WO2025200464A1 - 一种考虑虚拟储能的建筑综合能源系统双层优化调度方法 - Google Patents

一种考虑虚拟储能的建筑综合能源系统双层优化调度方法

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Publication number
WO2025200464A1
WO2025200464A1 PCT/CN2024/130263 CN2024130263W WO2025200464A1 WO 2025200464 A1 WO2025200464 A1 WO 2025200464A1 CN 2024130263 W CN2024130263 W CN 2024130263W WO 2025200464 A1 WO2025200464 A1 WO 2025200464A1
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load
energy
building
power
cooling
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French (fr)
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周霞
刘东林
解相朋
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Nanjing University of Posts and Telecommunications
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Nanjing University of Posts and Telecommunications
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • 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.
  • the Building Integrated Energy System 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.
  • 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.
  • the present invention provides a two-tier optimization scheduling method for a building integrated energy system considering virtual energy storage, comprising the following steps:
  • 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 converted into electricity through grid electricity, new energy power output and natural gas.
  • the energy generation system and the gas turbine power generation system work together to achieve this.
  • the heat load output is provided by the gas boiler, the cooling load output is achieved by the electric refrigerator and 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 cooling storage process is achieved by the building's virtual energy storage.
  • P load , H load , Q load and G load are the electricity, heat, cooling and gas load demands, respectively;
  • Ps , Hs , Qs and Gs are the power source, heat source, cooling source and gas source, respectively, including external energy input and various energy sources generated in BIES;
  • ⁇ P , ⁇ H , ⁇ Q and ⁇ G are the distribution coefficients of the electricity, heat, cooling and gas load demands among the power source, heat source, cooling source and gas source, respectively;
  • ⁇ GT , ⁇ GB , ⁇ AC , ⁇ AR and ⁇ P2G are the distribution coefficients of the energy conversion device among the corresponding energy sources, and each distribution coefficient changes with the change of the demand-side load;
  • ⁇ GT , ⁇ GB , ⁇ AC , ⁇ AR and ⁇ P2G are the energy conversion efficiencies of the energy conversion device, respectively.
  • 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;
  • 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;
  • 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;
  • 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;
  • t S , t D are the start time and end time of the movable load respectively;
  • 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;
  • the transfer period of the transferable load P tran is [t tr- ,t tr+ ], and use the variable It represents the transfer state of load P tran in a certain period of time t, that is, When the load is not transferred, When P tran starts to transfer from time period t, the minimum continuous working time is set for the transferable load, and the transfer power is limited, that is:
  • Load curtailment Participate in demand response by reducing energy consumption of building users
  • ⁇ t is the reduction coefficient, 0 ⁇ t ⁇ 1 ; is the initial power of load that can be reduced during period t;
  • 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 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;
  • ⁇ Q HQ (t) is the indoor heat change at time t
  • C is the specific heat capacity of air
  • ⁇ 0 is the air density
  • V is the building volume capacity
  • Tin is the indoor temperature
  • d is the differential symbol, that is, the derivative with respect to t;
  • Qt ,c represents the total cooling power of the chiller during time period t
  • R and C are the equivalent thermal resistance and equivalent heat capacity of the room in the cooling building respectively
  • 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.
  • MP and W P are the human metabolic rate and the mechanical power generated by human activities respectively;
  • Pa is the water vapor pressure of the air around the human body;
  • ta is the ambient temperature around the human body;
  • fcl is the surface area of the clothing covering the human body.
  • t cl and t r are the surface temperature and the average radiation temperature of the human body when clothed, respectively;
  • h c is the air convection heat transfer coefficient.
  • the constructed comprehensive user satisfaction index F BIES includes energy satisfaction, electricity price satisfaction, cost satisfaction, and comfort satisfaction;
  • Cost satisfaction The operating cost of BIES is the cost of interacting with external energy sources.
  • the cost satisfaction F co is defined as:
  • ESO and BIES are participants in the two-level optimization model; the strategy sets of both parties are That is, the energy price increase ⁇ and the electricity price sold by ESO to BIES and electricity purchase price Bilateral utility, that is, the objective functions of both parties that benefit the subject, min F ESO and max F BIES , that is, minimizing carbon emissions and maximizing comprehensive satisfaction.
  • FESO is the total carbon emissions of BIES
  • ⁇ C is the unit price of carbon emissions
  • Ct is the actual carbon emissions of the building integrated energy system
  • C0 is the initial carbon emission quota
  • 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.
  • Each energy conversion device, energy storage device, and building virtual energy storage has upper and lower output limits.
  • the upper and lower output limits of new energy equipment are as follows:
  • P wt and P pv are wind turbine and photovoltaic output power respectively; and They are wind turbine and photovoltaic predicted output respectively;
  • P wt and P pv are the output power of wind turbines and photovoltaics respectively;
  • P B2E is the interactive power between BIES and ESO;
  • P ES is the power released by the energy storage device;
  • P AC and PP2G are the power consumed by the electric refrigerator and P2G device respectively;
  • P base is the basic power load;
  • HGB and HAR are the heat generated by the gas boiler and the heat consumed by the absorption chiller, respectively; HVES is the thermal power released by the building's virtual energy storage.
  • Cooling power balance constraint Q load Q AR + Q AC + Q VES
  • QAR and QAC are the cooling capacities of the absorption chiller and electric chiller, respectively;
  • QVES is the cooling power released by the building's virtual energy storage.
  • G B2E is the gas purchase power of BIES from ESO
  • G GT and G GB are the gas consumption of gas boiler and gas turbine respectively
  • G P2G is the gas production of P2G device.
  • the method of the present invention constructs a low-carbon building integrated energy system based on an energy hub containing wind, solar, storage and energy conversion devices, comprehensively analyzes the characteristics of each load in the low-carbon building to improve its demand response capability; the proposed two-layer optimization model considers the building virtual energy storage and building user comfort index to improve the system scheduling flexibility; solves the two-layer optimization model to optimize the equipment output, demand response and power purchase and sales plan of the building integrated energy system to obtain the optimal scheduling strategy.
  • the present invention can finely regulate Various loads in the building's integrated energy system can improve energy utilization efficiency, alleviate the system's power supply pressure, and achieve the goal of building energy conservation and emission reduction.
  • FIG1 is a schematic flow diagram of the method of the present invention.
  • FIG2 is a schematic diagram of a building integrated energy system model provided by an embodiment of the present invention.
  • 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.
  • FIG9 is a schematic diagram of an electric power balance situation provided by an embodiment of the present invention.
  • FIG10 is a schematic diagram of indoor and outdoor temperatures of cooling and heating buildings provided by an embodiment of the present invention.
  • FIG11 is a schematic diagram of a cooling power balance according to an embodiment of the present invention.
  • FIG12 is a schematic diagram of thermal power balance according to an embodiment of the present invention.
  • 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.
