kW; P t This represents the grid connection power of the building at time t, in kW;

[0075] Second, the expression for the transferable load transfer constraint is:

[0076] ;

[0077] In the formula, / ε represents the power output assuming no transfer of transferable load occurs during the t / k time period; k,t This represents the transferable load transfer rate from time period k to time period t. Its upper and lower limits represent the user's acceptance of load transfer, implying the user's needs for comfort and convenience. For ε k,t The lower limit; For ε k,t The upper limit;

[0078] Third, the expression for the constraints of the energy storage system is:

[0079] ;

[0080] In the formula, P s,t P represents the discharge power of the energy storage system. s,t <0 indicates that the energy storage system is charging; P s,min and P s,max Indicates the upper and lower power limits of the energy storage system, in kW; E s,t E represents the state of charge of the energy storage system at time t. s,t+1 E represents the state of charge of the energy storage system at time t+1. s,min and E s,max Indicates the upper and lower limits of the state of charge, both in kWh.

[0081] In this embodiment, the optimization solution module includes:

[0082] The problem rewriting unit is used to rewrite the optimization problem of the dual-objective optimization model for low-carbon electricity consumption in buildings into a simplified form;

[0083] The optimization unit is used to perform bi-objective optimization on the simplified form by calling the embedded profit matrix algorithm and the modified augmented ε-constraint method, so as to obtain a series of Pareto solutions and the Pareto front formed therefrom.

[0084] The bi-objective optimization described above can be viewed as a special case of multi-objective optimization. Since conflicts may exist between multiple objectives—that is, optimizing one objective may require the deterioration of others—it is difficult to find an optimal solution where all objectives are optimal. Therefore, multi-objective optimization requires trade-offs to ensure the overall objective is as optimal as possible. In multi-objective optimization theory, a solution where improvement in one objective cannot be achieved without worsening other objectives is called a Pareto solution, and the set of all Pareto solutions is called the Pareto front.

[0085] Linear weighted methods and ε-constraint methods are commonly used multi-objective optimization methods in the electrical engineering field. Based on the ε-constraint method, the augmented ε-constraint method is proposed, which improves the performance of the traditional ε-constraint method and is also superior to the linear weighted method. However, when using the augmented ε-constraint method, it is necessary to obtain the payoff table of the optimization problem in advance; lexicographic optimization (LO) is an effective method for this. Furthermore, a correction part is added to the augmented ε-constraint method to obtain a solution set that is closer to the complete Pareto front.

[0086] The optimization solution process for the dual-objective optimization model of low-carbon electricity consumption in buildings is as follows:

[0087] (1) Rewrite the bi-objective optimization problem in a simplified form.

[0088] ;

[0089] In the formula, x represents the set of all variables in the model, Ω represents the domain of x, and g1(x) and g2(x) are the economic objective function and the low-carbon objective function, respectively. , .

[0090] (2) Construct a payoff matrix of size 2×2 for the dual objective optimization model of low-carbon electricity consumption in buildings.

[0091] ;

[0092] In the formula, g1(x1), g1(x2), g2(x1), and g2(x2) are the coordinate boundaries of the Pareto front for bi-objective optimization in the two-dimensional plane; the diagonal terms are the minimum values ​​of each optimization objective, i.e. The off-diagonal term equals the minimum value of other objective functions when a certain objective function is minimized, i.e.:

[0093] ;

[0094] ;

[0095] (3) Select g1(x) as the primary objective, and use the augmented ε-constraint to divide the range of maximum and minimum values ​​of other objectives besides the primary objective into S equal parts, that is:

[0096] ;

[0097] In the formula, ε 2,s As auxiliary parameters, To find the lower bound of g2(x) in the Pareto front range, Let s be the upper bound of g1(x) within the Pareto front range of g2(x), where s is the index value; the lower bound of g2(x) is obviously equal to the objective function value when the decision variable is x2, i.e. The upper bound of g2(x) is equal to the maximum value in the second column of the payoff matrix, that is:

[0098] ;

[0099] (4) Under different values ​​of s, By substituting the following optimization problem into the solution, we can obtain a series of Pareto solutions.

[0100] ;

[0101] In the formula, Δe2, r i As an auxiliary variable, These are the weighting coefficients.

[0102] (5) Since g1(x) does not usually decrease uniformly as g2(x) increases, in order to make the solution set more completely reflect the Pareto front, based on the distribution trend of the obtained Pareto solutions, a portion of the grid of g2(x) is further divided into U equal parts. Taking the smallest grid as an example, it is further subdivided to obtain a series of... ,Will By substituting the solutions and solving them, a series of Pareto solutions can be obtained, thus yielding a more complete and clear Pareto front.

