Day-ahead control method and device for electric-water-heat integrated energy system considering virtual energy storage

Abstract

The invention discloses a day-ahead control method and device for an electric-water-heat comprehensive energy system considering virtual energy storage, and the method comprises the steps: building an equipment basic model composed of a water pump model, a water storage tank model, a hot water load model, a solar heat collector model, a heat storage tank model, an air source heat pump model, and a photovoltaic model; the VES-I model has a water storage buffering characteristic, the VES-II model has a hot water storage buffering characteristic, and the expanded energy hub EEH model integrates energy flow and material flow; and establishing a day-ahead optimization control model of the electric-water-heat integrated energy system considering virtual energy storage, and further performing optimization according to the requirements of electricity, heat and water loads. The device comprises a processor and a memory. The system effectively balances the supply and demand of renewable energy sources, improves the utilization efficiency of the renewable energy sources, and improves the stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the field of electric, hydro and thermal integrated energy systems, and in particular to a day-ahead control method and device for an electric, hydro and thermal integrated energy system taking into account virtual energy storage. Background Art

[0002] Integrated Energy Systems (IES) are a key approach to achieving multi-energy complementarity and increasing the utilization of renewable energy. They have become a crucial component of building a low-carbon, high-efficiency energy system. Among the various IES energy supply models, the Electricity-Water-Hot Water Integrated Energy System (EWH-IES) typically consists of photovoltaic, solar thermal, water supply, and hot water systems. Through the organic coordination of electricity, water, and electricity, and utilizing photovoltaic power generation and solar thermal collection technologies, users can achieve the combined supply of electricity, water, and hot water. By considering the virtual energy storage effect brought about by the conversion and storage of energy and material flows between electricity, water, and electricity, and between electricity and heat, the optimization control method effectively improves the operational flexibility and renewable energy absorption capacity of EWH-IES.

[0003] With the increasing energy consumption for water and hot water supply and the increasing utilization of solar energy, the coupling between electricity, water, and heat is becoming increasingly tight. However, the uncertainties of photovoltaic and solar thermal systems pose challenges to the operational flexibility of EWH-IES. Numerous studies have been conducted to analyze the inter-coupling relationships between electricity, water, and heat. Many of these studies have considered the role of electric energy storage devices in accommodating renewable energy and enhancing flexibility. However, the current high installation and maintenance costs of electric energy storage and the large space required make its deployment not necessarily the optimal solution. Therefore, to fully leverage the advantages of EWH-IES, there is an urgent need to find an equivalent energy storage method that balances flexibility and universality.

[0004] In recent years, the use of virtual energy storage (VES) technology to exploit the synergistic flexibility of multiple energy sources and enhance the renewable energy absorption capacity of energy systems has attracted extensive research. However, most studies have used water as a carrier to study the complementary relationship between electricity and water or electricity and heat, and have used a single VES model as the target for optimization. This fails to fully consider the synergistic effects of multiple VESs that simultaneously include electricity, water, and heat in optimization control. Furthermore, while existing electric-heat VESs have primarily modeled the time-delay characteristics of heating networks, there is a lack of research on quantifying hot water VESs from the perspective of energy storage. Summary of the Invention

[0005] This invention provides a day-ahead optimization control method for an electric, water, and heat integrated energy system that takes virtual energy storage into account. This method addresses the problem that traditional methods use a single VES model as the subject for optimization control, failing to fully consider the synergistic effects of multiple VESs that simultaneously include electricity, water, and heat in optimization control. It also addresses the lack of quantified research on hot water-type VESs from an energy storage perspective in traditional research. Details are described below:

[0006] In a first aspect, a method for day-ahead control of an electric, hydro and thermal integrated energy system taking into account virtual energy storage is provided, the method comprising:

[0007] Establish a basic equipment model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a thermal storage tank model, an air source heat pump model, and a photovoltaic model; a VES-Ι model with water storage buffer characteristics; a VES-Ι model with hot water storage buffer characteristics; and an extended energy hub EEH model that integrates energy flow and material flow;

[0008] Based on the aforementioned equipment basic model, VES-I model, VES-II model, and EEH model, a day-ahead optimization control model for the electric, hydrothermal, and integrated energy systems, including virtual energy storage, is established.

[0009] The demands for electricity, heat and water loads are optimized based on the day-ahead optimization control model.

[0010] The day-ahead optimization control model is:

[0011] Objective function:

[0012] min f=C e +C PV +C SC

[0013] Where C e is the electricity purchase cost; C PV Penalty cost for abandoning PV; C SC Penalty costs for abandoned solar and thermal power;

[0014] C e calculate:

[0015]

[0016] Where p t is the electricity price during period t;

[0017] C PV calculate:

[0018]

[0019] Where p PV is the penalty factor;

[0020] C SC calculate:

[0021]

[0022] Where p SC is the penalty factor.

[0023] The constraints of the day-ahead optimization control model are:

[0024] 1) System energy flow and material flow balance constraints:

[0025]

[0026] in, is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; is the reference power of the water pump at time t; is the charge and discharge power of VES-1; is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump;

[0027] 2) VCDP Constraints:

[0028]

[0029] in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II;

[0030] 3) VSOC constraints:

[0031] 0≤VSOC1 t ≤1

[0032] 0≤VSOC 2,t ≤1

[0033] Among them, VSOC1 t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t is the ratio of the actual storage capacity of VES-II to that of VEC-II;

[0034] 4) Maximum output constraints for photovoltaic and solar thermal power:

[0035]

[0036] in, are the maximum output powers of photovoltaic and solar thermal, respectively.

