A low-carbon scheduling method for a virtual power plant considering biomass energy and a light storage charging station

Abstract

The present application relates to a kind of virtual power plant low carbon scheduling method considering biomass energy and light storage charging station, first propose a new dry, wet biomass garbage collaborative processing framework, through coal mixed with biomass and biocarbon recovery to carry out carbon reduction reconstruction to thermal power generating unit, and through "carbon bridge" communication wet garbage anaerobic fermentation realizes carbon cycle closure and multi-energy supply path;Second, combined with electric vehicle variable speed charging and vehicle network interaction technology, design light-storage-charging module collaborative charging and discharging strategy based on user's wishes light-storage-charging station;Finally, the source side biomass energy supply and the city virtual power plant low carbon optimization scheduling model of load side light storage charging regulation is constructed and is solved.The present application effectively improves the energy collaborative optimization ability and carbon emission reduction level of system under the premise of meeting system power balance and each equipment operation constraint, and has important engineering application value for promoting the green low-carbon transformation of urban energy system.

Description

Technical Field

[0001] This invention relates to the field of power system operation and dispatching, and in particular to a low-carbon dispatching method for virtual power plants that takes into account biomass energy and photovoltaic-storage charging stations. Background Technology

[0002] Cities, as core areas of energy production and consumption, face multiple challenges, including a surge in the pressure of municipal solid waste disposal and difficulties in the local absorption of renewable energy. Virtual power plants, as an advanced regional energy management model, can aggregate distributed power sources, energy storage systems, and flexible loads, making them an important means of achieving low-carbon dispatching of urban energy systems.

[0003] Current research on the resource utilization of urban biomass waste mainly falls into two categories: dry waste pyrolysis and gasification, and wet waste anaerobic fermentation. For dry biomass waste, some studies have proposed a "coal-fired +" coupled power generation technology. This technology promotes the low-carbon transformation of coal-fired power units by co-firing a certain proportion of biomass. Based on this, experimental analyses have been conducted on the energy consumption and carbon emission characteristics of coal-fired units under different biomass co-firing ratios and production technology routes. New process routes have been proposed for different scenarios to improve gasification efficiency. Simultaneously, abundant wind and solar resources are utilized for carbon capture, desulfurization, and denitrification of the flue gas after waste combustion, and gas storage equipment is used to decouple flue gas treatment from power generation. For wet biomass waste, domestic and international research focuses on the output efficiency of anaerobic fermentation. This involves controlling the fermentation tank temperature based on thermodynamic models or improving biogas adsorption and purification technologies to increase the required gas production. However, existing biomass energy utilization schemes mostly focus on optimizing equipment efficiency along a single path, without further exploring the synergistic processing between multiple paths, and lack closed-loop resource coupling and multi-grid energy supply integration.

[0004] On the other hand, as a typical load-side flexible resource in urban virtual power plants, photovoltaic-storage charging stations can form a coordinated operation mode of "stable power supply - flexible adjustment" under the unified scheduling framework of virtual power plants by integrating photovoltaics, energy storage and electric vehicle (EV) charging and discharging mechanisms. Together with biomass resource recycling, they can support the overall scheduling capability of system carbon reduction and source-load interaction scenarios. Existing research on virtual power plant scheduling strategies for photovoltaic-storage charging stations mainly follows the following paths: (1) In view of the uncertainty of charging load, an orderly charging strategy for electric vehicles is proposed, and a load probability model is constructed by combining user travel habits and charging behavior to characterize the load demand response capability of electric vehicles; (2) In view of the goal of low-carbon operation, the carbon emission flow theory is introduced, and the carbon emission cost is incorporated into the objective function and constraint system to constrain the power exchange of energy storage-photovoltaics-grid to achieve low-carbon scheduling; (3) In view of the configuration and response capability of energy storage, the dynamic response of power is enhanced by configuring hybrid energy storage or linearizing the energy storage model, suppressing curtailment of photovoltaics, and supporting the system scheduling goals of peak shaving and time shifting; (4) Photovoltaic-storage charging stations are further extended from the system operation layer to the market layer, and they are used as adjustable resources to participate in the electricity trading and secondary frequency regulation, backup and other auxiliary service links to explore their multiple values ​​in system scheduling.

[0005] The aforementioned studies analyze the impact of photovoltaic-storage charging stations on system scheduling from aspects such as load modeling, low-carbon optimization, energy storage modeling, and market participation. However, most studies still tend to discuss photovoltaic-storage-electric vehicle operation strategies in a unified manner, lacking dynamic synergistic optimization of photovoltaic, energy storage, and electric vehicle operation strategies from a unified perspective. This makes it difficult to fully leverage the comprehensive regulation advantages of photovoltaic-storage charging stations in the system source-load interaction scenario. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a closed-loop scheduling model that effectively improves the global regulation resilience and carbon emission reduction level of the virtual power plant by constructing a multi-energy coupling of biomass carbon cycle on the source side and deep coordination of photovoltaic storage and charging on the load side. This enables the efficient resource utilization of urban waste and the low-carbon economic operation of the system, and considers the low-carbon scheduling method of the virtual power plant that integrates biomass energy and photovoltaic storage charging stations.

[0007] The objective of this invention can be achieved through the following technical solutions: A low-carbon dispatching method for virtual power plants that considers biomass energy and photovoltaic-storage charging stations includes the following steps: Dry and wet biomass waste are subjected to anaerobic pyrolysis coupled with coal-fired power generation and anaerobic fermentation, respectively, and carbon capture is performed on the flue gas generated by the coal-fired power generation. Methane directly generated by the anaerobic fermentation is extracted, and the carbon dioxide extracted by carbon capture and anaerobic fermentation is used as raw material for the power-to-gas conversion equipment to convert it into methane to supply the equipment and gas load demand. Based on the biomass power plant framework, the operating state constraints are determined, including the output boundary constraints of the "coal-fired+" unit, the power balance constraints of the flue gas treatment unit and the anaerobic fermentation unit, and the equipment output ramp-up constraints. The system receives the expected departure time and target power command sent by the electric vehicle terminal connected to the photovoltaic-storage charging station, and calculates the discharge availability state based on the current state of charge of the electric vehicle; it obtains the current state of charge of the energy storage device, and assigns the corresponding charging power level to electric vehicles with different discharge availability states according to the preset interval threshold to which the current state of charge of the energy storage device belongs; it summarizes the real-time photovoltaic power data, electric vehicle charging load, electric vehicle discharge power, and energy storage device status, and calculates the interaction power boundary parameters between the photovoltaic-storage charging station and the virtual power plant main system. The real-time load data of the virtual power plant is collected, and the interactive power boundary parameters and equipment operating status constraints generated in the previous steps are input into the preset scheduling calculation model. The corresponding time series power scheduling instructions are generated with the goal of minimizing operating costs. The power scheduling command is sent through the communication network to the underlying controllers of the "coal-fired+" unit, the electricity-to-gas equipment, the energy storage equipment, and the charging pile, so as to control the corresponding physical entities to perform corresponding output power adjustment and charging / discharging actions.

