Large building virtual power plant scheduling method considering electric drive multi-energy coupling conversion

Abstract

The invention discloses a large-scale building virtual power plant scheduling method considering electric drive multi-energy coupling conversion, and the method comprises the steps: preferably adding a new energy generator set and an electric drive multi-energy coupling conversion device which are adaptive to each large-scale building, and forming various types of multi-building virtual power plants; constructing a multi-energy coupling management transaction platform to perform aggregation scheduling and transaction mechanism of electric energy among multiple building virtual power plants; building an upper-layer optimization model: taking benefit maximization of the multi-energy coupling management transaction platform as a target, and deciding electricity selling prices of the multi-energy coupling management transaction platform in each time period and transaction electric quantity of each building virtual power plant; and constructing a lower-layer optimization model: taking the minimum optimization operation cost of each building virtual power plant as a target, and deciding the transaction electric quantity, the electric quantity selling price, each internal flexible load adjusting quantity, the energy quantity purchased from an external energy network and the operation power of each internal device of the building virtual power plant and the multi-energy coupling management transaction platform in each time period.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant scheduling technology, specifically relating to a large-scale building virtual power plant scheduling method that considers the multi-energy coupling conversion of electric drive. Background Technology

[0002] A building virtual power plant is an energy management system that uses buildings as physical carriers and aggregates resources such as distributed energy, flexible loads, and energy storage devices within the building through an intelligent management and control system to achieve autonomous energy supply. With the promotion of clean energy technologies and the increase in energy demand and energy consumption types, it is necessary to make existing building virtual power plants more flexible. This will enable them to adapt to the building's own energy consumption behavior and environmental characteristics, meet internal energy needs, and also allow them to directly or indirectly trade surplus energy to meet the energy gaps of other building virtual power plants.

[0003] However, current solutions mostly involve storing surplus energy to mitigate load peak-valley fluctuations or conducting energy trading through shared energy storage. But with the retrofitting and deployment of multi-energy coupling conversion equipment such as air source heat pumps, water source heat pumps, and electric chillers, there will be flexible control mechanisms among multiple energy sources, including electricity, heat, and cooling. For example, if heat or cooling is reduced, the amount of electricity required to drive it will also decrease, resulting in surplus electricity. How can this surplus electricity be traded to other buildings? Or, if a building itself is short of energy, how can it prioritize making up for it through internal multi-energy coupling conversion devices, and then purchase surplus energy from other buildings to achieve energy synergy among multiple buildings, reduce their respective operating costs, and improve the reliability of energy supply? These are urgent problems that need to be solved.

[0004] Based on the above technical problems, it is necessary to design a new scheduling method for large-scale building virtual power plants that considers the multi-energy coupling conversion of electric drive. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a scheduling method for large-scale building virtual power plants that considers electric drive multi-energy coupling conversion. By using a multi-energy coupling management and trading platform to aggregate, schedule and trade electrical energy among multiple building virtual power plants, and constructing an optimization model for the upper-level trading platform and the lower-level building virtual power plants, the operating cost of building virtual power plants can be reduced, and the reliability of building energy use and the flexibility of energy scheduling among building virtual power plants can be improved.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: This invention provides a large-scale building virtual power plant scheduling method considering electric drive multi-energy coupling conversion, which includes: S1. Analyze the energy supply and demand and carbon emissions of different types of large buildings in the previous year, and combine the different energy consumption characteristics and natural environmental conditions of each large building to select and add new energy generator sets and electric drive multi-energy coupling conversion devices that are suitable for each large building to form various types of multi-building virtual power plants. S2. Each building virtual power plant supplies internal energy through the original energy supply equipment and energy storage equipment, combined with newly added new energy generator sets and electric drive multi-energy coupling conversion devices. At the same time, it interacts with other building virtual power plants through the preset multi-energy coupling management and trading platform, and obtains the missing energy through interaction with the external energy grid. S3. Construct a multi-energy coupling management and trading platform to aggregate, schedule, and trade electricity among multiple building virtual power plants: When a building virtual power plant has surplus electricity, it can sell it to the multi-energy coupling management and trading platform, which then aggregates and sells the electricity to other building virtual power plants; when a building virtual power plant lacks other energy sources, it prioritizes adjusting internal flexible loads, then converts the missing electricity, purchases electricity from the multi-energy coupling management and trading platform and converts it into the missing energy, and finally purchases the missing other energy sources from the external energy grid; and when a building virtual power plant lacks electricity, it prioritizes adjusting internal flexible loads, then purchases from the multi-energy coupling management and trading platform, and finally purchases the missing electricity from the external energy grid. S4. Each building virtual power plant obtains its own energy supply and demand status. Combined with the aggregation scheduling and trading mechanism of the multi-energy coupling management and trading platform in S3, an upper-level optimization model is constructed: with the goal of maximizing the benefits of the multi-energy coupling management and trading platform, the electricity sales price of the multi-energy coupling management and trading platform in each time period and the transaction volume with each building virtual power plant are determined. S5. Each building virtual power plant constructs a lower-level optimization model based on the electricity sales price and electricity trading parameters of the upper-level optimization model, combined with the aggregation scheduling and trading mechanism of the multi-energy coupling management trading platform in S3. With the goal of minimizing the optimized operating cost of each building virtual power plant, it decides the trading volume, electricity sales price, internal flexible load adjustment, energy purchased from the external energy grid, and operating power of internal equipment between the building virtual power plant and the multi-energy coupling management trading platform at each time period.

