Intelligent building centralized energy storage capacity segmentation method based on master-slave game

Abstract

The invention discloses an intelligent building centralized energy storage capacity segmentation method based on a master-slave game. The method comprises the steps that a building operator serves as a leader, and an objective function with the sum of the electric energy transaction cost and the energy storage construction operation cost as the minimum value is established; the user is used as a follower, and a target function with the sum of the electricity purchase cost and the electricity utilization uncomfortableness of the user as the minimum value is established; determining the operation constraint condition of each main body; the electricity consumption behavior of each building user is optimized by taking the electric energy transaction electricity price in the intelligent building system as a variable; and finally, determining the energy storage investment and construction capacity, and realizing economic operation of the system. According to the invention, the income of the operator is improved, the power utilization cost of each user is reduced, and the benefit win-win of the operator and the building user is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of building operation, and in particular to a method for dividing centralized energy storage capacity of an intelligent building based on master-slave game. Background Art

[0002] In smart building systems, the development background of new energy storage is mainly due to the widespread application of renewable energy and the growing demand for electricity. With the acceleration of urbanization, the demand for energy in buildings is increasing, while traditional energy supply methods face the problem of insufficient stability and flexibility. The emergence of new energy storage technologies, such as lithium batteries, solid-state batteries and other innovative solutions, provides a rich foundation for optimizing energy management. These technologies can not only effectively balance the intermittent nature of renewable energy, but also support demand response and peak and valley load regulation of smart buildings. In addition, with the development of smart grid and Internet of Things technologies, energy storage systems can be seamlessly integrated with the building's energy management system to improve overall energy efficiency and reduce operating costs, thereby promoting a sustainable smart building environment.

[0003] Currently, most building systems use centralized energy storage, that is, multiple users share one energy storage device. However, traditional modeling methods have potential power transmission behavior. Between multiple energy storage units, power transmission may cause the system to appear to have more available energy at certain times, so the system will overestimate the remaining power, stop charging early, or over-discharge. If there are errors in the estimation of energy storage capacity, these strategies will be affected, resulting in inaccurate energy storage scheduling. Summary of the invention

[0004] In view of the above problems, the present invention provides a method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game, which clarifies the energy storage construction capacity, takes into account the interests of both building operators and users, and achieves a win-win situation for both parties.

[0005] The technical solution of the present invention includes: a method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game, comprising the following steps:

[0006] With the building operator as the leader, an objective function is established with the sum of electricity transaction costs and energy storage construction and operation costs as the minimum value;

[0007] As a follower, the user establishes an objective function that takes the sum of the electricity purchase cost and the user's electricity discomfort as the minimum value;

[0008] Clarify the operating constraints of each subject;

[0009] The electricity trading price within the smart building system is used as a variable to optimize the electricity consumption behavior of each building user; ultimately, the investment and construction capacity of energy storage and the capacity divided among each user are determined to achieve economic operation of the system.

[0010] With the building operator as the leader, an objective function is established with the sum of the electricity transaction cost and the energy storage construction and operation cost as the minimum value:

[0011] F=C grid +C user +C s

[0012]

[0013] Where F is the objective function of the building operator; C grid is the transaction cost between the operator and the power grid; C user is the transaction cost between operators and users; C s The construction and operation costs of the centralized energy storage invested and built by the operator; T is the sampling point within a day, taking 24 hours; are the purchase and sale prices of electricity set by the power grid at time t; P t bg , P t sg They are the purchase and sale of electricity from the operator to the grid at time t; are the internal purchase and sale prices of electricity set by the operator at time t; P t bu , P t su are the purchase and sale of electricity by the user group at time t; δ is the annual loss rate of energy storage built by the user; m is the operating life of energy storage; r is the energy storage construction price; S is the total energy storage capacity of the user group; p om is the loss price of energy storage operation; P t c , P t dis They are respectively the charging and discharging power of the centralized energy storage at time t; △t is the time interval; it is taken as 1h.

[0014] Taking the user as a follower, establish an objective function with the sum of the electricity purchase cost and the user's electricity discomfort as the minimum value:

[0015] f=C bl +C loss

[0016]

[0017] Where f is the objective function of the building user, C bl The transaction cost between users and building operators; C loss The discomfort of electricity consumption when users respond to demand; L t The electricity demand after the user responds to the demand; L t,0 is the user’s original electricity cost; ε is the sensitivity of each user’s electricity consumption behavior adjustment.

