Planning method of commercial building hybrid energy storage system considering virtual energy storage effect

Abstract

This invention discloses a planning method for a commercial building hybrid energy storage system that considers the virtual energy storage effect. The method includes the following steps: equating the variable-frequency air conditioning-building system to a first-order circuit, establishing differential equations for first-order equivalent thermal parameters, and then constructing calculation models for virtual charge and discharge power and virtual energy storage capacity; constructing discharge loss models for new and old energy storage batteries at different depths of discharge; establishing an upper-level planning model, solving it using a particle swarm algorithm, and transferring decision variables to a lower-level operation model; establishing a lower-level operation model, using a Cplex solver to determine the new and old battery energy storage output and air conditioning operating power, and returning the calculated results to the upper-level planning model. This method considers the variable-frequency air conditioning-building system as virtual energy storage in the daily scheduling and operation of commercial buildings. While maintaining indoor thermal comfort, the system adjusts the air conditioning operating power and smoothes the air conditioning power curve, thereby reducing the pressure on the user-side physical energy storage operation.

Description

Technical Field

[0001] The present invention relates to a commercial building hybrid energy storage system planning method considering virtual energy storage effect. Background Art

[0002] Urban commercial buildings integrate a large number of office equipment, commercial lighting, air conditioning systems, and various other electrical utilities. In recent years, with industrial restructuring and improved living standards, winter and summer peak loads have continued to grow, and the peak-to-valley difference has continued to widen, posing severe challenges to the stable supply of urban electricity. Deploying battery energy storage systems in commercial buildings to provide peak-to-valley load shaving services can effectively reduce building peak loads, alleviate pressure on the distribution network, and lower building operating costs. However, current high prices for new batteries, coupled with a single business model and low return on investment, hinder widespread adoption on the demand side. Decommissioned automotive power batteries still have 80% or more of remaining capacity, allowing them to be reused in other scenarios with lower performance requirements, such as behind-the-meter energy storage, microgrids, lighting, and backup power.

[0003] On the other hand, the construction industry is a major contributor to carbon emissions in China. Air conditioning accounts for over 50% of building energy consumption. To alleviate the pressure on commercial buildings to supply air conditioning loads, State Grid Corporation of China has pioneered a pilot program in Ningbo to implement air conditioning load regulation and control systems for commercial buildings. This regulation utilizes the thermal inertia of the building envelope, allowing air conditioning operating power to fluctuate within a certain range. This approach considers the variable-frequency air conditioning (VVAC)-building system to have battery-like charging and discharging capabilities, treating it as a virtual energy storage resource for scheduling. However, most existing research focuses on VVAC demand response while ignoring the impact of its inherent regulation potential as virtual energy storage on operating costs. This results in a certain degree of resource waste in user-side energy storage planning schemes based on this approach. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a commercial building hybrid energy storage system planning method that takes into account the virtual energy storage effect with a simple algorithm.

[0005] The technical solution of the present invention to solve the above technical problems is: a commercial building hybrid energy storage system planning method considering virtual energy storage effect, comprising the following steps:

[0006] S1: New energy storage batteries are assembled into a centralized energy storage system to assist in peak shaving and valley filling of the base load. Screened old energy storage batteries are assembled into a distributed energy storage system to connect to the air conditioning load. The variable frequency air conditioning-building system is equated to a first-order circuit. Based on this, a differential equation for the first-order equivalent thermal parameters is established, and then a calculation model for virtual charging and discharging power and virtual energy storage capacity is constructed.

[0007] S2: Use the Bacon-watts model to describe the inflection point of energy storage battery capacity degradation, and then use exponential functions to build discharge loss models for new and old energy storage batteries at different discharge depths.

[0008] S3: Considering investment cost constraints, the installed capacity of new / old energy storage batteries and the number of energy storage replacements within the full planning period are used as optimization variables. An upper-level planning model is established, and the decision variables are passed to the lower-level operation model after solving them using the particle swarm algorithm.

[0009] S4: Considering building power balance constraints, indoor temperature constraints, virtual energy storage operation constraints, new / old battery energy storage system operation constraints, and tie line constraints, with the goal of minimizing the building's annual operating cost, a lower-level operation model is established. The Cplex solver is used to determine the new / old battery energy storage output and air conditioning operating power, and the calculated results are returned to the upper-level planning model.

[0010] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, in step S1, the differential equation of the first-order equivalent thermal parameter is established as follows:

[0011]

[0012] Where: is the indoor temperature of the kth area in the ε scene at the tth moment; T out,t,ε is the outdoor temperature at time t in the ε scenario; R j is the equivalent thermal resistance of the building; C s is the equivalent heat capacity of the building; is the air conditioning heating / cooling capacity of the kth zone at time t in the ε scenario. Its value is positive when heating and negative when cooling;

[0013] The relationship between the air conditioner's electrical power and the heat it releases is as follows:

[0014]

[0015] Where: is the air conditioning power of the kth area at time t in the ε scenario, also called dynamic power when calculating virtual energy storage parameters; η is the air conditioning energy efficiency ratio; is the frequency of the variable frequency air conditioner in the kth area at time t in the ε scenario; m and n are two coefficients that characterize the relationship between the frequency and power of the variable frequency air conditioner; T set Set the temperature for the air conditioner; T min,ε 、T max,ε are the lower and upper limits of the thermal comfort temperature range for human body in the ε scenario, respectively.

