A method for optimizing power sharing and scheduling in intelligent buildings based on weak robust optimization

Abstract

This invention discloses a smart building power sharing optimization scheduling method based on weak robustness optimization, comprising: modeling the power supply and consumption equipment contained in the smart building and clarifying the power flow direction of each equipment; formulating the shared power price for building users based on the supply and demand matching degree using the nonequilibrium cobweb theory; clarifying the objective function of the pricing model users; clarifying the system operation constraints; and using the equivalence transformation theory to transform the uncertain constraints into deterministic constraints, making the model a weakly robust model that takes into account both uncertainty and economy, and solving it to obtain the electricity cost of each building user. This invention uses the nonequilibrium cobweb theory to price the shared power among users and uses the equivalence transformation theory to transform the model into a weakly robust form, thereby ensuring the safety of the building system while improving the electricity economy for users.

Description

Technical Field

[0001] The invention relates to the technical field of building internal resource scheduling, and in particular to an intelligent building electric energy sharing optimization scheduling method based on weak robust optimization. Background Art

[0002] In smart building management, resource scheduling, as an important part of improving energy efficiency and optimizing operations, is facing increasingly complex challenges. With the widespread application of new energy, especially solar and wind energy, the uncertainty of their output has put forward higher requirements for building energy management. Traditional scheduling methods are often based on stable energy supply, while the volatility of new energy makes the prediction and scheduling process more difficult. Therefore, studying how to effectively consider the uncertainty of new energy output in resource scheduling can not only improve the flexibility and reliability of the system, but also promote the goal of sustainable development and achieve dual optimization of economic and environmental benefits. Exploration in this field will provide an important theoretical basis and practical guidance for the energy management of future smart buildings.

[0003] There are currently some significant shortcomings in smart building resource scheduling: insufficient management of the volatility of new energy sources makes it difficult for the system to effectively respond to random and uncertain energy output, resulting in an imbalance between supply and demand and an inability to meet dynamically changing needs. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0005] The technical solution of the present invention includes: a method for optimizing the sharing of electric energy in intelligent buildings based on weak robust optimization, comprising the following steps:

[0006] Model the power supply and consumption equipment in the smart building and clarify the power flow of each device;

[0007] Based on the matching degree of supply and demand, the shared electricity price of building users is formulated using the non-equilibrium cobweb theory;

[0008] Specify the objective function of the pricing model user;

[0009] Identify system operation constraints;

[0010] The uncertainty constraints are transformed into deterministic constraints using the equation transformation theory, making the model a weakly robust model that takes both uncertainty and economy into account. The electricity cost of each building user is then obtained by solving the model.

[0011] Model the power supply and consumption equipment in the smart building and clarify the power flow of each device:

[0012] (1) Supply-side photovoltaic devices

[0013] According to the purpose of PV, the power generation balance constraint of the nth user in the tth period can be obtained:

[0014]

[0015] Where: P t PV represents the power generation of the building photovoltaic device in the tth period; It represents the amount of electricity provided by PV to the nth user in period t; It represents the amount of electricity that the nth user shares with other users during period t; It represents the amount of electricity sold by the nth user to the grid during period t; represents the amount of electricity provided by PV to EV by the nth user in period t; X EV =1 means the nth user is not an EV user, X EV =0 means the nth user is an EV user; N is the number of users in the building system.

[0016] (2) Single electric vehicle on the electricity consumption side

[0017] The EV of the present invention adopts an orderly power consumption mode. Assume that the time when the EV of the nth user is connected to the building is T n,in , the time to leave the building is T n,out , then EV can participate in the scheduling parameter ω n,t for:

[0018]

[0019] Where: n,t 1 means that the EV of the nth user can participate in the scheduling in the tth time period; ω n,t A value of 0 indicates that the EV of the nth user cannot participate in the scheduling in the tth time period.

[0020] In the tth period, the actual charging and discharging power of the nth user's EV is:

[0021]

[0022] Where: are the charging and discharging powers of the n EVs that can participate in the scheduling in the tth period; η car,ch , η car,dis are the charging and discharging efficiency of EV respectively.

