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WO2025103217A1 - 一种虚拟电厂优化调度方法 - Google Patents

一种虚拟电厂优化调度方法 Download PDF

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WO2025103217A1
WO2025103217A1 PCT/CN2024/130714 CN2024130714W WO2025103217A1 WO 2025103217 A1 WO2025103217 A1 WO 2025103217A1 CN 2024130714 W CN2024130714 W CN 2024130714W WO 2025103217 A1 WO2025103217 A1 WO 2025103217A1
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power plant
output
virtual power
day
virtual
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English (en)
French (fr)
Inventor
方建亮
高小飞
刘国良
徐宏
刘海林
刘书涵
李鑫
朱鹏程
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Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Tongxiang Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Tongxiang Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Publication of WO2025103217A1 publication Critical patent/WO2025103217A1/zh
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers

Definitions

  • the present invention relates to the technical field of virtual power plant scheduling, and in particular to a virtual power plant optimization scheduling method.
  • VPP virtual power plant
  • Chinese patent CN202210828831.2 discloses a virtual power plant optimization scheduling method and device.
  • this technology only considers the time-of-use electricity price strategy based on price-based demand-side response to regulate the electricity consumption mode of user-side loads, and does not consider the incentive-based demand-side response strategy and electric vehicles participating in the virtual power plant optimization scheduling.
  • electric vehicles can provide energy storage capacity support and power supply.
  • Virtual power plants can use the energy storage capacity of electric vehicles to balance the power load, reduce the difference between power peaks and valleys, and reduce the load pressure of the power system. There is a problem of a single scheduling method.
  • one or more embodiments of this specification provide a virtual power plant optimization scheduling method.
  • the present invention provides a virtual power plant optimization scheduling method, which is applied to a virtual power plant scheduling control center in a virtual power plant scheduling system.
  • the method comprises:
  • next-day output plan reported by each output unit in the virtual power plant dispatching system, where the next-day output plan is determined by each output unit based on the day-ahead electricity price, electric vehicle charging and discharging strategy, and output characteristics and costs of other output units;
  • Coordinated optimization is performed based on the next-day power generation plan reported by the power unit and the virtual power plant optimization scheduling model that considers demand response and environmental costs to obtain a final next-day power generation plan, which is then sent to each power unit so that each power unit executes according to the final next-day power generation plan.
  • the method further comprises:
  • the actual operating status of each output unit during the day is obtained, and the gas turbine units and electric vehicles are optimized and adjusted according to the wind power, photovoltaic output and the daily electricity sales price to reduce the deviation value between the actual output of the virtual power plant and the final next-day output plan.
  • the virtual power plant dispatch control center includes a virtual power plant optimization dispatch model that takes into account demand response and environmental costs.
  • the virtual power plant optimization dispatch model is constructed in the following manner:
  • the virtual power plant optimization scheduling model is established based on wind power model, electric vehicle, energy storage equipment, gas turbine model and load.
  • the output power corresponding to the wind power model includes:
  • vi , vo and vr are the cut-in speed, cut-out speed and rated speed of the wind turbine respectively; a, b, c are the output coefficients of the wind turbine; and pr is the rated power.
  • the output power corresponding to the photovoltaic power generation system includes:
  • PSTC is the maximum output power of the component under standard test conditions
  • k1 represents the temperature coefficient of the component
  • Tr and Tc represent the reference temperature and the photovoltaic panel temperature, respectively
  • GSTC and GING are the irradiance under standard test conditions and estimated output, respectively.
  • the quadratic function expression corresponding to the gas turbine model includes:
  • P m (t) represents the power generation of the micro gas turbine at time t; ⁇ m , ⁇ m , and ⁇ m are the unit operating conditions of the gas turbine. Cost factor.
  • the objective function for maximizing net profit corresponding to the virtual power plant optimization scheduling model includes:
  • Y(t) is the revenue of the virtual power plant at time t
  • Ydr (t) is the revenue of electric vehicles at time t after price demand response
  • CG (t) is the operating and management cost of each output unit of the virtual power plant at time t
  • CM is the fuel cost generated by the output of the gas turbine at time t
  • D(t) is the cost of the virtual power plant purchasing electricity from the main power grid
  • Cdis (t) is the sum of the discharge revenue of the electric vehicle and the battery loss cost.
  • the revenue of the virtual power plant at time t includes:
  • T represents 24 moments in a day
  • t 1, 2, 3, 3, T
  • G(t) is the day-ahead electricity price at moment t
  • P wind (t), P pv (t), P gt (t), P evdis (t), and P evc (t) are the wind power, photovoltaic, gas turbine, electric vehicle output, and electric vehicle load at moment t, respectively.
  • the electric vehicle uses the revenue at time t after the price demand response, including:
  • P evc,0 (t)P evc (t) is the electric vehicle load before and after the price demand response at time t
  • F t0 and F t are
  • the operating management expenses of each output unit of the virtual power plant at time t include:
  • the present invention aims at the relationship between electric vehicle demand response and environmental cost, fully considers the harmful gas released by gas turbine output and excludes the uncertainty of new energy output, and proposes a virtual power plant optimization scheduling model.