[0016] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, in step S1, the process of constructing a calculation model of virtual charging and discharging power and virtual energy storage capacity is as follows:

[0017] When the indoor temperature is constant, the heat released by the air conditioner to the building is equal to the heat transferred from the building to the outside world. At this time, the power required by the air conditioner is is the steady-state power, which is calculated as follows:

[0018]

[0019] Where: is the heat exchanged between the building interior and the outside world at the kth area in the ε scenario at the tth moment;

[0020] The indoor temperature changes from the initial temperature T set0 Increase or decrease to T set , increase or decrease the energy Q k as follows:

[0021]

[0022] Where: is the starting temperature of the kth region within a period of time; is the termination temperature of the kth region within a period of time;

[0023] The equivalent virtual energy storage capacity E of a single variable frequency air conditioning-building system in the kth area at the tth time in the ε scenario t,ε (k) and rated capacity E N,ε The expression of (k) is as follows:

[0024]

[0025] Equivalent virtual energy storage charging and discharging power of the kth region in the ε scenario is the difference between the dynamic power and steady-state power of the air conditioner, and the calculation formula is as follows:

[0026]

[0027] The capacity of the entire building's virtual energy storage and charge and discharge power It is formed by the accumulation of the virtual energy storage capacity and charge and discharge power of a single area. Its model is as follows:

[0028]

[0029] Where: G is the total number of air conditioners participating in virtual energy storage control in the building at time t, and g is the number of the air conditioner participating in virtual energy storage control.

[0030] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, the specific process of step S2 is as follows:

[0031] Assuming that the battery is about to be eliminated when the number of cycles reaches the inflection point of capacity decline, the battery inflection point is identified by the Bacon-watts model:

[0032] F=α0+α1(xn knee )+α2(xn knee )×tanh[(xn knee ) / γ]+Z

[0033] Where: F is the battery capacity retention rate, γ is the battery life cycle fitting parameter; α0 is the capacity retention rate when the battery life reaches the inflection point; α1 and α2 are the parameters of the two fitting curves in the Bacon-watts model; Z is the error amount that conforms to the standard normal distribution; n knee is the number of cycles at the inflection point; x is the number of cycles of the battery;

[0034] The centralized energy storage system is a new battery energy storage system. The calculation method for the total discharge capacity of the new battery energy storage system over its entire life cycle is as follows:

[0035]

[0036] Where: The amount of energy that can be released over the entire life cycle of the new battery energy storage system at the rated depth of discharge; Rated number of discharge cycles for new batteries; Rated discharge depth for new batteries; E nb Install capacity for new batteries.

[0037] The distributed energy storage system is the old battery energy storage system. The total discharge capacity of the old battery energy storage system is calculated as follows:

[0038]

[0039] Where: The dischargeable capacity of the old battery energy storage system; n al E is the number of cycles used in electric vehicles before retirement; slb Install capacity on old batteries; Rated depth of discharge for old batteries.

[0040] The formula for calculating the loss of a single discharge of a new / old battery energy storage system converted to the loss under standard operating conditions is as follows:

[0041]

[0042] Where: is the power loss of the new battery energy storage system during the i-th discharge; is the power loss of the old battery energy storage system during the jth discharge; is the discharge depth of the new battery energy storage system for the i-th discharge; is the discharge depth of the old battery energy storage system during the jth discharge; The i-th discharge capacity of the new battery energy storage system; is the jth discharge capacity of the old battery energy storage system; p0 and p1 are battery life parameters;

[0043]

[0044] Where: The actual service life of the new battery energy storage system; is the actual service life of the old battery energy storage system; I and J are the total discharge times of the new and old battery energy storage systems in one year, respectively.

[0045] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, in step S3, the upper-level planning model includes the initial investment cost of the new / old battery energy storage system, the energy storage battery replacement cost, the energy storage operation and maintenance cost, and the operating cost;

[0046]

[0047] C inv =C inv,nb +C inv,slb =c nb,inv E nb +c slb,inv E slb

[0048]

[0049] Where: C total is the comprehensive planning cost; r is the annual discount rate; C inv is the energy storage investment cost; C inv,nb 、C inv,slb are the investment costs of new and old battery energy storage systems respectively; c nb,inv 、c slb,inv are the unit capacity prices of new and old battery energy storage systems respectively; C rep is the replacement cost of energy storage equipment; H is the planned period; C om is the annual maintenance cost of energy storage equipment; c nb,om 、c slb,om are the unit output maintenance costs of the new and old battery energy storage systems respectively; D ε is the number of days in a year occupied by the ε scenario; w is the number of scenarios; are the operating power of the new and old battery energy storage systems at time t in the ε scenario, respectively, with charging being positive and discharging being negative; Cop is the annual operating cost; T is the total number of time periods for optimal scheduling on a typical day; △t is the optimization step size.

