Virtual energy storage-considered double-layer optimization scheduling method for building integrated energy system

Abstract

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

Description

A two-layer optimization scheduling method for building integrated energy systems considering virtual energy storage Technical Field

[0001] The present invention belongs to the technical field of building integrated energy, and in particular relates to a double-layer optimization scheduling method for a building integrated energy system considering virtual energy storage. Background Art

[0002] As energy consumption continues to expand and environmental issues become increasingly severe, optimizing the energy mix has become a key societal issue. Integrated energy systems, characterized by integrated "source-grid-load-storage" systems and the complementary use of multiple energy sources, are gaining widespread attention as a key driver of CO2 emissions reduction. Buildings account for approximately 40% of total energy consumption and approximately 50% of total carbon emissions, a proportion that is expected to continue to rise as the number of buildings increases. The diversification of building functions has also led to increasingly close connections between multiple energy flows, such as cooling, heating, electricity, and gas, placing greater demands on integrated and systematic energy utilization. With the increasing adoption of renewable energy and the shift toward low-carbon energy, technologies such as wind power, photovoltaic power generation, and multi-energy conversion are being applied to various types of buildings, effectively improving energy efficiency, achieving energy conservation and emission reduction goals, and reducing building energy consumption. The Building Integrated Energy System (BIES) combines renewable energy systems, integrated energy systems and buildings, reshaping the demand-supply relationship of buildings, transforming buildings from single energy consumers to energy producers and sellers, and making the realization of zero-carbon buildings and net-zero energy buildings possible.

[0003] However, existing technologies lack comprehensive consideration of flexible resources on the building user side, such as building cooling, heating, electricity and gas integrated demand response, power-to-gas devices, building virtual energy storage, etc., and insufficient consideration of the various load characteristics of the building integrated energy system; in addition, the interaction between energy operators and building users also urgently needs further consideration.

[0004] Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a two-layer optimization scheduling method for a building integrated energy system considering virtual energy storage. Aiming at the demand response and low-carbon scheduling problems of the building integrated energy system, a two-layer optimization scheduling model for the building integrated energy system considering virtual energy storage and load characteristics is established. By formulating the purchase and sale electricity prices between the upper-level energy operators and the lower-level building users, the lower-level building users are guided to optimize, so as to realize the building energy conservation and emission reduction function.

[0006] The present invention provides a two-tier optimization scheduling method for a building integrated energy system considering virtual energy storage, comprising the following steps:

[0007] S1. Based on the concept of energy hub, a low-carbon building integrated energy system including wind, solar, storage and energy conversion devices is constructed;

[0008] S2. Comprehensively analyze the characteristics of cooling, heating, and electrical loads in the integrated energy system of low-carbon buildings, and consider the building's virtual energy storage and human comfort indicators to construct a comprehensive user satisfaction index;

[0009] S3. Construct a two-layer optimization model with the energy operator (ESO) as the upper optimization layer, minimizing system carbon emissions as the goal, and building users as the lower optimization layer, maximizing user satisfaction as the goal; determine the objective functions and constraints of the upper energy operator pricing layer and the lower building user optimization layer;

[0010] S4. Solve the two-layer optimization model, using a non-dominated genetic algorithm to solve the upper-layer energy operator pricing model, update the purchase and sale prices of electricity of the upper-layer leader ESO, call the solver to solve the lower-layer model, and finally obtain the optimal scheduling strategy for the building integrated energy system.

[0011] Furthermore, in S1, the low-carbon building integrated energy system uses grid electricity, new energy power output and natural gas as energy input; the energy conversion device includes a gas turbine, a gas boiler, an electric refrigerator, an absorption refrigerator, and a power-to-gas (P2G) device; the electric load output is achieved through the synergy of grid electricity, the new energy power generation system and the gas turbine power generation system, the heat load output is provided by the gas boiler, the cooling load output is achieved by the electric refrigerator and the absorption refrigerator, the gas load is converted by the gas energy of the gas grid, the power-to-gas device, the gas turbine and the gas boiler, the energy storage device is a battery, and the heat and cold storage processes are achieved by the building virtual energy storage;

[0012] The multi-energy flow coupling matrix is ​​used to describe the conversion relationship between various loads, equipment, and multiple energy flows for equivalent modeling. The energy coupling matrix of the demand side and the supply side is constructed as follows:

[0013] Where: P load 、H load , Q load and G load are electricity, heating, cooling and gas load demands respectively; P s 、H s , Q s and G s They are power source, heat source, cold source and gas source, including energy input from the outside and various energy sources generated in BIES; μ P 、μ H 、μ Q and μ Gμ are the distribution coefficients of electricity, heat, cooling and gas load demands among power source, heat source, cooling source and gas source respectively; GT 、μ GB 、μ AC 、μ AR and μ P2G are the distribution coefficients of the energy conversion devices in the corresponding energy, and their respective distribution coefficients change with the change of the demand side load; η GT ,η GB ,η AC ,η AR and η P2G are the energy conversion efficiencies of the energy conversion devices respectively.