[0037] The demand for electricity, heat and water loads is optimized based on the day-ahead optimization control model as follows:

[0038] Solve the day-ahead optimization control model to obtain the purchased power, photovoltaic output power, solar thermal output power, VES-1 virtual charge and discharge power, and VES-11 charge and discharge power for the entire period;

[0039] Based on the purchased electricity power, photovoltaic output power, solar thermal output power, VES-Ι virtual charge and discharge power, and VES-ΙΙ charge and discharge power during the entire period, the EWH-IES scheduling plan is corrected and adjusted to guide the operation of EWH-IES the next day.

[0040] In a second aspect, a day-ahead control device for an electric, hydrothermal integrated energy system taking into account virtual energy storage is provided, the device comprising:

[0041] The first modeling module is used to establish an equipment basic model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a heat storage tank model, an air source heat pump model, and a photovoltaic model; a VES-I model with water storage buffer characteristics, a VES-II model with hot water storage buffer characteristics, and an extended energy hub EEH model that integrates energy flow and material flow;

[0042] A second modeling module is used to establish a day-ahead optimization control model of the electric, hydrothermal integrated energy system taking into account virtual energy storage based on the first modeling module;

[0043] Optimization module is used to optimize the demand for electricity, heat and water loads.

[0044] Wherein, the optimization module includes:

[0045] The acquisition submodule is used to solve the day-ahead optimization control model to obtain the purchased power, photovoltaic output power, solar thermal output power, VES-1 virtual charge and discharge power, and VES-11 charge and discharge power for the entire period;

[0046] The correction and adjustment submodule is used to correct and adjust the EWH-IES scheduling plan based on the purchased power, photovoltaic output power, solar thermal output power, VES-Ι virtual charge and discharge power, and VES-ΙΙ charge and discharge power of the entire period, and guide the operation of EWH-IES the next day.

[0047] The day-ahead optimization control model is:

[0048] Objective function:

[0049] min f=C e +C PV +C SC

[0050] Where C e is the electricity purchase cost; C PV Penalty cost for abandoning PV; C SC Penalty costs for abandoned solar and thermal power;

[0051] C e calculate:

[0052]

[0053] Where p t is the electricity price during period t;

[0054] C PV calculate:

[0055]

[0056] Where p PV is the penalty factor;

[0057] C SC calculate:

[0058]

[0059] Where p SC is the penalty factor.

[0060] The constraints of the day-ahead optimization control model are:

[0061] 1) System energy flow and material flow balance constraints:

[0062]

[0063] in, is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; is the reference power of the water pump at time t; is the charge and discharge power of VES-1; is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump;

[0064] 2) VCDP Constraints:

[0065]

[0066] in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II;

[0067] 3) VSOC constraints:

[0068]

[0069] Among them, VSOC1 t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t is the ratio of the actual storage capacity of VES-II to that of VEC-II;

[0070] 4) Maximum output constraints for photovoltaic and solar thermal power:

[0071]

[0072] in, are the maximum output powers of photovoltaic and solar thermal, respectively.

[0073] In the third aspect, a day-ahead control device for an electric, hydrothermal integrated energy system taking into account virtual energy storage is provided, the device comprising: a processor and a memory, the memory storing program instructions, the processor calling the program instructions stored in the memory to enable the device to execute any one of the method steps described in the first aspect.

[0074] In a fourth aspect, a computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes any one of the method steps described in the first aspect.

[0075] The beneficial effects of the technical solution provided by the present invention are:

[0076] 1. Based on the electricity-water-heat coupling relationship in EWH-IES, this paper establishes a VES model for the water supply system and the hot water system. By storing excess energy and releasing it when needed, it smoothes the load fluctuations of the system, effectively balances the supply and demand of renewable energy, improves its utilization efficiency, and thus enhances the stability and reliability of the system.

[0077] 2. This invention integrates the VES model into the day-ahead optimization control method of the EWH-IES. While ensuring the user's electricity, water, and hot water needs, it can improve the operational flexibility of the EWH-IES, increase the photovoltaic absorption rate and solar energy guarantee rate, and thus enhance the environmental friendliness of the EWH-IES. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Schematic diagram of the structure of the electricity-water-heat integrated energy system (EWH-IES) of the present invention;

[0079] Figure 2 EEH model structure diagram of EWH-IES of the present invention;

[0080] Figure 3 Schematic diagram of the maximum output power of the EWH-IES under a typical day with water load;

[0081] Figure 4 The maximum output power curve of the EWH-IES under a typical day and the heat load at standard temperature is shown in FIG.

[0082] Figure 5 This is a graph showing the maximum photovoltaic and solar thermal output power under a typical day for the EWH-IES of the present invention;

[0083] Figure 6 This is a schematic diagram of the power purchase for scenarios 1 to 4 of the present invention;

[0084] Figure 7 Schematic diagram of actual photovoltaic output power for scenarios 1 to 4 of the present invention;

[0085] Figure 8 Schematic diagram of the actual thermal power output of photothermal energy in scenarios 1 to 4 of the present invention;

[0086] Figure 9 Schematic diagram of water storage capacity for scenarios 1, 2, and 4 of the present invention;

[0087] Figure 10 Schematic diagram of hot water temperature for scenarios 1, 3, and 4 of the present invention;

[0088] Figure 11 VSOC comparison diagram of scenarios 2 to 4 of the present invention;

[0089] Figure 12 This is a schematic diagram of the structure of a day-ahead control device for an electric, water and heat integrated energy system taking into account virtual energy storage;

[0090] Figure 13 This is a schematic diagram of the structure of the optimization module;