[0023] (3) Load on the electricity consumption side

[0024] Building loads include uncontrollable loads (lighting, etc.), movable loads (washing machines, water heaters, etc.), and interruptible loads (air conditioners, etc.). The actual power consumption in the t period of a day can be expressed as:

[0025]

[0026] Where: They represent the actual power consumption and the original power consumption of user n respectively; represents the load power that can be shifted by user n at time t; represents the interruptible load power of user n at time t; They represent the maximum values ​​of load that can be shifted and load that can be interrupted by user n respectively; T represents the total time period, which is 24.

[0027] Based on the matching degree of supply and demand, the shared electricity price of building users is formulated using the non-equilibrium cobweb theory:

[0028] Since the supply and demand inside the building are in an unbalanced state in many periods of time, the non-equilibrium spider web model can jointly adjust the price and quantity to achieve a state of supply and demand adaptation. In order to encourage users to participate in electricity sharing, based on supply and demand matching, the non-equilibrium spider web model is used to set different price packages for users to choose from.

[0029] The following formula is that the non-equilibrium spider web electricity price is subject to the constraints of the power grid transaction purchase and sale price. After the building users meet their own needs, the supply of the non-equilibrium spider web pricing model is the sum of excess electricity, and the demand is the sum of the electricity that the users themselves lack, that is:

[0030]

[0031] Where: p p,t represents the non-equilibrium spider price for the tth period during the pth iteration, and is the time-of-use price set for the grid in the process of solving the optimal form; It represents the amount of electricity shared by the nth user at time t, i.e. the supply S t ; It represents the amount of electricity that the nth user obtains from other users at time t, that is, the demand D t ; The non-equilibrium spider web price adjustment model is:

[0032]

[0033] Where: Q p,t is the absolute value of the difference between supply and demand; p p-1,t is the price at the p-1th time t; r is a very small positive number, and the iteration stops when the supply and demand difference corresponding to the p-th price differs from the supply and demand difference corresponding to the p-1th price by a very small positive number.

[0034] Specify the objective function of the user of the non-equilibrium spider pricing model:

[0035] In the stage of finding the best combination guided by the pricing model, the objective function based on the best combination and the non-equilibrium spider web pricing model is to minimize the total electricity cost of a combination:

[0036]

[0037] Number each user in turn, M is the set of user numbers belonging to a group; g n is the electricity cost of the nth user for one day, and the objective function is to minimize the total electricity cost; They represent the amount of electricity purchased or sold by the nth user from the power grid; represents the amount of electricity obtained by user n from other buildings at time t; is the electricity price set by the power grid. X is the number of users in each combination. F is the objective function, which minimizes the electricity cost of the user combination.

[0038] Identify system operation constraints:

[0039] ①Combined overall power balance constraints

[0040]

[0041] The above formula represents the power balance within the combination. n,t is the predicted power consumption of user n at time t. ② Power sharing constraints among users within the combination

[0042]

[0043] Where: is a user sharing Boolean variable, 1 means that user n receives power from other users during time period t, and 0 means that it shares power with other users; They represent the maximum amount of electricity received by user n from other users and the maximum amount of electricity shared with other users at time t.

[0044] The uncertainty constraints are transformed into deterministic constraints using the equation transformation theory, making the model a weakly robust model that takes both uncertainty and economy into account. The electricity cost of each building user is then obtained by solving the model.

[0045] Electric power constraint after introducing slack variables:

[0046]

[0047] Where: are the actual loads of electric vehicles of user n in period t; is the actual output of PV of user n in period t; is the power obtained by user n within the group during period t is the power shortage of user n at time t;

[0048] The actual output of the photovoltaic device of user n in period t Electrical equipment and electric vehicles The actual load is described as:

[0049]

[0050] Where: are the PV output and electric EV prediction values ​​of user n in period t, respectively; are the corresponding maximum forecast error amplitudes, which are the maximum values ​​within the corresponding day; They are the corresponding fluctuation ratios, and the value of the present invention is ±20%.

[0051] The budget value of the uncertainty set in the electric power constraint should satisfy:

[0052]

[0053] Where: Γ n,t is the uncertain set budget value of the electric power constraint of user n in the tth period.