  • 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:
  • 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.
  • NGA-II non-dominated genetic algorithm
  • a building integrated energy system model including wind, solar, and storage and various energy conversion devices is constructed as shown in Figure 2.
  • the building integrated energy system uses grid electricity, new energy power output and natural gas as energy input; the various energy conversion devices include gas turbines, gas boilers, electric chillers, absorption chillers, power to gas (P2G) devices, etc.; the electrical load output is achieved through the synergy of grid electricity, new energy power generation system and gas turbine power generation system, and the thermal load output is mainly achieved by gas boilers.
  • the cooling load output is realized by electric refrigerators and absorption refrigerators
  • the gas load is converted by the gas network
  • power-to-gas devices gas turbines and gas boilers
  • the energy storage devices are mainly batteries
  • the heat and cold storage processes are mainly realized by building virtual energy storage.
  • 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 turbine blades to generate mechanical energy, which is ultimately converted into electrical energy.
  • the relationship between its output electrical power P GT and the consumed gas power G GT is:
  • ⁇ GT is the gas-to-electricity efficiency of the gas turbine
  • P GT,t is the electric power generated by the gas turbine in period t
  • They are the upper and lower limits of gas turbine ramp speed, respectively.
  • the model of the gas boiler is as follows: the gas boiler generates thermal power by burning natural gas, and the relationship between its output thermal power H GB and the consumed gas power G GB is:
  • ⁇ GB is the combustion thermal efficiency of the gas boiler; is the rated thermal power of the gas boiler.
  • the model of the electric refrigerator is as follows: the electric refrigerator is a refrigeration device driven by electric energy, and the relationship between its output cooling power Q AC and consumed electric power P AC is:
  • ⁇ AC is the cooling efficiency of the electric refrigerator; is the rated power of the electric refrigerator.
  • the absorption refrigerator model is as follows:
  • the absorption refrigerator is a device that uses heat energy to achieve refrigeration.
  • the relationship between its cooling power output Q AR and the consumed heat power H AR is:
  • ⁇ AR is the refrigeration efficiency of the absorption chiller; is the rated power of the absorption chiller.
  • 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:
  • the model of the P2G device is as follows: P2G technology can convert the CO2 collected by the device into natural gas for use in the system.
  • the relationship between the output gas power G P2G and the consumed electrical power P P2G is:
  • ⁇ P2G is the electrical conversion efficiency of the P2G device; is the rated power of the P2G device.
  • 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:
  • P load , H load , Q load , and G load are the load demands for electricity, heat, cooling, and gas, respectively;
  • P s , H s , Q s , and G s are the power source, heat source, cooling source, and gas source, respectively, including external energy input and various energy sources generated by the BIES;
  • ⁇ P , ⁇ H , ⁇ Q , and ⁇ G are the distribution coefficients of the power, heat source, cooling source, and gas load demands among the power source, heat source, cooling source, and gas source, respectively;
  • ⁇ GT , ⁇ GB , ⁇ AC , ⁇ AR , and ⁇ P2G are the distribution coefficients of the above-mentioned energy conversion devices (gas turbine, gas boiler, electric chiller, absorption chiller, and power-to-gas) among the corresponding energy sources, and each distribution coefficient changes with changes in demand-side load to improve the overall system's regulation capability.
  • ⁇ GT , ⁇ GB , ⁇ AC , ⁇ AR , and ⁇ P2G are the energy conversion efficiencies of the above-mentioned energy conversion devices (gas turbine, gas boiler, electric chiller, absorption chiller, and power-to-gas).
  • 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.
  • 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.
  • the two-level optimization model can be expressed as:
  • 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 ⁇ and the electricity price sold by ESO to BIES and electricity purchase price Bilateral utility is the objective function of each party benefiting the subject.
  • ESO and BIES the strategy sets of both parties, namely the price increase ⁇ and the electricity price sold by ESO to BIES and electricity purchase price Bilateral utility is the objective function of each party benefiting the subject.
  • both parties cannot gain more benefits by changing their own strategies
  • both parties reach equilibrium.
  • the non-dominated genetic algorithm (NSGA-II) is used to solve the upper-level energy operator pricing model, update the purchase and sale prices of electricity of the upper-level leader ESO, and call the solver to solve the lower-level model, ultimately obtaining the optimal scheduling strategy for the building integrated energy system.
  • FIG. 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.
  • the ESO and BIES interact with each other.
  • the electricity price optimization curve of the ESO is shown in FIG7 .
  • the electricity load distribution on the building user side after demand response is shown in Figure 8.
  • the flexible electricity load including the shiftable electricity load and the transferable electricity load
  • 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.
  • FIG 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.
  • 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.
  • excess renewable energy output is converted into gas and cooling energy flows by the P2G devices and electric chillers, respectively.
  • the energy storage device primarily charges.
  • the energy storage device discharges, alleviating peak demand pressures.
  • 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.
  • Figure 13 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.
  • Scenario 1 the scheduling scenario proposed in this paper
  • Scenario 2 a scheduling scenario without considering the upper and lower layer interaction strategy
  • 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.
  • 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
  • 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.
  • 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.
  • 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.

