Building integrated energy system optimization scheduling model and method giving consideration to comfort level of light storage soft environment

Abstract

The invention discloses a building integrated energy system optimization scheduling method giving consideration to the comfort level of a light storage soft environment, and relates to the field of building energy system optimization scheduling, and the method comprises the steps: building a photovoltaic output uncertainty model based on Beta distribution according to typical daily solar radiation intensity data; secondly, constructing an energy storage model; thirdly, a comfort level quantitative model considering temperature regulation and control of the indoor air conditioner and the electric water heater is constructed, and the influence on the environmental comfort level is reflected laterally; and finally, according to the various formed models, constructing a building integrated energy system optimization scheduling model considering the light storage soft comfort degree, and performing solving based on a mixed integer linear programming method. The environment comfort level of the building integrated energy system can be quantified, the flexibility of the building integrated energy system is improved by introducing photovoltaic and energy storage equipment, flexible scheduling of load resources is facilitated, peak load shifting is better achieved, the operation cost of the building integrated energy system is reduced, and the operation efficiency of the building integrated energy system is improved.

Description

Technical Field

[0001] The present invention relates to the field of building energy system optimization and scheduling, and in particular to a building integrated energy system optimization and scheduling model that takes into account both photovoltaic energy storage and soft environmental comfort.

[0002] The present invention also relates to a method for optimizing and scheduling a building integrated energy system that takes into account both photovoltaic energy storage and soft environmental comfort. Background Art

[0003] Buildings account for a significant proportion of global energy consumption and carbon emissions; for example, in 2020, the total energy consumption of global buildings and construction industries accounted for 36% of the global total energy consumption and contributed 37% of the global carbon emissions; in addition, with the continuous advancement of urbanization, energy demand in the construction sector is expected to increase by 50% in the next 30 years; currently, buildings have become multi-energy carriers, with multiple load demands such as electricity, heat, and cooling; in order to meet users' multiple load demands, a building integrated energy system (Building Integrated Energy System, BIES) is gradually taking shape; the optimized scheduling of the building integrated energy system helps to ensure load supply, reduce energy consumption, and improve safety and economy.

[0004] Integrated energy systems can achieve peak load shaving, valley filling, and cost reduction by utilizing energy storage equipment and renewable energy. Photovoltaic renewable energy and the energy storage characteristics of EVs have been widely used in building integrated energy systems and other related fields. Related studies have established corresponding integrated load demand optimization models for the orderly charging and discharging characteristics of mobile battery energy storage systems (TBES) and EVs, and adopted opportunity constraints to deal with the uncertainty of renewable energy, which have the effect of peak load shaving and valley filling and cost reduction. Other researchers have achieved good results in improving the stability of regional integrated energy systems (RIES) by introducing hybrid energy storage systems (HESS) to deal with the uncertainty of renewable energy. Some studies focus on solving the dissatisfaction, discomfort or inconvenience on the user side, that is, the user comfort problem. By monitoring the use of air conditioning in residential buildings in the region, it was found that the impact of indoor air temperature on air conditioning use behavior cannot be ignored, and a regression model of air conditioning start probability and indoor air temperature was established based on monitoring data to improve the comfort of the environment.

[0005] Building integrated energy systems can draw on the research of the above-mentioned general integrated energy systems and optimize building operations by introducing renewable energy such as rooftop photovoltaic panels and various types of energy storage devices. In recent years, China has gradually paid more attention to building load optimization, but there is still a gap compared with foreign countries, especially in terms of renewable energy penetration and environmental comfort. Domestic building integrated energy systems focus more on the economic benefits of energy optimization configuration, and less on the environmental comfort requirements of building operations and the use of renewable energy. Summary of the invention

[0006] The purpose of the present invention is: to address the above-mentioned problems, the present invention provides a building integrated energy system optimization scheduling method that takes into account both photovoltaic storage and environmental comfort. By considering two flexible loads related to comfort, a comfort model required for efficient operation of the building is constructed, and by introducing photovoltaic storage and environmental comfort, the scheduling flexibility of the building integrated energy system is increased. Taking multiple factors such as economy, environment and environmental comfort as goals, the present invention aims to minimize the operating cost of the building integrated energy system (BIES) and promote the rapid development of the green economy of the building integrated energy system.

