Virtual power plant day-ahead optimization scheduling system and method considering low-carbon operation

Abstract

The invention discloses a virtual power plant day-ahead optimization scheduling system and method considering low-carbon operation. The method comprises the steps that a system model for calculating output parameters of all devices of a virtual power plant is constructed; constructing constraint conditions of a system model of each device by taking solving of wind and light absorption and / or operation of the virtual power plant at the lowest cost as an optimization target; and obtaining a load side demand according to the day-ahead data of the virtual power plant, solving the system model of each device of the virtual power plant according to the load side demand and the optimization target, and obtaining a day-ahead power scheduling cooperation optimal strategy of each device unit under the constraint condition of the system model of each device. According to the method, the P2G-CCS equipment unit is introduced to reduce the carbon emission of the virtual power plant, improve the energy consumption of the wind and light unit and realize green and environment-friendly operation; new energy, heating, refrigeration, energy storage unit output and load requirements are analyzed, and carbon transaction is considered. A branch and bound algorithm is adopted, a low-carbon operation day-ahead power scheduling scheme is provided, and scheduling feasibility and low-carbon performance are improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy control technology, and in particular to a day-ahead optimization scheduling system and method for a virtual power plant taking low-carbon operation into consideration. Background Art

[0002] In recent years, energy crisis and environmental pollution have become a major problem that needs to be solved all over the world. Renewable energy, mainly wind and solar power generation, has gradually been developed and applied to society. As a resource integration and dispatching center, virtual power plants need to solve urgent problems in terms of how to reasonably and effectively dispatch resources and reduce the carbon emissions of system equipment. Therefore, for virtual power plant systems containing P2G-CCS (Power-to-Gas, P2G; carbon capture and storage, CCS) equipment, it is very important to effectively optimize the power dispatch of virtual power plant system equipment on the day before and reduce the carbon emissions and operating costs of the system.

[0003] However, there are few studies on low-carbon optimization scheduling for virtual power plant systems containing P2G-CCS equipment. Most of them introduce P2G equipment into the integrated energy system of the park for economic optimization scheduling. Therefore, for the virtual power plant system, after the introduction of P2G-CCS equipment, how to optimize the power scheduling of the equipment on the day before, realize the rationality and efficiency of equipment scheduling, and reduce the carbon emissions of the system equipment are urgent problems to be solved. Summary of the invention

[0004] In order to optimize the day-ahead equipment power dispatching of a virtual power plant system that introduces P2G-CCS equipment, the present invention proposes a day-ahead optimization dispatching method and system for a virtual power plant that considers low-carbon operation.

[0005] A virtual power plant day-ahead optimization dispatching system considering low-carbon operation to achieve one of the purposes of the present invention includes:

[0006] System model building module: used to build a system model for calculating the output parameters of each device in the virtual power plant;

[0007] Objective constraint module: used to construct the constraint conditions of the system model of each device with the optimization goal of solving the problem of wind and solar power consumption and / or virtual power plant operation at the lowest cost; the ultimate goal of the optimization goal is to effectively dispatch the power distribution of each device, cut the peak and fill the valley, so as to minimize the system operation cost; the effective dispatch means that the system can operate at the lowest cost under the premise of stable operation; the solution of wind and solar power consumption means that the electricity generated by wind and solar power generation can be consumed;

[0008] Strategy determination module: used to obtain the load side demand according to the day-ahead data of the virtual power plant, solve the system model of each device in the virtual power plant according to the load side demand and the optimization target, and obtain the optimal strategy for the day-ahead power scheduling of each device unit under the constraints of the system model of each device; the load side demand includes: electric load demand, cold and hot load demand; the optimal strategy for the day-ahead power scheduling of each device unit includes: determining the output power of each device at each moment; the output power of each device includes: main grid exchange power, gas internal combustion engine power generation, centrifugal cooling capacity, energy storage and release of each energy storage device, heating capacity of waste heat device, electrolyzer power consumption, methane reactor hydrogen consumption, hydrogen fuel cell (HFC) power generation, CCS operating power, lithium bromide absorption refrigeration capacity, gas boiler heating capacity.

[0009] Furthermore, in the system model construction module, the system models of the equipment in the virtual power plant include an electrolyzer equipment system model, which is used to calculate the hydrogen production power of the electrolyzer equipment based on the energy conversion efficiency, power consumption and start / stop status of the electrolyzer; the electrolyzer equipment is a type of P2G equipment.

[0010] Furthermore, in the system model construction module, the system models of various equipment in the virtual power plant include a methane reactor equipment system model, which is used to calculate the natural gas flow rate generated by the methane reactor equipment based on the energy conversion efficiency of the methane reactor, the hydrogen energy consumed by the methane reactor, the start and stop status, and the calorific value of the natural gas; it is also used to calculate the amount of carbon dioxide consumed by the methane reactor based on the carbon dioxide density and the natural gas flow rate generated by the methane reactor equipment; the methane reactor equipment is a type of P2G equipment.

[0011] Furthermore, in the system model building module, the system model of each device of the virtual power plant includes a CCS device system model, which is used to calculate the power consumption of the CCS device according to the basic energy consumption, start and stop status and operating power consumption of the CCS device.

[0012] Furthermore, in the system model construction module, the system models of each device in the virtual power plant include a wind power generation system model, which is used to calculate the actual output power of wind power generation based on the cut-in wind speed, rated wind speed, cut-out wind speed, actual wind speed and rated power of the wind turbine.

[0013] Furthermore, in the system model construction module, the system models of each device in the virtual power plant include a photovoltaic power generation system model, which is used to calculate the output power of the photovoltaic system based on the rated output power of the photovoltaic system, light intensity, real-time temperature of the photovoltaic panel and reference temperature.

[0014] Furthermore, in the system model construction module, the system models of each equipment in the virtual power plant include a gas internal combustion engine system model, which is used to calculate the electrical energy generated by the gas internal combustion engine based on the gas-to-electricity efficiency and gas-to-heat efficiency of the gas internal combustion engine, the consumed natural gas flow rate, the generated waste heat, and the start and stop status of the gas internal combustion engine.

[0015] Furthermore, in the system model construction module, the system models of the various devices in the virtual power plant include a gas boiler system model, which is used to calculate the heating power of the gas boiler according to the gas consumption power and start-stop working status of the gas boiler.

[0016] Furthermore, in the system model construction module, the system models of the equipment in the virtual power plant include a lithium bromide absorption chiller system model, which is used to calculate the cooling capacity of the lithium bromide unit based on the heat absorbed and consumed by the lithium bromide unit and the start and stop status of the lithium bromide refrigeration unit.

[0017] Furthermore, in the system model construction module, the system models of the various devices in the virtual power plant include a centrifugal refrigerator system model, which is used to calculate the refrigeration power of the centrifugal refrigerator based on the operating power consumption and start / stop status of the centrifugal refrigerator.

[0018] Furthermore, in the system model construction module, the system models of the various equipment in the virtual power plant include a waste heat recovery device system model, which is used to calculate the heat generated by the waste heat recovery device based on the heat absorbed by the waste heat recovery device from the gas internal combustion engine and the start and stop status of the waste heat recovery device.

