A phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings

Abstract

The present invention relates to a phased collaborative optimization scheduling method for a combined heat, cooling and power system considering the comprehensive demand response of electric vehicles and / or buildings, comprising: in the first phase, with the optimization goal of reducing the charging cost and the superimposed load fluctuation of electric vehicles and buildings, according to the time-sharing power load of the building and the simulated electric vehicle behavior characteristic model, the electric vehicle cluster is scheduled to participate in the power load demand response using V2B, and the electric vehicle time-sharing load is obtained and provided to the second phase; in the second phase, with the optimization goal of reducing costs and improving energy utilization efficiency, according to the time-sharing load of the electric vehicle, the pre-built combined heat, cooling and power system and the comprehensive demand response model of the building, the building time-sharing cold, heat and electricity loads are obtained and provided to the first phase. The present invention improves the comprehensive benefits and energy efficiency of the multi-energy end through a multi-stage optimization strategy, promotes environmental protection and carbon emission reduction, and enhances the security of energy supply.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling optimization, and in particular to a phased collaborative optimization scheduling method for a combined cooling, heating and power system taking into account the comprehensive demand response of electric vehicles and / or buildings. Background Art

[0002] At present, the global energy demand is growing, and the daily energy consumption of residents is one of the important research areas of energy consumption. In particular, as a typical energy-demanding building complex, many studies consider the establishment of a multi-energy coupling cogeneration energy system to meet the needs of various energy loads such as residential cooling, heating and electricity. At the same time, with the growing demand for low-carbon travel, more and more people choose to travel by electric vehicles, and the centralized charging of a large number of electric vehicles can cause a shortage of electricity supply on the residential side.

[0003] At present, the relevant research is as follows: Literature 1: Wang Yun, Xie Haipeng, Sun Xiaotian, et al. Day-ahead economic dispatch of electric-thermal integrated energy system considering incentive-based integrated demand response [J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1926-1934; it constructs an incentive-based electric-thermal integrated demand response model and considers the electric-thermal conversion response, and optimizes the day-ahead dispatch of the established integrated energy system with the optimal economic performance as the objective function. The results show that after considering the demand response, it can play the role of "peak shaving and valley filling" and greatly improve the economy of system operation. Literature 2: Zheng W, Xu Z, Shao Z, et al. Optimal dispatch of nearly-zero carbon integrated energy system considering waste incineration plant-carbon capture system and market mechanisms [J]. International Journal of Hydrogen Energy, 2023; it considers the incentive-based flexible DR mechanism to participate in the optimal dispatch of the nearly zero-carbon integrated energy system, optimizes energy distribution, and improves economic and environmental benefits. Reference 3: Zhou Wei, Xu Congming, Yang Danxia, ​​et al. Research on demand response strategy of electric vehicle group considering dynamic adjustment of willingness under P2P energy sharing [J]. Proceedings of the CSEE, 2023, 43(21): 8217-8230; They proposed a demand response strategy of electric vehicle group considering dynamic adjustment of response willingness under the peer-to-peer (P2P) energy sharing market mechanism, and constructed a robust model considering the impact of photovoltaic fluctuations on the strategy. Through case analysis, it was verified that the proposed strategy can reduce charging costs and promote local photovoltaic consumption. Reference 4: Zhang L, Sun C, Cai G, et al. Charging and discharging optimization strategy for electric vehicles considering elasticity demand response. eTransportation 18, 100262 [J]. 2023. They combined the electric vehicle travel chain planning of multiple functional areas in the city and proposed an electric vehicle charging and discharging elastic demand response strategy, which can reduce costs by 40-110% and achieve a 19%-100% reduction in load peak-valley differences.

[0004] In summary, there are still some deficiencies in current research, especially the following problems: (1) Energy optimization is only considered from the perspective of electric vehicles or buildings, without considering the connection and mutual coupling between the two, nor focusing on a large system that includes the two; (2) Most of the existing multi-stage optimization studies start from interests, focusing on the purpose of improving benefits, considering fewer factors, and it is difficult to be practical; (3) Most of the existing multi-subject demand studies focus on electricity demand, without considering the coordinated optimization of other types of energy demand. Therefore, further exploration of the current research situation will have high theoretical and practical significance. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a phased collaborative optimization scheduling method for a combined heat, cooling and power system taking into account the comprehensive demand response of electric vehicles and / or buildings, which solves the technical problems existing in the existing research on demand response optimization for electric vehicles and buildings, such as not considering the connection and mutual coupling influence between multiple energy users, having few optimization considerations, and not considering the collaborative optimization of demand for multiple energy supplies.

[0007] (II) Technical solution

[0008] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, an embodiment of the present invention provides a phased collaborative optimization scheduling method for a combined heat, cooling and power system considering the comprehensive demand response of electric vehicles and / or buildings, including: in a first stage, taking reducing the charging cost and the superimposed load fluctuation of electric vehicles and buildings as the optimization goal, taking the initial time-sharing power load of the building as the parameter input in the first optimization, and taking the time-sharing power load of the building obtained in the second stage as the parameter input in the subsequent optimization, and combining the electric vehicle behavior characteristic model obtained by simulation, scheduling the electric vehicle cluster to participate in the power load demand response using V2B, obtaining the electric vehicle time-sharing load and providing it to the second stage; in the second stage, constructing a combined heat, cooling and power system by introducing renewable energy, equipment coupling multi-energy supply and multi-energy storage configuration, and taking reducing costs and improving energy utilization efficiency as the optimization goals, obtaining the building time-sharing load according to the obtained electric vehicle time-sharing load, the constructed combined heat, cooling and power system and the comprehensive demand response model of the building, and providing the building time-sharing power load to the first stage; wherein, both the first stage and the second stage use the intelligent algorithm to use the loads after each optimized scheduling as the parameters transmitted to each other to coordinate and optimize the different stages, and obtain the optimization results of each stage.

[0010] Optionally, in the first stage, the optimization goal is to reduce the charging cost and the fluctuation of the superimposed load of electric vehicles and buildings. In the first optimization, the initial time-sharing power load of the building is used as a parameter input. In the subsequent optimization, the time-sharing power load of the building obtained in the second stage is used as a parameter input, and combined with the electric vehicle behavior characteristic model obtained by simulation, the electric vehicle cluster is dispatched to participate in the electric load demand response using V2B, and the electric vehicle time-sharing load is obtained and provided to the second stage, including: simulating the obtained statistical data using the Monte Carlo method to obtain the electric vehicle behavior characteristic model; constructing the electric vehicle energy management item according to the electric vehicle travel energy consumption, the battery state of charge under spatial changes, and the battery state of charge change under charging and discharging; An economic indicator for reducing charging costs and a load indicator for reducing the fluctuation of the superimposed load of electric vehicles and buildings are introduced, and the economic indicator and the load indicator are unified in dimension respectively, and the weight coefficient method is used to obtain the total optimization indicator of the first stage; when scheduling electric vehicle clusters to interact with buildings using V2B, an electric vehicle demand response strategy is constructed from the perspective of time and charging demand according to the total optimization indicator of the first stage and the electric vehicle energy management item; the electric vehicle demand response strategy is executed, and the superimposed electric vehicle-building power load curve is output; the variables to be optimized and the electric vehicle behavior characteristic model are obtained from the electric vehicle-building power load curve, and the optimization is performed through an intelligent algorithm to output the electric vehicle time-sharing load.

[0011] Optionally, the electric vehicle behavior characteristic model includes an electric vehicle travel distance model, an electric vehicle arrival time model, and an electric vehicle departure time model, and the electric vehicle energy management item includes an energy consumption factor model, a charge state when the electric vehicle arrives, a charge state when the electric vehicle leaves, a limit on the charge / discharge power when the electric vehicle is charged / discharged, and a total electric power of the electric vehicle cluster;

[0012] The travel distance model of electric vehicles is:

[0013]

[0014] In formula (1), f jou (s) is the probability density of the travel distance, s is the travel distance of the electric vehicle, μ s and σ s are the expected and standard deviation of the travel distance of electric vehicles, respectively;

[0015] The electric vehicle arrival time model is:

[0016]

[0017] In formula (2), f arr (t1) is the probability density of arrival time, t1 is the time when the electric vehicle arrives after the trip, μ arr and σarr are the expected and standard deviation of the electric vehicle arrival time, respectively;

[0018] The electric vehicle departure time model is:

[0019]

[0020] In formula (3), f dep (t2) is the probability density of departure time, t2 is the time when the electric vehicle leaves, μ dep and σ dep are the expected and standard deviation of the departure time of electric vehicles, respectively;

[0021] The energy consumption factor model is:

[0022] W EV =0.21-0.001v+1.534 / v (4)

[0023] W n,sum =S n W EV (5)

[0024] In formulas (4) and (5), W EV is the energy consumption per kilometer of electric vehicle travel, v is the average travel speed of electric vehicle, S n is the travel distance of the nth electric car, W n,sum is the power consumption of the nth electric car;

[0025] The state of charge of the electric vehicle upon arrival is:

[0026] SOC n.t =1-W n,sum / E n,cap (6)

[0027] In formula (6), SOC n,t is the charge state of the nth electric car when it arrives at time t, E n,cap is the capacity of the onboard battery of the nth electric vehicle;

[0028] The state of charge constraint of the electric vehicle when leaving is:

[0029] W n,sum / E n,cap ≤SOC n.le ≤1 (7)

[0030] In formula (7), SOC n,le is the state of charge of the nth electric vehicle when it leaves;

[0031] When charging / discharging electric vehicles, the charging / discharging power is limited by:

[0032]

[0033] In formula (8), P dis,max and P cha,max They are the upper limit of charging power and the upper limit of discharging power when a single electric vehicle interacts with building power. and P n,t are the charging power, discharging power and building power interaction comprehensive power of the nth electric vehicle at time t and the electric rate of the electric vehicle cluster at time t, SOC n,t+1 is the charge state of the nth electric car at time t+1, E cap ;

[0034] The total electric power of the electric vehicle cluster is:

[0035]

[0036] In formula (9), P t EV is the total electric power of the electric vehicle cluster at time t, N t 、N 1,t and N 2,t They are the number of electric vehicles participating in scheduling, the number of electric vehicles participating in charging, and the number of electric vehicles participating in discharging at time t respectively.

