Multi-time-scale optimization scheduling method for micro-grid system containing photo-thermal power station and combined heat and power generation

Abstract

The invention relates to a multi-time scale optimization scheduling method for a micro-grid system containing a photo-thermal power station and combined heat and power generation, a micro-grid model containing the photo-thermal power station and the combined heat and power generation is adopted, an operation strategy of the micro-grid system is divided into a day-ahead stage and an intra-day stage, and intra-day operation is performed on the basis of a day-ahead scheduling result. Micro-grid operation constraints comprise power balance constraints, heat balance constraints, various power station output constraints, and constraints of a heat storage device and an energy storage power station, micro-grid day-ahead scheduling takes minimization of operation and maintenance cost and wind and light curtailment as an optimization target, and intra-day scheduling takes minimization of various power station processing fluctuations as a target function. The method has the advantages that the time scale is refined from the hour level to the minute level, and instantaneous load fluctuation and randomness of renewable energy sources can be coped with. Micro-grid optimization objectives are different before and within the day, and economic benefits and stability of a grid system are considered.

Description

Technical Field

[0001] The present invention belongs to the field of multi-energy microgrid power dispatching and relates to multi-time scale optimization dispatching of microgrid systems. Background Art

[0002] The multi-energy complementary microgrid system integrates multiple types of energy, such as wind energy, solar energy, energy storage power stations and thermal power. The multi-energy complementary microgrid utilizes the complementary characteristics of wind and solar energy resources and the flexible adjustment capabilities of energy storage power stations, while retaining a portion of thermal power as a guarantee for power energy regulation, greatly improving the consumption of renewable energy and realizing an economic, reliable and environmentally friendly supply model. However, due to the uncertainty of wind power and photovoltaic power generation output, insufficient power supply and power abandonment of wind power and photovoltaic power generation often occur, making it difficult to ensure the economic benefits and stability of the entire system.

[0003] In a multi-energy complementary microgrid system, the combination of wind power generation, photovoltaic power generation, solar thermal power generation, and energy storage systems (including batteries and thermal energy storage) can balance the difference between load demand and power supply. Optimization scheduling technology uses the prediction of power load and renewable energy generation in different time scales to reasonably allocate various energy outputs, reduce operating costs and environmental impacts, and ensure stable operation of the system. However, there is a problem of insufficient consideration of the uncertainty of renewable energy output in existing optimization scheduling technologies. The scheduling method of a single time scale is difficult to cope with the fluctuations of wind and light resources, resulting in insufficient regulation capacity of the power grid. Most of the existing scheduling schemes are scheduled at two levels: day-ahead and intraday. Day-ahead scheduling is usually based on a time interval of 1 hour, and the prediction accuracy is insufficient. So far, no multi-time scale scheduling method has been found for microgrid systems including solar thermal power stations and cogeneration, and multi-time scale optimization scheduling that takes into account the economic benefits and stability of the power grid system in multiple time scales.

[0004] Therefore, how to optimize the scheduling of the multi-energy complementary microgrid system, further improve the economic and environmental benefits of the multi-energy complementary microgrid system, and ensure the safety and stability of the entire power grid system is the technical problem to be solved by the present invention. Summary of the invention

[0005] The purpose of the present invention is to propose a multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration, which utilizes high-frequency scheduling within a short time scale to timely update the output forecast and load demand of renewable energy sources such as wind power and photovoltaics, reduce the impact of volatility on the system, and improve the accuracy and reliability of scheduling.