[0054] According to the weak robust optimization equation transformation, the electric power constraint can be transformed into:

[0055]

[0056] Finally, the objective function of the smart building power sharing scheduling model based on weak robust optimization is composed of the electricity cost F n and the penalty term F λ,n It consists of two parts, namely:

[0057]

[0058] Where: t is the penalty coefficient for the tth period; is the power shortage; obj is the objective function of the intelligent building sharing scheduling model.

[0059] The present invention provides an intelligent building power sharing optimization scheduling method based on weak robust optimization, formulates the price of shared power within users, and ensures the safety of the building system while improving the economic efficiency of users' electricity consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of the present invention;

[0061] Figure 2 is a pricing iteration diagram of the non-equilibrium spider web adopted by the present invention;

[0062] Figure 3 It is the price of the power sharing transaction among each combination of users in the present invention. DETAILED DESCRIPTION

[0063] 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.

[0064] The present invention Figure 1 As shown, a smart building power sharing optimization scheduling method based on weak robust optimization is provided, comprising the following steps:

[0065] Step 1): Model the power supply and consumption equipment in the smart building and clarify the power flow of each device:

[0066] (1) Supply-side photovoltaic devices

[0067] According to the purpose of PV, the power generation balance constraint of the nth user in the tth period can be obtained:

[0068]

[0069] Where: P t PV represents the power generation of the building photovoltaic device in the tth period; It represents the amount of electricity provided by PV to the nth user in period t; It represents the amount of electricity that the nth user shares with other users during period t; It represents the amount of electricity sold by the nth user to the grid during period t; represents the amount of electricity provided by PV to EV by the nth user in period t; X EV =1 means the nth user is not an EV user, X EV =0 means the nth user is an EV user; N is the number of users in the building system.

[0070] (2) Single electric vehicle on the electricity consumption side

[0071] The EV of the present invention adopts an orderly power consumption mode. Assume that the time when the EV of the nth user is connected to the building is T n,in , the time to leave the building is T n,out , then EV can participate in the scheduling parameter ω n,t for:

[0072]

[0073] Where: n,t 1 means that the EV of the nth user can participate in the scheduling in the tth time period; ω n,t A value of 0 indicates that the EV of the nth user cannot participate in the scheduling in the tth time period.

[0074] In the tth period, the actual charging and discharging power of the nth user's EV is:

[0075]

[0076] Where: are the charging and discharging powers of the n EVs that can participate in the scheduling in the tth period; η car,ch , η car,dis are the charging and discharging efficiency of EV respectively.

[0077] (3) Load on the electricity consumption side

[0078] Building loads include uncontrollable loads (lighting, etc.), movable loads (washing machines, water heaters, etc.), and interruptible loads (air conditioners, etc.). The actual power consumption in the t period of a day can be expressed as:

[0079]

[0080] Where: They represent the actual power consumption and the original power consumption of user n respectively; represents the load power that can be shifted by user n at time t; represents the interruptible load power of user n at time t; They represent the maximum values ​​of load that can be shifted and load that can be interrupted by user n respectively; T represents the total time period, which is 24.

[0081] Step 2): Based on the matching degree of supply and demand, the shared electricity price of building users is formulated using the non-equilibrium spider web theory:

[0082] Since the supply and demand inside the building are in an unbalanced state in many periods of time, the non-equilibrium spider web model can jointly adjust the price and quantity to achieve a state of supply and demand adaptation. In order to encourage users to participate in electricity sharing, based on supply and demand matching, the non-equilibrium spider web model is used to set different price packages for users to choose from.

[0083] The following formula is that the non-equilibrium spider web electricity price is subject to the constraints of the power grid transaction purchase and sale price. After the building users meet their own needs, the supply of the non-equilibrium spider web pricing model is the sum of excess electricity, and the demand is the sum of the electricity that the users themselves lack, that is:

[0084]

[0085] Where: p p,t represents the non-equilibrium spider price for the tth period during the pth iteration, and is the time-of-use price set for the grid in the process of solving the optimal form; It represents the amount of electricity shared by the nth user at time t, i.e. the supply St ; It represents the amount of electricity that the nth user obtains from other users at time t, that is, the demand D t ;

[0086] The non-equilibrium spider web price adjustment model is:

[0087]

[0088] Where: Q p,t is the absolute value of the difference between supply and demand; p p-1,t is the price at the p-1th time t; r is a very small positive number, and the iteration stops when the supply and demand difference corresponding to the p-th price differs from the supply and demand difference corresponding to the p-1th price by a very small positive number.