[0008] Furthermore, the output boundary constraint of the "coal-fired+" unit is used to limit the net output range composed of biomass power generation output, coal power generation output, and biochar power generation output.

[0009] Furthermore, the specific steps for carbon capture of the flue gas generated by the coal-fired power plant are as follows: the flue gas undergoes carbon capture, desulfurization, and denitrification processes in the reaction tower before being discharged into the air. The real-time flue gas flow rate entering the storage device and the reaction tower is controlled by a preset flow ratio, thereby achieving decoupled operation of the power generation stage and the flue gas treatment stage in the time dimension.

[0010] Furthermore, the anaerobic fermentation process employs a second-order pressure swing adsorption model, using a preset pressure and temperature gradient to cyclically extract the carbon dioxide gas and purify the generated methane.

[0011] Furthermore, the preset interval threshold includes a first preset charge threshold and a second preset charge threshold; the allocation of corresponding charging power levels for electric vehicles with different discharge availability states specifically involves: allocating a first charging power level when the current state of charge of the energy storage device is not lower than the first preset charge threshold; allocating a second charging power level when the current state of charge is between the first preset charge threshold and the second preset charge threshold; and allocating a third charging power level when the current state of charge is lower than the second preset charge threshold.

[0012] Furthermore, the calculation of the interaction power boundary parameters between the photovoltaic-storage charging station and the virtual power plant main system specifically involves: determining the internal net power requirement of the photovoltaic-storage charging station, and then combining the internal net power requirement with the discharge power of the electric vehicle to calculate the interaction power boundary parameters. The internal power allocation strategy is as follows: the acquired real-time photovoltaic power data is preferentially matched with the electric vehicle charging load; when there is a surplus of photovoltaic power, the energy storage device is controlled to perform a charging action; when the photovoltaic power is insufficient, the energy storage device is controlled to perform a discharging action.

[0013] Furthermore, the power scheduling instruction that generates the corresponding time series includes solving for the optimal total discharge power for vehicle-to-grid interaction; the method also includes: distributing the optimal total discharge power to each electric vehicle in a discharge-available state according to the maximum discharge capacity ratio of each connected vehicle.

[0014] Furthermore, the operating costs include: coal and dry biomass waste procurement costs, raw material costs of wet biomass waste, operating costs of flue gas treatment systems, operating costs of power-to-gas conversion equipment, operation and maintenance costs of renewable energy equipment, electricity purchase costs from the upper-level power grid, and user incentive subsidy costs for vehicle-to-grid interaction.

[0015] Furthermore, the virtual power plant also includes a combined heat and power (CHP) unit and a gas-fired boiler for multi-energy conversion; the power dispatching command also includes power control commands issued to the CHP unit and the gas-fired boiler; the power control commands are used to adjust the output of the CHP unit and the gas-fired boiler to meet the power balance constraints preset by the system in the operating state constraints.

[0016] Furthermore, the method also includes: after executing the power scheduling command, collecting feedback data from each underlying controller in real time, dynamically updating the real-time state of charge of the electric vehicle and the energy storage device, and using the updated state of charge as the initial input parameter for the next scheduling cycle.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. Achieved closed-loop carbon cycle and efficient coupling of multiple energy sources: Waste biomass is divided into dry and wet categories. Dry biomass is sent to the "coal-fired+" unit for anaerobic pyrolysis coupling coal-fired power generation and biochar recovery. At the same time, the carbon dioxide captured by the flue gas treatment unit and the carbon dioxide produced by the anaerobic fermentation of wet waste are used as raw materials for the power-to-gas equipment to convert into methane.

[0018] 2. It activates the interactive potential and realizes deep coordinated regulation of load-side photovoltaic, energy storage and charging resources: It receives the expected departure time and target power command sent by the electric vehicle terminal to calculate the discharge availability state, and allocates the corresponding charging power level to electric vehicles with different discharge availability states according to the preset interval threshold to which the current charge state of the energy storage device belongs, thereby summarizing and calculating the interactive power boundary parameters of the photovoltaic-energy storage charging station.

[0019] 3. Enhanced global resilience and achieved comprehensive optimization of low-carbon and economical operation of virtual power plants: Real-time load data of virtual power plants are collected, and generator output data, interactive power boundary parameters, and equipment operating status constraints, which include carbon emission reduction and energy conversion boundaries, are input into a preset scheduling calculation model. The corresponding time series power scheduling instructions are generated with the goal of minimizing operating costs.

