Zero-emission building virtual energy storage system scheduling strategy analysis method and device

Abstract

The invention discloses a zero-emission building virtual energy storage system scheduling strategy analysis method and device, and the method comprises the steps: constructing an inversion air-conditioning model which is used for describing a function relation between heat supply power and electricity consumption; the heat storage capacity of the zero-emission building envelope structure is converted into electricity storage capacity through the inversion air conditioner model; constructing a virtual energy storage system model according to the storage capacity of the zero-emission building envelope; the energy storage capacity of the zero-emission building envelope is quantified through the virtual energy storage system model; and solving and calculating the battery energy storage system model and the virtual energy storage system model through a weekly stage optimization operation strategy, a daily stage optimization operation strategy and an hour stage optimization operation strategy to obtain optimal scheduling strategies of the weekly stage, the daily stage and the hour stage. According to the three-stage optimization scheduling strategy provided by the invention, the utilization efficiency of the virtual energy storage system and the battery energy storage system is improved through weekly stage optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy scheduling, and in particular to a scheduling strategy analysis method and device for a zero-emission building virtual energy storage system. Background Art

[0002] Currently, in order to reduce energy consumption and carbon emissions in the building sector, the concept of zero emission building (ZEB) has been introduced. ZEB has two key features: excellent building performance and the ability to generate carbon-free energy and on-site storage, which enables ZEB to utilize the resources of battery energy storage system (BESS) and virtual energy storage system (VESS).

[0003] VESS quantifies the thermal storage capacity of the building envelope, indoor air, and indoor heat capacity, similar to the mathematical model of BESS, expressing the quantitative mathematical relationship between indoor and outdoor heat transfer from the perspective of energy conservation. In the context of ZEB, dispatchable resources include distributed photovoltaics, BESS, and controllable household appliances, including washing machines and dryers.

[0004] However, the existing technology uses indoor comfort temperature as a constraint for optimal scheduling, without quantifying the building's heat storage capacity (in the form of VESS or similar systems) as a scheduling resource participating in ZEB optimal scheduling. In addition, the scheduling cycle commonly used in the existing ZEB optimal scheduling strategy contains multiple time periods, including day-ahead and real-time. The existing technology does not incorporate VESS into the optimal scheduling strategy of ZEB. When VESS is taken into account, the operating status of the corresponding BESS and controllable household appliances will change according to the research results. In contrast, the existing day-ahead and intraday optimization methods require that the initial and final values ​​of the operating status of each device within a scheduling cycle are the same, so that the scheduling can be repeated. Therefore, how to invent a scheduling strategy analysis scheme for zero-emission building virtual energy storage systems has become an urgent problem to be solved. Summary of the invention

[0005] To this end, the present invention provides a zero-emission building virtual energy storage system scheduling strategy analysis method and device, which can divide the scheduling stage into weekly, daily and hourly stages, and achieve functions similar to BESS by building VESS. While maintaining user comfort, IAC load can be transferred and the building power load curve can be changed, thereby improving the economic feasibility of ZEB operation and photovoltaic consumption. At the same time, the three-stage optimization scheduling strategy improves the utilization efficiency of VESS and BESS through weekly stage optimization.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for analyzing a scheduling strategy of a zero-emission building virtual energy storage system, comprising:

[0007] Constructing an inverter air conditioning model to describe the functional relationship between heating power and power consumption; converting the heat storage capacity of the zero-emission building envelope into the power storage capacity through the inverter air conditioning model; constructing a virtual energy storage system model based on the power storage capacity of the zero-emission building envelope; quantifying the energy storage capacity of the zero-emission building envelope through the virtual energy storage system model;

[0008] By optimizing the operation strategy in the weekly stage, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy in the weekly stage;

[0009] By optimizing the operation strategy in the daily stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, the optimal scheduling strategy in the daily stage is obtained;

[0010] By optimizing the operation strategy in the hourly stage, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy in the hourly stage.

[0011] As a preferred solution of a zero-emission building virtual energy storage system scheduling strategy analysis method, in the process of constructing the virtual energy storage system model according to the storage capacity of the zero-emission building envelope structure, the virtual energy storage system model is described by virtual charging and discharging power and virtual charging state;

[0012] The expression of the virtual charge and discharge power is:

[0013] P VESS (t) = P IAC (t)-P base (t)

[0014] Q IAC (t) = mP IAC (t)+d

[0015]

[0016] Where P VESS (t) is the virtual charge and discharge power; P IAC (t) is the electric power; P base (t) is the reference power consumption; Q IAC is the heating power; m and d are the constant coefficients of the inverter air conditioning model IAC; i is the i-th type of building envelope, where i is 1-4, corresponding to the wall, window, roof, and door respectively; F i is the inner surface area of ​​the building envelope of type i; α in is the inner surface heat transfer coefficient; T in (t) is the indoor temperature at time t; is the inner surface temperature of the i-th type building envelope at time t; Qsolar (t) is the heat gain from solar radiation at time t;

[0017] The expression of the virtual charging state is:

[0018]

[0019] In the formula, SOC VESS (t) is the virtual charging state; E(t) is the virtual power storage at time t; C VESS (t) is the virtual capacity at time t; Δτ ca (t) is the time when IAC is closed at time t in From T max Reduce to T min time; Δτ E (t) is the time when IAC is closed at time t in From T in (t) is reduced to T min time; P sd (t) is the power reduced by IAC at time t.

