A building integrated energy system operation strategy and declaration capacity optimization method considering demand response effectiveness requirements

Abstract

The application discloses a building integrated energy system operation strategy and declaration capacity optimization method considering demand response effectiveness requirements. First, the basic information of the building user side and the power grid side is collected; second, a building integrated energy system optimization scheduling model is established; then, an optimization solving problem of the building integrated energy system optimization scheduling model considering the demand response effectiveness requirements is determined; finally, a mixed integer linear programming algorithm is used to solve the optimization solving problem of the building integrated energy system optimization scheduling model, and the optimal declaration capacity value scheme and the operation strategy scheme are obtained. By using the application, the requirements of the power grid on the user information declaration can be met when the building user participates in the invitation type demand response, the declaration capacity value recommendation scheme and the response strategy recommendation scheme are provided for the user; and based on the response effectiveness auditing mechanism formulated by the power grid, the actual response effect of the user is improved, and the target of further reducing the operation cost of the user is achieved.

Description

Technical fields:

[0001] This invention relates to the operation of integrated building energy systems, and more particularly to an operation strategy and capacity optimization method for integrated building energy systems that takes into account the requirements of demand response effectiveness. Background technology:

[0002] In recent years, the pressure on the power grid's supply and demand balance has been increasing. On the one hand, user-side load is showing a peak-driven development pattern, with a significant gap between peak and valley loads. On the other hand, the increasing prevalence of intermittent renewable energy has led to a continuous decrease in the number and scale of traditional fossil fuel power plants, increasing instability factors on the power grid's supply side and affecting the safety and economy of power system operation. Demand response, as a more efficient and economical way to maintain power grid supply and demand balance, has received widespread attention. Integrated energy systems are widely used due to their high energy utilization rate and low operating costs. Unlike single energy carrier systems, integrated energy systems, through the integration of electricity, heat, and other forms of energy, can fully utilize the interactive capabilities of building flexible resources to better participate in demand response. Effective peak shaving can be achieved by utilizing flexible resources such as photovoltaics, energy storage, and building thermal storage.

[0003] CN115619015A proposes an optimized operation method for integrated energy systems that considers user demand response. It incorporates the impact of electricity price incentive response on user load into the operation of the integrated energy system, and includes the demand response of interruptible loads represented by large factory users. It considers the characteristics of different flexible resources and constructs a basic electricity charge for declared electricity consumption constraints. A hybrid linear integer programming model is established, and the model is solved using the MILP algorithm to provide users with response schemes to achieve the goal of reducing energy purchase costs.

[0004] However, the aforementioned methods, on the one hand, do not fully utilize flexible resources, failing to consider resources such as building virtual energy storage; on the other hand, they cannot support the generation of response schemes under invitation-based demand response. Price-based demand response cannot impose clear constraints or requirements on user behavior, meaning that user participation in demand response is uncontrollable. This characteristic is particularly prominent in China, where time-of-use pricing or peak pricing with relatively fixed prices is being vigorously promoted. Currently, phenomena such as peak load impacting the power grid occur frequently. To more effectively guide user demand response behavior, incentive-based demand response is being widely promoted, posing new requirements for user participation in grid interaction. Summary of the Invention:

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a building integrated energy system operation strategy and capacity optimization method that considers the requirements of demand response effectiveness, serving building users' participation in invited demand response aimed at peak shaving. Based on the operational characteristics of distributed photovoltaics, energy storage, thermal storage, and passive building virtual energy storage, and fully considering the characteristics of the invited demand response market mechanism and the requirements for user response effectiveness, this invention provides building users with optimal capacity allocation and operation strategy schemes, achieving the goals of improving demand response effectiveness and reducing user operating costs.

[0006] The technical solution adopted to achieve the purpose of this invention is:

[0007] An operational strategy and capacity optimization method for a building integrated energy system that considers demand response effectiveness requirements includes the following steps:

[0008] S1. Collect basic information from the building user side and the power grid side;

[0009] S2. Establish an optimized scheduling equipment model for the building's integrated energy system, including a photovoltaic power output model, a battery model, a water tank model, a passive building virtual energy storage model, and an electric chiller model;

[0010] S3. Determine the optimization problem of the building integrated energy system optimization scheduling model that considers the requirements of demand response effectiveness. The objective function of the optimization problem of the integrated energy system optimization scheduling model is to minimize the system operating cost. The optimization scheduling constraints include inequality constraints and equality constraints.

