Virtual power plant optimization scheduling method based on two-stage dynamic game

Abstract

The invention discloses a virtual power plant optimal scheduling method based on a two-stage dynamic game, which is used for solving the problems that the current related technology has insufficient attention on a carbon transaction and power market cooperation mechanism and lacks modeling for interaction of supply and demand parties under the dynamic game, so that the carbon emission reduction effect and economic benefit are difficult to balance, and the scheduling efficiency is poor. And the sustainability of the scheduling strategy is insufficient. The method comprises the following steps: acquiring gas consumption data and energy storage charging and discharging data of a virtual power plant; constructing a gas turbine power output model based on the gas consumption data, and constructing an energy storage battery operation model based on the energy storage charging and discharging data; in combination with system standby constraints, introducing a sectional excitation type carbon emission mechanism, and constructing a constraint model containing carbon emission; based on a gas turbine power output model, an energy storage battery operation model and a carbon-containing emission constraint model, constructing a scheduling balance optimization model based on a two-stage dynamic game; and carrying out optimization solution on the scheduling balance optimization model to obtain an optimized scheduling result of the virtual power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant scheduling optimization, and in particular to a virtual power plant optimization scheduling method based on two-stage dynamic game, a virtual power plant optimization scheduling device based on two-stage dynamic game, an electronic device and a storage medium. Background Art

[0002] Virtual Power Plants (VPPs) are an emerging energy management technology. They aggregate distributed energy resources (such as renewable energy generation, energy storage systems, and demand response resources) to form a virtual, flexible power generation entity, enabling it to participate in power system operations and market transactions like a traditional power plant. With the transition to a cleaner, more intelligent, and decentralized energy structure, VPPs are playing an increasingly important role in the power system. Optimal scheduling is one of VPPs' core functions. Through scientific and rational resource allocation and scheduling strategies, VPPs achieve efficient resource utilization, minimize operating costs, and maximize system benefits.

[0003] In practice, virtual power plants, as an innovative model integrating distributed power generation, energy storage, and demand-side resources, have become a research hotspot for power dispatch. However, current research focuses primarily on economic optimization, with insufficient attention paid to the synergy between carbon trading and the power market.

[0004] Traditional virtual power plant scheduling models often employ single-layer optimization. This approach to scheduling optimization lacks modeling of the dynamic interaction between supply and demand. This makes it difficult to balance carbon reduction and economic benefits. Furthermore, most models fail to consider the mutual interests of both the power generation and user sides, resulting in unsustainable scheduling strategies. Summary of the Invention

[0005] The present invention provides a virtual power plant optimization scheduling method based on two-stage dynamic game, a virtual power plant optimization scheduling device based on two-stage dynamic game, an electronic device and a storage medium, which are used to solve or partially solve the technical problems that current related technologies pay insufficient attention to the coordination mechanism of carbon trading and electricity market, lack modeling of the interaction between supply and demand sides under dynamic game, resulting in difficulty in balancing carbon emission reduction effects and economic benefits, and insufficient sustainability of scheduling strategies.

[0006] The present invention provides a virtual power plant optimization scheduling method based on a two-stage dynamic game, the method comprising:

[0007] Obtain gas consumption data and energy storage charging and discharging data of virtual power plants;

[0008] Based on the gas consumption data, a gas turbine power output model is constructed, and based on the energy storage charge and discharge data, an energy storage battery operation model is constructed;

[0009] Combined with system reserve constraints, a segmented incentive-based carbon emission mechanism is introduced to build a constraint model containing carbon emissions;

[0010] Based on the gas turbine power output model, the energy storage battery operation model and the carbon emission constraint model, a scheduling equilibrium optimization model based on a two-stage dynamic game is constructed;

[0011] The scheduling equilibrium optimization model is optimized and solved to obtain the optimized scheduling result of the virtual power plant.

[0012] Optionally, the gas consumption data includes natural gas calorific value, power generation efficiency, and gas consumption of the gas turbine in the virtual power plant; and constructing the gas turbine power output model based on the gas consumption data includes:

[0013] The gas power generation power of the gas turbine is calculated according to the calorific value of the natural gas, the gas consumption and the power generation efficiency, so as to construct a gas turbine power output model.

