Commercial building hybrid energy storage system planning method considering virtual energy storage effect

Abstract

The invention discloses a commercial building hybrid energy storage system planning method considering a virtual energy storage effect, and the method comprises the following steps: enabling a variable frequency air conditioner-building system to be equivalent to a first-order circuit, building a differential equation of a first-order equivalent thermal parameter, and constructing a calculation model of virtual charging and discharging power and virtual energy storage capacity; constructing a discharge loss model of the new / old energy storage battery under different discharge depths; an upper-layer planning model is established, and decision variables are transmitted to a lower-layer operation model after solving through a particle swarm algorithm; and a lower-layer operation model is established, new / old battery energy storage output and air conditioner operation power are decided through a Cplex solver, and calculation results are returned to the upper-layer planning model. According to the method, the variable frequency air conditioner-building system serves as virtual energy storage to participate in daily dispatching operation of a commercial building, and on the premise that the indoor thermal comfort degree is met, the energy storage operation pressure of a user side entity is reduced by adjusting the air conditioner operation power and smoothing an air conditioner power curve.

Description

Technical Field

[0001] The 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 power facilities. In recent years, with the adjustment of industrial structure and the improvement of living standards, the peak load in winter and summer has continued to grow, and the peak-to-valley difference has continued to increase, posing severe challenges to the stable supply of urban electricity. Configuring battery energy storage systems in commercial buildings to provide peak-to-valley services can effectively reduce building peak loads, reduce the power supply pressure of distribution networks and reduce building operating costs. However, the current price of new batteries is still high, the business model is single, and the return on investment is low, making it difficult to be widely used on the demand side. After retirement, the on-board power battery still has 80% or more of the remaining capacity, which can be used in other scenarios with lower requirements for battery performance, such as user-side energy storage, microgrids, lighting, backup power supplies, etc.

[0003] On the other hand, the construction industry is a major carbon emitter in China. Among them, air conditioning energy consumption accounts for more than 50% of building energy consumption. In order to alleviate the power supply pressure of air conditioning loads in commercial buildings, the State Grid took the lead in carrying out the first batch of commercial building air conditioning load control and transformation work in Ningbo. In the actual control process, the thermal inertia characteristics of the building envelope are used to allow the air conditioning operating power to fluctuate within a certain range, that is, considering that the variable frequency air conditioning-building system has a similar charging and discharging function to a battery, it is equivalent to a virtual energy storage resource for scheduling. However, most of the existing studies consider the participation of variable frequency air conditioning in demand response, while ignoring the impact of its own regulation potential as a virtual energy storage on operating costs, resulting in a certain degree of resource waste in the user-side energy storage planning scheme based on this. 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 has a simple algorithm and takes into account the virtual energy storage effect.

[0005] The technical solution of the present invention to solve the above technical problem 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 built into a centralized energy storage system to assist the basic load in peak load shaving and valley filling. The screened old energy storage batteries are built 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 first-order equivalent thermal parameters is established, and then a calculation model of virtual charging and discharging power and virtual energy storage capacity is constructed.

[0007] S2: The Bacon-watts model is used to describe the inflection point of energy storage battery capacity decline, and then the exponential function is used to build discharge loss models for new / old energy storage batteries at different discharge depths;

[0008] S3: Considering the investment cost constraint, the installed capacity of new / old energy storage batteries and the number of energy storage replacements in the entire planning cycle are used as optimization variables to establish an upper-level planning model. After solving it through the particle swarm algorithm, the decision variables are passed to the lower-level operation model.

[0009] S4: Considering the building power balance constraints, indoor temperature constraints, virtual energy storage operation constraints, new / old battery energy storage system operation constraints, and interconnection line constraints, with the goal of minimizing the annual building operation cost, a lower-level operation model is established, and the new / old battery energy storage output and air conditioning operation power are determined through the Cplex solver, and the calculation 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 established is:

[0011]

[0012] Where: is the indoor temperature of the kth area in the ε scene at the tth time; T out,t,ε is the outdoor temperature at the tth moment 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 area at the tth time 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 the tth time 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 the tth moment 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 humans 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:

[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, and its calculation formula is as follows:

[0018]

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

[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 conditioner-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 virtual energy storage capacity and charging and discharging power in a single area. Its model is as follows:

[0028]

[0029] 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.