[0007] The technical solution adopted by the present invention is as follows:

[0008] An optimization scheduling model for building integrated energy systems that takes into account both photovoltaic and energy storage and environmental comfort, including:

[0009] Photovoltaic output uncertainty model, using building parameters and energy data to build a photovoltaic output uncertainty model to adjust photovoltaic output;

[0010] Energy storage equipment energy model, including static energy storage model and mobile energy storage model, to determine the storage capacity of energy storage equipment in the building;

[0011] Flexible load model, including transferable load, curtailable load and shiftable load, dispatches energy through load transfer, reduction and shift;

[0012] The temperature control comfort model includes indoor air conditioning temperature control and electric water heater temperature control, which is used to control the indoor environmental comfort.

[0013] The present invention also provides a building integrated energy system optimization scheduling method that takes into account both photovoltaic storage and soft environmental comfort, specifically comprising:

[0014] Collect building parameters and energy data;

[0015] Using the collected building parameters and energy data, a photovoltaic output uncertainty model is constructed based on Beta distribution to determine the photovoltaic output power;

[0016] According to the energy storage equipment inside the building, an energy storage equipment energy model including a static energy storage model and a mobile energy storage model is constructed to determine the energy storage capacity;

[0017] Construct a flexible load model including transferable loads, reducible loads and translational loads, and obtain the parameters of each load;

[0018] Construct the temperature control comfort model of indoor air conditioners and electric water heaters, and determine the control parameters of indoor air conditioners and electric water heaters;

[0019] The above-constructed models are integrated to establish an optimal scheduling model for the building integrated energy system that takes into account both photovoltaic and storage and soft environmental comfort. The model is solved based on the mixed integer linear programming method to determine the optimized scheduling model for the integrated energy system.

[0020] Further, the energy data includes energy data of typical days in summer and winter.

[0021] Furthermore, the photovoltaic output uncertainty model is as follows:

[0022]

[0023] In the formula, t is the time period, is the power generated by the photovoltaic device during period t, η PV is the power conversion efficiency of the photovoltaic device, is the solar radiation intensity during the period, A PV is the area of ​​the building photovoltaic panels, ε pe is the radiation intensity distribution conversion coefficient, θ is the temperature power coefficient, and T STC They are the actual operating temperature and reference temperature of the photovoltaic panel respectively.

[0024] Furthermore, the energy model of the energy storage device is as follows:

[0025]

[0026] In the formula, S soc,b,g,t+1 is the energy storage amount of static energy storage during period t+1; It is the energy storage amount of mobile energy storage in period t+1.

[0027] Furthermore, the flexible load model includes:

[0028] The load can be transferred, and the required electric energy remains unchanged before and after the load transfer, as shown in the following formula:

[0029]

[0030] In the formula, and They are the load powers that can be transferred before and after the dispatch period respectively;

[0031] The load can be reduced, and the power in period t after the load reduction As shown below:

[0032]

[0033] In the formula, is the power consumption in period t before load reduction, γ is the load reduction coefficient, δ t is a state variable for determining whether load reduction occurs. t =1 indicates that the load is reduced;

[0034] The acceptable translation range of the translatable load is [t sh- ,t sh+ ], when the load is shifted to the interval with τ as the starting time, in order to ensure the continuity of the running time, the following formula should be satisfied:

[0035]

[0036] Where, t s is the duration of the translatable load, y t is the state variable for determining whether the load is translated. t =1 indicates load translation during period t, L shift is the rated power of the load that can be translated.

[0037] Furthermore, the temperature control comfort model is as follows:

[0038] U=ω AC U AC,t +ω EWH U EWH,t (7)

[0039] Where U AC,t and U EWH,t The dissatisfaction was caused by air conditioners and electric water heaters, ω AC and ω EWH Convert weights for comfort.