[0014] Furthermore, in S2, the characteristics of cooling, heating and electrical loads in the low-carbon building integrated energy system include building flexible electrical load characteristics and building cooling / heating load characteristics;

[0015] The specific characteristics of the building's flexible electrical load are as follows: the participation of flexible electrical loads enables the system scheduling plan to have a positive impact on reducing load peak-valley differences. The electrical loads in the building are divided into four categories according to their ability to participate in flexible regulation: basic load, shiftable load, transferable load and curtailable load;

[0016] Base load: refers to uncontrolled load that cannot respond to user requirements and cannot change the user's energy consumption pattern and time. This part does not participate in scheduling;

[0017] Shiftable load: The load's power consumption time varies according to the plan, but the load needs to be moved as a whole and its value cannot be changed, so the energy consumption time can span multiple scheduling cycles;

[0018] Assume that the translation load P shift Vector distribution before scheduling for:

[0019] Where: t S ,t D are the start time and end time of the translatable load respectively;

[0020] Assume that the translation period is [t sh- ,t sh+ ], and use variable κ to represent the load P shift In the translation state of time period t, that is, when κ=0, the load P shift No translation, κ = 1 means load P shift Starting from the t period, the set of starting periods is for:

[0021] U represents the union;

[0022] If t∈[t sh- ,t sh+ -t D +1] and t≠t S , then it means the load From the component P of the starting time period t shift for:

[0023] On the contrary, if t=t S , represents the load P shift unchanged;

[0024] Transferable load: During the power consumption period, the power consumption of the transferable load can be flexibly adjusted. The power consumption period is allowed to be interrupted, and the duration is not required to be fixed. It is only necessary to ensure that the total load demand before and after the transfer remains unchanged;

[0025] Assume that the transferable load P tran The transfer period is [t tr- ,t tr+ ], using variables Indicates load P tran The transfer state in a certain period of time t, that is, When the load is not transferred, When P tran The transfer starts from the t period; the minimum continuous working time is set for the transferable load, and the transfer power is limited, that is:

[0026] Where: and are the minimum and maximum values ​​of load transfer power respectively; The continuous operation time of the equipment;

[0027] Load curtailment: Participate in demand response by reducing energy consumption of building users;

[0028] The variable σ represents the load that can be reduced P cut In the reduction state of time period t, that is, when σ=1, it means P cut If it is reduced in period t, the power of load that can be reduced in period t after participating in the dispatch becomes:

[0029] Where: η t is the reduction coefficient, 0≤η t ≤1; is the initial power of load that can be reduced during period t;

[0030] In order to ensure the rationality of energy reduction for building users, it is necessary to constrain the duration and number of load reductions, namely:

[0031] Where: are the minimum and maximum continuous reduction time respectively; N max is the maximum number of reductions;

[0032] The building cooling / heating load characteristics include the inertia and flexibility of the building cooling / heating load;

[0033] Specifically, the inertia of the building's cooling / heating load is as follows: compared with the "instantaneous use" and "real-time balancing" characteristics of the power system, due to the building's own dynamic cooling / heating characteristics, that is, when the building uses various devices to obtain cooling / heating energy, it takes a period of time to reach the preset temperature, which makes the cooling / heating system in the building have "inertia" and exhibits a certain "energy storage" capability, namely virtual energy storage;

[0034] Virtual energy storage can make full use of the thermal inertia and heat storage characteristics of buildings, which meets the basic laws of thermodynamics. The model is:

[0035] Where: ΔQ HQ (t) is the indoor heat change at time t; C is the specific heat capacity of air; ρ0 is the air density; V is the building volume capacity; T in is the indoor temperature, d is the differential symbol, that is, the derivative of t;

[0036] Introducing virtual energy storage charging and discharging power to describe virtual energy storage, we have:

[0037] Where: P VES Energy storage charging and discharging power; VES t and VES max are the capacity and maximum capacity of virtual energy storage at time t respectively; is the indoor temperature at time t; T in,max and T in,min are the upper and lower limits of the acceptable indoor temperature respectively; Δt is the time interval;

[0038] In the heating system, the autoregressive moving average time series ARMA model is used to calculate the water supply temperature T of the heating network during period t. t,g , return water temperature T t,h , indoor temperature of heating buildings and outdoor temperature The relationship between them is described as follows:

[0039] Where: α, β, γ, θ, and ω are the physical parameters of the building heating system, and the order J reflects the thermal inertia of the building heating system;

[0040] Among them, the heating building heat supply H t,h and water supply temperature T t,g , return water temperature T t,h The relationship is: H t,h =γ(T t,g -T t,h )

[0041] Where: γ is the relationship coefficient between the heating supply of the heating building and the supply and return water temperature of the heating network.

[0042] The "inertia" in the cooling building is described by the equivalent thermal parameter model ETP, specifically:

[0043] Where: and are the indoor and outdoor temperatures of the cooling building; Q t,c represents the total cooling power of the refrigerator during the period t; R and C are the equivalent thermal resistance and equivalent heat capacity of the cooling building room respectively;

[0044] The flexibility of building cooling and heating loads specifically refers to the ambiguity in building users' perception of indoor room temperature. Therefore, the cooling and heating loads can be converted from fixed values ​​to flexible values. Flexibility means that the cooling and heating loads are not fixed values, but rather can be adjusted to meet comfort requirements and room temperature constraints, allowing for flexible scheduling.

[0045] Furthermore, in S2, the predicted mean vote (PMV) is selected as the human comfort index. It is a comprehensive index that comprehensively considers the physical environment and individual behavior differences that affect human comfort, that is, the human metabolic rate, clothing thermal resistance, average radiation temperature, and air flow, and quantifies the user's hot and cold comfort. The PMV index value λ PMV Specifically:

[0046] Where: M P and W P are the human metabolic rate and the mechanical power generated by human activities; P a is the water vapor pressure of the air surrounding the human body; t a is the ambient temperature of the human body; f cl The ratio of the body's clothing-covered area to its exposed area; cl and t r are the surface temperature and average radiation temperature of the human body when wearing clothes; h c is the air convection heat transfer coefficient.

