Building power load adjustable potential assessment method considering uncertainty factors

Abstract

The invention discloses a building power load adjustable potential assessment method considering uncertainty factors, and relates to the field of data intelligent assessment. The method comprises the following steps: performing simulation modeling on a building energy system to obtain a simulation model, determining the minimum building total energy consumption according to the simulation model, and determining a building load baseline under the minimum building total energy consumption; determining a building up-regulation energy consumption curve corresponding to the negative electricity price and a building down-regulation energy consumption curve corresponding to the high electricity price; according to the building load base line and the building up-regulation and down-regulation energy consumption curve, the adjustable potential is determined; and determining the adjustable potential providing efficiency according to the additional energy consumption required by the adjustable potential. Through the independently researched and developed power load adjustable potential evaluation method, the influence of uncertain factors can be effectively considered, accurate and conservative adjustable potential estimation is provided for a power grid operator and an energy aggregator, and short-term and long-term energy supply plans can be made.

Description

Technical Field

[0001] The present invention relates to the field of intelligent data evaluation, and in particular to a method for evaluating the adjustable potential of building power loads taking uncertainty factors into consideration. Background Art

[0002] With the rapid growth of renewable energy, the power grid faces challenges such as load changes, intermittent energy supply, price fluctuations and new peak and valley periods. The assessment of the adjustable potential of residential buildings plays a key role in addressing these challenges. Adjustable potential is defined as the ability of distributed energy resources to adjust the building load curve at different time scales. However, the existing adjustable potential quantification framework fails to fully consider uncertainty factors, which may lead to an overestimation of flexibility potential and endanger the thermal comfort of users in actual operation. Therefore, it is of great significance to develop a method that can accurately quantify the adjustable potential of buildings under uncertain conditions. Summary of the invention

[0003] In view of the defects existing in the prior art, the technical problem solved by the present invention is to provide a method for evaluating the adjustable potential of building power load taking into account uncertainty factors.

[0004] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a method for evaluating the adjustable potential of a building power load taking into account uncertainty factors, the method comprising the following steps:

[0005] S1: Simulate and model the building energy system to obtain a simulation model, determine the minimum total building energy consumption based on the simulation model, and determine the building load baseline under the minimum total building energy consumption;

[0006] S2: setting a negative electricity price and a high electricity price within a simulated demand response event cycle, and determining an upward energy consumption curve of a building corresponding to the negative electricity price and a downward energy consumption curve of a building corresponding to the high electricity price according to the simulation model;

[0007] S3: Determine the adjustable potential according to the building load baseline, the building upward energy consumption curve and the building downward energy consumption curve; determine the efficiency provided by the adjustable potential according to the additional energy consumption required for the adjustable potential.

[0008] In combination with the first aspect, in one implementation, the specific process of S1 includes:

[0009] Construct a discrete time state space model of the building energy system. The formula of the discrete time state space model is:

[0010] x(t+1)=Ax(t)+Bu(t)+Ed(t)

[0011] y(t)=Fx(t)+Gu(t)

[0012] Among them, x(t) is the system state vector, u(t) is the control input vector, d(t) is the interference vector, A is the state matrix, B is the control matrix, E is the interference matrix, y(t) is the system output vector, F is the output matrix, and G is the control output matrix.

[0013] In combination with the first aspect, in one embodiment, the input of the discrete-time state-space model includes ambient temperature, global horizontal irradiance, internal heat and heating output of the heating system, and the output of the discrete-time state-space model includes indoor air temperature; the order of the discrete-time state-space model is selected by trial and error until the minimum fitting error is achieved on the test data.

[0014] In combination with the first aspect, in one implementation, the process of S1 further includes:

[0015] Based on the discrete time state space model established by S101, a building baseline load optimization simulation model is established with the optimization goal of minimizing the total energy consumption of the building. The optimization goal is set as:

[0016]

[0017] Among them, J(u(t),x(t)) is the energy consumption cost function;

[0018] J(u(t),x(t))=c u u(t)+c x x(t)

[0019] Among them, c u and c x are the weight coefficients of control input and state respectively.