[0050] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, in step S3, the upper-level planning model objective function constraint conditions are as follows:

[0051] Investment cost and configuration capacity constraints:

[0052]

[0053] Where: The upper and lower limits of energy storage investment costs.

[0054] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, in step S4, the lower-level operation model includes the annual electricity purchase cost, the annual energy storage discharge loss cost, and the peak-to-average ratio penalty cost;

[0055] minC op =min(C buy +C dec +C pena )

[0056]

[0057] Where: C buy is the annual electricity purchase cost; is the electricity price during period t; C is the power purchased from the grid at time t in scenario ε; dec is the annual discharge loss cost of the energy storage system; C pena is the peak-to-average ratio penalty cost; is the penalty value in the ε scenario; δ is the peak-to-average ratio threshold; α is the penalty coefficient; is the peak value of the electricity purchase curve from the grid in the ε scenario.

[0058] In the above-mentioned commercial building hybrid energy storage system planning method considering the virtual energy storage effect, in step S4, the lower-level objective function constraints are as follows:

[0059] Power balance constraints:

[0060]

[0061] Where: is the total building base load at time t in the ε scenario;

[0062] Indoor temperature constraints:

[0063]

[0064] Virtual energy storage operation constraints:

[0065]

[0066] Where: P AC,max 、P AC,min They are the upper and lower limits of the inverter air conditioner's operating power respectively;

[0067] New / old battery energy storage system operating constraints:

[0068]

[0069] Where: are the capacities of the new and old battery energy storage systems at time t in the ε scenario, respectively; are the capacities of the new and old battery energy storage systems at time t-1 in the ε scenario, respectively; are the self-discharge rates of the new and old battery energy storage systems, respectively; They are the charging and discharging efficiency of the new battery energy storage system; are the charging and discharging efficiency of the old battery energy storage system respectively; These are the upper and lower limits of the new battery energy storage system capacity; They are the upper and lower limits of the old battery energy storage system capacity; are the charging power of the new and old battery energy storage systems at time t in the ε scenario; are the discharge power of the new and old battery energy storage systems at time t in the ε scenario; These are the upper and lower operating power limits of the new battery energy storage system; They are the upper and lower limits of the operating power of the old battery energy storage system; are the capacities of the new and old battery energy storage at the start time of scheduling respectively; are the capacities of the new and old battery energy storage at the end of scheduling respectively;

[0070] Constraints on the tie lines between buildings and distribution networks:

[0071]

[0072] Where: is the maximum interaction power between the distribution network and commercial buildings.

[0073] The beneficial effects of the present invention are:

[0074] 1. The present invention first considers the capacity decay, discharge loss characteristics and cost differences between new and old batteries, and intends to select new batteries with large single capacity and good performance to form centralized energy storage, mainly to smooth the peak and fill the valley of the base load; the screened retired batteries are connected in series and parallel to form distributed energy storage, connected to the air-conditioning load, mainly to reduce the peak power supply pressure of the building load in extreme winter and summer weather.

[0075] 2. To reduce the investment cost of commercial building energy storage systems and mitigate the impact of peak air conditioning loads on the distribution network, this invention considers incorporating variable-frequency air conditioning-building systems into the daily scheduling and operation of commercial buildings as virtual energy storage. By considering the thermal inertia of the building envelope at the operational level and leveraging the cold and heat storage capacity of the walls, this invention adjusts the air conditioning operating power and smoothes the air conditioning power curve while maintaining indoor thermal comfort, thereby reducing the operational pressure on physical energy storage on the user side. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Flowchart of the present invention.

[0077] Figure 2 It is a framework diagram of the two-layer planning model of the present invention.

[0078] Figure 3 Find a flowchart for solving the bilevel programming model.

[0079] Figure 4 1 is a battery capacity decay curve diagram in an embodiment of the present invention.

[0080] Figure 5 Schematic diagram of load data and outdoor temperature of a commercial building in an embodiment of the present invention.

[0081] Figure 6 Schematic diagram of air conditioning power and room temperature changes in summer according to an embodiment of the present invention.

[0082] Figure 7 Schematic diagram of air conditioning power and room temperature changes in winter according to an embodiment of the present invention.

[0083] Figure 8 Schematic diagram of the virtual energy storage operating state in summer according to an embodiment of the present invention.

[0084] Figure 9 Schematic diagram of the virtual energy storage operating state in winter according to an embodiment of the present invention.

[0085] Figure 10 This is a schematic diagram of the operating status of batteries retired in summer according to an embodiment of the present invention.

[0086] Figure 11 This is a schematic diagram of the operating status of retired batteries in winter according to an embodiment of the present invention.

[0087] Figure 12 This is a schematic diagram of the operating status of a new battery in summer according to an embodiment of the present invention.

[0088] Figure 13 This is a schematic diagram of the operating status of a new battery in winter according to an embodiment of the present invention.