[0047] Furthermore, in S2, the user comprehensive satisfaction index F is constructedBIES Including energy consumption satisfaction, electricity price satisfaction, cost satisfaction and comfort satisfaction;

[0048] Energy consumption satisfaction: Before the electricity price is adjusted, users decide their electricity consumption time according to their own habits and preferences. At this time, the energy consumption satisfaction is 1. After the price is adjusted, customers adjust their own flexible loads and update the load curve. At this time, whether they increase or decrease electricity consumption, their satisfaction with electricity consumption will decrease. The energy consumption satisfaction F load Expressed as:

[0049] Where: The electricity load after electricity price adjustment;

[0050] Electricity price satisfaction: If building users cannot adjust electricity prices in a timely manner, it will have a greater impact on the real-time electricity prices released by ESO; electricity price satisfaction F a It is inversely proportional to the size of the real-time electricity price, that is:

[0051] Where: The price of electricity sold by the ESO to BIES; is the amount of electricity sold by ESO to BIES at time t; a is the initial electricity price, The initial amount of electricity sold by ESO to BIES;

[0052] Cost satisfaction: The operating cost of BIES is the cost of interacting with external energy sources. Cost satisfaction F co Defined as:

[0053] Where: is the external energy purchase cost of BIES at time t, It is the initial energy purchase cost to meet the initial load demand.

[0054] Comfort satisfaction: When the PMV index value λ PMV When the value is between -1 and 1, it is considered that the user is comfortable and satisfied. PMV High, that is: F PMV =1-|λ PMV |.

[0055] Furthermore, the two-layer optimization model is expressed as:

[0056] Among them, ESO and BIES are participants in the two-level optimization model; the strategy sets of both parties are That is, the energy price increase ψ and the electricity price sold by ESO to BIES and electricity purchase price Bilateral utility, that is, the objective function min F that benefits both parties ESO and max F BIES , that is, minimizing carbon emissions and maximizing overall satisfaction.

[0057] Furthermore, the objective function of the upper energy operator pricing layer is:

[0058] Where: F ESO is the total carbon emissions of BIES; γ C is the unit price of carbon emissions; C t is the actual carbon emissions of the building's integrated energy system; C0 is the initial carbon emission quota;

[0059] Among them, carbon emissions in the building integrated energy system are generated by purchased electricity and purchased natural gas, namely;

[0060] Where: and are the amount of electricity and natural gas sold by ESO to BIES at time t, respectively; and are the average carbon emission coefficients of the regional power grid and gas grid respectively;

[0061] The constraints of the upper energy operator pricing layer are:

[0062] (1) Interacting with the main network with power constraints:

[0063] In order to avoid adverse effects on the stability of the main grid caused by interaction with the main grid, the principle of "grid connection without access to the grid" is adopted, and electricity is only purchased from the upper-level grid, rather than sold to the upper-level grid.

[0064] Where: and are the upper and lower limits of the electricity that the ESO can purchase from the upper power grid at time t; and The upper and lower limits of gas energy that the ESO can purchase from the upper-level gas grid at time t;

[0065] (2) Electricity price constraints:

[0066] The price of electricity sold by ESO to BIES It is the basis of demand response and reflects the dynamic supply and demand relationship of the system. The formulation of electricity prices must not only maintain the operating profit of operators, but also take into account the willingness and acceptance of users.

[0067] Where: and are the upper and lower limits of electricity prices at time t respectively; is the average electricity price during the dispatch period.

[0068] Furthermore, the objective function of the underlying user optimization layer is: max F BIES =(F load +F a +F PMV +F co ) / 4

[0069] The constraints of the user optimization layer of the underlying structure are:

[0070] (1) Unit constraints

[0071] Each energy conversion device, energy storage device, and building virtual energy storage has output limits. The output limits of new energy equipment are as follows:

[0072] Where: P wt and P pv are wind turbine and photovoltaic output power respectively; and They are wind turbine and photovoltaic predicted output respectively;

[0073] (2) Load demand response constraints

[0074] Users participating in the electric load demand response adopt corresponding strategies based on the price information set by the upper-level energy operator, such as load shifting, load transfer, and load reduction. The corresponding constraints are as follows:

[0075] The demand response characteristics of cooling and heating loads are mainly reflected in the "inertia" and flexibility of cooling and heating loads. In order to meet the indoor temperature comfort requirements and the indoor temperature requirements of cooling buildings, the following constraints should be made:

[0076] Where: It is the range limit of PMV index; and They are the upper and lower limits of indoor temperature for heating buildings and cooling buildings respectively;

[0077] (3) Electric power balance constraint P load =P wt +P pv +P B2E +P ES +P GT -P AC -P P2G =P base +P shift +P tran +Pcut

[0078] Where: P wt and P pv are the output power of wind turbine and photovoltaic respectively; P B2E is the interaction electric power between BIES and ESO; P ES Release electric power to the energy storage device; P AC and P P2G are the electric power consumed by the electric refrigerator and the P2G device respectively; P base As the basic electrical load;

[0079] (4) Thermal power balance constraint H load =H GB -H AR +H VES

[0080] Where: H GB and H AR are the heat generated by the gas boiler and the heat consumed by the absorption chiller; H VES Free up thermal power for building virtual energy storage.

[0081] (5) Cold power balance constraint Q load =Q AR +Q AC +Q VES

[0082] Where: Q AR and Q AC are the cooling capacities of the absorption refrigerator and the electric refrigerator respectively; Q VES Free up cooling power for building virtual energy storage.