[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 will be eliminated when the number of cycles reaches the inflection point of capacity decline, the battery inflection point can be identified through 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 the 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 electricity that can be released by the new battery energy storage system over its entire life cycle at the rated discharge depth; Rated number of discharge cycles for new batteries; Rated discharge depth for new batteries; E nb Install capacity for new batteries.

[0037] 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 for old batteries; Rated depth of discharge for old batteries.

[0040] The loss calculation formula for converting the single discharge loss of the new / old battery energy storage system to the loss under standard working 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 at 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 both 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 operation 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 occupied by the ε scenario in a year; w is the number of scenarios; are the operating power of the new and old battery energy storage systems at the tth moment 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 length.

[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 discharge loss cost of the energy storage, 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 objective function constraint conditions 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 operating power respectively;

[0067] New / old battery energy storage system operation 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; They 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; They are respectively the charging and discharging efficiency of the old battery energy storage system; They are the upper and lower limits of the capacity of the new battery energy storage system; They are the upper and lower limits of the capacity of the old battery energy storage system; are the charging power of the new and old battery energy storage systems at time t in the ε scenario, respectively; are the discharge power of the new and old battery energy storage systems at time t in the ε scenario respectively; They are the upper and lower limits of the operating power of the new battery energy storage system respectively; They are the upper and lower limits of the operating power of the old battery energy storage system respectively; They are the capacity of the new and old battery energy storage at the start time of scheduling respectively; They are the capacities of the new and old batteries at the end of energy storage scheduling;

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

[0071]

[0072] Where: It 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 firstly considers the capacity decay, discharge loss characteristics and cost differences of new / 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 basic load; the screened retired batteries are connected in series and parallel to form groups as distributed energy storage, connected to the air-conditioning load, mainly to reduce the peak power supply pressure of the building load under extreme weather in winter and summer.

[0075] 2. In order to reduce the investment cost of commercial building energy storage systems and reduce the impact of air conditioning peak load on the distribution network, the present invention considers the variable frequency air conditioning-building system as a virtual energy storage to participate in the daily scheduling and operation of commercial buildings. The present invention considers the thermal inertia of the building envelope at the operation level, utilizes the cold and heat storage capacity of the wall, and adjusts the air conditioning operating power and smoothes the air conditioning power curve under the premise of meeting the indoor thermal comfort, thereby reducing the user-side physical energy storage operation pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of the present invention.

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

[0078] Figure 3 Solve the flow chart for the bilevel programming model.

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

[0080] Figure 5 The figure is a 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 in an embodiment of the present invention.

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

[0084] Fig. 9 Schematic diagram of the winter virtual energy storage operation state in an embodiment of the present invention.

[0085] Fig.10 Schematic diagram of the operating status of batteries retired in summer in an embodiment of the present invention.

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

[0087] Fig.12 Schematic diagram of the operating status of a new battery in summer according to an embodiment of the present invention.

[0088] Fig.13 Schematic diagram of the operating status of a new battery in winter according to an embodiment of the present invention.

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

[0090] The present invention will be further described below in conjunction with 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 built into a centralized energy storage system to assist the basic load in peak shaving and valley filling. The screened old energy storage batteries are built 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 the differential equation of the first-order equivalent thermal parameters is established, and then the calculation model of the virtual charging and discharging power and the virtual energy storage capacity is constructed.

[0093] Taking winter as an example, the air conditioning system maintains the indoor temperature through the heating function. On this basis, if the heating capacity of the air conditioner is enhanced, that is, more heat is released, the indoor temperature will rise accordingly. It is also possible to appropriately reduce the power of the air conditioner to reduce the indoor temperature without affecting the thermal comfort of the human body. Therefore, the variable frequency air conditioning-building system provides a virtual energy storage effect. Regulations: When the air conditioning system increases power and releases more heat, the system is in a "charging" state. On the contrary, when the power and heat output are reduced, the system is in a "discharging" state.

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

[0095]

[0096] Where: is the indoor temperature of the kth area in the ε scene at the tth time; T out,t,ε is the outdoor temperature at the tth moment 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 area at the tth time 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 the tth time 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 the tth moment 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 humans 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, and its calculation formula is as follows:

[0102]

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

[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 conditioner-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 the air conditioning heating condition 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 virtual energy storage charging and discharging power equivalent to the variable frequency air conditioning-building system is negative, and the system is in a "discharging" state. Conversely, the virtual energy storage charging and discharging power is positive, and the system is in a "charging" state.