[0011] S4. Use mixed-integer linear programming algorithm to solve the optimization problem of the building integrated energy system optimization scheduling model, and obtain the optimal application capacity value scheme and operation strategy scheme.

[0012] Furthermore, the basic information on the building user side includes energy system equipment configuration information, building electricity load demand, meteorological data, personnel schedules, equipment usage schedules, etc.; the basic information on the grid side includes time-of-use electricity pricing information, invitation-based demand response incentive prices, response validity review mechanisms, etc.

[0013] Furthermore, the photovoltaic output model in the building integrated energy system optimization scheduling equipment model is as follows:

[0014]

[0015] Where, N PV P represents the number of installed photovoltaic modules. PV_no This is the rated power of each photovoltaic module under test conditions, in kW; The solar radiation under the test conditions is expressed in W / m². 2 ;SRt The actual solar radiation at each moment, W / m 2 η PV Photovoltaic panel efficiency.

[0016] Furthermore, the battery model in the building integrated energy system optimization scheduling equipment model is as follows:

[0017] Q Ba,t =Q Ba,t-1 (1-α Ba,loss )+P BaI,t η BaI Δt-P BaO,t / η BaO Δt

[0018] Among them, Q Ba (t) represents the battery capacity at time t, in kWh; P BaI (t) and P BaO (t) represents the battery charging and discharging power, respectively, in kW; η BaI With η BaO These represent the battery charging and discharging efficiencies, respectively; α Ba,loss This represents the battery loss coefficient.

[0019] Furthermore, the water tank model in the building integrated energy system optimization scheduling equipment model is as follows:

[0020] Q Ws (t)=Q Ws (t-1)(1-α Ws,loss )+Q WsI (t)η WsI Δt-Q WsO (t) / η WsO Δt

[0021] Among them, Q Ws (t) represents the energy stored in the water tank at time t, in kWh; Q WsI (t) and Q WsO (t) represents the cold storage power and cold release power of the water tank, respectively, in kW:η WsI With η WsO These represent the energy storage and energy release efficiencies of the water tank, respectively; α Ws,loss This represents the loss coefficient of the water storage tank.

[0022] Furthermore, the passive building virtual energy storage model in the building integrated energy system optimization scheduling equipment model is as follows:

[0023]

[0024] Among them, CL t To predict cooling load, kW. t-mThis is the preliminary input data upon which the current cooling load depends. In this invention, the data for the ARX model includes dry-bulb temperature (TD), relative humidity (HD), solar irradiance (SR), occupancy schedule (OS), equipment schedule (ES), and indoor temperature schedule (RTS).

[0025] Furthermore, the electric chiller model in the building integrated energy system optimization scheduling equipment model is as follows:

[0026] P CC，t =Q CC,t / COP

[0027] Among them, P CC，t Q represents the chiller power, in kW. CC,t Cooling capacity of the refrigeration unit, kW. COP t This is the coefficient of performance (COP) of the chiller.

[0028] Furthermore, the objective of the optimization problem in the integrated energy system scheduling model is to minimize the total operating cost. The operating cost is calculated by subtracting the incentive subsidy (Ren) obtained by users participating in peak shaving demand response from the energy purchase cost. The optimization objective is expressed as follows:

[0029]

[0030] Among them, P grid，t Let Pr be the tie-line power at time t. grid，t Let be the electricity price at time t, Δt be the event interval, and Ren be the incentive subsidy received by users participating in peak shaving demand response. Considering the grid's requirements for the effectiveness of user demand response, an optimized calculation method for the incentive subsidy (Ren) received by users participating in peak shaving demand response is proposed:

[0031]

[0032] Among them, Pr DR The price for peak-shaving demand response incentive subsidies is [amount] yuan / kW. ΔP eff，τ Let P be the effective load response at time τ, in kW. grid_base，τ Let P be the baseline power of the tie line at time τ, in kW. grid,τ The tie-line response power, kW, at time τ after the response strategy is executed. ΔP set The user-reported capacity is in kW. ω is the penalty coefficient. R1, R2, and R3 are the coefficients for judging the validity interval of the response. α1 and α2 are the correction coefficients for the effective response quantity.

[0033] Furthermore, the inequality constraints include battery charging and discharging power constraints, battery available capacity constraints, water tank energy storage and release power constraints, water tank available capacity constraints, indoor temperature adjustable range constraints, and electric chiller available capacity constraints.

