A centralized energy storage capacity segmentation method for intelligent buildings based on principal-agent game

Abstract

The application discloses a kind of intelligent building centralized energy storage capacity segmentation methods based on master-slave game.The method includes: building operator is regarded as leader, and the target function with the sum of energy transaction cost and energy storage construction operation cost is minimum value is established;User is regarded as follower, and the target function with the sum of electricity purchase cost and user power consumption discomfort is minimum value is established;The operation constraint condition of each subject is clarified;The electricity transaction price in the internal of intelligent building system is variable, and the power consumption behavior of each building user is optimized;Finally, the construction capacity of energy storage is determined, and the economic operation of system is realized.The application improves the income of operator and reduces the power consumption cost of each user, realizes the win-win of interests of operator and building user.

Description

Technical Field

[0001] This invention relates to the field of building operation technology, specifically to a method for dividing centralized energy storage capacity in intelligent buildings based on master-slave game theory. Background Technology

[0002] The development of new energy storage technologies in smart building systems stems primarily from the widespread application of renewable energy and the continuous growth in electricity demand. With accelerating urbanization, buildings are increasingly demanding energy, while traditional energy supply methods face challenges in stability and flexibility. The emergence of new energy storage technologies, such as lithium-ion batteries, solid-state batteries, and other innovative solutions, provides a solid foundation for optimizing energy management. These technologies not only effectively balance the intermittency of renewable energy sources but also support demand response and peak-valley load regulation in smart buildings. Furthermore, with the development of smart grids and the Internet of Things (IoT), energy storage systems can be seamlessly integrated with building energy management systems, improving overall energy efficiency, reducing operating costs, and thus promoting a sustainable smart building environment.

[0003] Currently, most building systems utilize centralized energy storage, where multiple users share a single energy storage device. However, traditional modeling methods suffer from potential power transfer behavior. Between multiple energy storage units, power transfer can cause the system to appear to have more available energy at certain times, leading to an overestimation of remaining power and premature termination of charging or over-discharging. 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] To address the above problems, this invention provides a method for allocating centralized energy storage capacity in intelligent buildings based on master-slave game theory. This method clarifies the energy storage construction capacity, balances the interests of both building operators and users, and achieves a win-win situation for both parties.

[0005] The technical solution of this invention includes: a method for allocating centralized energy storage capacity in intelligent buildings based on master-slave game theory, comprising the following steps:

[0006] With building operators as the leaders, establish an objective function that minimizes the sum of electricity trading costs and energy storage construction and operation costs;

[0007] As followers, users establish an objective function that minimizes the sum of electricity purchase costs and user discomfort with electricity usage.

[0008] Clearly define the operational constraints for each entity;

[0009] The electricity trading price within the intelligent building system is used as a variable to optimize the electricity consumption behavior of each building user; ultimately, the energy storage capacity and the capacity allocated to each user are determined to achieve economical operation of the system.

[0010] With building operators as the leaders, establish an objective function that minimizes the sum of electricity trading costs and energy storage construction and operation costs:

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

[0012]

[0013] In the formula, F is the objective function of the building operator; C grid For the transaction costs between operators and the power grid; C user For the transaction costs between operators and users; C s The construction and operation costs of centralized energy storage for operators; T represents the sampling points within a day, taken over 24 hours; These are the electricity purchase and sale prices set by the power grid at time t; P t bg P t sg These represent the purchase and sale of electricity by the operator to the power grid at time t; These are the internal electricity purchase and sale prices set by the operator at time t; P t bu P t su δ represents the electricity purchased and sold by the user group at time t; m represents the annual loss rate of the energy storage built by the user; r represents the energy storage construction price; S represents the total energy storage capacity of the user group; p om The cost of energy storage operation losses; P t c P t dis Δt represents the charging and discharging power of the centralized energy storage at time t; Δt is the time interval; taken as 1 hour.

