Building energy system multi-type demand response operation strategy optimization method and system

Abstract

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building energy management and demand response optimization control, in particular to a building energy system multi-type demand response operation strategy optimization method and system. BACKGROUND

[0002] Demand side flexibility participating in demand response is an important means to relieve the pressure of power supply and demand balance at present. With the large-scale use of variable and intermittent renewable energy, the demand for demand side flexibility is also increasing. At the same time, with the continuous development of the electricity market, various interactive scenarios have gradually formed, such as price-based demand response represented by time-of-use electricity price and incentive-based demand response represented by peak shaving. Building energy systems include various types of flexible resources, such as distributed photovoltaic, battery, water storage tank, passive building virtual energy storage, etc., which are an important demand response participant. Determining the building energy system operation strategy and bidding strategy through dispatch optimization method is the key to exerting the flexibility of building energy.

[0003] The prior art gives an operation strategy optimization method in a time-of-use electricity price scenario and an operation strategy and bidding strategy optimization method in a peak shaving scenario, and understands the requirements of the power grid for demand response effectiveness.

[0004] However, the prior art focuses on operation strategy optimization in a single demand response market. In fact, building users can improve their economic benefits by participating in various types of demand response. However, the key information of different types of demand response markets is released at different times, such as the electricity price information in the time-of-use electricity price scenario is fixed information, while the invitation information in the peak shaving scenario is random information, which is randomly released only on the response day. Building users still face major challenges in how to allocate their flexibility to participate in various types of demand response markets. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that the existing building energy system operation optimization method has the problem of only facing a single type of demand response scenario, cannot simultaneously coordinate the resource conflict between time-of-use electricity price and invitation peak shaving, lacks a unified mechanism for system dispatch flexibility configuration, the flexible resource allocation response efficiency is low, and how to realize the dynamic regulation and control of the reserved capacity under the condition of unknown invitation frequency.

[0007] To solve the above technical problems, the application provides the following technical scheme: a building energy system multi-type demand response operation strategy optimization method, comprising collecting building user side multi-source information, establishing a building cold load prediction structure, incorporating an optimization scheduling model input system, and constructing a building energy system optimization scheduling equipment model; collecting power grid side information, based on the discrimination of next day power grid demand response type information, dynamically loading an optimization scheduling model facing a time-of-use price scenario and a peak shaving scenario; analyzing the optimization problem of a unified objective function in a double-type response scenario, introducing a power reservation coefficient based on the time-of-use price scenario, and controlling flexible resource retention; using a mixed integer linear programming algorithm to solve the multi-scenario optimization scheduling model, and generating corresponding strategies and schemes and power configuration plans.

[0008] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization method, the construction of the building energy system optimization scheduling equipment model comprises constructing a photovoltaic output model, collecting the number of installed photovoltaic components, determining the rated output power of each photovoltaic component under standard test conditions, introducing the solar radiation factor, and the photoelectric conversion efficiency of the photovoltaic component in actual operation; constructing a battery model, correcting the battery capacity at the last moment, combining the current charging increment and discharging consumption, and obtaining the current battery remaining capacity; constructing a water storage tank model, correcting the energy storage at the last moment, combining the current cold storage charging amount and cold release consumption, and obtaining the current water storage tank remaining energy storage; constructing a passive building virtual energy storage model, establishing an autoregressive external input structure, representing the current cold load as a weighted combination of past cold load values and ARX model data; and constructing an electric refrigerator model, deriving the power input according to the given moment refrigeration capacity of the refrigerator, and combining the refrigeration performance coefficient of the refrigerator.

[0009] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization method, the collection of power grid side information comprises extracting price distribution data and invitation peak shaving event related parameters, and dynamically loading a scene flag bit of a scheduling path according to the response type.

[0010] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization method, the dynamic loading of the optimization scheduling model facing the time-of-use price scenario and the peak shaving scenario comprises automatically switching the objective function and the power constraint strategy by identifying the trigger signal.

[0011] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization method, the power constraint strategy comprises setting the maximum power reservation proportion of the battery and the water storage tank based on the time-of-use price scenario, replacing to the peak shaving scenario, and removing the reservation proportion limit.