[0091] Figure 14 This is another structural schematic diagram of a day-ahead control device for an electric, hydrothermal integrated energy system taking into account virtual energy storage. DETAILED DESCRIPTION

[0092] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0093] To fully leverage the flexibility and synergistic potential of the water supply and hot water systems and improve the level of renewable energy consumption, an embodiment of the present invention proposes an EWH-IES day-ahead optimization control method that takes VES into account. Based on the water storage characteristics of the water supply system and the hot water storage characteristics of the hot water system, two types of VES models for the electricity-water and electricity-heat sectors in the EWH-IES were established. Three virtual energy storage parameters, namely virtual charge and discharge power, virtual capacity, and virtual state of charge, were defined to explore the water and hot water storage capacity to participate in the optimized operation of the EWH-IES. Furthermore, an extended energy hub model that integrates the VES model and couples energy and material flows was established to describe the mathematical relationship between the conversion of energy and material flows in the EWH-IES. By utilizing the flexibility of the two types of VES, the optimized operation of the EWH-IES is jointly supported.

[0094] Example 1

[0095] Based on the above research, the embodiment of the present invention further studies a day-ahead optimization control method for an electric, hydrothermal integrated energy system taking virtual energy storage into account based on the virtual energy storage theory. Figure 1 , the method comprises the following steps:

[0096] Step 1: Establish a basic equipment model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a thermal storage tank model, an air source heat pump model, and a photovoltaic model; a VES-I model with water storage buffer characteristics; a VES-II model with hot water storage buffer characteristics; and an extended energy hub EEH model that integrates energy flow and material flow;

[0097] Step 2: Establish a day-ahead optimization control model for the integrated electric, hydro, and thermal energy system taking into account virtual energy storage, and then optimize it based on the system's electricity, heat, and water load requirements.

[0098] The electric, water and heat integrated energy system studied in the embodiment of the present invention includes: photovoltaics (PV), solar collectors (SC), electric pumps (EP), heat pumps (HP), water tanks (WT), hot water storage tanks (ST) and electricity, water and hot water loads. The composition structure is as follows: Figure 1 As shown in the figure, the EWH-IES system includes both energy flows (electricity and heat) and material flows (water and hot water). The EP and WT form the water supply system, while the SC, HP, and ST form the hot water system with auxiliary heat sources. The two subsystems provide water and hot water to users, respectively. The EWH-IES system includes both energy flows (electricity and heat) and material flows (water and hot water).

[0099] In summary, the embodiments of the present invention address the problem that traditional methods use a single VES model as the object for optimization control, failing to fully consider the synergistic effect of multiple VESs that simultaneously include electricity, water, and heat in optimization control; and address the problem that traditional research lacks quantitative research on hot water type VES from the perspective of energy storage.

[0100] Example 2

[0101] The embodiment of the present invention further introduces the solution in Example 1 by combining specific calculation formulas and examples, as described below for details:

[0102] Step 1: Establish a basic equipment model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a thermal storage tank model, an air source heat pump model, and a photovoltaic model; a VES-I model with water storage buffer characteristics; a VES-II model with hot water storage buffer characteristics; and an extended energy hub EEH model that integrates energy flow and material flow;

[0103] The specific steps of step 1 include:

[0104] (1) Establish the basic model of EWH-IES equipment;

[0105] The specific steps of step (1) of step 1 include:

[0106] Water pump model:

[0107]

[0108] Where H in 、 are the input and output water heads of EP in period t; H0 is the static head of EP; c EP is the EP internal resistance coefficient, which can be obtained by fitting the working head curve; is the water flow rate of EP. EP , β EP is the power consumption characteristic of the operating parameters; where, is the upper limit of water flow of EP in period t; is the power consumption of EP in period t, and its value range is shown in formula (3); ρ is the density of water; η EP is the EP operating efficiency; λ is the EP fixed power loss; They are the upper and lower limits of EP electric power respectively.

[0109] Water tank model:

[0110]

[0111] Where, and are the water storage capacity of WT in period t and the initial moment respectively; and are the water flows in / out of WT during period t, respectively; is the actual flow of water load in period t; H is the flow rate of hot water load in period t. fix A is the altitude difference between WT and EP; wt is the WT bottom area; and are the upper and lower limits of WT water storage capacity respectively.

[0112]

[0113] Where, They represent the expected flow of water load in period t; H out,t 、 and They are the actual water head, demand water head, minimum water head and maximum water head of the water load in period t respectively.

[0114] Hot water load model:

[0115]

[0116] Where, hot water load The corresponding hot water supply power is c is the specific heat capacity of water; and T cw They are hot water supply temperature and tap water inlet temperature,

[0117] Solar Collector Model:

[0118]

[0119] In the formula, the maximum heat output power of SC is I t is the light intensity; A sc is the SC installation area; η sc is the efficiency of SC.

[0120] Thermal storage tank model:

[0121]

[0122]

[0123] Where, and are the hot water flow in / out of ST during period t. The hot water storage capacity is constant at S st , hot water temperature range is S st is the ST capacity; and are the net heat storage power of ST and other heat loss power caused by water withdrawal in period t; K r is the heat loss coefficient.

[0124] Air source heat pump model:

[0125]

[0126]

[0127] Where, is the HP heating power during period t; R COP is the HP efficiency ratio; is the HP power consumption during period t, They are the upper and lower limits of HP power consumption respectively.

[0128] Photovoltaic model:

[0129]

[0130]

[0131] Where, A is the maximum output power of PV during period t; pv is the installation area of PV; I t is the light intensity; η pv is the photoelectric conversion efficiency; is the actual output power of PV during period t.