[0019] Furthermore, in the system model construction module, the system models of each device in the virtual power plant include a hydrogen fuel cell system model, which is used to calculate the hydrogen power consumed by the hydrogen fuel cell based on the electrical power and thermal power generated by the hydrogen fuel cell and the start and stop status of the hydrogen fuel cell.

[0020] Furthermore, in the system model construction module, the system model of each device of the virtual power plant includes a system model of an electric energy storage device, which is used to calculate the stored electric power of the electric energy storage device based on the charging and discharging power and the working start and stop status of the electric energy storage device.

[0021] Furthermore, in the system model construction module, the system model of each device of the virtual power plant includes a cold energy storage device system model, which is used to calculate the storage cold power of the cold energy storage device according to the charging and discharging cold power and start and stop status of the cold energy storage device.

[0022] Furthermore, in the system model construction module, the system models of the various devices in the virtual power plant include a thermal energy storage device system model, which is used to calculate the stored thermal power of the thermal energy storage device based on the charging and discharging power of the thermal energy storage device and the start and stop status of the thermal energy storage device.

[0023] Furthermore, in the system model construction module, the system models of the various devices in the virtual power plant include a hydrogen energy storage device system model, which is used to calculate the stored hydrogen power of the hydrogen energy storage device based on the charging and discharging power of the hydrogen energy storage device and the start and stop status of the device.

[0024] Furthermore, in the target constraint module, the optimization objectives include: minimizing the sum of the cost of exchanging electricity between the virtual power plant system and the main grid, the cost of purchasing natural gas for the virtual power plant system, the initial construction cost of the virtual power plant system, the equipment operation cost of the virtual power plant system and the cost of the virtual power plant system participating in carbon trading.

[0025] A method for optimizing the day-ahead scheduling of a virtual power plant considering low-carbon operation to achieve the second objective of the present invention includes:

[0026] Construct a system model to calculate the output parameters of each device in the virtual power plant;

[0027] With the optimization goal of solving the problem of wind and solar power consumption and / or virtual power plant operation at the lowest cost, the constraints of the system model of each device are constructed;

[0028] The load side demand is obtained according to the day-ahead data of the virtual power plant, and the system model of each device of the virtual power plant is solved according to the load side demand and the optimization target to obtain the optimal strategy for power dispatch coordination of each device unit that meets the constraints.

[0029] Furthermore, the optimization objectives include: minimizing the sum of the cost of exchanging electricity between the virtual power plant system and the main grid, the cost of purchasing natural gas for the virtual power plant system, the initial construction cost of the virtual power plant system, the equipment operation cost of the virtual power plant system and the cost of the virtual power plant system participating in carbon trading.

[0030] The beneficial effects of the present invention include:

[0031] This method considers introducing P2G-CCS equipment units to reduce the carbon emissions of carbon-emitting equipment in virtual power plants, while improving the energy absorption capacity of wind and solar units in virtual power plants to achieve green and environmentally friendly operation of virtual power plants. The output characteristics of new energy units, heating units, refrigeration units, energy storage units and other equipment in virtual power plants, as well as the demand characteristics on the load side, are analyzed, and the participation of virtual power plants in the carbon trading market is considered. A branch and bound algorithm is used to propose a day-ahead power dispatching scheme for low-carbon operation of virtual power plants, and an optimal decision-making scheme for equipment dispatching is established. Compared with the existing day-ahead power dispatching optimization method for virtual power plants, P2G-CCS equipment units are added to the system, which improves the feasibility of the optimized dispatching results of virtual power plants and the low-carbon nature of equipment operation, and the rationality of the power dispatching coordination results of each device in the system is analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1is a flow chart of a method for optimizing the day-ahead scheduling of a virtual power plant considering low-carbon operation according to an embodiment of the present invention;

[0033] Figure 2 This is a model architecture diagram of a virtual power plant system according to an embodiment of the present invention;

[0034] Figure 3 It is a diagram of the system structure and algorithm association of an embodiment of the present invention;

[0035] Figure 4 This is a diagram of wind and solar power output data according to an embodiment of the present invention;

[0036] Figure 5 It is a local real-time ladder electricity price map according to an embodiment of the present invention;

[0037] Figure 6 is a flow chart of branch and bound algorithm optimization according to an embodiment of the present invention;

[0038] Figure 7 This is a power balance optimization scheduling diagram of the system according to an embodiment of the present invention;

[0039] Figure 8 This is a cooling balance optimization scheduling diagram of the system according to an embodiment of the present invention;

[0040] Fig. 9 is a heat supply balance optimization scheduling diagram of the system according to an embodiment of the present invention;

[0041] Fig.10 This is a hydrogen supply balance optimization scheduling diagram of the system according to an embodiment of the present invention;

[0042] Fig.11 This is a gas supply balance optimization scheduling diagram for the system according to an embodiment of the present invention;

[0043] Fig.12 This is a carbon emission relationship diagram of the system of the embodiment of the present invention;

[0044] Fig.13 This is a diagram of carbon capture and utilization in a system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following specific implementations are used to explain the technical solutions of the claims of the present invention so that those skilled in the art can understand the claims. The protection scope of the present invention is not limited to the following specific implementation structures. The technical solutions of the claims of the present invention made by those skilled in the art but different from the following specific implementations are also within the protection scope of the present invention.

[0046] In P2G-CCS equipment, P2G (Power-to-Gas) is a technology that converts electricity into gas. It converts electricity into hydrogen or methane and other gases through water electrolysis and other methods and stores them to achieve energy storage and utilization; CCS (Carbon Capture and Storage) is the abbreviation of carbon capture and storage technology; through specific technical means, the emitted carbon dioxide is captured and stored underground or in other safe places to reduce greenhouse gas emissions. In the process of energy production and consumption, CCS technology can effectively reduce carbon emissions and achieve low-carbon and environmental protection goals. P2G-CCS is a comprehensive energy technology that couples P2G technology and CCS technology. When working, P2G-CCS equipment first converts electricity into gas (such as hydrogen), and then captures and stores the generated carbon dioxide through CCS technology.

[0047] Example 1

[0048] See also Figure 1 The present invention proposes a virtual power plant day-ahead optimization scheduling method considering low-carbon operation. The system structure diagram is shown in Figure 2 As shown, the method specifically comprises the following steps:

[0049] S1: Introduce P2G-CCS equipment units, deploy a virtual power plant system information interaction platform, and rely on the information interaction platform to obtain the day-ahead data of the virtual power plant;

[0050] The day-ahead data include:

[0051] The day-ahead data of sunlight intensity, wind speed, real-time electricity price and natural gas price are obtained from weather forecast centers, carbon trading markets, power companies and natural gas price companies.

[0052] The photovoltaic unit output data and wind turbine unit output data obtained from the wind and solar unit system according to the established wind and solar output model;

[0053] The load-side electrical load demand data and cooling and heating load demand data of the virtual power plant source system are obtained from the load-side control center.

[0054] S2: Establish system models of various equipment in the virtual power plant, including: wind power generation system model, photovoltaic power generation system model, gas internal combustion engine system model, gas boiler system model, lithium bromide absorption refrigerator system model, centrifugal refrigerator system model, waste heat recovery device system model, P2G equipment system model, CCS equipment system model, hydrogen fuel cell system model, and energy storage device system model.