[0007] Furthermore, S1 includes: The system acquires raw equipment energy supply data, multi-energy demand data, operating periods of core energy-consuming equipment, adjustable flexible load potential, peak and valley periods and peak and valley values ​​of various loads, direct carbon emission data, and indirect carbon emission data for different types of large buildings from the previous year, and constructs an energy basic database for each type of large building. The different types of large buildings include commercial complex buildings, hospital buildings, industrial buildings, and office buildings. Analyze the energy supply and demand status of the energy database of each large building, and determine whether it is necessary to add new energy generator sets or electric drive multi-energy coupling conversion devices to make up for the shortage of electricity and other energy sources based on the period of supply and demand imbalance, the amount of supply and demand imbalance, and the cost budget. Calculate the carbon emissions per unit area of ​​each large building and identify high carbon emission links. Combine the cost budget to determine whether it is necessary to add new energy generator sets and use green electricity to drive the corresponding multi-energy coupling conversion device to reduce carbon emissions. Simultaneously, for large buildings requiring the addition of new energy generator sets and electric-driven multi-energy coupling conversion devices, the solar irradiance, wind energy resources, air ambient temperature, and water source conditions of the areas where each large building is located are obtained. These are then matched with the type, scope, and carbon reduction measures to screen the types and capacities of new energy generator sets and electric-driven multi-energy coupling conversion devices to be added to each large building. The types of new energy generator sets include photovoltaic generator sets and wind turbine generator sets; the types of electric-driven multi-energy coupling conversion devices include water source heat pumps, air source heat pumps, electric refrigeration devices, and electric boiler devices. Each large building is treated as a virtual power plant, forming various types of multi-building virtual power plants.

[0008] Furthermore, in S2, when each building virtual power plant supplies energy internally through its existing energy supply equipment and energy storage equipment, combined with newly added new energy generator sets and electric drive multi-energy coupling conversion devices, it obtains the peak and valley periods and supply and demand of each energy source. If supply and demand balance can be achieved through the coordination between devices and the remaining energy is in a low range, it does not interact with the multi-energy coupling management and trading platform and stores the remaining energy in the corresponding energy storage device. If supply and demand balance can be achieved through the coordination between devices and there is a remaining energy in a high range, it sells the excess electricity to the multi-energy coupling management and trading platform and / or reduces the operating power and driving power required by the electric drive multi-energy coupling conversion device, and adjusts the corresponding flexible load to reduce the remaining energy. If supply and demand balance cannot be achieved through the coordination between devices and the adjustment of flexible load, it interacts with the multi-energy coupling management and trading platform.

[0009] Furthermore, the external energy network includes an external power grid, an external heating network, and an external cooling network; the price at which the building virtual power plant purchases electricity from the multi-energy coupling management and trading platform is lower than the price at which it purchases electricity from the external power grid; the cost for the building virtual power plant to purchase electricity from the multi-energy coupling management and trading platform and then convert it into other energy sources using an electrically driven multi-energy coupling conversion device is lower than the cost of purchasing energy from the external heating network and the external cooling network; the other energy sources are energy types other than electricity, including heat energy and cold energy.

[0010] Furthermore, in S3, after the multi-energy coupling management and trading platform aggregates and sells electricity to other virtual power plants in buildings, if there is still surplus electricity, it will be sold to the external power grid to participate in the grid's peak shaving and frequency regulation auxiliary services and obtain additional subsidy income. The electricity price for each building's virtual power plant to trade electricity with the multi-energy coupling management and trading platform changes dynamically based on the trading period and the range of trading volume.

[0011] Furthermore, in S3, when other energy sources are lacking in the building's virtual power plant, priority is given to adjusting the internal flexible loads, followed by converting the missing electrical energy, purchasing electrical energy from the multi-energy coupling management trading platform and converting it into the missing energy, and finally purchasing other missing energy sources from the external energy grid, including: When the building's virtual power plant lacks heat energy, it prioritizes lowering the heating temperature in non-core areas, shortening unnecessary heating periods, and utilizing the heat storage in the building envelope to release heat to make up for the peak heat gap; when the building's virtual power plant lacks cooling energy, it prioritizes raising the cooling temperature in non-core areas, shortening unnecessary cooling periods, and utilizing the cold storage in the building envelope to release cold energy to make up for the peak cold energy gap. If the internal flexible load regulation cannot fill the gap in heat and cold energy, then based on the amount of heat and cold energy shortage and the energy efficiency ratio of the internal electric drive multi-energy coupling conversion device, calculate the amount of electricity required to generate the heat and cold energy shortage, purchase the required amount of electricity with the multi-energy coupling management trading platform, and start the corresponding electric drive multi-energy coupling conversion device to convert electrical energy into heat and cold energy. If the electricity aggregated by the multi-energy coupling management trading platform can meet the required electricity demand, electricity trading can be carried out. Otherwise, only a portion of the electricity can be traded, and the remaining shortfall in electricity is purchased from the external power grid. Alternatively, the amount of heat or cold energy that is ultimately missing after the traded portion of the electricity is converted into heat or cold energy is calculated and purchased from the external heating network or external cooling network.

[0012] Furthermore, in S3, when the building's virtual power plant is short of power, it prioritizes adjusting the internal flexible loads, then purchases power from the multi-energy coupling management and trading platform, and finally purchases the shortfall from the external energy grid, including: When the building's virtual power plant is short of power, priority is given to suspending non-essential lighting systems, implementing short-term reductions in the building's electrical load and shifting power consumption periods, and adjusting internal heat and cold energy load demands to indirectly regulate power consumption; If the internal flexible loads are unable to fill the power shortage, a power purchase request is sent to the multi-energy coupling management and trading platform based on the missing power, specifying the amount of power to be purchased, the time period for purchasing power, and the preferred type of power; the preferred type of power includes prioritizing the purchase of green electricity; If the electricity aggregated by the multi-energy coupling management and trading platform can meet the electricity purchase request, electricity trading can be carried out; otherwise, only a portion of the electricity can be traded, and the remaining shortfall in electricity will be purchased from the external power grid.