[0012] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization method, the analysis of the optimization problem of the unified objective function under the double-type response scenario comprises: based on the time-of-use electricity purchase cost and the subsidy and penalty function structure of the peak shaving response, a scheduling target expression is constructed.

[0013] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization method, the solving of the multi-scenario optimization scheduling model by using the mixed integer linear programming algorithm comprises: a multi-scenario optimization scheduling model initialization stage, a variable structure and a boundary condition are loaded, and an optimizer is called to generate scheduling instructions and response capacity configuration through iterative calculation.

[0014] Another object of the present application is to provide a building energy system multi-type demand response operation strategy optimization system, which can solve the problems of the existing building energy system that cannot uniformly process the time-of-use electricity price and the invited peak shaving response scheduling demand, the flexible resource power configuration conflict and the lack of response strategy switching mechanism by constructing a building side device model and a cold load prediction structure, identifying the response type and dynamically loading the corresponding optimization scheduling model, setting the power reservation coefficient to construct the unified objective function and solving by using the mixed integer linear programming algorithm.

[0015] As a preferred scheme of the building energy system multi-type demand response operation strategy optimization system, it comprises a building side modeling module, a response type identification and model loading module, a scheduling target construction and flexible constraint module, and an optimization solving and instruction generation module; the building side modeling module is used to collect building user side multi-source information, establish a building cold load prediction structure, and input into the optimization scheduling model input system, and construct a photovoltaic output model, a battery model, a water tank model, a passive building virtual energy storage model and an electric refrigerator model; the response type identification and model loading module is used to extract price distribution data and invited peak shaving event related parameters, and dynamically identify the trigger signal to automatically switch the objective function and the power constraint strategy; the scheduling target construction and flexible constraint module is used to analyze the optimization problem of the unified objective function under the double-type response scenario, set the maximum power reservation ratio of the battery and the water tank based on the time-of-use electricity price scenario, replace to the peak shaving scenario, and remove the reservation ratio limit; the optimization solving and instruction generation module is used to solve the multi-scenario optimization scheduling model by using the mixed integer linear programming algorithm, load the variable structure and the boundary condition, and generate the scheduling instructions and the response capacity configuration.

[0016] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the building energy system multi-type demand response operation strategy optimization method.

[0017] A computer readable storage medium, having stored thereon a computer program, the computer program being executed by a processor to implement steps of a building energy system multi-type demand response operation strategy optimization method.

[0018] The building energy system multi-type demand response operation strategy optimization method provided by the application introduces a power reservation coefficient to constrain the power of the battery and the water tank in the time-of-use electricity price scenario, can coordinate the operation boundary of flexible resources in multiple scenarios, and realizes strategy implementation without predicting the frequency of peak shaving events by constructing a power constraint strategy. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0020] Figure 1 The building energy system multi-type demand response operation strategy optimization method provided by the first embodiment of the application is shown in the overall flowchart.

[0021] Figure 2 The power reservation coefficient recommended value comparison result graph of the building energy system multi-type demand response operation strategy optimization method provided by the second embodiment of the application is shown in the graph.

[0022] Figure 3 The new scheme and the benchmark scheme building user reported response amount optimization result graph of the building energy system multi-type demand response operation strategy optimization method provided by the second embodiment of the application is shown in the graph.

[0023] Figure 4 The new scheme compared with the benchmark scheme running cost change level summary graph of the building energy system multi-type demand response operation strategy optimization method provided by the second embodiment of the application is shown in the graph.

[0024] Figure 5 The overall schematic diagram of the building energy system multi-type demand response operation strategy optimization system provided by the third embodiment of the application is shown in the graph. DETAILED DESCRIPTION

[0025] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0026] Embodiment 1, reference Figure 1 For an embodiment of the present application, a building energy system multi-type demand response operation strategy optimization method is provided, comprising:

[0027] S1: Collecting building user side multi-source information, establishing a building cold load prediction structure, incorporating into an optimization scheduling model input system, and constructing a building energy system optimization scheduling equipment model.