[0006] The technical solution of the present invention is a multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and a cogeneration system. The steps are as follows: 1) Modeling of a solar thermal power station and a cogeneration system. The solar thermal power station is divided into a heat collection module, a heat storage module and a power generation module. The model of the heat collection module is: Q SF =η SF *DNI, Q SF (t) = Q SF_HS (t)+Q SF_GE (t), where Q SF Represents the total heat collected by the solar collector module, η SF is the light-to-heat conversion efficiency, DNI is the direct normal irradiance, Q SF_HS (t), Q SF_GE (t) are the heat transferred from the heat collection module to the heat storage module and the power generation module at time t, Q SF (t) is the total heat collected by the heat collection module at time t; the model of heat storage module charging and releasing heat: Q HS_All (t) = Q HS_GE (t)+Q HS_LOAD (t), where Q HSC (t) and Q HSC (t-1) represents the heat stored by the heat storage module at time t and the previous moment, respectively, η in Q , η out Q They represent the efficiency of heat charging and heat release in the heat storage module, Q HS_All (t) represents the heat released by the heat storage component at time t, where Q HS_GE (t) represents the heat sent to the power generation device, Q HS_LOAD (t) represents the amount of heat sent to the heat load;

[0007] Power generation module: P CSP (t) = η GE (Q SF_GE (t)+Q HS_GE (t)), where P CSP (t) represents the output power of the CSP power station at time t, η GE Indicates the thermal-electric conversion efficiency of the CSP power generation module;

[0008] Combined Heat and Power Model: P CHP (t) = η CHP E P fuel (t), Q CHP_LOAD (t) = η CHP Q P fuel (t), where P CHP(t) and Q CHP_LOAD (t) respectively represent the electrical power and thermal power of the cogeneration at time t, η CHP E , η CHP Q Respectively represent the power generation efficiency and heating efficiency of cogeneration, P fuel (t) is the fuel consumption rate;

[0009] 2) Operation strategy of multi-energy complementary microgrid under multiple time scales. Step 1) The operation strategy of the model is divided into two stages: day-ahead optimization and intraday optimization. In the day-ahead optimization stage, the short-term forecasts of renewable energy generation, load and direct radiation intensity are used as inputs. Through optimization, the active power output plans of various energy sources are obtained. In the intraday optimization stage, the ultra-short-term forecast results of renewable energy generation, load and direct radiation intensity, as well as the active power output plan obtained from the first stage, are used as inputs. The operation interval on the time scale of the intraday stage is measured in minutes, and a rolling optimization strategy is adopted. For each moment after the first moment, the initial conditions are based on the latest values ​​of the previous moment.

[0010] 3) Operation constraints of the microgrid, including power balance constraints, heat balance constraints, power station output constraints, heat storage device constraints, and energy storage power station constraints;

[0011] 4) Objective function of microgrid at different time scales:

[0012]

[0013] In the formula, C OM Represents the total operation and maintenance cost; C OM PV (t), C OM WT (t), C OM THE (t), C OM CHP (t), C OM CSP (t), C OM ES (t) represents the operation and maintenance costs of photovoltaic power generation, wind power generation, thermal power generation, cogeneration, solar thermal power station and energy storage power station respectively; k OM PV , k OM WT , k OM THE , k OM CHP_E , k OM CHP_H , k OM CSP_E , kOM CSP_H , k OM ES They represent the operation and maintenance cost coefficients of photovoltaic power generation, wind power generation, thermal power generation, combined heat and power generation, combined heat and power generation, solar thermal power station power generation, solar thermal power station heat generation, and energy storage power station, respectively. PV (t), P WT (t), P THE (t), P DIS (t), P CH (t) represents wind power generation, photovoltaic power generation, thermal power generation, battery discharge power and battery charging power respectively; the penalty for abandoning wind and solar power is added to the objective function: In the formula, C pl represents the penalty power for wind and solar power abandonment, λ is the penalty coefficient for wind and solar power abandonment cost; P pl (t) is the wind and solar power curtailed at time t;

[0014] The optimization goal of day-ahead dispatch is to minimize the operation and maintenance costs of all power stations plus the penalties for wind and solar power abandonment, expressed as: C A =C OM +C pl , where C A It represents the sum of total operation and maintenance cost and penalty power for wind and solar power abandonment;

[0015] The optimization goal of intraday scheduling is to minimize the output fluctuation of various power sources, which can be expressed as: In the formula, C FLC represents the total volatility cost; ΔP WT (t), ΔP PV (t), ΔP THE (t), ΔP CHP (t), ΔP CSP (t), ΔP ES (t) represents the power fluctuation of wind power, photovoltaic power, thermal power, cogeneration, solar thermal power station and energy storage power station at time t respectively;

[0016] 5) Solution: Use MATLAB to call the commercial solver CPLEX for solution.