[0013] Furthermore, in S4, an upper-level optimization model is constructed: with the goal of maximizing the benefits of the multi-functional coupled management trading platform, it is expressed as: ; Z represents the benefits of the multi-functional coupled management trading platform; T represents the scheduling cycle. The price at which electricity is sold to the building's virtual power plant during time period t on the multi-energy coupling management trading platform; N represents the amount of electricity sold by the multi-energy coupling management trading platform to the i-th building virtual power plant during time period t; N is the number of building virtual power plants. The power transmission and distribution loss rate for the multi-energy coupling management trading platform; Additional subsidy revenue for the multi-functional coupling management trading platform during time period t; The price at which electricity is purchased from a building's virtual power plant during time period t on the multi-energy coupling management trading platform; Purchase electricity from the i-th building virtual power plant for the multi-energy coupling management trading platform during time period t; Meanwhile, constraints are set for the upper-level optimization model, including: the electricity sold by the building virtual power plant does not exceed its own remaining electricity, the total electricity sold by the multi-energy coupling management trading platform to the building virtual power plant does not exceed the aggregated electricity, and the reasonableness of the electricity sales price of the multi-energy coupling management trading platform.

[0014] Furthermore, in S5, a lower-level optimization model is constructed: with the objective of minimizing the optimized operating cost of each building's virtual power plant, it is expressed as: ; Let be the operating cost of the i-th virtual power plant in the building; The price at which the i-th building's virtual power plant purchases energy from the external energy grid during time period t; The energy purchased from the external energy grid by the i-th building virtual power plant during time period t; Let M be the flexible load adjustment cost within the i-th building virtual power plant during time period t; M is the number of energy supply devices within the i-th building virtual power plant. Let $t$ be the unit operating power price of the $j$-th power supply equipment in the $i$-th virtual power plant of the building during time period $t$. Let be the operating power of the j-th power supply device in the i-th building virtual power plant during time period t; Meanwhile, constraints are set for the lower-level optimization model, including: energy supply and demand balance constraints within the building virtual power plant, operating power constraints of energy supply equipment, flexible load adjustment constraints, constraints that the electricity sold by the building virtual power plant does not exceed its own remaining electricity, and constraints on the reasonableness of the electricity sales price of the building virtual power plant.

[0015] Furthermore, the solution algorithms for the upper-level optimization model and the lower-level optimization model include commercial solvers, genetic optimization algorithms, and particle swarm optimization algorithms; The solution of the lower-level optimization model of the upper-level optimization model is an iterative interactive solution: the initial decision results of the upper-level optimization model are transmitted to each building virtual power plant. Each building virtual power plant solves its own lower-level optimization model based on the initial decision results of the upper level, obtains the initial decision results of the lower level, and feeds back the transaction volume and electricity sales price of each building virtual power plant with the multi-energy coupling management and trading platform to the upper-level multi-energy coupling management and trading platform. Then, the multi-energy coupling management and trading platform solves the upper-level optimization model again based on the initial decision results fed back from the lower level, adjusts the platform's electricity sales price and the transaction volume with each building virtual power plant, and repeats the iteration until the objective function and constraints of the upper and lower levels are satisfied, and outputs the final decision results of the upper and lower levels.

[0016] The beneficial effects of this invention are: (1) By adding a new energy generator set and an electric drive multi-energy coupling conversion device, the present invention can realize the flexible conversion of electricity with heat, cold and other energy sources, reduce building carbon emissions and improve energy self-sufficiency. (2) This invention constructs a multi-energy coupling management and trading platform to aggregate, schedule and trade electricity among multiple virtual power plants in buildings. On the one hand, it sells the surplus electricity to the multi-energy coupling management and trading platform to realize the aggregation and trading of surplus electricity of each virtual power plant in buildings, which satisfies the energy trading and scheduling between virtual power plants in buildings and reduces their respective operating costs. On the other hand, each virtual power plant in buildings avoids energy shortages caused by extreme weather and equipment failures through a three-level energy guarantee mechanism of internal regulation, platform energy purchase and external energy network energy purchase, thereby improving the reliability of building energy use. (3) The upper-level optimization model of this invention aggregates the surplus energy of multiple building virtual power plants through a multi-energy coupling management and trading platform, decides the optimal trading volume and price, realizes the price difference revenue of buying at low price and selling at high price, and reduces the energy purchase cost and energy supply pressure of building virtual power plants; (4) The lower-level optimization model of this invention adopts the cost-optimal strategy of prioritizing internal adjustment, followed by platform energy purchase, and then external network energy purchase to avoid directly purchasing high-priced energy. At the same time, it sells surplus energy to the platform to obtain revenue, and decides the optimal transaction volume and price, the operating output of its own energy supply equipment, and the adjustment amount of flexible load, which can significantly reduce the operating cost of the building virtual power plant.