[0055] (1) The mathematical expression of the wind power generation system model is:

[0056]

[0057] Where: P t w is the actual output power of wind power generation at time t, kW; V i 、V r 、V o 、V w are the cut-in wind speed, rated wind speed, cut-out wind speed and actual wind speed of the wind turbine respectively; P r is the rated power.

[0058] (2) The mathematical expression of the photovoltaic power generation system model is:

[0059]

[0060] Where: P t pv is the output power of the photovoltaic system at time t, kW; t is the unit time in a day, ranging from 1 to 24 hours; η PV is the reaction coefficient of the photovoltaic panel to the shading and aging damage, and in this embodiment, the value is a constant of 0.9; c is the rated output power of the photovoltaic system under the standard test environment, kW; G is the light intensity at time t; G c is the light intensity under the standard test environment (1000W / m2, 25℃); α P is the power compensation coefficient; T s is the real-time temperature of the photovoltaic panel; T c is the reference temperature value of the photovoltaic panel under the standard test environment, which is 25°C in this embodiment.

[0061] (3) The mathematical expression of the gas internal combustion engine system model is:

[0062]

[0063] Where P t GE is the electrical energy generated by the gas internal combustion engine at time t, kW; η GE is the gas-to-electricity efficiency of the gas internal combustion engine, and the values ​​in this embodiment are shown in Table 1; is the natural gas flow consumed by the gas internal combustion engine at time t, m 3 / h; is the waste heat generated by the gas internal combustion engine at time t; η RE is the gas-to-heat efficiency of the gas internal combustion engine; is the start / stop state of the gas internal combustion engine. In this embodiment, the value of the start / stop state is 1 when the gas internal combustion engine is started, otherwise it is 0;

[0064] (4) The mathematical expression of the gas boiler system model is:

[0065]

[0066] Where: is the heating power of the gas boiler at time t, kW; is the thermal efficiency coefficient of the gas boiler, and the values ​​in this embodiment are shown in Table 1; is the gas consumption power of the gas boiler at time t, m 3 / h; is the start / stop working state of the gas boiler. In this embodiment, the value of the start / stop state is 1 when the gas boiler is started, otherwise it is 0.

[0067] (5) The mathematical expression of the lithium bromide absorption refrigerator system model is:

[0068]

[0069] Where: is the cooling capacity of the lithium bromide unit, kW; The heat absorbed and consumed by the lithium bromide unit for refrigeration, kW; η BL is the conversion efficiency of the lithium bromide refrigerator; is the start / stop state of the lithium bromide refrigeration unit. In this embodiment, the value of the start / stop state is 1 when the lithium bromide refrigeration unit is started, otherwise it is 0;

[0070] (6) The mathematical expression of the centrifugal refrigerator system model is:

[0071]

[0072] Where: is the cooling power of the centrifugal refrigerator at time t, kW; η EC is the conversion efficiency of the centrifugal refrigerator; P t EC is the operating power consumption of the centrifugal refrigerator at time t, kW; is the start / stop state of the centrifugal refrigeration unit. In this embodiment, the start / stop state value is 1 when the centrifugal refrigeration unit is started, otherwise it is 0;

[0073] (7) The mathematical expression of the waste heat recovery device system model is:

[0074]

[0075] In the formula, is the heat generated by the waste heat recovery device, kW; is the heat absorbed by the waste heat recovery device from the gas internal combustion engine, kW; η backis the energy recovery efficiency of the waste heat recovery device, and the values ​​in this embodiment are shown in Table 1; is the start / stop state of the waste heat recovery device. In this embodiment, the start / stop state value is 1 when the waste heat recovery device is started, otherwise it is 0;

[0076] (8) The P2G equipment system model described in this embodiment is divided into an electrolyzer device and a methane reactor device, wherein the mathematical expression of the electrolyzer device is:

[0077]

[0078] In the formula, is the hydrogen production power of the electrolyzer at time t, kW; η EL is the energy conversion efficiency of the electrolyzer; P t EL,e is the power consumption of the electrolytic cell at time t, kW; is a 0-1 variable, indicating the working state of the electrolytic cell equipment. In this embodiment, a 0-1 variable is introduced to indicate the start and stop state of the equipment. When the value is 0, it means that the device stops working at this moment. When the value is 1, it means that the device is working normally at this moment.

[0079] The mathematical expression of the methane reactor equipment is:

[0080]

[0081]

[0082] In the formula, is the natural gas flow rate generated by the methane reactor equipment at time t, m 3 / h; η MR is the energy conversion efficiency of the methane reactor, and the values ​​in this embodiment are shown in Table 1; is the hydrogen energy consumed by the methane reactor at time t, kW; is the start / stop state of the methane reactor. In this embodiment, the start / stop state value is 1 when the methane reactor is started, otherwise it is 0; is the calorific value of natural gas; The amount of carbon dioxide consumed by the methane reactor; is the density of carbon dioxide;

[0083] (9) The mathematical expression of the CCS equipment system model is as follows (12):

[0084]

[0085] Where P t CCSP is the power consumption of CCS equipment at time t, kW; t CCS,b is the basic energy consumption of CCS equipment at time t, kW; is the start / stop state of the CCS device at time t. In this embodiment, the start / stop state value is 1 when the CCS device is started, otherwise it is 0; t CCS,u is the power consumption of CCS equipment at time t, kW; is the carbon capture rate of the CCS equipment; The amount of carbon dioxide consumed to operate the CCS equipment; The amount of CO2 stored for CCS facilities; is the maximum storage capacity of carbon dioxide in the CCS equipment; is the amount of carbon dioxide consumed by the methane reactor as shown in formula (11).

[0086] (10) The mathematical expression of the hydrogen fuel cell system model is:

[0087]

[0088] In the formula, is the hydrogen power consumed by the hydrogen fuel cell at time t, kW; η HFC,e , η HFC,h is the power generation and heat generation efficiency of the hydrogen fuel cell, and the values ​​in this embodiment are shown in Table 1; P t HFC,e , P t HFC,h is the electrical power and thermal power generated by the hydrogen fuel cell at time t, kW; is the start / stop state of the hydrogen fuel cell. In this embodiment, the value of the start / stop state is 1 when the hydrogen fuel cell is started, otherwise it is 0.

[0089] (11) The energy storage device system model includes an electric energy storage device system model, a cold energy storage device system model, a thermal energy storage device system model and a hydrogen energy storage device system model;

[0090] The mathematical expression of the electric energy storage device system model is:

[0091]

[0092] Where: are the stored electric power of the electric energy storage device at time t and t-1, kW; η es,in , η es,out are the charging and discharging efficiency of the energy storage device; P t es,in , P t es,outare the charging and discharging power of the energy storage device at time t, kW; They are respectively the starting and stopping states of charging and discharging of the electric energy storage device. In this embodiment, the value of the starting and stopping state during startup is 1, otherwise it is 0.