[0034] The battery charging and discharging power constraint expression is as follows:

[0035] P BaI (t)≤P Bal,max

[0036] P BaO (t)≤P BaO，max

[0037] Among them, P BaI，max With P BaO，max These represent the maximum charging and discharging power of the battery, in kW.

[0038] The expression for the available battery capacity constraint is:

[0039] Q Ba_no SOC Ba_min ≤Q Ba,t ≤Q Ba_no SOC Ba_max

[0040] Among them, Q Ba_no Rated battery capacity, kWh; SOC Ba_min With SOC Ba_max These represent the minimum and maximum battery states of charge, respectively.

[0041] The constraint expression for the energy storage and release power of the water storage tank is:

[0042] Q WsI (t)≤Q WsI，max

[0043] Q WsO (t)≤Q WsO，max

[0044] Among them, Q WsI,max With Q WsO,max These are the maximum cold storage capacity and maximum cold release capacity of the water storage tank, respectively, in kW.

[0045] The expression for the available capacity constraint of the water tank is:

[0046]

[0047] Among them, Q Ws_no Rated capacity of the water storage tank, kW, SOC Ws_min With SOC Ws_max These represent the percentage of the minimum and maximum water storage tank capacity, respectively.

[0048] The constraint expression for the adjustable range of indoor temperature is:

[0049]

[0050] in, and RTS t These are the upper and lower limits of the acceptable indoor temperature for users during normal operating periods; and RTS_DR t These represent the upper and lower limits of the indoor temperature that users can accept during the demand response period.

[0051] The available capacity constraint expression for the electric chiller is:

[0052]

[0053] in, Q CC.t and These represent the upper and lower limits of the refrigeration capacity of the refrigeration unit, in kW.

[0054] Furthermore, the equality constraints include power balance constraints and thermal balance constraints.

[0055] The expression for the power balance constraint is:

[0056] P grid，t +P PV,t +P BaO，t =EL t +P BaI，t +P CC，t

[0057] Among them, EL t This represents the building's electrical load demand, excluding building cooling electrical load, in kW.

[0058] The thermal balance constraint expression is:

[0059] Q CC,t +Q WsO,t =CL t +Q WsI，t

[0060] Compared with the prior art, the present invention has the following advantages and positive effects:

[0061] 1. This invention fully considers the unique market mechanism characteristics of invitation-based demand response. Based on the demand response validity review mechanism established by the power grid, it innovatively optimizes the user incentive revenue calculation method with the goal of improving response validity. It can adapt to the mechanism characteristics of the power grid to reduce incentive subsidies or even impose penalties on invalid responses.

[0062] 2. This invention can meet the information interaction needs between buildings and the power grid. During the system operation strategy optimization phase, it collaboratively optimizes the user-declared capacity value, fully considers the operation characteristics of distributed photovoltaic power generation, batteries, water tanks, electric chillers, and building virtual energy storage, and achieves the effect of minimizing system operation costs through optimized scheduling models, providing building users with declared capacity value and response strategy solutions.

[0063] 3. Based on the building user's declared capacity and response strategy scheme given by the method of the present invention, on the one hand, it can ensure high-quality demand response effect, help the power grid complete the predetermined peak shaving task, and alleviate the pressure of power grid supply and demand balance; on the other hand, it can reduce user response incentive loss and further reduce system operating costs. Attached image description:

[0064] Figure 1 This invention provides a flowchart of a method for optimizing the declared capacity and operation strategy of a building integrated energy system that considers the effectiveness of demand response;

[0065] Figure 2 A schematic diagram of the physical form of the building integrated energy system in this embodiment of the invention;

[0066] Figure 3 A schematic diagram illustrating the necessary input data in this embodiment of the invention;

[0067] Figure 4 A schematic diagram of the demand response scheme in this embodiment of the invention;

[0068] Figure 5 A summary chart of the changes in operating costs of the traditional solution compared to the benchmark solution in this invention embodiment;

[0069] Figure 6 A summary chart of the changes in operating costs of the new solution compared to the baseline solution in this invention embodiment;

[0070] Figure 7 A summary chart of the changes in operating costs of the new solution compared to the traditional solution in this invention embodiment. Detailed Implementation

[0071] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0072] refer to Figure 1 An operational strategy and capacity optimization method for a building integrated energy system that considers demand response effectiveness requirements, comprising the following steps:

[0073] S1. Collect basic information from the building user side and the power grid side;

[0074] S2. Establish an optimized scheduling equipment model for the building's integrated energy system, including a photovoltaic power output model, a battery model, a water tank model, a passive building virtual energy storage model, and an electric chiller model;

[0075] S3. Determine the optimization problem of the building integrated energy system optimization scheduling model that considers the requirements of demand response effectiveness. The objective function of the optimization problem of the integrated energy system optimization scheduling model is to minimize the system operating cost. The optimization scheduling constraints include inequality constraints and equality constraints.