[0020] In combination with the first aspect, in one implementation, the process of S1 further includes: determining the constraint conditions of the optimization target based on the constructed state space model and the corresponding input and control signals, specifically:

[0021]

[0022] Among them, x mib and x max are the upper and lower limits of the state variables, u min and u max are the upper and lower limits of the control input; the random model predictive control method is adopted, the current state is taken as the initial condition, the system behavior at future moments is predicted, and the control input is iteratively optimized to obtain the building load baseline.

[0023] In combination with the first aspect, in one implementation, the process of S2 includes:

[0024] Simulate an upward energy consumption curve and set a negative electricity price during the response event period to encourage increased energy consumption, and simulate a downward energy consumption curve and set a high electricity price during the response event period to reduce building energy consumption;

[0025] The objective function of the simulation is:

[0026]

[0027] Among them, J(u(t),x(t)) is the energy cost function:

[0028] J(u(t),x(t))=c i u(t)+c x x(t)

[0029] For the simulation of upward energy consumption curve, the electricity price is set to negative value cu<0, and for the simulation of downward energy consumption curve, the electricity price c u Set to a high value c u >0.

[0030] In combination with the first aspect, in one implementation, the process of determining the adjustable potential in S3 includes:

[0031] According to the total energy consumption E of the upward simulation energy consumption curve up and the total energy consumption E of the building load baseline ref , calculate the up-regulation potential ΔE up , the calculation formula is:

[0032] ΔE up =E up -E ref ;

[0033] According to E ref and the total energy consumption E of the upward simulation energy consumption curve down , calculate the down-regulation potential ΔE down , the calculation formula is:

[0034] ΔE down =E ref -E down .

[0035] In combination with the first aspect, in one implementation, the process of determining the efficiency provided by the adjustable potential according to the additional energy consumption required by the adjustable potential in S3 includes:

[0036] The calculation formula for the additional energy consumption UpwardFlexibility of the upward adjustable potential is:

[0037] UpwardFlexibility=E up -E ref;

[0038] The calculation formula for increasing the adjustable potential to provide efficiency UpEfficiency is:

[0039]

[0040] The calculation formula for DownwardFlexibility, which is the additional energy consumption of the adjustable potential, is:

[0041] DownwardFlexibility=E ref -E down ;

[0042] The calculation formula for DownEfficiency provided by down-adjusting the adjustable potential is:

[0043]

[0044] Where E tot,up is the total energy consumption difference between the upward energy consumption curve and the baseline load in all periods of the building, E tot,down The total energy consumption difference between the downward energy consumption curve and the baseline load of the building during all time periods.

[0045] In a second aspect, an embodiment of the present application provides a device for evaluating the potential for adjustable power loads in a building taking into account uncertainty factors, wherein the device comprises a processor, a memory, and a program for evaluating the potential for adjustable power loads in a building taking into account uncertainty factors, the program being stored on the memory and executable by the processor. When the program for evaluating the potential for adjustable power loads in a building taking into account uncertainty factors is executed by the processor, the method steps provided in the first aspect are implemented.

[0046] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a program for evaluating the adjustable potential of a building's power load taking into account uncertainty factors. When the program for evaluating the adjustable potential of a building's power load taking into account uncertainty factors is executed, the method steps provided in the first aspect are implemented.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] The present invention can effectively consider the influence of uncertain factors through the independently developed power load adjustable potential assessment method, provide accurate and conservative adjustable potential estimates for power grid operators and energy aggregators, and help formulate short-term and long-term energy supply plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 A schematic diagram of a flow chart of a method for evaluating the adjustable potential of a building power load taking into account uncertainty factors in an embodiment of the present application;

[0051] Figure 2 This is a schematic diagram of the hardware structure of a device for evaluating the adjustable potential of a building's power load taking into account uncertainty factors involved in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0053] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0054] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0055] In a first aspect, the present application provides a method for evaluating the adjustable potential of building power loads taking into account uncertainty factors, see Figure 1 As shown, the steps of the method include:

[0056] S1: Use the stochastic model predictive control method to simulate the building energy system to obtain a simulation model, determine the minimum total building energy consumption based on the simulation model, and determine the building load baseline under the minimum total building energy consumption.