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Abstract

本发明属于建筑综合能源技术领域，公开了一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，其构建基于能源集线器的含风光储及能源转换装置的低碳建筑综合能源系统，综合分析系统中各负荷特性以提高其需求响应能力；进而提出一种包含上层能源运营商定价层、下层建筑用户优化层的双层优化模型，在该模型考虑建筑虚拟储能和建筑用户舒适度指标以提高系统调度灵活性，并构建用户综合满意度指标。最后，对所述双层优化模型求解，以优化建筑综合能源系统的设备出力、需求响应与购售电计划，获得最优调度策略。本发明能精细化调控建筑综合能源系统各类负荷，提高能源利用效率，缓解系统的供电压力，实现建筑节能减排目的。

Description

一种考虑虚拟储能的建筑综合能源系统双层优化调度方法 技术领域
本发明属于建筑综合能源技术领域，具体涉及一种考虑虚拟储能的建筑综合能源系统双层优化调度方法。
背景技术
随着能源消费规模不断扩大，环境问题日益严峻，实现能源结构的优化问题已逐渐成为社会的重要议题。而作为推动CO2减排的主要力量，以“源-网-荷-储”一体化及多能互补为特点的综合能源系统受到广泛重视。与此同时，在整个能源消费中，建筑物的能量消耗占到了40％左右，其碳排放总量约占总碳排放量的50％，并且随着建筑物总量的增加，该比例也将不断升高。建筑功能的多样化发展也使得冷-热-电-气等多能流间的联系日益密切，对能源的利用也提出了一体化和系统化的要求。随着可再生新能源的普及与能源低碳化转型的提出，风力发电、光伏发电、多能转换等技术应用于各类建筑，可以有效地提高能源利用效率，实现建筑节能减排的目的，减少建筑能耗。建筑综合能源系统(Building Integrated Energy System，BIES)将可再生能源系统、综合能源系统与建筑结合，重塑建筑需求-供应关系，使建筑从单一的能源消费者转变为能源产销者,也使得零碳建筑、净零能耗建筑的实现成为可能。
然而，现有技术缺少对建筑用户侧灵活性资源的综合考虑，如建筑冷-热-电-气综合需求响应、电转气装置、建筑虚拟储能等，对建筑综合能源系统的各负荷特性考虑不足；另外，针对能源运营商与建筑用户之间的互动也亟需进一步考虑。
发明内容
为解决上述技术问题，本发明提供了一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，针对建筑综合能源系统的需求响应与低碳调度问题，建立了考虑虚拟储能和负荷特性的建筑综合能源系统双层优化调度模型，通过制定上层能源运营商与下层建筑用户间的购售电价引导下层建筑用户优化，以实现建筑节能减排功能。
本发明所述的一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，包括以下步骤：
S1、基于能源集线器的概念构建含风光储及能源转换装置的低碳建筑综合能源系统；
S2、综合分析低碳建筑综合能源系统中冷热电气各负荷特性，并考虑建筑虚拟储能与人体舒适度指标，构建用户综合满意度指标；
S3、以能源运营商(ESO)为上层优化层，系统最小化碳排放量为目标，以建筑用户为下层优化层，最大化用户满意度为目标，构建双层优化模型；确定上层能源运营商定价层和下层建筑用户优化层目标函数与约束条件；
S4、对所述双层优化模型进行求解，其中利用非支配遗传算法求解上层能源运营商定价模型，更新上层领导者ESO的购售电价，调用求解器对下层模型求解，最终获取建筑综合能源系统最优调度策略。
进一步的，S1中，所述低碳建筑综合能源系统使用电网电能、新能源电源出力和天然气作为能源输入；能源转换装置包括燃气轮机、燃气锅炉、电制冷机、吸收式制冷机、电转气(powerto gas，P2G)装置；电负荷输出通过电网电能、新 能源发电系统和燃气轮机发电系统的协同作用实现，热负荷输出由燃气锅炉提供，冷负荷输出由电制冷机和吸收式制冷机实现，气负荷由气网气能、电转气装置、燃气轮机、燃气锅炉实现转换，储能装置为蓄电池，储热和储冷过程由建筑虚拟储能实现；
通过多能流耦合矩阵描述各类负荷、设备和多种能流的转换关系进行等效建模，构建需求侧和供能侧能量耦合矩阵如下所示：
式中：Pload、Hload、Qload和Gload分别为电、热、冷、气负荷需求；Ps、Hs、Qs和Gs分别为电源、热源、冷源和气源，包括外界输入的能源和BIES中产生的各种能源；μP、μH、μQ和μG分别为电、热、冷、气负荷需求在电源、热源、冷源和气源中的分配系数；μGT、μGB、μAC、μAR和μP2G分别为所述能源转换装置在对应能源中的分配系数，且各自的分配系数随需求侧负荷的改变而改变；ηGT、ηGB、ηAC、ηAR和ηP2G分别为所述能源转换装置的能源转换效率。
进一步的，S2中，低碳建筑综合能源系统中冷热电气各负荷特性包括建筑柔性电负荷特性和建筑冷/热负荷特性；
所述建筑柔性电负荷特性具体为：柔性电负荷的参与使系统调度计划对降低负荷峰谷差起到积极影响，建筑物内电负荷根据其参与柔性调节的能力分为四类：基础负荷、可平移负荷、可转移负荷和可削减负荷；
基础负荷：是指不受控制的负荷，不能对使用者的要求做出反应，且不能改变使用者的用能模式和用能时间，此部分不参与调度；
可平移负荷：该负荷的用电时刻根据计划变化，但是负荷需要整体移动且不能改变其值，因此用能时间可以跨越多个调度周期；
假设可平移负荷Pshift参与调度前的向量分布为：
式中：tS,tD分别为可平移负荷的起始时间和结束时间；
假设平移时段为[tsh-,tsh+]，用变量κ表示负荷Pshift在时段t的平移状态，即κ＝0时表示负荷Pshift不平移，κ＝1时表示负荷Pshift从t时段开始平移；则起始时段的集合为：
U表示并集；
若t∈[tsh-,tsh+-tD+1]且t≠tS，则表示负荷从起始时段为t的分量Pshift为：
反之，若t＝tS,代表负荷Pshift未改变；
可转移负荷：在用电时段，可转移负荷的用电量可以进行灵活调整，用电时段允许中断，并且持续时间并不要求固定，只需确保转移前后的负荷需求总量不变；
假设可转移负荷Ptran的转移时段为[ttr-,ttr+]，用变量表示负荷Ptran在某一时段t的转移状态，即时表示负荷不转移，时表示Ptran从t时段开始转移；对可转移负荷设定最短持续工作时间，并对转移功率加以限制，即：
式中：和分别为负荷转移功率的最小值和最大值；为设备的持续运行时间；
可削减负荷：通过减少建筑用户的用能参与需求响应；
用变量σ表示可削减负荷Pcut在时段t的削减状态，即σ＝1时表示Pcut在t时段被削减，则参与调度后t时段可削减负荷的功率变为：
式中：ηt为削减系数，有0≤ηt≤1；为t时段可削减负荷的初始功率；
为保证建筑用户用能削减的合理性，需要对负荷削减的时长及削减次数进行约束，即：
式中：分别为最小和最大连续削减时间；Nmax为最大削减次数；
所述建筑冷/热负荷特性包括建筑冷/热负荷的惯性和柔性；