[0017] The power balance constraint is expressed as:

[0018] P WT (t)+P PV (t)+P THE (t)+P CSP (t)+P CHP (t)+P DIS (t)-P CH (t) = P LOAD(t), where P LOAD (t) represents the electrical load power.

[0019] The heat balance constraint is expressed as:

[0020] Q HS_LOAD (t)+Q CHP_LOAD (t) = Q LOAD (t).

[0021] The power station output constraint is expressed as:

[0022] Where P WT_PRE (t), P PV_PRE (t) represents the predicted maximum output of wind power and photovoltaic power respectively; P THE_MAX , P CHP_MAX , P CSP_MAX , represent the maximum power of thermal power, cogeneration and solar thermal power stations respectively.

[0023] The heat storage device constraints include the heat storage device heat capacity constraint, the constraint that heat charging and heat release cannot be performed simultaneously, and the constraint that the heat capacity must be maintained consistent within a scheduling cycle, which are respectively expressed as:

[0024]

[0025] 0≤Q SF_HS (t)≤u(t)Q TES_in MAX ;

[0026] 0≤Q HS_All (t)≤[1-u(t)]Q TES_out MAX ;

[0027] Q HSC_initial =Q HSC_final ;

[0028] In the formula, H TES Indicates the number of hours at full load; Q TES_in MAX , Q TES_out MAX They represent the maximum charging power and maximum discharging power of the heat storage device respectively; u(t) represents a 0-1 variable at time t; Q HSC_initial , Q HSC_final They represent the initial heat and final heat of the heat storage device in one day respectively.

[0029] The energy storage power station constraints include the constraint that it cannot charge and discharge at the same time and the constraint that the energy storage power station's initial and final power consumption of the day must be consistent, which can be expressed as:

[0030] 0≤PCH (t)≤x(t)P CH MAX , 0≤P DIS (t)≤[1-x(t)]P DIS MAX ;

[0031] E BAT_initial =E BAT_final ;

[0032] Where P CH MAX , P DIS MAX are the maximum power of charging and discharging of the energy storage station; x(t) represents a 0-1 variable at time t; E BAT_initial 、E BAT_final They respectively represent the initial and final power consumption of the energy storage power station in a day.

[0033] Beneficial effects of the present invention: This method adds a more precise intraday scheduling on the basis of day-ahead scheduling, which is refined from hourly level to minute level (such as 15 minutes), and can cope with instantaneous load fluctuations and the randomness of renewable energy. Since the time scale becomes smaller and is based on more real-time data, the scheduling results are also more accurate, which significantly improves the coordinated control effect of distributed energy, and greatly improves the reliability of the scheduling results. This method adopts minimization of operation and maintenance costs and wind and solar power abandonment in the objective function of day-ahead scheduling optimization, and performs intraday scheduling based on the day-ahead scheduling results. Minimization of power fluctuations is used as the objective function in intraday scheduling, which takes into account the economic benefits and stability of the power grid system, and minimizes operating costs and environmental impacts. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of energy flow in microgrid system. DETAILED DESCRIPTION

[0035] The present invention proposes a multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration. The energy flow diagram of the microgrid system is as follows: Figure 1 shown.