[0089] Figure 14Schematic diagram of the operating status of a new battery in spring / autumn according to an embodiment of the present invention. DETAILED DESCRIPTION

[0090] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0091] like Figure 1-Figure 3 As shown, a commercial building hybrid energy storage system planning method considering virtual energy storage effect includes the following steps:

[0092] S1: New energy storage batteries are assembled into a centralized energy storage system to assist in peak shaving and valley filling of the base load. The screened old energy storage batteries are assembled into a distributed energy storage system to connect the air conditioning load. The variable frequency air conditioning-building system is equivalent to a first-order circuit, based on which a differential equation of the first-order equivalent thermal parameters is established, and then a calculation model of the virtual charging and discharging power and virtual energy storage capacity is constructed.

[0093] Taking winter as an example, air conditioning systems maintain indoor temperatures through heating. If the air conditioner's heating capacity is increased, meaning more heat is released, the indoor temperature will rise accordingly. Alternatively, the air conditioner's power can be appropriately reduced to lower the indoor temperature without affecting thermal comfort. Therefore, the variable-frequency air conditioning-building system provides a virtual energy storage effect. Specifically, when the air conditioning system increases its power and releases more heat, it is in a "charging" state. Conversely, when its power and heat output are reduced, the system is in a "discharging" state.

[0094] The established first-order differential equation of equivalent thermal parameters is:

[0095]

[0096] Where: is the indoor temperature of the kth area in the ε scene at the tth moment; T out,t,ε is the outdoor temperature at time t in the ε scenario; R j is the equivalent thermal resistance of the building; C s is the equivalent heat capacity of the building; is the air conditioning heating / cooling capacity of the kth zone at time t in the ε scenario. Its value is positive when heating and negative when cooling;

[0097] The relationship between the air conditioner's electrical power and the heat it releases is as follows:

[0098]

[0099] Where: is the air conditioning power of the kth area at time t in the ε scenario, also called dynamic power when calculating virtual energy storage parameters; η is the air conditioning energy efficiency ratio; is the frequency of the variable frequency air conditioner in the kth area at time t in the ε scenario; m and n are two coefficients that characterize the relationship between the frequency and power of the variable frequency air conditioner; T set Set the temperature for the air conditioner; T min,ε 、T max,ε are the lower and upper limits of the thermal comfort temperature range for human body in the ε scenario, respectively.

[0100] The process of constructing the calculation model of virtual charging and discharging power and virtual energy storage capacity is as follows:

[0101] When the indoor temperature is constant, the heat released by the air conditioner to the building is equal to the heat transferred from the building to the outside world. At this time, the power required by the air conditioner is is the steady-state power, which is calculated as follows:

[0102]

[0103] Where: is the heat exchanged between the building interior and the outside world at the kth area in the ε scenario at the tth moment;

[0104] The indoor temperature changes from the initial temperature T set0 Increase or decrease to T set , increase or decrease the energy Q k as follows:

[0105]

[0106] Where: is the starting temperature of the kth region within a period of time; is the termination temperature of the kth region within a period of time;

[0107] The equivalent virtual energy storage capacity E of a single variable frequency air conditioning-building system in the kth area at the tth time in the ε scenario t,ε (k) and rated capacity E N,ε The expression of (k) is as follows:

[0108]

[0109] Equivalent virtual energy storage charging and discharging power of the kth region in the ε scenario is the difference between the dynamic power and steady-state power of the air conditioner, and the calculation formula is as follows:

[0110]

[0111] Taking air conditioning heating as an example, when the actual operating power of the air conditioner is less than the power required to maintain a constant indoor temperature, the equivalent virtual energy storage charge and discharge power of the variable frequency air conditioning-building system is negative, and the system is in a "discharge" state. Conversely, the virtual energy storage charge and discharge power is positive, and the system is in a "charge" state.

[0112] The capacity of the entire building's virtual energy storage and charge and discharge power It is formed by the accumulation of the virtual energy storage capacity and charge and discharge power of a single area. Its model is as follows:

[0113]

[0114] Where: G is the total number of air conditioners participating in virtual energy storage regulation in the building at time t, and g is the number of the air conditioner participating in virtual energy storage regulation. Since the start-up time of air conditioners in each area of ​​commercial buildings is different, the time when the room temperature of a single area enters the temperature comfort zone is different. This patent stipulates that virtual energy storage regulation will only be participated in after the room temperature of the area enters the comfort zone, so G≤K.

[0115] S2: The Bacon-watts model is used to describe the inflection point of energy storage battery capacity degradation, and then an exponential function is used to build discharge loss models for new and old energy storage batteries at different discharge depths.

[0116] The capacity retention rate decay rate of the battery throughout its life cycle presents a two-stage characteristic. The initial battery capacity decay rate is relatively slow. After a period of use, the number of cycles exceeds the capacity decline inflection point, and the battery capacity decline rate accelerates.