[0014] Using users as followers, establish an objective function that minimizes the sum of electricity purchase costs and user discomfort with electricity:

[0015] f = C bl +C loss

[0016]

[0017] Where f is the objective function for building users, C bl For the transaction costs between users and building operators; C loss To address user discomfort related to electricity usage during demand response; L t For users, the electricity demand is determined after the demand response; L t,0 ε represents the user's original electricity cost; ε represents the sensitivity of each user to adjusting their electricity consumption behavior.

[0018] Clearly define the operational constraints for each entity:

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

[0020] Specifically, the following applies: Power balance constraints

[0021]

[0022] In the formula, These represent the power supplied to and purchased by the operator from the grid when user n transacts with the grid at time t, and when there is a power surplus or deficit. Let L be the power output of user n's photovoltaic power generation equipment at time t; n,t The actual electricity demand of user n at time t;

[0023] These represent the charging and discharging behavior of the energy storage device towards the user at time t;

[0024] Centralized energy storage operation constraints

[0025]

[0026] In the formula, e n,0 e n,24 These represent the energy storage capacity of user n at the beginning and end of the day, respectively; e n,t e represents the energy storage capacity of building user n at time t; n,t-1 E represents the energy storage capacity of user n at time t-1; n This represents the energy storage capacity allocated to user n in a centralized energy storage system; S is the centralized energy storage capacity built by the operator; N is the total number of users; η c η dis These represent the charge and discharge efficiencies of centralized energy storage devices; P c，max P dis，max These are the upper limits for charging and discharging of centralized energy storage equipment; This means that at most one value is not zero at any given time.

[0027] Internal electricity price constraints

[0028]

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

[0030] The operational constraints for building users as followers include: power balance constraints, load reduction constraints, and load transfer constraints.

[0031] Specifically, the following applies: Power balance constraints

[0032]

[0033]

[0034] In the formula, These represent the electrical energy that user n trades with the operator when there is a power surplus or deficit at time t; L represents the power generation of user n's photovoltaic equipment at time t; n,t L n,0 These represent the actual electricity demand and the original load demand of user n at time t, respectively. Let represent the load that user n can reduce and the load that can be transferred at time t, respectively.

[0035] Reduced load constraints

[0036]

[0037] In the formula, This indicates that the total load can be reduced; This represents the maximum load that user n can reduce;

[0038] Transferable load constraints

[0039]

[0040] In the formula, This indicates the total transferable load of the building; This represents the maximum value of the transferable load.

[0041] The electricity consumption behavior of each building user is optimized by using the electricity trading price within the smart building system as a variable; and the capacity for energy storage construction is ultimately determined.

[0042] The specific steps are as follows:

[0043] 1) As the leader, the operator aims to minimize operating costs, initially determines the centralized energy storage capacity and segmented energy storage capacity to be invested and constructed, the internal electricity trading price and charging and discharging strategy, and transmits the information to each building.

[0044] 2) Each building, as a follower, aims to minimize its own electricity costs. It determines its own electricity needs based on the information released by the upper-level leader and transmits them to the upper level. The upper-level leader adjusts the electricity price and formulates strategies until the game equilibrium is met, and neither party can gain greater benefits by adjusting its strategies.

[0045] This invention is based on a master-slave game theory approach, using internal transaction electricity prices as variables to determine the electricity demand of each building user, and ultimately determine the reasonable capacity for energy storage to be built by the operator. This avoids unnecessary capacity waste, increases operator revenue, and reduces electricity costs for each user, achieving a win-win situation for both the operator and the building user. Attached Figure Description

[0046] Figure 1 This is a flowchart of the present invention;

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

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

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

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

[0051] Figure 6 This is a schematic diagram of the internal electricity transaction prices set by the building operator for each user in this invention. Figure 1 ;

[0052] Figure 7 This is a schematic diagram of the internal electricity transaction prices set by the building operator for each user in this invention. Figure 2 . Detailed Implementation

[0053] The following description, in conjunction with the accompanying drawings, illustrates the embodiments and specific operating procedures of the present invention, but the scope of protection of the present invention is not limited to the specific descriptions below.