[0028] Further, collecting building user side multi-source information includes synchronously collecting real-time input parameters and static input parameters required for photovoltaic components, battery equipment, water storage tanks and passive building virtual energy storage models in the building.

[0029] Further, the real-time input parameters include battery state of charge, water storage tank energy level, photovoltaic output and indoor temperature; the static input parameters include component specifications, installation quantity and capacity constraints; the cold load prediction relies on an ARX (time frame based on cold load as prediction target variable) model, and the input covers dry bulb temperature, relative humidity, solar radiation and personnel and equipment time sequence table, realizes unified coding of building internal dynamic response characteristics and equipment state, and provides accurate time sequence input basis for subsequent multi-scenario scheduling model.

[0030] It should be noted that constructing the building energy system optimization scheduling equipment model includes constructing a photovoltaic output model, collecting the number of installed photovoltaic components, determining the rated output power of each photovoltaic component under standard test conditions, introducing the solar radiation factor, and the photoelectric conversion efficiency of the photovoltaic component in actual operation; constructing a battery model, correcting the capacity of the battery at the last moment, combining the charging increment and discharging consumption at the current moment, and obtaining the current battery remaining capacity; constructing a water storage tank model, correcting the energy storage at the last moment, combining the cold storage charging amount and cold release consumption at the current moment, and obtaining the current water storage tank remaining energy storage; constructing a passive building virtual energy storage model, establishing an autoregressive external input structure, representing the current cold load as a weighted combination of past cold load values and ARX model data; constructing an electric refrigerator model, according to the given cold machine refrigerating capacity, combining the refrigeration performance coefficient of the cold machine, and deducing the power input.

[0031] It should be noted that a preferred scheme of the photovoltaic output model is specifically represented as:

[0032]

[0033] where P PV (t) is the photovoltaic power output capacity at time t, 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 time t, η PV is the photovoltaic panel efficiency, a preferred solution of the battery model is specifically expressed as:

[0034] Q Ba (t) = Q Ba (t-1)(1-α Ba,loss )+P BaI (t)η BaI Δt-P BaO (t) / η BaO Δt

[0035] where Q Ba (t) is the battery capacity at time t, Q Ba (t-1) is the battery capacity at time t-1, P BaI (t) is the battery charging power, P BaO (t) is the battery discharging power, η BaI is the battery charging efficiency, η BaO is the battery discharging efficiency, α Ba,loss is the battery loss coefficient, and Δt is the time interval, a preferred solution of the water tank model is specifically expressed as:

[0036] Q Ws (t) = Q Ws (t-1)(1-α Ws,loss )+Q WsI (t)η WsI Δt-Q WsO (t) / η WsO Δt

[0037] where Q Ws (t) is the energy stored in the water tank at time t, Q Ws (t-1) is the energy stored in the water tank at time t-1, Q WsI (t) is the cold storage power of the water tank, Q WsO (t) is the cold release power of the water tank, η WsI is the energy storage of the water tank, η WsO is the energy release efficiency, and α Ws,lossFor the water storage tank loss efficiency, the passive building virtual energy storage model is a calculation structure with cold load prediction as the core. A preferred scheme of the passive building virtual energy storage model is specifically represented as:

[0038]

[0039] Wherein, CL(t) is a predicted cold load, m is an input lag order, a l is a regression coefficient, l is a lag order index of an autoregressive term, u(t-m) is a previous input data on which the current cold load depends, u is a set of external input variables, b m is an input response coefficient. In the present application, the data of the ARX model includes dry bulb temperature TD, relative humidity RH, solar radiation SR, personnel occupancy schedule OS, equipment schedule ES and indoor temperature RT. A preferred scheme of the electric chiller model is specifically represented as:

[0040] P CC (t) = Q CC (t) / COP

[0041] Wherein, P CC (t) is a chiller power, Q CC (t) is a chiller refrigeration capacity, and COP is a refrigeration performance coefficient of the chiller.

[0042] S2: Collect grid side information, based on the discrimination of next day grid demand response type information, dynamically load the optimization scheduling model facing the time-of-use price scene and the peak shaving scene.

[0043] Further, the collection of grid side information includes extracting price distribution data and invitation peak shaving event related parameters, and dynamically loading scene flag bits of scheduling paths according to response types.