[0117] Assuming that the battery is about to be eliminated when the number of cycles reaches the inflection point of capacity decline, the battery inflection point is identified by the Bacon-watts model:

[0118] F=α0+α1(xn knee )+α2(xn knee )×tanh[(xn knee ) / γ]+Z

[0119] Where: F is the battery capacity retention rate, γ is the battery life cycle fitting parameter; α0 is the capacity retention rate when the battery life reaches the inflection point; α1 and α2 are the parameters of the two fitting curves in the Bacon-watts model; Z is the error amount that conforms to the standard normal distribution; n knee is the number of cycles at the inflection point; x is the number of cycles of the battery;

[0120] The centralized energy storage system is a new battery energy storage system. The calculation method for the total discharge capacity of the new battery energy storage system over its entire life cycle is as follows:

[0121]

[0122] Where: The amount of energy that can be released over the entire life cycle of the new battery energy storage system at the rated depth of discharge; Rated number of discharge cycles for new batteries; Rated discharge depth for new batteries; E nb New battery installed capacity;

[0123] The distributed energy storage system is the old battery energy storage system. The total discharge capacity of the old battery energy storage system is calculated as follows:

[0124]

[0125] Where: The dischargeable capacity of the old battery energy storage system; n al E is the number of cycles used in electric vehicles before retirement; slb Install capacity on old batteries; Rated depth of discharge for old batteries;

[0126] The formula for calculating the loss of a single discharge of a new / old battery energy storage system converted to the loss under standard operating conditions is as follows:

[0127]

[0128] Where: is the power loss of the new battery energy storage system during the i-th discharge; is the power loss of the old battery energy storage system during the jth discharge; is the discharge depth of the new battery energy storage system for the i-th discharge; is the discharge depth of the old battery energy storage system during the jth discharge; The i-th discharge capacity of the new battery energy storage system; is the jth discharge capacity of the old battery energy storage system; p0 and p1 are battery life parameters;

[0129]

[0130] Where: The actual service life of the new battery energy storage system; is the actual service life of the old battery energy storage system; I and J are the total discharge times of the new and old battery energy storage systems in one year, respectively.

[0131] S3: Considering the investment cost constraint, the installed capacity of new / old energy storage batteries and the number of energy storage replacements within the entire planning cycle are used as optimization variables. An upper-level planning model is established, and the decision variables are passed to the lower-level operation model after being solved by the particle swarm algorithm.

[0132] The upper-level planning model includes the initial investment cost of the new / old battery energy storage system, the energy storage battery replacement cost, the energy storage operation and maintenance cost, and the operating cost;

[0133]

[0134] Cinv =C inv,nb +C inv,slb =c nb,inv E nb +c slb,inv E slb

[0135]

[0136] Where: C total is the comprehensive planning cost; r is the annual discount rate; C inv is the energy storage investment cost; C inv,nb 、C inv,slb are the investment costs of new and old battery energy storage systems respectively; c nb,inv 、c slb,inv are the unit capacity prices of new and old battery energy storage systems respectively; C rep is the replacement cost of energy storage equipment; H is the planned period; C om is the annual maintenance cost of energy storage equipment; c nb,om 、c slb,om are the unit output maintenance costs of the new and old battery energy storage systems respectively; D ε is the number of days in a year occupied by the ε scenario; w is the number of scenarios; are the operating power of the new and old battery energy storage systems at time t in the ε scenario, respectively, with charging being positive and discharging being negative; C op is the annual operating cost, T is the total number of time periods for typical day optimization scheduling; Δt is the optimization step length, which is 30 minutes in the present invention.

[0137] The objective function constraints of the upper-level planning model are as follows:

[0138] Investment cost and configuration capacity constraints:

[0139]

[0140] Where: The upper and lower limits of energy storage investment costs.

[0141] S4: Considering building power balance constraints, indoor temperature constraints, virtual energy storage operation constraints, new / old battery energy storage system operation constraints, and tie line constraints, with the goal of minimizing the building's annual operating cost, a lower-level operation model is established. The Cplex solver is used to determine the new / old battery energy storage output and air conditioning operating power, and the calculated results are returned to the upper-level planning model.

[0142] In step S4, the lower-level operation model includes annual electricity purchase costs, annual energy storage discharge loss costs, and peak-to-average ratio penalty costs;

[0143] minC op =min(C buy+C dec +C pena )

[0144]

[0145] Where: C buy is the annual electricity purchase cost; is the electricity price during period t; C is the power purchased from the grid at time t in scenario ε; dec is the annual discharge loss cost of the energy storage system; C pena is the peak-to-average ratio penalty cost; is the penalty value in the ε scenario; δ is the peak-to-average ratio threshold; α is the penalty coefficient; is the peak value of the electricity purchase curve from the grid in the ε scenario.

[0146] The constraints of the lower objective function are as follows:

[0147] Power balance constraints:

[0148]

[0149] Where: is the total building base load at time t in the ε scenario;

[0150] Indoor temperature constraints:

[0151]

[0152] Virtual energy storage operation constraints:

[0153]

[0154] Where: P AC,max 、P AC,min They are the upper and lower limits of the inverter air conditioner's operating power respectively;

[0155] New / old battery energy storage system operating constraints:

[0156]

[0157] Where: are the capacities of the new and old battery energy storage systems at time t in the ε scenario, respectively; are the capacities of the new and old battery energy storage systems at time t-1 in the ε scenario, respectively; are the self-discharge rates of the new and old battery energy storage systems, respectively; They are the charging and discharging efficiency of the new battery energy storage system; are the charging and discharging efficiency of the old battery energy storage system respectively; These are the upper and lower limits of the new battery energy storage system capacity; They are the upper and lower limits of the old battery energy storage system capacity; are the charging power of the new and old battery energy storage systems at time t in the ε scenario; are the discharge power of the new and old battery energy storage systems at time t in the ε scenario; These are the upper and lower operating power limits of the new battery energy storage system; They are the upper and lower limits of the operating power of the old battery energy storage system; are the capacities of the new and old battery energy storage at the start time of scheduling respectively; are the capacities of the new and old battery energy storage at the end of scheduling respectively;

[0158] Constraints on the tie lines between buildings and distribution networks:

[0159]

[0160] Where: is the maximum interaction power between the distribution network and commercial buildings.