[0037] Optionally,

[0038] The economic indicators for reducing charging costs are:

[0039]

[0040] In formula (10), F 1,1 is the economic index of the first stage, T is the scheduling period, μ grid,t and μ sell,t are the electricity purchase price and electricity sales price from the power grid at time t respectively;

[0041] The load index for the purpose of reducing the fluctuation of the superimposed load of electric vehicles and buildings is:

[0042]

[0043] In formula (11), F 1,2 is the load index of the first stage, P t and are the daily average values ​​of the building power load and the superimposed power load of electric vehicles and buildings at time t, respectively;

[0044] The total optimization index of the first stage is:

[0045]

[0046] In formula (12), ω 1,1 and ω 1,2 are all weight factors, Obj1 is the total objective function of the first stage, F 1,1,max is the maximum economic index value in the first stage, F 1,2,max It is the maximum load index value of the first stage.

[0047] Optionally, an electric vehicle demand response strategy is executed, and the output of the superimposed electric vehicle-building power load curve includes:

[0048] Compare the state of charge (SOC) of the electric vehicle when it arrives at time t n.t The lower limit of the state of charge (SOC) of the electric vehicle load battery min size;

[0049] If the electric vehicle arrives at the state of charge SOC n.t Less than or equal to SOC min , then the following cycle is performed: Calculate the capacity SOC of the vehicle battery when leaving n,le With the current capacity SOC n,t and calculate the difference between the current time t and the departure time t n,2 The average charging power at each moment in time is the interaction power P between the electric vehicle and the building at time t. n,t Assign the value to the average charging power of the electric vehicle obtained, and reassign the cycle time variable t by adding 1 to the cycle time variable t until t is not less than the departure time t n,2 End the loop;

[0050] If the electric vehicle arrives at the state of charge SOC n.t Greater than SOC min , calculate the minimum charging period T at the maximum charging rate cha,min , and determine the departure time t n,2 The difference between the current time t and the minimum charging time T cha,min size;

[0051] At departure time t n,2 The difference from the current time t is greater than T cha,min When t, the following cycle is performed: the interaction power P between the electric vehicle and the building at time t is n,t Randomly select a value within the upper limit of the electric vehicle's charge / discharge power and pre-determine the departure time t n,2 The difference between the next time t+1 and T cha,min +1 The size of the time period; if you leave at time t n,2 The difference with the next time t+1 is not less than T cha,min+1 time period, the loop time t plus 1 is reassigned to the loop time t variable; if the time t is left n,2 The difference with the next time t+1 is less than T cha,min +1, the interaction power P between the electric vehicle and the building at time t will be returned. n,t Randomly select a value within the upper limit of the charging and discharging power of the electric vehicle, and re-judge the pre-judgment departure time t n,2 The difference between the next time t+1 and T cha,min +1 time period, until the current t is not less than the departure time t n,2 End the loop;

[0052] At departure time t n,2 The difference with the current time t is not greater than the minimum charging time T cha,min When the interaction power P between the electric vehicle and the building at time t is n,t The upper limit of electric vehicle charging power P cha,max The loop time t plus 1 is reassigned to the loop time t variable until the current t is not less than the departure time t n,2 End the loop;

[0053] Determine the relationship between the number of dispatched electric vehicles n and the total number of electric vehicles N. If n>N, superimpose the dispatched electric vehicle time-sharing load and the building time-sharing load, and finally output the superimposed electric vehicle-building power load curve.

[0054] Optionally, in the second stage, a combined heat, power and cooling system is constructed by introducing renewable energy, equipment coupling multi-energy supply and multi-energy storage configuration, and with reducing costs and improving energy efficiency as optimization goals, the building time-sharing load is obtained according to the obtained electric vehicle time-sharing load, the constructed combined heat, power and cooling system and the comprehensive building demand response model, and the building time-sharing power load is provided to the first stage. The load includes: according to the energy demand and heat and cooling load of the energy-consuming end including electric vehicles and / or buildings, a combined heat, power and cooling system is constructed that introduces two types of energy, solar energy and geothermal energy, uses gas turbines as multi-energy coupling supply equipment, and sets up multi-energy storage including batteries and heat storage tanks; introduces economic indicators for the purpose of reducing energy supply costs and energy efficiency indicators for improving energy efficiency, and respectively converts the economic indicators and energy efficiency indicators into The dimensions are unified, and the weight coefficient method is used to obtain the total optimization index of the second stage through difference processing; according to the total optimization index of the second stage and the incentive-based demand response method, a comprehensive demand response strategy for electricity, heat and cooling is constructed from the perspective of the electric load and cooling and heating load at the energy consumption end; based on the combined heat and cooling system and the comprehensive demand response strategy for electricity, heat and cooling, the introduction of renewable energy in the system energy supply process, the coupling of multi-energy supply and multi-energy storage configuration of equipment are used to meet the various energy needs of the building, and some flexible loads existing in the building energy consumption process are used to cooperate with the system to participate in the dynamic response of the load to optimize the building's multi-energy load curve. Combined with the dual benefit factors of economy and energy efficiency, an intelligent algorithm is used to optimize the actual time-sharing load of the building and the operation planning information of the energy system, and the actual time-sharing electric load of the building is provided to the first stage.

[0055] Optionally, the combined heat, cooling and power system includes: a multi-energy input part, a multi-energy production part and a multi-energy conversion and storage part; the multi-energy input part provides multi-energy input including solar energy, power grid, gas grid and geothermal energy; the multi-energy production part uses production components including photovoltaic panels, gas turbines and ground source heat pumps to process and produce the input multi-energy; the multi-energy conversion and storage part uses waste heat recovery devices and absorption refrigerators to achieve conversion of hot and cold energy, and sets batteries and heat storage tanks to improve the system's energy regulation capabilities; among them, the energy consumption end including electric vehicles and buildings considers the demand for heating, cooling and electricity loads, the heating and cooling loads of buildings include rigid loads and flexible loads, and the electricity load of the building includes the total electricity load after the first stage of scheduling and at least part of the flexible electricity load.

[0056] Optionally, the equipment model of the combined heat and power system includes a gas turbine model, a waste heat recovery device model, a photovoltaic panel model, a battery assembly model, a heat storage tank model, a ground source heat pump model, and an absorption chiller model; the equipment constraints of the combined heat and power system include: power supply and demand balance constraints, heat supply and demand balance constraints, cooling supply and demand balance constraints, gas turbine constraints, photovoltaic power generation constraints, and energy storage constraints;

[0057] The gas turbine model is:

[0058] P t GT =η GT,e P gas,t (13)

[0059]

[0060] In formulas (13) and (14), P t GT , are the electrical power and thermal power output of the gas turbine at time t, respectively, GT,e , η GT,e / h are the power generation efficiency and thermal power ratio of the gas turbine, P gas,t is the gas turbine power consumption at time t

[0061] The model of the waste heat recovery device is:

[0062]

[0063] In formula (15), is the thermal power of the waste heat recovery device at time t, η HR is the waste heat recovery efficiency of the waste heat recovery device;

[0064] The model of the photovoltaic panel is:

[0065]

[0066] In formula (16), is the available photovoltaic power at time t, η PV is the photovoltaic power generation efficiency, A is the area of ​​the photovoltaic panel, Φ t is the radiation intensity;

[0067] The model of the battery component is:

[0068]

[0069] In formula (17), SOC t and SOC t-1 are the state of charge of the battery at time t and the state of charge of the battery at time t-1, respectively, Es,c and η Es,d are the charging efficiency and discharging efficiency of the battery at time t, respectively; E0 is the rated battery capacity of the battery; and They are the charging power of the battery and the discharging power of the battery respectively;

[0070] The model of the heat storage tank is:

[0071]

