Building energy consumption optimization method, system and equipment considering user comfort demand and energy consumption cost reduction demand in building

Abstract

The invention discloses a building energy consumption optimization method, a building energy consumption optimization system and building energy consumption optimization equipment, which consider the user comfort demand and the energy consumption cost reduction demand in a building, and the method comprises the following steps: converting influence factors with different attributes in the building energy consumption optimization process into a unified measurement standard by using psychological prices; the comfort level value and the economic value obtained by the user in the energy using process are uniformly converted into energy using welfare, and then the building energy using behavior is optimized. The optimization method can solve the problems that in the building energy consumption optimization process, measurement units of the user comfort and the energy consumption cost are not unified, and the target function weight is difficult to set, and the contradiction between the comfort and the energy consumption cost is well coordinated.

Description

Technical Field

[0001] The present invention relates to a method for optimizing building energy consumption, and in particular to a method, system and device for optimizing building energy consumption that takes into account the comfort requirements of users in the building and the requirements for reducing energy consumption costs. Background Art

[0002] The improvement of residents' living standards and the level of terminal electrification have led to a continuous increase in the internal load of buildings, while users' requirements for comfort inside buildings have also been continuously increasing. The key to optimizing building energy consumption is to coordinate the contradiction between comfort in the building and the building's energy cost. There are two main coordination optimization methods in existing research: the first coordination optimization method uses comfort as a constraint condition and minimizes the building's operating cost as the objective function to optimize the energy consumption of various flexible equipment in the building; the second coordination optimization method quantifies user comfort, and then also uses it as a cost to form an objective function together with the building's energy cost, and optimizes the building's energy behavior with the goal of minimizing the comprehensive energy cost. At present, these two optimization coordination methods have the following problems:

[0003] 1. The first coordination optimization method has the following problems: the comfort demand expressed in the form of constraints is not flexible enough. In the solution process, in order to minimize the energy cost of the objective function, the comfort level is often kept at the lower limit of the user's demand. It also cannot meet the needs of flexibly adjusting electricity consumption behavior when the electricity price is low or the comfort demand is high.

[0004] Second, the second coordination optimization method has the following problems: when considering multi-dimensional comfort, the weight setting is more complicated. The weight needs to consider the relative importance of multiple comfort levels and coordinate the overall weight of overall comfort and energy cost. When considering that the user's comfort needs may change over time or in the external environment, the setting of weights and objective functions is even more complicated. The experience-based weight setting cannot solve the problems of inconsistent measurement units of multi-dimensional influencing factors and difficulty in setting weights.

[0005] The above problems affect the effectiveness and optimization reliability of building energy management goals, and affect the design and arrangement of building energy optimization strategies. Summary of the invention

[0006] Purpose of the invention: The present invention aims to provide a building energy optimization method that takes into account the comfort needs of users in the building and the energy cost reduction needs, so as to solve the problems of inconsistent measurement units of user comfort and energy cost and difficulty in setting objective function weights in the process of building energy optimization; a second purpose of the present invention is to provide a building energy optimization system and equipment that takes into account the comfort needs of users in the building and the energy cost reduction needs.

[0007] Technical solution: The building energy optimization method of the present invention, which takes into account the comfort requirements of users in the building and the energy cost reduction requirements, comprises the following steps:

[0008] Based on the utility model of equipment power consumption and the utility model of power consumption time of the i-th comfort level, the maximum psychological price Pri of the user in the i-th comfort level is determined according to the collected subjective evaluation of the user on the i-th comfort level in the optimal heat, light and air environment. i,willing ;

[0009] Based on the per unit value of comfort index of each dimension at time t And the corresponding maximum psychological price Pri i,willing , determine the economic characteristics V of user comfort at time t i,t ;

[0010] According to the economic characteristics V of user comfort at time t i,t and energy cost, we can get the energy benefit B of users in the building during the future Δt period t , determine the overall energy welfare of users in the building throughout the day B wd ;

[0011] Taking into account the building operation constraints such as building power balance constraints, HVAC operation constraints, lighting system operation constraints, fresh air system operation constraints and energy storage system operation constraints, the maximization of the overall energy welfare of users in the building throughout the day is taken as the optimization goal. Combined with the building's photovoltaic power generation and rigid load forecast data in the future T period, the energy consumption behavior of the building in the future T period is optimized and simulated, and the equipment operation strategy with a control time interval of Δt is obtained.