[0036] The whole system includes wind power plants, photovoltaic power plants, energy storage power stations, thermal power generating units, solar thermal power stations and cogeneration devices. Wind farms, photovoltaic power stations, solar thermal power stations and energy storage power stations transmit electricity to the grid through external transmission channels to realize the consumption of renewable energy. When renewable energy power resources are abundant, the grid will transmit abundant electricity to the energy storage power station. However, when renewable energy power generation cannot meet the load demand of the system, the energy storage power station, the heat storage device of the solar thermal power station and the thermal power units will ensure the power supply of the system. In other words, when wind and solar energy resources are insufficient, the power balance will be ensured by the output of thermal power units, energy storage stations and heat storage devices. In addition, in order to ensure the power generation efficiency of the heat storage device and the performance of the solar thermal power generation device, the whole system also needs to ensure the balance of the heat load, and the heat of the heat load is jointly transmitted by the heat storage device and the cogeneration device of the solar thermal power station.

[0037] The technical solution of this patent mainly includes 5 parts, namely:

[0038] 1. Modeling of CSP power stations and cogeneration;

[0039] 2. Operation strategy of multi-energy complementary microgrid under multiple time scales;

[0040] 3. Operation constraints of microgrids;

[0041] 4. Objective functions of microgrids at different time scales;

[0042] 5. Solution method.

[0043] 1) Modeling of CSP plants and cogeneration

[0044] Solar thermal power stations use heat collection devices to convert solar energy into heat energy, which is then divided into two parts. One part is directly sent to the power generation device of the solar thermal power station for power generation, and the other part is sent to the heat storage device for storage. Therefore, the solar thermal power station is divided into a heat collection module, a heat storage module and a power generation module.

[0045] The model of the heat collection module is shown in equations (1) and (2):

[0046] Q SF =η SF *DNI (1)

[0047] Q SF (t) = Q SF_HS (t)+Q SF_GE (t) (2)

[0048] In the formula, Q SF Indicates the total heat collected by the collector module, Q SF (t) represents the heat collected by the solar collector module at time t, η SFis the light-to-heat conversion efficiency, DNI is the direct normal irradiance, Q SF_HS (t), Q SF_GE (t) are the heat transferred from the thermal collection module to the thermal storage module and the power generation module at time t, respectively.

[0049] The heat storage module is the key to the flexible adjustment capability of the CSP plant. Its heat charging and heat releasing models are shown in equations (3) and (4):

[0050]

[0051] Q HS_All (t) = Q HS_GE (t)+Q HS_LOAD (t) (4)

[0052] In the formula, Q HSC (t) and Q HSC (t-1) represents the heat stored by the heat storage module at time t and the previous moment, respectively, η in Q , η out Q They represent the efficiency of heat charging and heat release in the heat storage module, Q HS_All (t) represents the heat released by the heat storage component at time t, where Q HS_GE (t) represents the heat sent to the power generation device, Q HS_LOAD (t) represents the heat sent from the heat storage device to the heat load.

[0053] The power generation module converts thermal energy into electrical energy. The specific formula is shown in formula (5):

[0054] P CSP (t) = η GE (Q SF_GE (t)+Q HS_GE (t)) (5)

[0055] Where P CSP (t) represents the output power of the CSP power station at time t, η GE Indicates the thermal-electric conversion efficiency of the solar thermal power station's power generation module.

[0056] As an important way of producing electricity, cogeneration is similar to thermal power generation in principle, generating electricity through fuel combustion. However, unlike thermal power generation, cogeneration also focuses on generating heat to improve overall energy efficiency. The models for generating electricity and heat are shown in equations (6) and (7):

[0057] P CHP (t) = η CHP E P fuel (t) (6)

[0058] Q CHP_LOAD (t) = η CHP Q P fuel (t) (7)

[0059] Where P CHP (t) and Q CHP_LOAD (t) represent the electrical power and heat produced by CHP, respectively, and η CHP E , η CHP Q Respectively represent the power generation efficiency and heating efficiency of cogeneration, P fuel (t) is the fuel consumption rate.

[0060] 2) Operation strategy of multi-energy complementary microgrid at multiple time scales

[0061] The operating strategy of this patented model is divided into two stages: day-ahead optimization and intraday optimization.