[0072] In formula (18), H SSt and H SSt-1 are the heat storage state of the heat storage tank at time t and the heat storage state of the heat storage tank at time t-1, η Hs,c and η Hs,d are the heat storage efficiency and heat release efficiency of the heat storage tank, respectively; H0 is the rated heat storage capacity of the heat storage tank. and are the heat storage power of the heat storage tank and the heat release power of the heat storage tank respectively;

[0073] The model of ground source heat pump is:

[0074]

[0075] 0≤μ1+μ2≤1 (20)

[0076] In formulas (19) and (20), P t GSHP is the power consumption of the ground source heat pump at time t, and are the heating power and cooling power of the ground source heat pump at time t, COP h,GSHP and COP c,GSHP They are the coefficient of performance of the ground source heat pump in the heating state and the coefficient of performance in the cooling state, respectively. μ1 and μ2 are both 0-1 variables, indicating the switching state of the ground source heat pump heating and cooling;

[0077] The model of the absorption chiller is:

[0078]

[0079] In formula (21), is the cooling power output of the absorption chiller at time t, COP ABS is the coefficient of performance of the absorption refrigerator, Q Ah,t is the heat consumption power of the absorption refrigerator;

[0080] The power supply and demand balance constraint is:

[0081]

[0082] In formula (22), P t pv and P t Es are the photovoltaic power and battery power at time t, respectively, rig,t is the initial electrical load of the building at time t, P dr,t is the building load power reduced at time t, Pgrid,t is the electric power input into the grid at time t;

[0083] The heat supply and demand balance constraint is:

[0084]

[0085] In formula (23), Q h,t Central heating power for the cogeneration system, is the thermal power of the heat storage tank;

[0086] The cold energy supply and demand balance constraint is:

[0087]

[0088] In formula (24), Q c,t Central cooling power for the cogeneration system;

[0089] The gas turbine constraints are:

[0090]

[0091] In formula (25), and are the upper and lower limits of gas turbine output respectively;

[0092] The photovoltaic power generation constraints are:

[0093]

[0094] The energy storage constraint is:

[0095]

[0096] In formula (27), SOC max , SOC min ,HSS max and HSS min are the upper and lower limits of the battery state of charge and the upper and lower limits of the heat storage state of the heat storage tank, respectively. and are the maximum and minimum values ​​of the battery charging power, respectively. and are the maximum and minimum values ​​of the battery discharge power, respectively. and are the minimum and maximum values ​​of the thermal storage power of the thermal storage tank, and are the minimum and maximum values ​​of the heat release power of the heat storage tank respectively.

[0097] Optionally, the electric, heating and cooling integrated demand response strategy includes: an electric load driving strategy and a cooling / heating load driving strategy;

[0098] The electric load driving strategy includes: based on the signed load reduction contract and the electric demand response model, issuing electric demand response instructions to users, so that users can cooperate with the electric demand response instructions to reduce the electric load within a certain electric load power regulation range and obtain benefits;

[0099] The electricity demand response model is:

[0100]

[0101] P dr,min ≤P dr,t ≤P dr,max (29)

[0102] In formulas (28) and (29), C dr,t is the compensation cost for demand response at time t, P dr,min and P dr,max are the lower and upper limits of the building power demand response power, respectively, dr and b dr They are the quadratic coefficient and the linear coefficient of the compensation cost for users participating in the incentive-based demand response;

[0103] And, the cooling / heating load driving strategy includes: during central heating / cooling, issuing cooling / heating load control instructions to users based on the cooling / heating demand response model, so that users can adjust the indoor temperature within a certain heating / cooling temperature range;

[0104] The cooling and heating demand response model is:

[0105]

[0106] T in,min ≤T in,t ≤T in,max (31)

[0107] In formulas (30) and (31), T in,t and T out,t are the indoor and outdoor temperatures at time t, T in,t+1 and T out,t+1 are the indoor and outdoor temperatures at time t+1, Δt is the time step, R is the equivalent thermal resistance of the building, C air is the heat capacity of the indoor air, T in,min and T in,max They are the upper and lower limits of indoor temperature control at time t respectively.

[0108] Optionally, the economic indicator for the purpose of reducing energy supply costs is:

[0109]

[0110]

[0111] In formulas (32) and (33), F 2,1 is the economic indicator of the second stage, C buy,t and C ope,t are the energy purchase cost and operation and maintenance cost at time t, μ grid,t and μ gas,t are the prices of electricity purchased from the power grid and gas purchased from the gas grid, respectively; K1, K2, K3, K4, K5 and K6 are the operating cost coefficients of photovoltaic, ground source heat pump, absorption chiller, battery and thermal storage tank respectively;

[0112] The energy efficiency indicators for improving energy utilization efficiency are:

[0113]

[0114]

[0115]

[0116] In formulas (34), (35), and (36), F 2,2 is the energy efficiency index of the second stage, R e,t and R h,t are the energy consumption and energy storage of electrical energy or thermal energy, S e,t and S h,t are the energy supply and energy release of electrical energy or thermal energy, respectively, e , h,t and λ c,t are the energy quality coefficients of electrical load, heating load and cooling load respectively, λ E , G,t and λ H,t are the energy quality coefficients of electricity, natural gas and hot water at the system end, T gas is the temperature of natural gas after complete combustion, T sup and T ret are the supply and return water temperatures of hot water respectively;

[0117] The total optimization index of the second stage is:

[0118]

[0119] In formula (37), ω 2,1 and ω 2,2 are weight factors, F 2,1,max is the maximum value of the economic index in the second stage, F 2,1,max It is the maximum value of the energy efficiency index in the second stage.

[0120] (III) Beneficial effects

[0121] The beneficial effects of the present invention are as follows: in the two-stage collaborative optimization process of the present invention, two optimized energy-consuming entities, electric vehicles and buildings, are taken into consideration, and both entities have dynamic adjustment mechanisms. Among them, the electric vehicle cluster uses V2B on the building side to achieve dynamic response of charging / discharging power, and the flexible parts of the cold, hot and electric loads during the operation of the building can achieve flexible response. The two stages not only fully tap the flexible response potential of different entities, but also realize the coordinated cooperation of multi-energy load responses. Therefore, the present invention improves the comprehensive benefits and energy efficiency of the multi-energy end, promotes environmental protection and carbon emission reduction, and enhances the security of energy supply through the proposed staged optimization strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 A schematic diagram of a two-stage overall scheduling process considering the comprehensive demand response of electric vehicles and / or buildings proposed in an embodiment of the present invention;

[0123] Figure 2 A schematic diagram of an electric vehicle demand response strategy for a phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings proposed in an embodiment of the present invention;

[0124] Figure 3 A schematic diagram of the composition of a combined cooling, heating and power system of a combined cooling, heating and power system in a phased collaborative optimization scheduling method considering electric vehicles and / or building comprehensive demand response proposed in an embodiment of the present invention;

[0125] Figure 4 A two-stage optimization process based on a particle swarm algorithm is proposed in an embodiment of the present invention, which takes into account the comprehensive demand response of electric vehicles and / or buildings. DETAILED DESCRIPTION

[0126] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0127] like Figure 1As shown, a method for phased collaborative optimization scheduling of a combined heat and power system considering comprehensive demand response of electric vehicles and / or buildings proposed in an embodiment of the present invention includes: in a first stage, with the optimization goal of reducing charging costs and superimposed load fluctuations of electric vehicles and buildings, according to the acquired building time-sharing power load and the simulated electric vehicle behavior characteristic model, the electric vehicle cluster is scheduled to participate in the power load demand response using V2B (vehicle-to-building bidirectional charging and discharging technology), and the electric vehicle time-sharing load is obtained and provided to the second stage; in the second stage, a combined heat and power system is constructed by introducing renewable energy, equipment coupling multi-energy supply and multi-energy storage configuration, and with the optimization goal of reducing costs and improving energy utilization efficiency, according to the acquired electric vehicle time-sharing load, the constructed combined heat and power system and the comprehensive building demand response model, the building time-sharing load is obtained and provided to the first stage; wherein, in both the first stage and the second stage, the loads after each optimized scheduling are used as parameters for mutual coordination and optimization of different stages by using intelligent algorithms, and the optimization results of each stage are obtained. Preferably, the optimization results specifically include: outputting electric vehicle load power curve, building load power curve, electric vehicle-building superimposed load power curve and building time-sharing cooling and heating load power curve, and proposing output planning for each device in the cogeneration system.

[0128] In order to simultaneously analyze the complex impact of electric vehicles and building load demand response on the system energy supply, the present invention proposes a two-stage scheduling scheme that considers the comprehensive benefits of multiple subjects of electric vehicle building-system, wherein the first stage considers the participation of electric vehicles and buildings and mainly takes the participation of electric vehicles in scheduling, and the second stage considers the coordinated scheduling between the two subjects of buildings and energy supply systems. Secondly, in the first stage, a strategy for realizing demand response by regulating the charging and discharging behavior of electric vehicles is proposed by using electric vehicle V2B, and different charging conditions dominated by multiple factors are considered in the strategy; in the second stage, a combined heating, cooling and power system considering multi-energy coupling and cogeneration is constructed, and a building heating, cooling and power demand response model is constructed; and, when optimizing the proposed two-stage scheduling method, the scheduled load is used as a parameter for mutual transmission to achieve mutual coordination between different stages.