[0014] Optionally, the energy storage charge and discharge data includes the charge and discharge status, charge and discharge power, and charge and discharge efficiency of the energy storage battery in the virtual power plant; and constructing the energy storage battery operation model based on the energy storage charge and discharge data includes:

[0015] Constructing a charge and discharge state constraint of the energy storage battery according to the charge and discharge state and the charge and discharge power;

[0016] Constructing a state of charge model of the energy storage battery according to the charge and discharge state, the charge and discharge power, and the charge and discharge power, combined with the self-discharge rate and the energy storage rated capacity;

[0017] The charge and discharge state constraints and the state of charge model are integrated to construct an energy storage battery operation model.

[0018] Optionally, the system reserve constraint is combined with a segmented incentive-based carbon emission mechanism to construct a carbon emission constraint model, including:

[0019] Obtaining wind power generation power, photovoltaic power generation power and total power generation power of the virtual power plant;

[0020] According to the wind power generation power, the photovoltaic power generation power and the total power generation power, combined with the wind power reserve coefficient, the photovoltaic reserve coefficient and the user's interruptible load, a system reserve constraint related to the system spinning reserve demand is established;

[0021] Introducing a segmented incentive-based carbon emission mechanism, based on the pre-set carbon trading volume interval length, incentive coefficient and carbon price growth coefficient, combined with the benchmark carbon price and net carbon emissions, to construct carbon cost calculation sub-models for different carbon trading volume intervals, and integrating each of the carbon cost calculation sub-models to obtain an incentive-based ladder carbon emission model;

[0022] The system standby constraint and the incentive-based step-by-step carbon emission model are integrated to construct a constraint model containing carbon emissions.

[0023] Optionally, the carbon emission constraint model includes a system reserve constraint and an incentive-type stepped carbon emission model; and constructing a scheduling equilibrium optimization model based on a two-stage dynamic game based on the gas turbine power output model, the energy storage battery operation model, and the carbon emission constraint model includes:

[0024] Taking minimizing total operating costs and maximizing revenue as scheduling optimization goals, an operator utility sub-model is constructed based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based step-by-step carbon emission model;

[0025] Taking minimizing electricity expenditure as the scheduling optimization goal, a user utility sub-model related to electricity cost is constructed;

[0026] With the operator utility sub-model as the upper stage and the user utility sub-model as the lower stage, a scheduling equilibrium optimization model based on two-stage dynamic game is constructed.

[0027] Optionally, the scheduling optimization objective is to minimize total operating costs and maximize revenue, and to construct an operator utility sub-model based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based stepped carbon emission model, including:

[0028] constructing a gas turbine fuel cost model based on the gas turbine power output model;

[0029] Based on the energy storage battery operation model, construct an energy storage operation cost model;

[0030] Based on the system backup constraints, a system backup cost model is constructed;

[0031] At the same time, the user's interruptible load, the interruptible load subsidy price, the user's actual electricity load and the internal electricity price set by the operator are considered to build the user's electricity fee income model;

[0032] At the same time, the time-of-use electricity price of the external power grid, the electricity sold to the market and the electricity purchased from the market are considered to build a power market transaction revenue model;

[0033] The scheduling optimization objectives are to minimize the gas turbine fuel cost, energy storage operation cost, system backup cost and carbon trading cost, and to maximize the user electricity bill income and electricity market transaction income. An operator utility sub-model is constructed based on the gas turbine fuel cost model, the energy storage operation cost model, the system backup cost model, the incentive-based tiered carbon emission model, the user electricity bill income model and the electricity market transaction income model.

[0034] Optionally, the scheduling optimization objective is to minimize electricity expenditure, and a user utility sub-model related to electricity cost is constructed, including:

[0035] At the same time, the user's response power load, user's interruptible load and user's electricity price are considered to build the user's electricity cost model;

[0036] At the same time, the marginal utility coefficient of electricity consumption, electricity preference coefficient, user response electricity load and user interruptible load are considered to construct the user electricity utility model;

[0037] At the same time, the user's additional subsidy, the external grid peak benchmark subsidy and the user's interruptible load are taken into account to build an interruptible load subsidy benefit model;

[0038] Taking minimizing electricity expenditure as the scheduling optimization goal, a user utility sub-model related to electricity cost is constructed according to the user electricity cost model, the user electricity utility model and the interruptible load subsidy benefit model.