[0093] The mathematical expression of the cold energy storage device system model is:

[0094]

[0095] Where: are the storage cold power of the cold energy storage equipment at time t and t-1, kW; η cs,in , η cs,out They are the charging and discharging efficiency of cold energy storage equipment respectively; are the charging and discharging power of the cold energy storage equipment at time t, kW; are respectively the start and stop states of the charging and discharging of the cold energy storage device. In this embodiment, the value of the start and stop state at startup is 1, otherwise it is 0;

[0096] The mathematical expression of the thermal energy storage device system model is:

[0097]

[0098] Where: are the thermal power stored in the thermal energy storage device at time t and t-1, kW; η hs,in , η hs,out are the charging and discharging efficiencies of thermal energy storage equipment, respectively; are the charging and discharging power of the thermal energy storage device at time t, kW; are the start and stop states of the charging and discharging operation of the thermal energy storage device, respectively. In this embodiment, the value of the start and stop state during startup is 1, otherwise it is 0;

[0099] The mathematical expression of the hydrogen energy storage device system model is:

[0100]

[0101] Where: are the stored hydrogen power of the hydrogen energy storage device at time t and t-1, kW; are the charging and discharging power of hydrogen energy storage equipment at time t, kW; They are respectively the start and stop states of the hydrogen charging and discharging operation of the hydrogen energy storage device at time t. In this embodiment, the value of the start and stop state at startup is 1, otherwise it is 0.

[0102] S3: The optimization goal is to solve the problem of wind and solar power consumption and / or virtual power plant operation at the lowest cost, and to build corresponding system model constraints;

[0103] The mathematical expression of the optimization objective is as follows:

[0104]

[0105] In the formula, F min is the minimum operating cost of the virtual power plant system; F grid F is the cost of exchanging electricity between the virtual power plant system and the main grid; gas Cost of purchasing natural gas for the virtual power plant system; F is is the initial construction cost of the virtual power plant system; F os The equipment operating costs of the virtual power plant system; G is the cost of the virtual power plant system participating in carbon trading; gas is the price of natural gas; is the gas consumption power of the gas boiler at time t, m 3 / h; is the natural gas flow consumed by the gas internal combustion engine at time t, m 3 / h; is the natural gas flow rate generated by the methane reactor equipment at time t, m 3 / h; η m is the unit operating cost of the mth device in the virtual power plant system; P m (t) is the operating power of the mth device at time t; n is the total number of operating devices in the virtual power plant system.

[0106] The carbon trading mechanism model used by the virtual power plant system to calculate the cost of participating in carbon trading includes:

[0107]

[0108] In the formula, is the cost of the virtual power plant system participating in carbon trading; λ is the carbon trading base price; E tr,t is the actual carbon emission minus the carbon quota; l is the length of the carbon emission interval; α is the transaction price growth rate; The increase in the transaction base price.

[0109] The system model constraints of each device include: power balance equation constraints, cold power balance constraints, hot power balance constraints, hydrogen power balance constraints, natural gas balance constraints, main grid transmission line exchange power safety constraints, power storage equipment operation constraints, gas internal combustion engine equipment operation constraints, gas boiler operation constraints, waste heat recovery device equipment operation constraints, lithium bromide refrigerator equipment operation constraints, centrifugal refrigerator equipment operation constraints, thermal energy storage equipment operation constraints, electrolyzer equipment operation constraints, cold energy storage equipment operation constraints, methane reactor equipment operation constraints, hydrogen energy storage equipment operation constraints, CCS equipment operation constraints, hydrogen fuel cell operation constraints; the parameter values ​​of the constraints are shown in Table 1, and the expressions of the constraints specifically include:

[0110] (1) Power balance equation constraint:

[0111] P t load =P t grid +P t Z +P t GE +P t HFC,e +P t es,out -P t EL,e -P t CCS -P t EC -P t es,in (27)

[0112] P t Z =P t pv +P t w (28)

[0113] Where P t load is the electric load demand of the virtual power plant system; P t grid P is the exchange power between the virtual power plant system and the main grid at time t. A positive value indicates that the system purchases electricity from the main grid, and a negative value indicates that the system sells electricity to the main grid. t Z The output power P of the photovoltaic power generation system at time t is shown in formula (2): t pv The actual output power P of the wind power generation system shown in formula (1) t w The sum of tGE is the electric energy generated by the gas internal combustion engine at time t as shown in formula (3); t HFC,e is the electric power generated by the hydrogen fuel cell at time t as shown in formula (16); P t es,out is the discharge power of the energy storage device at time t as shown in formula (18); P t EL,e is the power consumption of the electrolytic cell at time t as shown in formula (9); t CCS is the power consumption of the CCS equipment shown in formula (12); P t EC is the power consumption of the centrifugal refrigerator at time t as shown in formula (7); P t es,in is the charging power of the electric energy storage device at time t as shown in formula (18);

[0114] (2) Cold power balance constraint

[0115]

[0116] In the formula, The cooling load demand of the virtual power plant system; is the cooling capacity of the lithium bromide unit shown in formula (6); is the cooling power of the centrifugal refrigerator at time t as shown in formula (7); are respectively the charging and discharging cold power of the cold energy storage device at time t as shown in formula (19).

[0117] (3) Thermal power balance constraints

[0118]

[0119] In the formula, is the heat load demand of the virtual power plant system; P t HFC,h is the thermal power generated by the hydrogen fuel cell at time t as shown in formula (17); is the heat generated by the waste heat recovery device shown in formula (8); is the heating power of the gas boiler at time t as shown in formula (5); and is the charging and discharging power of the thermal energy storage device at time t as shown in formula (20); It is the heat absorbed and consumed by the lithium bromide unit refrigeration as shown in formula (6).

[0120] (4) Hydrogen power balance constraints

[0121]

[0122] is the hydrogen production power of the electrolyzer at time t as shown in formula (9); are respectively the charging and discharging power of the hydrogen energy storage device at time t as shown in formula (21); is the hydrogen energy consumed by the methane reactor at time t as shown in formula (11); is the hydrogen power consumed by the hydrogen fuel cell at time t as shown in formula (16).

[0123] (5) Natural gas balance constraints

[0124]

[0125] In the formula, is the natural gas flow rate generated by the methane reactor equipment at time t as shown in formula (10); is the amount of natural gas purchased by the virtual power plant system from the natural gas company at time t; is the natural gas flow consumed by the gas internal combustion engine at time t as shown in formula (3); is the gas consumption power of the gas boiler at time t as shown in formula (5).

[0126] (6) Main network transmission line switching power safety constraints

[0127] -P t grid,max ≤P t grid ≤P t grid,max (33)

[0128] Where P t grid,max is the maximum value of the power exchanged between the virtual power plant system and the main grid at time t; P t grid It is the exchange power between the virtual power plant system and the main grid at time t. A positive value indicates that the virtual power plant system purchases electricity from the main grid, and a negative value indicates that the virtual power plant system sells electricity to the main grid.

[0129] (7) Operation constraints of power storage equipment

[0130]

[0131] Where P t es,max , P t es,min is the maximum and minimum charging and discharging power of the energy storage device at time t; P t es,in and P t es,out are respectively the charging and discharging power of the electric energy storage device at time t as shown in formula (18); are the charging and discharging start and stop states of the electric energy storage device shown in formula (18); R es It is the maximum number of charge and discharge times allowed for the energy storage device in one day; SOC is the installed capacity of the energy storage device, MWh; SOC es,min , SOC es,max It is the minimum and maximum state of charge threshold of the electric energy storage device.

[0132] (8) Operation constraints of gas internal combustion engine equipment

[0133]

[0134] In the formula, is the maximum and minimum operating power of the gas internal combustion engine; P t GE is the electrical energy generated by the gas internal combustion engine at time t as shown in formula (3); P is the electric energy generated by the gas internal combustion engine at time t-1; t GE,up , P t GE,down is the maximum upward and downward climbing power of the gas internal combustion unit; Δt is the time interval, that is, time t minus time t-1.