  • the model adds electric vehicles to the operation of virtual power plants in the form of distributed energy storage, and takes environmental costs such as harmful gas emissions from gas turbines into account in the model.
  • the operating cost of gas turbines is considered. Wind power and photovoltaic power, as clean energy, do not pollute the environment. Only the operating costs of the two are considered.
  • the maximum operating profit of the virtual power plant is used as the objective function of the optimization scheduling analysis.
  • FIG1 is a schematic diagram of the architecture of a virtual power plant scheduling system provided by an exemplary embodiment of the present invention.
  • FIG2 is a flow chart of a virtual power plant optimization scheduling method provided by an exemplary embodiment of the present invention.
  • FIG3 is a flow chart of a method for establishing a virtual power plant optimization scheduling model taking into account demand response and environmental costs, provided by an exemplary embodiment of the present invention.
  • FIG1 is a schematic diagram of the architecture of a virtual power plant dispatching system provided by an exemplary embodiment of the present invention.
  • distributed power sources such as photovoltaic generators, wind turbines and gas turbines, as well as distributed energy storage and internal loads are integrated in a virtual power plant.
  • the synergy of distributed power sources and energy storage systems during user-side regulation and virtual power plant operation constructs the dispatching architecture of the virtual power plant.
  • the virtual power plant dispatching system may include a main power grid 102, a virtual power plant dispatching control center 104, and corresponding output units.
  • Output is generally abbreviated as output power in the power system, so the output unit can be considered as a unit that can output a certain output power.
  • the present invention creatively adds electric vehicles, adds electric vehicles to the operation of virtual power plants in the form of distributed energy storage, and electric vehicles as a new type of distributed energy storage system can provide energy storage capacity support and power supply.
  • Virtual power plants can use the energy storage capacity of electric vehicles to balance power loads, reduce the difference between power peaks and valleys, and reduce the load pressure of power systems.
  • a virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy resources DER (Distributed Energy Resource) such as distributed power sources DG (distributed generator), energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant.
  • distributed energy resources DER distributed Energy Resource
  • DG distributed power sources
  • DG distributed generator
  • electric vehicles so as to participate in the power market and power grid operation as a special power plant.
  • the core of the concept of virtual power plants can be summarized as "communication” and "aggregation”.
  • the key technologies of virtual power plants mainly include coordinated control technology, smart metering technology, and information and communication technology.
  • the most attractive function of virtual power plants is that they can aggregate DER to participate in the operation of the power market and ancillary service market, and provide management and ancillary services for distribution and transmission networks.
  • FIG2 is a flow chart of a virtual power plant optimization scheduling method provided by an exemplary embodiment of the present invention, which may specifically include the following steps:
  • Step 202 forecast the wind power and photovoltaic output values and the electric vehicle power demand in the load agent for the next day.
  • the virtual power plant dispatch control center calculates the wind power and photovoltaic output values of the next day and the power consumption of electric vehicles in the load agent in the day before. As shown in Figure 1, the wind power output forecast and photovoltaic power output forecast shown in Figure 1 are forecast situations, and the electricity demand of electric vehicles also needs to be forecasted.
  • Step 204 Obtain the next-day power generation plan reported by each power generation unit, wherein the next-day power generation plan is determined by each power generation unit according to the day-ahead electricity price, the electric vehicle charging and discharging strategy, and the output characteristics and costs of other power generation units.
  • Each output unit can reasonably optimize the next day's output plan based on the day-ahead electricity price, electric vehicle charging and discharging strategy, and the output characteristics and costs of other output units to maximize the virtual power plant's revenue, and report the optimized plan as the next day's output plan to the virtual power plant control center.
  • the gas turbine unit can report the gas turbine's day-ahead output plan to the virtual power plant control center.
  • Step 206 Coordinate and optimize the next-day power generation plan reported by the power unit and the virtual power plant optimization scheduling model that considers demand response and environmental costs to obtain a final next-day power generation plan, and send it to each power unit so that each power unit executes according to the final next-day power generation plan.
  • the virtual power plant control center can coordinate and optimize according to the plans reported by each output unit of the virtual power plant, and reasonably arrange the final next-day output plan of the virtual power plant and each output unit.
  • the virtual power plant dispatching and control center can obtain the actual operating status of each output unit during the day, and optimize and adjust the gas turbine units and electric vehicles according to the wind power, photovoltaic output and the daily electricity price, so as to reduce the deviation between the actual output of the virtual power plant and the final next-day output plan.
  • the dispatching and control center can optimize and adjust the gas turbines and electric vehicles according to the wind power, photovoltaic output and the daily electricity price, so as to reduce the deviation between the actual output of the virtual power plant and the day-ahead output plan, and improve the actual operating income of the virtual power plant during the day.
  • FIG3 is a flow chart of a method for establishing a virtual power plant optimization scheduling model considering demand response and environmental costs provided by an exemplary embodiment of the present invention, which can be constructed in the following manner:
  • Step 302 Model the output units of the virtual power plant to obtain a wind power model, a photovoltaic power generation system, and a gas turbine model.