[0044] Further, the price distribution data and the invitation peak shaving event related parameters include time-of-use price and peak shaving, price level and distribution period, start time, duration, subsidy price, effective interval and penalty coefficient.

[0045] It should be noted that the dynamic loading of the optimization scheduling model facing the time-of-use price scene and the peak shaving scene includes automatically switching the objective function and the power constraint strategy through the identification of the trigger signal.

[0046] It should be noted that the automatic switching of the objective function and the power constraint strategy through the identification of the trigger signal includes judging whether there is a peak shaving response event according to the received grid signal, determining the next day demand response operation strategy optimization scheduling type, executing the building energy system optimization scheduling facing the time-of-use price when the information of the next day grid not initiating the peak shaving demand response is received before the day, and executing the building energy system optimization scheduling facing the peak shaving when the information of the next day grid initiating the peak shaving is received before the day.

[0047] It should be noted that the power constraint strategy includes setting the maximum power reservation ratio of the battery and the water tank based on the time-of-use electricity price scenario, and changing to the peak shaving scenario to release the reservation ratio limit.

[0048] It should be noted that setting the maximum power reservation ratio of the battery and the water tank includes introducing a power reservation coefficient α in the battery and water tank constraints to avoid all flexible resources being used in advance in the time-of-use electricity price scenario, and the maximum discharge and cold release power is constrained to be the product of the rated upper limit and 1-α, α∈(0, 1); after switching to the peak shaving scenario, the power reservation coefficient is fixed to 0, the power upper limit constraint is cancelled, and the energy storage device can release full power in the response period.

[0049] S3: Analyze the optimization problem of the unified objective function in the double-type response scenario, introduce a power reservation coefficient based on the time-of-use electricity price scenario, and control the flexible resource retention.

[0050] Further, analyzing the optimization problem of the unified objective function in the double-type response scenario includes constructing a dispatch target expression based on the time-of-use electricity purchase cost and the penalty function structure of the peak shaving response subsidy.

[0051] It should be noted that constructing the dispatch target expression includes based on the optimization dispatch constraint condition, the optimization solving problem of the building energy system optimization dispatch model for the time-of-use electricity price is to minimize the time-of-use electricity price daily operation cost, and the optimization target calculation expression is:

[0052]

[0053] Wherein, opt TOU (d TOU ) is the minimum time-of-use scenario daily operation cost, P grid (d TOU , t) is the time-of-use electricity price daily t time connection line power, Pr(t) is the t time electricity price, and Δt is the time interval; the optimization solving problem of the building energy system optimization dispatch model for the peak shaving is to minimize the peak shaving daily operation cost, and the peak shaving daily operation cost calculation method is the energy purchase cost minus the incentive subsidy (Sub) obtained by the user participating in the peak shaving demand response, including two parts of giving economic subsidies for effective response amount ΔP eff (t) and taking economic penalties for ineffective response, which is expressed as:

[0054]

[0055] Wherein, Pr DR is the peak shaving demand response incentive subsidy price, P represents the baseline power of the tie line at time t on the peak shaving response day, where t_start is the start time of the peak shaving response day, t_end is the end time of the peak shaving response day, and P is the baseline power of the tie line at time t. grid (t) represents the tie-line response power at time t after implementing the peak-shaving response strategy, ΔP set For the user's declared capacity, ω is the penalty coefficient, and ΔP eff (t) represents the effective response at time t, specifically the effective response ΔP. eff The magnitude of (t) depends on the user's hourly actual response quantity ΔP. act (t) and the declared capacity ΔP set The degree of proximity is categorized into four types of demand response: ineffective response, under-response, effective response, and over-response, each corresponding to a different calculation method for the effective response quantity. The calculation method for the effective response quantity is as follows:

[0056]

[0057] Where R1, R2, and R3 are the judgment coefficients for the response validity interval, and t start For peak-shaving demand response start time, t end The peak-shaving demand response end time, ΔP act (t) represents the user's actual hourly response volume. To reduce the peak response, the baseline power of the tie line at time t is ΔP. dec To calculate the peak load reduction target, the optimized target calculation expression is as follows:

[0058]

[0059] Among them, opt PS (d PS To minimize daily operating costs in peak shaving scenarios, P grid (d PS ,t) represents the tie-line power at time t on the peak shaving day, Pr(t) represents the electricity price at time t, Δt represents the time interval, and Sub represents the incentive subsidy received by users for participating in peak shaving demand response.