[0129] Therefore, in the two-stage collaborative optimization process of the present invention, two optimized energy-consuming entities, electric vehicles and buildings, are considered, and both entities have dynamic adjustment mechanisms. Among them, the electric vehicle cluster uses V2B on the building side to realize the dynamic response of charging / discharging power, and the flexible parts of the cold, hot and electric loads during the operation of the building can achieve flexible response. The two stages not only fully tap the flexible response potential of different entities, but also realize the coordinated cooperation of multi-energy load responses. Therefore, the present invention improves the comprehensive benefits and energy efficiency of the multi-energy end through the proposed staged optimization strategy, promotes environmental protection and carbon emission reduction, and enhances the security of energy supply. Therefore, the present invention improves the comprehensive benefits and energy efficiency of the multi-energy end through the above-mentioned multi-stage optimization strategy, promotes environmental protection and carbon emission reduction, and enhances the security of energy supply.

[0130] In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0131] Specifically, an embodiment of the present invention provides a phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings, comprising:

[0132] S1. In the first stage, the optimization goal is to reduce the charging cost and the fluctuation of the superimposed load of electric vehicles and buildings. In the first optimization, the initial time-sharing power load of the building is used as the parameter input. In the subsequent optimization, the time-sharing power load of the building obtained in the second stage is used as the parameter input, and combined with the electric vehicle behavior characteristic model obtained by simulation, the electric vehicle cluster is dispatched to use V2B to participate in the power load demand response, and the electric vehicle time-sharing load is obtained and provided to the second stage.

[0133] Further, step S1 includes: S11, simulating the acquired statistical data by using the Monte Carlo method to obtain an electric vehicle behavior characteristic model; S12, constructing electric vehicle energy management items according to the electric vehicle travel energy consumption, the battery state of charge under spatial changes, and the battery state of charge changes under charging and discharging; S13, introducing economic indicators for the purpose of reducing charging costs and load indicators for the purpose of reducing the superimposed load fluctuations of electric vehicles and buildings, and unifying the dimensions of economic indicators and load indicators respectively, and using the weight coefficient method to obtain the first-stage total optimization indicators; S14, when scheduling the electric vehicle cluster V2B to interact with the building for electricity, according to the first-stage total optimization indicators and electric vehicle energy management items, construct an electric vehicle demand response strategy from the perspective of time and charging demand; S15, executing the electric vehicle demand response strategy, and outputting the superimposed electric vehicle-building power load curve; S16, according to the variables to be optimized and the electric vehicle behavior characteristic model obtained from the electric vehicle-building power load curve, optimizing through intelligent algorithms, and outputting the electric vehicle time-sharing load.

[0134] Furthermore, the electric vehicle behavior characteristic model includes an electric vehicle travel distance model, an electric vehicle arrival time model, and an electric vehicle departure time model, and the electric vehicle energy management items include an energy consumption factor model, the charge state of the electric vehicle when it arrives, the charge state of the electric vehicle when it leaves, the charge / discharge power limitation of the electric vehicle when it is charging / discharging, and the total electric power of the electric vehicle cluster.

[0135] The following is a detailed introduction to each electric vehicle behavior characteristic model and electric vehicle energy management item.

[0136] According to statistics, the travel distance of each electric vehicle during its travel follows a log-normal distribution, and the electric vehicle travel distance model is:

[0137]

[0138] In formula (1), f jou(s) is the probability density of travel distance, S is the travel distance of electric vehicles, in km; μ s and σ s are the expected and standard deviation of the travel distance of electric vehicles, which are 17.6 and 3.4 respectively.

[0139] The departure time and arrival time of electric vehicles follow a normal distribution, so the arrival time model of electric vehicles is:

[0140]

[0141] In formula (2), farr(t1) is the probability density of arrival time, t1 is the time when the electric vehicle arrives after the trip, and the unit is h, μ arr and σ arr are the expected and standard deviation of the electric vehicle arrival time, which are 17.6 and 3.4 respectively.

[0142] The electric vehicle departure time model is:

[0143]

[0144] In formula (3), f dep (t2) is the probability density of departure time, t2 is the time when the electric vehicle leaves, in h; μ dep and σ dep are the expected and standard deviation of the departure time of electric vehicles, which are 8.92 and 3.24 respectively.

[0145] When calculating the energy consumption of electric vehicles, the energy consumption factor model is obtained by considering the influence of travel speed. The energy consumption factor model is:

[0146] W EV=0.21-0.001v+1.534 / v (4)

[0147] W n,sum =S n W EV (5)

[0148] In formulas (4) and (5), W EV is the travel energy consumption of electric vehicles, in kW / km; v is the average travel speed of electric vehicles, in km / h; S n is the travel distance of the nth electric car, in km; W n,sum is the travel power consumption of the nth electric car, in kW.

[0149] The state of charge of the electric vehicle upon arrival is:

[0150] SOC n.t =1-W n,sum / E n,cap (6)

[0151] In formula (6), SOC n,t is the charge state of the nth electric vehicle when it arrives at time t, in %; E n,cap is the capacity of the battery on the nth electric car, in kWh.

[0152] After the electric vehicle completes the dispatch, it needs to meet the power demand for the next trip and cannot exceed the battery capacity. Therefore, the charge state of the electric vehicle when it leaves is:

[0153] W n,sum / E n,cap ≤SOC n.le ≤1 (7)

[0154] In formula (7), SOC n,le is the state of charge of the nth electric vehicle when it leaves, in %.

[0155] When charging / discharging electric vehicles, the charging / discharging power is limited by:

[0156]

[0157] In formula (8), P dis,max and P cha,max They are the upper limit of charging power and the upper limit of discharging power when a single electric vehicle interacts with building electricity, both in kW; and P n,t They are the charging power, discharging power, building power interaction comprehensive power of the nth electric vehicle at time t and the electric power rate of the electric vehicle cluster at time t, all in kW; SOCn,t and SOC n,t+1 are the charge states of the battery of the nth electric car at time t and time t+1, both in %.

[0158] The total electric power of the electric vehicle cluster is:

[0159]

[0160] In formula (9), P t EV is the total electric power of the electric vehicle cluster at time t, in kW; N t 、N 1,t and N 2,t They are the number of electric vehicles participating in dispatching, the number of electric vehicles participating in charging, and the number of electric vehicles participating in discharging at time t, all in units of vehicle.

[0161] In the context of time-sharing electricity purchase and sales prices, economic factors are often one of the important factors that determine the driver's charging and discharging behavior. Therefore, the present invention comprehensively considers the cost of charging and the benefits of discharging for each electric vehicle, and proposes and optimizes economic indicators. The economic indicators for reducing charging costs are:

[0162]

[0163] In formula (10), F 1,1 is the economic index of the first stage, in yuan; T is the scheduling period, in hours; μ grid,t and μ sell,t They are respectively the purchase price and sales price of electricity from the power grid at time t, both in Yuan / kWh.

[0164] Considering that electric vehicles are controllable power loads, they have the ability to reduce the peak-to-valley difference of energy load during the power consumption process. Therefore, the present invention optimizes the load index by reducing the peak-to-valley difference of the total power load curve at the energy consumption end. The load index for the purpose of reducing the load fluctuation of electric vehicles and buildings is:

[0165]

[0166] In formula (11), F 1,2 is the load index of the first stage, in kW; P t and They are respectively the building power load at time t and the daily average value of the superimposed power load of electric vehicles and buildings, both in kW.

[0167] For the convenience of calculation, the optimization indicators in the first stage are unified in dimension, and the weight coefficient method is used to construct the multi-objective function into a single objective function. The two optimization indicators proposed in the first stage are both for seeking the minimum value. The two indicators need to be scaled separately and then added according to their respective weights. The total optimization indicator in the first stage is:

[0168]

[0169] In formula (12), ω 1,1 and ω 1,2 are weight factors, and their values ​​are all 0.5; Obj1 is the overall objective function of the first stage, F 1,1,max is the maximum economic indicator value in the first stage, in yuan; F 1,2,max It is the maximum load index value of the first stage, in kW.

[0170] Furthermore, considering that electric vehicles have the characteristics of mobile energy storage, they can interact with buildings in cluster V2B when participating in scheduling, and build an electric vehicle demand response model from the perspective of time and charging demand. The Monte Carlo method is used to simulate the travel characteristics of electric vehicles, considering various situations in the charging process, and reasonably arranging the size of electric vehicle charging / discharging power in different situations, which not only improves the overall benefits, but also meets the needs of the next trip. Figure 2 When scheduling the electric vehicle cluster V2B to interact with the building, according to the total optimization index of the first stage and the electric vehicle energy management item, the electric vehicle demand response strategy is constructed from the perspective of time and charging demand, and the electric vehicle demand response strategy is executed. The output superimposed electric vehicle-building power load curve includes:

[0171] Compare the state of charge (SOC) of the electric vehicle when it arrives at time t n.t The lower limit of the state of charge (SOC) of the electric vehicle load battery min size.

