Design method for enhancing low-carbon building energy flexibility and power grid interaction friendliness

Abstract

The invention discloses an innovative design method for improving the flexibility of a low-carbon building energy system and the interaction friendliness of a power grid, and the method systematically integrates the technologies in multiple fields, such as energy management, optimization algorithm and environmental impact assessment, and specifically comprises the following core steps: step 10, carrying out the optimization of the energy management, the optimization algorithm and the environmental impact assessment; the method comprises the following steps: constructing an accurate load calculation model in an EnergyPlus simulation platform based on multi-dimensional data such as a personnel activity mode, equipment energy consumption characteristics, illumination requirements and real-time meteorological parameters; step 20, scientifically defining key variables and potential search space thereof in the optimization process according to the specific structural characteristics and the scale of the building energy system; 30, three core objective functions, namely the life cycle cost, the total carbon emission and the power grid friendly interaction index, are creatively set up, and the functions serve as key indexes for evaluating the comprehensive performance of the low-carbon building energy system; step 40, building a mathematical model of the energy system by utilizing a Python programming language, and efficiently solving a Pareto optimal solution set of a multi-objective optimization problem by virtue of an NSGA-II (non-dominated sorting genetic algorithm-II) solver in a powerful algorithm library of the mathematical model; and step 50, performing normalization processing on each objective function value, reasonably allocating weights according to actual demands, and selecting a comprehensive optimal solution which most meets specific requirements of a project from the Pareto optimal solution set, thereby providing a scientific basis for design and implementation of a low-carbon building energy system.

Description

Technical Field

[0001] The present invention belongs to the field of low-carbon building design optimization, and specifically relates to a design method for enhancing the energy flexibility and grid interaction friendliness of low-carbon buildings. Background Art

[0002] The application of low-carbon buildings has brought new opportunities for building energy conservation. It is considered to be an effective solution to reduce energy consumption and carbon emissions in the building sector. The energy system of low-carbon buildings mainly consists of three parts: renewable energy system, energy storage system and energy use system. By using renewable energy and interacting with the power grid in a two-way manner, under an efficient control strategy, it shares the power generated by its own renewable energy system with the power grid, while achieving an annual balance between power generation and energy consumption, and reducing the demand for power from the power grid. However, the renewable energy system mainly relies on natural energy such as wind power and solar energy, which has strong uncertainty, resulting in strong volatility in the level of building production capacity. This makes it difficult for low-carbon buildings to achieve the expected zero energy consumption target, and also causes damage to the power grid. Therefore, appropriate energy system design is crucial for low-carbon buildings to achieve zero energy consumption targets. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a design method for enhancing the energy flexibility and grid interaction friendliness of low-carbon buildings, taking into account the flexibility of the energy system and the impact on the grid, and guiding the design of the building energy system. To solve the above technical problem, the embodiment of the present invention adopts the following technical solution, including the following steps:

[0004] Step 10. Based on the density of human activity, equipment energy consumption characteristics, lighting requirements, and detailed meteorological parameters, a high-precision load calculation model is constructed in the EnergyPlus environment to comprehensively evaluate the building's energy needs. First, a detailed building physics model is constructed in SketchUp software based on the specific design parameters of the building envelope; then, the model is imported into EnergyPlus for precise identification to generate a basic framework for cooling load calculation; and the internal parameters of the building are further set, including but not limited to the type of air-conditioning system, indoor temperature setting, ventilation frequency, envelope characteristics, internal heat source, and operation schedule, etc. By integrating the corresponding load output module, accurate calculation of cooling loads on an hourly and daily basis is achieved;

[0005] Step 20, according to the specific architecture and scale of the building energy system, strictly determine the key variables and their search space in the optimization process, and provide clear parameter boundaries for subsequent optimization design. The optimization variables are clearly selected as the rated power of wind power generation, the rated power of biodiesel power generation, and the storage capacity of the energy storage system; at the same time, in the optimization process, in order to ensure the annual energy balance of the building, the area of ​​the photovoltaic panel is set as the dependent variable that depends on the annual energy demand of the building;