[0040] Furthermore, based on the condition that the building includes distributed photovoltaic power sources, energy storage equipment and flexible loads, the above models are coordinated to establish the optimization objective function of the building integrated energy system. The objective function includes economic cost, environmental cost and environmental comfort cost, and constraints are established to determine the optimization scheduling model of the optimized integrated energy system.

[0041] Furthermore, the objective function is as follows:

[0042]

[0043] In the formula, is the objective function, is the economic cost, C dif is the environmental cost, μU is the environmental comfort cost;

[0044] Said is the objective function, is the economic cost, C dif is the environmental cost, μU is the environmental comfort cost, and the specific formula is as follows:

[0045]

[0046]

[0047] C dr =C trans +C cut +C shift (11)

[0048]

[0049] C dif =C pol +C car (16)

[0050]

[0051] In the formula, The cost of purchasing energy from outside for the building; is the natural gas purchased by the building at time t; is the unit price of electricity purchased at time t; is the unit price of natural gas purchased at time t; Δt is the time change; is the selling price of photovoltaic power generation at time t; is the photovoltaic power generation of the building at time t; C dr The compensation cost for flexible load changes participating in demand response; C trans Compensate for the cost of flexible transferable loads; C cut Flexible load compensation costs can be reduced; C shift Compensation cost for flexible translatable load; c trans is the compensation price per unit power load transfer; H is the total number of transferable load types; k is the corresponding load reduction type; K represents the total number of load reduction types; c cut is the compensation price for unit power load reduction; c shift is the compensation price for unit power load shift; C om Maintenance costs for building equipment; represents the unit maintenance cost of equipment j; Pj，t represents the output of device j in time period t; C dif is the environmental cost item; C pol is the pollutant emission control cost item; C car is the carbon emission cost item; m is a certain pollutant gas; M represents the total number of pollutant gas types; β i The cost required to treat the i-th pollutant gas; α i is the emission coefficient for the i-th pollutant gas; represents the amount of electricity purchased from the external power grid in the tth time period; represents carbon tax; Z im Indicates the carbon emission intensity of electricity purchased from the external grid.

[0052] Furthermore, the constraints are as follows:

[0053] Power balance constraints:

[0054]

[0055] Photovoltaic power generation constraints:

[0056]

[0057] In the formula, and They represent the lower and upper limits of the setting preference for photovoltaic power generation, respectively;

[0058] The air conditioner temperature setting and electric water heater temperature setting constraints are as follows:

[0059]

[0060] In the formula, and Represent the lower and upper limits of the temperature setting preferences for the air conditioner and electric water heater, respectively.

[0061] Energy storage constraints:

[0062]

[0063] In the formula, and are the charging and discharging power of EV respectively; and They are the charging and discharging power boundaries of EV respectively; is the state of charge; and They are the upper and lower limits of the state of charge allowed for the battery during the charging and discharging process. is the expected state of charge of the EV when it is off-grid the next day; T is the total dispatching time; N b is the number of smart buildings;

[0064] Flexible load constraints:

[0065]

[0066]

[0067] In the formula, and are the upper and lower limits of the power after load transfer; υ t is the state variable, when υ t =1 means that the load is transferred in period t; and are the minimum and minimum duration of load shedding respectively.

[0068] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0069] The building integrated energy system optimization scheduling method of the present invention that takes into account both photovoltaic and energy storage and soft environmental comfort can quantify the environmental comfort of the building integrated energy system. By introducing photovoltaic and energy storage equipment, the flexibility of the building integrated energy system is increased, which helps to flexibly schedule load resources, better achieve peak shaving and valley filling, reduce the operating cost of the building integrated energy system, and improve its operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a principle framework diagram of a building integrated energy system optimization scheduling method that takes into account both photovoltaic storage and soft environmental comfort of the present invention;

[0071] Figure 2 It is a schematic diagram of mobile energy storage modeling in a building integrated energy system optimization scheduling method that takes into account both photovoltaic storage and soft environmental comfort of the present invention. DETAILED DESCRIPTION

[0072] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0073] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0074] Example 1

[0075] The present invention provides a building integrated energy system optimization scheduling model that takes into account both solar energy storage and soft environmental comfort. Figure 1 As shown, specifically including:

[0076] Data collection model, used to collect building-related parameters and data, including solar radiation intensity curves for typical summer and winter days, daily load curves, energy market prices, and energy conversion equipment parameters;

[0077] Photovoltaic, energy storage and load models, including photovoltaic output uncertainty model, energy storage equipment energy model and flexible load model; photovoltaic output power of the building is adjusted through the photovoltaic output uncertainty model; energy storage equipment energy model includes static energy storage model and mobile energy storage model, and building energy storage situation is determined through energy storage equipment energy model; flexible load model includes transferable load, reducible load and shiftable load, and the introduction of flexible load model increases the flexibility of building integrated energy system optimization scheduling model;

[0078] Comfort quantification model: build a comfort quantification model for the temperature control of indoor air conditioners and electric water heaters, which indirectly reflects the control effect on environmental comfort;

[0079] Based on the various models formed above, an optimization scheduling model for the building integrated energy system taking into account the soft comfort of photovoltaic storage is constructed, and the lowest cost and most efficient operation mode of the building integrated energy system is determined on the premise of meeting the total load demand, environmental comfort demand and the energy supply constraints of the building energy system.

[0080] Example 2

[0081] The present invention provides a building integrated energy system optimization scheduling method that takes into account both photovoltaic storage and soft environmental comfort, specifically comprising:

[0082] Collect building-related parameters and data, including solar radiation intensity curves, daily load curves, energy market prices, and energy conversion equipment parameters for typical days in summer and winter. According to the solar radiation intensity data for typical days, including typical days in summer and typical days in winter, a photovoltaic output uncertainty model is constructed based on the Beta distribution, as shown in the following formula:

[0083]

[0084] In the formula, t is the time period, is the power generated by the photovoltaic device during period t, η PV is the power conversion efficiency of the photovoltaic device, is the solar radiation intensity during the period, A PV is the area of ​​the building photovoltaic panels, ε pe is the radiation intensity distribution conversion coefficient, θ is the temperature power coefficient, and T STC They are the actual operating temperature and reference temperature of the photovoltaic panel respectively.

[0085] Correctly simulating the solar irradiance distribution is the key to calculating the power generation output of photovoltaic equipment. Modeling is performed. In a certain period of time, generally a few hours, the solar irradiance approximately follows the Beta distribution. The distribution function of the solar irradiance intensity in a given period of time is shown as follows:

[0086]

[0087] In the formula, represents the solar radiation rate in a given time period, is the maximum value of solar radiation in a given time period, Γ(·) is the gamma function, parameters α and β are the parameters for adjusting the shape of Beta distribution, and μ and σ represent the mean and variance of solar radiation in the time period, respectively.

[0088] According to the energy storage equipment inside the building, an energy storage equipment energy model including a static energy storage model and a mobile energy storage model is constructed, as shown in the following formula:

[0089]

[0090] In the formula, S soc,b,g,t+1 is the energy storage amount of static energy storage during period t+1; It is the energy storage amount of mobile energy storage in period t+1.

[0091] The static energy storage model includes the dynamic mathematical model of the electric energy storage device represented by the following formula:

[0092]

[0093] S soc,b,g,1 =S soc,b,g,T (41)

[0094] In the formula, S soc,b,g,t+1 is the energy storage capacity of the energy storage device during period t+1; σ b,t is the energy self-consumption rate of the energy storage device; and are the input and output energy powers of the time period energy storage device respectively; and are the input and output energy efficiencies of the energy storage device, S soc,b,g,1 and S soc,b,g,T They respectively represent the energy storage status at the beginning and end of the operation scheduling of the energy storage equipment, and T represents the total number of time periods.

[0095] The mobile energy storage model specifically includes:

[0096] like Figure 2As shown, the following formula is the electric vehicle energy storage model, which is established on the basis of the previous energy and describes the discharge process of EV considering the consumption per unit mileage when in a moving state. It is the energy change component in the electric vehicle energy storage model.