[0039] The present invention also provides a virtual power plant optimization scheduling device based on two-stage dynamic game, comprising:

[0040] A data acquisition unit, used to acquire the gas consumption data and energy storage charging and discharging data of the virtual power plant;

[0041] a basic operation model construction unit, configured to construct a gas turbine power output model based on the gas consumption data, and to construct an energy storage battery operation model based on the energy storage charge and discharge data;

[0042] The carbon emission constraint model construction unit is used to combine the system reserve constraint, introduce a segmented incentive carbon emission mechanism, and construct a carbon emission constraint model;

[0043] a dispatch equilibrium optimization model construction unit, configured to construct a dispatch equilibrium optimization model based on a two-stage dynamic game based on the gas turbine power output model, the energy storage battery operation model, and the carbon emission constraint model;

[0044] The optimization scheduling result solving unit is used to optimize and solve the scheduling equilibrium optimization model to obtain the optimization scheduling result of the virtual power plant.

[0045] The present invention further provides an electronic device, comprising a processor and a memory:

[0046] The memory is used to store program code and transmit the program code to the processor;

[0047] The processor is used to execute the virtual power plant optimization scheduling method based on two-stage dynamic game as described in any of the above items according to the instructions in the program code.

[0048] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the virtual power plant optimization scheduling method based on two-stage dynamic game as described in any of the above items.

[0049] It can be seen from the above technical solutions that the present invention has the following advantages:

[0050] A virtual power plant (VPP) scheduling optimization method based on a two-stage dynamic game is proposed. First, gas consumption data and energy storage charging and discharging data for the VPP are obtained. A gas turbine power output model is constructed based on the gas consumption data, and an energy storage battery operation model is constructed based on the energy storage charging and discharging data. This physical model defines the VPP's core equipment operating rules, ensuring the feasibility of power generation, energy storage, and load. Incorporating system reserve constraints, a segmented incentive-based carbon emission mechanism is introduced to construct a carbon emission constraint model. By constructing this constraint model, an incentive-based tiered carbon price directly links carbon emission costs to strategies, encouraging operators to prioritize clean energy in the dynamic game. Based on the gas turbine power output model, the energy storage battery operation model, and the carbon emission constraint model, a scheduling equilibrium optimization model based on the two-stage dynamic game is constructed. This scheduling equilibrium optimization model is then optimized and solved to obtain the VPP's optimal scheduling results. By constructing and optimizing the two-stage dynamic game model, balanced scheduling between operators and users is achieved with the goal of maximizing supply and demand utility. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A flowchart of the steps of a virtual power plant optimization scheduling method based on two-stage dynamic game;

[0053] Figure 2This is a schematic diagram of the overall process of a virtual power plant optimization scheduling method based on a two-stage dynamic game;

[0054] Figure 3 This is a structural block diagram of a virtual power plant optimization scheduling device based on two-stage dynamic game. DETAILED DESCRIPTION

[0055] The embodiments of the present invention provide a virtual power plant optimization scheduling method based on two-stage dynamic game, a virtual power plant optimization scheduling device based on two-stage dynamic game, an electronic device and a storage medium, which are used to solve or partially solve the technical problems that the current related technologies do not pay enough attention to the coordination mechanism of carbon trading and electricity market, lack modeling of the interaction between supply and demand sides under dynamic game, resulting in difficulty in balancing carbon emission reduction effects and economic benefits, and insufficient sustainability of scheduling strategies.

[0056] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] As an example, in practice, virtual power plants, as an innovative model integrating distributed power generation, energy storage, and demand-side resources, have become a research hotspot for power dispatch. However, current research focuses primarily on economic optimization, with insufficient attention paid to the synergy between carbon trading and the power market.

[0058] Traditional virtual power plant scheduling models often employ single-layer optimization. This approach to scheduling optimization lacks modeling of the dynamic interaction between supply and demand. This makes it difficult to balance carbon reduction and economic benefits. Furthermore, most models fail to consider the mutual interests of both the power generation and user sides, resulting in unsustainable scheduling strategies.