[0135] (9) Gas boiler operation constraints

[0136]

[0137] In the formula, The maximum and minimum operating power of the gas boiler; The maximum upward and downward climbing power of the gas boiler system; and are the heating powers of the gas boiler at time t-1 and time t as shown in formula (5) respectively; Δt is the time interval, i.e., time t minus time t-1.

[0138] (10) Operation constraints of waste heat recovery equipment

[0139]

[0140] In the formula, It is the minimum and maximum operating power of the waste heat recovery device; is the heat generated by the waste heat recovery device shown in formula (8).

[0141] (11) Lithium bromide refrigeration equipment operation constraints

[0142]

[0143] In the formula, are the maximum and minimum operating power of the lithium bromide system; is the cooling capacity of the lithium bromide unit shown in formula (6).

[0144] (12) Centrifugal refrigeration equipment operation constraints

[0145]

[0146] In the formula, is the maximum and minimum operating power of the centrifugal chiller system; is the cooling power of the centrifugal refrigerator at time t as shown in formula (7).

[0147] (13) Thermal energy storage equipment operation constraints

[0148]

[0149] In the formula, is the maximum and minimum charging and discharging power of the thermal energy storage device at time t; are respectively the charging and discharging power of the thermal energy storage device at time t as shown in formula (20); are the start and stop states of the charging and discharging operation of the thermal energy storage device shown in formula (20); is the thermal power stored in the thermal energy storage device at time t; R hs The maximum number of charging and discharging times allowed for a thermal energy storage device in one day; SOC h is the installed capacity of thermal energy storage equipment, MWh;

[0150] SOC hs,min , SOC hs,max It is the minimum and large capacity state threshold of the thermal energy storage device.

[0151] (14) Electrolyzer equipment operation constraints

[0152]

[0153] Where P t EL,max , P t EL,min is the maximum and minimum operating power of the electrolytic cell equipment; P t EL,up , P t EL,down The maximum upward and downward climbing power of the electrolyzer system; and P t EL,e are the power consumption of the electrolytic cell at time t-1 and time t as shown in formula (9) respectively; Δt is the time interval, that is, time t minus time t-1.

[0154] (15) Operation constraints of cold energy storage equipment

[0155]

[0156] In the formula, is the maximum and minimum charging and discharging cold power of the cold energy storage device at time t; are respectively the charging and discharging cold power of the cold energy storage device at time t as shown in formula (19); are the start and stop states of the charging and discharging operation of the cold energy storage device shown in formula (19); R cs The maximum number of charge and discharge times allowed for cold energy storage equipment in one day; SOC c is the installed capacity of cold energy storage equipment, MWh; is the storage cold power of the cold energy storage device at time t as shown in formula (19); SOC cs,min , SOC cs,max It is the minimum and large capacity state threshold of the cold energy storage device.

[0157] (16) Methane reactor equipment operation constraints

[0158]

[0159] Where P t MR,max , P t MR,min is the maximum and minimum operating power of the methane reactor equipment; and are the hydrogen energy consumed by the methane reactor at time t-1 and time t as shown in formula (10); P t MR,up , P t MR,down is the maximum upward and downward climbing power of the methane reactor system; Δt is the time interval, that is, time t minus time t-1.

[0160] (17) Operation constraints of hydrogen energy storage equipment

[0161]

[0162] In the formula, is the maximum and minimum charging and discharging cooling power of the hydrogen energy storage device at time t; are respectively the charging and discharging power of the hydrogen energy storage device at time t as shown in formula (21); are the start and stop states of the hydrogen charging and discharging operation of the hydrogen energy storage device shown in formula (21); is the stored hydrogen power of the hydrogen energy storage device at time t as shown in formula (21); The maximum number of charging and discharging times allowed for a hydrogen energy storage device in one day; is the installed capacity of hydrogen energy storage equipment, MWh; It is the minimum and large capacity state threshold of the hydrogen energy storage device.

[0163] (18) CCS equipment operation constraints

[0164] P t CCS,min ≤P t CCS ≤P t CCS,max (45)

[0165] Where P t CCS,max , P t CCS,min is the maximum and minimum operating power of the CCS system; P t CCS is the power consumption of the CCS equipment shown in formula (12).

[0166] (19) Hydrogen fuel cell operation constraints

[0167]

[0168] Where P t HFC,max , P t HFC,min The maximum and minimum operating power of the hydrogen fuel cell equipment; and are the hydrogen power consumed by the hydrogen fuel cell at time t-1 and time t as shown in formula (16); P t HFC,e , P t HFC,h is the electrical power and thermal power generated by the hydrogen fuel cell at time t as shown in formula (17); P t HFC,up , P t HFC,down The maximum upward and downward climbing power of the hydrogen fuel cell system; The upper and lower limits of the thermal-to-electric ratio of hydrogen fuel cells.

[0169] S4: Based on the day-ahead data and optimization objectives, the system model of each device in the virtual power plant is solved to obtain the optimal strategy for power dispatch coordination of each device unit in the virtual power plant system; the optimal strategy includes the study of the operating power dispatch strategy of the energy storage system equipment, natural gas equipment, gas boiler, P2G and other system equipment in the system;

[0170] According to the system model, optimization target and constraint conditions of each device in the virtual power plant established above, the system model of each device in the virtual power plant is a 0-1 mixed integer linear programming model. The optimization variable in the system model of each device includes the 24-hour output power of each device. In the embodiment of the present invention, the P2G device is divided into an electrolytic cell (EL) and a methane reactor (MR). Therefore, the optimization variable of the device in the model changes from one to two, such as Figure 3 The EL power consumption x13 and the MR hydrogen consumption x14 in the embodiment of the present invention adopt the branch and bound algorithm to solve the established 0-1 mixed integer linear programming model, and the system optimization scheduling flow chart is as follows: Figure 6 shown.

[0171] The branch and bound algorithm is a search and iteration method for solving integer programming problems, including pure integer programming and mixed integer programming problems. The algorithm repeatedly divides the feasible solution space into smaller and smaller subsets (called branches) and calculates a target lower bound (for minimum value problems) or upper bound (for maximum value problems) for each solution set in the subset, which is called bounding. After each branch, no further branching is performed for the subset whose bound exceeds the known feasible solution, thereby narrowing the search range and improving the solution efficiency.

[0172] In the branch and bound algorithm, by removing integer restrictions and branching methods, the system model of each device in the virtual power plant in the present invention is converted from a 0-1 mixed integer programming problem to a non-integer programming problem and the best solution is found. After branching, no further branching is performed for those subsets that may exceed the known range. At the same time, many subgroups can be eliminated through this method to narrow the search range, and the above steps are repeated until a feasible solution is found, whose value is not greater than the optimal integer solution of any subset of the boundary. Therefore, in solving the system model of each device in the virtual power plant, for the mixed integer linear programming problem, the branch and bound algorithm is used, and the CPLEX solver is called to solve it to find the best optimization scheduling solution. The solution model is as follows Figure 6 The specific solution process is as follows:

[0173] (1) Determine the day-ahead data of the load and wind and solar power output at each end of the system and determine the energy purchase and sale price.