  • vi , vo and vr are the cut-in speed, cut-out speed and rated speed of the wind turbine respectively; a, b, c are the output coefficients of the wind turbine; and pr is the rated power.
  • photovoltaic power generation output power P pv is expressed as:
  • PSTC is the maximum output power of the component under standard test conditions
  • k1 represents the temperature coefficient of the component
  • Tr and Tc represent the reference temperature and the photovoltaic panel temperature, respectively
  • GSTC and GING are the irradiance under standard test conditions and estimated output, respectively.
  • the model As for the gas turbine model, it mainly consists of two parts: fuel cost and maintenance cost.
  • the model When optimizing the system, the model can be expressed in the form of a quadratic function:
  • P m (t) represents the power generation of the micro gas turbine at time t; ⁇ m , ⁇ m , and ⁇ m are the unit operating cost coefficients of the gas turbine.
  • Gas turbine power generation cost includes environmental value cost and penalty cost, the function is as follows
  • CH (t) is the environmental cost of pollutant gases emitted by gas turbine power generation
  • ⁇ is the operating cost coefficient of wind power output
  • PMT (t) is the power output of the gas turbine in period t
  • Wgt,r is the emission of the rth pollutant gas emitted by the gas turbine power generation output
  • Hr and Yr are the environmental value and penalty of the rth gas, respectively.
  • Step 304 Establish the virtual power plant optimization scheduling model based on the wind power model, electric vehicle, energy storage equipment, gas turbine model, and load.
  • a virtual power plant optimization scheduling model can be constructed.
  • the virtual power plant takes the maximum net profit as its objective function, as shown in formula (5):
  • Y(t) is the revenue of the virtual power plant at time t
  • Ydr (t) is the revenue of electric vehicles at time t after price demand response.
  • CG (t) is the operating and management cost of each output unit of the virtual power plant at time t
  • CM is the fuel cost generated by the gas turbine output at time t
  • D(t) is the cost of the virtual power plant purchasing electricity from the main power grid
  • Cdis (t) is the sum of the discharge income of the electric vehicle and the battery loss cost.
  • the revenue Y(t) of the virtual power plant at time t includes:
  • G(t) is the day-ahead electricity price at moment t;
  • P wind (t), P pv (t), P gt (t), P evdis (t), and P evc (t) are the wind power, photovoltaic, gas turbine, electric vehicle output, and electric vehicle load at moment t, respectively.
  • P evc,0 (t) and P evc (t) are the electric vehicle loads before and after the price demand response at time t
  • F t0 and F t are the electric vehicle loads before and after the price demand response at time t.
  • X wt , X pv , X m and X ev are the operation and management cost coefficients of wind power, photovoltaic output units, gas turbines and electric vehicles respectively.
  • the penalty cost D(t) for the deviation between the virtual power plant's day-ahead output plan and actual output is as follows:
  • G 2 (t) is the unit power purchase cost of the virtual power plant in the main grid at time t; ⁇ P vpp (t) is the deviation between the planned output and the actual output of the virtual power plant at time t; P′(t) is the planned output before time t.
  • C d,n is the average loss cost per unit discharge of the nth electric vehicle
  • N evf (t) is the number of electric vehicles in the discharge state at time t
  • 0.5G 1 (t) is the benefit per unit discharge of the electric vehicle.
  • step 1 is the virtual power plant optimization scheduling architecture
  • step 2 is the modeling of typical VPP equipment (such as wind turbines, photovoltaic units, gas turbine units, etc.)
  • step 3 is to construct a virtual power plant optimization scheduling model considering demand response and environmental costs.
  • the intraday dispatch constraints of the virtual power plant may include power balance constraints, photovoltaic output constraints, wind power output constraints, gas turbine output constraints, and electric vehicle charging and discharging constraints.
  • the present invention describes the above constraints in detail below.
  • the power balance constraint can be shown as follows:
  • the PV output constraint can be shown as follows:
  • the wind power output constraint can be shown as follows:
  • P wt,max and P wt,min are the maximum and minimum output of the wind turbine respectively.
  • P evc (t) and P evf (t) are the total charging and discharging powers of the electric vehicle at time t, respectively;
  • N evcn (t) and N evfn (t) are the number of electric vehicles in the charging and discharging state at time t, respectively;
  • P evc,n (t) and P evf,n (t) are the charging and discharging powers of the nth electric vehicle at time t, respectively;
  • P evc,max and P evf,max are the maximum charging and discharging power limits of the electric vehicle, respectively;
  • K evc,n (t) and K evf,n (t) are the 0-1 variables corresponding to the charging and discharging powers of the nth electric vehicle, respectively;
  • t arr,n and t dep,n are the end time and start time of the trip of the nth electric vehicle, respectively;
  • SOC ev,n (t)
  • the present invention aims at the relationship between electric vehicle demand response and environmental costs, fully considers that the output of gas turbines will release harmful gases to the outside, and excludes factors such as uncertainty in the output of new energy, and provides a virtual power plant optimization scheduling model.
  • the model adds electric vehicles to the operation of the virtual power plant in the form of distributed energy storage, and takes environmental costs such as harmful gas emissions from gas turbines into account in the model.
  • the operating costs of the gas turbines are considered.