[0059] Therefore, one of the core inventions of the embodiment of the present invention is that: in response to the shortcomings of the current technology, a virtual power plant optimization scheduling method based on two-stage dynamic game is proposed. On the one hand, a segmented carbon emission mechanism based on step incentives is proposed. When calculating carbon emissions, the role of carbon trading in promoting emission reduction can be enhanced by adjusting the growth range of carbon trading volume in different stages and its corresponding incentive coefficient. On the other hand, a two-stage Stackelberg dynamic game scheduling equilibrium optimization model is constructed. The model coordinates the interests of both parties through two-layer optimization. The operator optimizes the power generation plan and market transactions, and the user adjusts the load in response to the electricity price. Therefore, with the goal of maximizing the utility of supply and demand, balanced scheduling of operators and users is achieved, so that both supply and demand sides can reach Pareto optimality under the low-carbon and economic goals.

[0060] Reference Figure 1 , shows a flowchart of a virtual power plant optimization scheduling method based on a two-stage dynamic game provided by an embodiment of the present invention, which may specifically include the following steps:

[0061] Step 101: Obtain gas consumption data and energy storage charging and discharging data of the virtual power plant;

[0062] Gas consumption data can primarily include natural gas calorific value, power generation efficiency, and gas consumption of gas turbines in virtual power plants. Energy storage charging and discharging data can primarily include the charge and discharge status, charge and discharge power (charging power, discharging power), and charge and discharge efficiency (charging efficiency, discharging efficiency) of energy storage batteries in virtual power plants.

[0063] Step 102: constructing a gas turbine power output model based on the gas consumption data, and constructing an energy storage battery operation model based on the energy storage charge and discharge data;

[0064] This step mainly establishes a basic equipment operation constraint model based on the physical conditions of the system equipment to provide a strategy space for subsequent dynamic games.

[0065] In a specific implementation, a gas turbine power output model is constructed based on gas consumption data. The gas power generation power of the gas turbine is calculated according to the calorific value of natural gas, gas consumption and power generation efficiency to construct the gas turbine power output model.

[0066] By constructing a gas turbine power output model, the relationship between gas turbine power generation and fuel consumption can be quantified, providing input for cost calculation. The gas turbine power output model is shown below:

[0067]

[0068] Where, Indicates that the gas turbine is in the period Power generation capacity; Indicates that the gas turbine is in the period Gas consumption; Indicates the calorific value of natural gas; Indicates power generation efficiency.

[0069] In a specific implementation, an energy storage battery operation model is constructed based on energy storage charge and discharge data. The following methods are used to construct the charge and discharge state constraints of the energy storage battery according to the charge and discharge state and charge and discharge power; to construct the state of charge model of the energy storage battery according to the charge and discharge state, charge and discharge power and charge and discharge power, combined with the self-discharge rate and the rated capacity of the energy storage; and to construct the energy storage battery operation model by integrating the charge and discharge state constraints and the state of charge model.

[0070] Furthermore, the charge and discharge state constraints of the energy storage battery are as follows:

[0071]

[0072] Where, and 0-1 variables representing the charge and discharge states respectively; Indicates charging power; Indicates discharge power; and Respectively represent the minimum value of charge and discharge power; and Represent the maximum value of charge and discharge power respectively.

[0073] The State of Charge (SOC) model of the energy storage battery is as follows:

[0074]

[0075] Where, Indicates energy storage period State of charge; Indicates the self-discharge rate; and Indicates the charging and discharging efficiency; Indicates the rated capacity of energy storage.

[0076] This step defines the operating rules for the core virtual power plant components through a physical model, ensuring the feasibility of power generation, energy storage, and load. For example, gas turbines, as controllable units, provide flexible adjustment capabilities, while energy storage batteries smooth out fluctuations in renewable energy. This operational constraint model provides the physical foundation for operators to formulate power generation plans and users to adjust loads in subsequent game scenarios, representing the "feasible region" boundary for dynamic game strategies.

[0077] Step 103: Incorporating system reserve constraints, introducing a segmented incentive-based carbon emission mechanism, and constructing a carbon emission constraint model;

[0078] This step mainly analyzes the constraints that the system needs to meet, introduces an incentive-based carbon emission mechanism, and establishes a constraint model containing carbon emissions.