[0174] (2) Construct a carbon trading mechanism model based on the system models of each device in the virtual power plant system constructed in step S2 (wind power generation system model, photovoltaic power generation system model, gas internal combustion engine system model, gas boiler system model, lithium bromide absorption chiller system model, centrifugal chiller system model, waste heat recovery device system model, P2G device system model, CCS device system model, hydrogen fuel cell, energy storage device system model). Determine the optimization variables and parameters of each device in the system; the optimization variables are the output power of each device in each model established in step S2; the parameters of each device in the system are the parameters of each device in the mathematical expression of each model established in step S2.

[0175] Step 3: Run the mixed integer linear programming problem for the virtual power plant system, remove the integer constraints, turn the problem into a linear programming problem without integer constraints, and call CPLEX for the optimal solution.

[0176] Step 4: Determine whether there is an optimal solution. If there is a solution, determine whether the solution is an integer. If not, select a non-integer solution variable to generate a branch. If so, determine that all branches have been searched, and loop to compare and solve to obtain the optimal solution.

[0177] Step 5: Obtain the solution results and output the output data of each terminal device at each time, system operation cost, and carbon trading cost.

[0178] In some embodiments, it also includes an allocation rationality analysis of the output power scheduling and coordination of each device, specifically including: system wind and solar power absorption capacity, system carbon emission analysis, system power supply and demand balance analysis, energy storage capacity change analysis, and main grid power exchange volume analysis.

[0179] Table 1 shows the value table of some parameters in the system model of each device. The full life cycle carbon emission coefficient of the carbon emission source used to calculate the system carbon emission in the virtual power plant system is shown in Table 2. After the comprehensive energy system performs multi-variable and multi-period optimization scheduling with the optimization goal of solving the problem of wind and solar power consumption and virtual power plant operation at the lowest cost (effectively scheduling the power allocation of each device), the optimization scheduling diagram corresponding to the day-ahead power scheduling of the system equipment and the optimal strategy is shown in Figure 7-11 As shown in the figure, it can be seen that the virtual power plant, while meeting the load-side demand response, achieves the rationality and efficiency of equipment scheduling through day-ahead power optimization scheduling, thereby reducing the carbon emissions of low-system equipment.

[0180] Table 1 Parameter value table

[0181]

[0182] Table 2 Carbon emission coefficients of system carbon emission sources throughout the life cycle

[0183]

[0184]

[0185] Table 3 Virtual power plant system cost table before and after the introduction of P2G-CCS

[0186]

[0187] Table 3 shows the comparison of the operating costs of the virtual power plant system within one day before and after the introduction of P2G-CCS. Among them, the positive value of carbon trading cost represents the cost of the system to purchase carbon quotas, and the negative value represents the economic benefits of the system selling carbon quotas. It can be seen from the table that after the introduction of the P2G-CCS system, the CCS equipment captures carbon dioxide by consuming electricity, and the P2G equipment can generate hydrogen energy and natural gas for the system equipment through energy consumption. After the CCS equipment captures, stores and utilizes carbon dioxide for one day, the final carbon emissions of the system are reduced. At the same time, the hydrogen and natural gas generated by the P2G equipment flow to other coupled systems of the virtual power plant, which reduces the system's energy purchase cost. Therefore, the introduction of P2G-CCS can effectively reduce the overall operating cost and carbon emissions of the virtual power plant system.

[0188] Figure 7 In the system energy balance optimization scheduling diagram shown, the positive data represents the energy output information of each device in the system, and the negative data represents the energy demand of the system. It can be seen from the figure that after the optimized scheduling, the scheduling model achieves the supply and demand balance of the system, verifying the effectiveness and economy of the model algorithm. Fig.12 Shown is the carbon emissions and CCS carbon capture amount of each system equipment at each moment. The initial carbon emissions of the system are calculated based on the energy output of the system's carbon emission equipment. Fig.13 The following table shows the carbon storage and carbon utilization information after carbon dioxide is captured by CCS. Fig.13 It can be seen that the carbon dioxide captured by CCS is mainly used to supply the methane reactor (MR) to produce natural gas, and the rest is used to store carbon dioxide, thereby reducing the system's carbon emissions and operating costs.

[0189] The system operation cost in Table 3 includes the start-up and shutdown operation cost of each system equipment. It can be seen from the table that the introduction of P2G-CCS equipment reduces the system operation cost and carbon emissions, effectively improves the economic benefits of the virtual power plant, enables the virtual power plant to operate with high efficiency, economy and environmental protection, and effectively promotes the sustainable green economic development of the virtual power plant.

[0190] The scheduling optimization simulation proves the rationality and effectiveness of the optimization scheduling strategy module. After optimization, the initiative of power scheduling coordination among various devices in the system is enhanced, the carbon emissions of the system are reduced, the practical application ability of the method is improved, and the superiority and reference value of the proposed optimization method are proved.

[0191] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0192] Example 2

[0193] A virtual power plant day-ahead optimization dispatching system considering low-carbon operation, comprising:

[0194] System model building module: used to build a system model for calculating the output parameters of each device in the virtual power plant;

[0195] Objective constraint module: used to solve the problem of wind and solar power consumption and / or virtual power plant operation at the lowest cost as the optimization goal, and to build the constraint conditions of the system model of each device;

[0196] Strategy determination module: used to obtain the load-side demand based on the day-ahead data of the virtual power plant, solve the system model of each device in the virtual power plant according to the load-side demand and the optimization target, and obtain the optimal strategy for the day-ahead power dispatch of each device unit under the constraints of the system model of each device. The load-side demand includes: electric load demand, cold and hot load demand; the optimal strategy for the day-ahead power dispatch of each device unit includes: determining the output power of each device at each moment; the output power of each device includes: main grid exchange power, gas internal combustion engine power generation, centrifugal cooling capacity, energy storage and release of each energy storage device, heating capacity of waste heat device, power consumption of electrolyzer, hydrogen consumption of methane reactor, power generation power of hydrogen fuel cell (HFC), CCS operating power, cooling capacity of lithium bromide absorption chiller, and heating capacity of gas boiler.

[0197] In some embodiments, the constraints of the system model of each device include:

[0198] Power balance equation constraints: Constrain the power load demand of the virtual power plant system based on the exchange power between the virtual power plant system and the main power grid, the output power of the photovoltaic power generation system, the actual output power of the wind power generation system, the power generated by the gas internal combustion engine, the power generated by the hydrogen fuel cell, the discharge power of the electric energy storage device, the power consumption of the electrolyzer; the power consumption of the CCS equipment; the operating power consumption of the centrifugal refrigerator; and the charging power of the electric energy storage device. The calculation formulas are shown in formulas (27) and (28);

[0199] Cold power balance constraint: The cold load demand of the virtual power plant system is constrained according to the cooling capacity of the lithium bromide unit, the cooling power of the centrifugal chiller, and the charging and discharging cooling power of the cold energy storage equipment. The calculation formula is shown in formula (29);

[0200] Thermal power balance constraint: According to the thermal power generated by the hydrogen fuel cell, the heat generated by the waste heat recovery device, the heating power of the gas boiler, the charging and discharging power of the thermal energy storage equipment, and the heat absorbed and consumed by the lithium bromide unit refrigeration, the thermal load demand of the virtual power plant system is constrained. The calculation formula is shown in formula (30);