[0006] Step 30, clearly set three core objective functions - life cycle cost, total carbon emissions and grid-friendly interaction index, as key quantitative indicators for evaluating the comprehensive performance of low-carbon building energy systems, and use them as indicators to measure the pros and cons of low-carbon building energy systems;

[0007] Step 40, constructing a mathematical model of the energy system in a Python programming environment, and using the NSGA-II (non-dominated sorting genetic algorithm-II) solver in the Python algorithm library to efficiently solve the Pareto optimal solution set of the multi-objective optimization problem;

[0008] Step 50, using a scientific normalization method, such as deviation normalization, to determine the relative weight of each objective function, and to select the solution with the best comprehensive performance from the Pareto optimal solution set.

[0009] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: the embodiment of the present invention provides a design method for enhancing the energy flexibility and grid interaction friendliness of low-carbon buildings. First, a load calculation model is established in energyplus according to personnel, equipment, lighting and meteorological parameters, and the optimization variables and search space are determined according to the structure and scale of the building energy system; then, three objective functions are defined respectively: life cycle cost, carbon emissions and grid-friendly interaction index, which are used as indicators to measure the quality of low-carbon building energy systems; finally, an energy system model is established in python, and the NSGA-Ⅱ solver provided by the algorithm library of python is used to find the Pareto optimal solution set, and the weights of each objective function are determined after normalization to select the optimal solution. The design method for enhancing the energy flexibility and grid interaction friendliness of low-carbon buildings provided by the present invention takes into account the flexibility of the energy system and the impact on the grid, and can guide the design of the building energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0011] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0012] Figure 2 It is a flexible design schematic diagram of a low-carbon building energy system in an embodiment method of the present invention. DETAILED DESCRIPTION

[0013] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0014] The energy system of low-carbon buildings uses renewable energy and interacts with the power grid in a two-way manner. Under an efficient control strategy, it shares the power generated by its own renewable energy system with the power grid, while achieving an annual balance between power generation and energy consumption, and reducing the demand for power from the power grid. However, the renewable energy system mainly relies on natural energy such as wind power and solar energy, which has a strong uncertainty, resulting in a strong volatility in the building's power generation level. This makes it difficult for low-carbon buildings to achieve the expected zero energy consumption goals, and also causes damage to the power grid.

[0015] To this end, the present invention provides a design method for enhancing the energy flexibility and grid interaction friendliness of low-carbon buildings, such as Figure 1 As shown, the following steps are included:

[0016] Step 10, establish a load calculation model in energyplus based on personnel, equipment, lighting and meteorological parameters;

[0017] Step 20, determining optimization variables and search space according to the structure and scale of the building energy system;

[0018] In step 30, three objective functions are defined respectively: life cycle cost, carbon emission and grid-friendly interaction index, which are used as indicators to measure the quality of low-carbon building energy systems;

[0019] Step 40, building an energy system model in Python, and using the NSGA-Ⅱ solver provided by the Python algorithm library to find the Pareto optimal solution set;

[0020] Step 50, determine the weights of each objective function and select the optimal solution.

[0021] In the optimization design of low-carbon building energy systems, cost-effectiveness, environmental benefits and grid interaction friendliness are of great significance to building designers and grid operators, and are also commonly used indicators for evaluating the performance of building energy systems. In the multi-objective optimization process, this patent sets three objective functions. By searching for different test values ​​in the optimization variable search interval, the test values ​​are substituted into the building energy system to obtain the objective function value, which is further used to find the optimal result. The NSGA-Ⅱ solver provided by the python algorithm library is used to obtain the Pareto optimal solution set.

[0022] In step 10, a building physical model is constructed in SketchUp according to the design parameters of the building envelope. Then, the generated building physical model is imported into EnergyPlus for identification, and the building physical framework of the cooling load calculation model is generated. The internal parameters of the building are set, including the air conditioning system form, indoor temperature setting value, ventilation frequency, envelope parameters, internal heat source, and operation schedule, and the corresponding load output module is added to output the hourly and daily cooling load.