[0103]

[0104] In the formula, , Here, u is the index value and U is the number of grid cells, which are auxiliary parameters.

[0105] Example 2

[0106] like Figure 2 As shown, the difference between this embodiment and Embodiment 1 is that this embodiment further provides a building low-carbon electricity management method based on the modified augmented ε-constraint method. This method is based on the building low-carbon electricity management system based on the modified augmented ε-constraint method of Embodiment 1; the method includes:

[0107] Obtain basic electricity consumption data; basic data includes electricity price, carbon factor, renewable energy generation capacity, energy storage system state of charge, non-transferable load, transferable load and its transfer ratio limit parameters;

[0108] Based on basic electricity consumption data, a dual-objective optimization model for low-carbon electricity consumption in buildings is constructed; the dual-objective optimization model for low-carbon electricity consumption in buildings deeply integrates the economic objective function and the low-carbon objective function;

[0109] The modified augmented ε-constraint solution algorithm is used to optimize the dual-objective optimization model of building low-carbon electricity consumption, and Pareto front is generated under the condition of satisfying the electricity consumption constraints of each building.

[0110] Based on the Pareto frontier, personalized power solutions are selected for users based on their preferences.

[0111] Based on the selected personalized power consumption plan, the personalized power consumption plan is executed, and the power of the transferable load is adjusted at different times by controlling the switching or power of the transferable load, so as to achieve cross-time coordination of the overall building power load.

[0112] As a further implementation, the dual-objective optimization model for low-carbon electricity consumption in buildings includes:

[0113] The economic objective function is the first optimization objective function for the electricity purchase cost of the building power grid.

[0114] The low-carbon objective function is the second optimization objective function for building indirect carbon emissions. The second optimization objective function is the minimum building indirect carbon emissions from electricity consumption calculated within a preset time interval, combining the nodal carbon factor and the power output of the grid-connected nodes. The nodal carbon factor refers to the amount of carbon dioxide emissions generated per kilowatt-hour of electricity consumed at a certain geographical location or node in the power system.

[0115] As a further implementation, the expression for the economic objective function is:

[0116] ;

[0117] In the formula, C e The cost of electricity purchased from the grid by the building is expressed in yuan; T represents the total number of time periods optimized before the day, T=24; ΔT represents the time interval, which is 1 hour; P t λ represents the grid connection power of a building at time t, in kW; t This represents the electricity price at time t, expressed in yuan / kWh.

[0118] The expression for the low-carbon objective function is:

[0119] ;

[0120] In the formula, C c This indicates the indirect carbon emissions from building electricity consumption, expressed in kgCO2; μ t The nodal electrocarbon factor at time t represents the node where the building is located, in units of kgCO2 / kWh.

[0121] As a further implementation, building electricity constraints include power balance constraints, transferable load transfer constraints, and energy storage system constraints; the power balance constraint means that the sum of renewable energy generation such as rooftop photovoltaic power generation and grid-purchased power equals the load power.

[0122] As a further implementation, the optimization of the solution process includes:

[0123] The optimization problem of the dual-objective optimization model for low-carbon electricity consumption in buildings is rewritten in a simplified form;

[0124] By invoking the embedded payoff matrix algorithm and the modified augmented ε-constraint method to solve the simplified form, a series of Pareto solutions and the Pareto front formed therefrom are obtained.

[0125] Meanwhile, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned building low-carbon electricity management method based on the modified augmented ε-constraint method.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A building low-carbon electricity management system based on the modified augmented ε-constraint method, characterized in that, The system includes: The data interaction module is used to collect basic electricity consumption data and transmit the basic electricity consumption data to the model building module; and to allow users to input or select user preferences based on the interaction interface with the user. The model building module is used to construct a dual-objective optimization model for low-carbon electricity consumption in buildings based on the aforementioned basic electricity consumption data; the dual-objective optimization model for low-carbon electricity consumption in buildings deeply integrates the economic objective function and the low-carbon objective function; The optimization solution module is used to optimize and solve the dual-objective optimization model of building low-carbon electricity consumption using the modified augmented ε-constraint solution algorithm, and generate the Pareto front under the condition of satisfying each building electricity consumption constraint. The preference selection module is used to select a personalized electricity plan for the user based on the Pareto front and the user's preferences. The scheme execution module is used to execute the selected personalized electricity scheme and adjust the power of the transferable load at different time periods by controlling the switching or power of the transferable load, so as to realize cross-time coordination of the overall building electricity load.