[0201] Hydrogen power balance constraint: The hydrogen production power of the electrolyzer is constrained according to the charging and discharging power of the hydrogen energy storage device, the hydrogen energy consumed by the methane reactor at time t, and the hydrogen power consumed by the hydrogen fuel cell. The calculation formula is shown in formula (31);

[0202] Natural gas balance constraint: The natural gas flow rate generated by the methane reactor equipment is constrained according to the natural gas usage purchased by the virtual power plant system from the natural gas company, the natural gas flow rate consumed by the gas internal combustion engine, and the gas power consumption of the gas boiler. The calculation formula is shown in formula (32);

[0203] Main grid transmission line exchange power security constraint: The exchange power between the virtual power plant system and the main grid is constrained according to the exchange power between the virtual power plant system and the main grid. The calculation formula is shown in formula (33);

[0204] Power storage equipment operation constraints: used to constrain the charging and discharging power, charging and discharging times, and storage capacity of the power storage equipment. The calculation formula is shown in formula (34);

[0205] Gas internal combustion engine equipment operation constraints: According to the maximum and minimum operating power of the gas internal combustion engine and the maximum upward and downward climbing power of the gas internal combustion engine unit, the electric energy generated by the gas internal combustion engine at each moment is constrained. The calculation formula is shown in formula (35);

[0206] Gas boiler operation constraints: The heating power of the gas boiler is constrained according to the maximum and minimum operating powers of the gas boiler, as well as the maximum upward and downward climbing powers of the gas boiler system. The calculation formula is shown in formula (36);

[0207] Waste heat recovery device equipment operation constraints: The heat generated by the waste heat recovery device is constrained according to the minimum and maximum operating powers of the waste heat recovery device. The calculation formula is shown in formula (37);

[0208] LiBr refrigeration equipment operation constraints: The refrigeration capacity of the LiBr unit is constrained according to the maximum and minimum operating power of the LiBr system. The calculation formula is shown in formula (38);

[0209] Centrifugal refrigeration equipment operation constraints: The refrigeration power of the centrifugal refrigeration machine is constrained according to the maximum and minimum operating powers of the centrifugal refrigeration machine system. The calculation formula is shown in formula (39);

[0210] Thermal energy storage equipment operation constraints: used to constrain the charging and discharging power, charging and discharging times, and stored thermal power of the thermal energy storage equipment. The calculation formula is shown in formula (40);

[0211] Electrolyzer equipment operation constraints: The power consumption of the electrolyzer is constrained according to the maximum and minimum operating powers of the electrolyzer equipment and the maximum upward and downward climbing powers of the electrolyzer system. The calculation formula is shown in formula (41);

[0212] Cold energy storage equipment operation constraints: According to the maximum and minimum operating powers of the methane reactor equipment, the charging and discharging power, charging and discharging times, and storage cold power of the cold energy storage equipment are constrained. The calculation formula is shown in formula (42);

[0213] Methane reactor equipment operation constraints: The hydrogen energy consumed by the methane reactor is constrained according to the maximum and minimum operating powers of the methane reactor equipment and the maximum upward and downward climbing powers of the methane reactor system. The calculation formula is shown in formula (43);

[0214] Operation constraints of hydrogen energy storage equipment: constraints are imposed on the charging and discharging power, charging and discharging times, and stored hydrogen power of hydrogen energy storage equipment. The calculation formula is shown in formula (44);

[0215] CCS equipment operation constraints: The power consumption of CCS equipment is constrained according to the maximum and minimum operating power of the CCS system. The calculation formula is shown in formula (45);

[0216] Hydrogen fuel cell operation constraints: The hydrogen power consumed by the hydrogen fuel cell is constrained based on the maximum and minimum operating power of the methane reactor equipment, the electrical power and thermal power generated by the hydrogen fuel cell, and the maximum upward and downward climbing power of the methane reactor system. The calculation formula is shown in formula (46).

[0217] In certain embodiments, in the system model building module, the system models of the equipment in the virtual power plant include an electrolyzer equipment system model, which is used to calculate the hydrogen production power of the electrolyzer equipment based on the energy conversion efficiency, power consumption and start / stop status of the electrolyzer, and the calculation formula is shown in Formula (9).

[0218] In certain embodiments, in the system model building module, the system models of the various devices of the virtual power plant include a methane reactor equipment system model, which is used to calculate the natural gas flow rate generated by the methane reactor equipment based on the energy conversion efficiency of the methane reactor, the hydrogen energy consumed by the methane reactor, the start and stop status, and the calorific value of the natural gas, and the calculation formula is shown in the above formula (10); it is also used to calculate the amount of carbon dioxide consumed by the methane reactor based on the carbon dioxide density and the natural gas flow rate generated by the methane reactor equipment, and the total calculation formula is shown in the above formula (11).

[0219] In some embodiments, in the system model construction module, the system model of each device of the virtual power plant includes a CCS device system model, which is used to calculate the power consumption of the CCS device based on the basic energy consumption, start and stop status and operating power consumption of the CCS device, and the calculation formula is shown in the above formula (12).

[0220] In certain embodiments, in the system model building module, the system model of each device of the virtual power plant includes a hydrogen energy storage device system model, which is used to calculate the stored hydrogen power of the hydrogen energy storage device based on the charging and discharging power of the hydrogen energy storage device and the start and stop status of the device, and the calculation formula is shown in formula (21).

[0221] In some embodiments, in the system model building module, the system model of each device in the virtual power plant includes a wind power generation system model, which is used to calculate the actual output power of wind power generation based on the cut-in wind speed, rated wind speed, cut-out wind speed, actual wind speed and rated power of the wind turbine.

[0222] In some embodiments, in the system model building module, the system model of each device in the virtual power plant includes a photovoltaic power generation system model, which is used to calculate the output power of the photovoltaic system based on the rated output power of the photovoltaic system, the light intensity, the real-time temperature of the photovoltaic panel and the reference temperature, and the calculation formula is shown in the above formula (2).

[0223] In certain embodiments, in the system model building module, the system models of the equipment in the virtual power plant include a gas internal combustion engine system model, which is used to calculate the electric energy generated by the gas internal combustion engine based on the gas-to-electricity efficiency and gas-to-heat efficiency of the gas internal combustion engine, the consumed natural gas flow rate, the generated waste heat, and the start and stop status of the gas internal combustion engine, and the calculation formula is shown in formula (3) above.

[0224] In some embodiments, in the system model construction module, the system models of the equipment in the virtual power plant include a gas boiler system model, which is used to calculate the heating power of the gas boiler based on the gas consumption power and start-stop working status of the gas boiler, and the calculation formula is shown in the above formula (4).

[0225] In certain embodiments, in the system model building module, the system models of the equipment in the virtual power plant include a lithium bromide absorption chiller system model, which is used to calculate the cooling capacity of the lithium bromide unit based on the heat absorbed and consumed by the lithium bromide unit and the start and stop status of the lithium bromide chiller, and the calculation formula is shown in the above formula (6).

[0226] In some embodiments, in the system model building module, the system model of each device of the virtual power plant includes a centrifugal chiller system model, which is used to calculate the cooling power of the centrifugal chiller based on the operating power consumption and start and stop status of the centrifugal chiller, and the calculation formula is shown in the above formula (7).