[0172] If the electric vehicle arrives at the state of charge SOC n.t Less than or equal to SOC min , then the following cycle is performed: Calculate the capacity SOC of the vehicle battery when leaving n,le With the current capacity SOC n,t and calculate the difference between the current time t and the departure time t n,2 The average charging power at each moment in time is the interaction power P between the electric vehicle and the building at time t. n,t Assign the value to the average charging power of the electric vehicle obtained, and reassign the cycle time variable t by adding 1 to the cycle time variable t until t is not less than the departure time t n,2 End the loop.

[0173] If the electric vehicle arrives at the state of charge SOC n.t Greater than SOC min , calculate the minimum charging period T at the maximum charging rate cha,min , and determine the departure time t n,2 The difference between the current time t and the minimum charging time T cha,min .

[0174] At departure time t n,2 The difference with the current time t is greater than the minimum charging time T cha,min When t, the following cycle is performed: the interaction power P between the electric vehicle and the building at time t is n,t Randomly select a value within the upper limit of the electric vehicle's charge / discharge power and pre-determine the departure time t n,2 The difference between the next time t+1 and T cha,min +1 The size of the time period; if you leave at time t n,2 The difference with the next time t+1 is not less than T cha,min +1 time period, the loop time t plus 1 is reassigned to the loop time t variable; if the time t is left n,2 The difference with the next time t+1 is less than T cha,min +1, the interaction power P between the electric vehicle and the building at time t will be returned. n,t Randomly select a value within the upper limit of the charging and discharging power of the electric vehicle, and re-judge the pre-judgment departure time t n,2 The difference between the next time t+1 and T cha,min +1 time period, until the current t is not less than the departure time t n,2 End the loop.

[0175] At departure time t n,2 The difference with the current time t is not greater than the minimum charging time T cha,min When the interaction power P between the electric vehicle and the building at time t is n,t The upper limit of electric vehicle charging power P cha,max The loop time t plus 1 is reassigned to the loop time t variable until the current t is not less than the departure time t n,2 End the loop.

[0176] S2. In the second stage, a combined heat, power and cooling system is constructed by introducing renewable energy, equipment coupling multi-energy supply and diversified energy storage configuration, and reducing costs and improving energy efficiency are optimized. According to the obtained electric vehicle time-sharing load, the constructed combined heat, power and cooling system and the comprehensive building demand response model, the building time-sharing load is obtained and provided to the first stage.

[0177] Further, step S2 includes: S21, based on the energy demand and cooling and heating load of the energy-consuming end including electric vehicles and / or buildings, construct a combined heat and power system that introduces two types of energy, solar energy and geothermal energy, uses gas turbines as multi-energy coupling and cogeneration equipment, and sets up multi-energy storage including batteries and heat storage tanks; S22, introduces economic indicators for the purpose of reducing energy supply costs and energy efficiency indicators for improving energy utilization efficiency, and unifies the dimensions of economic indicators and energy efficiency indicators respectively, and uses the weight coefficient method to obtain the second-stage total optimization index through difference processing; S23, according to the second-stage total optimization index and the incentive-based demand response method, from the user Construct an integrated electricity, heating and cooling demand response strategy from the perspective of the electric load and the heating and cooling load at the energy end; S24. Based on the cogeneration system and the integrated electricity, heating and cooling demand response strategy, the introduction of renewable energy in the system energy supply process, the coupling of multi-energy supply and diversified energy storage configuration of equipment are used to meet the various energy needs of the building, and some flexible loads in the building energy use process are used to cooperate with the system to participate in the dynamic response of the load to optimize the building's multi-energy load curve. Combined with the dual benefit factors of economy and energy efficiency, an intelligent algorithm is used to optimize the actual time-sharing load of the building and the operation planning information of the energy system, and the actual time-sharing electric load of the building is provided to the first stage.

[0178] refer to Figure 3 It can be seen that the combined heat, cooling and power system includes: a multi-energy input part, a multi-energy production part and a multi-energy conversion and storage part; the multi-energy input part provides multi-energy input including solar energy, power grid, gas grid and geothermal energy; the multi-energy production part uses production components including photovoltaic panels, gas turbines and ground source heat pumps to process and produce the input multi-energy; the multi-energy conversion and storage part uses waste heat recovery devices and absorption refrigerators to realize the conversion of hot and cold energy, and sets batteries and heat storage tanks to improve the system's energy regulation ability; among them, the energy consumption end including electric vehicles and buildings considers the demand for heating, cooling and electricity loads, the heating and cooling loads of buildings include rigid loads and flexible loads, and the electricity load of buildings includes the total electricity load after the first stage of scheduling and at least part of the flexible electricity load.

[0179] In a specific embodiment, the present invention establishes a combined heat and power system to meet the energy needs of users, taking into account the actual energy consumption characteristics of urban building users. The combined power system mainly consists of three parts: multi-energy input, multi-energy production, and multi-energy conversion and storage. In the multi-energy input stage, the two types of renewable resources, solar energy and geothermal energy, of the building are fully utilized, and the basic urban energy supply network composed of power grid and gas grid is introduced to provide the system with initial energy; in the multi-energy production stage, photovoltaic panels, gas turbines and ground source heat pumps are used to further process and produce on the basis of the initial energy; in the multi-energy conversion and storage stage, waste heat recovery devices and absorption refrigerators are used to realize the conversion of hot and cold energy to realize direct supply to residents. At the same time, considering the balance of energy supply and demand, batteries and heat storage tanks are set to improve the energy regulation capacity of the system. The energy consumption end considers the demand for heat, electricity and cold loads, where the heat and cold loads of the building are composed of rigid loads and flexible loads, and the electric load of the building is composed of the total power load after the first stage of scheduling and part of the flexible power load.

[0180] Next, we build the following device model:

[0181] The gas turbine model is:

[0182] P t GT =η GT,e P gas,t (13)

[0183]

[0184] In formulas (13) and (14), P t GT , are the electrical power and thermal power output of the gas turbine at time t, in kW; η GT,e , η GT,e / h are the power generation efficiency and thermal power ratio of the gas turbine, P gas,t is the gas turbine power consumption at time t, in kW.

[0185] The model of the waste heat recovery device is:

[0186]

[0187] In formula (15), is the thermal power of the waste heat recovery device at time t, in kW; η HR is the waste heat recovery efficiency of the waste heat recovery device, in %.

[0188] The model of the photovoltaic panel is:

[0189]

[0190] In formula (166), is the maximum available photovoltaic output power at time t, in kW; η PV is the photovoltaic power generation efficiency, A is the area of ​​the photovoltaic panel, in m 2 ; Φ t is the radiation intensity at time t, in W / m 2 .

[0191] The model of the battery component is:

[0192]

[0193] In formula (17), SOC t 、SO Ct-1 are the state of charge of the battery at time t and time t-1, both in %; η Es,c , η Es,d They are the charging efficiency and discharging efficiency of the battery, both in %; E0 is the rated battery capacity of the battery, in kWh; and They are the charging power and discharging power of the battery at time t, both in kW.

[0194] The model of the heat storage tank is:

[0195]

[0196] In formula (18), H SSt , H SSt-1 are the state of charge of the battery at time t and time t-1, both in %; η Hs,c , η Hs,d are the heat storage efficiency and heat release efficiency of the heat storage tank, both in %; H0 is the rated heat storage capacity of the heat storage tank, in kWh; and They are respectively the heat storage power of the heat storage tank and the heat release power of the heat storage tank at time t, both in kW.

[0197] The model of ground source heat pump is:

[0198]

[0199] 0≤μ1+μ2≤1 (20)

[0200] In formulas (19) and (20), P t GSHP is the power consumption of the ground source heat pump at time t, in kW; and are the heating power and cooling power of the ground source heat pump at time t, both in kW; COP h,GSHP and COPc,GSHP They are the coefficient of performance of the ground source heat pump in the heating state and the coefficient of performance in the cooling state respectively. μ1 and μ2 are both 0-1 variables, indicating the switching state of the ground source heat pump heating and cooling.

[0201] The model of the absorption chiller is:

[0202]

[0203] In formula (21), is the cooling power output of the absorption chiller at time t, in kW; COP ABS is the coefficient of performance of the absorption refrigerator, in %; Q Ah,t It is the heat consumption of absorption chiller, in kW.

[0204] The power supply and demand balance constraint is:

[0205]

[0206] In formula (22), P t PV , P t GT , P t Es , P t EV and P t GSHP They are photovoltaic power, gas turbine power, battery power, electric vehicle cluster power and ground source heat pump power consumption at time t, all in kW; and are the charging power and discharging power of the battery, both in kW; P t , P rig,t and P dr,t are the building load at time t, the initial building load and the reduced building load power, all in kW; P grid,t It is the electric power input into the grid at time t, in kW.