[0097]

[0098] Where D j,u and They are the driving distance and power consumption per unit mileage of EV; is the rated capacity of the EV battery; t a,u and t 1,u They are the length of the EV access period and grid connection period respectively; and They are the energy conversion efficiency in EV charging and discharging modes respectively.

[0099] In the optimization and scheduling of building integrated energy systems, a flexible load model is introduced to construct a flexible load model including transferable loads, reducible loads and shiftable loads, as follows:

[0100] The transferable load has no continuity constraints, and the working time and working period are adjustable. The operation flexibility is high, and the total power consumption must be maintained unchanged within a scheduling cycle. The acceptable transfer interval of the transferable load is [t sh- ,t sh+ ], the required electric energy remains unchanged before and after the load transfer, as shown in the following formula:

[0101]

[0102] In the formula, and They are the load powers that can be transferred before and after scheduling respectively.

[0103] The load that can be reduced can be partially reduced while meeting the energy demand of the building; the power of the load after the load reduction in period t As shown below:

[0104]

[0105] In the formula, is the power consumption in period t before load reduction, γ is the load reduction coefficient, δ t is a state variable for determining whether load reduction occurs. t =1 indicates that the load is reduced;

[0106] The load that can be moved has continuous power consumption, fixed working hours, and adjustable working hours. It needs to be moved as a whole and cannot be moved in sections. The acceptable range of movement for the load that can be moved is [t sh- ,tsh+ ], when the load is shifted to the interval with τ as the starting time, in order to ensure the continuous operation time, it should be satisfied

[0107]

[0108] Where, t s is the duration of the translatable load, y t is the state variable for determining whether the load is translated. t =1 indicates load translation during period t, L shift is the rated power of the load that can be translated.

[0109] A quantitative model of temperature control comfort including indoor air conditioning and electric water heater is constructed, as follows:

[0110] U=ω AC U AC,t +ω EWH U EWH,t (7)

[0111] Where U AC,t and U EWH,t The dissatisfaction was caused by air conditioners and electric water heaters, ω AC and ω EWH Convert weights for comfort.

[0112] The indoor air conditioning model specifically includes:

[0113] The air conditioning model under different modes is shown as follows:

[0114]

[0115] In the formula, is the operating power of the air conditioner (W); U AC,t It is a binary variable, where 1 indicates that the air conditioner is running in the ON state and 0 indicates that the air conditioner is running in the OFF state; is the temperature setting value of the air conditioner (℃); δ AC is the thermostat dead zone (℃); is the cooling / heating provided by the air conditioner (W); η is the energy input ratio of the air conditioner; T en is the outdoor temperature (℃); η nom is the normal energy input ratio of the air conditioner; χ1 and χ2 are the conversion coefficients of the air conditioner energy input and temperature. In this embodiment, χ1 and χ2 are constants;

[0116] is the operating power of the air conditioner in the ON state, and is the average value of the monitored power in the ON state; It is the operating power of the air conditioner in the OFF state and the average value of the monitored power in the OFF state.

[0117] In addition to the above air-conditioning model, the thermodynamic model of the air-conditioned room is another key component of the air-conditioning model. This embodiment uses a grey model to establish the thermodynamic model of the air-conditioned room, which is expressed by the following formula, where T a is the indoor air temperature, T en is the outdoor temperature, is the cooling capacity provided by the air conditioner, I s is the solar irradiance, k1, k2, k3 and k4 are the model parameters that need to be determined; these model parameters can be estimated by multi-linear regression method based on monitoring data. In addition, the estimation of indoor air temperature is recursive, that is, according to the previous Repeated estimates of

[0118]

[0119] The electric water heater model is established as shown below:

[0120]

[0121] In the formula, is the operating power of the electric water heater (W); The heat provided by the electric water heater (watt); UEWH is a binary variable, 1 means the electric water heater is on, 0 means the electric water heater is off; is the temperature setting value of the electric water heater (degrees Celsius); δ EWH is the thermostat dead zone (degrees Celsius); η EWH is the heating efficiency of the electric water heater; It is the power of the electric water heater in the ON state, which is equal to the average value of the monitored power in the ON state.