[0020] As a preferred solution for the analysis method of the scheduling strategy of the virtual energy storage system of a zero-emission building, in the process of optimizing the operation strategy in the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the weekly stage, during each week, the goal of the environmental management system is to reduce the weekly operating cost; the objective function of the weekly cycle is to minimize the energy cost, and the expression of the objective function is:

[0021]

[0022] In the formula, C grid (t) is the time-of-use electricity price at time t; P grid (t) is the energy of interaction with the power grid, P grid A positive value of (t) indicates that energy is purchased from the grid, and a negative value indicates that energy is sold to the grid; t is the time interval.

[0023] As a preferred solution for the analysis method of the scheduling strategy of the virtual energy storage system of a zero-emission building, in the process of optimizing the operation strategy in the daily stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the daily stage, the initial state of the equipment on the scheduling day is determined within the daily period, and the energy scheduling of each period of the day is established; the goal of the daily cycle is to minimize the difference between the scheduling results of the weekly cycle and the daily cycle, and the expression of the objective function is:

[0024]

[0025] In the formula, It is the energy of interaction between the system and the power grid during a daily cycle.

[0026] As a preferred solution of the zero-emission building virtual energy storage system scheduling strategy analysis method, in the process of optimizing the operation strategy in the hourly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the hourly stage, the model predictive control strategy is used for rolling update within the hourly cycle, and the scheduling strategy is adjusted every hour; the hourly cycle scheduling strategy steps are:

[0027] Initialize parameters and set the calculation period within the total scheduling period;

[0028] Calculate the power of each device in each scheduling interval and the charging and discharging power of the battery energy storage system model and the virtual energy storage system model to obtain a power distribution diagram;

[0029] The control state of each rolling optimization cycle is used as the feedback correction value to enter the next scheduling cycle; the goal of the next round of optimization is to meet the constraints of the energy system;

[0030] Each scheduling cycle is iterated and calculated until all scheduling cycles are completed.

[0031] The present invention also provides a zero-emission building virtual energy storage system scheduling strategy analysis device, based on the above zero-emission building virtual energy storage system scheduling strategy analysis method, comprising:

[0032] A virtual energy storage system model building module is used to build an inverter air conditioning model to describe the functional relationship between heating power and power consumption; the thermal storage capacity of the zero-emission building envelope is converted into electrical storage capacity through the inverter air conditioning model; a virtual energy storage system model is built according to the electrical storage capacity of the zero-emission building envelope; the energy storage capacity of the zero-emission building envelope is quantified through the virtual energy storage system model;

[0033] The weekly optimal scheduling strategy acquisition module is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through the weekly optimization operation strategy to obtain the optimal scheduling strategy for the weekly stage;

[0034] A daily optimal scheduling strategy acquisition module is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through the daily optimization operation strategy to obtain the optimal scheduling strategy for the daily stage;

[0035] The module for obtaining the optimal dispatching strategy in the hourly stage is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through optimizing the operation strategy in the hourly stage to obtain the optimal dispatching strategy in the hourly stage.

[0036] As a preferred solution of a zero-emission building virtual energy storage system scheduling strategy analysis device, in the virtual energy storage system model construction module, in the process of constructing the virtual energy storage system model according to the storage capacity of the zero-emission building envelope structure, the virtual energy storage system model is described by virtual charging and discharging power and virtual charging state;

[0037] The expression of the virtual charge and discharge power is:

[0038] P VESS (t) = P IAC (t)-P base (t)

[0039] Q IAC (t) = mP IAC (t)+d

[0040]

[0041] Where P VESS (t) is the virtual charge and discharge power; P IAC (t) is the electric power; P base (t) is the reference power consumption; Q IAC is the heating power; m and d are the constant coefficients of the inverter air conditioning model IAC; i is the i-th type of building envelope, where i is 1-4, corresponding to the wall, window, roof, and door respectively; F i is the inner surface area of ​​the building envelope of type i; α in is the inner surface heat transfer coefficient; T in (t) is the indoor temperature at time t; is the inner surface temperature of the i-th type building envelope at time t; Q solar (t) is the heat gain from solar radiation at time t;

[0042] The expression of the virtual charging state is:

[0043]

[0044] In the formula, SOC VESS (t) is the virtual charging state; E(t) is the virtual power storage at time t; C VESS (t) is the virtual capacity at time t; Δτ ca (t) is the time when IAC is closed at time t in From T max Reduce to T min time; Δτ E (t) is the time when IAC is closed at time t in From T in (t) is reduced to Tmin time; P sd (t) is the power reduced by IAC at time t.

[0045] As a preferred solution of a zero-emission building virtual energy storage system scheduling strategy analysis device, in the weekly optimal scheduling strategy acquisition module, in the process of obtaining the optimal scheduling strategy of the weekly stage by optimizing the operation strategy of the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, during each week, the goal of the environmental management system is to reduce the weekly operating cost; the objective function of the weekly cycle is to minimize the energy cost, and the expression of the objective function is:

[0046]

[0047] In the formula, C grid (t) is the time-of-use electricity price at time t; P grid (t) is the energy of interaction with the power grid, P grid A positive value of (t) indicates that energy is purchased from the grid, and a negative value indicates that energy is sold to the grid; t is the time interval.