[0060] It should also be noted that the optimization scheduling constraints include both inequality constraints and equality constraints.

[0061] It should also be noted that the inequality constraints include battery charging and discharging power constraints, expressed as:

[0062] P BaI (t)≤P BaI,max

[0063] P BaO (t)≤(1-α)P BaO,max

[0064] Among them, P BaI,maxTo maximize battery charge, P BaO,max Let α be the battery discharge power, and α be the power reserve coefficient, which ranges from (0, 1) on time-of-use pricing days and is a fixed value of 0 on peak-shaving days. The available battery capacity is constrained and expressed as:

[0065] Q Ba_no SOC Ba_min ≤Q Ba (t)≤Q Ba_no SOC Ba_max

[0066] Among them, Q Ba_no For the battery's rated capacity, SOC Ba_min At minimum battery state of charge, SOC Ba_max The energy storage and release power constraint of the water tank at the maximum battery state of charge is expressed as:

[0067] Q WsI (t)≤Q WsI,max

[0068] Q WsO (t)≤(1-α)Q WsO,max

[0069] Among them, Q WsI,max Q represents the maximum cold storage capacity of the water storage tank. WsO,max The maximum cooling capacity of the water storage tank, constrained by the available capacity of the water tank, is expressed as:

[0070] Q Ws_no SOC Ws_min ≤Q Ws (t)≤Q Ws_no SOC Ws_max

[0071] Among them, Q Ws_no For the rated capacity of the water tank, SOC Ws_min SOC is the minimum water tank capacity percentage. Ws_max The maximum water tank capacity percentage, constrained by the adjustable indoor temperature range, is expressed as:

[0072] RT(t) min ≤RT(t)≤RT(t) max

[0073] 2.5 ≤ RT(t+1) - RT(t) ≤ 2.5

[0074] Where, RT(t) min RT(t) represents the upper limit of the acceptable indoor temperature for the user during the operating period, and RT(t+1) represents the acceptable indoor temperature for the user in the next operating period. maxis the upper limit of the indoor temperature acceptable by the user during the operation period, the available capacity of the electric chiller, represented as:

[0075]

[0076] wherein, Q CC (t) is the lower limit of the chiller cooling capacity, is the upper limit of the chiller cooling capacity; the equation constraint includes the power balance constraint, represented as:

[0077] P grid (t) + P PV (t) + P BaO (t) = EL(t) + P BaI (t) + P CC (t)

[0078] wherein, P grid (t) is the tie-line power at time t of the time-of-use electricity price, P PV (t) is the photovoltaic output capacity at time t, EL(t) is the building electrical load demand, P BaI (t) is the battery charging power, P BaO (t) is the battery discharging power, excluding the building refrigeration electrical load, P CC (t) is the chiller power, the heat balance constraint, represented as:

[0079] Q CC (t) + Q WSO (t) = CL(t) + Q WSI (t)

[0080] wherein, Q CC (t) is the upper and lower limit of the chiller cooling capacity, Q WSO (t) is the cold release power of the water storage tank, Q WSI (t) is the cold storage power of the water storage tank, and CL(t) is the predicted cooling load.

[0081] S4: a mixed integer linear programming algorithm is used to solve the multi-scenario optimization scheduling model to generate corresponding strategies and schemes and power configuration plans.

[0082] Further, the mixed integer linear programming algorithm is used to solve the multi-scenario optimization scheduling model, including a multi-scenario optimization scheduling model initialization stage, loading variable structures and boundary conditions, and calling an optimizer to generate scheduling instructions and response capacity configurations through iteration.