[0122] The thermodynamic model of the electric water heater is expressed by the following formula without consuming hot water:

[0123]

[0124] C EWH =C p ρ w V (54)

[0125] The thermodynamic model of the electric water heater when consuming hot water is expressed by the following formula:

[0126]

[0127] Where, T w and T sur Respectively represent the water temperature in the electric water heater and the ambient temperature around the electric water heater; REWH is the thermal resistance of the water tank insulation layer; C EWH is the equivalent heat capacity; C p is the specific heat of water; ρ w is the density of water; V represents the volume of the electric water heater; V con Indicates the volume of hot water consumed; T w，inlet is the temperature of the make-up water, γ EWH Indicates the thermal conductivity of the electric water heater;

[0128] Integrate the various models formed above to build an optimal scheduling model for the building integrated energy system that takes into account the soft comfort of photovoltaic storage. Under the premise of meeting the total load demand, environmental comfort demand and the energy supply constraints of the building energy system, determine the lowest cost and most efficient operation mode of the building integrated energy system, as follows:

[0129] The objective function is as follows:

[0130]

[0131] In the formula, is the objective function, is the economic cost, C dif is the environmental cost, μU is the environmental comfort cost;

[0132] Said is the objective function, is the economic cost, C dif is the environmental cost, μU is the environmental comfort cost, and the specific formula is as follows:

[0133]

[0134] C dr =C trans +C cut +C shift (11)

[0135]

[0136]

[0137] C dif =C pol +C car (16)

[0138]

[0139] In the formula, The cost of purchasing energy from outside for the building; is the natural gas purchased by the building at time t; is the unit price of electricity purchased at time t; is the unit price of natural gas purchased at time t; Δt is the time change; is the selling price of photovoltaic power generation at time t; is the photovoltaic power generation of the building at time t; C dr The compensation cost for flexible load changes participating in demand response; C trans Compensate for the cost of flexible transferable loads; C cut Flexible load compensation costs can be reduced; C shift Compensation cost for flexible translatable load; c trans is the compensation price per unit power load transfer; H is the total number of transferable load types; k is the corresponding load reduction type; K represents the total number of load reduction types; c cut is the compensation price for unit power load reduction; c shift is the compensation price for unit power load shift; C om Maintenance costs for building equipment; represents the unit maintenance cost of equipment j; P j，t represents the output of device j in time period t; C dif is the environmental cost item; C pol is the pollutant emission control cost item; C car is the carbon emission cost item; m is a certain pollutant gas; M represents the total number of pollutant gas types; β i The cost required to treat the i-th pollutant gas; α i is the emission coefficient for the i-th pollutant gas; represents the amount of electricity purchased from the external power grid in the tth time period; represents carbon tax; Z im Indicates the carbon emission intensity of electricity purchased from the external grid.

[0140] Furthermore, the constraints are as follows:

[0141] Power balance constraints:

[0142]

[0143]

[0144] Photovoltaic power generation constraints:

[0145]

[0146] In the formula, and They represent the lower and upper limits of the setting preference for photovoltaic power generation, respectively;

[0147] The air conditioner temperature setting and electric water heater temperature setting constraints are as follows:

[0148]

[0149] In the formula, and Represent the lower and upper limits of the temperature setting preferences for the air conditioner and electric water heater, respectively.

[0150] Energy storage constraints:

[0151]

[0152] In the formula, and are the charging and discharging power of EV respectively; and They are the charging and discharging power boundaries of EV respectively; is the state of charge; and They are the upper and lower limits of the state of charge allowed for the battery during the charging and discharging process. is the expected state of charge of the EV when it is off-grid the next day; T is the total dispatching time; N b is the number of smart buildings;

[0153] Flexible load constraints:

[0154]

[0155]

[0156] In the formula, and are the upper and lower limits of the power after load transfer; υ t is the state variable, when υ t =1 means that the load is transferred in period t; and are the minimum and minimum duration of load shedding respectively.