[0023] In step 20, the optimization variables are selected as the rated power of wind power generation, the rated power of biodiesel power generation and the capacity of the energy storage system. In the optimization process, in order to meet the annual energy balance requirements of the building, this paper sets the photovoltaic area as the dependent variable determined by the annual energy balance of the building.

[0024] In step 30, three objective functions are defined: life cycle cost, carbon emissions, and grid-friendly interaction index, and they are used as indicators to measure the pros and cons of low-carbon building energy systems. Among them, life cycle cost (Formula (1)) and carbon emissions (Formula (2)) to (Formula (4)) respectively represent the considerations of investment cost, operating cost, and environmental friendliness of the building during the design process. In addition, considering the current situation of two-way power interaction between buildings and power grids, and the adverse effects that the interaction between building renewable energy generation and the power grid may have on the power grid, this paper adopts the grid-friendly interaction index (Formula (5)) to measure the impact of building production capacity on the power grid, and enhances the friendliness of the interaction between buildings and the power grid by maximizing the target value. The expressions and meanings of the three objective functions are as follows:

[0025] (1) Optimization goal 1: total life cycle cost

[0026] TC=Cost ic +Cost oc (1)

[0028] In the formula, Cost ic Indicates the initial investment in building capacity equipment (yuan), Cost oc It represents the operating cost of the building (RMB), which mainly refers to the electricity costs of the air conditioning system, lighting and other plug-in electrical equipment inside the building. This paper considers using batteries as the energy storage system, so the cost of the battery also needs to be considered.

[0029] (2) Optimization goal 2: Carbon emissions

[0030] CDE=CDE ele +CDE BDG (2)

[0031] CDE ele =(E inport -Eexport )×c ele (3)

[0032] CDE BDG =F bio ×c bio (4)

[0033] In the formula, CDE ele It represents the carbon emissions of electricity of the building, which is the total amount of electricity E imported from the grid by the building. inport The total amount of electricity exported from the building to the grid E export The difference between the actual annual electricity consumption of the building is used to calculate the building’s carbon emissions; BDG represents the carbon emissions generated by the combustion of biomass during the power generation process of biodiesel generators; c ele and c bio represent the carbon emission factor of electricity and the carbon emission factor of biodiesel combustion respectively.

[0034] (3) Optimization goal three: grid-friendly interaction indicators

[0035]

[0036] Where Pow pos Indicates the beneficial interaction power (kWh), Pow neg Indicates the unfavorable interaction power (kWh), P supply represents the amount of electricity purchased from the grid by the building (kWh), P export Indicates the amount of electricity (kWh) sold by the building to the grid.

[0037] The grid-friendly interaction index is used to measure the friendliness of a building's interaction with the grid. This index evaluates the quality of a building's interaction with the grid based on the total power exchange (purchase and sale) in a year, the amount of favorable interaction (the sum of the amount of electricity sold to the grid at high electricity prices and the amount of electricity purchased from the grid at low electricity prices), and the amount of unfavorable interaction (the sum of the amount of electricity purchased from the grid at high electricity prices and the amount of electricity sold to the grid at low electricity prices). When the index is less than 0, it means that the negative impact of the building on the grid is greater than the positive impact, and the building is a grid-unfriendly building; when the index is equal to 0, it means that the building has neither a positive nor a negative impact on the grid, and this building can be considered a grid-friendly neutral building, which will neither increase the burden on the grid, but also cannot exert the potential of zero-energy buildings to shift peak power generation and electricity consumption valleys of the grid; when the index is greater than 0, it means that the positive impact of the building on the grid is greater than the negative impact, and the building is a grid-friendly building.