[0062] In the day-ahead optimization phase, short-term forecasts of renewable energy generation, load, and direct radiation intensity are used as inputs. Through optimization, active power output plans for various energy sources are obtained. These plans are the initial conditions for the second phase - the intraday optimization phase.

[0063] In the intraday optimization phase, ultra-short-term forecast results of renewable energy generation, load and direct radiation intensity, as well as the active power output plan obtained from the first phase, are used as input. First, the intraday optimization is adjusted according to the day-ahead plan and ultra-short-term forecast. Unlike the day-ahead phase, the intraday phase operates on a finer time scale with an interval of 15 minutes and adopts a rolling optimization strategy. For each moment after the first moment, the initial conditions are based on the latest values ​​of the previous moment, making the intraday optimization more precise.

[0064] 3) Operation constraints of microgrids

[0065] In a multi-energy complementary microgrid system, its operation constraints mainly include power balance constraints, heat balance constraints, power station output constraints, heat storage device constraints and energy storage power station constraints.

[0066] The power balance constraint and heat balance constraint are expressed as equation (8) and equation (9) respectively:

[0067] P WT (t)+P PV (t)+P THE (t)+P CSP (t)+P CHP (t)+P DIS (t)-P CH(t) = P LOAD (t) (8)

[0068] Q HS_LOAD (t)+Q CHP_LOAD (t) = Q LOAD (t) (9)

[0069] Where P WT (t), P PV (t), P THE (t), P DIS (t), P CH (t) represent wind power generation power, photovoltaic power generation power (referring only to photovoltaic panel power generation), thermal power generation power, battery discharge power and battery charging power respectively; P LOAD (t) represents the electric load power; Q LOAD (t) represents the heat load.

[0070] The output constraints of various power stations are mainly limited by the output upper limits of various energy units, which can be expressed as formula (10):

[0071]

[0072] Where P WT_PRE (t), P PV_PRE (t) represents the predicted maximum output of wind power and photovoltaic power respectively; P THE_MAX , P CHP_MAX , P CSP_MAX They represent the maximum power of thermal power, cogeneration and solar thermal power stations respectively.

[0073] The operation constraints of the heat storage device mainly include the heat capacity constraint of the heat storage device, the constraint that charging and releasing heat cannot be performed at the same time, and the constraint that the heat capacity must be maintained consistent within a scheduling cycle, which are respectively expressed as formula (11), formula (12), formula (13), and formula (14):

[0074]

[0075] 0≤Q SF_HS (t)≤u(t)Q TES_in MAX (12)

[0076] 0≤Q HS_All (t)≤[1-u(t)]Q TES_out MAX (13)

[0077] Q HSC_initial =Q HSC_final (14)

[0078] In the formula, H TESIndicates the number of hours at full load; Q TES_in MAX , Q TES_out MAX They represent the maximum charging power and maximum discharging power of the heat storage device respectively; u(t) represents a 0-1 variable at time t; Q HSC_initial , Q HSC_final They represent the initial heat and final heat of the heat storage device in one day respectively.

[0079] The operation constraints of the energy storage power station are also the constraints that it cannot charge and discharge at the same time and the constraints that the energy storage power station's initial and final power consumption must be consistent, which are respectively expressed as equations (15), (16), and (17):

[0080] 0≤P CH (t)≤x(t)P CH MAX (15)

[0081] 0≤P DIS (t)≤[1-x(t)]P DIS MAX (16)

[0082] E BAT_initial =E BAT_final (17)

[0083] Where P CH MAX , P DIS MAX are the maximum power of charging and discharging of the energy storage station; x(t) represents a 0-1 variable at time t; E BAT_initial 、E BAT_final They respectively represent the initial and final power consumption of the energy storage power station in a day.

[0084] 4) Objective functions of microgrids at different time scales

[0085] In the day-ahead dispatch model, the objective function is to maximize the economic benefits of renewable energy and maximize the consumption of wind power and photovoltaic power.