[0012] Furthermore, the maximum psychological price of the i-th comfort level is Pri i,willing for

[0013] Pri i,willing =max[μ(Q)]·max[v(t)]

[0014] Among them, μ(Q) is the electricity consumption utility of the equipment corresponding to the i-th comfort level when the electricity consumption is Q; v(t) is the electricity consumption time utility of the equipment corresponding to the i-th comfort level when the electricity consumption time is t.

[0015] Furthermore, the utility model of the equipment electricity consumption of the i-th comfort level is as follows:

[0016] For equipment with fixed power consumption, the equipment power consumption utility model is:

[0017]

[0018] Among them, p i q iis the utility value of the equipment in normal use, p i is the maximum electricity price that users stop using, q i The power consumption Q is the rated power consumption of the equipment in the time period Δt;

[0019] For devices whose power consumption can be adjusted, the utility model of the equipment power consumption is:

[0020]

[0021] μ s =a(1-e -bx )

[0022] Among them, q min and q max are the minimum and maximum power consumption of the equipment within Δt, p i2 is the electricity price when the user's electricity consumption reaches the minimum, p i1 is the electricity price when the user's electricity consumption reaches the maximum, μ s (·) is a standard convex utility function, and a and b are the basic parameters of the convex utility function.

[0023] Furthermore, the utility model of electricity consumption time of the i-th comfort level is:

[0024]

[0025] Among them, v s (·) is the standard sigmoid function, t min is the earliest acceptable usage time for users, t max is the latest usage time acceptable to the user, and a, b and c are the basic parameters of the standard s-type function.

[0026] Furthermore, the economic characteristic V of user comfort at time t i,t for

[0027]

[0028] Among them, the per unit value It is any one of the per-unit value of the thermal comfort index, the per-unit value of the visual comfort index and the per-unit value of the air quality comfort index.

[0029] Furthermore, the energy welfare of users in the building during the future Δt period is B t for

[0030]

[0031] V profit,t =E sell,t ·p sell,t +V product,t

[0032] C t =E buy,t ·p buy,t +C other,t

[0033] Where n is the number of comfort levels, ω i,t represents the weight of the i-th comfort level; V profit,t Represents the economic value generated by users in the building participating in electricity sales or production; C t represents the energy cost of the building in the period [t, t+1]; E sell,t and E buy,t They represent the electricity sales and purchases of the building in the period [t, t+1] respectively; p sell,t and p buy,t They are respectively represented as the electricity selling price and electricity buying price in the period [t, t+1]; V product,t represents the economic value generated by the building through production equipment during the period [t, t+1]; C other,t It represents the operating costs of various equipment and other costs of the building in the period [t, t+1].

[0034] Furthermore, the overall energy consumption benefit of users in the building throughout the day is B wd for

[0035]

[0036] Furthermore, the optimization goal of maximizing the overall energy benefits of users in the building throughout the day is as follows:

[0037]

[0038] The building energy optimization system of the present invention taking into account the comfort requirements of users in the building and the requirements for reducing energy costs comprises:

[0039] The model building module is used to build the utility model of equipment power consumption and the utility model of power consumption time. Based on the collected subjective evaluation of the user on the i-th comfort level under the optimal heat, light and air environment, the maximum psychological price Pri of the user on the i-th comfort level is determined. i,willing , combined with the per unit value of each dimension comfort index at time t Determine the economic characteristic V of user comfort at time t i,t ;

[0040] The decision-making solution module is used to maximize the overall energy welfare of users in the building throughout the day as the optimization goal based on the building power balance constraints, HVAC operation constraints, lighting system operation constraints, fresh air system operation constraints and energy storage system operation constraints. Combined with the building's photovoltaic power generation and rigid load forecast data in the future T period, the energy consumption behavior of the building in the future T period is optimized and simulated to obtain the equipment operation strategy with a control time interval of Δt.

[0041] The computer device of the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the steps of the above method are implemented when the processor executes the computer program.