[0017] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a large-scale building virtual power plant scheduling method considering electric drive multi-energy coupling conversion according to the present invention; Figure 2 This is a schematic block diagram illustrating the scheduling principle between the large-scale building virtual power plant and the multi-energy coupling management and trading platform of this invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 , Figure 2 As shown, this embodiment provides a large-scale building virtual power plant scheduling method considering electric drive multi-energy coupling conversion, which includes: S1. Analyze the energy supply and demand and carbon emissions of different types of large buildings in the previous year, and combine the different energy consumption characteristics and natural environmental conditions of each large building to select and add new energy generator sets and electric drive multi-energy coupling conversion devices that are suitable for each large building to form various types of multi-building virtual power plants. S2. Each building virtual power plant supplies internal energy through the original energy supply equipment and energy storage equipment, combined with newly added new energy generator sets and electric drive multi-energy coupling conversion devices. At the same time, it interacts with other building virtual power plants through the preset multi-energy coupling management and trading platform, and obtains the missing energy through interaction with the external energy grid. S3. Construct a multi-energy coupling management and trading platform to aggregate, schedule, and trade electricity among multiple building virtual power plants: When a building virtual power plant has surplus electricity, it can sell it to the multi-energy coupling management and trading platform, which then aggregates and sells the electricity to other building virtual power plants; when a building virtual power plant lacks other energy sources, it prioritizes adjusting internal flexible loads, then converts the missing electricity, purchases electricity from the multi-energy coupling management and trading platform and converts it into the missing energy, and finally purchases the missing other energy sources from the external energy grid; and when a building virtual power plant lacks electricity, it prioritizes adjusting internal flexible loads, then purchases from the multi-energy coupling management and trading platform, and finally purchases the missing electricity from the external energy grid. S4. Each building virtual power plant obtains its own energy supply and demand status. Combined with the aggregation scheduling and trading mechanism of the multi-energy coupling management and trading platform in S3, an upper-level optimization model is constructed: with the goal of maximizing the benefits of the multi-energy coupling management and trading platform, the electricity sales price of the multi-energy coupling management and trading platform in each time period and the transaction volume with each building virtual power plant are determined. S5. Each building virtual power plant constructs a lower-level optimization model based on the electricity sales price and electricity trading parameters of the upper-level optimization model, combined with the aggregation scheduling and trading mechanism of the multi-energy coupling management trading platform in S3. With the goal of minimizing the optimized operating cost of each building virtual power plant, it decides the trading volume, electricity sales price, internal flexible load adjustment, energy purchased from the external energy grid, and operating power of internal equipment between the building virtual power plant and the multi-energy coupling management trading platform at each time period.

[0023] In this embodiment, S1 includes: The system acquires raw equipment energy supply data, multi-energy demand data, operating periods of core energy-consuming equipment, adjustable flexible load potential, peak and valley periods and peak and valley values ​​of various loads, direct carbon emission data, and indirect carbon emission data for different types of large buildings from the previous year, and constructs an energy basic database for each type of large building. The different types of large buildings include commercial complex buildings, hospital buildings, industrial buildings, and office buildings. Analyze the energy supply and demand status of the energy database of each large building, and determine whether it is necessary to add new energy generator sets or electric drive multi-energy coupling conversion devices to make up for the shortage of electricity and other energy sources based on the period of supply and demand imbalance, the amount of supply and demand imbalance, and the cost budget. Calculate the carbon emissions per unit area of ​​each large building and identify high carbon emission links. Combine the cost budget to determine whether it is necessary to add new energy generator sets and use green electricity to drive the corresponding multi-energy coupling conversion device to reduce carbon emissions. Simultaneously, for large buildings requiring the addition of new energy generator sets and electric-driven multi-energy coupling conversion devices, the solar irradiance, wind energy resources, air ambient temperature, and water source conditions of the areas where each large building is located are obtained. These are then matched with the type, scope, and carbon reduction measures to screen the types and capacities of new energy generator sets and electric-driven multi-energy coupling conversion devices to be added to each large building. The types of new energy generator sets include photovoltaic generator sets and wind turbine generator sets; the types of electric-driven multi-energy coupling conversion devices include water source heat pumps, air source heat pumps, electric refrigeration devices, and electric boiler devices. Each large building is treated as a virtual power plant, forming various types of multi-building virtual power plants.

[0024] In practical applications, distributed photovoltaic generator sets are suitable for buildings in high-irradiation areas, wind turbine generator sets are suitable for buildings with abundant wind energy resources, water source heat pumps are suitable for buildings with abundant water resources, air source heat pumps are suitable for buildings with suitable air ambient temperature, electric heat pumps are suitable for buildings with high heating demand, electric-driven chillers are suitable for buildings with high cooling demand, and new energy units and electric-driven multi-energy coupling conversion devices are suitable for buildings with high carbon emissions, thereby reducing the output of traditional energy supply equipment.

[0025] It should be noted that the peak energy consumption of office buildings is concentrated between 8:30 am and 6:00 pm on weekdays, while the peak energy consumption of commercial complex buildings is concentrated during the noon period on non-working days, between 5:00 pm and 9:00 pm and during weekend business hours. Hospital buildings also require stable energy consumption during the daytime working hours (consultation rooms, operating rooms, cashier departments, etc.) and at night (emergency rooms, inpatient departments, etc.). Furthermore, office buildings exhibit consistent electricity consumption patterns throughout the year (electricity consumption increases during summer when air conditioning is used for cooling and during winter when air conditioning is used for heating, indicating a strong coupling between cooling, heating, and electricity consumption). Industrial buildings, on the other hand, have energy consumption patterns related to their production processes. Industrial products also experience peak and off-peak seasons. During peak seasons, ensuring sufficient product supply requires overtime production, while during off-peak seasons, production is only needed on demand to avoid product backlog. Therefore, energy supply equipment can be increased based on the energy requirements of the production process to ensure energy supply meets process needs. For example, some processes require maintaining a specific ambient temperature; electric heating equipment can be added to generate heat to meet these temperature requirements. If the production process generates significant carbon emissions, renewable energy units can be added to obtain green electricity and drive electric heating equipment, reducing some carbon emissions. Additionally, different types of virtual power plants in buildings can complement each other's energy needs. By aggregating surplus energy from buildings during peak load shifts, complementary virtual power plants can be formed. For instance, peak nighttime energy loads in commercial complexes can be supplemented by photovoltaic power generation and energy storage in office buildings.