[0054] The present invention is as follows Figure 1 As shown, a method for allocating centralized energy storage capacity in smart buildings based on master-slave game theory is provided, including the following steps:

[0055] Step 1): Taking the building operator as the leader, establish an objective function that minimizes the sum of electricity trading costs and energy storage construction and operation costs:

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

[0057]

[0058] In the formula, F is the objective function of the building operator; C grid For the transaction costs between operators and the power grid; C user For the transaction costs between operators and users; C s The construction and operation costs of centralized energy storage for operators; T represents the sampling points within a day, taken over 24 hours; These are the electricity purchase and sale prices set by the power grid at time t; P t bg P t sg These represent the purchase and sale of electricity by the operator to the power grid at time t; These are the internal electricity purchase and sale prices set by the operator at time t; P t bu P t su δ represents the electricity purchased and sold by the user group at time t; m represents the annual loss rate of the energy storage built by the user; r represents the energy storage construction price; S represents the total energy storage capacity of the user group; p om The cost of energy storage operation losses; P t c P t dis Δt represents the charging and discharging power of the centralized energy storage at time t; Δt is the time interval; taken as 1 hour.

[0059] Step 2): Using users as followers, establish an objective function that minimizes the sum of electricity purchase cost and user discomfort with electricity usage.

[0060]

[0061] Where f is the objective function for building users, C bl For the transaction costs between users and building operators; C loss To address user discomfort related to electricity usage during demand response; L t For users, the electricity demand is determined after the demand response; L t,0 ε represents the user's original electricity cost; ε represents the sensitivity of each user to adjusting their electricity consumption behavior.

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

[0063] Operator constraints include:

[0064] ① Power balance constraints

[0065]

[0066] To avoid potential power transfer between users, each user is modeled separately, where, These represent the power supplied to and purchased by the operator from the grid when user n transacts with the grid at time t, and when there is a power surplus or deficit. Let L be the power output of user n's photovoltaic power generation equipment at time t; n,t The actual electricity demand of user n at time t; These represent the charging and discharging behavior of the energy storage device towards the user at time t.

[0067] ②Constraints of centralized energy storage operation

[0068]

[0069]

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

[0071] At most one value is not zero at any given time;

[0072] ③ Internal electricity price constraints

[0073] λ t sg ≤λ t su ≤λ t bu ≤λ t bg

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

[0075] To effectively avoid the problem of power transfer between users leading to a small energy storage capacity, this invention treats each user as an independent entity for energy storage, achieving the effect of energy storage capacity segmentation. That is, each user's energy storage first operates independently to meet its own electricity needs, and then if there is surplus or shortage of power, it uses the power stored by other users.

[0076] The constraints for traditional building users to access energy storage are:

[0077]

[0078] N represents the number of building users; These represent the energy storage charging and discharging power of user n in traditional energy storage dispatch; P t D P t C These represent the overall energy storage charging and discharging power of the building system. However, there is a potential for energy storage power transfer between users during the charging and discharging process. For example, when there are three users in the building, and each user's charging and discharging power is -3, -2, or +5 (negative values ​​represent discharging, and positive values ​​represent charging), the sum of the overall building power is 0. From the perspective of the building operator, it is possible not to build energy storage, i.e., the capacity is 0. However, it is clear that the energy storage capacity is not 0. Therefore, this invention models users separately, and each user has its own objective function, namely, minimizing the sum of electricity purchase cost and user discomfort with electricity. Simulations are performed with the operator 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 built by the building operator.

[0079] Building user constraints include:

[0080] ① Power balance constraints

[0081]

[0082]

[0083] In the formula, These represent the electrical energy that user n trades with the operator when there is a power surplus or deficit at time t; L represents the power generation of user n's photovoltaic equipment at time t; n,t L n,0 These represent the actual electricity demand and the original load demand of user n at time t, respectively. Let represent the load that user n can reduce and the load that can be transferred at time t, respectively.