[0207] The heat supply and demand balance constraint is:

[0208]

[0209] In formula (23), Q h,t and Q Ah,t They are the heat power of the waste heat recovery device, the heating power of the ground source heat pump, the central heating power of the cogeneration system and the heat power consumed by the absorption chiller, all in kW; and They are respectively the thermal power of the heat storage tank, the heat storage power of the heat storage tank and the heat release power of the heat storage tank, all in kW.

[0210] The cold energy supply and demand balance constraint is:

[0211]

[0212] In formula (24), and Q c,t They are respectively the cooling power of absorption chiller, the cooling power of ground source heat pump and the centralized cooling power of cogeneration system, all in kW.

[0213] The gas turbine constraints are:

[0214]

[0215] The photovoltaic power generation constraints are:

[0216]

[0217] The energy storage constraint is:

[0218]

[0219] In formula (27), SOC max , SOC min ,HSS max and HSS min They are the upper and lower limits of the battery state of charge and the upper and lower limits of the heat storage state of the heat storage tank, both in %; and They are the maximum and minimum values ​​of the battery charging power, both in kW; and They are the maximum and minimum values ​​of the battery discharge power, both in kW; and They are the minimum and maximum values ​​of the thermal storage power of the thermal storage tank, both in kW; and They are respectively the minimum and maximum values ​​of the heat release power of the heat storage tank, both in kW.

[0220] Then, the comprehensive demand response strategy for electric heating and cooling includes: from the perspective of electric load, residents of buildings are limited by the fixed characteristics of electricity consumption time, and often implement power regulation of electric load in the form of incentive-type demand response. This form is to sign a load reduction contract with residential electricity users, and then users need to cooperate with the dispatching instructions to reduce the power load within a certain range of electric load power regulation and obtain benefits. From the perspective of cooling / heating load, the size of heating / cooling power is directly controlled by the indoor temperature. During centralized heating / cooling, building users can cooperate with the load regulation needs and adjust the indoor temperature within a certain heating / cooling temperature range, which will not change the user's comfort on a large scale and can also achieve the effect of increasing or decreasing the heating / cooling load.

[0221] Therefore, the comprehensive demand response strategy for electricity, heating and cooling includes: electricity load driving strategy and cooling / heating load driving strategy.

[0222] Among them, the electric load driving strategy includes: based on the signed load reduction contract and the power demand response model, the power demand response instruction is issued to the user, so that the user can cooperate with the power demand response instruction to reduce the power load within a certain power load adjustment range and obtain benefits; the incentive type electric load signs a contract with the residential user, stipulates that the user cooperates with the load reduction within a specified time range, and gives the user corresponding compensation. Since the present invention involves a large number of users, household scheduling is relatively complex, and it will be uniformly scheduled in the form of a load aggregator. Among them, the power demand response model is:

[0223]

[0224] P dr,min ≤P dr,t ≤P dr,max (29)

[0225] In formulas (28) and (29), C dr,t is the compensation cost for demand response at time t, in yuan; P dr,t is the load power reduced at time t, in kW; a dr and b dr The quadratic coefficient and linear coefficient of the compensation cost for users participating in incentive-based demand response are 0.00002 and 0.5 respectively.

[0226] And, the cold / heat load driving strategy includes: during centralized heating / cooling, cold / heat load control instructions are issued to users based on the cold / heat demand response model, so that users can adjust the indoor temperature within a certain heating / cooling temperature range. Since the change of indoor temperature within a small range will not have a great impact on the user's comfort, the characteristics of interval temperature control can be fully utilized to change the energy consumption, and cooperate with the energy supply system to further achieve mutual coordinated optimization, which can not only reduce the peak-to-valley difference of the load curve, but also has great significance for improving the overall economic benefits. The equivalent thermal parameter model is often used to characterize that the indoor temperature of a building not only depends on the size of the air-conditioning heating / cooling power, but also has a strong correlation with the size of the outdoor temperature, building materials, energy supply time, etc. Among them, the cold and heat demand response model is:

[0227]

[0228] T in,min ≤T in,t ≤T in,max (31)

[0229] In formulas (30) and (31), T in,t and T out,t are the indoor and outdoor temperatures at time t, T in,t and T out,t are the indoor and outdoor temperatures at time t+1, in °C; Δt is the time step, in h; R is the equivalent thermal resistance of the building, C air is the heat capacity of indoor air, in kW·h / ℃; Q h,t and Q c,t are the central heating power and the central cooling power of the cogeneration system at time t, in kW; T in,min and T in，max They are the upper and lower limits of indoor temperature adjustment at time t respectively. According to the provisions of the "Design Code for Heating, Ventilation and Air Conditioning of Civil Buildings", when the thermal comfort level is met, the indoor heating temperature range in winter is 22-24°C, and the indoor cooling temperature range in summer is 24-26°C.

[0230] It is worth mentioning that the mathematical model of air conditioning heating / cooling power obtained by transforming formula (29) is as follows:

[0231]

[0232] The economic indicators for reducing energy supply costs are:

[0233]

[0234]

[0235] In formulas (32) and (33), F 2,1 is the economic indicator of the second stage, C buy,t and C ope,t , C dr,t They are respectively the cost of purchasing energy, the cost of operation and maintenance, and the compensation fee for demand response, in RMB. The cost of purchasing energy mainly includes the cost of purchasing electricity from the power grid and gas from the gas grid. The cost of operation and maintenance mainly includes the cost of operation and maintenance of photovoltaic, ground source heat pump, absorption chiller, battery, and heat storage tank during operation. grid，t and μ gas,t are the prices of electricity purchased from the power grid and gas purchased from the gas grid, K i (i=1,2…) are the operating cost coefficients of photovoltaic, ground source heat pump and absorption chiller respectively, and the unit is yuan / kW.

[0236] In order to achieve efficient use of energy, it is necessary to consider energy efficiency in order to deeply analyze the impact of comprehensive demand response on the energy efficiency of the system. However, the traditional method of analyzing and considering energy efficiency only considers the energy superposition method, which fails to reflect the difference in energy quality and work capacity. Therefore, the present invention introduces a method based on The energy efficiency indicators analyzed realize the comprehensive evaluation of energy in various links such as energy production, transportation, conversion and utilization. The energy efficiency indicators for improving energy utilization efficiency are:

[0237]

[0238]

[0239]

[0240] In formulas (34), (35), and (36), F 2,2 is the energy efficiency index of the second stage, L e,t He He L h,t , L c,t are the electricity, heating and cooling loads of the building at time t, in kW; R e,t and R h,t They are respectively the energy consumption and energy storage of electrical energy or thermal energy, both in kW; S e,t and S h,t are the energy supply and energy release of electrical energy or thermal energy, in kW; P grid,t is the electric power input to the grid at time t, in kW; e , h,t and λ c,t are the energy quality coefficients of electric load, heat load and cooling load respectively, where the energy quality coefficient of electric load is 1, and the energy quality coefficient of cooling and heating load is related to indoor and outdoor temperature; E ,G,t and λ H,t are the energy quality coefficients of electricity, natural gas and hot water at the system end, respectively. The energy quality coefficient of electricity is 1, the energy quality coefficient of natural gas is related to the outdoor temperature and combustion temperature, and the energy quality coefficient of hot water is related to the outdoor temperature and supply and return water temperatures; T out,t and T in , t are the outdoor and indoor temperatures at time t, in K; T gas is the temperature of natural gas after complete combustion, which is 1573.15K for the sake of simplicity; T sup and T ret They are the supply and return water temperatures of hot water respectively. The present invention refers to the optimal temperature design parameters in the radiator central heating solution using hot water as the heat medium, and takes the supply and return water temperatures as 348.15K and 323.15K respectively.

[0241] The calculation method of the second stage is the same as that of the first stage, so it will not be repeated here. It is worth noting that the objective function of the second stage has the situation of seeking the maximum value, so it needs to be processed by difference. Among them, the total optimization index of the second stage is:

[0242]

[0243] In formula (37), ω 2,1 and ω 2,2 are all weight factors. Theoretically, the weight factors are all between 0 and 1. In the present invention, the values ​​are all 0.5 during calculation. 2,1,max is the maximum value of the economic indicator in the second stage, in yuan; F 2,1,max It is the maximum value of the energy efficiency index of the second stage, in %.

[0244] Then, if Figure 4 As shown, the simulation process of this method is programmed based on the Matlab R2022a software environment. Since the optimization goal proposed in this invention involves the multiplication and division of multiple variables, the existing commonly used linear programming problem solvers are difficult to solve quickly. Considering that the particle swarm algorithm has strong applicability, simple algorithm, fast convergence speed, and is widely used in solving problems in the field of microgrids and virtual power plants, this case uses the particle swarm algorithm to solve the two-stage problem. The computer configuration used is AMD Ryzen 536006-Core six-core CPU, 16GB memory.