[0132] (2) Two VES models were established: the VES-Ι model with water storage buffer characteristics and the VES-ΙΙ model with hot water storage buffer characteristics;

[0133] The specific steps of step (2) include:

[0134] EP is combined with WT, and HP, SC and ST are combined, and the water storage and heat storage capacities are modeled as VES-Ι and VES-ΙΙ, respectively, from the perspective of electricity storage.

[0135] VES-1 Model:

[0136] VCDP-I is the charge and discharge power of VES-I, expressed as Describes the power change before and after VES-I participates in the optimization control; VEC-I is the capacity of VES-I, expressed as Describes the capacity that VES-I can provide for optimization control; VSOC-I is the ratio of VES-I storage capacity to capacity, expressed as VSOC 1,t The relationship between the three parameters is shown in formula (21).

[0137]

[0138] The power consumption of EP in period t is the baseline power consumption The calculation is shown in formula (22):

[0139]

[0140] when hour, Keep constant; otherwise, Deviation The power difference can be defined as the virtual charge and discharge power (VCDP-1), as shown in formula (23):

[0141]

[0142] The maximum charge and discharge power of VES-1 is shown in equations (24) and (25):

[0143]

[0144] Where, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 during period t.

[0145] Self-discharge power It is defined as the EP power consumption corresponding to the water volume that meets the water load, and is calculated as shown in formula (26):

[0146]

[0147] When EP is closed, the WT water storage capacity changes from down to The corresponding power consumption The calculation is shown in formula (27):

[0148]

[0149] VSOC-1 is defined as the ratio of the actual storage capacity of VES-1 to VEC-1, and is calculated as shown in formula (28):

[0150]

[0151] Where, E 1,t is the actual storage capacity of VES-Ι during period t.

[0152] The definition of actual storage capacity of VES-Ι is similar to that of VEC-Ι. This parameter is defined as the amount of water stored in WT when EP is closed. down to The corresponding power consumption is calculated as shown in formula (29):

[0153]

[0154] VES-II model:

[0155] VCDP-III, VEC-III, and VSOC-III. VCDP-III is the charge and discharge power of VES-III, expressed as VEC-III is the capacity of VES-III, expressed as VSOC-III is the ratio of VES-III storage capacity to capacity, expressed as VSOC 2,t The relationship between the three parameters can be expressed as shown in formula (30):

[0156]

[0157] Base heating power The calculation is shown in formula (31):

[0158]

[0159] Baseline power consumption The calculation of is shown in formula (32):

[0160]

[0161] Similarly, you can Deviation The power difference is defined as VCDP-II, as shown in formula (33):

[0162]

[0163] Similarly, VCDP-II is affected by Operating power range and ST water temperature limit. At this time, the maximum power of HP operation will no longer be Considering the base power required by HP Therefore, the maximum charge and discharge power is shown in equations (34) and (35):

[0164]

[0165] Where, The maximum charging power of VES-II. is the maximum discharge power of VES-II.

[0166] Self-discharge power Can be defined as the power required to meet the heat dissipation requirement The corresponding HP electric power, The calculation is shown in formula (36):

[0167]

[0168] Self-discharge power The calculation is shown in formula (37):

[0169]

[0170] When HP is turned off, the hot water temperature in ST changes from down to The corresponding power consumption is calculated as shown in formula (38):

[0171]

[0172] Where t0 is the initial time of optimal control; When HP is turned off, the water temperature in ST changes from down to The corresponding time can be determined by formula (39).

[0173]

[0174] Assumptions The ST heat balance equation can be expressed as (40) and the water temperature in ST during period t is obtained. and The relationship between them is shown in formula (41):

[0175]

[0176] VSOC-II is defined as the ratio of the actual storage capacity of VES-II to VEC-II, and is calculated as shown in formula (42):

[0177]

[0178] Where, E 2,t The actual storage capacity of VES-II.

[0179] The actual storage capacity of VES-II is defined as the amount of water stored in ST when the HP is turned off and the temperature of the hot water in ST changes from down to The corresponding power consumption is calculated as shown in formula (43):

[0180]

[0181] Where, When HP is turned off, the water temperature in ST changes from down to The corresponding time can be determined by formula (44).

[0182]

[0183] (3) Fusion energy flow and material flow EEH model

[0184] The specific steps of step (3) include:

[0185] The EEH model structure of EWH-IES is as follows Figure 2 The establishment process of the fusion energy flow and material flow EEH model is as follows:

[0186] The calculation is shown in formula (45):

[0187]

[0188] The calculation is shown in formula (46):

[0189]

[0190] and The calculations are shown in equations (47) and (48):

[0191]

[0192] Where α is the distribution ratio of water flowing out of the water storage tank to the heat storage tank for hot water production.

[0193] H out,t The calculation is shown in formula (49):

[0194]

[0195] By expressing Equations (45)-(49) in matrix form, the EEH model of EWH-IES can be obtained as shown in Equation (50):

[0196]

[0197] in, is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; is the reference power of the water pump at time t; is the charge and discharge power of VES-1; is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump.

[0198] Equation (50) couples the energy flow and material flow in the EWH-IES containing VES, which is the key and basis for the next step of realizing the optimal control of EWH-IES.

[0199] Step 2: Establish a day-ahead optimization control model for the electric, hydro, and thermal integrated energy system that takes virtual energy storage into account, and then optimize the system based on the demands of electricity, heat, and water loads.