[0038] In step 40, a low-carbon building energy system model is constructed according to the actual characteristics of the low-carbon building, which mainly includes three parts: a production capacity model, an energy consumption model and an energy storage model. The production capacity model is mainly composed of a photovoltaic power generation (PV) model, a wind turbine power generation (WT) model and a biodiesel power generation model (BDG), and the power grid serves as a backup power source. The energy consumption system model is mainly composed of power consumption models of various components in the HVAC system (chillers, cooling towers, fans and water pumps) and power consumption models of lighting and other office electrical equipment. The power consumption of lighting and other office electrical equipment depends on the building usage schedule. The energy storage system model is mainly composed of a battery model. The energy storage system mainly plays a role in regulating production capacity and peak energy consumption in the entire building energy system, and improves the friendliness of the interaction between the building and the power grid. The energy flow diagram of each system is shown in Figure 2.

[0039] In step 50, the normalization method is a deviation normalization method, so that the result value is mapped to between [0-1]. The weights of each objective function are equal, and the optimal solution is the solution with the highest multi-objective mean.

[0040] The method of the embodiment of the present invention can be used for the flexible design of low-carbon building energy systems, or for the design of energy systems for grid-friendly interaction, or for the optimization of low-carbon building energy-saving designs.

[0041] A specific example is provided below.

[0042] A typical low-carbon building was selected for design optimization, and the building load calculation model and energy system model were established according to the method proposed in this patent. The average hourly energy consumption of the building under TMY meteorological parameters was about 21.5kW. Referring to the actual energy consumption of the building and the market conditions of related products, the rated power search range of wind power generation in the building energy system was set to 10-40kW, the rated power search range of biodiesel power generation was set to 20-60kW, and the capacity search range of the energy storage system was set to 50-500kWh, as shown in Table 1. In the optimization process, in order to meet the annual energy balance requirements of the building, this paper sets the photovoltaic area as the dependent variable determined by the annual energy balance of the building. This search range will also be used in the subsequent optimization design of building energy systems considering parameter uncertainty.

[0043] Table 1 Optimization variable search interval

[0044]

[0045]

[0046] The object of optimization design in this example is the energy system of the building. Generally speaking, reducing the total cost of the building energy system requires reducing the investment in the renewable energy system, that is, by reducing the scale of the renewable energy system. However, the reduction in the scale of the renewable energy system will lead to a reduction in the building's own power generation. Since this article takes into account the needs of the power grid, the real-time electricity price is added as a basis for judging the interaction between the building and the grid. When the building has excess electricity and the grid is at peak power consumption, the building supplies power to the grid to alleviate the peak load problem of the grid. When the battery has excess capacity and the grid is at a low power consumption period, the building purchases electricity from the grid at a low price. In a sense, this operation strategy encourages friendly interaction between the building and the grid. This example applies the multi-objective optimization method to the deterministic optimization design of the scale of the renewable energy system, obtains the Pareto frontier solution set, and selects the optimal design point, as shown in Table 2.

[0047] Table 2 Optimal scale combination of building energy system

[0048]

[0049] The basic principles, main features and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above specific embodiments, and the descriptions in the above specific embodiments and the specification are only for further illustrating the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed for protection.

Claims

1. A design method for enhancing the energy flexibility and grid interaction friendliness of low-carbon buildings, characterized in that: The following steps are involved: Step 10: Based on the density of human activity, equipment energy consumption characteristics, lighting requirements, and detailed meteorological parameters, a high-precision load calculation model is constructed in the EnergyPlus environment to comprehensively evaluate the building energy demand. Step 20: According to the specific architecture and scale of the building energy system, the key variables and their search space in the optimization process are rigorously determined to provide clear parameter boundaries for subsequent optimization design. Step 30, clearly set three core objective functions - life cycle cost, total carbon emissions and grid-friendly interaction index, as key quantitative indicators for evaluating the comprehensive performance of low-carbon building energy systems; Step 40, constructing a mathematical model of the energy system in a Python programming environment, and using the NSGA-II (non-dominated sorting genetic algorithm-II) solver in the Python algorithm library to efficiently solve the Pareto optimal solution set of the multi-objective optimization problem; Step 50, using a scientific normalization method, such as deviation normalization, to determine the relative weight of each objective function, and to select the solution with the best comprehensive performance from the Pareto optimal solution set.