[0076] S4. Use mixed-integer linear programming algorithm to solve the optimization problem of the building integrated energy system optimization scheduling model, and obtain the optimal application capacity value scheme and operation strategy scheme.

[0077] Furthermore, the basic information on the building user side includes energy system equipment configuration information, building electricity load demand, meteorological data, personnel schedules, equipment usage schedules, etc.; the basic information on the grid side includes time-of-use electricity pricing information, invitation-based demand response incentive prices, response validity review mechanisms, etc.

[0078] Time-of-use pricing information generally includes off-peak pricing, normal pricing, and peak pricing.

[0079] Among them, the incentive price for invitation-based demand response is a fixed price, which subsidizes the user's hourly reduction during the response period.

[0080] The response effectiveness review mechanism refers to the power grid providing different levels of incentive subsidies or even penalties based on the actual response effect of users. Table 1 shows a typical peak-shaving demand response effectiveness review mechanism. Demand responses are categorized into effective responses, under-response, over-response, and ineffective responses based on the ratio of the user's actual response volume to the declared capacity. Under-response and over-response occur when the actual load response volume deviates from the effective range, and the power grid recognizes a lower effective response volume than the actual response volume, resulting in lower-than-expected incentive subsidies for the user. When an ineffective response occurs, the power grid cancels the incentive subsidies for the user and charges additional economic penalties. Based on the proposed assumptions, the key issue faced by building users participating in day-ahead invitation-based demand response is how to formulate the optimal response strategy and declared capacity. The goal is to achieve high-quality response results, thereby ensuring optimal system operating economy. R1, R2, and R3 are the response effectiveness range judgment coefficients. α1 and α2 are the effective response volume correction coefficients.

[0081] Table 1. Review Mechanism for the Effectiveness of Invitation-Based Peak Shaving Demand Response

[0082]

[0083] Furthermore, the photovoltaic output model in the building integrated energy system optimization scheduling equipment model is as follows:

[0084]

[0085] Where, N PV P represents the number of installed photovoltaic modules. PV_no This is the rated power of each photovoltaic module under test conditions, in kW; The solar radiation under the test conditions is expressed in W / m². 2 ;SR t The actual solar radiation at each moment, W / m 2 η PV Photovoltaic panel efficiency.

[0086] Furthermore, the battery model in the building integrated energy system optimization scheduling equipment model is as follows:

[0087] Q Ba，t =Q Ba,t-1 (1-α Ba,loss )+P BaI,t η BaI Δt-P BaO,t / η BaO Δt

[0088] Among them, Q Ba (t) represents the battery capacity at time t, in kWh; P Bal (t) and P BaO (t) represents the battery charging and discharging power, respectively, in kW; η BaI With η BaO These represent the battery charging and discharging efficiencies, respectively; α Ba,loss This represents the battery loss coefficient.

[0089] Furthermore, the water tank model in the building integrated energy system optimization scheduling equipment model is as follows:

[0090] Q Ws (t)=Q Ws (t-1)(1-α Ws,loss )+Q WsI (t)η WsI Δt-Q WsO (t) / η WsO Δt

[0091] Among them, Q Ws (t) represents the energy stored in the water tank at time t, in kWh; Q WsI (t) and Q WsO (t) represents the cold storage power and cold release power of the water tank, respectively, in kW; η WsI With η WsOThese represent the energy storage and energy release efficiencies of the water tank, respectively; α Ws,loss This represents the loss coefficient of the water storage tank.

[0092] Furthermore, the passive building virtual energy storage model in the building integrated energy system optimization scheduling equipment model is as follows:

[0093]

[0094] Among them, CL t To predict cooling load, kW. t-m This is the preliminary input data upon which the current cooling load depends. In this invention, the data for the ARX model includes dry-bulb temperature (TD), relative humidity (HD), solar irradiance (SR), occupancy schedule (OS), equipment schedule (ES), and indoor temperature schedule (RTS).