[0026] In this embodiment, in step S2, when each building virtual power plant supplies energy internally using existing energy supply equipment and energy storage equipment, combined with newly added new energy generator sets and electric drive multi-energy coupling conversion devices, it obtains the peak and valley periods and supply and demand of each energy source. If supply and demand balance can be achieved through inter-device collaboration and the remaining energy is in a low range, it does not interact with the multi-energy coupling management and trading platform and stores the remaining energy in the corresponding energy storage device. If supply and demand balance can be achieved through inter-device collaboration and there is a remaining energy in a high range, the excess electricity is sold to the multi-energy coupling management and trading platform, and / or the operating power and driving power required for the electric drive multi-energy coupling conversion device are reduced, and the corresponding flexible load is adjusted to reduce the remaining energy. If supply and demand balance cannot be achieved through inter-device collaboration and flexible load adjustment, then it interacts with the multi-energy coupling management and trading platform.

[0027] In practical applications, while the multi-energy coupling management and trading platform trades electricity with each building's virtual power plant, some of this electricity is obtained by the building's virtual power plants through adjusting the operating strategies of other energy sources. Therefore, it also indirectly trades other energy sources. This primarily involves heat and cooling, which typically require the deployment of corresponding energy transmission pipelines for trading. However, in practice, it's impractical to establish such pipelines between building virtual power plants, as this would be costly and inflexible. Therefore, electricity is traded, which can be achieved using the existing power grid (or distributed power routers) without the need for additional pipelines, reducing physical pipeline construction costs and improving flexible and coordinated energy scheduling across buildings. If a building's virtual power plant can meet its internal energy needs through the coordination of its energy supply equipment and has minimal surplus energy, it can directly store the small amount of surplus energy in corresponding energy storage devices (such as a daytime surplus electricity storage device for nighttime use) without interacting with the multi-energy coupling management and trading platform. This avoids the costs of frequent trading and improves the utilization rate of internal energy storage. If a building virtual power plant can meet its internal energy needs through the coordinated operation of its energy supply equipment and has a significant surplus of energy, it can sell the excess electricity to a multi-energy coupling management and trading platform to generate some revenue. If it wants to store the excess energy for later peak shaving, it can choose to store some of the surplus energy or reduce the operating power of the electric multi-energy coupling device. For example, it can reduce the operating power of the electric multi-energy coupling conversion device for heating or cooling, thereby reducing the output of heat or cold and indirectly reducing the consumption of electricity driven by the device. It can also reduce energy demand by adjusting flexible loads and reducing energy supply. This can reduce energy waste and lower the operating costs of the building virtual power plant's internal equipment, while also monetizing the surplus energy through the multi-energy coupling management and trading platform. If the building virtual power plant still cannot achieve supply and demand balance through the coordination of energy supply-related equipment and flexible load adjustment, it indicates that there is still an energy gap. For example, if extreme weather causes a sharp drop in photovoltaic output, or there is a sudden surge in medical / commercial load, it can interact with the multi-energy coupling management and trading platform to purchase electricity from the platform. The cost is lower than that of the external power grid, which can ensure the stability of energy use. By obtaining low-cost energy through the platform, the high cost of purchasing electricity directly from the external power grid can be avoided.

[0028] For example, taking a virtual power plant in an industrial park as an example, the equipment configuration includes traditional energy supply equipment, photovoltaic generator sets, energy storage equipment, electric-driven heat pumps (combined cooling and heating), and flexible loads (air conditioning, lighting, process operation period adjustments, etc.). On a sunny noon, the traditional energy supply equipment generates 350kW of electricity, the photovoltaic output reaches 200kW, and the industrial park's electricity demand is 400kW. The remaining 100kW is sold to the trading platform, while the heat pump operating power is reduced (reducing electricity consumption by 50kW). On a cloudy evening, the photovoltaic output is insufficient, and even after the energy storage discharges and the equipment works together, there is still a shortage of 100kW. Therefore, the remaining electricity from other buildings is purchased from the trading platform.

[0029] It should be noted that when each building's virtual power plant sends a power purchase request to the multi-energy coupling management and trading platform, after specifying the purchase amount and time period, if the total aggregated power on the platform cannot meet the purchase requests of all building virtual power plants, power will be sold tiered according to the energy consumption priority of each building virtual power plant. For example, hospital buildings have a higher power priority; if some departments or areas lack power, causing the hospital's operation to halt, power will be sold to hospital buildings first. For buildings with lower priority, the higher the purchase amount, the higher the priority. The priority order can be negotiated with the trading platform in advance. If the trading platform cannot sell power, the building must be notified in advance. Of course, the building's power purchase needs must also be communicated to the trading platform in advance.

[0030] In this embodiment, the external energy network includes an external power grid, an external heating network, and an external cooling network; the price at which the building virtual power plant purchases electricity from the multi-energy coupling management and trading platform is lower than the price at which it purchases electricity from the external power grid; the cost for the building virtual power plant to purchase electricity from the multi-energy coupling management and trading platform and convert it into other energy sources using an electrically driven multi-energy coupling conversion device is lower than the cost of purchasing energy from the external heating network and the external cooling network; the other energy sources are energy types other than electrical energy, including thermal energy and cold energy.

[0031] In this embodiment, in step S3, after the multi-energy coupling management and trading platform aggregates and sells electricity to other virtual power plants in buildings, if there is still surplus electricity, it will be sold to the external power grid to participate in the grid's peak shaving and frequency regulation auxiliary services and obtain additional subsidy income. The electricity price for each building's virtual power plant to trade electricity with the multi-energy coupling management and trading platform changes dynamically based on the trading period and the range of trading volume.