[0083] (6) Gas power balance constraint G load =G B2E -G GT -G GB +G P2G

[0084] Where: G B2E G is the gas purchase power of BIES from ESO; GT and G GB are gas consumption of gas boiler and gas turbine respectively; G P2G It is the gas production of the P2G device.

[0085] The beneficial effects described in the present invention are as follows: the method described in the present invention constructs a low-carbon building integrated energy system based on an energy hub, including wind, solar, storage, and energy conversion devices, and comprehensively analyzes the characteristics of various loads in the low-carbon building to improve its demand response capability; the proposed two-layer optimization model considers the building's virtual energy storage and building user comfort indicators to improve system scheduling flexibility; the two-layer optimization model is solved to optimize the equipment output, demand response, and power purchase and sales plan of the building's integrated energy system to obtain the optimal scheduling strategy. The present invention can finely regulate various loads in the building's integrated energy system, improve energy utilization efficiency, alleviate the system's power supply pressure, and achieve the goal of building energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] FIG1 is a schematic flow diagram of the method of the present invention;

[0087] FIG2 is a schematic diagram of a building integrated energy system model provided by an embodiment of the present invention;

[0088] FIG3 is a schematic diagram of an interactive framework of a two-layer optimization model provided by an embodiment of the present invention;

[0089] FIG4 is a schematic diagram of a solution to a two-layer optimization scheduling strategy for a building integrated energy system considering virtual energy storage according to the present invention;

[0090] FIG5 is a schematic diagram of a prediction curve of new energy output, load demand and outdoor temperature provided by an embodiment of the present invention;

[0091] FIG6 is a schematic diagram of the user-side electric load distribution before optimization provided by an embodiment of the present invention;

[0092] FIG7 is a schematic diagram of an ESO electricity price optimization curve provided by an embodiment of the present invention;

[0093] FIG8 is a schematic diagram of the optimized user-side electric load distribution according to an embodiment of the present invention;

[0094] FIG9 is a schematic diagram of an electric power balance situation provided by an embodiment of the present invention;

[0095] FIG10 is a schematic diagram of indoor and outdoor temperatures of cooling and heating buildings provided by an embodiment of the present invention;

[0096] FIG11 is a schematic diagram of a cooling power balance according to an embodiment of the present invention;

[0097] FIG12 is a schematic diagram of thermal power balance according to an embodiment of the present invention;

[0098] FIG13 is a schematic diagram of gas power balance according to an embodiment of the present invention;

[0099] FIG14 is a schematic diagram of comprehensive satisfaction and carbon emissions of BIES under different operation scenarios provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0100] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0101] As shown in FIG1 , the present invention discloses a two-tiered optimization scheduling method for a building integrated energy system considering virtual energy storage, which can finely control various loads of the building integrated energy system, improve energy utilization efficiency, alleviate the power supply pressure of the system, and achieve the goal of building energy conservation and emission reduction. The method comprises the following steps:

[0102] S1: Based on the concept of energy hub, a building integrated energy system model including wind, solar, storage and various energy conversion devices is constructed;

[0103] S2: Comprehensively analyze the characteristics of cooling, heating, and electrical loads in the low-carbon Building Integrated Energy System (BIES). Consider building virtual energy storage and human comfort indicators to construct a comprehensive user satisfaction index.

[0104] S3: With energy operators as the upper optimization layer and the goal of minimizing carbon emissions, and the lower building users as the lower follower optimization layer, with the goal of maximizing user satisfaction, a two-layer optimization model is constructed to determine the objective functions and constraints of the upper energy operator pricing layer and the lower building user optimization layer.

[0105] S4: Solve the two-layer optimization model, using the non-dominated genetic algorithm (NSGA-II) to solve the upper-layer energy operator pricing model, update the purchase and sale prices of the upper-layer leader ESO, call the solver to solve the lower-layer model, and finally obtain the optimal scheduling strategy for the building integrated energy system.

[0106] Figure 2 shows a model of a building integrated energy system based on the concept of an energy hub, including wind, solar, and storage, as well as various energy conversion devices. The building integrated energy system uses grid electricity, renewable energy power output, and natural gas as energy inputs. The energy conversion devices include gas turbines, gas boilers, electric chillers, absorption chillers, and power-to-gas (P2G) devices. Electric load output is achieved through the synergy of grid electricity, renewable energy power generation systems, and gas turbine power generation systems. Heat load output is mainly provided by gas boilers, and cooling load output is achieved by electric chillers and absorption chillers. Gas load conversion is achieved by the gas grid, power-to-gas devices, gas turbines, and gas boilers. Energy storage devices are mainly batteries, and heat and cooling storage processes are mainly achieved by building virtual energy storage.

[0107] The gas turbine model is as follows: The gas turbine uses the combustion of natural gas to release heat energy to drive the rotation of the turbine blades to generate mechanical energy, which is eventually converted into electrical energy. Its output power P GT and the gas power consumed G GT The relationship is:

[0108] Where: η GT is the gas-to-electricity efficiency of the gas turbine; P GT,t is the electric power generated by the gas turbine during period t; is the rated electrical power of the gas turbine; and They are the upper and lower limits of gas turbine ramp speed, respectively.

[0109] The model of the gas boiler is as follows: the gas boiler generates thermal power by burning natural gas, and its output thermal power H GB and the gas power consumed G GB The relationship is:

[0110] Where: η GB is the combustion thermal efficiency of the gas boiler; is the rated thermal power of the gas boiler.