[0084] ② Load constraints can be reduced

[0085]

[0086] In the formula, This indicates that the total load can be reduced; This represents the maximum load that user n can reduce.

[0087] ③ Transferable load constraints

[0088]

[0089] In the formula, This indicates the total transferable load of the building; This represents the maximum value of the transferable load.

[0090] In summary, this invention provides a method for allocating centralized energy storage capacity in smart buildings based on a master-slave game theory approach. When a smart building system does not contain energy storage devices, and the operator undertakes centralized energy storage construction, this invention first establishes an objective function with the building operator as the leader, minimizing the sum of electricity transaction costs and energy storage construction and operation costs. Then, with users as followers, it establishes an objective function minimizing the sum of electricity purchase costs and user discomfort with electricity usage. Next, it clarifies the operational constraints of each entity, and, combining these constraints with the model, optimizes the electricity demand of each building user using the internal electricity transaction price as a variable. This clarifies the centralized energy storage construction capacity and the capacity allocated to each user, ensuring no waste of energy storage capacity and maximizing the interests of both the leader and followers.

[0091] This invention selects four users, assigns them numbers, and performs simulation verification based on their reported electricity consumption information.

[0092] Table 1. Cost Comparison of Individual Modeling and Collaborative Modeling

[0093]

[0094] Table 2. Operator's Energy Storage Capacity and Revenue

[0095]

[0096] As shown in Table 1, compared with user joint modeling, the transaction costs between users and the power grid are lower when users are modeled individually, but the transaction costs with operators are higher when users are modeled jointly. This is because potential power transfer is not eliminated in joint modeling, so users are less willing to trade with operators and more willing to trade with the power grid. Due to the preferential treatment in internal transactions, the total electricity cost is higher when users are modeled jointly. Although the transaction costs between users and operators are higher when users are modeled individually, it indicates that users are more willing to trade with operators and less willing to trade with the power grid, thus reducing the total electricity cost.

[0097] Table 2 shows that the total energy storage capacity when users are modeled individually is greater than that when users are modeled jointly. This is because individual modeling avoids potential power transfer between users, reveals implicit capacity, increases user transaction volume, and ultimately increases the operator's total revenue. Therefore, the user-individual modeling proposed in this invention for energy storage capacity segmentation can effectively improve operator revenue and reduce user electricity costs.

[0098] from Figures 6-7 It can be seen that the internal user purchase and sale electricity price set by the operator falls between the electricity price set by the power grid. When the power grid electricity price is at a low point, combined with... Figures 2-5 It can be seen that operators are increasingly willing to utilize energy storage for charging, and when internal user electricity purchase prices are at their peak, operators are more willing to discharge stored energy to maximize profits. Furthermore, for their own benefit, operators set electricity sales prices for each user that are not significantly different from the electricity sales prices set by the power grid, but there is a substantial difference in the purchase price. This is to use electricity prices to adjust users' electricity consumption behavior and maximize the interests of both parties.

[0099] This invention achieves capacity segmentation of energy storage by modeling each user individually, which can better realize demand response strategies, flexibly dispatch energy storage according to electricity trading prices and load demand, and optimize energy use efficiency.

[0100] The methods, steps, and data described in this invention are merely specific embodiments of the invention, serving as a general exposition and illustrative example of the spirit of the invention. Those skilled in the art will recognize the various possibilities of variations or optional embodiments, and various modifications, additions, improvements, or substitutions can be made under the inspiration of the spirit and principles of this invention. It is understood that these modifications, additions, improvements, or substitutions are considered to be included in this invention and do not depart from the spirit of the invention or exceed the scope defined by the appended claims.