2. The building low-carbon electricity management system based on the modified augmented ε-constraint method according to claim 1, characterized in that, The basic data includes electricity price, carbon factor, renewable energy power generation, state of charge of energy storage system, non-transferable load, transferable load and its transfer ratio limit parameters.

3. The building low-carbon electricity management system based on the modified augmented ε-constraint method according to claim 1, characterized in that, The dual-objective optimization model for low-carbon electricity consumption in buildings includes: The economic objective function is the first optimization objective function for the electricity purchase cost of the building power grid. The low-carbon objective function is the second optimization objective function for building indirect carbon emissions. The second optimization objective function is the minimum building indirect carbon emissions calculated by combining the nodal carbon factor and the power output of the grid-connected nodes within a preset time interval.

4. The building low-carbon electricity management system based on the modified augmented ε-constraint method according to claim 3, characterized in that, The nodal carbon factor refers to the amount of carbon dioxide emissions generated per kilowatt-hour of electricity consumed at a specific geographical location or node in a power system.

5. The building low-carbon electricity management system based on the modified augmented ε-constraint method according to claim 3, characterized in that, The expression for the economic objective function is: ； In the formula, C e The cost of electricity purchased by the building from the grid is expressed in yuan; T represents the total number of optimized time periods, T=24. ΔT represents the time interval, which is 1 hour; P t λ represents the grid connection power of a building at time t, in kW; t This represents the electricity price at time t, expressed in yuan / kWh. The expression for the low-carbon objective function is: ； In the formula, C c This indicates the indirect carbon emissions from building electricity consumption, expressed in kgCO2; μ t The nodal electrocarbon factor at time t represents the node where the building is located, in units of kgCO2 / kWh.

6. The building low-carbon electricity management system based on the modified augmented ε-constraint method according to claim 1, characterized in that, The building power constraints include power balance constraints, transferable load transfer constraints, and energy storage system constraints. The power balance constraint means that the sum of the building's own rooftop photovoltaic renewable energy generation and the power purchased from the grid equals the load power.

7. The building low-carbon electricity management system based on the modified augmented ε-constraint method according to claim 1, characterized in that, The optimization solution module includes: The problem rewriting unit is used to rewrite the optimization problem of the dual-objective optimization model for low-carbon electricity consumption in buildings into a simplified form; An optimized solution unit is used to solve the simplified form by calling the embedded profit matrix algorithm and the modified augmented ε-constraint method, to obtain a series of Pareto solutions and the Pareto front formed therefrom.

8. A building low-carbon electricity management method based on the modified augmented ε-constraint method, characterized in that, The method includes: Obtain basic electricity consumption data; the basic data includes electricity price, carbon factor, renewable energy power generation, energy storage system state of charge, non-transferable load, transferable load and its transfer ratio limit parameters; Based on the aforementioned basic electricity consumption data, a dual-objective optimization model for low-carbon electricity consumption in buildings is constructed; the dual-objective optimization model for low-carbon electricity consumption in buildings deeply integrates the economic objective function and the low-carbon objective function; The modified augmented ε-constraint solution algorithm is used to optimize the dual-objective optimization model of building low-carbon electricity consumption, and Pareto front is generated under the condition of satisfying each building electricity consumption constraint. Based on the Pareto front, a personalized electricity plan is selected for the user based on the user's input preferences. Based on the selected personalized power consumption plan, the personalized power consumption plan is executed, and the power of the transferable load is adjusted at different times by controlling the switching or power of the transferable load, so as to achieve cross-time coordination of the overall building power load.

9. The building low-carbon electricity management method based on the modified augmented ε-constraint method according to claim 8, characterized in that, The dual-objective optimization model for low-carbon electricity consumption in buildings includes: The economic objective function is the first optimization objective function for the electricity purchase cost of the building power grid. The low-carbon objective function is the second optimization objective function for building indirect carbon emissions. The second optimization objective function is the minimum building indirect carbon emissions from electricity consumption calculated within a preset time interval, combining the nodal carbon factor and the power output of the grid-connected node. The nodal carbon factor refers to the amount of carbon dioxide emissions generated per kilowatt-hour of electricity consumed at a certain geographical location or node in the power system.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the building low-carbon electricity management method based on the modified augmented ε-constraint method as described in any one of claims 8 to 9.

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