[0020] 4. Ensures the engineering application feasibility and physical automation closed loop of the system scheduling capability: The optimized power scheduling command is sent to the underlying controllers of generator sets, power-to-gas equipment, energy storage equipment and charging piles through the communication network, directly controlling the corresponding physical entities to perform corresponding output power adjustment and charging / discharging actions. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the virtual power plant system structure framework; Figure 3 This is a schematic diagram of the structural framework of a biomass power plant. Figure 4 A schematic diagram of the charging and discharging strategy for a photovoltaic-storage charging station. Figure 5 For the load curve; Figure 6 Optimize the results for low-carbon applications in various scenarios; Figure 7 EV charging load curve; Figure 8 To balance the power output of photovoltaic-storage charging stations; Figure 9 The results of low-carbon optimization for scenarios one and five under extreme workday conditions; Figure 10 To balance the power distribution of the five-photovoltaic-storage charging stations under extreme workday scenarios; Figure 11 This is the EV charging load curve under extreme workday conditions. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0023] Example 1 like Figure 1 As shown in the figure, the main steps of the low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations proposed in this embodiment are as follows: S1: Dry and wet biomass waste are subjected to anaerobic pyrolysis coupled with coal-fired power generation and anaerobic fermentation, respectively, and carbon capture is performed on the flue gas generated by the coal-fired power generation; methane directly generated by the anaerobic fermentation is extracted, and the carbon capture and carbon dioxide extracted by anaerobic fermentation are used as raw materials to convert into methane to supply the equipment and gas load requirements; based on the biomass power plant framework, the operating state constraints are determined, including the output boundary constraints of the "coal-fired+" unit, the power balance constraints of each unit, and the equipment output ramp-up constraints. S2: Receive the expected departure time and target power command sent by the electric vehicle terminal connected in the photovoltaic-storage charging station, and calculate the discharge availability state based on the current state of charge of the electric vehicle; obtain the current state of charge of the energy storage device, and allocate the corresponding charging power level to electric vehicles with different discharge availability states according to the preset interval threshold to which the current state of charge of the energy storage device belongs; summarize the real-time photovoltaic power data, electric vehicle charging load, electric vehicle discharge power, and energy storage device status, and calculate the interaction power boundary parameters between the photovoltaic-storage charging station and the virtual power plant main system; S3: Collect the real-time load data of the virtual power plant, and input the interactive power boundary parameters and equipment operating status constraints generated in the previous steps into the preset scheduling calculation model, and solve to generate the corresponding time series power scheduling instructions with the goal of minimizing operating costs; S4: The power scheduling command is sent through the communication network to the underlying controller of the "coal-fired+" unit, the electricity-to-gas equipment, the energy storage equipment, and the charging pile, so as to control the corresponding physical entities to perform corresponding output power adjustment and charging / discharging actions.

[0024] The core architecture of the method is as follows Figure 2As shown, the system mainly consists of two parts: a biomass power plant and a photovoltaic-storage charging station. The biomass power plant sorts urban waste biomass, which is then used in the coupled power generation of a "coal-fired +" unit and the natural gas adsorption and purification of an anaerobic fermentation unit. Through combined heat and power (CHP), a gas boiler (GB), and power-to-gas (P2G) equipment, it achieves multi-energy conversion, supplying energy to the city's electricity, gas, and heat energy network. The photovoltaic-storage charging station uses photovoltaic, energy storage, and EV variable-speed charging strategies to smooth out fluctuations in photovoltaic output. The energy storage equipment is connected to the biomass power plant via interconnection lines, and V2G technology enables joint scheduling of the city's virtual power plant. The technical solution adopted in this embodiment is as follows: 1. Frame Structure and Modeling of Biomass Power Plant Biomass power plants recycle and process urban waste biomass, utilizing surplus wind power resources as internal energy supply. Different biomass energy conversion and treatment methods are employed for dry and wet biomass waste. A unified carbon recovery pathway is used to deepen the coupling constraints between different treatment processes, improving the overall energy efficiency and carbon emission reduction level of biomass power plants. Its production structure framework is as follows: Figure 3 As shown. Figure 3 The biomass power plant is divided into three parts: a "coal-fired +" unit, a flue gas treatment unit, and an anaerobic fermentation unit.

[0025] (1) "Coal-fired +" unit model Currently, most operating thermal power units generate high levels of pollution. To achieve carbon reduction in these units, "coal-plus" co-generation replaces coal-fired power generation by blending a certain proportion of clean energy. Biomass waste is clean and low-carbon; by recycling organic waste, fossil fuel consumption can be reduced, promoting the low-carbon transformation of thermal power units.

[0026] The "Coal+" unit employs an anaerobic pyrolysis coupled with a coal-fired power plant process. It pre-treats biomass dry waste through anaerobic pyrolysis, then transfers the pyrolysis oil and gas to a coal-fired boiler. The coal-fired boiler then feeds the high-temperature flue gas back to the biomass pyrolysis gasification boiler to achieve waste heat recycling and improve combustion efficiency. Simultaneously, the carbon from the pyrolysis residue is recovered and fed back into the coal-fired boiler for co-firing and power generation. The "Coal+" unit's output consists of biomass power generation, coal power generation, and recovered biochar power generation. (1) (2) In the formula, , , , They are respectively Net output of "coal-fired+" units, biomass output, coal output, and biochar output during the time period; , These are the biochar proportion coefficient and the biomass pyrolysis gasification efficiency, respectively.

[0027] The proportion of biomass co-firing affects pyrolysis and gasification efficiency, plant power consumption, coal consumption, and carbon emissions. In order to maintain boiler efficiency and not affect the maximum output of the unit under load, the proportion of biomass co-firing is limited to no more than 20%.

[0028] (3) In the formula, The biomass co-firing ratio is set to 20% in this embodiment; , These are the amounts of biomass dry waste co-burned and the total combustion volume of the "coal+" unit, respectively. , The calorific values ​​are biomass and coal, respectively.

[0029] (2) Flue gas treatment unit model To further reduce the environmental harm caused by thermal power units and improve the environmental friendliness of the virtual power plant system, a flue gas treatment unit is installed. After the flue gas generated by the "coal-fired+" unit is extracted, it is diverted into a storage device and a reaction tower. In the reaction tower, the flue gas undergoes carbon capture, desulfurization, and denitrification processes before being released into the air. By controlling the proportion of flue gas entering the storage device and the reaction tower, the time of "coal-fired+" power generation and flue gas treatment is decoupled.

[0030] After the flue gas is generated, it is diverted into storage devices and a reaction tower. (4) (5) (6) In the formula, for Total amount of flue gas generated by the "coal-fired+" power generation unit during the time period; , , These represent the amount of flue gas generated per unit of electricity generated from biomass, coal, and biochar, respectively. , They are respectively The amount of flue gas entering the reaction tower and flue gas storage device during a given period; for Time period flue gas split ratio.