Claims

1. A method for allocating centralized energy storage capacity in intelligent buildings based on master-slave game theory, characterized in that, Includes the following steps: With building operators as the leaders, establish an objective function that minimizes the sum of electricity trading costs and energy storage construction and operation costs; To avoid potential power transfer between users, each user is modeled separately, and an objective function is established that minimizes the sum of electricity purchase cost and user discomfort with electricity usage. This includes: ， ， in, The objective function for building users, For transaction costs between users and building operators; To address user discomfort related to electricity usage when responding to their needs; To determine the electricity demand after responding to user needs; Based on the user's original electricity demand; Adjust sensitivity based on each user's electricity consumption behavior; The operational constraints for each entity are clearly defined, specifically as follows: The operational constraints for operators as leaders include: power balance constraints, centralized energy storage operation constraints, and internal electricity trading price constraints. The operational constraints for building users as followers include: power balance constraints, load reduction constraints, and load transfer constraints. Operator constraints include: Power balance constraints ， In the formula, , These represent the power supplied to and purchased by the operator from the grid when user n transacts with the grid at time t, and when there is a power surplus or deficit. Let be the power generation capacity of user n's photovoltaic power generation equipment at time t; The actual electricity demand of user n at time t; , These represent the charging and discharging behavior of the energy storage device towards the user at time t; Centralized energy storage operation constraints ， ， In the formula, , These represent the energy storage capacity of user n at the beginning and end of the day, respectively. This represents the energy storage capacity of building user n at time t; This represents the energy storage capacity of user n at time t-1; This represents the energy storage capacity allocated to user n by centralized energy storage. Centralized energy storage capacity built for operators; N is the total number of users; , These refer to the charging and discharging efficiency of centralized energy storage devices; , These are the upper limits for charging and discharging of centralized energy storage equipment; This means that at most one value is not zero at any given time. For time intervals; Internal electricity price constraints ， In the formula, , These are the purchase and sale prices of electricity set by the power grid at time t, respectively. , These are the electricity sales and purchase prices set by the operators for their internal users; Building user constraints include: Power balance constraints ， ， In the formula, , These represent the electrical energy that user n trades with the operator when there is a power surplus or deficit at time t; This represents the power generation of user n's photovoltaic equipment at time t; , These represent the actual electricity demand and the original load demand of user n at time t, respectively. , Let represent the load that user n can reduce and the load that can be transferred at time t, respectively. Reduced load constraints ， In the formula, This indicates that the total load can be reduced; This represents the maximum load that user n can reduce; Transferable load constraints ， In the formula, This indicates the total transferable load of the building; This represents the maximum transferable load. By using the electricity trading price within the smart building system as a variable to optimize the electricity consumption behavior of each building user, the final energy storage capacity and the allocated capacity for each user are determined. The specific steps are as follows: 1) As the leader, the operator aims to minimize operating costs, initially determining the centralized and segmented energy storage capacity to be invested in, the internal electricity trading price, and the charging and discharging strategy, and then transmitting the information to each building. 2) Each building, as a follower, aims to minimize its own electricity costs. Based on the information released by the upper-level leader, it determines its own electricity demand and transmits it to the upper level. The upper-level leader adjusts the electricity price and formulates strategies until the game equilibrium is met, and neither party can gain greater benefits by adjusting its strategies.

2. The method for allocating centralized energy storage capacity in intelligent buildings based on master-slave game theory as described in claim 1, characterized in that, With building operators as the leaders, establish an objective function that minimizes the sum of electricity trading costs and energy storage construction and operation costs; include: ， ， In the formula, The objective function for the building operator; For transaction costs between operators and the power grid; For transaction costs between operators and users; The construction and operation costs of centralized energy storage for operators; T represents the number of sampling points within a day; , These are the purchase and sale prices of electricity set by the power grid at time t, respectively. , These represent the purchase and sale of electricity by the operator to the power grid at time t; , These are the internal electricity purchase and sale prices set by the operator at time t; , These represent the electricity purchased and sold by the user group at time t. The annual loss rate of energy storage built for users; m is the operating life of the energy storage; The cost of building energy storage facilities; Total energy storage capacity for the user group; The cost of energy storage operation losses; , These represent the charging and discharging power of the centralized energy storage at time t, respectively. For time intervals.

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