[0086] In terms of economic benefits, the main consideration is the operation and maintenance costs of the microgrid. The operation and maintenance costs mainly include the operation and maintenance costs of wind power, photovoltaic power, solar thermal power, thermal power, and the operation and maintenance costs of battery systems and heat storage devices. Various costs are expressed as formula (18):

[0087]

[0088] In the formula, C OM Represents the total operation and maintenance cost; C OM PV (t), C OMWT (t), C OM THE (t), C OM CHP (t), C OM CSP (t), C OM ES (t) represents the operation and maintenance costs of photovoltaic power generation, wind power generation, thermal power generation, cogeneration, solar thermal power station and energy storage power station respectively; k OM PV , k OM WT , k OM THE , k OM CHP_E , k OM CHP_H , k OM CSP_E , k OM CSP_H , k OM ES They respectively represent the operation and maintenance cost coefficients of photovoltaic power generation, wind power generation, thermal power, cogeneration power generation, cogeneration heat production, solar thermal power station power generation, solar thermal power station heat production and energy storage power station.

[0089] In addition, in order to maximize the consumption of clean energy such as wind and solar energy, it is also necessary to add penalties for abandoning wind and solar power to the objective function, which can be expressed as formula (19):

[0090]

[0091] In the formula, C pl represents the penalty power for wind and solar power abandonment, λ is the penalty coefficient for wind and solar power abandonment cost; P pl (t) is the wind and solar power curtailed at time t.

[0092] Finally, the optimization goal of the day-ahead dispatch is to minimize the operation and maintenance costs of all power stations plus the penalties for wind and solar power abandonment, which can be expressed as formula (20):

[0093] C A =C OM +C pl (20)

[0094] In the formula, C A It represents the sum of total operation and maintenance cost and penalty power for wind and solar power abandonment.

[0095] In the daily optimization scheduling, in order to further improve the stability of the microgrid system, the objective function is to minimize the output fluctuation of various power sources, which can be expressed as formula (21).

[0096]

[0097] In the formula, C FLC represents the total volatility cost; ΔP WT (t), ΔP PV (t), ΔP THE (t), ΔP CHP (t), ΔP CSP (t), ΔP ES (t) respectively represent the power fluctuations of wind power, photovoltaic power, thermal power, cogeneration, solar thermal power station and energy storage power station at time t, that is, the difference between various powers and the previous moment.

[0098] 5) Solution method

[0099] The model used in this method is a linear integer programming model. For convenience, MATLAB is used to call the commercial solver CPLEX for solution.

[0100] The output of renewable energy sources such as wind power and photovoltaic power is affected by external factors such as weather conditions and seasonal changes, and has obvious randomness and volatility. The existing optimization scheduling methods are difficult to accurately predict the power generation of wind and solar power, resulting in the inability to effectively deal with the imbalance between supply and demand of such energy in actual scheduling, increasing the operating costs of the system and the instability of the power system. Most of the existing scheduling schemes are mainly concentrated at the day-ahead scheduling level, and the scheduling cycle is usually set to an hour or even longer time interval. Due to the large time interval, the scheduling system cannot respond to the rapid fluctuations in wind and solar power generation in real time, especially when the load changes frequently or encounters extreme weather. The scheduling strategy is not updated in a timely manner and cannot accurately guide the scheduling work. The robustness of the system is reduced, affecting the balance of the power grid and the reliability of power supply.

[0101] Compared with the existing optimization scheduling method, this patent proposes a multi-time scale scheduling optimization algorithm, which uses high-frequency scheduling within a short time scale, such as a 15-minute time interval, to timely update the output forecast and load demand of renewable energy such as wind power and photovoltaics, reduce the impact of volatility on the system, and improve the accuracy and reliability of scheduling. In addition, the present invention plans to use a multi-time scale refined scheduling strategy to minimize the cost of power generation and wind and solar abandonment on a large time scale (such as an interval of 1 hour before the day), and introduce a small time scale (such as 15 minutes within a day) of real-time scheduling to optimize the smoothness of various energy outputs, ensuring that the scheduling system can take into account both economic benefits and stability.