[0200] The specific steps of step 2 include:

[0201] The main goal of EWH-IES optimization control taking VES into account is to improve the economic efficiency of the optimization control results and enhance the level of photovoltaic and solar thermal energy consumption while meeting the user's electricity, water, and hot water load requirements. The operating cost includes the system's electricity purchase cost and the cost of curtailed solar power. The following objective function is constructed:

[0202] min f=C e +C PV +C SC (51)

[0203] Where C e is the electricity purchase cost; C PV Penalty cost for abandoning PV. C SC Penalty costs for abandoned solar thermal power.

[0204] C e The calculation is shown in formula (52):

[0205]

[0206] Where p t is the electricity price during period t.

[0207] C PV The calculation is shown in formula (53):

[0208]

[0209] Where p PV is the penalty factor.

[0210] C SC The calculation is shown in formula (54):

[0211]

[0212] Where p SC is the penalty factor.

[0213] The constraints of the EWH-IES day-ahead optimization control model taking VES into account are as follows:

[0214] 1) System energy flow and material flow balance constraints:

[0215] The constructed EWH-IES must satisfy the multi-energy coupling relationship described by the EEH model, as shown in Equation (50).

[0216] 2) VCDP Constraints:

[0217]

[0218]

[0219] in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II.

[0220] 3) VSOC constraints:

[0221] 0≤VSOC1 t ≤1 (57)

[0222] 0≤VSOC 2,t ≤1 (58)

[0223] Among them, VSOC1 t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t It is the ratio of the actual storage capacity of VES-II to that of VEC-II.

[0224] 4) Maximum output constraints for photovoltaic and solar thermal power:

[0225]

[0226]

[0227] in, are the maximum output powers of photovoltaic and solar thermal, respectively.

[0228] The day-ahead optimization control model of the electric, hydrothermal integrated energy system taking into account virtual energy storage in the embodiment of the present invention is composed of the objective functions shown in equations (51)-(54) and the constraints shown in equations (50) and (55)-(60).

[0229] The above optimization control model is a mixed integer linear programming (MILP) optimization problem, which can be solved by the interior point method. By solving the above optimization control model, the day-ahead optimization control scheme of the electric, hydro and thermal integrated energy system taking into account virtual energy storage can be obtained, that is, the purchased power of the entire period Photovoltaic output power Photothermal output power Virtual charge and discharge power of VES-Ι And VES-II charging and discharging power Based on the resulting EWH-IES day-ahead optimization control scheme, the EWH-IES scheduling scheme derived from traditional scheduling methods (known in the art) can be modified and adjusted to guide the EWH-IES's operation the following day. Because traditional scheduling methods only consider the operational economics of the IWH-IES for optimization and control, and fail to account for the virtual energy storage effect brought about by the conversion and storage of energy and material flows between electricity and water, and electricity and heat, the EWH-IES day-ahead optimization control scheme, which accounts for virtual energy storage, obtained by this invention, can improve the EWH-IES's photovoltaic absorption rate and solar thermal utilization efficiency.

[0230] Example 3

[0231] In order to verify the effectiveness of the day-ahead optimization control method for the electric, hydrothermal and integrated energy system taking into account virtual energy storage proposed in the embodiment of the present invention, a typical EWH-IES is selected as an example. The structure is as follows: Figure 1 The water load, electricity load, heat load at standard temperature, and maximum output power of photovoltaic and solar thermal energy under a typical day of the EWH-IES are shown as follows: Figures 3 to 5 As shown in Table 1, the system parameters are shown in Table 1, and the VES and equipment related parameters are shown in Table 2.

[0232] Table 1 System parameters

[0233]

[0234]

[0235] Table 2 VES and equipment related parameters

[0236]

[0237] To verify the effectiveness of the proposed EWH-IES day-ahead optimization control method considering VES, three comparison scenarios are set:

[0238] Scenario 1: VES-Ι and VES-ΙΙ are not considered;

[0239] Scenario 2: Consider only VES-1;

[0240] Scenario 3: Consider only VES-II;

[0241] Scenario 4: Consider both VES-Ι and VES-ΙΙ.

[0242] Photovoltaic absorption rate η in the optimized control scheme corresponding to scenarios 1 to 4 pv , Solar energy guarantee rate η scTable 3 shows the power purchase amount and cost of electricity purchased. Compared with Scenario 1, the power purchase amount in Scenarios 2 through 4 decreased by 1.3%, 0.7%, and 2%, respectively, and the cost decreased by 4.6%, 0.92%, and 5.58%, respectively. Compared with Scenario 1, the PV integration rate in Scenarios 2 through 4 increased by 1.68%, 0.51%, and 2.19%, respectively. The solar energy guarantee rate in Scenarios 3 and 4 increased by 3.03%. It can be seen that the improvement in the PV integration rate is related to the consideration of both VES-I and VES-II, while the improvement in the solar energy guarantee rate is only related to the consideration of VES-II. Furthermore, VES-I and VES-II exhibit certain synergistic effects. In Scenario 4, the combined consideration of VES-I and VES-II further improves the PV integration rate and operational flexibility.

[0243] Table 3 Photovoltaic absorption rate, solar energy guarantee rate, electricity purchase amount and electricity purchase cost for scenarios 1 to 4

[0244]

[0245] EWH-IES optimization control scheme and The changes in one day are as follows Figures 6 to 8 To further analyze the effectiveness of the optimization control method of the present invention, the specific optimization control schemes for each scenario are further analyzed below.