[0227] In certain embodiments, in the system model building module, the system models of the various devices in the virtual power plant include a waste heat recovery device system model, which is used to calculate the heat generated by the waste heat recovery device based on the heat absorbed by the waste heat recovery device from the gas internal combustion engine and the start and stop status of the waste heat recovery device, and the calculation formula is shown in the above formula (8).

[0228] In certain embodiments, in the system model building module, the system model of each device in the virtual power plant includes a hydrogen fuel cell system model, which is used to calculate the hydrogen power consumed by the hydrogen fuel cell based on the electric power and thermal power generated by the hydrogen fuel cell and the start and stop status of the hydrogen fuel cell, and the calculation formula is shown in the above formula (16).

[0229] In some embodiments, in the system model construction module, the system model of each device of the virtual power plant includes a system model of an electric energy storage device, which is used to calculate the stored electric power of the electric energy storage device based on the charging and discharging power and the start and stop status of the electric energy storage device, and the calculation formula is shown in the above formula (18).

[0230] In some embodiments, in the system model building module, the system model of each device of the virtual power plant includes a cold energy storage device system model, which is used to calculate the storage cold power of the cold energy storage device according to the charging and discharging cold power and the start and stop status of the cold energy storage device, and the calculation formula is shown in the above formula (19).

[0231] In some embodiments, in the system model building module, the system model of each device of the virtual power plant includes a thermal energy storage device system model, which is used to calculate the stored thermal power of the thermal energy storage device according to the charging and discharging power of the thermal energy storage device and the working start and stop status of the thermal energy storage device, and its calculation formula is shown in the above formula (20).

[0232] In certain embodiments, in the system model building module, the system model of each device of the virtual power plant includes a hydrogen energy storage device system model, which is used to calculate the stored hydrogen power of the hydrogen energy storage device based on the charging and discharging power of the hydrogen energy storage device and the start and stop status of the device, and the calculation formula is shown in the above formula (21).

[0233] In some embodiments, in the target constraint module, the optimization objective is to minimize the sum of the cost of exchanging electricity between the virtual power plant system and the main grid, the cost of purchasing natural gas for the virtual power plant system, the initial construction cost of the virtual power plant system, the equipment operation cost of the virtual power plant system, and the cost of the virtual power plant system participating in carbon trading. The calculation formula of the optimization objective is shown in formula (22).

[0234] Example 3

[0235] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the steps of the method described in the present invention are implemented, which will not be repeated here.

[0236] The computer-readable storage medium may be the data transmission device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (smartmedia card, SMC), a secure digital (secure digital, SD) card, a flash card (flash card), etc., provided on the computer device.

[0237] Furthermore, the computer-readable storage medium may include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data to be output or has been output.

[0238] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0239] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0240] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0241] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0242] Example 4

[0243] A computer program product includes a computer program / instruction, which, when executed by a processor, implements any step of the method for optimizing the day-ahead scheduling of a virtual power plant taking into account low-carbon operation.

[0244] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A virtual power plant day-ahead optimization dispatching system considering low-carbon operation, characterized in that: include: System model building module: used to build a system model for calculating the output parameters of each device in the virtual power plant; Objective constraint module: used to solve the problem of wind and solar power consumption and / or virtual power plant operation at the lowest cost as the optimization goal, and to build the constraint conditions of the system model of each device; Strategy determination module: used to obtain the load side demand based on the day-ahead data of the virtual power plant, solve the system model of each device in the virtual power plant according to the load side demand and the optimization target, and obtain the optimal strategy for the day-ahead power dispatch of each device unit under the constraints of the system model of each device.

2. The virtual power plant day-ahead optimization dispatching system considering low-carbon operation as claimed in claim 1 is characterized in that: In the system model construction module, the system models of the virtual power plant equipment include an electrolyzer equipment system model, which is used to calculate the hydrogen production power of the electrolyzer equipment based on the energy conversion efficiency, power consumption and start / stop status of the electrolyzer.

3. The virtual power plant day-ahead optimization dispatching system considering low-carbon operation as claimed in claim 1 or 2, characterized in that: In the system model construction module, the system models of the virtual power plant equipment include a methane reactor equipment system model, which is used to calculate the natural gas flow rate generated by the methane reactor equipment based on the energy conversion efficiency of the methane reactor, the hydrogen energy consumed by the methane reactor, the start and stop status, and the calorific value of the natural gas; and is also used to calculate the amount of carbon dioxide consumed by the methane reactor based on the carbon dioxide density and the natural gas flow rate generated by the methane reactor equipment.

4. The virtual power plant day-ahead optimization dispatching system considering low-carbon operation as claimed in claim 1 is characterized in that: In the system model building module, the system models of the virtual power plant equipment include a CCS equipment system model, which is used to calculate the power consumption of the CCS equipment according to the basic energy consumption, start and stop status and operating power consumption of the CCS equipment.

5. The virtual power plant day-ahead optimization dispatching system considering low-carbon operation as claimed in claim 1 is characterized in that: In the system model construction module, the system models of the equipment in the virtual power plant include a hydrogen energy storage device system model, which is used to calculate the stored hydrogen power of the hydrogen energy storage device based on the charging and discharging power of the hydrogen energy storage device and the start and stop status of the device.

6. The virtual power plant day-ahead optimization dispatching system considering low-carbon operation as claimed in claim 1 is characterized in that: In the target constraint module, the optimization goal is to minimize the sum of the cost of exchanging electricity between the virtual power plant system and the main grid, the cost of purchasing natural gas for the virtual power plant system, the initial construction cost of the virtual power plant system, the equipment operation cost of the virtual power plant system and the cost of the virtual power plant system participating in carbon trading.

7. A method for optimizing the day-ahead scheduling of a virtual power plant considering low-carbon operation, characterized in that: include: Construct a system model to calculate the output parameters of each device in the virtual power plant; With the optimization goal of solving the problem of wind and solar power consumption and / or virtual power plant operation at the lowest cost, the constraints of the system model of each device are constructed; The load side demand is obtained according to the day-ahead data of the virtual power plant, and the system model of each device of the virtual power plant is solved according to the load side demand and the optimization target to obtain the optimal strategy for power dispatch coordination of each device unit that meets the constraints.

8. The method for optimizing the day-ahead scheduling of a virtual power plant taking low-carbon operation into consideration according to claim 7, characterized in that: The system models of the equipment in the virtual power plant include an electrolyzer equipment system model and / or a methane reactor equipment system model. The electrolyzer equipment system model is used to calculate the hydrogen production power of the electrolyzer equipment based on the energy conversion efficiency, power consumption and start / stop status of the electrolyzer; the methane reactor equipment system model is used to calculate the natural gas flow generated by the methane reactor equipment based on the energy conversion efficiency of the methane reactor, the hydrogen energy consumed by the methane reactor, the start / stop status and the calorific value of the natural gas; and is also used to calculate the amount of carbon dioxide consumed by the methane reactor based on the carbon dioxide density and the natural gas flow generated by the methane reactor equipment.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for day-ahead optimization scheduling of a virtual power plant considering low-carbon operation as described in any one of claims 7 to 8 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, any step of the method for optimizing the day-ahead dispatch of a virtual power plant taking into account low-carbon operation as described in claims 7 to 8 is implemented.

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