[0102] The key point of the present invention is that a multi-time scale scheduling optimization of the day-ahead and intra-day is adopted in a multi-energy complementary microgrid system, and different objective functions are adopted at different time scales. In the day-ahead scheduling optimization, the optimization goal focuses on improving the economic benefits of the power grid and the consumption of new energy. In the existing optimization scheduling methods at different time scales, maximizing economic benefits is used as the objective function at different time scales, which to a certain extent ignores the demand for stability of the microgrid system. In the intra-day scheduling optimization of the present invention, the optimization goal focuses on reducing the output fluctuations of various power sources, thereby improving the stability of the power grid.

Claims

1. A multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and a cogeneration system, characterized by: The steps are as follows: 1) Modeling of CSP power station and cogeneration. CSP power station is divided into heat collection module, heat storage module and power generation module. The model of heat collection module: Q SF =η SF *DNI, Q SF (t) = Q SF_HS (t)+Q SF_GE (t), where Q SF Represents the total heat collected by the solar collector module, η SF is the light-to-heat conversion efficiency, DNI is the direct normal irradiance, Q SF_HS (t), Q SF_GE (t) are the heat transferred from the heat collection module to the heat storage module and the power generation module at time t, Q SF (t) is the total heat collected by the heat collection module at time t; the model of heat storage module charging and releasing heat: Q HS_All (t) = Q HS_GE (t)+Q HS_LOAD (t), where Q HSC (t) and Q HSC (t-1) represents the heat stored by the heat storage module at time t and the previous moment, respectively, η in Q , η out Q They represent the efficiency of heat charging and heat release in the heat storage module, Q HS_All (t) represents the heat released by the heat storage component at time t, where Q HS_GE (t) represents the heat sent to the power generation device, Q HS_LOAD (t) represents the amount of heat sent to the heat load; Power generation module: P CSP (t) = η GE (Q SF_GE (t)+Q HS_GE (t)), where P CSP (t) represents the output power of the CSP power station at time t, η GE Indicates the thermal-electric conversion efficiency of the CSP power generation module; Combined Heat and Power Model: P CHP (t) = η CHP E P fuel (t), Q CHP_LOAD (t) = η CHP Q P fuel (t), where P CHP (t) and Q CHP_LOAD (t) respectively represent the electrical power and thermal power of the cogeneration at time t, η CHP E , η CHP Q Respectively represent the power generation efficiency and heating efficiency of cogeneration, P fuel (t) is the fuel consumption rate; 2) Operation strategy of multi-energy complementary microgrid under multiple time scales. Step 1) The operation strategy of the model is divided into two stages: day-ahead optimization and intraday optimization. In the day-ahead optimization stage, the short-term forecasts of renewable energy generation, load and direct radiation intensity are used as inputs. Through optimization, the active power output plans of various energy sources are obtained. In the intraday optimization stage, the ultra-short-term forecast results of renewable energy generation, load and direct radiation intensity, as well as the active power output plan obtained from the first stage, are used as inputs. The operation interval on the time scale of the intraday stage is measured in minutes, and a rolling optimization strategy is adopted. For each moment after the first moment, the initial conditions are based on the latest values ​​of the previous moment. 3) Operation constraints of the microgrid, including power balance constraints, heat balance constraints, power station output constraints, heat storage device constraints, and energy storage power station constraints; 4) Objective function of microgrid at different time scales: In the formula, C OM Represents the total operation and maintenance cost; C OM PV (t), C OM WT (t), C OM THE (t), C OM CHP (t), C OM CSP (t), C OM ES (t) represents the operation and maintenance costs of photovoltaic power generation, wind power generation, thermal power generation, cogeneration, solar thermal power station and energy storage power station respectively; k OM PV , k OM WT , k OM THE , k OM CHP_E , k OM CHP_H , k OM CSP_E , k OM CSP_H , k OM ES They represent the operation and maintenance cost coefficients of photovoltaic power generation, wind power generation, thermal power generation, combined heat and power generation, combined heat and power generation, solar thermal power station power generation, solar thermal power station heat generation, and energy storage power station, respectively. PV (t), P WT (t), P THE (t), P DIS (t), P CH (t) represent wind power generation power, photovoltaic power generation power, thermal power generation power, battery discharge power and battery charging power respectively, Q CHP_LOAD (t) represents the heat produced by cogeneration, Q HS_LOAD (t) represents the heat sent from the thermal storage device to the thermal load; the penalty for wind and solar abandonment is added to the objective function: In the formula, C pl represents the penalty power for wind and solar power abandonment, λ is the penalty coefficient for wind and solar power abandonment cost; P pl (t) is the wind and solar power curtailed at time t; The optimization goal of day-ahead dispatch is to minimize the operation and maintenance costs of all power stations plus the penalties for wind and solar power abandonment, expressed as: C A =C OM +C pl , where C A It represents the sum of total operation and maintenance cost and penalty power for wind and solar power abandonment; The optimization goal of intraday scheduling is to minimize the output fluctuation of various power sources, which can be expressed as: In the formula, C FLC represents the total volatility cost; ΔP WT (t), ΔP PV (t), ΔP THE (t), ΔP CHP (t), ΔP CSP (t), ΔP ES (t) represents the power fluctuation of wind power, photovoltaic power, thermal power, cogeneration, solar thermal power station and energy storage power station at time t respectively; 5) Solution: Use MATLAB to call the commercial solver CPLEX for solution.