[0048] As a preferred solution of a zero-emission building virtual energy storage system scheduling strategy analysis device, in the daily optimal scheduling strategy acquisition module, in the process of solving and calculating the battery energy storage system model and the virtual energy storage system model through the daily optimization operation strategy, and obtaining the optimal scheduling strategy for the daily stage, the initial state of the equipment on the scheduling day is determined within the daily time period, and the energy scheduling of each time period of the day is established; the goal of the daily cycle is to minimize the difference between the scheduling results of the weekly cycle and the daily cycle, and the expression of the objective function is:

[0049]

[0050] In the formula, It is the energy of interaction between the system and the power grid during a daily cycle.

[0051] As a preferred solution of a zero-emission building virtual energy storage system scheduling strategy analysis device, in the hourly optimal scheduling strategy acquisition module, the hourly period scheduling strategy submodule includes:

[0052] The parameter initialization submodule is used to initialize parameters and set the calculation cycle within the total scheduling cycle;

[0053] A power calculation submodule, used to calculate the power of each device in each scheduling interval and the charging and discharging power of the battery energy storage system model and the virtual energy storage system model to obtain a power distribution diagram;

[0054] The correction optimization submodule is used to use the control state of each rolling optimization cycle as a feedback correction value to enter the next scheduling cycle; the goal of the next round of optimization is to meet the constraints of the energy system;

[0055] The iterative calculation optimization submodule is used to perform iterative calculation processing on each scheduling cycle until all scheduling cycles are completed.

[0056] The present invention has the following advantages: constructing an inverter air conditioning model to describe the functional relationship between heating power and power consumption; converting the heat storage capacity of the zero-emission building envelope into the electricity storage capacity through the inverter air conditioning model; constructing a virtual energy storage system model according to the electricity storage capacity of the zero-emission building envelope; quantifying the energy storage capacity of the zero-emission building envelope through the virtual energy storage system model; optimizing the operation strategy in the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the weekly stage; optimizing the operation strategy in the daily stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the daily stage; optimizing the operation strategy in the hourly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the hourly stage. The present invention can divide the scheduling stage into weekly, daily and hourly stages, and realizes functions similar to BESS by constructing VESS. While maintaining user comfort, it can transfer IAC load and change the building power load curve, thereby improving the economic feasibility of ZEB operation and photovoltaic consumption. At the same time, the three-stage optimization scheduling strategy improves the utilization efficiency of VESS and BESS through weekly stage optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0058] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0059] Figure 1A schematic flow chart of a method for analyzing a scheduling strategy for a zero-emission building virtual energy storage system provided in Example 1 of the present invention;

[0060] Figure 2 A schematic diagram of three-stage optimization scheduling of a VESS zero-emission building in a scheduling strategy analysis method for a zero-emission building virtual energy storage system provided in Example 1 of the present invention;

[0061] Figure 3 A schematic diagram of an outdoor temperature curve and a photovoltaic power generation prediction curve in a possible embodiment provided in Embodiment 1 of the present invention;

[0062] Figure 4 This is a schematic diagram of the results of the mid-term operation strategy in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a) is Scenario I, and (b) is Scenario II;

[0063] Figure 5 Schematic diagram of power supply and consumption of ZEB in a possible embodiment provided in Embodiment 1 of the present invention; wherein (a) is Scenario I, and (b) is Scenario II;

[0064] Figure 6 In a possible embodiment provided in Embodiment 1 of the present invention, VESS and SOC VESS Schematic diagram of the optimization results; where (a) is P VESS The best operating result, (b) is SOC VESS The best running results;

[0065] Figure 7 This is a schematic diagram of the architecture of a zero-emission building virtual energy storage system scheduling strategy analysis device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0066] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Example 1

[0068] See also Figure 1 and Figure 2 Embodiment 1 of the present invention provides a method for analyzing a scheduling strategy of a zero-emission building virtual energy storage system, comprising the following steps:

[0069] S1. Construct an inverter air conditioning model to describe the functional relationship between heating power and power consumption; convert the heat storage capacity of the zero-emission building envelope structure into the power storage capacity through the inverter air conditioning model; construct a virtual energy storage system model based on the power storage capacity of the zero-emission building envelope structure; quantify the energy storage capacity of the zero-emission building envelope structure through the virtual energy storage system model;

[0070] S2. By optimizing the operation strategy in the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, the optimal scheduling strategy in the weekly stage is obtained;

[0071] S3. By optimizing the operation strategy in the daily stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the daily stage;

[0072] S4. By optimizing the operation strategy in hourly stages, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy in hourly stages.

[0073] In this embodiment, in step S1, an inverter air conditioning model is constructed to describe the functional relationship between heating power and power consumption; the heat storage capacity of the zero-emission building envelope is converted into the power storage capacity through the inverter air conditioning model; according to the power storage capacity of the zero-emission building envelope, a virtual energy storage system model is constructed; the energy storage capacity of the zero-emission building envelope is quantified through the virtual energy storage system model;

[0074] Specifically, first, define the characteristic heat dissipation power parameters and the thermal inertia of the building envelope; establish an inverter air conditioning (IAC) model to describe the functional relationship between heating power and power consumption; convert the thermal storage capacity of the zero-emission building envelope into electrical storage capacity through the inverter air conditioning model; then construct a virtual energy storage system model to quantify the energy storage capacity of the zero-emission building envelope. The virtual energy storage system model constructed by the present invention can be expressed as a virtual charge and discharge power (P VESS ) and virtual state of charge (SOC VESS ) are described by two parameters.