[0031] The flue gas in the reaction tower comes from two parts: the part directly fed into the "coal-fired +" product and the part fed into the storage device. Flue gas volume to be treated in the reaction tower during the time period for (7) (8) (9) In the formula, These represent CO2, SO2, and NO, respectively. X Pollutant categories; for The first time period reaction tower Pollutant quantity; For the first The proportion of these pollutants in flue gas; for The amount of flue gas entering the reaction tower from the time-period storage device; , for The amount of CO2 captured by carbon capture during the time period is expressed in terms of volume and weight, respectively. This is the CO2 unit conversion factor.

[0032] Total energy consumption of flue gas treatment unit during the period Energy consumption from carbon capture Desulfurization energy consumption and denitrification energy consumption composition, (10) (11) In the formula, , , These are the energy consumption coefficients per unit capture mass for carbon capture, desulfurization, and denitrification, respectively. , They are respectively Desulfurization and denitrification rates during specific time periods.

[0033] (3) Anaerobic fermentation unit model Anaerobic digestion of organic waste to produce biogas is a low-carbon treatment method. However, the high CO2 content in biogas limits its further utilization. How to separate and utilize CO2 in biogas is the key to solving the problem of high-value, low-carbon utilization of biogas. Currently, biogas purification widely adopts pressure swing adsorption (PSA) and membrane separation procedures (MSP). PSA has lower energy consumption and higher recovery rate than MSP. This embodiment uses a second-order PSA model to further recover methane and CO2, reducing the harm of residual emission gases.

[0034] The two-stage PSA is set with pressure and temperature for CH4 and CO2 precipitation respectively. The remaining gas is then transported to a buffer tank in another stage for repeated PSA. This process is repeated to obtain pure gas.

[0035] (12) (13) (14) (15) In the formula, , , They are respectively The amount of biogas, CH4, and CO2 generated during a given period; To improve the utilization rate of biomass wet waste; It is a gas-producing factor; for Quality of biomass wet waste over a specific period; The CH4 precipitation efficiency of the PSA process, where , This is the precipitation stage; and These represent the precipitation efficiencies of the first-order PSA and second-order PSA processes, respectively. , These represent the percentage content of CH4 and CO2 in biogas, respectively. Let be the CO2 loss coefficient of the PSA process, where , and These represent the loss coefficients of the first-order PSA and second-order PSA processes, respectively. for Power consumption of second-order pressure swing adsorption during the time period; The unit gas production power consumption coefficient.

[0036] The CO2 captured by flue gas treatment and the CO2 produced by second-order pressure swing adsorption are used as raw materials for P2G equipment. Total CO2 consumed by P2G devices during the period for (16) In the formula, for P2G energy consumption during specific time periods; P2G conversion factor; This refers to the P2G conversion efficiency.

[0037] The volume of CH4 generated during the P2G period for (17) In the formula, This refers to the calorific value of natural gas.

[0038] The model expressions for CHP units and gas-fired boilers are as follows. (18) (19) In the formula, , They are respectively The electrical and thermal power output of the CHP unit during the specified time period; , These are the electrical and thermal conversion efficiencies of the CHP unit, respectively. for The output heat power of the gas-fired boiler during a specific time period; For gas-fired boiler efficiency; , They are respectively Natural gas consumption of CHP units and gas-fired boilers during the specified time period.

[0039] 2. Response mechanism and charge / discharge coordination strategy of photovoltaic-storage charging stations As a new type of flexible resource, the key to improving the economy and flexibility of photovoltaic-storage charging stations lies in how to prioritize the consumption of local renewable energy, efficiently utilize energy storage systems, and coordinate the charging and discharging behavior of electric vehicles. A photovoltaic-storage charging station consists of a photovoltaic system, energy storage equipment, and electric vehicle charging piles. The EV load supplied by photovoltaic power generation is consumed locally, and the state of charge (SOC) of the energy storage equipment complements and mitigates the uncertainty of photovoltaic output, improving photovoltaic utilization. The overflow and shortage of energy storage, along with vehicle-to-grid (V2G) communication between electric vehicles and the main system, provides energy support to the virtual power plant, further enhancing the responsiveness and dispatchability of the photovoltaic-storage charging station.

[0040] Considering that electric vehicles operate in various charging modes, including fast, regular, and slow charging, different charging speeds correspond to different power levels and grid impacts: fast charging has a high load and short charging time, but it has a significant impact on the grid and battery life; slow charging has low power and long charging time, but its impact on the grid is relatively small. Based on this, a variable-speed charging-V2G collaborative strategy that respects users' charging mode choices and discharging intentions is designed. While ensuring the completion of charging tasks, it guides users to differentiate their access and optimizes charging and discharging timing, promoting local photovoltaic consumption and peak EV load shifting, further enhancing the scheduling resilience of photovoltaic-storage charging stations and virtual power plant systems. The specific process is as follows: Figure 4 As shown, the specific steps are as follows: 1) Determine and classify the charging mode and charging / discharging behavior of electric vehicles based on whether the user is willing to participate in variable speed charging and V2G: When the user is willing to participate in V2G, calculate the difference between the remaining connection time and the minimum charging time of the vehicle, and determine whether the EV battery SOC is qualified to discharge. The minimum charging time is calculated according to the conventional charging speed to ensure the completion of the charging task; the remaining vehicles that are not qualified to discharge are charged.

[0041] (20) In the formula, for Time period The charging time required for the vehicle; for Time period The vehicle's battery SOC status; For EV battery capacity; For EV slow charging speed.

[0042] 2) Charging vehicles according to The initial SOC status of the energy storage device and the user's preferred charging speed are used to allocate charging speeds. When the SOC of the energy storage device is ≥70%, vehicles willing to participate in variable-speed charging are assigned fast charging speeds, and the remaining vehicles are charged at the normal speed. When the SOC of the energy storage device is 40% ≤ SOC < 70%, all vehicles are charged at the normal speed. When the SOC of the energy storage device is <40%, vehicles willing to participate in variable-speed charging are assigned slow charging speeds, and the remaining vehicles are charged at the charging speed required.