[0161] Simulation example:

[0162] The battery data set used in this paper comes from the NASA Center of Excellence in Testing. The test object is a commercial lithium-ion battery of model 18650. The relationship between the number of charge and discharge cycles and the capacity retention rate is as follows: Figure 4 To shield the interference of fluctuation errors in actual measurement data and accurately identify the turning points of the battery capacity decay curve, the present invention uses the Levenberg-Marquardt least squares method to optimize the parameters of the Bacon-Watts model. Table 1 lists the optimized Bacon-Watts model parameters.

[0163] Table 1

[0164]

[0165] The commercial building operation data used in this paper comes from a commercial building in Wuhan. The building covers an area of ​​2,000 square meters, has 50 floors, and is equipped with 590 air conditioners. The detailed load data is as follows: Figure 5 As shown in the figure, the peak air conditioning load hours in summer and winter are 12:30-18:00 and 9:30-11:00, respectively, while the base load peak hour is 9:30-11:00. Considering the operating characteristics of different departments within the building, they are equivalently divided into three zones, with air conditioning start-up times at 8:30, 9:00, and 9:30, respectively. The operational model of this invention considers four scenarios: spring, summer, autumn, and winter, with each season comprising 92, 93, 92, and 89 days, respectively. Time-of-use electricity pricing information is detailed in Table 2.

[0166] Table 2

[0167]

[0168] The equivalent virtual energy storage parameters of the “inverter air conditioning-building” system are detailed in Table 3, and the technical and economic parameters of new and old batteries are detailed in Table 4.

[0169] Table 3

[0170]

[0171] Table 4

[0172]

[0173] The present invention sets two scenarios to verify the effectiveness of the proposed method:

[0174] Option 1: Considering the virtual energy storage effect of the "inverter air conditioning-building" system;

[0175] Option 2: The virtual energy storage effect of the “inverter air conditioning-building” system is not considered.

[0176] The planning results are detailed in Table 5. Because Option 2 fails to consider the virtual energy storage effect of the "variable frequency air conditioning-building" system, it requires the installation of additional retired batteries to support the air conditioning load. Consequently, the configured capacity of retired batteries increases 2.9 times compared to Option 1, increasing the annual investment cost by 81% and reducing the annual return on investment from 9.3% to 8.3%. This demonstrates that the proposed collaborative planning method for new and old energy storage batteries in commercial buildings, which considers the virtual energy storage effect, fully accounts for the virtual energy storage regulation potential of the "variable frequency air conditioning-building" system, effectively reducing the planned capacity of energy storage batteries at the planning level and significantly reducing building operating costs at the operational level.

[0177] Table 5

[0178]

[0179] Figure 6-Figure 9 The equivalent virtual energy storage operation state of the variable frequency air conditioning-building system is shown. Figure 6 、 Figure 7 It can be seen that after considering the virtual energy storage effect, the peak load period of the building's total air-conditioning is delayed, and the total daily air-conditioning electricity consumption is significantly reduced. Among them, the air-conditioning electricity consumption on a typical day in summer is reduced by 17.7%, and the air-conditioning electricity consumption on a typical day in winter is reduced by 14.5%.

[0180] Before 10:00 in summer, the room temperature in all areas can meet the thermal comfort temperature range of human body, and then continue to increase the air conditioning power to cool down and store cold. Figure 8It can be seen that the virtual energy storage capacity has increased to 590kWh. During the period of 10:00-15:00, the air conditioning output power is appropriately reduced to save electricity without affecting the thermal comfort of the human body. During the period of 13:30-16:30, due to the increase in outdoor temperature, the heat transmitted from the outside to the interior of the building increases, which causes the air conditioning power to maintain the room temperature to increase accordingly. In winter, the room temperature in area 1 enters the human thermal comfort temperature range before the electricity price increases. In order to reduce the electricity consumption during peak hours, the variable frequency air conditioner further increases the indoor temperature to store heat. Figure 9 It can be seen that at this time the virtual energy storage charging power can reach 175kW and the accumulated energy can reach 92kWh.

[0181] Figure 10 、 Figure 11 The display shows the operating status of retired batteries, which strictly adhere to a "low charge, high discharge" strategy. First, charging occurs between 6:00 and 7:00 a.m., when electricity prices are lower. Second, after 10:00 p.m., the retired batteries are recharged to their initial state using off-peak electricity prices to ensure regulation capacity for the next day. Discharge is concentrated between 10:30 a.m. and 3:00 p.m., during the peak electricity price period. Because the peak air conditioning load in winter coincides with the peak base load, the retired batteries increase their output during this period to reduce the peak load.