[0075] Among them, the calculation steps of virtual charging and discharging power are:

[0076] Using IAC to heat ZEB, the electrical power (P IAC ) and heating power (Q IAC ) is shown in formula (1):

[0077] Q IAC (t) = mP IAC (t)+d(1)

[0078] Where m and d are constant coefficients of the IAC model.

[0079] The IAC power consumption to maintain the current room temperature is defined as the reference power consumption, as shown in formula (2):

[0080]

[0081] Where i is the i-th type of building envelope structure, where i ranges from 1 to 4, corresponding to walls, windows, roofs, and doors, respectively; F i is the inner surface area of ​​the building envelope of type i; α in is the inner surface heat transfer coefficient; T in (t) is the indoor temperature at time t; is the inner surface temperature of the i-th type building envelope at time t; Q solar (t) is the heat gain from solar radiation at time t; The detailed calculation formula of can be found in the literature (Mu, Y. et al. 2023).

[0082] When P IAC (t) = P base (t), T in (t) Keep the current and temperature constant. IAC (t)≠P base (t), P IAC (t) and P base (t), defined as P at time t. VESS , as shown in formula (3):

[0083] P VESS (t) = P IAC (t)-P base (t) (3)

[0084] When P VESS When (t)>0, VESS is in charging state. VESS When (t)<0, VESS is in the discharge state.

[0085] The calculation steps of the virtual charging state are:

[0086] The building heat balance equation (Liu, X. et al. 2018) is expressed as formula (4):

[0087]

[0088] In the formula, C b is the equivalent heat capacity of the building.

[0089] Define the SOC at time t VESS is E(t) and C VESS (t), as shown in formula (5), the value range is 0 to 1.in (t) = T min When SOC VESS (t)=0; when T in (t) = T max When SOC VESS (t)=1.

[0090]

[0091] Where E(t) is the virtual power storage at time t; C VESS (t) is the virtual capacity at time t;

[0092] C VESS The calculation formula of (t) is shown in formula (6):

[0093]

[0094] In the formula, Δτ ca (t) is the time when IAC is closed at time t in From T max Reduce to T min time; by converting Q of formula (4) IAC The term (t) is set to zero to solve.

[0095] The calculation formula of E(t) is shown in formula (7):

[0096]

[0097] In the formula, Δτ E (t) is the time when IAC is closed at time t in From T in (t) is reduced to T min time; P sd (t) is the power reduced by IAC at time t, which is calculated by converting Q IAC The term (t) is set to zero to solve.

[0098] In this embodiment, in step S2, the battery energy storage system model and the virtual energy storage system model are solved and calculated by optimizing the operation strategy in the weekly stage to obtain the optimal scheduling strategy in the weekly stage;

[0099] Specifically, during each week, the goal of the environmental management system is to minimize the weekly operating costs. The time scale of the cycle strategy is one week or longer. Through scheduling, the initial SOC value of the BESS and the operating status of the household appliances in the daily period of each scheduling day are determined.

[0100] The objective function of the weekly cycle is to minimize the energy cost. The expression of the objective function is:

[0101]

[0102] In the formula, C grid (t) is the time-of-use electricity price at time t; P grid (t) is the energy of interaction with the power grid, P grid A positive value of (t) indicates that energy is purchased from the grid, and a negative value indicates that energy is sold to the grid; t is the time interval.

[0103] The constraints include power balance constraints, VESS operation constraints, BESS operation constraints and controllable load constraints.

[0104] Among them, the power balance constraints are shown in equations (9) and (10):

[0105]

[0106] P grid (t)|≤P PCS (10)

[0107] Where P PV is the photovoltaic power generation power; and are the charging and discharging power of BESS respectively; P app is the power of household appliances; P PCS It is the power limit of the power control system.

[0108] The VESS operation constraints are shown in equations (11)-(13):

[0109]

[0110] In the formula, and are the upper and lower limits of VES respectively.

[0111] The BESS operation constraints are shown in equations (14)-(18):

[0112]

[0113]

[0114] In the formula, SOC BESS is the state of charge (SOC) of the BESS; where, Batteries and Battery represents the charging and discharging efficiency of BESS; E battmax is the energy capacity of BESS; P battmax is the maximum power of the BESS; and are the upper and lower limits of BESS; Formula (18) indicates that charging and discharging cannot be performed simultaneously.

[0115] The controllable load constraints are shown in equations (19)-(22):

[0116] P app (t) = P app,un (t)+P app,con (t)(19)

[0117]

[0118] In the formula, load P app Including uncontrollable load P app,un , controllable load P app,con The present invention uses VES power to describe P app,cut .

[0119] For the controllable load P at time t app,con,i , such as washing machines and clothes drying machines, an operating time interval is defined as and are the earliest start time and the latest end time of the ith controllable load respectively. The working time constraint of the ith controllable load is shown in formula (21); app,con,i The power at time t is shown in formula (22);

[0120]

[0121] In the formula, Duration of operation of controllable appliances; is the start time of the controllable electrical appliances; The rated power of the i-th controllable electrical appliance provided by the manufacturer.