[0042] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. In the process of optimizing building energy consumption, the present invention unifies the measurement standards of user comfort and energy consumption cost, and transforms the optimization target into maximizing "energy consumption benefits", making it more suitable for the setting of the objective function in the process of optimizing building energy consumption; 2. In the process of giving economic characteristics to user comfort, the present invention transforms the comfort value and economic value obtained by users in the process of using energy into the economic value equivalent to the comfort obtained by users through energy consumption, and the economic value and electricity purchase cost obtained by users by selling electricity at different electricity prices and participating in demand response; 3. The present invention realizes the decoupling of the weight setting between multi-dimensional comfort and between comfort and energy consumption cost, which can flexibly increase or decrease the comfort dimension in the objective function, and flexibly set the weights of various influencing factors under the unified "energy consumption benefits" standard; 4. The present invention solves the problems of inconsistent measurement units of user comfort and energy consumption cost and difficulty in setting the weight of the objective function in the process of optimizing building energy consumption, and better coordinates the contradiction between comfort and energy consumption cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the optimized air conditioning system operation strategy and building room temperature fluctuation;

[0044] Figure 2 Schematic diagram of the operation strategy of the flexible lighting system and the fluctuation of visual comfort after optimization;

[0045] Figure 3 Schematic diagram of the optimized fresh air system operation strategy and air quality comfort fluctuations. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] The building energy optimization method according to the present invention, which takes into account the comfort requirements of users in the building and the energy cost reduction requirements, comprises the following steps:

[0048] (1) Determine the overall optimization time domain T = 24h and the control time interval Δt = 15min for building energy optimization, and then collect the building photovoltaic power generation and rigid load forecast data within the future time range of 24h;

[0049] (2) Collect the normalized comfort index of users in the building's thermal, light, and air environments: thermal comfort index per unit value Visual comfort index per unit value Air quality comfort index per unit value

[0050] (3) Test and collect users’ subjective evaluations of thermal comfort, visual comfort, and air quality comfort under the most suitable heat, light, and air environment: For thermal comfort, construct a HVAC energy use behavior utility model μ ac (Q) and the electricity time utility model v ac (t), and then determine the maximum psychological price Pri of the user in thermal comfort ac,willing ; For visual comfort, construct a flexible lighting system behavior utility model μ light (Q) and the electricity time utility model v light (t), and then determine the maximum psychological price Pri of the user in terms of visual comfort light,willing ; For air quality comfort, construct a flexible lighting system behavior utility model μ air (Q) and the electricity time utility model v air (t), and then determine the maximum psychological price Pri of the user in terms of visual comfort air,willing .

[0051] The method for constructing the electricity consumption utility model is as follows:

[0052] For equipment with fixed power consumption, such as fixed-frequency air conditioners, non-adjustable lighting equipment, fresh air systems, etc., the power consumption Q is the rated power consumption of the equipment in the time period Δt, q i , when the equipment is in normal use, the utility value remains unchanged. Excluding the influence of other factors, the highest electricity price p when the user stops using the equipment is collected i , determine the utility value of the normal use of the equipment is p i q i , then the power consumption utility model of this equipment can be expressed as μ(Q):

[0053]

[0054] For devices whose power consumption can be adjusted, such as variable frequency air conditioners, adjustable lighting equipment, etc., the power consumption range of the equipment within Δt is [q min ,q max ], excluding the influence of other factors, collect the electricity price p when the user's electricity consumption reaches the maximumi1 When the electricity price continues to rise, users begin to appropriately reduce their comfort level to reduce electricity consumption; collect the electricity price p when the user's electricity consumption reaches the minimum i2 ; When the electricity price continues to rise, the user completely abandons the use of the device, and the utility value becomes 0. Considering the impact of electricity price fluctuations on user energy consumption behavior, the revised electricity consumption utility model is as follows:

[0055]

[0056] Further: In the step 3), the electricity time utility model construction method and the maximum psychological price Pri i,willing The determination method is as follows:

[0057] Assume that the power consumption time of the equipment is [t min ,t max ], where the best time is t min (The utility of delayed use of equipment decreases) or t max (The utility of equipment used in advance decreases), so the utility model of equipment's electricity time can be expressed as:

[0058]

[0059] Based on the utility model of electricity consumption of equipment with adjustable comfort level i and the utility model of electricity consumption time, the maximum price that users are willing to pay to maintain this comfort level can be obtained, that is, the maximum psychological price Pri i,willing :

[0060] Pri i,willing =max[μ(Q)]·max[v(t)]

[0061] (4) The economic characteristics of user comfort are determined based on the per unit value of each dimension of comfort index and the corresponding maximum psychological price of comfort:

[0062] Energy consumption benefits are mainly composed of three parts. The first part is the economic value equivalent to the current comfort level. The second part is the economic value obtained by users through selling electricity at different electricity prices, participating in demand response, etc., and also includes the economic value obtained by production equipment participating in flexible regulation. The third part is the electricity purchase cost, which is the energy and operating cost that users must pay for energy consumption, so this part needs to be subtracted.