[0043] 3) Summarize the total charging load With V2G maximum discharge power Based on the current photovoltaic output Hierarchical scheduling of energy storage device SOC: Photovoltaic power supply to EV charging load is prioritized; when photovoltaic power meets EV charging power and there is overflow, energy storage device charges; when photovoltaic power is insufficient, energy storage device discharges to make up the gap; when energy storage SOC is below 10% or above 90% exceeding the limit, it participates in the internal resource coordination and scheduling of the virtual power plant, calling other energy sources or sending out excess power.

[0044] 4) After determining the external interaction power between the energy storage device and the charging station, solve for the optimal V2G discharge power using the optimization model, and allocate it proportionally according to the maximum discharge capacity. (twenty one) In the formula, , They are respectively Actual discharge power during the period and the first Maximum discharge power of the vehicle.

[0045] 5) Update the SOC status of electric vehicles and energy storage based on the charging and discharging results.

[0046] (twenty two) In the formula, , They are respectively Before and after charging and discharging during the period The vehicle's SOC (State of Charge) status; , For charge and discharge efficiency; for Time period The charging power of the vehicle; , They are respectively State of charge (SOC) of the energy storage device before and after charging and discharging during a specific period; , for Charging and discharging power of time-limited energy storage devices; This refers to the capacity of the energy storage device.

[0047] The charging and discharging strategies of photovoltaic-storage charging stations and the virtual power plant scheduling model exhibit a logical relationship of "bottom-level boundary setting and upper-level optimization." The bottom-level photovoltaic-storage charging station, prioritizing electric vehicle charging needs, determines the interaction power between the energy storage device and the virtual power plant main system, as well as the available discharge power boundaries for V2G. Upon receiving these boundary constraints, the upper-level virtual power plant scheduling model combines the main system's multi-energy complementarity requirements and time-of-use pricing to find the optimal solution. Finally, the optimized actual V2G discharge power is returned to the bottom-level system to update and correct the real-time SOC status of electric vehicles and energy storage devices.

[0048] 3. Objective function The urban virtual power plant scheduling model proposed in this embodiment achieves overall economic optimization while meeting all constraints. The objective function is: (twenty three) In the formula, This represents the total daily operating cost of the virtual power plant. The scheduling period; , They are respectively Raw material costs for coal-fired power plants and anaerobic digesters during specific periods; for Operating costs of a time-limited flue gas treatment system; for Time-based P2G costs; for Operation and maintenance costs of wind and solar power during specific time periods; They are respectively Electricity purchase cost from the upstream power grid during the specified time period; for V2G subsidy cost for users during specific time periods. The specific expressions for each part are as follows: 1) Raw material purchase costs for "coal-fired+" power plants (twenty four) (25) (26) In the formula, , They are respectively Cost of purchasing coal and biomass dry waste during specific periods; , , This is the characteristic coefficient of coal consumption; , These are the cost of biomass dry waste consumed per unit of power generation and the government subsidy for per unit of biomass power generation, respectively.

[0049] 2) Raw material cost for anaerobic fermentation tank (27) In the formula, The unit price is for biomass wet waste.

[0050] 3) Flue gas treatment cost (28) (29) In the formula, , , They are respectively Costs of carbon capture, desulfurization, and denitrification during specific time periods; , , These are the cost coefficients for carbon capture, desulfurization, and denitrification, respectively.

[0051] 4) P2G cost (30) In the formula, This represents the operating cost coefficient for P2G.

[0052] 5) Wind and solar operation and maintenance costs (31) In the formula, , They are respectively The scenery during that period contributed its strength; , These represent the unit operation and maintenance costs for wind and solar power generation, respectively.

[0053] 6) Electricity purchase cost (32) In the formula, for Time-of-use electricity pricing; for Electricity purchase during specific time periods.

[0054] 7) V2G cost (33) In the formula, This is the discharge cost coefficient for V2G-incentivized vehicle owners.

[0055] 4. Constraints 1) Power balance constraint Equation (34) represents the power balance of the virtual power plant system, and Equation (35) represents the power balance of the photovoltaic-storage charging station.

[0056] (34) (35) In the formula, for The power dispatch of energy storage equipment during different time periods is positive for photovoltaic-storage charging stations, while the demand is negative. for Fixed energy consumption of biomass power plants during specific time periods; for Periodic electrical load; for The power transmitted between the photovoltaic-energy storage charging station and the energy storage equipment during the time period is positive when the energy storage equipment is charging and negative when it is discharging.

[0057] 2) Thermal power and gas power balance constraints (36) (37) In the formula, , They are respectively Heat load and gas load during different time periods.

[0058] 3) Constraints of "Coal-fired+" Units (38) (39) In the formula, , These are the upper and lower limits of power output for "coal-fired power+" respectively; for Net output of "coal-fired+" units during the specified time period; Constraints on the ramp-up rate of "coal-fired power generation".

[0059] 4) CHP unit electrothermal output and ramp-up constraints (40) (41) (42) In the formula, , , , These are the upper and lower limits of the electrical and thermal output of the CHP unit, respectively. for CHP unit output power during the specified period; Constraints on the ramp-up rate of CHP unit output.

[0060] 5) Thermal output and ramp-up constraints of gas-fired boilers (43) (44) In the formula, , These are the upper and lower limits of the output of the gas-fired boiler; for The output heat power of the gas-fired boiler during a specific time period; Constraints on the ramp-up rate of gas-fired boiler output.

[0061] 6) P2G operational constraints (45) In the formula, This represents the maximum operating power of the P2G device.

[0062] 7) Constraints of the flue gas treatment system (46) In the formula, This represents the maximum flow rate of the flue gas duct per hour. , It is a 0-1 variable, where a value of 1 represents flue gas entering the system, and otherwise represents flue gas being discharged. , The flue gas storage device stores the gas volume before and after storage; for The amount of flue gas entering the flue gas storage device during a given period; for The amount of flue gas entering the reaction tower from the time-period storage device; This is the maximum gas storage capacity of the flue gas storage device.

[0063] (47) In the formula, , , These are the carbon capture, desulfurization, and denitrification efficiencies, respectively.

[0064] 8) Constraints of energy storage devices (48) 9) Electric vehicle constraints (49) (50) In the formula, , These are the safe operating boundaries for electric vehicle batteries; for Time period Remaining dwell time for the vehicle; This is the V2G discharge redundancy time threshold.