[0122] In this embodiment, in step S3, the battery energy storage system model and the virtual energy storage system model are solved and calculated by optimizing the operation strategy in the daily stage to obtain the optimal scheduling strategy in the daily stage;

[0123] Specifically, within the daily time period, after determining the initial state of the equipment on each scheduling day, the energy scheduling for each time period of the day is established. The scheduling cycle is set to 1 hour.

[0124] The goal of the daily cycle is to minimize the difference between the weekly cycle and the daily cycle scheduling results. The expression of the objective function is:

[0125]

[0126] In the formula, It is the energy of interaction between the system and the power grid during a daily cycle.

[0127] Similar to the weekly phase constraints, the daily cycle constraints also include power balance constraints, BESS operation constraints, VESS operation constraints and controllable load constraints, as shown in formula (23):

[0128]

[0129] In the formula, the superscript daily represents a one-day cycle, and the last two constraints in formula (23) ensure that the scheduling strategy runs continuously.

[0130] In this embodiment, in step S4, the operation strategy is optimized in hourly stages, and the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy in hourly stages.

[0131] Specifically, within the hourly cycle, the model predictive control (MPC) method is used for rolling updates, and the scheduling strategy is adjusted every hour. The steps of the hourly cycle scheduling strategy are:

[0132] S41, initializing parameters and setting a calculation cycle within the total scheduling cycle;

[0133] Specifically, initialize the parameters and set the calculation period k within the total scheduling period p Assuming that the current operation is in the tth scheduling period, collect the scheduling period [t, t+k p -1] weather information, load data, photovoltaic power generation, user-specific arrangements (such as watching a movie, dinner), and the current SOC of the BESS.

[0134] S42, calculating the power of each device in each scheduling interval and the charging and discharging power of the battery energy storage system model and the virtual energy storage system model to obtain a power distribution diagram;

[0135] Specifically, the power distribution diagram of each device in each scheduling interval is calculated, including the charging and discharging power of BESS and VESS. In the rolling optimization stage, the initial control strategy is converted into an optimization problem with a rolling prediction level equal to the scheduling period, and the MPC strategy is applied. The initial value is continuously updated to solve the optimization problem, and the daily value of each rolling optimization period is obtained. and These values ​​are entered into hourly cycles.

[0136] S43, taking the control state of each rolling optimization cycle as a feedback correction value and entering the next scheduling cycle; the goal of the next round of optimization is to meet the constraints of the energy system;

[0137] S44, performing iterative calculation processing on each scheduling cycle until all scheduling cycles are completed.

[0138] Specifically, step S41 to step S43 are repeated for each subsequent scheduling cycle until all scheduling cycles are completed.

[0139] During the hourly period, the EMS focuses on determining the primary power supply components, namely BESS, VESS, and the power grid, and considers whether there is boundary logic for each device to ensure safe operation.

[0140] In a possible embodiment, an example of ZEB scheduling strategy analysis on a typical winter day in Hebei Province, China is provided as follows:

[0141] The outdoor temperature, photovoltaic power, and building envelope parameter settings are consistent with (Mu, Y. et al. 2023). The electricity price setting refers to (Ju, L. et al. 2022). The indoor temperature range of the building is set to [19.71℃, 25.02℃] according to (Mu, Y. et al., 2023). Figure 3 The outdoor temperature curve and the photovoltaic power prediction curve are shown in Figure 1. The electricity price is given in Table 1. The weekly simulation period is from January 1 to January 7. The simulation results of January 1 are used to analyze the daily cycle operation strategy and the hourly cycle operation strategy.

[0142] Time Cgrid (yuan / kW) 00:00-8:00,18:00-21:00,22:00-24:00 0.3 08:00-11:00,12:00-16:00,17:00-18:00 0.52 Others 0.55

[0143] Table 1 Electricity prices for different time periods

[0144] In order to analyze the value of VESS to the three-stage optimization scheduling method of the present invention, the following two scenarios are considered:

[0145] Scenario I: Without considering VESS, the three-stage optimization scheduling method proposed in this invention is used to formulate the operation strategy of ZEB;

[0146] Scenario II: Considering VESS, the three-stage optimization scheduling method proposed in this invention is adopted to formulate the operation strategy of ZEB.

[0147] The scheduling results of the weekly operation strategy are as follows: Figure 4 As shown in Figure 1, the energy balance of ZEB on a weekly time scale is shown. The weekly operation strategy can obtain the initial SOC values ​​of BESS and VESS on each scheduling day as the input conditions of the daily cycle operation strategy. Figure 5 It can be seen that on a one-week time scale, the operating costs of ZEB under scenario I and scenario II are 69.89 yuan and 152.44 yuan respectively. This shows that VESS can significantly reduce the operating costs of ZEB.

[0148] By implementing the proposed three-stage optimization operation strategy, in scenario II, ZEB's electricity purchase cost is 25.66 yuan and sales revenue is 6.23 yuan. In scenario I, ZEB's electricity purchase cost is 19.84 yuan, which is 22.68% lower than scenario II; sales revenue is 11.80 yuan, which is 89.44% higher than scenario II. Figure 5 As shown in (a), ZEB only has photovoltaic power generation during the period of 8:00-20:00. During the periods of 8:00-9:00 and 19:00-20:00, ZEB supplements the shortage of photovoltaic power generation by purchasing electricity from the grid. During the periods of 0:00-8:00 and 20:00-24:00, ZEB relies on purchasing electricity from the grid and using battery energy storage to meet its electricity demand. During the period of 9:00-19:00, ZEB uses photovoltaic power generation and battery energy storage to meet its electricity demand, and sells excess photovoltaic power generation to the grid to obtain income.