[0079] In some embodiments, a segmented incentive-based carbon emission mechanism is introduced in combination with system standby constraints to construct an execution process of a constraint model containing carbon emissions, including the following sub-steps S01 to S04:

[0080] Step S01: Obtain the wind power generation power, photovoltaic power generation power and total power generation power of the virtual power plant;

[0081] Step S02: Based on the wind power generation power, photovoltaic power generation power and total power generation power, combined with the wind power reserve coefficient, photovoltaic reserve coefficient and user interruptible load, a system reserve constraint related to the system spinning reserve demand is constructed;

[0082] By setting system backup constraints, the virtual power plant can still provide stable power supply when renewable energy fluctuates. The system backup constraints constructed in this embodiment of the present invention are as follows:

[0083]

[0084] Where, Indicates the maximum power generation capacity of the virtual power plant; Indicates that the virtual power plant Power generation at the moment; Indicates that the user can interrupt the load; and represent wind power and photovoltaic reserve coefficients respectively; and Represents wind power and photovoltaic power in Power generation at the moment; Indicates the system spinning reserve requirement.

[0085] Step S03: Introducing a segmented incentive-based carbon emission mechanism, based on the pre-set carbon trading volume interval length, incentive coefficient, and carbon price growth coefficient, combined with the benchmark carbon price and net carbon emissions, constructing carbon cost calculation sub-models for different carbon trading volume intervals, and integrating each carbon cost calculation sub-model to obtain an incentive-based ladder carbon emission model;

[0086] The purpose of building an incentive-based tiered carbon emission model is to encourage operators to reduce carbon emissions through segmented carbon prices. ), then the unit carbon price will be The incentive-based step-by-step carbon emission model constructed in this embodiment of the present invention is as follows:

[0087]

[0088] Where, represents the carbon cost; represents the benchmark carbon price; represents net carbon emissions (actual emissions minus free allowances); Indicates the length of the carbon trading volume interval; Indicates the carbon price growth coefficient in each stage; Represents the excitation coefficient.

[0089] Step S04: Integrate the system reserve constraint and the incentive-based ladder carbon emission model to construct a constraint model containing carbon emissions.

[0090] By building a constraint model that incorporates carbon emissions, the incentive-based tiered carbon price directly links carbon emission costs to strategies, prompting operators to prioritize clean energy in a dynamic game. For example, a faster carbon price increase could incentivize operators to reduce gas turbine output and increase energy storage and renewable energy consumption.

[0091] Step 104: constructing a scheduling equilibrium optimization model based on a two-stage dynamic game based on the gas turbine power output model, the energy storage battery operation model, and the carbon emission constraint model;

[0092] This step mainly establishes a two-stage Stackelberg dynamic game equilibrium optimization model. By constructing the Stackelberg dynamic game model, the equilibrium strategy between operators and users can be derived.

[0093] In combination with the above content, it can be seen that the carbon emission constraint model includes a system reserve constraint and an incentive-based step-by-step carbon emission model. In some embodiments, based on the gas turbine power output model, the energy storage battery operation model, and the carbon emission constraint model, an execution process of a two-stage dynamic game-based scheduling equilibrium optimization model is constructed, including the following sub-steps S11 to S13:

[0094] Step S11: With minimizing total operating costs and maximizing revenue as the scheduling optimization objectives, an operator utility sub-model is constructed based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based step-by-step carbon emission model;

[0095] The upper-level operator utility sub-model is constructed with the goal of minimizing total operating costs (fuel, energy storage, backup, carbon trading) and maximizing revenue.

[0096] Furthermore, step S11 can be implemented by executing the following sub-steps S11-1 to S11-6:

[0097] Step S11-1: constructing a gas turbine fuel cost model based on the gas turbine power output model;

[0098] Step S11-2: constructing an energy storage operation cost model based on the energy storage battery operation model;

[0099] Step S11-3: constructing a system backup cost model based on the system backup constraints;

[0100] Step S11-4: Build a user electricity revenue model by taking into account the user's interruptible load, the interruptible load subsidy price, the user's actual electricity load, and the operator's internal electricity price;

[0101] Step S11-5: Considering the external grid time-of-use electricity price, the amount of electricity sold to the market, and the amount of electricity purchased from the market, a power market transaction revenue model is constructed;

[0102] Step S11-6: Taking minimizing gas turbine fuel costs, energy storage operating costs, system standby costs and carbon trading costs, and maximizing user electricity bill revenue and electricity market transaction revenue as the scheduling optimization goals, an operator utility sub-model is constructed based on the gas turbine fuel cost model, energy storage operating cost model, system standby cost model, incentive-based step-by-step carbon emission model, user electricity bill revenue model and electricity market transaction revenue model.