[0063] The goal of building energy optimization is to maximize the overall energy benefits of users in the building throughout the day. The energy benefits of users in the building are expressed as follows:

[0064]

[0065] V profit,t =E sell,t·p sell,t +V product,t

[0066] C t =E buy,t ·p buy,t +C other,t

[0067] Among them, n is the number of comfort levels considered, B t represents the energy welfare in the period [t, t+1]; ω i,t represents the comfort weight of the i-th item; V profit,t Represents the economic value generated by users in the building participating in electricity sales or production; C t represents the energy cost of the building in the period [t, t+1]; E sell,t and E buy,t They represent the electricity sales and electricity purchase of the building in the period [t, t+1] respectively; p sell,t and p buy,t They are respectively represented as the electricity selling price and electricity buying price in the period [t, t+1]; V product,t It represents the economic value generated by the building through production equipment during the period [t, t+1]. If there is no production equipment in the building, this item is 0. other,t It represents the operating costs of various equipment and other costs of the building in the period [t, t+1].

[0068] Based on the user's comfort preference for each dimension in different time and space states, determine the corresponding comfort weight ω i,t Calculate the economic value V generated by the building based on the building's future 24h photovoltaic power generation and rigid load forecast data, time-of-use electricity price data and simulated energy consumption behavior. product,t and energy cost C t , forming the goal of maximizing the overall energy benefits of users in the building throughout the day and optimizing energy consumption:

[0069] (5) Considering various equality constraints and inequality constraints during the operation of the building, the maximization of the overall energy welfare of users in the building throughout the day is taken as the optimization goal to form a building energy optimization problem; the building photovoltaic power generation and rigid load forecast data within the future time range of 24 hours are substituted into the formed building energy optimization problem for solution and the energy consumption behavior of the building in the future 24 hours is optimized and simulated to obtain the equipment operation strategy with a control time interval of 15 minutes.

[0070] This paper uses an office building in southern China as a calculation scenario to evaluate the performance of this building energy optimization method. The basic situation of the building is as follows: the building area is 528.08 square meters, there are two floors above ground, and the building height is 7.2 meters. The main function of the building is to provide office and meeting space. The building is an energy-saving building, with photovoltaic tiles and photovoltaic railings installed as photovoltaic power generation equipment, and energy storage equipment configured. The building envelope has been energy-savingly transformed, and insulation measures have been adopted for the exterior walls, roofs, doors and windows. The operating status of HVAC and energy storage equipment can be controlled by IoT devices. The building energy optimization method will optimize the operation strategies of various flexible energy-consuming equipment in the next 24 hours, with an optimization time interval of 15 minutes.

[0071] Table 1 compares the optimization method with a day-ahead optimization method and user autonomous adjustment. Figure 1 , Figure 2 and Figure 3 As shown, the optimization results show that the present invention can achieve energy conservation and emission reduction of buildings on the basis of satisfying the multi-dimensional comfort of users, which illustrates the effectiveness of the objective function setting and energy optimization method based on "psychological price".

[0072] Table 1

[0073]

Claims

1. A building energy optimization method considering the user comfort requirements and energy cost reduction requirements in the building, characterized in that: The following steps are involved: Based on the utility model of equipment power consumption and the utility model of power consumption time of the i-th comfort level, the maximum psychological price Pri of the user in the i-th comfort level is determined according to the collected subjective evaluation of the user on the i-th comfort level in the optimal heat, light and air environment. i,willing ; Based on the per unit value of comfort index of each dimension at time t And the corresponding maximum psychological price Pri i,willing , determine the economic characteristics V of user comfort at time t i,t ; According to the economic characteristics V of user comfort at time t i,t and energy cost, we can get the energy benefit B of users in the building during the future Δt period t , determine the overall energy welfare of users in the building throughout the day B wd ; Considering the building operation constraints, maximizing the overall energy welfare of users in the building throughout the day is taken as the optimization goal. Combined with the building photovoltaic power generation and rigid load forecast data in the future T period, the energy consumption behavior of the building in the future T period is optimized and simulated, and the equipment operation strategy with a control time interval of Δt is obtained.