[0149] Taking into account the addition of VESS, the method proposed in this invention tends to purchase more electricity from the grid during periods of low electricity prices when photovoltaic power generation cannot meet the electricity demand of ZEB. It aims to reduce the purchase of electricity during periods of high electricity prices, thereby reducing the cost of electricity purchase. Figure 5 (a) and Figure 6 It can be seen that during the 4:00-8:00 period, the amount of electricity purchased increases, P VESS is positive, causing VESS to charge, resulting in VESS SOC VESS Add to SOC VESS =1. 8:00-14:00 period, P VESS If P is negative, VESS will release the energy stored in the period of 4:00-8:00, resulting in a decrease in ZEB power purchases and a reduction in power purchase costs. VESS To promote friendly interaction between ZEB and the power grid within its capacity limits, thereby reducing the operating costs of ZEB.

[0150] In summary, the present invention constructs an inverter air conditioning model to describe the functional relationship between heating power and power consumption; the inverter air conditioning model is used to convert the heat storage capacity of the zero-emission building envelope into the electricity storage capacity; according to the electricity storage capacity of the zero-emission building envelope, a virtual energy storage system model is constructed; the energy storage capacity of the zero-emission building envelope is quantified by the virtual energy storage system model; through the weekly stage optimization operation strategy, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy for the weekly stage; through the daily stage optimization operation strategy, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy for the daily stage; through the hourly stage optimization operation strategy, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy for the hourly stage. The present invention can divide the scheduling stage into weekly, daily and hourly stages, and realizes functions similar to BESS by constructing VESS. While maintaining user comfort, it can transfer IAC load and change the building power load curve, thereby improving the economic feasibility of ZEB operation and photovoltaic consumption. At the same time, the three-stage optimization scheduling strategy improves the utilization efficiency of VESS and BESS through weekly stage optimization.

[0151] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0152] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0153] Example 2

[0154] See also Figure 7 Embodiment 2 of the present invention further provides a zero-emission building virtual energy storage system scheduling strategy analysis device, comprising:

[0155] A virtual energy storage system model construction module 001 is used to construct an inverter air conditioning model to describe the functional relationship between heating power and power consumption; the thermal storage capacity of the zero-emission building envelope structure is converted into electrical storage capacity through the inverter air conditioning model; a virtual energy storage system model is constructed according to the electrical storage capacity of the zero-emission building envelope structure; and the energy storage capacity of the zero-emission building envelope structure is quantified through the virtual energy storage system model;

[0156] The weekly optimal scheduling strategy acquisition module 002 is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through the weekly optimization operation strategy to obtain the optimal scheduling strategy for the weekly stage;

[0157] The daily optimal dispatching strategy acquisition module 003 is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through the daily optimization operation strategy to obtain the optimal dispatching strategy for the daily stage;

[0158] The module 004 for obtaining the optimal dispatching strategy in the hourly stage is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through optimizing the operation strategy in the hourly stage to obtain the optimal dispatching strategy in the hourly stage.

[0159] In this embodiment, in the virtual energy storage system model construction module 001, in the process of constructing the virtual energy storage system model according to the storage capacity of the zero-emission building envelope structure, the virtual energy storage system model is described by virtual charging and discharging power and virtual charging state;

[0160] The expression of the virtual charge and discharge power is:

[0161] P VESS (t) = P IAC (t)-P base (t)

[0162] Q IAC (t) = mP IAC (t)+d

[0163]

[0164] Where P VESS (t) is the virtual charge and discharge power; P IAC (t) is the electric power; P base (t) is the reference power consumption; Q IAC is the heating power; m and d are the constant coefficients of the inverter air conditioning model IAC; i is the i-th type of building envelope, where i is 1-4, corresponding to the wall, window, roof, and door respectively; F i is the inner surface area of ​​the building envelope of type i; α inis the inner surface heat transfer coefficient; T in (t) is the indoor temperature at time t; is the inner surface temperature of the i-th type building envelope at time t; Q solar (t) is the heat gain from solar radiation at time t;

[0165] The expression of the virtual charging state is:

[0166]

[0167]

[0168] In the formula, SOC VESS (t) is the virtual charging state; E(t) is the virtual power storage at time t; C VESS (t) is the virtual capacity at time t; Δτ ca (t) is the time when IAC is closed at time t in From T max Reduce to T min time; Δτ E (t) is the time when IAC is closed at time t in From T in (t) is reduced to T min time; P sd (t) is the power reduced by IAC at time t.

[0169] In this embodiment, in the weekly optimal scheduling strategy acquisition module 002, in the process of obtaining the optimal scheduling strategy of the weekly stage by optimizing the operation strategy of the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, during each week, the goal of the environmental management system is to reduce the weekly operating cost; the objective function of the weekly cycle is to minimize the energy cost, and the expression of the objective function is:

[0170]

[0171] In the formula, C grid (t) is the time-of-use electricity price at time t; P grid (t) is the energy of interaction with the power grid, P grid A positive value of (t) indicates that energy is purchased from the grid, and a negative value indicates that energy is sold to the grid; t is the time interval.