[0095] Furthermore, the electric chiller model in the building integrated energy system optimization scheduling equipment model is as follows:

[0096] P CC，t =Q CC，t / COP

[0097] Among them, P CC，t Q represents the chiller power, in kW. CC，t Cooling capacity of the refrigeration unit, kW. COP t This is the coefficient of performance (COP) of the chiller.

[0098] Furthermore, the objective function of the optimization problem in the integrated energy system optimal scheduling model is expressed as follows:

[0099]

[0100] Among them, P grid，t Let Pr be the tie-line power at time t. grid，t Let be the electricity price at time t, Δt be the event interval, and Ren be the incentive subsidy received by users participating in peak shaving demand response. Considering the grid's requirements for the effectiveness of user demand response, an optimized calculation method for the incentive subsidy (Ren) received by users participating in peak shaving demand response is proposed:

[0101]

[0102] Among them, Pr DR The price for peak-shaving demand response incentive subsidies is [amount] yuan / kW. ΔP eff，τ Let P be the effective load response at time τ, in kW. grid_base，τ Let P be the baseline power of the tie line at time τ, in kW. grid，τ The tie-line response power, kW, at time τ after the response strategy is executed. ΔP setThe user-reported capacity is in kW. ω is the penalty coefficient. R1, R2, and R3 are the coefficients for judging the validity interval of the response. α1 and α2 are the correction coefficients for the effective response quantity.

[0103] Furthermore, the inequality constraints include battery charging and discharging power constraints, battery available capacity constraints, water tank energy storage and release power constraints, water tank available capacity constraints, indoor temperature adjustable range constraints, and electric chiller available capacity constraints.

[0104] The battery charging and discharging power constraint expression is as follows:

[0105] P BaI (t)≤P BaI,max

[0106] P BaO (t)≤P BaO,max

[0107] Among them, P BaI,max With P BaO,max These represent the maximum charging and discharging power of the battery, in kW.

[0108] The expression for the available battery capacity constraint is:

[0109] Q Ba_no SOC Ba_min ≤Q Ba，t ≤Q Ba_no SOC Ba_max

[0110] Among them, Q Ba_no Rated battery capacity, kWh; SOC Ba_min With SOC Ba_max These represent the minimum and maximum battery states of charge, respectively.

[0111] The constraint expression for the energy storage and release power of the water storage tank is:

[0112] Q WsI (t)≤Q WsI,max

[0113] Q WsO (t)≤Q WsO,max

[0114] Among them, Q WsI,max With Q WsO，max These are the maximum cold storage capacity and maximum cold release capacity of the water storage tank, respectively, in kW.

[0115] The expression for the available capacity constraint of the water tank is:

[0116]

[0117] Among them, Q Ws_noRated capacity of the water storage tank, kW, SOC Ws_min With SOC Ws_max These represent the percentage of the minimum and maximum water storage tank capacity, respectively.

[0118] The constraint expression for the adjustable range of indoor temperature is:

[0119]

[0120] in, and RTS t These are the upper and lower limits of the acceptable indoor temperature for users during normal operating periods; and RTS_DR t These represent the upper and lower limits of the indoor temperature that users can accept during the demand response period.

[0121] The available capacity constraint expression for the electric chiller is:

[0122]

[0123] in, Q CC.t and These represent the upper and lower limits of the refrigeration capacity of the refrigeration unit, in kW.

[0124] Furthermore, the equality constraints include power balance constraints and thermal balance constraints.

[0125] The expression for the power balance constraint is:

[0126] P grid，t +P PV，t +P BaO，t =EL t +P BaI，t +P CC，t

[0127] Among them, EL t This represents the building's electrical load demand, excluding building cooling electrical load, in kW.

[0128] The thermal balance constraint expression is:

[0129] Q cc,t +Q WsO,t =CL t +Q WsI，t

[0130] Example:

[0131] The data used comes from the measured data of a building's integrated energy system from July 1, 2022 to December 31, 2022.

[0132] S1: Calibration Preparation: Collect basic information from the building user side and the power grid side;

[0133] The example described is an integrated energy system for an industrial building in Shenzhen. The target building primarily manufactures batteries and is located in a hot-summer, warm-winter region with no heating requirement throughout the year. An integrated energy system is installed within the industrial park to meet the building's electrical and cooling load demands. The energy system structure is as follows: Figure 2 As shown, it includes a variety of flexible resources such as photovoltaics, energy storage, cold storage, and building virtual energy storage.