[0018] Clarify the operating constraints of each entity:

[0019] The operating constraints for operators as leaders include: power balance constraints, centralized energy storage operation constraints, and internal transaction electricity price constraints.

[0020] The details are as follows: Power balance constraints

[0021]

[0022] In the formula, They are respectively the power delivered and purchased by the operator to the grid when there is a power surplus or shortage when user n trades with the grid at time t; is the power generation of the photovoltaic power generation equipment of user n at time t; L n,t is the actual electricity demand of user n at time t;

[0023] They are respectively the charging and discharging behaviors of the energy storage device on the user at time t;

[0024] Centralized energy storage operation constraints

[0025]

[0026] In the formula, e n,0 、e n,24 They represent the energy storage capacity of user n at the beginning and end of the day respectively; e n,t represents the energy storage capacity of building user n at time t; e n,t-1 represents the energy storage capacity of user n at time t-1; E n represents the energy storage capacity allocated to user n by centralized energy storage; S is the centralized energy storage capacity built by the operator; N is the total number of users; η c , η dis are the charging and discharging efficiency of centralized energy storage equipment; P c，max , P dis，max They are the upper limits of charging and discharging of centralized energy storage equipment respectively; It means that at most one value is not 0 at the same time;

[0027] Internal transaction price constraints

[0028]

[0029] In the formula, λ t sg , t bg are the purchase and sale electricity prices set by the power grid at time t; t su ,t bu The electricity sales and purchase prices are set by operators for internal users respectively.

[0030] The operating constraints of building users as followers include: power balance constraints, curtailable load constraints, and transferable load constraints.

[0031] The details are as follows: Power balance constraints

[0032]

[0033]

[0034] In the formula, They represent the electric energy traded between user n and the operator when there is a surplus or shortage of power at time t; represents the power generation of user n’s photovoltaic equipment at time t; L n,t , L n,0 are the actual power demand and original load demand of user n at time t respectively; They represent the reducible and transferable loads of user n at time t respectively;

[0035] Reducible load constraints

[0036]

[0037] In the formula, Indicates the total amount of load that can be reduced; represents the maximum value of load reduction that can be achieved by user n;

[0038] Transferable load constraints

[0039]

[0040] In the formula, It indicates the total amount of transferable load of the building; Indicates the maximum value of the transferable load.

[0041] Optimize the electricity consumption behavior of each building user by taking the electricity trading price within the smart building system as a variable; ultimately determine the investment and construction capacity of energy storage;

[0042] The specific steps are:

[0043] 1) As a leader, the operator aims to minimize operating costs, preliminarily determines the centralized energy storage capacity and segmented energy storage capacity, internal transaction electricity prices and charging and discharging strategies to be invested and constructed, and transmits the information to each building.

[0044] 2) As a follower, each building aims to minimize its own electricity cost. It determines its own electricity demand based on the information released by the upper-level leader and passes it to the upper level. The upper-level leader adjusts the electricity price and formulates a strategy until the game equilibrium is met. No party can obtain greater benefits by adjusting the strategy.

[0045] The present invention is based on the master-slave game form, takes the internal transaction electricity price as a variable, determines the electricity demand of each building user, and finally determines the reasonable capacity of the operator to invest in energy storage, avoids unnecessary capacity waste, increases the operator's revenue and reduces the electricity cost of each user, and achieves a win-win situation for both the operator and the building users. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the present invention;

[0047] Figure 2 is a power balance diagram after demand response of building user 1 of the present invention;

[0048] Figure 3 is a power balance diagram after demand response of building user 2 of the present invention;

[0049] Figure 4 is a power balance diagram after demand response of building user 3 of the present invention;

[0050] Figure 5 is a power balance diagram after demand response of building user 4 of the present invention;

[0051] Figure 6 This is a schematic diagram of the internal transaction electricity price for each user formulated by the building operator of the present invention. Figure 1 ;

[0052] Figure 7 This is a schematic diagram of the internal transaction electricity price for each user formulated by the building operator of the present invention. Figure 2 . DETAILED DESCRIPTION

[0053] The implementation scheme and specific operation process of the present invention are described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following specific description.