[0182] Figure 12-14 This represents the operating status of a new battery. The charge and discharge strategy for new batteries is similar to that for retired batteries, following the principle of "low charge, high discharge." New batteries are discharged evenly during the 10:00 AM to 3:00 PM and 6:00 PM to 9:00 PM. Furthermore, building load peaks in spring, autumn, and winter occur between 9:00 AM and 10:00 AM. To reduce the peak-to-average ratio of the building's total load curve, new batteries increase their output during these periods.

[0183] Based on the above analysis, the proposed commercial building hybrid energy storage system planning method, which considers the virtual energy storage effect, can fully leverage the cost advantages of retired batteries and the regulation potential of air conditioning-building virtual energy storage systems. This approach promotes the large-scale application of behind-the-meter energy storage in commercial buildings, fully realizes the full lifecycle value of energy storage batteries, and provides strong support for the development of intelligent and electrified buildings.

Claims

1. A commercial building hybrid energy storage system planning method considering virtual energy storage effect, characterized in that: The following steps are involved: S1: New energy storage batteries are assembled into a centralized energy storage system to assist in peak shaving and valley filling of the base load. Screened old energy storage batteries are assembled into a distributed energy storage system to connect to the air conditioning load. The variable frequency air conditioning-building system is equated to a first-order circuit. Based on this, a differential equation for the first-order equivalent thermal parameters is established, and then a calculation model for virtual charging and discharging power and virtual energy storage capacity is constructed. S2: Use the Bacon-watts model to describe the inflection point of energy storage battery capacity degradation, and then use exponential functions to build discharge loss models for new and old energy storage batteries at different discharge depths. S3: Considering investment cost constraints, the installed capacity of new / old energy storage batteries and the number of energy storage replacements within the full planning period are used as optimization variables. An upper-level planning model is established, and the decision variables are passed to the lower-level operation model after solving them using the particle swarm algorithm. S4: Considering building power balance constraints, indoor temperature constraints, virtual energy storage operation constraints, new / old battery energy storage system operation constraints, and tie line constraints, with the goal of minimizing the building's annual operating cost, a lower-level operation model is established. The Cplex solver is used to determine the new / old battery energy storage output and air conditioning operating power, and the calculated results are returned to the upper-level planning model.

2. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 1 is characterized in that: In step S1, the differential equation of the first-order equivalent thermal parameters is established as follows: Where: is the indoor temperature of the kth area in the ε scene at the tth moment; T out,t,ε is the outdoor temperature at time t in the ε scenario; R j is the equivalent thermal resistance of the building; C s is the equivalent heat capacity of the building; is the air conditioning heating / cooling capacity of the kth zone at time t in the ε scenario. Its value is positive when heating and negative when cooling; The relationship between the air conditioner's electrical power and the heat it releases is as follows: Where: is the air conditioning power of the kth area at time t in the ε scenario, also called dynamic power when calculating virtual energy storage parameters; η is the air conditioning energy efficiency ratio; is the frequency of the variable frequency air conditioner in the kth area at time t in the ε scenario; m and n are two coefficients that characterize the relationship between the frequency and power of the variable frequency air conditioner; T set Set the temperature for the air conditioner; T min,ε 、T max,ε are the lower and upper limits of the thermal comfort temperature range for human body in the ε scenario, respectively.

3. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 2 is characterized in that: In step S1, the process of constructing a calculation model of virtual charge and discharge power and virtual energy storage capacity is as follows: When the indoor temperature is constant, the heat released by the air conditioner to the building is equal to the heat transferred from the building to the outside world. At this time, the power required by the air conditioner is is the steady-state power, which is calculated as follows: Where: is the heat exchanged between the building interior and the outside world at the kth area in the ε scenario at the tth moment; When the indoor temperature changes from the initial temperature T set0 Increase or decrease to T set , increase or decrease the energy Q k as follows: Where: is the initial temperature of the kth region over a period of time; is the termination temperature of the kth region within a period of time; The equivalent virtual energy storage capacity E of a single variable frequency air conditioning-building system in the kth area at the tth time in the ε scenario t,ε (k) and rated capacity E N,ε The expression of (k) is as follows: Equivalent virtual energy storage charging and discharging power of the kth region in the ε scenario is the difference between the dynamic power and steady-state power of the air conditioner, and the calculation formula is as follows: The capacity of the entire building's virtual energy storage and charge and discharge power It is formed by the accumulation of the virtual energy storage capacity and charge and discharge power of a single area. Its model is as follows: Where G is the total number of air conditioners participating in virtual energy storage control in the building at time t, and g is the number of the air conditioner participating in virtual energy storage control.

4. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 3 is characterized in that: The specific process of step S2 is: Assuming that the battery is about to be eliminated when the number of cycles reaches the inflection point of capacity decline, the battery inflection point is identified by the Bacon-watts model: F=α0+α1(xn knee )+α2(xn knee )×tanh[(xn knee ) / c]+Z Where: F is the battery capacity retention rate, γ is the battery life cycle fitting parameter; α0 is the capacity retention rate when the battery life reaches the inflection point; α1 and α2 are the parameters of the two fitting curves in the Bacon-watts model; Z is the error amount that conforms to the standard normal distribution; n knee is the number of cycles at the inflection point; x is the number of cycles of the battery; The centralized energy storage system is a new battery energy storage system. The total discharge capacity of the new battery energy storage system over its entire life cycle is calculated as follows: Where: The amount of energy that can be released over the entire life cycle of the new battery energy storage system at the rated depth of discharge; Rated number of discharge cycles for new batteries; Rated discharge depth for new batteries; E nb Install capacity for new batteries; The distributed energy storage system is the old battery energy storage system. The total discharge capacity of the old battery energy storage system is calculated as follows: Where: The dischargeable capacity of the old battery energy storage system; n al E is the number of cycles used in electric vehicles before retirement; slb Install capacity on old batteries; Rated depth of discharge for old batteries; The formula for calculating the loss of a single discharge of a new / old battery energy storage system converted to the loss under standard operating conditions is as follows: Where: is the power loss of the new battery energy storage system during the i-th discharge; is the power loss of the old battery energy storage system during the jth discharge; is the discharge depth of the new battery energy storage system for the i-th discharge; is the discharge depth of the old battery energy storage system during the jth discharge; The i-th discharge capacity of the new battery energy storage system; is the jth discharge capacity of the old battery energy storage system; p0 and p1 are battery life parameters; Where: The actual service life of the new battery energy storage system; is the actual service life of the old battery energy storage system; I and J are the total discharge times of the new and old battery energy storage systems in one year, respectively.

5. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 4 is characterized in that: In step S3, the upper-level planning model includes the initial investment cost of the new / old battery energy storage system, the energy storage battery replacement cost, the energy storage operation and maintenance cost, and the operating cost; C inv =C inv,nb +C inv,slb =c nb,inv E nb +c slb,inv E slb Where: C total is the comprehensive planning cost; r is the annual discount rate; C inv is the energy storage investment cost; C inv,nb 、C inv,slb are the investment costs of new and old battery energy storage systems respectively; c nb,inv 、c slb,inv are the unit capacity prices of new and old battery energy storage systems respectively; C rep is the replacement cost of energy storage equipment; H is the planned period; C om is the annual maintenance cost of energy storage equipment; c nb,om 、c slb,om are the unit output maintenance costs of the new and old battery energy storage systems respectively; D ε is the number of days in a year occupied by the ε scenario; w is the number of scenarios; are the operating power of the new and old battery energy storage systems at time t in the ε scenario, respectively, with charging being positive and discharging being negative; C op is the annual operating cost; T is the total number of time periods for optimal scheduling on a typical day; △t is the optimization step size.

6. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 5 is characterized in that: In step S3, the upper-level planning model objective function constraint conditions are as follows: Investment cost and configuration capacity constraints: Where: The upper and lower limits of energy storage investment costs.

7. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 6 is characterized in that: In step S4, the lower-level operation model includes annual electricity purchase costs, annual energy storage discharge loss costs, and peak-to-average ratio penalty costs; minC op =min(C buy +C dec +C pena ) Where: C buy is the annual electricity purchase cost; is the electricity price during period t; C is the power purchased from the grid at time t in scenario ε; dec is the annual discharge loss cost of the energy storage system; C pena is the peak-to-average ratio penalty cost; is the penalty value in the ε scenario; δ is the peak-to-average ratio threshold; α is the penalty coefficient; is the peak value of the electricity purchase curve from the grid in the ε scenario.

8. The commercial building hybrid energy storage system planning method considering virtual energy storage effect according to claim 7 is characterized in that: In step S4, the lower objective function constraints are as follows: Power balance constraints: Where: is the total building base load at time t in the ε scenario; Indoor temperature constraints: Virtual energy storage operation constraints: Where: P AC,max 、P AC,min They are the upper and lower limits of the inverter air conditioner's operating power respectively; New / old battery energy storage system operating constraints: Where: are the capacities of the new and old battery energy storage systems at time t in the ε scenario, respectively; are the capacities of the new and old battery energy storage systems at time t-1 in the ε scenario, respectively; are the self-discharge rates of the new and old battery energy storage systems, respectively; They are the charging and discharging efficiency of the new battery energy storage system; are the charging and discharging efficiency of the old battery energy storage system respectively; These are the upper and lower limits of the new battery energy storage system capacity; They are the upper and lower limits of the old battery energy storage system capacity; are the charging power of the new and old battery energy storage systems at time t in the ε scenario; are the discharge power of the new and old battery energy storage systems at time t in the ε scenario; These are the upper and lower operating power limits of the new battery energy storage system; They are the upper and lower limits of the operating power of the old battery energy storage system; are the capacities of the new and old battery energy storage at the start time of scheduling respectively; are the capacities of the new and old battery energy storage at the end of scheduling respectively; Constraints on the tie lines between buildings and distribution networks: Where: is the maximum interaction power between the distribution network and commercial buildings.

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