[0089] Here, the non-equilibrium spider web pricing model above is the previous pricing, and the price adjustment below is adjusted according to the previous pricing result and carried out iteratively.

[0090] Step 3): Clarify the objective function of the user of the non-equilibrium spider pricing model

[0091] In the stage of finding the best combination guided by the pricing model, the objective function based on the best combination and the non-equilibrium spider web pricing model is to minimize the total electricity cost of a combination:

[0092]

[0093] Number each user in turn, M is the set of user numbers belonging to a group; g n is the electricity cost of the nth user for one day, and the objective function is to minimize the total electricity cost; They represent the amount of electricity purchased or sold by the nth user from the power grid; represents the amount of electricity obtained by user n from other buildings at time t; is the electricity price set by the power grid. X is the number of users in each combination. F is the objective function, which minimizes the electricity cost of the user combination.

[0094] Step 4): Identify system operation constraints

[0095] ①Combined overall power balance constraints

[0096]

[0097] The above formula represents the power balance within the combination. n,t is the predicted electricity consumption of user n at time t.

[0098] ②Combined internal user sharing power constraints

[0099]

[0100] Where: is a user sharing Boolean variable, 1 means that user n receives power from other users during time period t, and 0 means that it shares power with other users; They represent the maximum amount of electricity received by user n from other users and the maximum amount of electricity shared with other users at time t.

[0101] Step 5): Use the equation transformation theory to transform the uncertainty constraints into deterministic constraints, making the model a weak robust model that takes both uncertainty and economy into account, and then solve it to obtain the electricity cost of each building user.

[0102] Electric power constraint after introducing slack variables:

[0103]

[0104] Where: are the actual loads of electric vehicles of user n in period t; is the actual output of PV of user n in period t; is the power obtained by user n within the group during period t; is the power shortage of user n at time t;

[0105] The actual output of the photovoltaic device of user n in period t Electrical equipment and electric vehicles The actual load is described as:

[0106]

[0107] Where: are the PV output and electric EV prediction values ​​of user n in period t, respectively; are the corresponding maximum forecast error amplitudes, which are the maximum values ​​within the corresponding day; They are the corresponding fluctuation ratios, and the value of the present invention is ±20%.

[0108] The budget value of the uncertainty set in the electric power constraint should satisfy:

[0109]

[0110] Where: Γ n,t is the uncertain set budget value of the electric power constraint of user n in the tth period.

[0111] According to the weak robust optimization equation transformation, the electric power constraint can be transformed into:

[0112]

[0113] Finally, the objective function of the smart building power sharing scheduling model based on weak robust optimization is composed of the electricity cost F n and the penalty term F λ,n It consists of two parts, namely:

[0114]

[0115] Where: t is the penalty coefficient for the tth period; is the power shortage; obj is the objective function of the intelligent building sharing scheduling model.

[0116] In the operation of the present invention, step 1) understanding the electrical equipment contained in the intelligent building and modeling to obtain a physical model of the equipment operation and the direction of electric energy flow;

[0117] Step 2) Based on the flow of electric energy in the electric equipment in step 1), the supply-demand ratio of the user is determined, and the non-equilibrium spider web pricing model is used to iteratively optimize the price.

[0118] Step 3) Based on the price optimization result of step 2), a model for minimizing the user's electricity cost is constructed to guide each user to adjust their electricity consumption behavior.

[0119] Step 4) Determine the constraints of each electrical device and shared electrical energy based on the model of step 3).

[0120] Step 5) When solving the model in steps 3) to 4), the uncertainty on both sides of the source and the load is considered to make the optimization result closer to the actual situation.

[0121] The present invention selects 25 users and numbers them, and performs simulation verification based on their electricity consumption information. The effectiveness of the strategy proposed by the present invention is verified by setting the following three schemes:

[0122] Method 1: Deterministic optimization;

[0123] Method 2: Traditional two-stage robust optimization;

[0124] Method 3: Weakly robust optimization.