[0246] (1) Analysis of optimized control results in scenario 1

[0247] In scenario 1, the water supply system and hot water system adopt traditional optimization control method. Figures 9 and 10 They are the changes in water storage in WT and hot water temperature in ST. It can be seen that in scenario 1, the water supply system WT maintains a constant water storage, and the hot water system ST maintains a constant hot water temperature. In this scenario, since there are two peak water load periods at 6:00-12:00 and 17:00-22:00, which coincide with the peak electricity load period, the electricity consumption of the water supply system EP will increase the power supply burden. Figure 8 and Figure 10 It can be seen that during the time periods of 8:00-11:00 and 17:00-19:00, photovoltaic power generation cannot meet all the electricity demand of EWH-IES. Figure 7 It can be seen that during the periods of 8:00-11:00 and 17:00-18:00 when electricity prices are higher, EWH-IES needs to purchase electricity from the distribution network, resulting in low operating flexibility of EWH-IES.

[0248] (2) Analysis of optimized control results in scenario 2

[0249] In scenario 2, the water storage capacity of the water supply system WT is considered and the flexibility of the water supply system VES-1 is used for control. Figure 7 and Figure 8 As can be seen, in Scenario 2, EWH-IES increases PV output between 11:00 AM and 3:00 PM, and reduces purchased electricity during the 3:00 PM to 6:00 PM period, when electricity prices are higher. This improves both PV absorption and EWH-IES operational flexibility. Regarding CSP absorption, since this scenario doesn't consider the flexibility of the hot water system and the CSP system is used only to generate heat to meet hot water demand, there is no change in CSP absorption.

[0250] (3) Analysis of optimized control results in scenario 3

[0251] In scenario 3, the hot water storage capacity of the hot water system ST is taken into consideration, and the flexibility of the hot water system VES-II is used for optimal control. Figure 7 、 Figure 8 and Figure 11 As can be seen, during the 6:00-19:00 period, the thermal energy required for hot water supply in Scenario 3 primarily comes from CSP. During the 11:00-18:00 period, CSP output increases compared to Scenario 1, and the hot water temperature in the ST rises to near the upper limit. This is because after 19:00, the thermal energy required for hot water supply is provided by HP, and the electricity price is higher between 19:00 and 22:00. Therefore, EWH-IES increases CSP output during the 11:00-18:00 period, while simultaneously reducing purchased electricity and improving operational flexibility. Regarding PV consumption, the increase in CSP output during the 11:00-12:00 and 14:00-15:00 periods in Scenario 3 coincides with an increase in PV output. This is because the thermal energy required for the hot water system is primarily provided by CSP. The higher water temperature during these periods allows for further utilization of PV power for HP heating, improving PV consumption.

[0252] (4) Analysis of optimized control results in scenario 4

[0253] VSOC for scenarios 2 to 4 is as follows Figure 11As shown, in Scenario 4, VSOC-1 increases between 00:00 and 4:00, corresponding to the charging state of VES-1, while in Scenario 2, VES-1 is discharging. Therefore, VES-1 can better utilize charging during the early morning hours when electricity prices are lower, improving the operational flexibility of the EWH-IES. Furthermore, compared to Scenario 2, in Scenario 4, VES-11 can charge between 11:00 and 12:00 and 14:00 and 15:00 to increase PV consumption. Meanwhile, during the higher electricity price period of 19:00 and 22:00, VES-11 can discharge to reduce the amount of electricity purchased by the EWH-IES during these hours. Therefore, considering both VES-1 and VES-11 can further improve the PV consumption of the EWH-IES and enhance operational flexibility.

[0254] Based on the coupling relationship between energy flow and material flow, the present invention proposes an EWH-IES day-ahead economic optimization control method taking into account VES. The conclusions are as follows:

[0255] 1) Based on the electricity-water-heat coupling relationship in EWH-IES, the power consumption of the water supply system and the hot water system can be adjusted while ensuring the water supply and hot water supply needs. The established VES-Ι model and VES-ΙΙ model realize the quantification of the virtual energy storage capacity of the two systems.

[0256] 2) VES-Ι utilizes the coordinated operation of EP and WT to absorb solar energy during periods of high PV output and convert it into water potential energy for storage; VES-Ι utilizes the coordinated operation of HP and ST, while considering the PV and CSP outputs to absorb solar energy and convert it into thermal energy for storage, thus providing flexibility for EWH-IES.

[0257] 3) Taking both VES-Ι and VES-II into account in the day-ahead optimization control of EWH-IES can provide more operational flexibility for EWH-IES and further improve the PV absorption rate and solar energy guarantee rate.

[0258] Example 4

[0259] The embodiment of the present invention provides a day-ahead control device for an electric, water and heat integrated energy system taking into account virtual energy storage, see Figure 12 , the device comprises:

[0260] The first modeling module is used to establish an equipment basic model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a heat storage tank model, an air source heat pump model, and a photovoltaic model; a VES-I model with water storage buffer characteristics, a VES-II model with hot water storage buffer characteristics, and an extended energy hub EEH model that integrates energy flow and material flow;

[0261] A second modeling module is used to establish a day-ahead optimization control model of the electric, hydrothermal integrated energy system taking into account virtual energy storage based on the first modeling module;

[0262] Optimization module is used to optimize the demand for electricity, heat and water loads.

[0263] Among them, see Figure 13 , the optimization modules include:

[0264] The acquisition submodule is used to solve the day-ahead optimization control model to obtain the purchased power, photovoltaic output power, solar thermal output power, VES-1 virtual charge and discharge power, and VES-11 charge and discharge power for the entire period;

[0265] The correction and adjustment submodule is used to correct and adjust the EWH-IES scheduling plan based on the purchased power, photovoltaic output power, solar thermal output power, VES-Ι virtual charge and discharge power, and VES-ΙΙ charge and discharge power of the entire period, and guide the operation of EWH-IES the next day.