[0083] It should be noted that the multi-scenario optimization scheduling model initialization stage includes structured definition of scheduling variables, covering photovoltaic output, battery charging and discharging power, water tank energy storage and release power, cold load supply power and declared capacity variables; at the same time, the upper and lower limit constraints of each type of variable are set, and the equality constraints and inequality constraints are loaded to ensure the feasibility of the scheduling solution.

[0084] It should also be noted that the corresponding strategy and scheme and power configuration plan include the optimal operation strategy for the time-of-use electricity price scenario; the optimal operation strategy for the peak shaving scenario; the declared capacity configuration plan and the corresponding scheduling control instruction generated when the peak shaving response exists, realizing the structured expression of the response capability of the building user side.

[0085] Embodiment 2, refer to Figures 2-4 For an embodiment of the present application, a building energy system multi-type demand response operation strategy optimization method is provided, in order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiment are carried out for scientific demonstration.

[0086] The scene analysis method is adopted, different peak shaving event frequency mixed demand response scenes are constructed, the effectiveness of the method is verified, and finally the recommended value of the power reservation coefficient under different frequencies is given.

[0087] As shown in Table 1, the experimental mixed scene is constructed:

[0088] Table 1 Mixed scene data table

[0089]

[0090] The experimental scheme involves:

[0091] Baseline scheme: the building user only participates in the time-of-use electricity price scenario.

[0092] Optimization scheme: the scheduling optimization method proposed in the present application, and three typical power reservation coefficient value schemes are set:

[0093] α=0.2

[0094] α=0.5

[0095] α=0.8

[0096] The comparison results are referred to Figure 2 According to the comparison results, the recommended value scheme of the power reservation coefficient in the time-of-use electricity price scenario can be given, as shown in Table 2:

[0097] Table 2 Recommended value table of power reservation coefficient in time-of-use electricity price scenario

[0098] Serial number Peak clipping event frequency Recommended value 1 1:30～1:9 0.2 2 1:9～1:3 0.5 3 >1:3 0.8

[0099] The data used is from the measured data of a building comprehensive energy system from 2023.07.01 to 2023.07.06, the building energy system of the example object of an industrial park, the park is located in a hot summer and warm winter area, only building electricity load and building cooling load demand exist, which are supplied by a set of building energy system, and the energy system structure includes photovoltaic, electricity storage, cold storage, building virtual energy storage and various flexible resources.

[0100] The specific situation of the next day demand response type in the grid side information collected in the experiment is that the first to fifth days are time-of-use price scenarios, and the sixth day is a peak shaving scenario; the price level and distributed time period in the time-of-use price information, and the peak shaving demand response incentive subsidy price, response effectiveness interval judgment coefficient and penalty coefficient in the peak shaving information are shown in Table 3. The starting time of the peak shaving response is 12:00, and the duration is 2h.

[0101] Table 3 Grid side basic information table

[0102]

[0103] The core innovation of the application is to introduce a power reservation coefficient to constrain the operation of the battery and the water tank in the time-of-use price scenario, thereby reserving peak shaving capacity for potential peak shaving scenarios. In order to better demonstrate the effectiveness of the application, three comparison schemes are set for the power reservation coefficient, i.e. 0.2, 0.5 and 0.8. At the same time, a benchmark scheme is set, i.e. the power reservation coefficient in the time-of-use price scenario is set to 0 for comparison with the advancement of the application.

[0104] Figure 3 The display is the declared response amount under different value schemes of the power reservation coefficient. The results of the verification in the mixed scenario with an invitation peak shaving event frequency of 1:5 show that the traditional method without power reservation can only provide a load reduction of 24kW during the demand response period when facing a sudden peak shaving response event. Using the method proposed in the application, when the power reservation coefficient is 0.2, 0.5 and 0.8, the declared response amount is increased to 103kW, 235kW and 472kW respectively. The actual load reduction capacity provided by the building user is significantly improved.