[0125] 1User Trading Portfolio

[0126]

[0127] The number of iterations of non-equilibrium spider pricing is Figure 2 As shown in the figure, at the 50th generation, the system supply and demand difference reaches the minimum and no longer changes. The corresponding user electricity transaction price is as follows: Figure 3 As shown in Table 1, the corresponding user transaction combination is shown in Table 1. Figure 3It can be seen that the prices of various combination packages are between the electricity prices set by the power grid, which can effectively attract users to participate in electricity sharing and motivate users to respond to demand.

[0128] Table 2 Total electricity costs for different combinations under three methods

[0129]

[0130] Table 2 shows the total electricity costs of the four combinations under the three methods. As can be seen from the table, the total electricity costs of each combination of method one are the lowest, because this is the optimal scheduling result without considering the uncertainty of photovoltaic output and user demand. The total electricity costs of each combination of method two are the highest. This is because the possible uncertainty is taken into account, and the economy is reduced to ensure the safety of the results, which has strong robustness. The total electricity costs of each combination of method three are between method one and method two, indicating that method three saves costs to the maximum extent while ensuring safety.

[0131] Table 3 Combined total electricity costs under different penalty coefficients

[0132]

[0133] Let the penalty coefficient P = λ t , analyze the electricity costs of each combination under different penalty coefficients. Table 3 shows the electricity costs of each combination with penalty coefficients between P and 3.4P. It can be seen from Table 5 that with the increase of the penalty coefficient, the electricity cost of each combination gradually increases. This is because the larger the penalty coefficient of the power shortage, the greater the cost of generating the power shortage. In order to reduce the power shortage, the entire building scheduling will increase the supply of the power grid or reduce the use of household photovoltaic power generation, which will inevitably lead to worse economy and stronger robustness. When the penalty coefficient increases to 2.6P, the electricity cost of each combination no longer increases, which shows that the economy and robustness of weak robust optimization scheduling can be effectively weighed by adjusting the size of the penalty coefficient. The larger the penalty coefficient, the worse the economy, but it has stronger security.

[0134] The present invention makes improvements in terms of both the safety and economy of building operation, thereby reducing the electricity costs of building users while ensuring system safety.

[0135] 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 optimizing the sharing of electric energy in intelligent buildings based on weak robust optimization, characterized in that: The following steps are involved: Model the power supply and consumption equipment in the smart building and clarify the power flow of each device; Based on the matching degree of supply and demand, the shared electricity price of building users is formulated using the non-equilibrium cobweb theory; Specify the objective function of the pricing model user; Identify system operation constraints; The uncertainty constraints are transformed into deterministic constraints using the equation transformation theory, making the model a weakly robust model that takes both uncertainty and economy into account. The electricity cost of each building user is then obtained by solving the model.

2. According to the method of intelligent building power sharing optimization scheduling based on weak robust optimization according to claim 1, it is characterized in that: Model the power supply and consumption equipment in the smart building and clarify the power flow of each device, including: (1) Supply-side photovoltaic devices According to the purpose of PV, the power generation balance constraint of the nth user in the tth period can be obtained: Where: represents the power generation of the building photovoltaic device in the tth period; It represents the amount of electricity provided by PV to the nth user in period t; It represents the amount of electricity that the nth user shares with other users during period t; It represents the amount of electricity sold by the nth user to the grid during period t; represents the amount of electricity provided by PV to EV by the nth user in period t; X EV =1 means the nth user is not an EV user, X EV =0 means the nth user is an EV user; N is the number of building system users; (2) Single electric vehicle on the electricity consumption side EV adopts orderly power consumption mode, assuming that the time when the nth user's EV is connected to the building is T n,in , the time to leave the building is T n,out , then EV can participate in the scheduling parameter ω n,t for: Where: n,t 1 means that the EV of the nth user can participate in the scheduling in the tth time period; ω n,t A value of 0 indicates that the EV of the nth user cannot participate in the scheduling in the tth time period; In the tth period, the actual charging and discharging power of the EV of the nth user for: Where: represents the power required by the electric vehicle of user n at time t; are the charging and discharging powers of the n EVs that can participate in the scheduling in the tth period; η car,ch , η car,dis They are the charging and discharging efficiency of EV respectively; (3) Load on the electricity consumption side The building load includes uncontrollable load, shiftable load and interruptible load. The actual power consumption in the t period of a day is expressed as: Where: They represent the actual power consumption and the original power consumption of user n respectively; represents the load power that can be shifted by user n at time t; represents the interruptible load power of user n at time t; They represent the maximum values ​​of load that can be shifted and load that can be interrupted by user n respectively; T represents the total time period.