[0266] Among them, the day-ahead optimization control model is:

[0267] Objective function:

[0268] min f=C e +C PV +C SC

[0269] Where C e is the electricity purchase cost; C PV Penalty cost for abandoning PV; C SC Penalty cost for abandoned solar thermal power plant; C e calculate:

[0270]

[0271] Where p t is the electricity price during period t;

[0272] C PV calculate:

[0273]

[0274] Where p PV is the penalty factor;

[0275] C SC calculate:

[0276]

[0277] Where p SC is the penalty factor.

[0278] The constraints of the day-ahead optimization control model are:

[0279] 1) System energy flow and material flow balance constraints:

[0280]

[0281] in, is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; is the reference power of the water pump at time t; is the charge and discharge power of VES-1; is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump;

[0282] 2) VCDP Constraints:

[0283]

[0284] in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II;

[0285] 3) VSOC constraints:

[0286] 0≤VSOC1 t ≤1

[0287] 0≤VSOC 2,t ≤1

[0288] Among them, VSOC1t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t is the ratio of the actual storage capacity of VES-II to that of VEC-II;

[0289] 4) Maximum output constraints for photovoltaic and solar thermal power:

[0290]

[0291] in, are the maximum output powers of photovoltaic and solar thermal, respectively.

[0292] In summary, the embodiments of the present invention, through the above-mentioned device, solve the problem that traditional optimization control only uses a single VES model as the object, but fails to fully consider the synergistic effect of multiple VESs that simultaneously include electricity, water, and heat in optimization control; and solves the problem that traditional research lacks quantitative research on hot water type VES from the perspective of energy storage.

[0293] Example 5

[0294] The embodiment of the present invention provides a day-ahead control device for an electric, water and heat integrated energy system taking into account virtual energy storage, see Figure 14 The device includes a processor and a memory. The memory stores program instructions. The processor calls the program instructions stored in the memory to enable the device to perform the following method steps in Example 1:

[0295] Establish a basic equipment model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a thermal storage tank model, an air source heat pump model, and a photovoltaic model; a VES-Ι model with water storage buffer characteristics; a VES-Ι model with hot water storage buffer characteristics; and an extended energy hub EEH model that integrates energy flow and material flow;

[0296] Based on the aforementioned equipment basic model, VES-I model, VES-II model, and EEH model, a day-ahead optimization control model for the electric, hydrothermal, and integrated energy systems, including virtual energy storage, is established.

[0297] The demand for electricity, heat and water loads is optimized based on the day-ahead optimization control model.

[0298] Among them, the day-ahead optimization control model is:

[0299] Objective function:

[0300] min f=C e +C PV +C SC

[0301] Where C e is the electricity purchase cost; CPV Penalty cost for abandoning PV; C SC Penalty costs for abandoned solar and thermal power;

[0302] C e calculate:

[0303]

[0304] Where p t is the electricity price during period t;

[0305] C PV calculate:

[0306]

[0307] Where p PV is the penalty factor;

[0308] C SC calculate:

[0309]

[0310] Where p SC is the penalty factor.

[0311] Among them, the constraints of the day-ahead optimization control model are:

[0312] 1) System energy flow and material flow balance constraints:

[0313]

[0314] in, is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; is the reference power of the water pump at time t; is the charge and discharge power of VES-1; is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump.

[0315] 2) VCDP Constraints:

[0316]

[0317] in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II.

[0318] 3) VSOC constraints:

[0319] 0≤VSOC1 t ≤1

[0320] 0≤VSOC 2,t ≤1

[0321] Among them, VSOC1 t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t The ratio of the actual storage capacity of VES-II to that of VEC-II

[0322] 4) Maximum output constraints for photovoltaic and solar thermal power:

[0323]

[0324] in, are the maximum output powers of photovoltaic and solar thermal, respectively.

[0325] It should be noted here that the device description in the above embodiment corresponds to the method description in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0326] The execution subjects of the above-mentioned processor 1 and memory 2 can be computers, single-chip microcomputers, microcontrollers and other devices with computing functions. In specific implementation, the embodiment of the present invention does not limit the execution subject and it is selected according to the needs of actual application.

[0327] Data signals are transmitted between the memory 2 and the processor 1 via the bus 3 , which will not be described in detail in the embodiment of the present invention.

[0328] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored program, and when the program is running, controls the device where the storage medium is located to execute the method steps in the above embodiment.

[0329] The computer-readable storage medium includes but is not limited to a flash memory, a hard disk, a solid-state drive, and the like.

[0330] It should be noted here that the description of the readable storage medium in the above embodiment corresponds to the description of the method in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0331] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part.

[0332] The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in or transmitted via computer-readable storage media. Computer-readable storage media can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. Available media can include magnetic media or semiconductor media, etc.

[0333] Unless otherwise specified, the embodiments of the present invention do not limit the models of the components. Any component that can perform the above functions may be used.

[0334] Those skilled in the art will understand that the accompanying drawings are only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0335] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for day-ahead control of an electric, hydro and thermal integrated energy system taking into account virtual energy storage, characterized in that: include: Establish a basic equipment model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a thermal storage tank model, an air source heat pump model, and a photovoltaic model; a VES-Ι model with water storage buffer characteristics; a VES-Ι model with hot water storage buffer characteristics; and an extended energy hub EEH model that integrates energy flow and material flow; Based on the aforementioned equipment basic model, VES-I model, VES-II model, and EEH model, a day-ahead optimization control model for the electric, hydrothermal, and integrated energy systems, including virtual energy storage, is established. The demands for electricity, heat and water loads are optimized based on the day-ahead optimization control model.