[0245] In addition, the method provided by the present invention can be applied to corresponding systems, devices and media. All systems, devices and media adopted by the methods of the above embodiments of the present invention belong to the scope of protection of the present invention.

[0246] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0247] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0248] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0249] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include these modifications and variations.

Claims

1. A phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings, characterized in that: include: In the first stage, the optimization goal is to reduce the charging cost and the fluctuation of the superimposed load of electric vehicles and buildings. In the first optimization, the initial time-sharing power load of the building is used as the parameter input. In the subsequent optimization, the time-sharing power load of the building obtained in the second stage is used as the parameter input. Combined with the electric vehicle behavior characteristic model obtained by simulation, the electric vehicle cluster is dispatched to participate in the electric load demand response using V2B, and the electric vehicle time-sharing load is obtained and provided to the second stage; In the second stage, a combined heat, cooling and power system is constructed by introducing renewable energy, equipment coupling multi-energy cogeneration and multi-energy storage configuration, and with the optimization goal of reducing costs and improving energy efficiency, the building time-sharing load is obtained based on the obtained electric vehicle time-sharing load, the constructed combined heat, cooling and power system and the building comprehensive demand response model, and the building time-sharing power load is provided to the first stage; In the first and second stages, intelligent algorithms are used to transfer the loads after optimization scheduling to each other so as to coordinate and optimize different stages and obtain the optimization results of each stage. In the first stage, the optimization goal is to reduce the charging cost and the fluctuation of the superimposed load of electric vehicles and buildings. In the first optimization, the initial time-sharing power load of the building is used as the parameter input. In the subsequent optimization, the time-sharing power load of the building obtained in the second stage is used as the parameter input. Combined with the electric vehicle behavior characteristic model obtained by simulation, the electric vehicle cluster is dispatched to participate in the electric load demand response using V2B, and the electric vehicle time-sharing load is obtained and provided to the second stage, including: By using the Monte Carlo method to simulate the acquired statistical data, the electric vehicle behavior characteristic model is obtained; Construct electric vehicle energy management items based on electric vehicle travel energy consumption, battery state of charge under spatial changes, and battery state of charge changes under charging and discharging; Introduce economic indicators for reducing charging costs and load indicators for reducing the fluctuation of electric vehicle and building superimposed loads, unify the dimensions of economic indicators and load indicators, and use the weight coefficient method to obtain the total optimization index of the first stage; When scheduling electric vehicle clusters to interact with buildings using V2B, an electric vehicle demand response strategy is constructed from the perspective of time and charging demand according to the first-stage total optimization index and electric vehicle energy management items; Execute electric vehicle demand response strategy and output superimposed electric vehicle-building power load curve; Based on the variables to be optimized and the electric vehicle behavior characteristic model obtained from the electric vehicle-building power load curve, the intelligent algorithm is used to optimize and output the electric vehicle time-sharing load; The economic indicators for reducing charging costs are: In formula (10), F 1,1 is the economic index of the first stage, T is the scheduling period, N 1,t is the number of electric vehicles involved in charging, N 2,t is the number of electric vehicles involved in discharge, The charging power of the nth electric car at time t, is the discharge power of the nth electric vehicle at time t and the interactive comprehensive power of the building power, μ grid,t and μ sell,t are the electricity purchase price and electricity sales price from the power grid at time t respectively; The load index for the purpose of reducing the fluctuation of the superimposed load of electric vehicles and buildings is: In formula (11), F 1,2 is the load index of the first stage, P t and are the daily average power of building load and the superimposed load of electric vehicles and buildings at time t, P t EV is the total electric power of the electric vehicle cluster at time t; The total optimization index of the first stage is: In formula (12), ω 1,1 and ω 1,2 are all weight factors, Obj1 is the total objective function of the first stage, F 1,1,max is the maximum economic index value in the first stage, F 1,2,max is the maximum load index value of the first stage; In the second stage, a combined heat, cooling and power system is constructed by introducing renewable energy, equipment coupling multi-energy supply and multi-energy storage configuration, and reducing costs and improving energy efficiency are optimized. According to the obtained electric vehicle time-sharing load, the constructed combined heat, cooling and power system and the building comprehensive demand response model, the building time-sharing load is obtained and provided to the first stage. The building time-sharing power load includes: According to the energy demand and heating and cooling load of the energy-consuming end including electric vehicles and / or buildings, a combined heating, cooling and power system is constructed that introduces solar energy and geothermal energy, uses gas turbines as multi-energy coupling equipment, and sets up multi-energy storage including batteries and heat storage tanks; Introduce economic indicators for reducing energy supply costs and energy efficiency indicators for improving energy utilization efficiency, unify the dimensions of economic indicators and energy efficiency indicators, and use the weight coefficient method to obtain the second-stage total optimization index through difference processing; According to the second phase total optimization index and incentive-based demand response method, a comprehensive demand response strategy for electricity, heating and cooling is constructed from the perspective of electricity load and cooling and heating load at the energy consumption end; Based on the combined heating, cooling and power system and the comprehensive demand response strategy of electricity, heating and cooling, the introduction of renewable energy in the system energy supply process, equipment coupling multi-energy cogeneration and multi-energy storage configuration are used to meet the various energy needs of the building. Some flexible loads in the building energy use process are used to cooperate with the system to participate in the dynamic response of the load to optimize the building multi-energy load curve. Combining the dual benefit factors of economy and energy efficiency, an intelligent algorithm is used to optimize the actual time-sharing load of the building and the operation planning information of the energy system, and the actual time-sharing electric load of the building is provided to the first stage; The economic indicators for reducing energy supply costs are: In formulas (32) and (33), F 2,1 is the economic indicator of the second stage, C buy,t and C ope,t are the energy purchase cost and operation and maintenance cost at time t, C dr,t is the compensation cost for demand response at time t, μ grid,t and μ gas,t are the prices of electricity purchased from the power grid and gas purchased from the gas grid, P grid,t is the electric power input from the grid at time t, P gas,t is the gas turbine power consumption at time t, K1, K2, K3, K4, K5 and K6 are the operating cost coefficients of photovoltaic, ground source heat pump, absorption chiller, gas turbine, battery and heat storage tank respectively, P t pv is the photovoltaic power at time t, is the heating power of the ground source heat pump at time t Or the cooling power of the ground source heat pump at time t is the cooling power output of the absorption refrigerator at time t, P t GT is the electrical power output of the gas turbine at time t, P t Es is the battery power at time t, is the thermal power of the heat storage tank; The energy efficiency indicators for improving energy utilization efficiency are: In formulas (34), (35), and (36), F 2,2 is the energy efficiency index of the second stage, R e,t is the energy consumption and energy storage of electric energy, R h,t is the energy consumption and energy storage of thermal energy, Q c,t is the central cooling power of the cogeneration system, S e,t is the energy supply and energy release of electric energy, S h,t is the energy supply and energy release of thermal energy, λ e , h,t and λ c,t are the energy quality coefficients of electrical load, heating load and cooling load respectively, λ E , G,t and λ H,t are the energy quality coefficients of electricity, natural gas and hot water at the system end, η Es,c is the charging efficiency of the battery at time t, is the charging power of the battery, Q h,t is the central heating power of the cogeneration system, η Hs,c is the heat storage efficiency of the heat storage tank, is the heat storage power of the heat storage tank, is the discharge power of the battery, η Es,d is the discharge efficiency of the battery, is the heat release power of the heat storage tank, η Hs,d is the heat release efficiency of the heat storage tank, T in,t and T out,t are the indoor and outdoor temperatures at time t, T gas is the temperature of natural gas after complete combustion, T sup and T ret are the supply and return water temperatures of hot water respectively; The total optimization index of the second stage is: In formula (37), ω 2,1 and ω 2,2 are all weight factors, Obj2 is the total objective function of the second stage, F 2,1,max is the maximum value of the economic index in the second stage, F 2,2,max It is the maximum value of the energy efficiency index in the second stage.