  • Wind power and photovoltaics, as clean energy do not pollute the environment. Only the operating costs of the two are considered, and the maximum operating profit of the virtual power plant is used as the optimization scheduling analysis objective function.
  • Through the virtual power plant multiple types of energy are integrated and coordinated and optimized, and the output of each type of energy is obtained separately to construct an optimization scheduling model for a virtual power plant including wind power, photovoltaics, gas turbines and electric vehicles.
  • an electronic device which includes a processor, a network interface, a memory, a non-volatile memory, and an internal bus, and may also include hardware required for other functions.
  • the electronic device is configured with a virtual switch, and the virtual switch reads the corresponding computer program from the non-volatile memory into the memory and then runs it, and the computer program executes the above method when it runs.
  • the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

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Abstract

本发明公开了一种虚拟电厂优化调度方法，应用于虚拟电厂调度系统中的虚拟电厂调度控制中心，方法包括：在日前对次日风电、光伏出力值和负荷代理商中的电动汽车用电需求情况进行预测；获取虚拟电厂调度系统中的由各出力单元上报的次日出力方案，次日出力方案由各出力单元根据日前售电电价、电动汽车充放电策略、以及其它出力单元出力特性及成本所确定；根据出力单元上报的次日出力方案以及考虑需求响应与环境成本的虚拟电厂优化调度模型进行协调优化，得到最终次日出力计划，并下发至各出力单元，以使各出力单元根据最终次日出力计划执行，本发明使得虚拟电厂负荷调度更加灵活。

Description

一种虚拟电厂优化调度方法 技术领域
本发明涉及虚拟电厂调度技术领域，尤其涉及一种虚拟电厂优化调度方法。
背景技术
在能源数字技术不断运用的背景下，能够提升能源领域管理，监察和支持水平，切实促进能源领域高质量发展。能源数字中的虚拟电厂(Virtual Power Plant，VPP)可将不同的能源有机整合到一起，形成多能互补的能源系统，促进解决不同电源因运行特性而产生的调度问题，能够有效地提高能源利用率，助力“碳达峰”、“碳中和”目标的实现。
由于以传统能源为主体的发电方式将逐渐被淘汰，而清洁能源发电的发展给减少环境污染提供了机遇，因此清洁能源发电必然成为今后电网能源供给侧的主体结构。然而，清洁能源的发电出力具有波动性和随机性，而电动汽车作为一种灵活性资源，兼具源荷双重属性，能够更好的满足虚拟电厂的灵活性需求，因此可以对电动汽车在虚拟电厂内进行聚合统一调控。随着新能源渗透率的逐步提高，如何减少各类型有害气体的环境成本、充分挖掘需求侧灵活运行能力已成为VPP实现低碳经济调度亟需解决的关键问题。
例如，中国专利CN202210828831.2公开了一种虚拟电厂优化调度方法及装置。但是其并没有考虑到电动汽车这一灵活性资源，该技术在考虑需求侧响应的虚拟电厂优化模型中，只考虑基于价格的需求侧响应的分时电价策略用来规范用户侧负荷的用电方式，未考虑基于激励的需求侧响应策略和电动汽车参与虚拟电厂优化调度。电动汽车作为新型分布式储能系统可以提供储能能力支持和电力供应。虚拟电厂可以利用电动汽车的储能能力平衡电力负荷，减少电力峰值和谷值之间的差异，降低电力系统的负荷压力，存在调度方式单一的问题。
发明内容