2. The method for day-ahead control of an electric, hydro and thermal integrated energy system taking into account virtual energy storage according to claim 1, characterized in that: The day-ahead optimization control model is: Objective function: minf=C e +C PV +C SC Where C e is the electricity purchase cost; C PV Penalty cost for abandoning PV; C SC Penalty costs for abandoned solar and thermal power; C e calculate: Where p t is the electricity price during period t; C PV calculate: Where p PV is the penalty factor; C SC calculate: Where p SC is the penalty factor.

3. The method for optimizing and controlling a day-ahead integrated energy system of electricity, water and heat taking into account virtual energy storage according to claim 2, characterized in that: The constraints of the day-ahead optimization control model are: 1) System energy flow and material flow balance constraints: Among them, P t ele,L is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; P t EP ,base is the reference power of the water pump at time t; is the charge and discharge power of VES-1; P t HP,base is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump; 2) VCDP Constraints: in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II; 3) VSOC constraints: 0≤VSOC1 t ≤1 0≤VSOC 2,t ≤1 Among them, VSOC1 t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t is the ratio of the actual storage capacity of VES-II to that of VEC-II; 4) Maximum output constraints for photovoltaic and solar thermal power: Among them, P t pv,T 、 are the maximum output powers of photovoltaic and solar thermal, respectively.

4. The method for day-ahead control of an electric, hydro and thermal integrated energy system taking into account virtual energy storage according to claim 1, characterized in that: The demand for electricity, heat and water loads is optimized based on the day-ahead optimization control model as follows: Solve the day-ahead optimization control model to obtain the purchased power, photovoltaic output power, solar thermal output power, VES-1 virtual charge and discharge power, and VES-11 charge and discharge power for the entire period; Based on the purchased electricity power, photovoltaic output power, solar thermal output power, VES-Ι virtual charge and discharge power, and VES-ΙΙ charge and discharge power during the entire period, the EWH-IES scheduling plan is corrected and adjusted to guide the operation of EWH-IES the next day.

5. A day-ahead control device for an electric, hydro and thermal integrated energy system taking into account virtual energy storage, characterized in that: The device comprises: The first modeling module is used to establish an equipment basic model consisting of a water pump model, a water storage tank model, a hot water load model, a solar collector model, a heat storage tank model, an air source heat pump model, and a photovoltaic model; a VES-I model with water storage buffer characteristics, a VES-II model with hot water storage buffer characteristics, and an extended energy hub EEH model that integrates energy flow and material flow; A second modeling module is used to establish a day-ahead optimization control model of the electric, hydrothermal integrated energy system taking into account virtual energy storage based on the first modeling module; Optimization module is used to optimize the demand for electricity, heat and water loads.

6. The day-ahead control device for an electric, hydro and thermal integrated energy system taking into account virtual energy storage according to claim 5, characterized in that: The optimization module includes: The acquisition submodule is used to solve the day-ahead optimization control model to obtain the purchased power, photovoltaic output power, solar thermal output power, VES-1 virtual charge and discharge power, and VES-11 charge and discharge power for the entire period; The correction and adjustment submodule is used to correct and adjust the EWH-IES scheduling plan based on the purchased power, photovoltaic output power, solar thermal output power, VES-Ι virtual charge and discharge power, and VES-ΙΙ charge and discharge power of the entire period, and guide the operation of EWH-IES the next day.

7. The day-ahead control device for an electric, hydro and thermal integrated energy system taking into account virtual energy storage according to claim 6, characterized in that: The day-ahead optimization control model is: Objective function: minf=C e +C PV +C SC Where C e is the electricity purchase cost; C PV Penalty cost for abandoning PV; C SC Penalty costs for abandoned solar and thermal power; C e calculate: Where p t is the electricity price during period t; C PV calculate: Where p PV is the penalty factor; C SC calculate: Where p SC is the penalty factor.

8. The day-ahead control device for an electric, hydro and thermal integrated energy system taking into account virtual energy storage according to claim 7, characterized in that: The constraints of the day-ahead optimization control model are: 1) System energy flow and material flow balance constraints: Among them, P t ele,L is the electrical load at time t; and are the hot water load and the corresponding hot water supply power at time t respectively; and H out,t are the actual flow and head of water load in period t respectively; is the purchased power at time t; The actual thermal power output of the solar thermal collector; is the water flow of the pump at time T; H in is the input water head of the water pump at time t; P t EP,base is the reference power of the water pump at time t; is the charge and discharge power of VES-1; P t HP,base is the base power of the heat pump at time t; is the charge and discharge power of VES-II; α is the distribution ratio of water flowing out of the water storage tank to the hot water storage tank for hot water production; c EP is the internal resistance coefficient of the water pump; and are the net heat storage power of the hot water tank and other heat loss power caused by water withdrawal during period t; and are the water flow rates flowing into / out of the water storage tank during period t; H0 is the static head of the water pump; 2) VCDP Constraints: in, is the maximum charging power of VES-1 in period t; is the maximum discharge power of VES-1 in period t; The maximum charging power of VES-II; is the maximum discharge power of VES-II; 3) VSOC constraints: 0≤VSOC1 t ≤1 0≤VSOC 2,t ≤1 Among them, VSOC1 t VSOC is the ratio of the actual storage capacity of VES-Ι to VEC-Ι; 2,t is the ratio of the actual storage capacity of VES-II to that of VEC-II; 4) Maximum output constraints for photovoltaic and solar thermal power: Among them, P t pv,T 、 are the maximum output powers of photovoltaic and solar thermal, respectively.

9. A day-ahead control device for an electric, hydro and thermal integrated energy system taking into account virtual energy storage, characterized in that: The device includes: a processor and a memory, wherein program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute the method steps according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method steps according to any one of claims 1 to 4.

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