[0065] Example 2 This embodiment uses wind, solar, and electric vehicle data from a certain area in Pudong, Shanghai as a foundation. Charging behavior data is generated through online surveys of user willingness probabilities. Five scenarios are set up to perform 24-hour optimization and scheduling analysis on the city's virtual power plant system. Specific parameters of the equipment within the virtual power plant system are shown in Table 1. Table 1. Parameters of various equipment in the virtual power plant The electricity purchase price from the upstream power grid is shown in Table 2: Table 2 Time-of-use electricity prices Electricity, gas, and heat load curves are shown below. Figure 5 The fast, regular, and slow charging power for electric vehicles are set to 45 kW, 25 kW, and 10 kW respectively. Table 3 shows the scenario settings. Table 3 Scene Settings Scenario 1: Depict the low-carbon energy supply characteristics of a biomass power plant under independent operation conditions, serving as a baseline for comparison with traditional centralized energy supply models, and conducting comparative analysis with various subsequent collaborative scheduling scenarios; Scenario 2: Based on Scenario 1, a photovoltaic-storage charging station is introduced to reflect the local consumption characteristics of photovoltaic power output within the virtual power plant and its impact on system power distribution; Scenario 3: Based on Scenario 2, introduce a variable-speed charging strategy to evaluate the regulatory role of the flexible charging mechanism in the photovoltaic-storage-charging system and its marginal impact on EV load characteristics and power allocation results. Scenario 4: Based on Scenario 2, introduce the V2G strategy to evaluate the impact of the vehicle-to-grid interaction mechanism on the power balance and system scheduling results of the virtual power plant system, and characterize the effect of electric vehicles participating in scheduling as adjustable resources. Scenario 5: By comprehensively introducing optical storage and charging, variable speed charging and V2G scheduling strategies, a complete collaborative scheduling model is proposed in this embodiment. The overall advantages of multi-strategy collaborative configuration in terms of system scheduling capabilities and engineering application feasibility are evaluated from the perspective of the whole.

[0066] The example analysis was performed using MATLAB R2023b with YALMIP calling the CPLEX 12.10.0 solver. The power balance optimization results for scenarios one through five are as follows: Figure 6 As shown in Table 4, key indicators are compared across different scenarios.

[0067] Table 4 Comparison of key indicators in different scenarios The charging load curves for scenarios two to five EVs are shown below. Figure 7 .

[0068] 1. Analysis of the operating characteristics of biomass power plants Figure 6 (a) to (e) represent the low-carbon optimized power balance distribution of virtual power plants under five scenarios. As the core of low-carbon energy supply on the source side, the biomass power plant plays multiple roles in different time periods, such as basic output support, flexible power regulation and carbon resource collaborative processing, depending on the load level, power structure and carbon resource processing needs.

[0069] The "coal-fired+" unit is the main source of power for the biomass power plant. Although the co-firing ratio of biomass dry waste is set at 20% due to boiler compatibility and thermal parameters, the scheduling results show that biomass pyrolysis and gasification still accounts for a significant share of the total power output of the biomass power plant. Under the same heat load conditions, there is a significant difference in carbon emission factors between biomass and coal. Even with the limited co-firing ratio, Scenario 1 reduced carbon emissions by 312 tons. Simultaneously, through the synergistic process of biomass coupled with coal combustion and waste heat recovery recycling, the overall thermal efficiency of the "coal-fired+" unit is improved, reducing basic coal-fired energy consumption and increasing coal utilization efficiency.

[0070] from Figure 6 The scheduling results show that the CHP unit's output accounts for one-third of the total output of the biomass power plant, exhibiting an overall operating characteristic of "high at night and low during the day." The natural gas consumed by the CHP unit mainly comes from the anaerobic fermentation process of biomass wet waste, effectively replacing part of the traditional thermal power output and contributing to source-side carbon emission reduction in the virtual power plant system. Simultaneously, the CO2 produced by the anaerobic fermentation and flue gas treatment systems is converted into methane via P2G, achieving efficient utilization of carbon resources and a closed-loop energy synergy, further enhancing the system's ecological benefits.

[0071] During peak daytime electricity load periods, the system prioritizes coal-fired power units as the primary power source, leveraging their high power output and low cost to maintain a power advantage during medium-to-high load periods. To avoid excessive energy consumption and suppress the operation of flue gas treatment and P2G equipment, natural gas supply primarily relies on biogas purification pathways. Heat load is mainly handled by gas-fired boilers, with CHP units being removed from the main operating sequence under decoupled thermal and electrical conditions, releasing their power regulation capabilities to adapt to dynamic system changes. At night, as electricity load decreases and wind power output increases, the system utilizes redundant renewable energy to drive flue gas treatment and P2G equipment, concentrating on carbon resource conversion. Simultaneously, CHP unit output is gradually increased, ensuring nighttime heat load supply while participating in power balancing, reducing the intensity of coal-fired power unit operation, and achieving low-carbon scheduling optimization under multi-source complementarity.

[0072] 2. Analysis of the operational characteristics of photovoltaic-storage charging stations Figure 6 In scenario (a) 1, although photovoltaic (PV) power generation provides a large amount of electricity to the system during the day, effectively alleviating the peak load pressure, a large amount of curtailment occurs between 13:00 and 17:00 due to reaching the power transmission limit. Scenario 2 builds upon scenario 1 by establishing three PV-storage charging stations. EV charging load is allocated from the total load of the virtual power plant. After meeting the EV charging load, PV power is prioritized for storage, and any excess energy from the storage system is then supplied to other loads in the virtual power plant. The PV-storage charging stations significantly alleviate the curtailment problem, but because the storage system does not participate in discharge before reaching its limit, PV power is not directly supplied to the main load of the virtual power plant, leading to increased local power supply pressure and a rise in electricity demand between 11:00 and 13:00, indicating that the regulation potential has not yet been effectively released.

[0073] Scenario 3 and Scenario 4 introduce variable speed charging and V2G strategies respectively, based on Scenario 2. Figure 8 The power distribution of photovoltaic-storage-charging stations in scenarios two through five is balanced.