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

[0113]

[0114] In the formula: G is the total number of air conditioners participating in virtual energy storage regulation in the building at time t, g is the number of the air conditioner participating in virtual energy storage regulation. Since the start-up time of the air conditioners in each area of ​​the commercial building is different, the time when the room temperature of a single area enters the temperature comfort zone is different. This patent stipulates that the virtual energy storage regulation is only 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 decline, and then the exponential function is used to build discharge loss models for new / 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 will be eliminated when the number of cycles reaches the inflection point of capacity decline, the battery inflection point can be identified through 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 the 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 electricity that can be released by the new battery energy storage system over its entire life cycle at the rated discharge depth; Rated number of discharge cycles for new batteries; Rated discharge depth for new batteries; E nb New battery installation capacity;

[0123] 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 for old batteries; Rated depth of discharge for old batteries;

[0126] The loss calculation formula for converting the single discharge loss of the new / old battery energy storage system to the loss under standard working 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 at 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 both 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 to establish an upper-level planning model. After solving it through the particle swarm algorithm, the decision variables are passed to the lower-level operation model.

[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 occupied by the ε scenario in a year; w is the number of scenarios; are the operating power of the new and old battery energy storage systems at the tth moment 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; △t 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 the building power balance constraints, indoor temperature constraints, virtual energy storage operation constraints, new / old battery energy storage system operation constraints, and interconnection line constraints, with the goal of minimizing the annual building operation cost, a lower-level operation model is established, and the new / old battery energy storage output and air conditioning operation power are determined through the Cplex solver, and the calculation 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 operating power respectively;

[0155] New / old battery energy storage system operation 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; They 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; They are respectively the charging and discharging efficiency of the old battery energy storage system; They are the upper and lower limits of the capacity of the new battery energy storage system; They are the upper and lower limits of the capacity of the old battery energy storage system; are the charging power of the new and old battery energy storage systems at time t in the ε scenario, respectively; are the discharge power of the new and old battery energy storage systems at time t in the ε scenario respectively; They are the upper and lower limits of the operating power of the new battery energy storage system respectively; They are the upper and lower limits of the operating power of the old battery energy storage system respectively; They are the capacity of the new and old battery energy storage at the start time of scheduling respectively; They are the capacities of the new and old batteries at the end of energy storage scheduling;

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

[0159]

[0160] Where: It 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 NASA's Center of Excellence in Testing. The test object is a commercial lithium-ion battery of model 18650. The relationship between the number of cycle charge and discharge and the capacity retention rate is as follows: Figure 4 In order to shield the interference of fluctuation errors in actual measurement data and accurately identify the turning point 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 invention 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. The peak hours of air conditioning load in summer and winter are 12:30-18:00 and 9:30-11:00 respectively, and the peak hour of basic load is 9:30-11:00. Considering the operating characteristics of different departments in the building, they are equivalent to 3 areas, and the air conditioning start-up time is 8:30, 9:00, and 9:30 respectively. The present invention considers four scenarios of spring, summer, autumn, and winter in the operation model, and each season contains 92, 93, 92, and 89 days respectively. Time-of-use electricity price information is detailed in Table 2.

[0166] Table 2

[0167]

[0168] The details of the equivalent virtual energy storage parameters of the “inverter air conditioning-building” system are shown in Table 3, and the details of the new / old battery technology and economic parameters are shown 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] Solution 1: Consider 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. Since Scheme 2 does not consider the virtual energy storage effect of "inverter air conditioner-building", it is necessary to install additional retired batteries to support the air conditioning load. Therefore, the configuration capacity of retired batteries is increased by 2.9 times compared with Scheme 1, the annual investment cost is increased by 81%, and the annual return on investment is reduced from 9.3% to 8.3%. It proves that the collaborative planning method of new / old energy storage batteries for commercial buildings considering the virtual energy storage effect proposed in the present invention can fully consider the virtual energy storage regulation potential of the "inverter air conditioner-building" system, effectively reduce the planned capacity of energy storage batteries at the planning level, and significantly reduce the building operation cost at the operation 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 rises to 590kWh at this time. During the 10:00-15:00 period, the air conditioning output power is appropriately reduced to save electricity without affecting the thermal comfort of the human body. During the 13:30-16:30 period, due to the increase in outdoor temperature, the heat transmitted from the outside to the interior of the building increases, which increases the air conditioning power to maintain the room temperature. In winter, the room temperature in area 1 enters the human thermal comfort temperature range before the electricity price rises. In order to reduce the power consumption during peak hours, the variable frequency air conditioner further increases the indoor temperature to store heat. Fig. 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] Fig.10 , Fig.11 The operating status of retired batteries is shown, which strictly follows the strategy of "low charge and high discharge". First, charging is carried out from 6 to 7 in the morning, taking advantage of the period with lower electricity prices; second, after 22:00, the retired batteries are charged to the initial state using the off-peak electricity price to ensure the regulation capacity of the next day. The discharge is concentrated in the peak electricity price period from 10:30 to 15:00. Since the peak of air conditioning load in winter coincides with the peak of basic load, the retired batteries will increase their output during this period to reduce the load peak.