[0054] The present invention Figure 1 As shown, a method for dividing the centralized energy storage capacity of an intelligent building based on a master-slave game is provided, comprising the following steps:

[0055] Step 1): Take the building operator as the leader and establish an objective function that minimizes the sum of the electricity transaction cost and the energy storage construction and operation cost:

[0056] F=C grid +C user +Cs

[0057]

[0058] Where F is the objective function of the building operator; C grid is the transaction cost between the operator and the power grid; C user is the transaction cost between operators and users; C s The construction and operation costs of the centralized energy storage invested and built by the operator; T is the sampling point within a day, taking 24 hours; are the purchase and sale prices of electricity set by the power grid at time t; P t bg , P t sg They are the purchase and sale of electricity from the operator to the grid at time t; are the internal purchase and sale prices of electricity set by the operator at time t; P t bu , P t su are the purchase and sale of electricity by the user group at time t; δ is the annual loss rate of energy storage built by the user; m is the operating life of energy storage; r is the energy storage construction price; S is the total energy storage capacity of the user group; p om is the loss price of energy storage operation; P t c , P t dis They are respectively the charging and discharging power of the centralized energy storage at time t; △t is the time interval; it is taken as 1h.

[0059] Step 2): Take the user as a follower and establish an objective function with the sum of the electricity purchase cost and the user's electricity discomfort as the minimum value:

[0060]

[0061] Where f is the objective function of the building user, C bl The transaction cost between users and building operators; C loss The discomfort of electricity consumption when users respond to demand; L t The electricity demand after the user responds to the demand; L t,0 is the user’s original electricity cost; ε is the sensitivity of each user’s electricity consumption behavior adjustment.

[0062] Step 3): Clarify the operating constraints of each entity

[0063] Operator constraints include:

[0064] ① Power balance constraints

[0065]

[0066] In order to avoid potential power transfer between users, each user is modeled separately, where: They are respectively the power delivered and purchased by the operator to the grid when there is a power surplus or shortage when user n trades with the grid at time t; is the power generation of the photovoltaic power generation equipment of user n at time t; L n,t is the actual electricity demand of user n at time t; They are respectively the charging and discharging behaviors of the energy storage device on the user at time t.

[0067] ② Centralized energy storage operation constraints

[0068]

[0069]

[0070] In the formula, e n,0 、e n,24 They represent the energy storage capacity of user n at the beginning and end of the day respectively; e n,t represents the energy storage capacity of building user n at time t; e n,t-1 represents the energy storage capacity of user n at time t-1; E n represents the energy storage capacity allocated to user n by centralized energy storage; S is the centralized energy storage capacity built by the operator; N is the total number of users; η c , η dis are the charging and discharging efficiency of centralized energy storage equipment; P c，max , P dis，max They are the upper limits of charging and discharging of centralized energy storage equipment respectively; express

[0071] At the same time, at most one of them is not 0;

[0072] ③ Internal transaction electricity price constraints

[0073] λ t sg ≤λ t su ≤λ t bu ≤λ t bg

[0074] In the formula, λ t sg , t bg are the purchase and sale electricity prices set by the power grid at time t; t su , t buThe electricity sales and purchase prices are set by operators for internal users respectively.

[0075] In order to effectively avoid the situation where power transmission between users leads to a small energy storage capacity, the present invention calls for energy storage for each user as an independent individual, thereby achieving the effect of dividing the energy storage capacity; that is, the energy storage of each user first operates independently to meet its own electricity needs, and then if there is surplus electricity or power shortage, the electricity stored by other users is used.

[0076] The constraints for traditional building user groups to call for energy storage are:

[0077]

[0078] N is the number of building users; are the energy storage charging and discharging power of user n in traditional energy storage scheduling; P t D , P t C They are the energy storage charging and discharging power of the entire building system respectively; however, at this time, there is potential mutual transmission of energy storage power between users in the energy storage charging and discharging behavior. For example, when there are 3 building users, when the charging and discharging power of each user is -3, -2, +5, negative values ​​represent discharge, and positive values ​​represent charging. At this time, the sum of the power of the entire building is 0; from the building operator's point of view, energy storage can be omitted, that is, the capacity is 0, but obviously the energy storage capacity is not 0. Therefore, the users of the present invention are modeled separately, and each user has his own objective function, that is, the sum of the electricity purchase cost and the user's electricity discomfort is minimized. Simulations are performed with the operator separately to obtain the energy storage capacity of each user; however, the energy storage capacity of each building should be equal to the energy storage capacity constructed by the building operator.

[0079] Building user constraints include:

[0080] ① Power balance constraints

[0081]

[0082]

[0083] In the formula, They represent the electric energy traded between user n and the operator when there is a surplus or shortage of power at time t; represents the power generation of user n’s photovoltaic equipment at time t; L n,t , L n,0 are the actual power demand and original load demand of user n at time t respectively; They represent the reducible and transferable loads of user n at time t respectively.