有鉴于此，为克服上述问题，本说明书一个或多个实施例提供一种虚拟电厂优化调度方法。
为实现上述目的，本说明书一个或多个实施例提供技术方案如下：
本发明提供一种虚拟电厂优化调度方法，应用于虚拟电厂调度系统中的虚拟电厂调度控制中心，所述方法包括：
在日前对次日风电、光伏出力值和负荷代理商中的电动汽车用电需求情况进行预测；
获取虚拟电厂调度系统中的由各出力单元上报的次日出力方案，所述次日出力方案由各出力单元根据日前售电电价、电动汽车充放电策略、以及其它出力单元出力特性及成本所确定；
根据所述出力单元上报的次日出力方案以及所述考虑需求响应与环境成本的虚拟电厂优化调度模型进行协调优化，得到最终次日出力计划，并下发至各出力单元，以使所述各出力单元根据所述最终次日出力计划执行。
作为优选，所述方法还包括：
获取所述各出力单元在日内的实际运行状况，根据风电、光伏出力以及日内售电电价，对燃气轮机组、电动汽车进行优化调整，以减少虚拟电厂实际出力与所述最终次日出力计划偏差值。
作为优选，所述虚拟电厂调度控制中心中包含考虑需求响应与环境成本的虚拟电厂优化调度模型，所述虚拟电厂优化调度模型通过下述方式所构建：
对所述虚拟电厂的所述各出力单元进行建模，得到风电模型、光伏发电系统以及燃气轮机模型；
基于风电模型、电动汽车、储能设备、燃气轮机模型、负荷建立所述虚拟电厂优化调度模型。
作为优选，所述风电模型对应的输出功率包括：
其中，vi、vo和vr分别为风力涡轮机的切入速度、切出速度和额定速度；a、b、c为风力涡轮机的出力系数；pr为额定功率。
作为优选，所述光伏发电系统对应的输出功率包括：
其中，PSTC为标准测试条件下组件输出功率最大值；k1表示组件的温度系数；Tr和Tc分别表示参考温度和光伏面板温度；GSTC以及GING分别是在标准测试条件及估算输出时的辐照强度。
作为优选，所述燃气轮机模型对应的二次函数表达式包括：
其中，Pm(t)表示t时刻时微型燃气轮机的发电量；ρm、γm、αm为燃气轮机的单位运行 成本系数。
作为优选，所述虚拟电厂优化调度模型对应的净利润最大的目标函数包括：
其中，Y(t)为虚拟电厂t时刻收益、Ydr(t)为电动汽车使用基于价格需求响应后t时刻的收益、CG(t)为虚拟电厂各出力单元t时刻运行管理费用、CM为燃气轮机t时刻出力产生的燃料成本、D(t)为虚拟电厂向主电网购电产生的成本、Cdis(t)为电动汽车放电收益和电池损耗成本之和。
作为优选，所述虚拟电厂t时刻收益包括：
Y(t)＝G1(t)(Pwt(t)+Ppv(t)+Pgt(t)+Pevdis(t)+Pevc(t))
其中，T表示一天24个时刻，t＝1,2,3,3,T；G(t)为t时刻日前售电电价；Pwind(t)、Ppv(t)、Pgt(t)、Pevdis(t)、Pevc(t)分别为t,时刻风电、光伏、燃气轮机、电动汽车出力值和电动汽车负荷。
作为优选，所述电动汽车使用基于价格需求响应后t时刻的收益，包括：
Ydr(t)＝Pevc(t)Ft-Pevc,0(t)Ft0
其中，Pevc,0(t)Pevc(t)为t时刻基于价格需求响应前后的电动汽车负荷，Ft0、Ft为t时刻
、
使用基于价格需求响应前后的电价。
作为优选，所述虚拟电厂各出力单元t时刻运行管理费用，包括：
CG(t)＝XwtPwt(t)+XpvPpv(t)+XgtPm(t)+Xev(Pevc(t)+Pevdis(t))
其中，Xwt、Xpv、Xm、Xev分别为风电、光伏出力机组，燃气轮机、电动汽车运行管理成本系数。
本发明的有益效果是：
本发明针对电动汽车需求响应与环境成本之间的关系，充分考虑燃气轮机出力将对外释放有害气体以及排除新能源出力不确定等因素，给出了一种虚拟电厂优化调度模型，该模型将电动汽车以分布式储能的形式添加到虚拟电厂运行中，并把燃气轮机排放有害气体等环境费用考虑到模型当中，同时考虑燃气轮机的运行成本，风电、光伏作为清洁能源并不会对环境造成污染，仅考虑两者运行成本，并基于虚拟电厂的最大运行收益作为优化调度分析目标函数，通过虚拟电厂对多类型能源进行整合协调优化，分别获取各类能源出力情况并构建含 风电，光伏，燃气轮机及电动汽车虚拟电厂优化调度模型，使得虚拟电厂负荷调度更加灵活。
附图说明
图1是本发明一示例性实施例提供的一种虚拟电厂调度系统的架构示意图；
图2是本发明一示例性实施例提供的一种虚拟电厂优化调度方法的流程图；
图3是本发明一示例性实施例提供的一种考虑需求响应与环境成本的虚拟电厂优化调度模型的建立方法的流程图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，通过下述实施例并结合附图，对本发明实施例中的技术方案的进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定发明。