[0074] Compare Figure 8 The findings in (a) and (b) indicate that, compared to the static charging strategy in Scenario 2, the introduction of the EV variable-speed charging strategy in Scenario 3, with the increase in photovoltaic output, causes some vehicles to initiate fast charging at the initial connection stage. The charging behavior, originally concentrated at midday peak, is shifted to the morning and early midday periods, achieving a reconstruction of the charging sequence and a shift before the peak, thus smoothing out the overall peak load of the virtual power plant system to some extent. As shown in Table 4, with the charging load shifted forward, the photovoltaic-storage charging station has a greater discharge capacity during the 14:00-16:00 period, providing more support for the main system of the virtual power plant. However, from... Figure 6(c) revealed that the virtual power plant still experienced significant power purchase load between 11:00 and 13:00. This was primarily because, although the energy storage devices were fully charged, their discharge strategy was conservative, resulting in a large amount of stored energy remaining unreleased and failing to translate into effective regulation capacity. The variable-speed charging strategy optimized the local load structure through load shifting and mitigated peak system pressure to some extent, but there is still room for improvement in its coordination with the energy storage dispatch strategy.

[0075] Scenario 4 introduces a V2G strategy based on Scenario 2, enabling vehicles willing to discharge to participate in direct discharge to the virtual power plant main system under the premise of meeting SOC and dwell time constraints. Figure 8 As shown in (c), after introducing V2G, the total charging load of EV discharge and recharge increased compared to Scenario 2. However, under the virtual power plant system dispatch, this strategy did not increase the power supply pressure of the virtual power plant main system. On the contrary, the V2G released 37.34 MW more electricity than the energy storage external transmission in Scenario 2, and the distribution spread from the original peak photovoltaic power generation period to the entire 24 hours. V2G effectively released some of the electricity that was originally stored in the energy storage system. Especially during periods of abundant photovoltaic power and when the energy storage SOC is close to the upper limit, V2G discharge freed up capacity for the energy storage system, enabling it to have a stronger subsequent adjustment and response capability. The V2G strategy improved the energy cycle efficiency within the station and enhanced the system flexibility and energy storage utilization efficiency without changing the system's energy boundary conditions.

[0076] Scenario 5: The photovoltaic-storage charging station combines EV variable speed charging and V2G discharge strategies to achieve coordinated scheduling of local photovoltaic consumption and cross-time period distribution. Figure 8 As shown in (d), during peak photovoltaic output periods, the variable-speed charging strategy effectively guides some vehicles to charge rapidly, improving the immediate utilization rate of photovoltaic output. The V2G strategy guides some vehicles to actively discharge during peak system load periods after completing photovoltaic absorption, achieving synergy between local power support and peak load reduction. Compared with scenarios three and four, in scenario five, the SOC growth of the energy storage system tends to level off during midday. The electricity released by V2G effectively alleviates the charging pressure on the energy storage system and avoids energy stagnation. At the same time, some of the discharge tasks undertaken by EVs replace the energy output originally performed by the energy storage system, reducing the operating frequency of the energy storage equipment, helping to delay the degradation of electrochemical performance, and improving the long-term operating economy of the system.

[0077] Compared to Scenario 1, Scenario 5 demonstrates greater temporal adaptability and regulatory resilience in energy flow scheduling, with a significant decrease in purchased electricity and a smoother overall system load curve. Through multi-strategy collaboration, the source-load interaction structure is strengthened, providing effective support for virtual power plants to achieve efficient clean energy utilization and flexible supply-demand balance.

[0078] 3. Analysis of Virtual Power Plant Dispatch Capability under Extreme EV Load Scenarios The EV load data for scenarios one through five above are referenced from charging data at a charging station in a certain area of ​​Pudong. To evaluate the scheduling capability of the virtual power plant system under extreme EV load conditions and to examine the changing characteristics of system scheduling results under conditions of significant EV load amplification or concentrated access, this section, without changing the model structure and solution configuration, samples and reconstructs the original data based on scenarios one and five, constructing two types of extreme workdays and regular workdays for comparison: extreme congestion days and days of participation in traffic collapse. On extreme congestion days, the number of EVs increases by 50%, the peak amplitude of the charging load increases and the peak width narrows, the SOC is low and the preference for fast charging increases, and the demand for energy replenishment is concentrated; on days of participation in traffic collapse, the number of EVs decreases by 50%, the peak collapses and the valley extends, the attendance rate decreases, the dwell time is shortened and the willingness for V2G decreases, and the EV charging load is sparse. Figure 9 The results show the power balance optimization of the virtual power plant in scenarios one and five under extreme workday conditions. Figure 10 This diagram illustrates the power balance distribution of the photovoltaic-storage charging station in Scenario 5 under extreme workday conditions. The original EV charging load curves for Scenario 1 and the charging load curves for Scenario 1 and Scenario 5 under extreme workday conditions are shown below. Figure 11 .

[0079] Compared to regular days, the charging load curve rises significantly on extremely congested days. For example... Figure 9 As shown in (a)-(b), without a photovoltaic-storage charging station, the EV charging load is directly integrated into the main system load, resulting in a sharper and longer-lasting peak in power consumption for the main system. Although the photovoltaic system has a higher output at noon, its transmission capacity is limited by the upper limit of the transmission lines, making it difficult to effectively mitigate the evening peak. After introducing the photovoltaic-storage-charging strategy, although the peak charging power at the vehicle end further increases, a peak-shifting closed loop of "daytime absorption - evening release" is formed between the main system and the station. Figure 10 (a) shows that during the midday period, photovoltaic output is preferentially converted to energy storage charging state, and during the evening peak, energy storage discharge and a small amount of V2G return flow provide local support for fast charging demand, thereby avoiding peak charging spillover and higher electricity purchase costs. Under extremely congested working conditions, peak impacts are mitigated and absorbed internally by the virtual power plant: during the day, energy storage charging and discharging at charging stations, a small amount of slow charging, and V2G alleviate and transfer more concentrated charging loads; while some of the daytime charging load is transferred and filled into the evening trough, the peak-to-valley difference of the load curve is further converged, and the intraday load profile tends to be smoother and more controllable.