[0057] S2: During the simulated demand response event cycle, negative electricity prices and high electricity prices are set respectively, and the upward energy consumption curve of the building corresponding to the negative electricity price and the downward energy consumption curve of the building corresponding to the high electricity price are determined according to the simulation model in S1.

[0058] S3: Determine the adjustable potential based on the building load baseline, the building upward energy consumption curve and the building downward energy consumption curve; determine the efficiency provided by the adjustable potential based on the additional energy consumption required for the adjustable potential.

[0059] The specific process of S1 includes:

[0060] S101: Construct a discrete-time state-space model of the building energy system and use the subspace method to systematically identify the matrix parameters of the discrete-time state-space model.

[0061] Specifically, the formula (equation) of the discrete-time state-space model is:

[0062] x(t+1)=Ax(t)+Bu(t)+Ed(t)

[0063] y(t)=Fx(t)+Gu(t)

[0064] Among them, x(t) is the system state vector, u(t) is the control input vector, d(t) is the interference vector, A is the state matrix, B is the control matrix, E is the interference matrix, y(t) is the system output vector, F is the output matrix, and G is the control output matrix.

[0065] The above equations are used to describe the dynamic behavior of a building and its energy system, and these matrix parameters are extracted through system identification methods (such as subspace methods).

[0066] In one embodiment, the input of the discrete time state space model includes ambient temperature, global horizontal irradiance (GHI), internal heat and heating output of the heating system, and the output of the discrete time state space model includes indoor air temperature; the order of the discrete time state space model is selected by trial and error until the minimum fitting error is achieved on the test data. The training data used to train the model was generated in TRNSYS from January 1, 2017 to May 31, 2017, randomly generated using different heating outputs, and the sampling rate was 1h.

[0067] S102: Based on the discrete time state space model established in S101, a building baseline load optimization simulation model is established with the optimization goal of minimizing the total energy consumption of the building. The optimization goal is set as:

[0068]

[0069] Among them, J(u(t),x(t)) is the energy consumption cost function;

[0070] J(u(t),c(t))=c u u(t)+c x x(t)

[0071] Among them, c u and c x are the weight coefficients of control input and state respectively.

[0072] S103: Based on the constructed state space model and the corresponding input and control signals, determine the constraints of the optimization target, specifically:

[0073]

[0074] Among them, x min and x max are the upper and lower limits of the state variables, u min and u max are the upper and lower limits of the control input; the random model predictive control method is used to gradually solve the above optimization problem through online optimization. It takes the current state as the initial condition, predicts the system behavior at future times, iteratively optimizes the control input, and finally obtains the building load baseline.

[0075] The S2 process includes:

[0076] S201: Similar to the building load baseline optimization simulation, the upward energy consumption curve is simulated, and a negative electricity price is set during the response event period to encourage increased energy consumption. The optimization goal becomes to maximize the building energy consumption; the downward energy consumption curve is simulated, and a high electricity price is set during the response event period to reduce the building energy consumption.

[0077] The objective function of S201 is:

[0078]

[0079] Among them, J(u(t),x(t)) is the energy cost function, which is usually defined as:

[0080] H(u(t),x(t))=c u u(t)+c x x(t)

[0081] For the simulation of upward energy consumption curve, the electricity price is set to negative value cu<0, and for the simulation of downward energy consumption curve, the electricity price c u Set to a high value c u >0.

[0082] The constraints are similar to the building load baseline simulation. The optimization problem is solved based on the SMPC method. Through the rolling optimization method, the control input of the future period is re-optimized at each moment to cope with the influence of uncertain factors and ensure the realization of different optimization goals. The upward and downward energy consumption curves of the building are calculated.

[0083] The process of determining adjustable potential in S3 includes:

[0084] According to the total energy consumption E of the upward simulation energy consumption curve up and the total energy consumption E of the building load baseline ref , calculate the up-regulation potential ΔE up , the calculation formula is:

[0085] ΔE up =E up -E ref ;

[0086] According to E ref and the total energy consumption E of the upward simulation energy consumption curve down , calculate the down-regulation potential ΔE down , the calculation formula is:

[0087] ΔE down =E ref -E down .