2. The design method for enhancing low-carbon building energy flexibility and grid interaction friendliness according to claim 1 is characterized in that: In step 10, first, a detailed building physical model is constructed in SketchUp software based on the specific design parameters of the building envelope structure; then, the model is imported into EnergyPlus for accurate identification to generate a basic framework for cooling load calculation; and further internal parameters of the building are set, including but not limited to the type of air-conditioning system, indoor temperature setting, ventilation frequency, envelope structure characteristics, internal heat source and operation schedule, etc., and the corresponding load output module is integrated to achieve accurate calculation of cooling load on an hourly and daily basis.

3. The design method for enhancing low-carbon building energy flexibility and grid interaction friendliness according to claim 1 is characterized in that: In step 20, the optimization variables are explicitly selected as the rated power of wind power generation, the rated power of biodiesel power generation, and the storage capacity of the energy storage system; at the same time, in the optimization process, in order to ensure the annual energy balance of the building, the area of ​​the photovoltaic panel is set as a dependent variable that depends on the annual energy demand of the building.

4. The design method for enhancing low-carbon building energy flexibility and grid interaction friendliness according to claim 1 is characterized in that: In step 30, three objective functions are defined: life cycle cost, carbon emissions, and grid-friendly interaction index, and they are used as indicators to measure the quality of low-carbon building energy systems. Among them, life cycle cost (Formula (1)) and carbon emissions (Formula (2)) to (Formula (4)) respectively represent the considerations of investment cost, operating cost, and environmental friendliness of the building during the design process. In addition, considering the current situation of two-way power interaction between buildings and power grids, and the adverse effects that the interaction between building renewable energy generation and the power grid may have on the power grid, the grid-friendly interaction index (Formula (5)) is used to measure the impact of building production capacity on the power grid, and the friendliness of the interaction between buildings and the power grid is enhanced by maximizing the target value. The expressions and meanings of the three objective functions are as follows: (1) Optimization goal 1: total life cycle cost TC=Cost ic +Cost oc (1) In the formula, Cost ic Indicates the initial investment in building capacity equipment (yuan), Cost oc It represents the operating cost of the building (RMB), which mainly refers to the electricity costs of the air conditioning system, lighting and other plug-in electrical equipment inside the building. This paper considers using batteries as the energy storage system, so the cost of the battery also needs to be considered. (2) Optimization goal 2: Carbon emissions CDE=CDE ele +CDE BDG (2) CDE ele =(And inport -AND export )×c ele (3) CDE BDG =F bio ×c bio (4) In the formula, CDE ele It represents the carbon emissions of electricity of the building, which is the total amount of electricity E imported from the grid by the building. inport The total amount of electricity exported from the building to the grid E export The difference between the actual annual electricity consumption of the building is used to calculate the building’s carbon emissions; BDG represents the carbon emissions generated by the combustion of biomass during the power generation process of biodiesel generators; c ele and c bio represent the carbon emission factor of electricity and the carbon emission factor of biodiesel combustion respectively. (3) Optimization goal three: grid-friendly interaction indicators Where Pow pos Indicates the beneficial interaction power (kWh), Pow neg Indicates the unfavorable interaction power (kWh), P supply represents the amount of electricity purchased from the grid by the building (kWh), P export Indicates the amount of electricity (kWh) sold by the building to the grid.

5. The design method for enhancing low-carbon building energy flexibility and grid interaction friendliness according to claim 5 is characterized in that: The grid-friendly interaction index is specifically used to evaluate the positive and negative effects of building capacity on grid operation, and is quantified by calculating the ratio of favorable to unfavorable interactive electricity.

6. The design method for enhancing low-carbon building energy flexibility and grid interaction friendliness according to claim 1 is characterized in that: In step 50, the deviation normalization method is used to normalize the objective function values ​​to ensure that all values ​​fall within the interval [0,1]; and it is assumed that each objective function has an equal weight, and finally the solution with the highest multi-objective mean is selected as the optimal design solution.

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