[0172] In this embodiment, in the daily optimal scheduling strategy acquisition module 003, in the process of solving and calculating the battery energy storage system model and the virtual energy storage system model through the daily optimization operation strategy to obtain the optimal scheduling strategy for the daily stage, the initial state of the equipment on the scheduling day is determined within the daily time period, and the energy scheduling for each time period of the day is established; the goal of the daily cycle is to minimize the difference between the scheduling results of the weekly cycle and the daily cycle, and the expression of the objective function is:

[0173]

[0174] In the formula, It is the energy of interaction between the system and the power grid during a daily cycle.

[0175] In this embodiment, in the hourly optimal scheduling strategy acquisition module 004, the hourly periodic scheduling strategy submodule includes:

[0176] The parameter initialization submodule 041 is used to initialize parameters and set the calculation cycle within the total scheduling cycle;

[0177] The power calculation submodule 042 is used to calculate the power of each device in each scheduling interval and the charging and discharging power of the battery energy storage system model and the virtual energy storage system model to obtain a power distribution diagram;

[0178] The correction optimization submodule 043 is used to use the control state of each rolling optimization cycle as a feedback correction value to enter the next scheduling cycle; the goal of the next round of optimization is to meet the constraints of the energy system;

[0179] The iterative calculation optimization submodule 044 is used to perform iterative calculation processing on each scheduling cycle until all scheduling cycles are completed.

[0180] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0181] Example 3

[0182] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a zero-emission building virtual energy storage system scheduling strategy analysis method is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of a zero-emission building virtual energy storage system scheduling strategy analysis method.

[0183] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0184] Example 4

[0185] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0186] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a zero-emission building virtual energy storage system scheduling strategy analysis method of Example 1 or any possible implementation thereof.

[0187] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0188] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0189] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0190] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for analyzing scheduling strategies of a zero-emission building virtual energy storage system, characterized in that: include: Constructing an inverter air conditioning model to describe the functional relationship between heating power and power consumption; converting the heat storage capacity of the zero-emission building envelope into the power storage capacity through the inverter air conditioning model; constructing a virtual energy storage system model based on the power storage capacity of the zero-emission building envelope; quantifying the energy storage capacity of the zero-emission building envelope through the virtual energy storage system model; By optimizing the operation strategy in the weekly stage, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy in the weekly stage; By optimizing the operation strategy in the daily stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, the optimal scheduling strategy in the daily stage is obtained; By optimizing the operation strategy in the hourly stage, the battery energy storage system model and the virtual energy storage system model are solved and calculated to obtain the optimal scheduling strategy in the hourly stage.

2. A zero-emission building virtual energy storage system scheduling strategy analysis method according to claim 1, characterized in that: In the process of constructing the virtual energy storage system model according to the storage capacity of the zero-emission building envelope structure, the virtual energy storage system model is described by virtual charging and discharging power and virtual charging state; The expression of the virtual charge and discharge power is: P VESS (t)=P IAC (t)-P base (t) Q IAC (t)=mP IAC (t)+d Where P VESS (t) is the virtual charge and discharge power; P IAC (t) is the electric power; P base (t) is the reference power consumption; Q IAC is the heating power; m and d are the constant coefficients of the inverter air conditioning model IAC; i is the i-th type of building envelope, where i is 1-4, corresponding to the wall, window, roof, and door respectively; F i is the inner surface area of ​​the building envelope of type i; α in is the inner surface heat transfer coefficient; T in (t) is the indoor temperature at time t; is the inner surface temperature of the i-th type building envelope at time t; Q solar (t) is the heat gain from solar radiation at time t; The expression of the virtual charging state is: In the formula, SOC VESS (t) is the virtual charging state; E(t) is the virtual power storage at time t; C VESS (t) is the virtual capacity at time t; Δτ ca (t) is the time when IAC is closed at time t in From T max Reduce to T min time; Δτ E (t) is the time when IAC is closed at time t in From T in (t) is reduced to T min time; P sd (t) is the power reduced by IAC at time t.

3. A zero-emission building virtual energy storage system scheduling strategy analysis method according to claim 2, characterized in that: In the process of optimizing the operation strategy in the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, and obtaining the optimal scheduling strategy in the weekly stage, during each week, the goal of the environmental management system is to reduce the weekly operating cost; the objective function of the weekly cycle is to minimize the energy cost, and the expression of the objective function is: In the formula, C grid (t) is the time-of-use electricity price at time t; P grid (t) is the energy of interaction with the power grid, P grid A positive value of (t) indicates that energy is purchased from the grid, and a negative value indicates that energy is sold to the grid; t is the time interval.

4. A zero-emission building virtual energy storage system scheduling strategy analysis method according to claim 3, characterized in that: In the process of optimizing the operation strategy in the daily stage, solving and calculating the battery energy storage system model and the virtual energy storage system model to obtain the optimal scheduling strategy in the daily stage, the initial state of the equipment on the scheduling day is determined within the daily period, and the energy scheduling of each period of the day is established; the goal of the daily cycle is to minimize the difference between the scheduling results of the weekly cycle and the daily cycle, and the expression of the objective function is: In the formula, It is the energy of interaction between the system and the power grid during a daily cycle.