[0111] The model of the electric refrigerator is: the electric refrigerator is a refrigeration device driven by electric energy, and its output cooling power Q AC and power consumption P AC The relationship is:

[0112] Where: η AC is the cooling efficiency of the electric refrigerator; is the rated power of the electric refrigerator.

[0113] The absorption refrigerator model is as follows: The absorption refrigerator is a device that uses heat energy to achieve refrigeration. Its cooling power output Q AR and the consumed heat power H AR The relationship is:

[0114] Where: η AR is the refrigeration efficiency of the absorption chiller; is the rated power of the absorption chiller.

[0115] The model of the energy storage device is as follows: the energy storage capacity and charge / discharge frequency before and after energy storage are subject to the following constraints:

[0116] Where: P ES,t is the capacity state of the energy storage device; and are the charge and discharge efficiency of the energy storage device; and are the input and output electric power of the energy storage device respectively; and are the minimum and maximum states of charge of the energy storage device, respectively; and It is the charge and discharge symbol of the energy storage device.

[0117] The model of the P2G device is as follows: P2G technology can convert CO2 collected by the device into natural gas for use by the system, and its output gas power G P2G and the consumed electrical power P P2G The relationship is:

[0118] Where: η P2G is the electrical conversion efficiency of the P2G device; is the rated power of the P2G device.

[0119] In BIES, the coupling relationships between the cooling, heating, electricity, and gas subsystems are very close, making system operation more significantly affected by the coupling characteristics of these subsystems. That is, when a certain energy source is in short supply, the supply and demand relationship of the entire system needs to be disrupted, and the energy shortage needs to be filled by reducing the corresponding load or leveraging the coupling performance of various loads. The multi-energy flow coupling matrix can be used to describe the conversion relationship between various loads, equipment, and multiple energy flows for equivalent modeling. The energy coupling matrix for the demand side and the supply side is constructed as shown below:

[0120] Where: P load 、H load , Q load and G load are electricity, heating, cooling and gas load demands respectively; P s 、H s , Q s and G s They are power source, heat source, cold source and gas source, including energy input from the outside and various energy sources generated in BIES; μ P 、μ H 、μ Q and μ G μ are the distribution coefficients of electricity, heat, cooling and gas load demands among power source, heat source, cooling source and gas source respectively; GT 、μ GB 、μ AC 、μ AR and μ P2Gare the distribution coefficients of the above energy conversion devices (gas turbine, gas boiler, electric chiller, absorption chiller, power-to-gas) in the corresponding energy sources, and their respective distribution coefficients change with the change of demand-side load to improve the regulation capacity of the entire system. GT ,η GB ,η AC ,η AR and η P2G They are the energy conversion efficiencies of the above energy conversion devices (gas turbine, gas boiler, electric refrigerator, absorption refrigerator, power-to-gas).

[0121] The interaction between the participants in a building's integrated energy system begins with the energy operator's strategy. Taking into account the system's carbon emissions, the operator sends a pricing strategy to building users and obtains their energy usage strategies. Rational users react to electricity prices and develop an optimal strategy for their energy usage, while still meeting their energy needs. This strategy is then communicated to the energy operator, who updates its pricing strategy based on the load demand responses received from users and sends the revised pricing strategy to users. This interactive process iterates continuously, and the game reaches equilibrium when both parties cannot improve their efficiency by changing their strategies.

[0122] As shown in Figure 3, the objective functions and constraints of the upper energy operator pricing layer and the lower building user optimization layer are determined based on the two-layer optimization model.

[0123] The two-level optimization model can be expressed as:

[0124] The model includes two participants in the two-level optimization model, namely ESO and BIES; the strategy sets of both parties, namely the price increase ψ and the electricity price sold by ESO to BIES and electricity purchase price Bilateral utility refers to the objective function that benefits each party. When neither party can achieve a more beneficial objective by changing their strategy, equilibrium is reached. A non-dominated genetic algorithm (NSGA-II) is used to solve the upper-level energy operator pricing model, update the upper-level leader ESO's electricity purchase and sales prices, and invoke the solver to solve the lower-level model, ultimately obtaining the optimal scheduling strategy for the building's integrated energy system.

[0125] As shown in FIG4 , the two-layer optimization model is solved, and the specific solution process is as follows:

[0126] 1) Input the operating data of each device and the initial system data, such as electricity load demand, gas load demand, predicted output of new energy, and outdoor temperature data;

[0127] 2) Use the NSGA-II algorithm to initialize the purchase and sale price population of the upper optimization model and send it to the lower model; the lower model calls the solver to complete the optimization strategy and feeds the results back to the upper model; the upper model calculates the current carbon emissions of the system based on the strategy;

[0128] 3) Perform selection, crossover, and mutation operations on the purchase and sale price population, update the purchase and sale price and send it to the upper model, and repeat step 2);

[0129] 4) Until the maximum number of iterations is reached or the upper and lower models reach game equilibrium, the scheduling results such as the minimum carbon emissions of the system, the maximum comprehensive satisfaction, and the output of each unit are obtained.

[0130] In order to verify the beneficial effects of the present invention, scientific demonstration was carried out through experiments: using the method of the present invention, a simulation experiment was first carried out on the scheduling of electricity, cooling, heating and gas energy in the building integrated energy system, and then the real effect of the method of the present invention was verified by comparing the carbon emissions and comprehensive user satisfaction of different scheduling schemes.