其中，所述建筑冷/热负荷的惯性的具体为：与电力系统的“即发即用”和“实时平衡”特点相比，由于建筑物具有自身冷/热动态特性，即当建筑利用各种设备获得冷/热能后，要经过一段时间延迟才可达到预设的温度，使得建筑内供冷/供热系统具有“惯性”，表现出一定的“储能”能力，即虚拟储能；
虚拟储能可以充分利用建筑的热惯性和储热特性，其满足热力学基本定律，模型为：
式中：ΔQHQ(t)为t时刻室内热量变化；C为空气比热容；ρ0为空气密度；V为建筑体积容量；Tin为室内温度，d是微分符号，即对t求导；
引入虚拟储能充放功率描述虚拟储能，则有：
式中：PVES为储能充放功率；VESt和VESmax分别为虚拟储能t时刻容量和最大容量；为t时刻室内温度；Tin,max和Tin,min分别为室内可接受温度上、下限；Δt为时间间隔；
在供热系统中，采用自回归滑动平均时间序列ARMA模型对t时段热网供水温度Tt,g、回水温度Tt,h、采暖建筑室内温度和室外温度之间的关系进行描述，具体为：
式中：α,β,γ,θ,和ω为建筑供热系统的物理参数，阶次J体现建筑供热系统的热惯性；
其中，采暖建筑供热量Ht,h与供水温度Tt,g、回水温度Tt,h的关系为：
Ht,h＝γ(Tt,g-Tt,h)
式中：γ为采暖建筑供热量与热网供回水温度间的关系系数。
采冷建筑内的“惯性”用等值热参数模型ETP描述,具体为：
式中：和分别为采冷建筑室内和室外温度；Qt,c表示t时段内制冷机的总制冷功率；R和C分别为采冷建筑房间的等效热阻和等效热容；
所述建筑冷/热负荷的柔性具体为：建筑内用户对建筑物房间内冷暖的感知存在模糊性，因此可以将冷/热负荷由固定值转换为柔性值。柔性是指冷热负荷并不是固定值，而是在满足舒适度要求和室温约束条件下冷热负荷的值可变，具有可灵活调度的特点。
进一步的，S2中，选取预测平均投票数(Predicted Mean Vote，PMV)作为人体舒适度指标，其由对人体舒适度感觉产生影响的物理环境与个体行为差异，即对人体新陈代谢率、服装热阻、平均辐射温度、空气流进行综合考虑的综合性指标，对用户的冷热舒适度进行量化，PMV指标值λPMV具体为：
式中：MP和WP分别为人体代谢率和人体活动所产生的机械功率；Pa为人体周围环境空气的水蒸气压力；ta为人体周围环境温度；fcl为人体覆盖服装面 积与裸露面积之比；tcl和tr分别为人体着装时表面温度和平均辐射温度；hc为空气对流热传递系数。
进一步的，S2中，构建的用户综合满意度指标FBIES包括用能满意度、电价满意度、成本满意度和舒适满意度；
用能满意度：在电价没有调整前，用户按自己的习惯、喜好来决定用电时间，此时的购能满意程度为1；在价格调整之后，客户调节自身的柔性负荷，并更新负荷曲线，此时不论增加用电还是减少用电，客户对用电的满意度都会下降，则用能满意度Fload表示为：
式中：为电价调整后的用电负荷；
电价满意度：若建筑用户不能及时对电价进行调整，将会对ESO发布的实时电价产生较大的影响；电价满意度Fa与实时电价的大小成反比，即：
式中：为ESO向BIES的售电价；为t时刻ESO向BIES售卖电能的量；a为初始售电价，ESO向BIES售电初始量；
成本满意度：BIES的运行成本为与外部能源交互成本，成本满意度Fco定义为：
式中：为t时刻BIES向外部的购能成本，为初始满足初始负荷需求的购能成本。
舒适满意度：当PMV指标值λPMV介于-1到1之间时认为用户舒适满意度FPMV高，即：
FPMV＝1-|λPMV|。
进一步的，所述双层优化模型表示为：
其中，ESO和BIES为双层优化模型的参与者；双方的策略集即能源价格增长幅度ψ和ESO向BIES的售电价与购电价双方效用，即双方各自益于主体的目标函数min FESO和max FBIES，即最小化碳排放量和最大化综合满意度。
进一步的，所述上层能源运营商定价层的目标函数为：
式中：FESO为BIES的总碳排放量；γC为碳排放单价；Ct为建筑综合能源系统实际碳排放量；C0为初始碳排放配额；
其中，建筑综合能源系统中碳排放由外购电力和外购天然气产生，即；
式中：和分别为t时刻ESO向BIES售卖电能和天然气的量；和分别为区域电网、气网平均排碳系数；
所述上层能源运营商定价层的约束条件为：
(1)与主网交互功率约束：
为了避免与主网互动而对主网的稳定性产生不利影响，采取“并网不上网”的原则，及只从上级电网购电，而不向上级电网售电；则有：
式中：与分别为t时刻ESO向上级电网购买电能的上下限；和为t时刻ESO向上级气网购买气能的上下限；
(2)电价约束：
ESO向BIES的售电价是需求响应的基础，反映着系统的动态供需关系。电价的制定不仅要维持运营商的经营利润，同时还要考虑用户的意愿与接受程度，则有：
式中：和分别为t时刻电价的上下限；为调度周期内电价的均值。
进一步的，所述下层建筑用户优化层的目标函数为：
max FBIES＝(Fload+Fa+FPMV+Fco)/4
所述下层建筑用户优化层的约束条件为：
(1)机组约束
各能源转化设备和储能装置、建筑虚拟储能具有出力上下限约束；新能源设备的出力上下限如下：
式中：Pwt和Ppv分别为风机、光伏输出功率；和分别为风机、光伏预测出力；
(2)负荷需求响应约束
参与电负荷需求响应的用户根据上层能源运营商制定的价格信息采取相应的策略，如负荷平移、负荷转移、负荷削减等，其相应的约束如下：
冷热负荷需求响应特性主要体现在冷热负荷的“惯性”和柔性，为了满足室内温度满足舒适度的要求及采冷建筑室内的温度要求，应做如下约束：
式中：为PMV指标范围限制；和分别为采暖建筑室内和采冷建筑室内温度的上、下限；
(3)电功率平衡约束
Pload＝Pwt+Ppv+PB2E+PES+PGT-PAC-PP2G＝Pbase+Pshift+Ptran+Pcut
式中：Pwt和Ppv分别为风机、光伏的输出电功率；PB2E为BIES与ESO的交互电功率；PES为储能装置释放电功率；PAC和PP2G分别为电制冷机、P2G装置消耗的电功率；Pbase为基础电负荷；
(4)热功率平衡约束
Hload＝HGB-HAR+HVES
式中：HGB和HAR分别为燃气锅炉产热和吸收式制冷机耗热；HVES为建筑虚拟储能释放热功率。
(5)冷功率平衡约束
Qload＝QAR+QAC+QVES
式中：QAR和QAC分别为吸收式制冷机和电制冷机的制冷量；QVES为建筑虚拟储能释放冷功率。
(6)气功率平衡约束
Gload＝GB2E-GGT-GGB+GP2G
式中：GB2E为BIES向ESO的购气功率；GGT和GGB分别为燃气锅炉和燃气轮机耗气；GP2G为P2G装置产气量。
本发明所述的有益效果为：本发明所述方法构建基于能源集线器的含风光储及能源转换装置的低碳建筑综合能源系统，综合分析低碳建筑中各负荷特性以提高其需求响应能力；提出的双层优化模型考虑建筑虚拟储能和建筑用户舒适度指标以提高系统调度灵活性；对所述双层优化模型求解，以优化建筑综合能源系统的设备出力、需求响应与购售电计划，获得最优调度策略。本发明能精细化调控 建筑综合能源系统各类负荷，提高能源利用效率，缓解系统的供电压力，实现建筑节能减排目的。