[0032] In this embodiment, in step S3, when other energy sources are insufficient in the building virtual power plant, priority is given to adjusting the internal flexible loads, followed by converting the missing electrical energy, purchasing electrical energy from the multi-energy coupling management trading platform and converting it into the missing energy, and finally purchasing other missing energy sources from the external energy grid, including: When the building's virtual power plant lacks heat energy, it prioritizes lowering the heating temperature in non-core areas, shortening unnecessary heating periods, and utilizing the heat storage in the building envelope to release heat to make up for the peak heat gap; when the building's virtual power plant lacks cooling energy, it prioritizes raising the cooling temperature in non-core areas, shortening unnecessary cooling periods, and utilizing the cold storage in the building envelope to release cold energy to make up for the peak cold energy gap. If the internal flexible load regulation cannot fill the gap in heat and cold energy, then based on the amount of heat and cold energy shortage and the energy efficiency ratio of the internal electric drive multi-energy coupling conversion device, calculate the amount of electricity required to generate the heat and cold energy shortage, purchase the required amount of electricity with the multi-energy coupling management trading platform, and start the corresponding electric drive multi-energy coupling conversion device to convert electrical energy into heat and cold energy. If the electricity aggregated by the multi-energy coupling management trading platform can meet the required electricity demand, electricity trading can be carried out. Otherwise, only a portion of the electricity can be traded, and the remaining shortfall in electricity is purchased from the external power grid. Alternatively, the amount of heat or cold energy that is ultimately missing after the traded portion of the electricity is converted into heat or cold energy is calculated and purchased from the external heating network or external cooling network.

[0033] In this embodiment, in step S3, when the building's virtual power plant is short of power, it prioritizes adjusting the internal flexible loads, then purchases power from the multi-energy coupling management trading platform, and finally purchases the shortfall from the external energy grid, including: When the building's virtual power plant is short of power, priority is given to suspending non-essential lighting systems, implementing short-term reductions in the building's electrical load and shifting power consumption periods, and adjusting internal heat and cold energy load demands to indirectly regulate power consumption; If the internal flexible loads are unable to fill the power shortage, a power purchase request is sent to the multi-energy coupling management and trading platform based on the missing power, specifying the amount of power to be purchased, the time period for purchasing power, and the preferred type of power; the preferred type of power includes prioritizing the purchase of green electricity; If the electricity aggregated by the multi-energy coupling management and trading platform can meet the electricity purchase request, electricity trading can be carried out; otherwise, only a portion of the electricity can be traded, and the remaining shortfall in electricity will be purchased from the external power grid.

[0034] In this embodiment, in step S4, an upper-level optimization model is constructed: with the goal of maximizing the benefits of the multi-functional coupled management trading platform, it is expressed as: ; Z represents the benefits of the multi-functional coupled management trading platform; T represents the scheduling cycle. The price at which electricity is sold to the building's virtual power plant during time period t on the multi-energy coupling management trading platform; N represents the amount of electricity sold by the multi-energy coupling management trading platform to the i-th building virtual power plant during time period t; N is the number of building virtual power plants. The power transmission and distribution loss rate for the multi-energy coupling management trading platform; Additional subsidy revenue for the multi-functional coupling management trading platform during time period t; The price at which electricity is purchased from a building's virtual power plant during time period t on the multi-energy coupling management trading platform; Purchase electricity from the i-th building virtual power plant for the multi-energy coupling management trading platform during time period t; Meanwhile, constraints are set for the upper-level optimization model, including: the electricity sold by the building virtual power plant does not exceed its own remaining electricity, the total electricity sold by the multi-energy coupling management trading platform to the building virtual power plant does not exceed the aggregated electricity, and the reasonableness of the electricity sales price of the multi-energy coupling management trading platform.

[0035] In this embodiment, in step S5, a lower-level optimization model is constructed: with the objective of minimizing the optimized operating cost of each building's virtual power plant, it is expressed as: ; Let be the operating cost of the i-th virtual power plant in the building; The price at which the i-th building's virtual power plant purchases energy from the external energy grid during time period t; The energy purchased from the external energy grid by the i-th building virtual power plant during time period t; Let M be the flexible load adjustment cost within the i-th building virtual power plant during time period t; M is the number of energy supply devices within the i-th building virtual power plant. Let $t$ be the unit operating power price of the $j$-th power supply equipment in the $i$-th virtual power plant of the building during time period $t$. Let be the operating power of the j-th power supply device in the i-th building virtual power plant during time period t; Meanwhile, constraints are set for the lower-level optimization model, including: energy supply and demand balance constraints within the building virtual power plant, operating power constraints of energy supply equipment, flexible load adjustment constraints, constraints that the electricity sold by the building virtual power plant does not exceed its own remaining electricity, and constraints on the reasonableness of the electricity sales price of the building virtual power plant.

[0036] In this embodiment, the solution algorithms for the upper-level optimization model and the lower-level optimization model include a commercial solver, a genetic optimization algorithm, and a particle swarm optimization algorithm. The solution of the lower-level optimization model of the upper-level optimization model is an iterative interactive solution: the initial decision results of the upper-level optimization model are transmitted to each building virtual power plant. Each building virtual power plant solves its own lower-level optimization model based on the initial decision results of the upper level, obtains the initial decision results of the lower level, and feeds back the transaction volume and electricity sales price of each building virtual power plant with the multi-energy coupling management and trading platform to the upper-level multi-energy coupling management and trading platform. Then, the multi-energy coupling management and trading platform solves the upper-level optimization model again based on the initial decision results fed back from the lower level, adjusts the platform's electricity sales price and the transaction volume with each building virtual power plant, and repeats the iteration until the objective function and constraints of the upper and lower levels are satisfied, and outputs the final decision results of the upper and lower levels.