2. The phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings as claimed in claim 1 is characterized in that: The electric vehicle behavior characteristic model includes the electric vehicle travel distance model, the electric vehicle arrival time model and the electric vehicle departure time model. The electric vehicle energy management items include the energy consumption factor model, the charge state of the electric vehicle when it arrives, the charge state of the electric vehicle when it leaves, the charge / discharge power limit of the electric vehicle when it is charged / discharged, and the total power of the electric vehicle cluster. The travel distance model of electric vehicles is: In formula (1), f jou (s) is the probability density of the travel distance, s is the travel distance of the electric vehicle, μ s and σ s are the expected and standard deviation of the travel distance of electric vehicles, respectively; The electric vehicle arrival time model is: In formula (2), f arr (t1) is the probability density of arrival time, t1 is the time when the electric vehicle arrives after the trip, μ arr and σ arr are the expected and standard deviation of the electric vehicle arrival time, respectively; The electric vehicle departure time model is: In formula (3), f dep (t2) is the probability density of departure time, t2 is the time when the electric vehicle leaves, μ dep and σ dep are the expected and standard deviation of the departure time of electric vehicles, respectively; The energy consumption factor model is: W EV =0.21-0.001v+1.534 / v (4) W n,sum =S n W EV (5) In formulas (4) and (5), W EV is the energy consumption per kilometer of electric vehicle travel, v is the average travel speed of electric vehicle, S n is the travel distance of the nth electric car, W n,sum is the travel power consumption of the nth electric car; The state of charge of the electric vehicle upon arrival is: SOC n.t =1-W n,sum / E n,cap (6) In formula (6), SOC n,t is the charge state of the nth electric car when it arrives at time t, E n,cap is the capacity of the onboard battery of the nth electric vehicle; The state of charge constraint of the electric vehicle when leaving is: W n,sum / HAVE BEEN n,cap ≤SOC n.le ≤1 (7) In formula (7), SOC n,le is the state of charge of the nth electric vehicle when it leaves; When charging / discharging electric vehicles, the charging / discharging power is limited by: In formula (8), P dis,max and P cha,max are the upper limit of charging power and the upper limit of discharging power when a single electric vehicle interacts with building power, P n,t is the electric vehicle cluster power rate at time t, SOC n,t+1 is the charge state of the nth electric car at time t+1; The total electric power of the electric vehicle cluster is: In formula (9), N t is the number of electric vehicles participating in the dispatch at time t.

3. The phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings as claimed in claim 1 is characterized in that: Execute the electric vehicle demand response strategy, and output the superimposed electric vehicle-building power load curve including: Compare the state of charge (SOC) of the electric vehicle when it arrives at time t n.t The lower limit of the state of charge (SOC) of the electric vehicle load battery min size; If the electric vehicle arrives at the state of charge SOC n.t Less than or equal to SOC min , then the following cycle is performed: Calculate the capacity SOC of the vehicle battery when leaving n,le With the current capacity SOC n,t and calculate the difference between the current time t and the departure time t n,2 The average charging power at each moment in time is the interaction power P between the electric vehicle and the building at time t. n,t Assign the value to the average charging power of the electric vehicle obtained, and reassign the cycle time variable t by adding 1 to the cycle time variable t until t is not less than the departure time t n,2 End the loop; If the electric vehicle arrives at the state of charge SOC n.t Greater than SOC min , calculate the minimum charging period T at the maximum charging rate cha,min , and determine the departure time t n,2 The difference between the current time t and the minimum charging time T cha,min size; At departure time t n,2 The difference from the current time t is greater than T cha,min When t, the following cycle is performed: the interaction power P between the electric vehicle and the building at time t is n,t Randomly select a value within the upper limit of the electric vehicle's charge / discharge power and pre-determine the departure time t n,2 The difference between the next time t+1 and T cha,min +1 The size of the time period; if you leave at time t n,2 The difference with the next time t+1 is not less than T cha,min +1 time period, the loop time t plus 1 is reassigned to the loop time t variable; if the time t is left n,2 The difference with the next time t+1 is less than T cha,min +1, the interaction power P between the electric vehicle and the building at time t will be returned. n,t Randomly select a value within the upper limit of the charging and discharging power of the electric vehicle, and re-judge the pre-judgment departure time t n,2 The difference between the next time t+1 and T cha,min +1 time period, until the current t is not less than the departure time t n,2 End the loop; At departure time t n,2 The difference with the current time t is not greater than the minimum charging time T cha,min When the interaction power P between the electric vehicle and the building at time t is n,t The upper limit of electric vehicle charging power P cha,max The loop time t plus 1 is reassigned to the loop time t variable until the current t is not less than the departure time t n,2 End the loop; Determine the relationship between the number of dispatched electric vehicles n and the total number of electric vehicles N. If n>N, superimpose the dispatched electric vehicle time-sharing load and the building time-sharing load, and finally output the superimposed electric vehicle-building power load curve.

4. The phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings as claimed in claim 1 is characterized in that: The combined cooling, heating and power system includes: a multi-energy input part, a multi-energy production part, and a multi-energy conversion and storage part; The multi-energy input part provides multi-energy input including solar energy, power grid, gas grid and geothermal energy; The multi-energy production part uses production components including photovoltaic panels, gas turbines and ground-source heat pumps to process and produce input multi-energy; The multi-energy conversion and storage part uses waste heat recovery devices and absorption refrigerators to achieve the conversion of hot and cold energy, and sets up batteries and heat storage tanks to improve the system's energy regulation capabilities; Among them, the energy consumption end including electric vehicles and buildings considers the demand for heating, cooling and electricity loads. The heating and cooling loads of buildings include rigid loads and flexible loads. The electricity load of buildings includes the total electricity load after the first stage scheduling and at least part of the flexible electricity load.

5. The phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings as claimed in claim 4 is characterized in that: The equipment model of the cogeneration system includes the gas turbine model, the waste heat recovery device model, the photovoltaic panel model, the battery assembly model, the heat storage tank model, the ground source heat pump model and the absorption chiller model; the equipment constraints of the cogeneration system include: power supply and demand balance constraints, heat supply and demand balance constraints, cooling supply and demand balance constraints, gas turbine constraints, photovoltaic power generation constraints and energy storage constraints; The gas turbine model is: In formulas (13) and (14), is the thermal power output of the gas turbine at time t, η GT,e , η GT,e / h are the power generation efficiency and thermal-to-electric power ratio of the gas turbine, respectively; The model of the waste heat recovery device is: In formula (15), is the thermal power of the waste heat recovery device at time t, η HR is the waste heat recovery efficiency of the waste heat recovery device; The model of the photovoltaic panel is: In formula (16), is the available photovoltaic power at time t, η PV is the photovoltaic power generation efficiency, A is the area of ​​the photovoltaic panel, Φ t is the radiation intensity; The model of the battery component is: In formula (17), SOC t and SOC t-1 are the state of charge of the battery at time t and the state of charge of the battery at time t-1, respectively, and E0 is the rated battery capacity of the battery; The model of the heat storage tank is: In formula (18), HSS t and HSS t-1 are the heat storage state of the heat storage tank at time t and the heat storage state of the heat storage tank at time t-1, respectively, and H0 is the rated heat storage capacity of the heat storage tank; The model of ground source heat pump is: 0≤μ1+μ2≤1 (20) In formulas (19) and (20), P t GSHP is the power consumption of the ground source heat pump at time t, COP h,GSHP and COP c,GSHP They are the coefficient of performance of the ground source heat pump in the heating state and the coefficient of performance in the cooling state, respectively. μ1 and μ2 are both 0-1 variables, indicating the switching state of the ground source heat pump heating and cooling; The model of the absorption chiller is: In formula (21), COP ABS is the coefficient of performance of the absorption refrigerator, Q Ah,t is the heat consumption power of the absorption refrigerator; The power supply and demand balance constraint is: In formula (22), P rig,t is the initial electrical load of the building at time t, P dr,t is the building load power reduced at time t; The heat supply and demand balance constraint is: The cold energy supply and demand balance constraint is: The gas turbine constraints are: In formula (25), and are the lower and upper limits of gas turbine output respectively; The photovoltaic power generation constraints are: The energy storage constraint is: In formula (27), SOC max , SOC min ,HSS max and HSS min are the upper and lower limits of the battery state of charge and the upper and lower limits of the heat storage state of the heat storage tank, respectively. and are the minimum and maximum values ​​of the battery charging power, respectively. and are the minimum and maximum values ​​of the battery discharge power, respectively. and are the minimum and maximum values ​​of the thermal storage power of the thermal storage tank, and are the minimum and maximum values ​​of the heat release power of the heat storage tank respectively.

6. The phased collaborative optimization scheduling method for a combined cooling, heating and power system considering the comprehensive demand response of electric vehicles and / or buildings as claimed in claim 5 is characterized in that: The comprehensive demand response strategy for electricity, heating and cooling includes: electricity load driving strategy and cooling / heating load driving strategy; The electric load driving strategy includes: based on the signed load reduction contract and the electric demand response model, issuing electric demand response instructions to users, so that users can cooperate with the electric demand response instructions to reduce the electric load within a certain electric load power regulation range and obtain benefits; The electricity demand response model is: P dr,min ≤P dr,t ≤P dr,max (29) In formulas (28) and (29), P dr,min and P dr,max are the lower and upper limits of the building power demand response power, respectively, dr and b dr They are the quadratic coefficient and the linear coefficient of the compensation cost for users participating in the incentive-based demand response; And, the cooling / heating load driving strategy includes: during central heating / cooling, issuing cooling / heating load control instructions to users based on the cooling / heating demand response model, so that users can adjust the indoor temperature within a certain heating / cooling temperature range; The cooling and heating demand response model is: T in,min ≤T in,t ≤T in,max (31) In formulas (30) and (31), T in,t and T out,t are the indoor and outdoor temperatures at time t, T in,t+1 and T out,t+1 are the indoor and outdoor temperatures at time t+1, Δt is the time step, R is the equivalent thermal resistance of the building, C air is the heat capacity of the indoor air, T in,min and T in,max They are the lower limit and upper limit of indoor temperature control at time t respectively.

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