[0080] On collapse days, unlike extremely congested days, EV attendance decreases, on-site time shortens, and V2G desire diminishes, resulting in an overall downward trend in the charging curve and a more sparse charging pace. Figure 9As can be seen from (c)-(d), the photovoltaic-storage charging station first absorbs and then transmits the energy, which meets the charging load while avoiding the energy accumulation caused by the transmission line blockage. It gradually absorbs the energy of the daytime photovoltaic power generation period rather than the peak photovoltaic power generation period to supply the main system. At night, it "gradually releases and continues" to continue the residual demand in a gentle discharge manner.

[0081] In summary, the advantages and characteristics of the photovoltaic-storage-charging synergistic mechanism are more fully demonstrated under extreme conditions: during high-load periods, local peak absorption and time-shifting are achieved through variable-speed charging and V2G, while during low-load periods, energy storage creates a smooth channel, effectively absorbing the real-line photovoltaic power. Through effective regulation of the main system and the photovoltaic-storage-charging station, system resources are absorbed in a timely manner, the load curve becomes smoother, and the resilience of the virtual power plant system is effectively verified under extreme conditions.

[0082] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A low-carbon scheduling method for virtual power plants considering biomass energy and photovoltaic-storage charging stations, characterized in that, Includes the following steps: Dry and wet biomass waste are subjected to anaerobic pyrolysis coupled with coal-fired power generation and anaerobic fermentation, respectively, and carbon capture is performed on the flue gas generated by the coal-fired power generation. Methane directly generated by the anaerobic fermentation is extracted, and the carbon capture and carbon dioxide extracted by anaerobic fermentation are used as raw materials for the power-to-gas conversion equipment to convert into methane to supply the equipment and gas load requirements. Based on the biomass power plant framework, the operating state constraints are determined, including the output boundary constraints of the "coal-fired+" unit, the power balance constraints of the flue gas treatment unit and the anaerobic fermentation unit, and the equipment output ramp-up constraints. The system receives the expected departure time and target power command sent by the electric vehicle terminal connected to the photovoltaic-storage charging station, and calculates the discharge availability state based on the current state of charge of the electric vehicle; it obtains the current state of charge of the energy storage device, and assigns the corresponding charging power level to electric vehicles with different discharge availability states according to the preset interval threshold to which the current state of charge of the energy storage device belongs; it summarizes the real-time photovoltaic power data, electric vehicle charging load, electric vehicle discharge power, and energy storage device status, and calculates the interaction power boundary parameters between the photovoltaic-storage charging station and the virtual power plant main system. The real-time load data of the virtual power plant is collected, and the interactive power boundary parameters and equipment operating status constraints generated in the previous steps are input into the preset scheduling calculation model. The corresponding time series power scheduling instructions are generated with the goal of minimizing operating costs. The power scheduling command is sent through the communication network to the underlying controllers of the "coal-fired+" unit, the electricity-to-gas equipment, the energy storage equipment, and the charging pile, so as to control the corresponding physical entities to perform corresponding output power adjustment and charging / discharging actions.

2. The low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The output boundary constraint of the "coal-fired+" unit is used to limit the net output range composed of biomass power generation output, coal power generation output, and biochar power generation output.

3. The low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The specific steps for carbon capture of the flue gas generated by the coal-fired power plant are as follows: the flue gas undergoes carbon capture, desulfurization, and denitrification processes in the reaction tower before being discharged into the air. The real-time flue gas flow rate entering the storage device and the reaction tower is controlled by a preset flow ratio, thereby achieving decoupled operation of the power generation stage and the flue gas treatment stage in the time dimension.

4. The low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The anaerobic fermentation process employs a second-order pressure swing adsorption model, which uses a preset pressure and temperature gradient to cyclically extract carbon dioxide gas and purify the generated methane.

5. The low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The preset range thresholds include a first preset charge threshold and a second preset charge threshold; the method of assigning corresponding charging power levels to electric vehicles with different discharge availability states specifically involves: when the current state of charge of the energy storage device is not lower than the first preset charge threshold, a first charging power level is assigned; when the current state of charge is between the first preset charge threshold and the second preset charge threshold, a second charging power level is assigned; and when the current state of charge is lower than the second preset charge threshold, a third charging power level is assigned.

6. A low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The calculation of the interaction power boundary parameters between the photovoltaic energy storage charging station and the virtual power plant main system specifically involves: determining the internal net power requirement of the photovoltaic energy storage charging station, and then combining the internal net power requirement with the discharge power of the electric vehicle to calculate the interaction power boundary parameters. The internal power allocation strategy is as follows: the acquired real-time photovoltaic power data is preferentially matched with the electric vehicle charging load; when there is a surplus of photovoltaic power, the energy storage device is controlled to perform a charging action; when the photovoltaic power is insufficient, the energy storage device is controlled to perform a discharging action.

7. A low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The power scheduling instruction that generates the corresponding time series includes solving for the optimal total discharge power for vehicle-to-grid interaction; the method further includes distributing the optimal total discharge power to each electric vehicle with a discharge availability state according to the maximum discharge capacity ratio of each connected vehicle.

8. A low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The operating costs include: coal and dry biomass waste procurement costs, wet biomass waste raw material costs, flue gas treatment system operating costs, power-to-gas conversion equipment operating costs, renewable energy equipment operation and maintenance costs, electricity purchase costs from the upper-level power grid, and user incentive subsidy costs for vehicle-to-grid interaction.

9. A low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The virtual power plant also includes a combined heat and power (CHP) unit and a gas-fired boiler for multi-energy conversion; the power dispatch command also includes power control commands issued to the CHP unit and the gas-fired boiler; the power control commands are used to adjust the output of the CHP unit and the gas-fired boiler to meet the power balance constraints preset by the system in the operating state constraints.

10. A low-carbon dispatching method for a virtual power plant considering biomass energy and photovoltaic-storage charging stations according to claim 1, characterized in that, The method further includes: after executing the power scheduling command, collecting feedback data from each underlying controller in real time, dynamically updating the real-time state of charge of the electric vehicle and the energy storage device, and using the updated state of charge as the initial input parameter for the next scheduling cycle.

Images