附图说明
图1是本发明所述方法的流程示意图；
图2是本发明一实施例提供的建筑综合能源系统模型示意图；
图3是本发明一实施例提供的双层优化模型互动框架示意图；
图4是本发明一种考虑虚拟储能的建筑综合能源系统双层优化调度策略求解示意图；
图5是本发明一实施例提供的新能源出力、负荷需求与室外温度预测曲线示意图；
图6是本发明一实施例提供的优化前用户侧电负荷分布示意图；
图7是本发明一实施例提供的ESO的电价优化曲线示意图；
图8是本发明一实施例提供的优化后用户侧电负荷分布示意图；
图9是本发明一实施例提供的电功率平衡情况示意图；
图10是本发明一实施例提供的采冷、采暖建筑室内温度与室外温度示意图；
图11是本发明一实施例提供的冷功率平衡情况示意图；
图12是本发明一实施例提供的热功率平衡情况示意图；
图13是本发明一实施例提供的气功率平衡情况示意图；
图14是本发明一实施例提供的不同运行场景下BIES的综合满意度和碳排放量示意图。
具体实施方式
为了使本发明的内容更容易被清楚地理解，下面根据具体实施例并结合附图，对本发明作进一步详细的说明。
如图1所示，本发明所述的一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，能够精细化调控建筑综合能源系统各类负荷，提高能源利用效率，缓解系统的供电压力，实现建筑节能减排目的；包括如下步骤：
S1：基于能源集线器的概念构建含风光储及各能源转换装置的建筑综合能源系统模型；
S2：综合分析低碳建筑综合能源系统中(Building Integrated Energy System，BIES)冷热电气各负荷特性。并考虑建筑虚拟储能与人体舒适度指标，构建用户综合满意度指标；
S3：以能源运营商为上层优化层，以系统最小化碳排放量为目标；下层建筑用户为下层追随者优化层，以最大化用户满意度为目标，构建双层优化模型，确定上层能源运营商定价层和下层建筑用户优化层目标函数与约束条件；
S4：对所述双层优化模型进行求解，其中利用非支配遗传算法(NSGA-II)求解上层能源运营商定价模型，更新上层领导者ESO的购售电价，调用求解器对下层模型求解，最终获取建筑综合能源系统最优调度策略。
基于能源集线器的概念构建含风光储及各能源转换装置的建筑综合能源系统模型如图2所示，该建筑综合能源系统使用电网电能、新能源电源出力和天然气作为能源输入；各能源转换装置包括燃气轮机、燃气锅炉、电制冷机、吸收式制冷机、电转气(power to gas，P2G)装置等；电负荷输出通过电网电能、新能源发电系统和燃气轮机发电系统的协同作用实现，热负荷输出主要由燃气锅炉 提供，冷负荷输出由电制冷机和吸收式制冷机实现，气负荷由气网、电转气装置、燃气轮机、燃气锅炉实现转换，储能装置主要为蓄电池，储热和储冷过程主要由建筑虚拟储能实现。
所述燃气轮机模型为：燃气轮机利用天然气的燃烧释放热能带动涡轮叶片的旋转产生机械能，最终转化为电能，其输出电功率PGT与所消耗的气功率GGT关系为：
式中：ηGT为燃气轮机的气转电效率；PGT,t为燃气轮机t时段产生的电功率；为燃气轮机的额定电功率；和分别为燃气轮机爬坡上下限。
所述燃气锅炉的模型为：燃气锅炉通过燃烧天然气产生热功率，其输出热功率HGB与所消耗的气功率GGB关系为：
式中：ηGB为燃气锅炉的燃烧热效率；为燃气锅炉的额定热功率。
所述电制冷机的模型为：电制冷机是一种电能驱动的制冷设备，其输出冷功率QAC与消耗电功率PAC的关系为：
式中：ηAC为电制冷机的制冷效率；为电制冷机的额定功率。
所述吸收式制冷机的模型为：吸收式制冷机是一种利用热能来实现制冷的设备，其冷功率输出QAR与所消耗的热功率HAR的关系为：
式中：ηAR为吸收式制冷机的制冷效率；为吸收式制冷机的额定功率。
所述储能装置的模型为：储能充放电前后的储能容量和充放电频率有如下约束：
式中：PES,t为储能装置的容量状态；和分别为储能装置的充放电效率；和分别为储能装置的输入、输出电功率；和分别为储能 装置最小、最大荷电状态；和为储能装置的充放电标志。
所述P2G装置的模型为：P2G技术可将装置收集的CO2转换为天然气供系统使用，其输出的气功率GP2G与所消耗的电功率PP2G关系为：
式中：ηP2G为P2G装置的电气转换效率；为P2G装置的额定功率。
在BIES中，冷-热-电-气子系统之间的耦合关系非常密切，使得系统运行受到各子系统之间耦合特征的影响更加显著，即当某种能源出现短缺时，需要破坏整个系统的供求关系，通过削减响应的负荷或利用各类负荷的耦合性能填补用能缺额。可通过多能流耦合矩阵描述各类负荷、设备和多种能流的转换关系进行等效建模，构建需求侧和供能侧能量耦合矩阵如下所示：
式中：Pload、Hload、Qload和Gload分别为电、热、冷、气负荷需求；Ps、Hs、Qs和Gs分别为电源、热源、冷源和气源，包括外界输入的能源和BIES中产生的各种能源；μP、μH、μQ和μG分别为电、热、冷、气负荷需求在电源、热源、冷源和气源中的分配系数；μGT、μGB、μAC、μAR和μP2G分别为上述能源转换装置(燃气轮机、燃气锅炉、电制冷机、吸收式制冷机、电转气)在对应能源中的分配系数，且各自的分配系数随需求侧负荷的改变而改变，以提高整个系统的调节能力。ηGT、ηGB、ηAC、ηAR和ηP2G分别为上述能源转换装置(燃气轮机、燃气锅炉、电制冷机、吸收式制冷机、电转气)的能源转换效率。
建筑综合能源系统参与主体之间的互动从能源运营商的策略开始，考虑系统碳排放量，向建筑用户发送价格策略并获取其用能策略。理性用户会对电价做出反应，在满足用能需求的前提下制定出适合自己能源使用的最佳策略。然后，该策略传达给能源运营商，能源运营商根据从用户获得的负荷需求响应更新其价格策略，并向用户发送修改后的价格策略。这种相互作用的过程不断迭代，当博弈双方不能通过改变自身策略来提升效益时，博弈达到平衡。
如图3所示，基于双层优化模型分别确定上层能源运营商定价层和下层建筑用户优化层目标函数与约束条件。
所述双层优化模型可表示为：
该模型包含双层优化模型的参与者，即ESO和BIES；双方的策略集，即价格增长幅度ψ和ESO向BIES的售电价与购电价双方效用，即双方各自益于主体的目标函数。当双方都不能通过改变自身策略而获得更益于自身 的目标时，双方达到均衡。利用非支配遗传算法(NSGA-II)求解上层能源运营商定价模型，更新上层领导者ESO的购电价与售电价，调用求解器对下层模型求解，最终获取建筑综合能源系统最优调度策略。
如图4所示，对所述双层优化模型进行求解，具体求解流程如下：
1)输入各设备的运行数据和系统初始数据，如电负荷需求、气负荷需求、新能源预测出力和室外温度数据等；
2)利用NSGA-II算法初始化上层优化模型的购售电价种群，并发送给下层模型；下层模型调用求解器完成优化策略的求解，并将结果反馈给上层模型；上层模型根据该策略计算系统当前碳排放量；
3)对购售电价种群进行选择、交叉、变异操作，更新购售电价并发送到上层模型，重复步骤2)；