[0134] Table 1 shows the energy system equipment configuration parameters of the embodiment. Information such as building electrical load demand, meteorological data, personnel schedules, and equipment usage schedules are included. Figure 3 As shown.

[0135] The time-of-use pricing information, the incentive price for invited demand response, and the response validity review mechanism in the basic grid-side information are shown in Table 2. The incentive subsidy method uses a fixed price subsidy; users receive a subsidy of 3.5 yuan for each 1kW load reduction at each response time. To avoid invalid response events, the penalty coefficient in Shenzhen is currently set at 0.6.

[0136] Table 1. Energy System Equipment Configuration Parameters for Examples

[0137]

[0138]

[0139] Table 2 Basic Information on the Power Grid Side

[0140]

[0141] S2. Establish an optimized scheduling equipment model for the building's integrated energy system, including a photovoltaic power output model, a battery model, a water tank model, a passive building virtual energy storage model, and an electric chiller model;

[0142] The performance parameters for the photovoltaic power output model, battery model, water tank model, and electric chiller model are set according to Table 1. EnergyPlus is used to build a building model of the research object to calculate the load data for training the ARX model. The input data for training and validating the ARX model is referenced in Table 1. Figure 3 The data settings are shown below. Hourly load data for July were calculated based on a fixed indoor temperature setting. 70% of the data was selected as the training dataset to identify ARX model parameters, and the remaining 30% was used to verify the model's prediction accuracy. Table 3 shows the results of ARX model parameter identification; the model accuracy verification result (CV) (RMSE) is 4.10%.

[0143] Table 3. Parameter representation of ARX model

[0144]

[0145] S3. Determine the optimization problem of the building integrated energy system optimization scheduling model that considers the requirements of demand response effectiveness. The objective function of the optimization problem of the integrated energy system optimization scheduling model is to minimize the system operating cost. The optimization scheduling constraints include inequality constraints and equality constraints.

[0146] The objective function for solving the optimization problem of the integrated energy system optimal scheduling model is expressed as follows:

[0147]

[0148]

[0149] The constraints on battery charging and discharging power, battery available capacity, water tank energy storage and release power, water tank available capacity, and electric chiller available capacity should be set according to Table 2. The indoor temperature adjustable range constraint is also included. RTS t The temperatures are 24℃ and 27℃, respectively. RTS_DR t The temperatures are 24℃ and 29℃ respectively.

[0150] S4. Use mixed-integer linear programming algorithm to solve the optimization problem of the building integrated energy system optimization scheduling model, and obtain the optimal application capacity value scheme and operation strategy scheme.

[0151] For the data boundaries in July, 31 demand response scenarios were set up based on different response start times and durations, such as... Figure 4 As shown. To better demonstrate the effectiveness of the method, two comparison schemes, the traditional scheme and the new scheme, are set up based on different methods for determining the declared capacity and the demand response scheme. At the same time, a baseline scheme is set up to obtain the user baseline load and serve as a benchmark for comparing the economics of the demand response strategy.

[0152] Baseline Scenario: This refers to the operational scenario where the user does not participate in invited demand response. In the baseline scenario, available flexible resources include photovoltaic (PV), active energy storage, and passive energy storage. The user still faces the problem of optimizing the dispatch strategy under time-of-use pricing boundaries. The optimization objective calculation method is as follows:

[0153]

[0154] Traditional approach: Demand response strategy is determined using traditional incentive subsidy calculation methods. This method assumes that all actual response quantities are effective, maximizing total energy reduction and yielding the corresponding ideal operating cost. Subsequently, to meet the information exchange requirements with the power grid, the average load reduction during the response period is used as the declared capacity. During the response review phase, the power grid determines the user incentive subsidy and actual operating cost based on the demand response effectiveness review mechanism. Optimized target calculation expression and declared capacity calculation expression:

[0155]

[0156] New solution: Employs the scheduling optimization method for the service building-grid interaction proposed in this invention. Considering the grid's requirements for the validity of user-reported information and responses, it collaboratively optimizes the demand response strategy and the value of the reported capacity.

[0157] Figure 5 This demonstrates the cost reduction rate of the traditional approach. Users can reduce operating costs by participating in peak-shaving demand response and receiving incentive subsidies. However, there are instances where the operating costs are higher than the baseline approach (without participating in incentive-based demand response). This is because the traditional method lacks constraints on the stability of hourly load reduction and neglects optimization of declared capacity, resulting in unsatisfactory actual user response. When a large number of invalid response events occur, the existence of penalty mechanisms can lead to actual user operating costs exceeding ideal operating costs, and even exceeding the baseline approach.