[0131] The above two-tiered optimization scheduling strategy was validated using a building in a specific area as the research object, with a 24-hour scheduling cycle and a 1-hour step size. Figure 5 shows the predicted curves for the renewable energy output of the building's integrated energy system, the building's internal electrical load demand, the gas load demand, and the outdoor temperature on a typical day. The building's user-side load consists of a base load (fixed load), a shiftable load, a curtailable load, and a transferable load. Figure 6 shows the load composition at different time periods before optimization.

[0132] (1) Electric energy dispatch results

[0133] According to the strategy of the present invention, the ESO and BIES interact with each other. When equilibrium is reached, the electricity price optimization curve of the ESO is shown in FIG7 .

[0134] Next, using the electricity price optimization results in Figure 7 as a reference, the electricity load distribution on the building user side after demand response is shown in Figure 8. Observing the electricity load optimization results, it can be seen that the flexible electricity load, including the shiftable electricity load and the transferable electricity load, generally shows a trend of shifting from peak electricity consumption periods to valley electricity consumption and normal periods. In addition, the optimized electricity load has also been reduced to varying degrees, and the reduction period is mostly during peak electricity consumption periods. The periods of electricity load reduction are mostly periods with higher ESO electricity prices (11:00-16:00, 19:00-22:00), and the electricity load in these periods is shifted to periods with lower electricity prices (4:00-9:00). This not only reflects the role of the building's flexible electricity load in peak shaving and valley filling, but also alleviates the power supply pressure of the system and ensures the economic efficiency of scheduling.

[0135] Figure 9 shows the power balance. BIES's electrical load is primarily met by renewable energy output and electricity purchased from the ESO, supplemented by a small amount of gas turbine power. Between 8:00 AM and 3:00 PM, renewable energy output already meets the majority of the load. The amount of electricity purchased during this period is much lower than at other times, boosting the renewable energy consumption rate and reducing overall system operating costs. During this period, excess renewable energy output is converted into gas and cooling energy flows by the P2G devices and electric chillers, respectively. During the 1:00 AM to 7:00 AM period, when electricity demand is low and electricity prices are low, the energy storage device primarily charges. At 1:00 PM and 8:00 PM, when wind resources are abundant and electricity demand is high, the energy storage device discharges, alleviating peak demand pressures.

[0136] (2) Cold and heat energy scheduling results

[0137] Set the indoor PMV index of the heating building to -1≤λ PMV ≤1, the initial indoor temperature of the cooling building is -15℃; the indoor temperatures of the cooling building and the heating building after optimization are shown in Figure 10.

[0138] The corresponding cooling and heating power balances are shown in Figures 11 and 12.

[0139] To meet BIES low-carbon requirements, the cooling load within the cooling building is primarily provided by electricity, especially during periods of abundant renewable energy output (10:00-21:00). This avoids the increased natural gas consumption associated with absorption chillers carrying the cooling load. A portion of the cooling load is met by the building's virtual energy storage. During periods of lower room temperatures (1:00-7:00), the virtual energy storage is in a charging state, allowing it to participate in subsequent cooling load regulation. The building's thermal load is primarily met by heat released by gas boilers, supplemented by the charging and discharging of the virtual energy storage. Besides meeting the comfort requirements of the building's heating system, the remaining thermal load is converted to cooling load via the absorption chillers, enhancing system flexibility.

[0140] (3) Gas energy dispatch results

[0141] The corresponding gas power balance is shown in Figure 13. Analysis of Figure 13 shows that natural gas is primarily purchased from the gas grid, with a small portion converted via P2G devices. Beyond meeting the base gas load, the remaining natural gas is converted into electricity and heat for system use via gas turbines and gas boilers.

[0142] (4) Optimization results in different scenarios

[0143] The simulation experiment considered three scheduling scenarios: Scenario 1: the scheduling scenario proposed in this paper; Scenario 2: a scheduling scenario without considering the upper and lower layer interaction strategy; and Scenario 3: a scheduling scenario without considering virtual energy storage. The comprehensive BIES satisfaction and carbon emissions under different operating scenarios are shown in Figure 14.

[0144] The carbon emissions of the BIES two-layer optimization scheduling model considering building virtual energy storage proposed in this invention are lower than those of scenarios two and three, while the overall user satisfaction is improved.

[0145] Among them, the total carbon emissions of the system in scenario one are 1271.2kg, the total carbon emissions of the system in scenario two are 1367.8kg, and the total carbon emissions of the system in scenario three are 1453.5kg. The carbon emissions of scenario one are reduced by 7.06% and 12.5% ​​respectively compared with other operating scenarios. This is because the energy price strategy formulated by the ESO under the upper and lower layer interaction strategy further takes into account the energy costs of building users; at the same time, considering the building's own virtual energy storage characteristics can effectively reduce the energy consumption of indoor heating and cooling buildings, thereby reducing the carbon emissions of the entire building's integrated energy system. In addition, the average user satisfaction of the system in scenario one is 0.89, which is 9.8% and 17.1% higher than that of operating scenario two (0.81) and operating scenario three (0.76), respectively. It can be seen that the model and scheduling strategy proposed in this invention can effectively improve the low carbon nature and user satisfaction of the building's integrated energy system.

[0146] The above description is only a preferred embodiment of the present invention and is not intended to further limit the present invention. All equivalent changes made using the contents of the present invention description and drawings are within the scope of protection of the present invention.