[0084] ② Load constraints can be reduced

[0085]

[0086] In the formula, Indicates the total amount of load that can be reduced; It represents the maximum value of load reduction that can be achieved by user n.

[0087] ③Transferable load constraints

[0088]

[0089] In the formula, It indicates the total amount of transferable load of the building; Indicates the maximum value of the transferable load.

[0090] In summary, the present invention provides a method for dividing the centralized energy storage capacity of smart buildings based on master-slave game. When the smart building system does not contain energy storage equipment, when the operator invests in the construction of centralized energy storage, the present invention first takes the building operator as the leader, establishes an objective function with the sum of the electricity transaction cost and the energy storage construction and operation cost as the minimum value, and takes the user as the follower, establishes an objective function with the sum of the electricity purchase cost and the user's electricity discomfort as the minimum value. Then, the operating constraints of each subject are clarified, and the operating constraints and models are combined. With the internal transaction electricity price as the variable, the electricity demand of each building user is optimized, the centralized energy storage construction capacity and the capacity allocated to each user are clarified, so as to achieve no waste of energy storage capacity and maximize the benefits of the leader and the follower.

[0091] The present invention selects 4 users, numbers them, and performs simulation verification based on the electricity usage information reported by them.

[0092] Table 1 Comparison of the cost of user individual modeling and joint modeling

[0093]

[0094] Table 2: Operators' energy storage capacity and revenue

[0095]

[0096] It can be seen from Table 1 that, compared with user joint modeling, the transaction costs between each user and the power grid in the individual modeling are lower than those in the user joint modeling, and the transaction costs with the operator are higher than those in the user joint modeling. This is because the potential power transmission is not eliminated in the joint modeling, the transaction willingness between users and operators is not high, and the transaction with the power grid increases. Due to the preferential internal transactions, the total electricity cost in the user joint modeling is ultimately high. Although the transaction costs between users and operators increase in the individual modeling, it shows that users are more willing to trade with operators, and their willingness to trade with the power grid decreases, and the total electricity cost is ultimately reduced.

[0097] It can be seen from Table 2 that the total energy storage capacity when the user is individually modeled is greater than the total energy storage capacity when the user is jointly modeled. This is because individual modeling avoids the potential power transmission between users, makes the implicit capacity visible, increases the user's transaction volume, and ultimately increases the operator's total revenue. It can be seen that the user individual modeling proposed in the present invention to divide the energy storage capacity can effectively increase the operator's revenue and reduce the user's electricity cost.

[0098] from Figure 6-7 It can be seen that the internal user purchase and sale electricity prices set by operators are between the electricity prices set by the power grid. When the power grid price is at a low point, combined with Figure 2 to Figure 5 It can be seen that the willingness of operators to call for energy storage charging has increased, and when the internal user's electricity purchase price is at a peak, operators are more willing to discharge energy storage to maximize their benefits. In addition, for their own interests, the electricity sales price set by the operator for each user is not much different from the electricity sales price set by the power grid, but there is a big difference in the electricity purchase price. This is to use the electricity price to adjust the user's electricity consumption behavior and maximize the interests of both parties.

[0099] The present invention realizes the capacity division of energy storage by individually modeling each user, which can better implement the demand response strategy, flexibly dispatch energy storage according to the transaction electricity price and load demand, and optimize the energy utilization efficiency.

[0100] The method steps and data described in the present invention are only specific embodiments of the present invention, and are an overall description and illustration of the spirit of the present invention. Those skilled in the art to which the present invention belongs may also be aware of the various possibilities of variant or optional embodiments, and make various modifications, supplements, improvements or substitutions inspired by the spirit and principles of the present invention. It is understood that these modifications, supplements, improvements or substitutions are considered to be included in the present invention, and will not deviate from the spirit of the present invention or exceed the scope defined by the attached claims.

Claims

1. A method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game, characterized in that: The following steps are involved: With the building operator as the leader, an objective function is established with the sum of electricity transaction costs and energy storage construction and operation costs as the minimum value; As a follower, the user establishes an objective function that takes the sum of the electricity purchase cost and the user's electricity discomfort as the minimum value; Clarify the operating constraints of each subject; The electricity trading price within the smart building system is used as a variable to optimize the electricity consumption behavior of each building user, and ultimately determine the investment and construction capacity of energy storage and the capacity allocated to each user.