[0158] Figure 6 and Figure 7 The figures show the cost reduction rate of the new solution and its comparison with the traditional solution. It is clear that the proposed scheduling optimization method can further reduce the user's actual operating costs. This advantage becomes more pronounced as the response duration increases. When the response duration is 4 hours, the operating cost of the new solution is reduced by an average of 8% compared to the traditional solution.

[0159] Table 6 shows the percentage of different types of response times for typical monthly users. In a total response time of 91 hours, the proposed method increased the effective response time from 18.86% to 59.34%. On the one hand, this reduces user revenue loss and further lowers user operating costs; on the other hand, high-quality demand response behavior helps the power grid complete its designated peak-shaving tasks and alleviate supply-demand balance pressures.

[0160] Table 6. Percentage of Response Time for Different User Types under Traditional and New Plans in Typical Months

[0161]

[0162] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing operation strategy and declared capacity of a building integrated energy system considering demand response effectiveness requirement, characterized in that, Including the following steps: S1. Collect basic information from the building user side and the power grid side; S2. Establish an optimized scheduling equipment model for the building's integrated energy system, including a photovoltaic power output model, a battery model, a water tank model, a passive building virtual energy storage model, and an electric chiller model; S3. Introduce the declared capacity parameter to construct an effective response quantity calculation model and a demand response incentive subsidy calculation model that conform to the power grid side validity review mechanism; S4. Determine the optimization problem of the building integrated energy system optimization scheduling model considering the requirements of demand response effectiveness. The objective function of the optimization problem of the integrated energy system optimization scheduling model is to minimize the system operating cost, which includes two parts: time-of-use electricity price operating cost and demand response incentive revenue. The optimization scheduling inequality constraints include battery charging and discharging power constraints, battery available capacity constraints, water tank energy storage and release power constraints, water tank available capacity constraints, indoor temperature adjustable range constraints, and electric chiller available capacity constraints. The equality constraints include power balance constraints and heat balance constraints. S5. Use mixed-integer linear programming algorithm to solve the optimization problem of the building integrated energy system optimization scheduling model, and obtain the operation strategy scheme and the optimal application capacity value scheme.

2. The method of claim 1, wherein the method further comprises: The basic information on the building user side includes energy system equipment configuration information, building electricity load demand, meteorological data, personnel schedules, equipment usage schedules, etc.; the basic information on the grid side includes time-of-use pricing information, invitation-based demand response incentive prices, response validity review mechanisms, etc. 3.The method of claim 1, wherein, The photovoltaic output model described in the building integrated energy system optimization scheduling equipment model is: where N PV is the number of installed photovoltaic modules, P PV_no is the rated power of each photovoltaic module under test conditions, is the solar radiation under test conditions, SR t is the actual solar radiation at each time, η PV is the photovoltaic panel efficiency; The battery model is as follows: Q Ba,t = Q Ba，t-1 (1 - α Ba，loss ) + P BaI，t η BaI Δt - P BaO,t / η BaO Δt Among them, Q Ba (t) represents the battery capacity at time t, in kWh; P BaI (t) and P BaO (t) represents the battery charging and discharging power, respectively, and η represents the battery charging and discharging power. BaI With η BaO These represent the battery charging and discharging efficiencies, α. Ba,loss This refers to the battery loss coefficient. The water storage tank model is as follows: Q Ws (t) = Q Ws (t - 1)(1 - a Ws,loss )+ Q WsI (t) η WsI Δt - Q WsO (t) / η WsO Δt where Q Ws (t) is the energy stored in the tank at time t, Q WsI (t) and Q WsO (t) are the cold storage and release power of the tank, respectively, η WsI and η WsO are the energy storage and release efficiency of the tank, respectively, and a Ws,loss is the loss coefficient of the tank. The passive building virtual energy storage model is an autoregressive structure with external input, which represents the current cooling load as a weighted combination of past cooling load values ​​and ARX (a time frame based on cooling load as the target variable for prediction) model data: Among them, CL t To predict cooling load, m is the input lag order, and a l Here, l represents the regression coefficient, and u represents the lag order index of the autoregressive term. t-m The current cooling load depends on the preceding input data, where u is the set of external input variables, and b is the set of external input variables. m For input response coefficients; in this invention, the data of the ARX model includes dry bulb temperature (TD), relative humidity (RH), solar irradiance (SR), occupancy schedule (OS), equipment schedule (ES), and indoor temperature schedule (RTS). The electric chiller model is as follows: P CC，t = Q CC,t / COP where P CC，t is the power of the cold machine, Q CC,t is the cold machine refrigeration capacity, COP t is the refrigeration performance coefficient of the cold machine. 4.The method of claim 1, wherein, The effective response amount (ΔP eff，t ) depends on the actual hourly response amount of the user The closeness of the declared capacity ΔP set includes four types of demand response: invalid response, under-response, effective response, and over-response, each corresponding to a different effective response amount calculation method. Wherein, R1, R2, R3 are response effectiveness interval judgment coefficients, α1 is an under-response correction coefficient, and α2 is an over-response correction coefficient, P is a tie-line baseline power at time t grid，t ΔP is a tie-line response power at time t after the response strategy is executed set is a user declared capacity.