图1是本发明一示例性实施例提供的一种虚拟电厂调度系统的架构示意图，如图1所示，分布式电源如光伏发电机群，风力发电机群和燃气轮机以及分布式储能和内部负荷集成在一个虚拟电厂之中。用户侧调控和虚拟电厂运营过程中分布式电源和储能系统的协同作用构建了虚拟电厂的调度架构，该虚拟电厂调度系统可以包括主电网102、虚拟电厂调度控制中心104、以及对应的出力单元，出力在电力系统中一般是输出功率的简称，故出力单元可以认为是可以输出一定输出功率的单元，具体的其可以包括如图1所示的风电机组106、光伏机组108、燃气轮机组110和目标区域内的电动汽车112。需要说明的是，本发明创造性的加入了电动汽车，将电动汽车以分布式储能的形式添加到虚拟电厂运行中，电动汽车作为新型分布式储能系统可以提供储能能力支持和电力供应。虚拟电厂可以利用电动汽车的储能能力平衡电力负荷，减少电力峰值和谷值之间的差异，降低电力系统的负荷压力。
虚拟电厂是一种通过先进信息通信技术和软件系统，实现分布式电源DG(distributed generator)、储能系统、可控负荷、电动汽车等分布式能源资源DER(Distributed Energy Resource)的聚合和协调优化，以作为一个特殊电厂参与电力市场和电网运行的电源协调管理系统。虚拟电厂概念的核心可以总结为“通信”和“聚合”。虚拟电厂的关键技术主要包括协调控制技术、智能计量技术以及信息通信技术。虚拟电厂最具吸引力的功能在于能够聚合DER参与电力市场和辅助服务市场运行，为配电网和输电网提供管理和辅助服务。
图2是本发明一示例性实施例提供的一种虚拟电厂优化调度方法的流程图，具体可以包括以下步骤：
步骤202、在日前对次日风电、光伏出力值和负荷代理商中的电动汽车用电需求情况进行预测。
虚拟电厂调度控制中心在日前对次日风电、光伏出力值和负荷代理商中的电动汽车用电 需求情况进行预测。如图1所示，图1所示的风电日前出力预测、光伏日前出力预测即为预测情况，电动汽车的用电需求情况也需要进行预测。
步骤204、获取由各出力单元上报的次日出力方案，所述次日出力方案由各出力单元根据日前售电电价、电动汽车充放电策略、以及其它出力单元出力特性及成本所确定。
各出力单元可以根据日前售电电价、电动汽车充放电策略、以及其它出力单元出力特性及成本，合理优化次日出力方案，最大化虚拟电厂收益，并将优化方案作为次日出力计划上报至虚拟电厂控制中心。举例而言，如图1所示，燃气轮机组可以将燃气轮日前出力计划上报至虚拟电厂控制中心。
步骤206、根据所述出力单元上报的次日出力方案以及所述考虑需求响应与环境成本的虚拟电厂优化调度模型进行协调优化，得到最终次日出力计划，并下发至各出力单元，以使所述各出力单元根据所述最终次日出力计划执行。
虚拟电厂控制中心可以根据虚拟电厂各出力单元上报计划进行协调优化，并合理安排虚拟电厂及各出力单元最终的次日出力计划。
在一实施例中，为了提高虚拟电厂日内实际运行收益，虚拟电厂调度控制中心可以获取所述各出力单元在日内的实际运行状况，根据风电、光伏出力以及日内售电电价，对燃气轮机组、电动汽车进行优化调整，以减少虚拟电厂实际出力与所述最终次日出力计划偏差值。换言之，虚拟电厂在日内实际运行时，调度控制中心可以根据风电、光伏出力以及日内电价，对燃气轮机、电动汽车进行优化调整，以减少虚拟电厂实际出力与日前出力计划偏差值，提高虚拟电厂日内实际运行收益。
为了更好的理解本发明所述的需求响应与环境成本的虚拟电厂优化调度模型的建立与应用，下面进行详细介绍。
图3是本发明一示例性实施例提供的一种考虑需求响应与环境成本的虚拟电厂优化调度模型的建立方法的流程图，具体可以通过下述方式所构建：
步骤302、对所述虚拟电厂的所述各出力单元进行建模，得到风电模型、光伏发电系统以及燃气轮机模型。
一般认为风力发电服从威布尔分布，即在不同的风速下，风力涡轮机的输出功率Pwt是不同的。具体如下：
其中，vi、vo和vr分别为风力涡轮机的切入速度、切出速度和额定速度；a、b、c为风力涡轮机的出力系数；pr为额定功率。
对于光伏发电系统而言，影响光伏发电出力的主要因素有太阳能电池板温度、环境温度以及太阳辐射强度等。光伏系统输出功率Ppv表示为：
其中，PSTC为标准测试条件下组件输出功率最大值；k1表示组件的温度系数；Tr和Tc分别表示参考温度和光伏面板温度；GSTC以及GING分别是在标准测试条件及估算输出时的辐照强度。
而对于燃气轮机模型，其主要由燃料成本和维护成本两部分组成，系统优化时可采用二次函数的形式表达其模型：
其中，Pm(t)表示t时刻时微型燃气轮机的发电量；ρm、γm、αm为燃气轮机的单位运行成本系数。
燃气轮机发电成本，环境成本包括环境价值成本和罚款成本，函数如下
其中，CH(t)是燃气轮机发电排出污染气体的环境成本；α是风电出力的运行成本系数；PMT(t)是t时段的燃气轮机的发电出力；Wgt,r是燃气轮机发电出力排出的第r种污染气体的排放量；Hr、Yr,分别是第r种气体的环境价值与罚款。
步骤304、基于风电模型、电动汽车、储能设备、燃气轮机模型、负荷建立所述虚拟电厂优化调度模型。
基于风力发电、电动汽车、储能设备、燃气轮机、负荷可以构建虚拟电厂优化调度模型。虚拟电厂以净利润最大为目标函数，如式(5)所示：