2. According to claim 1, a method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game, characterized in that: With the building operator as the leader, an objective function is established with the sum of the electricity transaction cost and the energy storage construction and operation cost as the minimum value; including: F=C grid +C user +C s Where F is the objective function of the building operator; C grid is the transaction cost between the operator and the power grid; C user is the transaction cost between operators and users; C s The construction and operation costs of the centralized energy storage invested and built by the operator; T is the sampling point within a day; are the purchase and sale prices of electricity set by the power grid at time t; They are the purchase and sale of electricity from the operator to the grid at time t; are the internal purchase and sale prices of electricity set by the operator at time t; are the purchase and sale of electricity by the user group at time t; δ is the annual loss rate of energy storage built by the user; m is the operating life of energy storage; r is the energy storage construction price; S is the total energy storage capacity of the user group; p om The loss price for energy storage operation; are the charging and discharging powers of the centralized energy storage at time t respectively; △t is the time interval.

3. According to claim 1, a method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game, characterized in that: Taking the user as a follower, an objective function is established with the sum of the electricity purchase cost and the user's electricity discomfort as the minimum value, including: f=C bl +C loss Among them, f is the objective function of the building user, C bl The transaction cost between users and building operators; C loss The discomfort of electricity consumption when users respond to demand; L t The electricity demand after the user responds to the demand; L t,0 is the user’s original electricity cost; ε is the sensitivity of each user’s electricity behavior adjustment.

4. According to claim 1, a method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game, characterized in that: Clarify the operating constraints of each entity: The operating constraints of operators as leaders include: power balance constraints, centralized energy storage operation constraints, and internal transaction electricity price constraints; The operating constraints of building users as followers include: power balance constraints, curtailable load constraints, and transferable load constraints.

5. The method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game according to claim 4 is characterized in that: Operator constraints include: Power balance constraints In the formula, They are respectively the power delivered and purchased by the operator to the grid when there is a power surplus or shortage when user n trades with the grid at time t; is the power generation of the photovoltaic power generation equipment of user n at time t; L n,t is the actual electricity demand of user n at time t; They are respectively the charging and discharging behaviors of the energy storage device on the user at time t; Centralized energy storage operation constraints In the formula, e n,0 、e n,24 They represent the energy storage capacity of user n at the beginning and end of the day respectively; e n,t represents the energy storage capacity of building user n at time t; e n,t-1 represents the energy storage capacity of user n at time t-1; E n represents the energy storage capacity allocated to user n by centralized energy storage; S is the centralized energy storage capacity built by the operator; N is the total number of users; η c , η dis are the charging and discharging efficiency of centralized energy storage equipment; P c，max , P dis，max They are the upper limits of charging and discharging of centralized energy storage equipment respectively; It means that at most one value is not 0 at the same time; Internal transaction price constraints In the formula, are the purchase and sale electricity prices set by the power grid at time t; The electricity sales and purchase prices are set by operators for internal users respectively.

6. The method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game according to claim 4 is characterized in that: Building user constraints include: Power balance constraints In the formula, They represent the electric energy traded between user n and the operator when there is a surplus or shortage of power at time t; represents the power generation of user n’s photovoltaic equipment at time t; L n,t , L n,0 are the actual power demand and original load demand of user n at time t respectively; They represent the reducible and transferable loads of user n at time t respectively; Reducible load constraints In the formula, Indicates the total amount of load that can be reduced; represents the maximum value of load reduction that can be achieved by user n; Transferable load constraints In the formula, It indicates the total amount of transferable load of the building; Indicates the maximum value of the transferable load.

7. The method for dividing the centralized energy storage capacity of an intelligent building based on master-slave game according to claim 1 is characterized in that: Optimize the electricity consumption behavior of each building user by taking the electricity trading price within the smart building system as a variable; ultimately determine the investment and construction capacity of energy storage and the capacity divided among each user; The specific steps are: 1) As a leader, the operator aims to minimize operating costs, preliminarily determines the centralized energy storage capacity and segmented energy storage capacity, internal transaction electricity prices and charging and discharging strategies to be invested and constructed, and transmits the information to each building; 2) As a follower, each building aims to minimize its own electricity cost. It determines its own electricity demand based on the information released by the upper-level leader and passes it to the upper level. The upper-level leader adjusts the electricity price and formulates a strategy until the game equilibrium is met. No party can obtain greater benefits by adjusting the strategy.

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