[0182] Figure 12-14 The operating status of a new battery. The charging and discharging strategies of new batteries are similar to those of retired batteries, both following the principle of "low charging and high discharging". New batteries are evenly discharged in the two periods of 10:00-15:00 and 18:00-21:00. In addition, the peak load period of buildings in spring, autumn and winter occurs between 9:00 and 10:00. In order to reduce the peak-to-average ratio of the total load curve of the building, the new battery increases its output during this period.

[0183] Based on the above analysis, the commercial building hybrid energy storage system planning method considering the virtual energy storage effect proposed in this invention can give full play to the cost advantage of retired batteries and the regulation potential of the air-conditioning-building virtual energy storage system. It promotes the large-scale application of user-side energy storage in commercial buildings, gives full play to the full life cycle value of energy storage batteries, and provides strong support for the development of building intelligence and electrification.

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 built into a centralized energy storage system to assist the basic load in peak load shaving and valley filling. The screened old energy storage batteries are built 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 first-order equivalent thermal parameters is established, and then a calculation model of virtual charging and discharging power and virtual energy storage capacity is constructed. S2: The Bacon-watts model is used to describe the inflection point of energy storage battery capacity decline, and then the exponential function is used to build discharge loss models for new / old energy storage batteries at different discharge depths; S3: Considering the investment cost constraint, the installed capacity of new / old energy storage batteries and the number of energy storage replacements in the entire planning cycle are used as optimization variables to establish an upper-level planning model. After solving it through the particle swarm algorithm, the decision variables are passed to the lower-level operation model. S4: Considering the building power balance constraints, indoor temperature constraints, virtual energy storage operation constraints, new / old battery energy storage system operation constraints, and interconnection line constraints, with the goal of minimizing the annual building operation cost, a lower-level operation model is established, and the new / old battery energy storage output and air conditioning operation power are determined through the Cplex solver, and the calculation 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 parameter established is: Where: is the indoor temperature of the kth area in the ε scene at the tth time; T out,t,ε is the outdoor temperature at the tth moment 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 area at the tth time 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 the tth time 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 the tth moment 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 humans 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, and its calculation formula is as follows: Where: is the heat exchanged between the building interior and the outside world in the kth area at the tth moment in the ε scenario; 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 conditioner-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 virtual energy storage capacity and charging and discharging power in a single area. Its model is as follows: 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.

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 will be eliminated when the number of cycles reaches the inflection point of capacity decline, the battery inflection point can be identified through 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 the 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: Where: The amount of electricity that can be released by the new battery energy storage system over its entire life cycle at the rated discharge depth; Rated number of discharge cycles for new batteries; Rated discharge depth for new batteries; E nb Install capacity for new batteries; 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 the electric vehicle before retirement; slb Install capacity for old batteries; Rated depth of discharge for old batteries; The loss calculation formula for converting the single discharge loss of the new / old battery energy storage system to the loss under standard working 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 at 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 both 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 operation 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 occupied by the ε scenario in a year; w is the number of scenarios; are the operating power of the new and old battery energy storage systems at the tth moment 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 length.

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 operating power respectively; New / old battery energy storage system operation 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; They 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; They are respectively the charging and discharging efficiency of the old battery energy storage system; They are the upper and lower limits of the capacity of the new battery energy storage system; They are the upper and lower limits of the capacity of the old battery energy storage system; are the charging power of the new and old battery energy storage systems at time t in the ε scenario, respectively; are the discharge power of the new and old battery energy storage systems at time t in the ε scenario respectively; They are the upper and lower limits of the operating power of the new battery energy storage system respectively; They are the upper and lower limits of the operating power of the old battery energy storage system respectively; They are the capacity of the new and old battery energy storage at the start time of scheduling respectively; They are the capacities of the new and old batteries at the end of energy storage scheduling; Constraints on the connection lines between buildings and distribution networks: Where: It is the maximum interaction power between the distribution network and commercial buildings.

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