其中，Y(t)为虚拟电厂t时刻收益、Ydr(t)为电动汽车使用基于价格需求响应后t时刻的收 益、CG(t)为虚拟电厂各出力单元t时刻运行管理费用、CM为燃气轮机t时刻出力产生的燃料成本、D(t)为虚拟电厂向主电网购电产生的成本、Cdis(t)为电动汽车放电收益和电池损耗成本之和。
虚拟电厂t时刻收益Y(t)包括：
Y(t)＝G1(t)(Pwt(t)+Ppv(t)+Pgt(t)+Pevdis(t)+Pevc(t)) (6)
式中，T表示一天24个时刻，t＝1,2,3,3,T；G(t)为t时刻日前售电电价；Pwind(t)、Ppv(t)、Pgt(t)、Pevdis(t)、Pevc(t)分别为t,时刻风电、光伏、燃气轮机、电动汽车出力值和电动汽车负荷。
电动汽车使用基于价格需求响应后t时刻的收益Ydr(t)如下：
Ydr(t)＝Pevc(t)Ft-Pevc,0(t)Ft0 (7)
式中，Pevc,0(t)、Pevc(t)为t时刻基于价格需求响应前后的电动汽车负荷，Ft0、Ft为t时刻
使用基于价格需求响应前后的电价。
虚拟电厂各出力单元t时刻运行管理费用CG(t)如下：
CG(t)＝XwtPwt(t)+XpvPpv(t)+XgtPm(t)+Xev(Pevc(t)+Pevdis(t)) (8)
其中，Xwt、Xpv、Xm、Xev分别为风电、光伏出力机组，燃气轮机、电动汽车运行管理成本系数。
虚拟电厂日前出力计划与实际出力偏差惩罚成本D(t)如下：
D(t)＝G2(t)|ΔPvpp(t)| (9)
ΔPvpp(t)＝P′(t)-Pwt(t)-Ppv(t)-Pm(t)-Pevf(t) (10)
其中，G2(t)为t时刻虚拟电厂在主电网中的单位购电成本；ΔPvpp(t)为虚拟电厂t时刻计划出力与实际出力的偏差值；P′(t)为t时刻日前计划出力。
电动汽车放电收益和电池损耗成本之和Cf(t)如下：
其中，Cd,n为第n辆电动汽车单位放电量的平均损耗成本；Nevf(t)为t时刻处于放电状态的电动汽车数量；0.5G1(t)为电动汽车单位放电量收益。
实际上，基于图1、图2和图3所示的实施例，也可以进行简化，即认为本发明通过三个核心步骤，实现虚拟电厂的调度，即步骤1为虚拟电厂优化调度架构，步骤2为VPP典型设备(如风电机组、光伏机组、燃气轮机组等)建模，步骤3为构建考虑需求响应与环境成本构建虚拟电厂优化调度模型。
进一步的，结合实际情况，也需要对模型设置约束条件，从而进行一定的限制，得到符合预期的调度结果。具体的，虚拟电厂日内调度约束条件可以包括功率平衡约束、光伏出力约束、风电出力约束、燃气轮机出力约束、电动汽车充放电约束，下面本发明对上述约束进行详细描述。
功率平衡约束可以如下所示：
P′(t)＝Pwt(t)+Ppv(t)+Pm(t)+Pevdis(t)+△Pvpp(t) (12)
光伏出力约束可以如下所示：
Ppv,min≤Ppv(t)≤Ppv,max (13)
风电出力约束可以如下所示：
Pwt,min≤Pwt(t)≤Pwt,max (14)
其中，Pwt,max、Pwt,min分别为风电机组出力最大、最小值。
电动汽车充放电约束可以如下所示：
0≤Pevc,n(t)≤Pevc,max (18)
0≤Pevf,n(t)≤Pevf,max (19)


SOCev,min(t)≤SOCev,n(t)≤SOCev,max(t) (23)
其中，Pevc(t)、Pevf(t)分别为电动汽车t时刻总充电、放电功率；Nevcn(t)、Nevfn(t)分别为t时刻的在充、放电状态的电动汽车数量；Pevc,n(t)、Pevf,n(t)分别为第n辆电动汽车t时刻充、放电功率；Pevc,max、Pevf,max分别为电动汽车最大充、放电功率限值；Kevc,n(t)、Kevf,n(t)分别为第n辆电动汽车充、放电功率对应的0-1变量；tarr,n、tdep,n分别为第n辆电动汽车的出行结束时刻、出行开始时刻；SOCev,n(t)为第n辆电动汽车t时刻荷电状态；ηevc、ηevf分别为电动汽车充、放电效率；C为电动汽车电池容量；△t为优化调度时间步长；SOCev,max(t)、SOCev,min(t)分别为电动汽车荷电状态的最大、最小值。
基于上述实施例可知，本发明针对电动汽车需求响应与环境成本之间的关系，充分考虑燃气轮机出力将对外释放有害气体以及排除新能源出力不确定等因素，给出了一种虚拟电厂优化调度模型，该模型将电动汽车以分布式储能的形式添加到虚拟电厂运行中，并把燃气轮机排放有害气体等环境费用考虑到模型当中，同时考虑燃气轮机的运行成本，风电、光伏作为清洁能源并不会对环境造成污染，仅考虑两者运行成本，并基于虚拟电厂的最大运行收益作为优化调度分析目标函数，通过虚拟电厂对多类型能源进行整合协调优化，分别获取各类能源出力情况并构建含风电，光伏，燃气轮机及电动汽车虚拟电厂优化调度模型。