2. The building energy optimization method according to claim 1, which takes into account the user comfort requirements and energy cost reduction requirements in the building, is characterized in that: The maximum psychological price of the i-th comfort level Pri i,willing for Pri i,willing =max[μ(Q)]·max[v(t)] Among them, μ(Q) is the electricity consumption utility of the equipment corresponding to the i-th comfort level when the electricity consumption is Q; v(t) is the electricity consumption time utility of the equipment corresponding to the i-th comfort level when the electricity consumption time is t.

3. The building energy optimization method according to claim 2, which takes into account the user comfort requirements and energy cost reduction requirements in the building, is characterized in that: The utility model of the equipment electricity consumption of the i-th comfort level is as follows: For equipment with fixed power consumption, the equipment power consumption utility model is: Among them, p i q i is the utility value of the equipment in normal use, p i is the maximum electricity price that users stop using, q i The power consumption Q is the rated power consumption of the equipment in the time period Δt; For devices whose power consumption can be adjusted, the utility model of the equipment power consumption is: m s =a(1-e -bx ) Among them, q min and q max are the minimum and maximum power consumption of the equipment within Δt, p i2 is the electricity price when the user's electricity consumption reaches the minimum, p i1 is the electricity price when the user's electricity consumption reaches the maximum, μ s (·) is a standard convex utility function, and a and b are the basic parameters of the convex utility function.

4. The building energy optimization method according to claim 3, which takes into account the user comfort requirements and energy cost reduction requirements in the building, is characterized in that: The utility model of electricity consumption time for the i-th comfort level is: Among them, v s (·) is the standard sigmoid function, t min is the earliest acceptable usage time for users, t max is the latest usage time acceptable to the user, and a, b and c are the basic parameters of the standard s-type function.

5. The building energy optimization method according to claim 4, which takes into account the user comfort requirements and energy cost reduction requirements in the building, is characterized in that: Economic characteristics of user comfort at time t V i,t for Among them, the per unit value It is any one of the per-unit value of the thermal comfort index, the per-unit value of the visual comfort index and the per-unit value of the air quality comfort index.

6. The building energy optimization method according to claim 5, characterized in that: Energy welfare of users in the building during the future Δt period B t for V profit,t =E sell,t ·p sell,t +V product,t C t =E buy,t ·p buy,t +C other,t Where n is the number of comfort levels, ω i,t represents the weight of the i-th comfort level; V profit,t Represents the economic value generated by users in the building participating in electricity sales or production; C t represents the energy cost of the building in the period [t, t+1]; E sell,t and E buy,t They represent the electricity sales and purchases of the building in the period [t, t+1] respectively; p sell,t and p buy,t They are respectively represented as the electricity selling price and electricity buying price in the period [t, t+1]; V product,t represents the economic value generated by the building through production equipment during the period [t, t+1]; C other,t It represents the operating costs of various equipment and other costs of the building in the period [t, t+1].

7. The building energy optimization method according to claim 6, characterized in that: The overall energy consumption benefit of users in the building throughout the day B wd for 8. The building energy optimization method according to claim 7, which takes into account the user comfort requirements and energy cost reduction requirements in the building, is characterized in that: The optimization goals for maximizing the overall energy benefits of users in the building throughout the day are as follows:

9. A building energy optimization system that takes into account the comfort requirements of building users and the energy cost reduction requirements, characterized in that: include The model building module is used to build the utility model of equipment power consumption and the utility model of power consumption time. Based on the collected subjective evaluation of the user on the i-th comfort level under the optimal heat, light and air environment, the maximum psychological price Pri of the user on the i-th comfort level is determined. i,willing , combined with the per unit value of each dimension comfort index at time t Determine the economic characteristic V of user comfort at time t i,t ; The decision-making solution module is used to maximize the overall energy welfare of users in the building throughout the day as the optimization goal based on the building power balance constraints, HVAC operation constraints, lighting system operation constraints, fresh air system operation constraints and energy storage system operation constraints. Combined with the building's photovoltaic power generation and rigid load forecast data in the future T period, the energy consumption behavior of the building in the future T period is optimized and simulated to obtain the equipment operation strategy with a control time interval of Δt.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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