4)直至求解达到最大迭代次数或上下层模型达到博弈均衡，得到系统最小碳排放量、最大综合满意度、各机组出力等调度结果。
为了验证本发明的有益效果，通过实验进行科学论证：采用本发明所述方法，首先对建筑综合能源系统中电、冷、热、气各能源调度进行了仿真实验，接着通过对不同调度方案的碳排放量和用户综合满意度对比验证本发明方法所具有的真实效果。
以某地区建筑为研究对象，以一天24h为调度周期、步长为1h来验证上述双层优化调度策略。某典型日该建筑综合能源系统中新能源出力情况、建筑内电负荷需求、气负荷需求与室外温度预测曲线如图5所示。建筑用户侧的电负荷由基础电负荷(固定电负荷)、可平移电负荷、可削减电负荷和可转移电负荷组成。优化前不同时段电负荷组成如图6所示。
(1)电能调度结果
按本发明的策略进行ESO和BIES之间的交互，当达到均衡时，ESO的电价优化曲线如图7所示。
接着以图7电价优化结果为参考，需求响应后建筑用户侧的电负荷分布如图8所示。观察电负荷优化结果可知，柔性电负荷中可平移电负荷和可转移电负荷总体上呈现由用电峰时段向用电谷、平时段转移的趋势。此外，经过优化的电负荷也受到了不同程度的削减，且削减时段多为用电高峰期。电负荷减少的时段多为ESO售电价较高的时段(11:00-16:00,19:00-22:00)，而将该时段的电负荷转移到电价较低的时段(4:00-9:00)，这不仅体现建筑柔性电负荷削峰填谷的作用，缓解了系统的供电压力，也保证了调度的经济性。
电功率平衡情况如图9所示。BIES的电负荷主要由新能源的出力和向ESO购电以满足需求，同时少量由燃气轮机的发电量作为补充。在08:00-15:00时新能源出力已经满足大部分的电负荷需求，该时段的电能购买量远低于其他时段，促进了新能源的消纳率，也降低了整个系统的运行成本，该时段同时将多余的新能源出力分别由P2G装置和电制冷机转换为气能流和冷能流。在01:00-07:00时段电负荷需求较少且电价较低时，储能装置在该时段以充电状态为主，在13:00和20:00时风力资源充沛且电负荷需求较高，储能装置的放电缓解了高峰期的用电压力。
(2)冷、热能调度结果
设定采暖建筑室内PMV指标为-1≤λPMV≤1，采冷建筑室内初始温度为-15℃；则优化后采冷建筑和采暖建筑室内温度如图10所示。
其对应的冷热功率平衡如图11、图12所示。
为了满足BIES低碳性的要求，采冷建筑室内的冷负荷基本由电能提供，尤其在新能源出力充足的时段(10:00-21:00)，避免了由吸收式制冷机承担冷负荷时增大天然气的消耗。部分冷负荷由建筑虚拟储能实现，在室温较低的时段(1:00-7:00)，建筑虚拟储冷处于充冷状态，以便建筑虚拟储能参与到后续的冷负荷调节之中。建筑内热负荷主要由燃气锅炉放热实现，部分由建筑虚拟储能的充放热过程作为补充，热负荷除满足建筑用户采暖系统的舒适度要求外，其余经由吸收式制冷机转换为冷负荷，提高了系统的灵活性。
(3)气能调度结果
对应的气功率平衡如图13所示。由图13分析可得，天然气主要由气网购买获得，少部分由P2G装置转换而来。所获天然气除满足基本气负荷外，其余全部经由燃气轮机与燃气锅炉转换为电能和热能供系统使用。
(4)不同场景下优化结果
仿真实验考虑了三种调度场景，即场景一：本发明所提的调度场景；场景二：不考虑上下层交互策略的调度场景；场景三：不考虑虚拟储能的调度场景。不同运行场景下BIES的综合满意度和碳排放量如图14所示。
本发明所提的考虑建筑虚拟储能的BIES双层优化调度模型碳排放量相比于场景二和场景三都有所降低，而用户综合满意度有所提升。
其中，场景一中系统总碳排放量为1271.2kg，场景二中系统总碳排放量为1367.8kg，场景三中系统总碳排放量为1453.5kg，场景一碳排放量对比其他运行场景分别降低7.06％和12.5％，这是因为上下层交互策略下ESO制定的能源价格策略进一步考虑到建筑用户的用能成本；同时，考虑建筑自身的虚拟储能特性能有效降低采暖建筑室内和采冷建筑室内的能源消耗，从而降低整个建筑综合能源系统的碳排放量。另外，场景一中系统的平均用户满意度为0.89，对比运行场景二(0.81)、运行场景三(0.76)分别提升9.8％和17.1％。由此可见，本发明所提模型和调度策略能有效提升建筑综合能源系统的低碳性和用户满意度。
以上所述仅为本发明的优选方案，并非作为对本发明的进一步限定，凡是利用本发明说明书及附图内容所作的各种等效变化均在本发明的保护范围之内。

Claims (5)

  1. 一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，其特征在于，包括：
    S1、基于能源集线器的概念构建含风光储及能源转换装置的低碳建筑综合能源系统BIES；
    S2、综合分析低碳建筑综合能源系统中冷热电气各负荷特性，并考虑建筑虚拟储能与人体舒适度指标，构建用户综合满意度指标；
    S3、以能源运营商ESO为上层优化层，系统最小化碳排放量为目标，以建筑用户为下层优化层，最大化用户满意度为目标，构建双层优化模型；确定上层能源运营商定价层和下层建筑用户优化层目标函数与约束条件；
    其中，所述双层优化模型表示为：
    其中，ESO和BIES为双层优化模型的参与者；双方的策略集即能源价格增长幅度ψ和ESO向BIES的售电价与购电价双方效用，即双方各自益于主体的目标函数min FESO和max FBIES，即最小化碳排放量和最大化综合满意度；
    所述上层能源运营商定价层的目标函数为：
    式中：FESO为BIES的总碳排放量；γC为碳排放单价；Ct为建筑综合能源系统实际碳排放量；C0为初始碳排放配额；
    其中，建筑综合能源系统中碳排放由外购电力和外购天然气产生，即；
    式中：和分别为t时刻ESO向BIES售卖电能和天然气的量；和分别为区域电网、气网平均排碳系数；
    所述上层能源运营商定价层的约束条件为：
    (1)与主网交互功率约束：
    式中：与分别为t时刻ESO向上级电网购买电能的上下限；和为t时刻ESO向上级气网购买气能的上下限；
    (2)电价约束：
    式中：和分别为t时刻电价的上下限；为调度周期内电价的均值；
    所述下层建筑用户优化层的目标函数为：
    max FBIES＝(Fload+Fa+FPMV+Fco)/4
    所述下层建筑用户优化层的约束条件为：
    (1)机组约束
    各能源转化设备和储能装置、建筑虚拟储能具有出力上下限约束；新能源设备的出力上下限如下：
    式中：Pwt和Ppv分别为风机、光伏输出功率；和分别为风机、光伏预测出力；
    (2)负荷需求响应约束
    柔性电负荷相应的约束如下：
    冷热负荷需求响应约束：
    式中：为PMV指标范围限制；和分别为采暖建筑室内和采冷建筑室内温度的上、下限；
    (3)电功率平衡约束
    Pload＝Pwt+Ppv+PB2E+PES+PGT-PAC-PP2G＝Pbase+Pshift+Ptran+Pcut