3. According to the method of intelligent building power sharing optimization scheduling based on weak robust optimization according to claim 1, it is characterized in that: Based on the matching degree of supply and demand, the shared electricity price of building users is formulated using the non-equilibrium cobweb theory, including: After the building users have met their own needs, the supply of the non-equilibrium spider web pricing model is the sum of excess electricity, and the demand is the sum of the electricity that the users themselves lack, that is: Where: p p,t represents the non-equilibrium spider price for the tth period during the pth iteration, and is the time-of-use price set for the grid in the process of solving the optimal form; It represents the amount of electricity shared by the nth user at time t, i.e. the supply S t ; It represents the amount of electricity that the nth user obtains from other users at time t, that is, the demand D t ; The non-equilibrium spider web price adjustment model is: Where: Q p,t is the absolute value of the difference between supply and demand; p p-1,t is the price at the p-1th time t; r is a very small positive number, and the iteration stops when the supply and demand difference corresponding to the p-th price differs from the supply and demand difference corresponding to the p-1th price by a very small positive number.

4. According to the method of intelligent building power sharing optimization scheduling based on weak robust optimization according to claim 1, it is characterized in that: Clarify the objective function of the user of the non-equilibrium spider pricing model, including: In the stage of finding the best combination guided by the pricing model, the objective function based on the best combination and the non-equilibrium spider web pricing model is to minimize the total electricity cost of a combination: Number each user in turn, M is the set of user numbers belonging to a group; g n The electricity cost of the nth user for one day; They represent the amount of electricity purchased or sold by the nth user from the power grid; The electricity prices set for the power grid; represents the amount of electricity obtained by user n from other buildings at time t; X is the number of users in each combination; F is the objective function, and the electricity cost of the user combination is minimized.

5. The method for optimizing and scheduling power sharing of intelligent buildings based on weak robust optimization according to claim 1 is characterized in that: Identify system operation constraints: ①Combined overall power balance constraints Where, L n,t is the predicted power consumption of user n at time t; ②Combined internal user sharing power constraints Where: is a user sharing Boolean variable, 1 means that user n receives power from other users during time period t, and 0 means that it shares power with other users; They represent the maximum amount of electricity received by user n from other users and the maximum amount of electricity shared with other users at time t.

6. The method for optimizing and scheduling power sharing of intelligent buildings based on weak robust optimization according to claim 1 is characterized in that: The uncertainty constraints are transformed into deterministic constraints using the equivalence transformation theory, making the model a weakly robust model that takes both uncertainty and economy into account. The electricity cost of each building user is obtained by solving the model, including: Electric power constraint after introducing slack variables: Where: are the actual loads of electric vehicles of user n in period t; is the actual output of PV of user n in period t; is the power obtained by user n within the group during period t; is the power shortage of user n at time t; The actual output of the photovoltaic device of user n in period t Electrical equipment and electric vehicles The actual load is described as: Where: are the PV output and electric EV prediction values ​​of user n in period t, respectively; They are the maximum prediction error amplitudes of photovoltaic power generation, user power consumption, and electric vehicle demand, respectively; are the corresponding fluctuation ratios respectively; The budget value of the uncertainty set in the electric power constraint should satisfy: Where: Γ n,t is the uncertain set budget value of the electric power constraint of user n in period t; According to the weak robust optimization equation transformation, the electric power constraint can be transformed into: Finally, the objective function of the smart building power sharing scheduling model based on weak robust optimization is composed of the electricity cost F n and the penalty term F λ,n It consists of two parts, namely: Where: t is the penalty coefficient for the tth period; is the power shortage; obj is the objective function of the intelligent building sharing scheduling model.

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