[0105] Figure 4The operation cost growth rate of the method is compared with the benchmark scheme. In the mixed scene of the invitation peak shaving event frequency of 1:5, with the increase of the power reservation correction coefficient, the operation cost of the time-of-use electricity price scene increases, and the operation cost of the building integrated energy system in the peak shaving response day decreases. According to the calculation, when the power reservation correction coefficient is 0.2, 0.5 and 0.8, the average value of the operation cost growth rate of the time-of-use electricity price scene is 0.1%, 0.58% and 2.4% respectively, and the operation cost growth rate of the peak shaving scene is-4.3%, -10.4% and-16.5% respectively. In the multi-type demand response scene, the economic benefit of the building integrated energy system is best when the power reservation coefficient is 0.5, and the total operation cost is reduced by 1.3% compared with the traditional method. The present application can improve the overall economic benefit of the building user in participating in the multi-type demand response market.

[0106] Embodiment 3, refer to Figure 5 For an embodiment of the present application, a building energy system multi-type demand response operation strategy optimization system is provided, comprising a building side modeling module 100, a response type identification and model loading module 200, a scheduling target construction and flexible constraint module 300, and an optimization solving and instruction generation module 400.

[0107] X1: The building side modeling module 100 comprises a device modeling sub-module 101 and a virtual energy storage and cold load prediction sub-module 102.

[0108] It should be noted that the device modeling sub-module 101 is used to obtain the power output of various energy devices in the building according to the component quantity, power and energy efficiency parameters, and to determine the operation constraints and boundary conditions of each device; the virtual energy storage and cold load prediction sub-module 102 establishes a load prediction structure based on the past cold load and external input structure, and constructs a building cold load prediction structure.

[0109] It should also be noted that the device modeling sub-module 101 provides physical device data modeling, and the virtual energy storage and cold load prediction sub-module 102 provides cold load time series prediction, and the two sub-modules are processed in parallel to become constraint sources.

[0110] X2: The response type identification and model loading module 200 comprises a response parameter extraction sub-module 201 and a scene flag discrimination and model switching sub-module 202.

[0111] It should be noted that the response parameter extraction sub-module 201 is used to obtain index data such as time-of-use electricity price, peak shaving event subsidy, start and end time, penalty coefficient, etc., as the basis for scene identification; the scene flag discrimination and model switching sub-module 202 is used to identify whether it is a peak shaving event, control the switching of the scheduling model, and determine the scheduling model path to be executed.

[0112] It should be noted that the response parameter extraction submodule 201 obtains the required parameters of the scene to provide the scene sign discrimination and model switching submodule 202 for scheduling model switching.

[0113] X3: The scheduling target construction and flexible constraint module 300 includes a unified target function construction submodule 301 and a power reservation constraint configuration submodule 302.

[0114] It should be noted that the unified target function construction submodule 301 is used to uniformly process the time-of-use electricity purchase cost and the subsidy and penalty function structure of the peak shaving response, and to construct a scheduling target expression; the power reservation constraint configuration submodule 302 is used to limit the output of flexible resources through a power reservation coefficient, and to reserve peak shaving capacity.

[0115] It should be noted that the unified target function construction submodule 301 and the power reservation constraint configuration submodule 302 are matched and processed in parallel to optimize the problem target.

[0116] X4: The optimization solving and instruction generation module 400 includes a model initialization and optimizer calling submodule 401 and a strategy output and declaration generation submodule 402.

[0117] It should be noted that the model initialization and optimizer calling submodule 401 is used to load variable structures and boundary conditions, to call an optimizer to generate scheduling instructions and response capacity configurations in a time-by-time iteration, and to set upper and lower limit constraints; the strategy output and declaration generation submodule 402 is used to output time-of-use electricity operation strategies, peak shaving operation strategies, and response declaration control plans.

[0118] It should be noted that the initialization and optimizer calling submodule 401 provides solving results for the strategy output and declaration generation submodule 402 to organize and form operation strategies and declaration instructions.

[0119] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0120] The logic and / or steps represented in the flowcharts and / or otherwise described herein, for example, can be considered as a sequence of instructions executed in an order to achieve a logic function, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0121] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In the context of this document, a "computer-readable medium" can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can have a transient or transient nature, for example, transitory signals.