    式中：Pwt和Ppv分别为风机、光伏的输出电功率；PB2E为BIES与ESO的交互电功率；PES为储能装置释放电功率；PAC和PP2G分别为电制冷机、P2G装置消耗的电功率；Pbase为基础电负荷；
    (4)热功率平衡约束
    Hload＝HGB-HAR+HVES
    式中：HGB和HAR分别为燃气锅炉产热和吸收式制冷机耗热；HVES为建筑虚拟储能释放热功率；
    (5)冷功率平衡约束
    Qload＝QAR+QAC+QVES
    式中：QAR和QAC分别为吸收式制冷机和电制冷机的制冷量；QVES为建筑虚拟储能释放冷功率；
    (6)气功率平衡约束
    Gload＝GB2E-GGT-GGB+GP2G
    式中：GB2E为BIES向ESO的购气功率；GGT和GGB分别为燃气锅炉和燃气轮机耗气；GP2G为P2G装置产气量；
    S4、对所述双层优化模型进行求解，其中利用非支配遗传算法求解上层能源运营商定价模型，更新上层领导者ESO的购售电价，调用求解器对下层模型求解，最终获取建筑综合能源系统最优调度策略。
  2. 根据权利要求1所述的一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，其特征在于，S1中，所述低碳建筑综合能源系统使用电网电能、新能源电源出力和天然气作为能源输入；能源转换装置包括燃气轮机、燃气锅炉、电制冷机、吸收式制冷机、电转气装置P2G；
    构建需求侧和供能侧能量耦合矩阵如下所示：
    式中：Pload、Hload、Qload和Gload分别为电、热、冷、气负荷需求；Ps、Hs、Qs和Gs分别为电源、热源、冷源和气源，包括外界输入的能源和BIES中产生的各种能源；μP、μH、μQ和μG分别为电、热、冷、气负荷需求在电源、热源、冷源和气源中的分配系数；μGT、μGB、μAC、μAR和μP2G分别为所述能源转换装置在对应能源中的分配系数，且各自的分配系数随需求侧负荷的改变而改变；ηGT、ηGB、ηAC、ηAR和ηP2G分别为所述能源转换装置的能源转换效率。
  3. 根据权利要求1所述的一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，其特征在于，S2中，低碳建筑综合能源系统中冷热电气各负荷特性包括建筑柔性电负荷特性和建筑冷/热负荷特性；
    建筑物内电负荷根据其参与柔性调节的能力分为：基础负荷、可平移负荷、可转移负荷和可削减负荷；
    基础负荷：是指不受控制的负荷，不能对使用者的要求做出反应，且不能改变使用者的用能模式和用能时间，其不参与调度；
    可平移负荷：该负荷的用电时刻根据计划变化，但是负荷需要整体移动且不能改变其值，因此用能时间可以跨越多个调度周期；
    假设可平移负荷Pshift参与调度前的向量分布为：
    式中：tS,tD分别为可平移负荷的起始时间和结束时间；
    假设平移时段为[tsh-,tsh+]，用变量κ表示负荷Pshift在时段t的平移状态，即κ＝0时表示负荷Pshift不平移，κ＝1时表示负荷Pshift从t时段开始平移；则起始时段的集合为：
    U表示并集；
    若t∈[tsh-,tsh+-tD+1]且t≠tS，则表示负荷从起始时段为t的分量Pshift为：
    反之，若t＝tS,代表负荷Pshift未改变；
    可转移负荷：该负荷的用电时段允许中断，并且持续时间并不要求固定，只需确保转移前后的负荷需求总量不变；
    假设可转移负荷Ptran的转移时段为[ttr-,ttr+]，用变量表示负荷Ptran在某一时段的转移状态，即时表示负荷不转移，时表示Ptran从t时段开始转移；对可转移负荷设定最短持续工作时间，并对转移功率加以限制，即：
    式中：和分别为负荷转移功率的最小值和最大值；为设备的持续运行时间；
    可削减负荷：指通过减少建筑用户的用能参与需求响应；
    用变量σ表示可削减负荷Pcut在时段t的削减状态，即σ＝1时表示Pcut在t时段被削减，则参与调度后t时段可削减负荷的功率变为：
    式中：ηt为削减系数，有0≤ηt≤1；为t时段可削减负荷的初始功率；
    对负荷削减的时长及削减次数进行约束，即：
    式中：分别为最小和最大连续削减时间；Nmax为最大削减次数；
    所述建筑冷/热负荷特性包括建筑冷/热负荷的惯性和柔性；
    其中，所述建筑冷/热负荷的惯性的具体为：由于建筑物具有自身冷/热动态特性，即当建筑利用各种设备获得冷/热能后，要经过一段时间延迟才可达到预设的温度，使得建筑内供冷/供热系统具有惯性，表现出储能能力，即虚拟储能；
    虚拟储能模型为：
    式中：ΔQHQ(t)为t时刻室内热量变化；C为空气比热容；ρ0为空气密度；V为建筑体积容量；Tin为室内温度，d是微分符号，即对t求导；
    引入虚拟储能充放功率描述虚拟储能，则有：
    式中：PVES为储能充放功率；VESt和VESmax分别为虚拟储能t时刻容量和最大容量；为t时刻室内温度；Tin,max和Tin,min分别为室内可接受温度上、下限；Δt为时间间隔；
    在供热系统中，采用自回归滑动平均时间序列ARMA模型对t时段热网供水温度Tt,g、回水温度Tt,h、采暖建筑室内温度和室外温度之间的关系进行描述，具体为：

    式中：α,β,γ,θ,和ω为建筑供热系统的物理参数，阶次J体现建筑供热系统的热惯性；
    其中，采暖建筑供热量Ht,h与供水温度Tt,g、回水温度Tt,h的关系为：
    Ht,h＝γ(Tt,g-Tt,h)
    式中：γ为采暖建筑供热量与热网供回水温度间的关系系数；
    采冷建筑内的惯性用等值热参数模型ETP描述,具体为：
    式中：和分别为采冷建筑室内和室外温度；Qt,c表示t时段内制冷机的总制冷功率；R和C分别为采冷建筑房间的等效热阻和等效热容；
    所述建筑冷/热负荷的柔性具体为：建筑内用户对建筑物房间内冷暖的感知存在模糊性，将冷/热负荷由固定值转换为柔性值。
  4. 根据权利要求3所述的一种考虑虚拟储能的建筑综合能源系统双层优化调度方法，其特征在于，S2中，选取预测平均投票数PMV作为人体舒适度指标，对用户的冷热舒适度进行量化，PMV指标值λPMV具体为：
    式中：MP和WP分别为人体代谢率和人体活动所产生的机械功率；Pa为人体周围环境空气的水蒸气压力；ta为人体周围环境温度；fcl为人体覆盖服装面积与裸露面积之比；tcl和tr分别为人体着装时表面温度和平均辐射温度；hc为空气对流热传递系数。
  5. 根据权利要求4所述的一种考虑虚拟储能的建筑综合能源系统双层优化调度方法， 其特征在于，S2中，构建的用户综合满意度指标FBIES包括用能满意度、电价满意度、成本满意度和舒适满意度；
    用能满意度Fload表示为：
    式中：为电价调整后的用电负荷；
    电价满意度Fa为：