Claims

1. A two-layer optimization scheduling method for building integrated energy systems considering virtual energy storage, characterized in that: include: S1. Based on the concept of energy hub, a low-carbon building integrated energy system (BIES) is constructed, which includes wind, solar, storage and energy conversion devices. S2. Comprehensively analyze the characteristics of cooling, heating, and electrical loads in the integrated energy system of low-carbon buildings, and consider the building's virtual energy storage and human comfort indicators to construct a comprehensive user satisfaction index; S3, a two-layer optimization model is constructed with the energy operator (ESO) as the upper optimization layer, minimizing the system's carbon emissions as the goal, and the building users as the lower optimization layer, maximizing user satisfaction as the goal; Determine the objective functions and constraints of the upper energy operator pricing layer and the lower building user optimization layer; Wherein, the two-layer optimization model is expressed as: Among them, ESO and BIES are participants in the two-level optimization model; the strategy sets of both parties are That is, the energy price increase ψ and the electricity price sold by ESO to BIES and electricity purchase price Bilateral utility, that is, the objective function min F that benefits both parties ESO and max F BIES , i.e. minimizing carbon emissions and maximizing overall satisfaction; The objective function of the upper energy operator pricing layer is: Where: F ESO is the total carbon emissions of BIES; γ C is the unit price of carbon emissions; C t is the actual carbon emissions of the building's integrated energy system; C0 is the initial carbon emission quota; Among them, carbon emissions in the building integrated energy system are generated by purchased electricity and purchased natural gas, namely; Where: and are the amount of electricity and natural gas sold by ESO to BIES at time t, respectively; and are the average carbon emission coefficients of the regional power grid and gas grid respectively; The constraints of the upper energy operator pricing layer are: (1) Interacting with the main network with power constraints: Where: and are the upper and lower limits of the electricity that the ESO can purchase from the upper power grid at time t; and The upper and lower limits of gas energy that the ESO can purchase from the upper-level gas grid at time t; (2) Electricity price constraints: Where: and are the upper and lower limits of electricity prices at time t respectively; is the average electricity price within the dispatch period; The objective function of the infrastructure user optimization layer is: max F BIES =(F load +F a +F PMV +F co ) / 4 The constraints of the user optimization layer of the lower layer are: (1) Unit constraints Each energy conversion device, energy storage device, and building virtual energy storage has output limits. The output limits of new energy equipment are as follows: Where: P wt and P pv are wind turbine and photovoltaic output power respectively; and They are wind turbine and photovoltaic predicted output respectively; (2) Load demand response constraints The corresponding constraints of flexible electric loads are as follows: Cooling and heating load demand response constraints: Where: It is the range limit of PMV index; and They are the upper and lower limits of indoor temperature for heating buildings and cooling buildings respectively; (3) Electric power balance constraints P load =P wt +P pv +P B2E +P ES +P GT -P AC -P P2G =P base +P shift +P tran +P cut Where: P wt and P pv are the output power of wind turbine and photovoltaic respectively; P B2E is the interaction electric power between BIES and ESO; P ES Release electric power to the energy storage device; P AC and P P2G are the electric power consumed by the electric refrigerator and the P2G device respectively; P base As the basic electrical load; (4) Thermal power balance constraints H load =H GB -H AR +H VES Where: H GB and H AR are the heat generated by the gas boiler and the heat consumed by the absorption chiller; H VES Release thermal power for building virtual energy storage; (5) Cooling power balance constraint Q load =Q AR +Q AC +Q VES Where: Q AR and Q AC are the cooling capacities of the absorption refrigerator and the electric refrigerator respectively; Q VES Release cold power for building virtual energy storage; (6) Gas power balance constraint G load =G B2E -G GT -G GB +G P2G Where: G B2E G is the gas purchase power of BIES from ESO; GT and G GB are gas consumption of gas boiler and gas turbine respectively; G P2G is the gas production of the P2G device; S4. Solve the two-layer optimization model, using a non-dominated genetic algorithm to solve the upper-layer energy operator pricing model, update the purchase and sale prices of electricity of the upper-layer leader ESO, call the solver to solve the lower-layer model, and finally obtain the optimal scheduling strategy for the building integrated energy system.

2. A two-layer optimization scheduling method for building integrated energy systems considering virtual energy storage according to claim 1, characterized in that: In S1, the low-carbon building integrated energy system uses grid electricity, new energy power output and natural gas as energy input; the energy conversion device includes a gas turbine, a gas boiler, an electric chiller, an absorption chiller, and a power-to-gas device P2G; The energy coupling matrix of the demand side and the supply side is constructed as follows: Where: P load 、H load , Q load and G load are electricity, heating, cooling and gas load demands respectively; P s 、H s , Q s and G s They are power source, heat source, cold source and gas source, including energy input from the outside and various energy sources generated in BIES; μ P 、μ H 、μ Q and μ G μ are the distribution coefficients of electricity, heat, cooling and gas load demands among power source, heat source, cooling source and gas source respectively; GT 、μ GB 、μ AC 、μ AR and μ P2G are respectively the distribution coefficients of the energy conversion devices in the corresponding energy, and the respective distribution coefficients change with the change of the demand side load; η GT ,η GB ,η AC ,η AR and η P2G are the energy conversion efficiencies of the energy conversion devices respectively.