[0122] It should be understood that each part of the present application can be realized by hardware, software, firmware or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be realized by software or firmware stored in the memory and executed by a suitable instruction execution system. For example, if realized by hardware, and as in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuit with logic gate circuit for implementing logic functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing multi-type demand response operation strategy of a building energy system, characterized in that, The method comprises the following steps: Collecting building user side multi-source information, establishing building cold load prediction structure, incorporating into optimization scheduling model input system, and constructing building energy system optimization scheduling equipment model; Collecting power grid side information, based on the discrimination of next day power grid demand response type information, dynamically loading optimization scheduling model facing time-of-use price scene and peak shaving scene; Analyzing optimization problem of unified objective function in double type response scene, introducing power reservation coefficient based on time-of-use price scene, and controlling flexible resource reservation; Using mixed integer linear programming algorithm to solve multi-scene optimization scheduling model, and generating corresponding strategy and scheme and power configuration plan. 2.The method of claim 1, wherein: The construction of the building energy system optimization scheduling equipment model comprises: Constructing photovoltaic output model, collecting installed photovoltaic component quantity, determining rated output power of each photovoltaic component under standard test condition, introducing solar radiation factor, and light-electric conversion efficiency of photovoltaic component in actual operation; Constructing battery model, correcting battery capacity at last moment, combining current charging increment and discharging consumption, and obtaining current battery residual capacity; Constructing water storage tank model, correcting energy storage at last moment, combining current cold storage charging amount and cold release consumption, and obtaining residual energy storage of the water storage tank at the current moment; Constructing passive building virtual energy storage model, establishing autoregressive structure with external input, and representing current cold load as weighted combination of past cold load values and ARX model data; Constructing electric refrigerator model, combining refrigeration capacity of the electric refrigerator at a given moment, and deducing power input according to the refrigeration performance coefficient of the electric refrigerator. 3.The method of claim 2, wherein: The collection of power grid side information comprises: Extracting price distribution data and invitation peak shaving event related parameters, and dynamically loading scene flag bit of scheduling path according to response type. 4.The method of claim 3, wherein: The dynamic loading of optimization scheduling model facing time-of-use price scene and peak shaving scene comprises: Switching target function and power constraint strategy automatically through identification of trigger signal.

5. The method of claim 4, wherein: The power constraint strategy comprises: Based on time-of-use price scene, setting maximum power reservation proportion of the battery and the water storage tank, replacing to peak shaving scene, and removing the reservation proportion limit. 6.The method of claim 5, wherein: The analysis of optimization problem of unified objective function in double type response scene comprises: Based on time-of-use price purchase cost and peak shaving response subsidy and penalty function structure, constructing scheduling target expression.

7. The method of claim 6, wherein: The multi-scene optimization scheduling model solving by using mixed integer linear programming algorithm comprises: Multi-scene optimization scheduling model initialization stage, loading variable structure and boundary condition, and calling optimizer to generate scheduling instruction and response capacity configuration through iteration.

8. A building energy system multi-type demand response operation strategy optimization system, adopting the building energy system multi-type demand response operation strategy optimization method according to any one of claims 1-7, characterized in that: The method comprises a building side modeling module (100), a response type identification and model loading module (200), a scheduling target construction and flexible constraint module (300), and an optimization solving and instruction generation module (400); The building side modeling module (100) is used for collecting building user side multi-source information, establishing building cold load prediction structure, incorporating into optimization scheduling model input system, and constructing photovoltaic output model, battery model, water storage tank model, passive building virtual energy storage model and electric refrigerator model. The response type identification and model loading module (200) is used to extract the electricity price distribution data and the invitation peak shaving event related parameters, and dynamically identify the trigger signal to automatically switch the objective function and the power constraint strategy; The scheduling target construction and flexible constraint module (300) is used to analyze the optimization problem of the unified objective function in the double type response scene, set the maximum power reservation ratio of the battery and the water tank based on the time-of-use electricity price scene, change to the peak shaving scene, and remove the reservation ratio limit; The optimization solving and instruction generation module (400) is used to solve the multi-scene optimization scheduling model by using a mixed integer linear programming algorithm, load the variable structure and the boundary condition, and generate the scheduling instruction and the response capacity configuration. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the building energy system multi-type demand response operation strategy optimization method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the building energy system multi-type demand response operation strategy optimization method in any one of claims 1 to 7.

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