[0157] The present invention constructs a photovoltaic output uncertainty model based on the Beta distribution according to the typical daily solar radiation intensity data; secondly, constructs an EV mobile energy storage model considering the mobility characteristics; thirdly, constructs a comfort quantification model considering the temperature control of indoor air conditioners and electric water heaters, which indirectly reflects the impact on environmental comfort; finally, according to the various models formed above, constructs an optimization scheduling model of a building integrated energy system considering the soft comfort of light storage, and solves it based on the mixed integer linear programming method; the present invention can quantify the environmental comfort of the building integrated energy system, increase the flexibility of the building integrated energy system by introducing photovoltaic and energy storage equipment, contribute to the flexible scheduling of load resources, better realize peak shaving and valley filling, reduce the operating cost of the building integrated energy system, and improve its operating efficiency.

[0158] The principles and implementation methods of the present invention are described in this article using specific embodiments. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An optimization scheduling model for building integrated energy system that takes into account both photovoltaic and energy storage and soft environmental comfort, characterized in that: include: Photovoltaic output uncertainty model, using building parameters and energy data to build a photovoltaic output uncertainty model to adjust photovoltaic output; Energy storage equipment energy model, including static energy storage model and mobile energy storage model, to determine the storage capacity of energy storage equipment in the building; Flexible load model, including transferable load, curtailable load and shiftable load, dispatches energy through load transfer, reduction and shift; The comfort quantification model, including indoor air conditioning temperature control and electric water heater temperature control, is used to control the indoor environmental comfort.

2. A building integrated energy system optimization scheduling method that takes into account both photovoltaic and energy storage and soft environmental comfort, characterized in that: Specifically include: Collect building parameters and energy data; Using the collected building parameters and energy data, a photovoltaic output uncertainty model is constructed based on Beta distribution to determine the photovoltaic output power; According to the energy storage equipment inside the building, an energy storage equipment energy model including a static energy storage model and a mobile energy storage model is constructed to determine the energy storage capacity; Construct a flexible load model including transferable loads, reducible loads and translational loads, and obtain the parameters of each load; Construct a quantitative model of temperature control comfort for indoor air conditioners and electric water heaters, and determine the control parameters of indoor air conditioners and electric water heaters; The above-constructed models are integrated to establish an optimal scheduling model for the building integrated energy system that takes into account both photovoltaic and storage and soft environmental comfort. The model is solved based on the mixed integer linear programming method to determine the optimized scheduling model for the integrated energy system.

3. According to claim 2, a building integrated energy system optimization scheduling method that takes into account both photovoltaic storage and soft environmental comfort is characterized in that: The energy data includes energy data of typical days in summer and winter.

4. According to claim 2, a building integrated energy system optimization scheduling method that takes into account both photovoltaic and energy storage and soft environmental comfort is characterized in that: The photovoltaic output uncertainty model is specifically as follows: In the formula, t is the time period, is the power generated by the photovoltaic device during period t, η PV is the power conversion efficiency of the photovoltaic device, is the solar radiation intensity during the period, A PV is the area of ​​the building photovoltaic panels, ε pe is the radiation intensity distribution conversion coefficient, θ is the temperature power coefficient, and T STC They are the actual operating temperature and reference temperature of the photovoltaic panel respectively.

5. According to claim 2, a building integrated energy system optimization scheduling method that takes into account both photovoltaic and energy storage and soft environmental comfort is characterized in that: The energy model of the energy storage device is as follows: In the formula, S soc,b,g,t+1 is the energy storage amount of static energy storage during period t+1; It is the energy storage amount of mobile energy storage in period t+1.

6. According to claim 2, a building integrated energy system optimization scheduling method that takes into account both photovoltaic and energy storage and soft environmental comfort is characterized in that: The flexible load model includes: The load can be transferred, and the required electric energy remains unchanged before and after the load transfer, as shown in the following formula: In the formula, and They are the load powers that can be transferred before and after the dispatch period respectively; The load can be reduced, and the power in period t after the load reduction As shown below: In the formula, is the power consumption in period t before load reduction, γ is the load reduction coefficient, δ t is a state variable for determining whether load reduction occurs. t =1 indicates that the load is reduced; The acceptable translation range of the translatable load is [t sh- ,t sh+ ], when the load is shifted to the interval with τ as the starting time, in order to ensure the continuity of the running time, the following formula should be satisfied: Where, t s is the duration of the translatable load, y t is the state variable for determining whether the load is translated. t =1 indicates load translation during period t, L shift is the rated power of the load that can be translated.