3. A two-layer optimization scheduling method for building integrated energy systems considering virtual energy storage according to claim 1, characterized in that: In S2, the characteristics of cooling, heating and electrical loads in the low-carbon building integrated energy system include the building flexible electrical load characteristics and the building cooling / heating load characteristics; The electrical loads in buildings are divided into basic loads, shiftable loads, transferable loads and curtailable loads according to their ability to participate in flexible regulation. Base load: refers to uncontrolled load that cannot respond to user requirements and cannot change the user's energy consumption pattern and time. It does not participate in scheduling; Shiftable load: The load's power consumption time varies according to the plan, but the load needs to be moved as a whole and its value cannot be changed, so the energy consumption time can span multiple scheduling cycles; Assume that the translation load P shift Vector distribution before scheduling for: Where: t S ,t D are the start time and end time of the translatable load respectively; Assume that the translation period is [t sh- ,t sh+ ], and use variable κ to represent the load P shift In the translation state of time period t, that is, when κ=0, the load P shift No translation, κ = 1 means load P shift Starting from the t period, the set of starting periods is for: U represents the union; If t∈[t sh- ,t sh+ -t D +1] and t≠t S , then it means the load From the component P of the starting time period t shift for: On the contrary, if t=t S , represents the load P shift unchanged; Transferable load: The power consumption period of this load is allowed to be interrupted, and the duration is not required to be fixed. It only needs to ensure that the total load demand before and after the transfer remains unchanged; Assume that the transferable load P tran The transfer period is [t tr- ,t tr+ ], using variables Indicates load P tran The transfer state in a certain period of time, that is, When the load is not transferred, When P tran The transfer starts from the t period; the minimum continuous working time is set for the transferable load, and the transfer power is limited, that is: Where: and are the minimum and maximum values ​​of load transfer power respectively; The continuous operation time of the equipment; Reducible load: refers to participating in demand response by reducing the energy consumption of building users; The variable σ represents the load that can be reduced P cut In the reduction state of time period t, that is, when σ=1, it means P cut If it is reduced in period t, the power of load that can be reduced in period t after participating in the dispatch becomes: Where: η t is the reduction coefficient, 0≤η t ≤1; is the initial power of load that can be reduced during period t; The duration and number of load reductions are constrained, namely: Where: are the minimum and maximum continuous reduction time respectively; N max is the maximum number of reductions; The building cooling / heating load characteristics include the inertia and flexibility of the building cooling / heating load; The inertia of the building's cooling / heating load is specifically due to the building's inherent cooling / heating dynamic characteristics. That is, after the building uses various devices to obtain cooling / heating energy, it takes a period of time to reach the preset temperature. This makes the building's cooling / heating system have inertia, showing energy storage capacity, namely virtual energy storage. The virtual energy storage model is: Where: ΔQ HQ (t) is the indoor heat change at time t; C is the specific heat capacity of air; ρ0 is the air density; V is the building volume capacity; T in is the indoor temperature, d is the differential symbol, that is, the derivative of t; Introducing virtual energy storage charging and discharging power to describe virtual energy storage, we have: Where: P VES Energy storage charging and discharging power; VES t and VES max are the capacity and maximum capacity of virtual energy storage at time t respectively; is the indoor temperature at time t; T in,max and T in,min are the upper and lower limits of the acceptable indoor temperature respectively; Δt is the time interval; In the heating system, the autoregressive moving average time series ARMA model is used to calculate the water supply temperature T of the heating network during period t. t,g , return water temperature T t,h , indoor temperature of heating buildings and outdoor temperature The relationship between them is described as follows: In the formula: α, β, γ, θ, and ω are the physical parameters of the building heating system, and the order J reflects the thermal inertia of the building heating system; Among them, the heating building heat supply H t,h and water supply temperature T t,g , return water temperature T t,h The relationship is: H t,h =γ(T t,g -T t,h ) Where: γ is the relationship coefficient between the heating supply of the heating building and the supply and return water temperature of the heating network; The inertia in the cooling building is described by the equivalent thermal parameter model ETP, specifically: Where: and are the indoor and outdoor temperatures of the cooling building; Q t,c represents the total cooling power of the refrigerator during the period t; R and C are the equivalent thermal resistance and equivalent heat capacity of the cooling building room respectively; The flexibility of the building's cooling / heating load is specifically that: there is ambiguity in the perception of users in the building about the temperature inside the building, and the cooling / heating load is converted from a fixed value to a flexible value.

4. A two-layer optimization scheduling method for building integrated energy systems considering virtual energy storage according to claim 3, characterized in that: In S2, the predicted mean vote number PMV is selected as the human comfort index to quantify the user's hot and cold comfort. The PMV index value λ PMV Specifically: Where: M P and W P are the human metabolic rate and the mechanical power generated by human activities; P a is the water vapor pressure of the air surrounding the human body; t a is the ambient temperature of the human body; f cl The ratio of the body's clothing-covered area to its exposed area; cl and t r are the surface temperature and average radiation temperature of the human body when wearing clothes; h c is the air convection heat transfer coefficient.

5. A two-layer optimization scheduling method for building integrated energy systems considering virtual energy storage according to claim 4, characterized in that: In S2, the user comprehensive satisfaction index F is constructed BIES Including energy consumption satisfaction, electricity price satisfaction, cost satisfaction and comfort satisfaction; Energy satisfaction F load Expressed as: Where: The electricity load after electricity price adjustment; Electricity price satisfaction F a for: Where: The price of electricity sold by the ESO to BIES; is the amount of electricity sold by ESO to BIES at time t; a is the initial electricity price, The initial amount of electricity sold by ESO to BIES; Cost satisfaction F co for: Where: is the external energy purchase cost of BIES at time t, The cost of purchasing energy to initially meet the initial load demand; Comfort satisfaction is: F PMV =1-|λ PMV |。

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