在本发明的一示例性实施例中提供一种电子设备，该电子设备包括处理器、网络接口、内存、非易失性存储器以及内部总线，当然还可能包括其他功能所需要的硬件。所述电子设备配置有虚拟交换机，所述虚拟交换机从非易失性存储器中读取对应的计算机程序到内存中然后运行，所述计算机程序运行时执行上述方法。当然，除了软件实现方式之外，本发明并不排除其他实现方式，比如逻辑器件抑或软硬件结合的方式等等，也就是说以下处理流程的执行主体并不限定于各个逻辑单元，也可以是硬件或逻辑器件。
以上所述的实施例只是本发明的一种较佳的方案，并非对本发明作任何形式上的限制，在不超出权利要求所记载的技术方案的前提下还有其它的变体及改型。

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  1. 一种虚拟电厂优化调度方法，其特征在于，应用于虚拟电厂调度系统中的虚拟电厂调度控制中心，所述方法包括：
    在日前对次日风电、光伏出力值和负荷代理商中的电动汽车用电需求情况进行预测；
    获取虚拟电厂调度系统中的由各出力单元上报的次日出力方案，所述次日出力方案由各出力单元根据日前售电电价、电动汽车充放电策略、以及其它出力单元出力特性及成本所确定；
    根据所述出力单元上报的次日出力方案以及所述考虑需求响应与环境成本的虚拟电厂优化调度模型进行协调优化，得到最终次日出力计划，并下发至各出力单元，以使所述各出力单元根据所述最终次日出力计划执行。
  2. 根据权利要求1所述的一种虚拟电厂优化调度方法，其特征在于，所述方法还包括：
    获取所述各出力单元在日内的实际运行状况，根据风电、光伏出力以及日内售电电价，对燃气轮机组、电动汽车进行优化调整，以减少虚拟电厂实际出力与所述最终次日出力计划偏差值。
  3. 根据权利要求1所述的一种虚拟电厂优化调度方法，其特征在于，所述虚拟电厂调度控制中心中包含考虑需求响应与环境成本的虚拟电厂优化调度模型，所述虚拟电厂优化调度模型通过下述方式所构建：
    对所述虚拟电厂的所述各出力单元进行建模，得到风电模型、光伏发电系统以及燃气轮机模型；
    基于风电模型、电动汽车、储能设备、燃气轮机模型、负荷建立所述虚拟电厂优化调度模型。
  4. 根据权利要求3所述的一种虚拟电厂优化调度方法，其特征在于，所述风电模型对应的输出功率包括：
    其中，vi、vo和vr分别为风力涡轮机的切入速度、切出速度和额定速度；a、b、c为风力涡轮机的出力系数；pr为额定功率。
  5. 根据权利要求3所述的一种虚拟电厂优化调度方法，其特征在于，所述光伏发电系统对应的输出功率包括：
    其中，PSTC为标准测试条件下组件输出功率最大值；k1表示组件的温度系数；Tr和Tc分别表示参考温度和光伏面板温度；GSTC以及GING分别是在标准测试条件及估算输出时的辐照强度。
  6. 根据权利要求3所述的一种虚拟电厂优化调度方法，其特征在于，所述燃气轮机模型对应的二次函数表达式包括：
    其中，Pm(t)表示t时刻时微型燃气轮机的发电量；ρm、γm、αm为燃气轮机的单位运行成本系数。
  7. 根据权利要求3所述的一种虚拟电厂优化调度方法，其特征在于，所述虚拟电厂优化调度模型对应的净利润最大的目标函数包括：
    其中，Y(t)为虚拟电厂t时刻收益、Ydr(t)为电动汽车使用基于价格需求响应后t时刻的收益、CG(t)为虚拟电厂各出力单元t时刻运行管理费用、CM为燃气轮机t时刻出力产生的燃料成本、D(t)为虚拟电厂向主电网购电产生的成本、Cdis(t)为电动汽车放电收益和电池损耗成本之和。
  8. 根据权利要求7所述的一种虚拟电厂优化调度方法，其特征在于，所述虚拟电厂t时刻收益包括：
    Y(t)＝G1(t)(Pwt(t)+Ppv(t)+Pgt(t)+Pevdis(t)+Pevc(t))
    其中，T表示一天24个时刻，G(t)为t时刻日前售电电价；分别为t,时刻风电、光伏、燃气轮机、电动汽车出力值和电动汽车负荷。
  9. 根据权利要求7所述的一种虚拟电厂优化调度方法，其特征在于，所述电动汽车使用基于价格需求响应后t时刻的收益，包括：
    Ydr(t)＝Pevc(t)Ft-Pevc,0(t)Ft0
    其中，Pevc,0(t)、Pevc(t)为t时刻基于价格需求响应前后的电动汽车负荷，Ft0、Ft为t时刻使用基于价格需求响应前后的电价。
  10. 根据权利要求7所述的一种虚拟电厂优化调度方法，其特征在于，所述虚拟电厂各出力单元t时刻运行管理费用，包括：
    CG(t)＝XwtPwt(t)+XpvPpv(t)+XgtPm(t)+Xev(Pevc(t)+Pevdis(t))
    其中，Xwt、Xpv、Xm、Xev分别为风电、光伏出力机组，燃气轮机、电动汽车运行管理成本系数。
PCT/CN2024/130714 2023-11-14 2024-11-08 一种虚拟电厂优化调度方法 Pending WO2025103217A1 (zh)

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