[0088] In S3, the process of determining the efficiency provided by the adjustable potential based on the additional energy consumption required by the adjustable potential includes:

[0089] The calculation formula for the additional energy consumption UpwardFlexibility of the upward adjustable potential is:

[0090] UpwardFlexibility=E up -E ref ;

[0091] The calculation formula for increasing the adjustable potential to provide efficiency UpEfficiency is:

[0092]

[0093] The calculation formula for DownwardFlexibility, which is the additional energy consumption of the adjustable potential, is:

[0094] DownwardFlexibility=E ref -E down ;

[0095] The calculation formula for DownEfficiency provided by down-adjusting the adjustable potential is:

[0096]

[0097] Where E tot,up is the total energy consumption difference between the upward energy consumption curve and the baseline load in all periods of the building, E tot,down The total energy consumption difference between the downward energy consumption curve and the baseline load of the building during all time periods.

[0098] In the second aspect, an embodiment of the present application provides a device for evaluating the adjustable potential of a building's power load taking into account uncertainty factors. The device for evaluating the adjustable potential of a building's power load taking into account uncertainty factors may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0099] Reference Figure 2 , Figure 2 The hardware structure diagram of the device for evaluating the adjustable potential of power loads in buildings considering uncertainty factors involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the device for evaluating the adjustable potential of power loads in buildings considering uncertainty factors may include a processor, a memory, a communication interface, and a communication bus.

[0100] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0101] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the building power load adjustable potential assessment device considering uncertainty factors, and an interface used to interconnect the building power load adjustable potential assessment device considering uncertainty factors with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0102] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0103] The processor may be a general-purpose processor, which may call the building power load adjustable potential assessment program considering uncertainty factors stored in the memory, and execute the building power load adjustable potential assessment method considering uncertainty factors provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the building power load adjustable potential assessment program considering uncertainty factors is called may refer to the various embodiments of the building power load adjustable potential assessment method considering uncertainty factors of the present application, which will not be repeated here.

[0104] Those skilled in the art will understand that Figure 2 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0105] In a third aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0106] The computer-readable storage medium of the present application stores a program for evaluating the potential for adjusting power loads in buildings taking into account uncertainty factors. When the program for evaluating the potential for adjusting power loads in buildings taking into account uncertainty factors is executed by a processor, the steps of the method for evaluating the potential for adjusting power loads in buildings taking into account uncertainty factors as described above are implemented.

[0107] Among them, the method implemented when the building power load adjustable potential assessment program considering uncertainty factors is executed can refer to the various embodiments of the building power load adjustable potential assessment method considering uncertainty factors in this application, and will not be repeated here.

[0108] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0110] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0111] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0112] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0113] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0114] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0115] The above are only specific implementations of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the embodiments of the present invention, and these modifications or replacements should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for evaluating the adjustable potential of building power loads taking into account uncertainty factors, characterized in that: The method comprises the following steps: S1: Simulate and model the building energy system to obtain a simulation model, determine the minimum total building energy consumption based on the simulation model, and determine the building load baseline under the minimum total building energy consumption; S2: setting a negative electricity price and a high electricity price within a simulated demand response event cycle, and determining an upward energy consumption curve of a building corresponding to the negative electricity price and a downward energy consumption curve of a building corresponding to the high electricity price according to the simulation model; S3: Determine the adjustable potential according to the building load baseline, the building upward energy consumption curve and the building downward energy consumption curve; determine the efficiency provided by the adjustable potential according to the additional energy consumption required for the adjustable potential.

2. The method for evaluating the adjustable potential of building power loads considering uncertainty factors according to claim 1, characterized in that: The specific process of S1 includes: Construct a discrete time state space model of the building energy system. The formula of the discrete time state space model is: x(t+1)=Ax(t)+Bu(t)+Ed(t) y(t)=Fx(t)+Gu(t) Among them, x(t) is the system state vector, u(t) is the control input vector, d(t) is the interference vector, A is the state matrix, B is the control matrix, E is the interference matrix, y(t) is the system output vector, F is the output matrix, and G is the control output matrix.