2. The multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration according to claim 1 is characterized by: The power balance constraint is expressed as: P WT (t)+P PV (t)+P THE (t)+P CSP (t)+P CHP (t)+P DIS (t)-P CH (t)=P LOAD (t), Where P LOAD (t) represents the electrical load power.

3. The multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration according to claim 1 is characterized by: The heat balance constraint is expressed as: Q HS_LOAD (t)+Q CHP_LOAD (t)=Q LOAD (t)。 4. The multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration according to claim 1 is characterized by: The power station output constraint is expressed as: Where P WT_PRE (t), P PV_PRE (t) represents the predicted maximum output of wind power and photovoltaic power respectively; P THE_MAX , P CHP_MAX , P CSP_MAX They represent the maximum power of thermal power, cogeneration and solar thermal power stations respectively.

5. The multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration according to claim 1, characterized in that: The heat storage device constraints include the heat storage device heat capacity constraint, the constraint that heat charging and heat release cannot be performed simultaneously, and the constraint that the heat capacity must be maintained consistent within a scheduling cycle, which are respectively expressed as: 0≤Q SF_HS (t)≤u(t)Q TES_in MAX ; 0≤Q HS_All (t)≤[1-u(t)]Q TES_out MAX ; Q HSC_initial =Q HSC_final ; In the formula, H TES Indicates the number of hours at full load; Q TES_in MAX , Q TES_out MAX They represent the maximum charging power and maximum discharging power of the heat storage device respectively; u(t) represents a 0-1 variable at time t; Q HSC_initial , Q HSC_final They represent the initial heat and final heat of the heat storage device in one day respectively.

6. The multi-time scale optimization scheduling method for a microgrid system including a solar thermal power station and cogeneration according to claim 1 is characterized by: The energy storage power station constraints include the constraint that it cannot charge and discharge at the same time and the constraint that the energy storage power station's initial and final power consumption of the day must be consistent, which can be expressed as: 0≤P CH (t)≤x(t)P CH MAX ,0≤P DIS (t)≤[1-x(t)]P DIS MAX ; AND BAT_initial =And BAT_final ; Where P CH MAX , P DIS MAX are the maximum power of charging and discharging of the energy storage station; x(t) represents a 0-1 variable at time t; E BAT_initial 、E BAT_final They respectively represent the initial and final power consumption of the energy storage power station in a day.

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