[0037] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0038] Furthermore, the functional modules in the various embodiments of this invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0039] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for scheduling large-scale building virtual power plants considering electric drive multi-energy coupling conversion, characterized in that, It includes: S1. Analyze the energy supply and demand and carbon emissions of different types of large buildings in the previous year, and combine the different energy consumption characteristics and natural environmental conditions of each large building to select and add new energy generator sets and electric drive multi-energy coupling conversion devices that are suitable for each large building to form various types of multi-building virtual power plants. S2. Each building virtual power plant supplies internal energy through the original energy supply equipment and energy storage equipment, combined with newly added new energy generator sets and electric drive multi-energy coupling conversion devices. At the same time, it interacts with other building virtual power plants through the preset multi-energy coupling management and trading platform, and obtains the missing energy through interaction with the external energy grid. S3. Construct a multi-energy coupling management and trading platform to aggregate, schedule, and trade electricity among multiple building virtual power plants: When a building virtual power plant has surplus electricity, it can sell it to the multi-energy coupling management and trading platform, which will then aggregate and sell the electricity to other building virtual power plants; when a building virtual power plant lacks other energy sources, it will prioritize adjusting its internal flexible loads, then convert the missing electricity, purchase electricity from the multi-energy coupling management and trading platform and convert it into the missing energy, and finally purchase other missing energy sources from the external energy grid. In addition, when the building virtual power plant is short of power, it will prioritize adjusting the internal flexible loads, then purchase power from the multi-energy coupling management and trading platform, and finally purchase the short-supply power from the external energy grid. S4. Each building virtual power plant obtains its own energy supply and demand status. Combined with the aggregation scheduling and trading mechanism of the multi-energy coupling management and trading platform in S3, an upper-level optimization model is constructed: with the goal of maximizing the benefits of the multi-energy coupling management and trading platform, the electricity sales price of the multi-energy coupling management and trading platform in each time period and the transaction volume with each building virtual power plant are determined. S5. Each building virtual power plant constructs a lower-level optimization model based on the electricity sales price and electricity trading parameters of the upper-level optimization model, combined with the aggregation scheduling and trading mechanism of the multi-energy coupling management trading platform in S3. With the goal of minimizing the optimized operating cost of each building virtual power plant, it decides the trading volume, electricity sales price, internal flexible load adjustment, energy purchased from the external energy grid, and operating power of internal equipment between the building virtual power plant and the multi-energy coupling management trading platform at each time period.

2. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, S1 includes: The system acquires raw equipment energy supply data, multi-energy demand data, operating periods of core energy-consuming equipment, adjustable flexible load potential, peak and valley periods and peak and valley values ​​of various loads, direct carbon emission data, and indirect carbon emission data for different types of large buildings from the previous year, and constructs an energy basic database for each type of large building. The different types of large buildings include commercial complex buildings, hospital buildings, industrial buildings, and office buildings. Analyze the energy supply and demand status of the energy database of each large building, and determine whether it is necessary to add new energy generator sets or electric drive multi-energy coupling conversion devices to make up for the shortage of electricity and other energy sources based on the period of supply and demand imbalance, the amount of supply and demand imbalance, and the cost budget. Calculate the carbon emissions per unit area of ​​each large building and identify high carbon emission links. Combine the cost budget to determine whether it is necessary to add new energy generator sets and use green electricity to drive the corresponding multi-energy coupling conversion device to reduce carbon emissions. Simultaneously, for large buildings requiring the addition of new energy generator sets and electric-driven multi-energy coupling conversion devices, the solar irradiance, wind energy resources, air ambient temperature, and water source conditions of the areas where each large building is located are obtained. These are then matched with the type, scope, and carbon reduction measures to screen the types and capacities of new energy generator sets and electric-driven multi-energy coupling conversion devices to be added to each large building. The types of new energy generator sets include photovoltaic generator sets and wind turbine generator sets; the types of electric-driven multi-energy coupling conversion devices include water source heat pumps, air source heat pumps, electric refrigeration devices, and electric boiler devices. Each large building is treated as a virtual power plant, forming various types of multi-building virtual power plants.

3. The large-scale building virtual power plant scheduling method according to claim 2, characterized in that, In S2, when each building virtual power plant supplies energy internally through the original energy supply equipment and energy storage equipment, combined with the newly added new energy generator sets and electric drive multi-energy coupling conversion devices, it obtains the peak and valley periods and supply and demand of each energy source. If the supply and demand balance can be achieved through the coordination between the equipment and the remaining energy is in a low range, it will not interact with the multi-energy coupling management and trading platform and will store the remaining energy in the corresponding energy storage device. If supply and demand can be balanced through the coordination between devices and there is a high energy surplus, the excess electricity can be sold to the multi-energy coupling management and trading platform, and / or the operating power and driving power required of the electric drive multi-energy coupling conversion device can be reduced, and the corresponding flexible load can be adjusted to reduce the energy surplus. If supply and demand cannot be balanced through coordination between devices and adjustment of flexible loads, then power interaction will be carried out with the multi-energy coupling management and trading platform.

4. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, The external energy network includes an external power grid, an external heating network, and an external cooling network; the price at which the building virtual power plant purchases electricity from the multi-energy coupling management and trading platform is lower than the price at which it purchases electricity from the external power grid; the cost for the building virtual power plant to purchase electricity from the multi-energy coupling management and trading platform and convert it into other energy sources using an electrically driven multi-energy coupling conversion device is lower than the cost of purchasing energy from the external heating network and the external cooling network; the other energy sources are energy types other than electricity, including heat energy and cold energy.

5. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, In S3, after the multi-energy coupling management and trading platform aggregates and sells electricity to other virtual power plants in buildings, if there is still surplus electricity, it will be sold to the external power grid to participate in the grid's peak shaving and frequency regulation auxiliary services and obtain additional subsidy income. The electricity price for each building's virtual power plant to trade electricity with the multi-energy coupling management and trading platform changes dynamically based on the trading period and the range of trading volume.

6. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, In S3, when other energy sources are insufficient in the building's virtual power plant, priority is given to adjusting internal flexible loads, followed by converting the missing energy into available power, purchasing energy from the multi-energy coupling management trading platform and converting it into the missing energy source, and finally purchasing other missing energy sources from the external energy grid, including: When the building's virtual power plant lacks heat energy, it prioritizes lowering the heating temperature in non-core areas, shortening unnecessary heating periods, and utilizing the heat storage in the building envelope to release heat to make up for the peak heat gap; when the building's virtual power plant lacks cooling energy, it prioritizes raising the cooling temperature in non-core areas, shortening unnecessary cooling periods, and utilizing the cold storage in the building envelope to release cold energy to make up for the peak cold energy gap. If the internal flexible load regulation cannot fill the gap in heat and cold energy, then based on the amount of heat and cold energy shortage and the energy efficiency ratio of the internal electric drive multi-energy coupling conversion device, calculate the amount of electricity required to generate the heat and cold energy shortage, purchase the required amount of electricity with the multi-energy coupling management trading platform, and start the corresponding electric drive multi-energy coupling conversion device to convert electrical energy into heat and cold energy. If the electricity aggregated by the multi-energy coupling management trading platform can meet the required electricity demand, electricity trading can be carried out. Otherwise, only a portion of the electricity can be traded, and the remaining shortfall in electricity is purchased from the external power grid. Alternatively, the amount of heat or cold energy that is ultimately missing after the traded portion of the electricity is converted into heat or cold energy is calculated and purchased from the external heating network or external cooling network.

7. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, In S3, when the building's virtual power plant experiences a power shortage, it prioritizes adjusting the internal flexible loads, then purchases power from the multi-energy coupling management and trading platform, and finally purchases the missing power from the external energy grid, including: When the building's virtual power plant is short of power, priority is given to suspending non-essential lighting systems, implementing short-term reductions in the building's electrical load and shifting power consumption periods, and adjusting internal heat and cold energy load demands to indirectly regulate power consumption; If the internal flexible loads are unable to fill the power shortage, a power purchase request is sent to the multi-energy coupling management and trading platform based on the missing power, specifying the amount of power to be purchased, the time period for purchasing power, and the preferred type of power; the preferred type of power includes prioritizing the purchase of green electricity; If the electricity aggregated by the multi-energy coupling management and trading platform can meet the electricity purchase request, electricity trading can be carried out; otherwise, only a portion of the electricity can be traded, and the remaining shortfall in electricity will be purchased from the external power grid.

8. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, In S4, an upper-level optimization model is constructed: with the goal of maximizing the benefits of the multi-functional coupled management trading platform, it is expressed as: ； Z represents the benefits of a multi-functional coupled management trading platform; T is the scheduling period; The price at which electricity is sold to the building's virtual power plant during time period t on the multi-energy coupling management trading platform; The amount of electricity sold by the multi-energy coupling management trading platform to the i-th building virtual power plant during time period t; N represents the number of virtual power plants in the building; The power transmission and distribution loss rate for the multi-energy coupling management trading platform; Additional subsidy revenue for the multi-functional coupling management trading platform during time period t; The price at which electricity is purchased from a building's virtual power plant during time period t on the multi-energy coupling management trading platform; Purchase electricity from the i-th building virtual power plant for the multi-energy coupling management trading platform during time period t; Meanwhile, constraints are set for the upper-level optimization model, including: the electricity sold by the building virtual power plant does not exceed its own remaining electricity, the total electricity sold by the multi-energy coupling management trading platform to the building virtual power plant does not exceed the aggregated electricity, and the reasonableness of the electricity sales price of the multi-energy coupling management trading platform.

9. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, In S5, a lower-level optimization model is constructed: with the objective of minimizing the optimized operating cost of each building's virtual power plant, it is expressed as: ； Let be the operating cost of the i-th virtual power plant in the building; The price at which the i-th building's virtual power plant purchases energy from the external energy grid during time period t; The energy purchased from the external energy grid by the i-th building virtual power plant during time period t; Let M be the flexible load adjustment cost within the i-th building virtual power plant during time period t; M is the number of energy supply devices within the i-th building virtual power plant. Let $t$ be the unit operating power price of the $j$-th power supply equipment in the $i$-th virtual power plant of the building during time period $t$. Let be the operating power of the j-th power supply device in the i-th building virtual power plant during time period t; Meanwhile, constraints are set for the lower-level optimization model, including: energy supply and demand balance constraints within the building virtual power plant, operating power constraints of energy supply equipment, flexible load adjustment constraints, constraints that the electricity sold by the building virtual power plant does not exceed its own remaining electricity, and constraints on the reasonableness of the electricity sales price of the building virtual power plant.

10. The large-scale building virtual power plant scheduling method according to claim 1, characterized in that, The solution algorithms for the upper-level optimization model and the lower-level optimization model include commercial solvers, genetic optimization algorithms, and particle swarm optimization algorithms. The solution of the lower-level optimization model of the upper-level optimization model is an iterative interactive solution: the initial decision results of the upper-level optimization model are transmitted to each building virtual power plant. Each building virtual power plant solves its own lower-level optimization model based on the initial decision results of the upper level, obtains the initial decision results of the lower level, and feeds back the transaction volume and electricity sales price of each building virtual power plant with the multi-energy coupling management and trading platform to the upper-level multi-energy coupling management and trading platform. Then, the multi-energy coupling management and trading platform solves the upper-level optimization model again based on the initial decision results fed back from the lower level, adjusts the platform's electricity sales price and the transaction volume with each building virtual power plant, and repeats the iteration until the objective function and constraints of the upper and lower levels are satisfied, and outputs the final decision results of the upper and lower levels.

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