5. The method of claim 1, wherein the method further comprises: The incentive subsidy (Ren) includes a reward for an effective response amount ΔP eff，τ The economic subsidy is given and the economic penalty is taken for the ineffective response, and the calculation expression is: wherein Pr DR is the peak-cut demand response incentive subsidy price, ΔP eff，t is the effective load response amount at time t, P grid_base，t is the baseline power of the tie line at time t, P grid，t is the response power of the tie line at time t after the response strategy is executed, ΔP set is the user declared capacity, ω is the penalty coefficient, t_start is the start time of the peak-cut response day, t_end is the end time of the peak-cut response day, and α1 is the under-response correction coefficient. 6.The method of claim 1, wherein, The objective of solving the optimization problem of the integrated energy system optimal scheduling model is to minimize the total operating cost. The operating cost is calculated by subtracting the incentive subsidy (Ren) obtained by users participating in peak shaving demand response from the energy purchase cost. The optimization objective is expressed as follows: where P grid，t is the tie-line power at time t, Pr grid，t is the electricity price at time t, Δt is the time interval, and Ren is the incentive subsidy obtained by the user participating in peak clipping demand response.

7. The method of claim 1, wherein the method further comprises: The battery charging and discharging power constraint expression is as follows: P BaI (t)≤P BaI，max P BaO (t)≤P BaO,max where P BaI,max and P BaO,max are the maximum charging and discharging power of the battery, respectively. The expression for the available battery capacity constraint is: Q Ba_no SOC Ba_min ≤Q Ba,t ≤Q Ba_no SOC Ba_max where Q Ba_no is the battery rated capacity, SOC Ba_min is the state of charge of the battery, and Ba_max are the minimum and maximum battery state of charge, respectively. The constraint expression for the energy storage and release power of the water storage tank is: Q WsI (t)≤Q WsI,max Q WsO (t)≤Q WsO,max wherein Q WsI，max and Q WsO,max are the maximum cold storage and maximum cold release power of the water tank, respectively. The expression for the available capacity constraint of the water tank is: wherein Q Ws_no is the rated capacity of the water reservoir, SOC Ws_min is the state of charge of the water reservoir, and ws_mAX are the minimum and maximum water reservoir capacity fractions, respectively. The constraint expression for the adjustable range of indoor temperature is: wherein, and RTS t are, respectively, upper and lower limits of acceptable indoor temperature for the user during a regular runtime period, and RTS_DR t are, respectively, upper and lower limits of acceptable indoor temperature for the user during a demand response period. The available capacity constraint expression for the electric chiller is: wherein, Q CC.t with are the upper and lower limits of the cold machine refrigeration capacity, respectively. 8.The method of claim 1, wherein, The expression for the power balance constraint is: P grid，t +P PV，t +P BaO,t = EL t +P BaI，t +P CC,t P grid，t is the tie-line power at time t, P PV，t is the photovoltaic power output at time t, P BaO,t is the battery discharge power, P BaI，t is the battery charge power, P CC，t is the cold machine power, EL t is the building electrical load demand, excluding building refrigeration electrical load; The thermal balance constraint expression is: Q CC,t +Q WsO,t = CL t +Q WsI,t where Q CC,t is the cooling capacity of the cold machine, Q WsO，t is the release cooling power of the water tank, Q WsI,t is the storage cooling power of the water tank, CL t is the predicted cooling load.

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