3. The method for evaluating the adjustable potential of building power loads considering uncertainty factors as claimed in claim 2, characterized in that: The input of the discrete-time state-space model includes ambient temperature, global horizontal irradiance, internal heat and heating output of the heating system, and the output of the discrete-time state-space model includes indoor air temperature; the order of the discrete-time state-space model is selected by trial and error until the minimum fitting error is achieved on the test data.

4. The method for evaluating the adjustable potential of building power loads considering uncertainty factors according to claim 2, characterized in that: The S1 process also includes: Based on the discrete time state space model established by S101, a building baseline load optimization simulation model is established with the optimization goal of minimizing the total energy consumption of the building. The optimization goal is set as: Among them, J(u(t),x(t)) is the energy consumption cost function; J(u(t),x(t))=c u u(t)+c x x(t) Among them, c u and c x are the weight coefficients of control input and state respectively.

5. The method for evaluating the adjustable potential of building power loads considering uncertainty factors according to claim 4, characterized in that: The process of S1 also includes: determining the constraints of the optimization target based on the constructed state space model and the corresponding input and control signals, specifically: Among them, x min and x max are the upper and lower limits of the state variables, u min and u max are the upper and lower limits of the control input; the random model predictive control method is adopted, the current state is taken as the initial condition, the system behavior at future moments is predicted, and the control input is iteratively optimized to obtain the building load baseline.

6. The method for evaluating the adjustable potential of building power loads considering uncertainty factors according to claim 1, characterized in that: The S2 process includes: Simulate an upward energy consumption curve and set a negative electricity price during the response event period to encourage increased energy consumption, and simulate a downward energy consumption curve and set a high electricity price during the response event period to reduce building energy consumption; The objective function of the simulation is: Among them, J(u(t),x(t)) is the energy cost function: J(u(t),x(t))=c u u(t)+c x x(t) For the simulation of upward energy consumption curve, the electricity price is set to negative value cu<0, and for the simulation of downward energy consumption curve, the electricity price c u Set to a high value c u >0.

7. The method for evaluating the adjustable potential of building power loads considering uncertainty factors according to claim 1, characterized in that: The process of determining adjustable potential in S3 includes: According to the total energy consumption E of the upward simulation energy consumption curve up and the total energy consumption E of the building load baseline ref , calculate the up-regulation potential ΔE up , the calculation formula is: ΔE up =E up -E ref ; According to E ref and the total energy consumption E of the upward simulation energy consumption curve down , calculate the down-regulation potential ΔE down , the calculation formula is: ΔE down =E ref -E down 。 8. The method for evaluating the adjustable potential of building power loads considering uncertainty factors according to claim 7, characterized in that: In S3, the process of determining the efficiency provided by the adjustable potential based on the additional energy consumption required by the adjustable potential includes: The calculation formula for the additional energy consumption UpwardFlexibility of the upward adjustable potential is: UpwardFlexibility=E up -E ref ; The calculation formula for increasing the adjustable potential to provide efficiency UpEfficiency is: The calculation formula for DownwardFlexibility, which is the additional energy consumption of downward adjustable potential, is: DownwardFlexibility=E ref -E down ; The calculation formula for DownEfficiency provided by down-adjusting the adjustable potential is: Where E tot,up is the total energy consumption difference between the upward energy consumption curve and the baseline load in all periods of the building, E tot,down The total energy consumption difference between the downward energy consumption curve and the baseline load of the building during all time periods.

9. A device for evaluating the adjustable potential of building power loads taking into account uncertainty factors, characterized in that: The device for evaluating the potential for adjusting power loads in a building taking uncertainty factors into consideration comprises a processor, a memory, and a program for evaluating the potential for adjusting power loads in a building taking uncertainty factors into consideration, which is stored in the memory and can be executed by the processor. When the program for evaluating the potential for adjusting power loads in a building taking uncertainty factors into consideration is executed by the processor, the steps of the method for evaluating the potential for adjusting power loads in a building taking uncertainty factors into consideration are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for evaluating the potential for adjusting power loads in a building taking into account uncertainty factors. When the program for evaluating the potential for adjusting power loads in a building taking into account uncertainty factors is executed, the steps of the method for evaluating the potential for adjusting power loads in a building taking into account uncertainty factors as described in any one of claims 1 to 8 are implemented.

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