    式中：为ESO向BIES的售电价；为t时刻ESO向BIES售卖电能的量；a为初始售电价，ESO向BIES售电初始量；
    成本满意度Fco为：
    式中：为t时刻BIES向外部的购能成本，为初始满足初始负荷需求的购能成本；
    舒适满意度为：
    FPMV＝1-|λPMV|。
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN121169039A (zh) * 2025-11-20 2025-12-19 华电电力科学研究院有限公司 基于v2g充电站的储能系统协同优化配置与调度方法
CN121303769A (zh) * 2025-12-10 2026-01-09 四川农业大学 一种区域建筑碳排放优化系统及方法
CN121461404A (zh) * 2025-12-29 2026-02-03 国网吉林省电力有限公司营销服务中心 一种不同情景下虚拟电厂的自适应多能动态存储方法及装置
CN121474678A (zh) * 2026-01-08 2026-02-06 宁波棠里绿色科技有限公司 基于动态负荷调节的中央空调智能控制方法
CN121580564A (zh) * 2026-01-26 2026-02-27 中国海洋大学 贯穿源网荷全链条的区域供冷系统协同模型预测控制方法
CN121840795A (zh) * 2026-03-13 2026-04-10 上海智慧之光信息科技有限公司 基于供需匹配度的光储建筑群多目标协同优化调度系统

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117952402B (zh) * 2024-03-27 2024-06-07 南京邮电大学 一种考虑虚拟储能的建筑综合能源系统双层优化调度方法
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CN118839801B (zh) * 2024-06-21 2025-04-18 华北电力大学 考虑热网调节特性的综合能源系统稳定性优化方法及装置
CN119398257A (zh) * 2024-10-25 2025-02-07 贵州电网有限责任公司 一种考虑虚拟储热的综合能源系统优化调度模型构建方法
CN119671179A (zh) * 2024-12-06 2025-03-21 国网内蒙古东部电力有限公司经济技术研究院 电网连接能源枢纽的灵活性定价方法及相关装置
CN119761716A (zh) * 2024-12-11 2025-04-04 华中科技大学 一种结合建筑虚拟储能的数据中心电力-热力调度方法及系统
CN120597729A (zh) * 2025-08-06 2025-09-05 陕西延长石油（集团）有限责任公司 一种数字油藏孪生的构建方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023134254A1 (zh) * 2022-01-11 2023-07-20 云南电网有限责任公司电力科学研究院 一种能源互联系统的设备选型方法
CN116822697A (zh) * 2023-04-18 2023-09-29 河北工业大学 一种考虑主从博弈和需求响应的综合能源系统低碳经济优化方法
CN117371675A (zh) * 2023-04-06 2024-01-09 南京工程学院 含碳捕获和电转气设备的综合能源系统主从博弈优化方法
CN117455164A (zh) * 2023-10-30 2024-01-26 国网上海市电力公司 考虑碳交易和需求响应的电热气综合能源系统的优化运行方法
CN117952402A (zh) * 2024-03-27 2024-04-30 南京邮电大学 一种考虑虚拟储能的建筑综合能源系统双层优化调度方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114462724B (zh) * 2022-04-12 2022-07-01 中建安装集团有限公司 一种考虑多能源价格激励综合需求响应模型的园区聚合商双层优化调度方法
CN115146834B (zh) * 2022-06-16 2025-01-24 国网江苏省电力有限公司经济技术研究院 基于改进粒子群算法的阶梯型碳交易机制参数优化方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023134254A1 (zh) * 2022-01-11 2023-07-20 云南电网有限责任公司电力科学研究院 一种能源互联系统的设备选型方法
CN117371675A (zh) * 2023-04-06 2024-01-09 南京工程学院 含碳捕获和电转气设备的综合能源系统主从博弈优化方法
CN116822697A (zh) * 2023-04-18 2023-09-29 河北工业大学 一种考虑主从博弈和需求响应的综合能源系统低碳经济优化方法
CN117455164A (zh) * 2023-10-30 2024-01-26 国网上海市电力公司 考虑碳交易和需求响应的电热气综合能源系统的优化运行方法
CN117952402A (zh) * 2024-03-27 2024-04-30 南京邮电大学 一种考虑虚拟储能的建筑综合能源系统双层优化调度方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHANG HONG, MA HONGJUN;YAN HE;SHI HUA;ZHANG QIAN: "Two-stage Day-ahead Optimal Microgrid Scheduling with Coordination Between Supply and Demand Considering WCVaR Assessment", AUTOMATION OF ELECTRIC POWER SYSTEMS, vol. 45, no. 2, 22 September 2020 (2020-09-22), CN , pages 55 - 63, XP093358543, ISSN: 1000-1026 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN121169039A (zh) * 2025-11-20 2025-12-19 华电电力科学研究院有限公司 基于v2g充电站的储能系统协同优化配置与调度方法
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CN121840795A (zh) * 2026-03-13 2026-04-10 上海智慧之光信息科技有限公司 基于供需匹配度的光储建筑群多目标协同优化调度系统

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