5. A zero-emission building virtual energy storage system scheduling strategy analysis method according to claim 4, characterized in that: In the process of optimizing the operation strategy in the hourly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model to obtain the optimal scheduling strategy in the hourly stage, the model predictive control strategy is used to perform rolling updates within the hourly cycle, and the scheduling strategy is adjusted every hour; the hourly cycle scheduling strategy steps are: Initialize parameters and set the calculation period within the total scheduling period; Calculate the power of each device in each scheduling interval and the charging and discharging power of the battery energy storage system model and the virtual energy storage system model to obtain a power distribution diagram; The control state of each rolling optimization cycle is used as the feedback correction value to enter the next scheduling cycle; the goal of the next round of optimization is to meet the constraints of the energy system; Each scheduling cycle is iterated and calculated until all scheduling cycles are completed.

6. A zero-emission building virtual energy storage system scheduling strategy analysis device, using a zero-emission building virtual energy storage system scheduling strategy analysis method according to any one of claims 1 to 5, characterized in that: include: A virtual energy storage system model building module is used to build an inverter air conditioning model to describe the functional relationship between heating power and power consumption; the thermal storage capacity of the zero-emission building envelope is converted into electrical storage capacity through the inverter air conditioning model; a virtual energy storage system model is built according to the electrical storage capacity of the zero-emission building envelope; the energy storage capacity of the zero-emission building envelope is quantified through the virtual energy storage system model; The weekly optimal scheduling strategy acquisition module is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through the weekly optimization operation strategy to obtain the optimal scheduling strategy for the weekly stage; A daily optimal scheduling strategy acquisition module is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through the daily optimization operation strategy to obtain the optimal scheduling strategy for the daily stage; The module for obtaining the optimal dispatching strategy in the hourly stage is used to solve and calculate the battery energy storage system model and the virtual energy storage system model through optimizing the operation strategy in the hourly stage to obtain the optimal dispatching strategy in the hourly stage.

7. A zero-emission building virtual energy storage system scheduling strategy analysis device according to claim 6, characterized in that: In the virtual energy storage system model construction module, in the process of constructing the virtual energy storage system model according to the storage capacity of the zero-emission building envelope structure, the virtual energy storage system model is described by virtual charging and discharging power and virtual charging state; The expression of the virtual charge and discharge power is: P VESS (t)=P IAC (t)-P base (t) Q IAC (t)=mP IAC (t)+d Where P VESS (t) is the virtual charge and discharge power; P IAC (t) is the electric power; P base (t) is the reference power consumption; Q IAC is the heating power; m and d are the constant coefficients of the inverter air conditioning model IAC; i is the i-th type of building envelope, where i is 1-4, corresponding to the wall, window, roof, and door respectively; F i is the inner surface area of ​​the building envelope of type i; α in is the inner surface heat transfer coefficient; T in (t) is the indoor temperature at time t; is the inner surface temperature of the i-th type building envelope at time t; Q solar (t) is the heat gain from solar radiation at time t; The expression of the virtual charging state is: In the formula, SOC VESS (t) is the virtual charging state; E(t) is the virtual power storage at time t; C VESS (t) is the virtual capacity at time t; Δτ ca (t) is the time when IAC is closed at time t in From T max Reduce to T min time; Δτ E (t) is the time when IAC is closed at time t in From T in (t) is reduced to T min time; P sd (t) is the power reduced by IAC at time t.

8. A zero-emission building virtual energy storage system scheduling strategy analysis device according to claim 7, characterized in that: In the weekly optimal scheduling strategy acquisition module, in the process of obtaining the optimal scheduling strategy of the weekly stage by optimizing the operation strategy of the weekly stage, solving and calculating the battery energy storage system model and the virtual energy storage system model, during each week, the goal of the environmental management system is to reduce the weekly operating cost; the objective function of the weekly cycle is to minimize the energy cost, and the expression of the objective function is: In the formula, C grid (t) is the time-of-use electricity price at time t; P grid (t) is the energy of interaction with the power grid, P grid A positive value of (t) indicates that energy is purchased from the grid, and a negative value indicates that energy is sold to the grid; t is the time interval.

9. A zero-emission building virtual energy storage system scheduling strategy analysis device according to claim 8, characterized in that: In the daily optimal scheduling strategy acquisition module, in the process of solving and calculating the battery energy storage system model and the virtual energy storage system model through the daily optimization operation strategy to obtain the optimal scheduling strategy for the daily stage, the initial state of the equipment on the scheduling day is determined within the daily time period, and the energy scheduling for each time period of the day is established; the goal of the daily cycle is to minimize the difference between the scheduling results of the weekly cycle and the daily cycle, and the expression of the objective function is: In the formula, It is the energy of interaction between the system and the power grid during a daily cycle.

10. A zero-emission building virtual energy storage system scheduling strategy analysis device according to claim 9, characterized in that: In the hourly optimal scheduling strategy acquisition module, the hourly periodic scheduling strategy submodule includes: The parameter initialization submodule is used to initialize parameters and set the calculation cycle within the total scheduling cycle; A power calculation submodule, used to calculate the power of each device in each scheduling interval and the charging and discharging power of the battery energy storage system model and the virtual energy storage system model to obtain a power distribution diagram; The correction optimization submodule is used to use the control state of each rolling optimization cycle as a feedback correction value to enter the next scheduling cycle; the goal of the next round of optimization is to meet the constraints of the energy system; The iterative calculation optimization submodule is used to perform iterative calculation processing on each scheduling cycle until all scheduling cycles are completed.

Images