7. According to claim 2, a building integrated energy system optimization scheduling method that takes into account both photovoltaic and energy storage and soft environmental comfort is characterized in that: The comfort quantification model is specifically as follows: U=ω AC IN AC,t +ω EWH IN EWH,t (7) Where U AC,t and U EWH,t The dissatisfaction was caused by air conditioners and electric water heaters, ω AC and ω EWH Convert weights for comfort.

8. A building integrated energy system optimization scheduling method that takes into account both photovoltaic and energy storage and environmental comfort according to any one of claims 3 to 7, characterized in that: Based on the condition that the building includes distributed photovoltaic power sources, energy storage equipment and flexible loads, the above models are coordinated to establish the optimization objective function of the building integrated energy system. The objective function includes economic cost, environmental cost and environmental comfort cost, establish constraints, and determine the optimization scheduling model of the optimized integrated energy system.

9. The method for optimizing and scheduling a building integrated energy system that takes into account both photovoltaic and energy storage and environmental comfort according to claim 8 is characterized in that: The objective function is specifically as follows: In the formula, is the objective function, is the economic cost, C dif is the environmental cost, μU is the environmental comfort cost; Said is the objective function, is the economic cost, C dif is the environmental cost, μU is the environmental comfort cost, and the specific formula is as follows: C dr =C trans +C cut +C shift (11) C dif =C pol +C car (16) In the formula, The cost of purchasing energy from outside for the building; is the natural gas purchased by the building at time t; is the unit price of electricity purchased at time t; is the unit price of natural gas purchased at time t; Δt is the time change; is the selling price of photovoltaic power generation at time t; is the photovoltaic power generation of the building at time t; C dr The compensation cost for flexible load changes participating in demand response; C trans Compensate for the cost of flexible transferable loads; C cut Flexible load compensation costs can be reduced; C shift Compensation cost for flexible translatable load; c trans is the compensation price per unit power load transfer; H is the total number of transferable load types; k is the corresponding load reduction type; K represents the total number of load reduction types; c cut is the compensation price for unit power load reduction; c shift is the compensation price for unit power load shift; C om Maintenance costs for building equipment; represents the unit maintenance cost of equipment j; P j，t represents the output of device j in time period t; C dif is the environmental cost item; C pol is the pollutant emission control cost item; C car is the carbon emission cost item; m is a certain type of pollutant gas; M represents the total number of types of pollutants; β i The cost required to treat the i-th type of pollutant gas; α i is the emission coefficient for the i-th pollutant gas; represents the amount of electricity purchased from the external power grid in the tth time period; represents carbon tax; Z im Indicates the carbon emission intensity of electricity purchased from the external grid.

10. The method for optimizing and scheduling a building integrated energy system that takes into account both photovoltaic and energy storage and environmental comfort according to claim 8, characterized in that: The constraints are as follows: Power balance constraints: Photovoltaic power generation constraints: In the formula, and They represent the lower and upper limits of the setting preference for photovoltaic power generation, respectively; The air conditioner temperature setting and electric water heater temperature setting constraints are as follows: In the formula, and Represent the lower and upper limits of the temperature setting preferences for the air conditioner and electric water heater, respectively. Energy storage constraints: In the formula, and are the charging and discharging power of EV respectively; and They are the charging and discharging power boundaries of EV respectively; is the state of charge; and They are the upper and lower limits of the state of charge allowed for the battery during the charging and discharging process. is the expected state of charge of the EV when it is off-grid the next day; T is the total dispatching time; N b is the number of smart buildings; Flexible load constraints: In the formula, and are the upper and lower limits of the power after load transfer; υ t is the state variable, when υ t =1 means that the load is transferred in period t; and are the minimum and minimum duration of load shedding respectively.

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