[0103] The operator utility sub-model constructed in the embodiment of the present invention is as follows:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] Where, a sub-model representing the operator utility of the virtual power plant; represents the gas turbine fuel cost; represents the energy storage operating cost; represents the system standby cost; Indicates the user's electricity revenue; represents the revenue from electricity market transactions; 、 and represents the cost coefficient; and Indicates time period Energy storage charging and discharging prices; represents the spinning reserve compensation price; Indicates the upper limit of spare capacity; represents the internal electricity price set by the operator; Indicates the actual power load of the user; Indicates time period Interruptible load subsidy price; Indicates the time-of-use electricity price of the external power grid; Indicates the amount of electricity sold to the market; Indicates the amount of electricity purchased from the market.

[0111] Step S12: Taking minimizing electricity expenditure as the scheduling optimization goal, construct a user utility sub-model related to electricity cost;

[0112] The lower-level user utility sub-model is constructed with the goal of minimizing electricity expenditure while obtaining subsidies by participating in demand response.

[0113] Furthermore, step S12 can be implemented by executing the following sub-steps S12-1 to S12-4:

[0114] Step S12-1: Considering the user's response power load, the user's interruptible load, and the user's electricity price, a user electricity cost model is constructed;

[0115] Step S12-2: Considering the marginal utility coefficient of electricity consumption, the electricity preference coefficient, the user's response electricity load and the user's interruptible load, a user electricity utility model is constructed;

[0116] Step S12-3: Considering the user's additional subsidy, the external grid's peak-time benchmark subsidy, and the user's interruptible load, a model for interruptible load subsidy revenue is constructed;

[0117] Step S12-4: Taking minimizing electricity expenditure as the scheduling optimization goal, a user utility sub-model related to electricity cost is constructed based on the user electricity cost model, the user electricity utility model and the interruptible load subsidy benefit model.

[0118] The user utility sub-model constructed in the embodiment of the present invention is as follows:

[0119]

[0120]

[0121]

[0122]

[0123] Where, represents the user utility sub-model; Indicates the user's electricity cost; Indicates the user's electricity utility; represents the interruptible load subsidy income; Represents a user In the period Response electricity load; Represents a user In the period interruptible load; Represents a user In the period electricity prices; represents the marginal utility coefficient of electricity consumption; represents the electricity preference coefficient; Represents a user In the period additional subsidies; Represents the external grid peak benchmark subsidy.

[0124] Step S13: With the operator utility sub-model as the upper stage and the user utility sub-model as the lower stage, a scheduling equilibrium optimization model based on a two-stage dynamic game is constructed.

[0125] Therefore, by constructing a two-stage Stackelberg dynamic game model, the balanced scheduling of operators and users is achieved with the goal of maximizing the supply and demand utility.

[0126] Step 105: Optimize and solve the scheduling equilibrium optimization model to obtain the optimized scheduling result of the virtual power plant.

[0127] Finally, by optimizing and solving the scheduling equilibrium optimization model constructed based on the previous steps, the optimized scheduling results of the virtual power plant can be obtained.

[0128] In an embodiment of the present invention, a virtual power plant optimization scheduling method based on a two-stage dynamic game is proposed. On the one hand, a segmented carbon emission mechanism based on step incentives is proposed. When calculating carbon emissions, the carbon trading volume growth range and its corresponding incentive coefficient can be adjusted in different stages to enhance the role of carbon trading in promoting emission reduction. On the other hand, a two-stage Stackelberg dynamic game scheduling equilibrium optimization model is constructed. The model coordinates the interests of both parties through two-layer optimization. The operator optimizes the power generation plan and market transactions, and the user adjusts the load in response to the electricity price. Thus, with the goal of maximizing the supply and demand utility, the balanced scheduling of operators and users is achieved, so that both the supply and demand sides reach the Pareto optimality under the low-carbon and economic goals.

[0129] For better explanation, refer to Figure 2, showing a schematic diagram of the overall process of a virtual power plant optimization scheduling method based on a two-stage dynamic game, provided by an embodiment of the present invention. It should be noted that this embodiment only briefly describes the general process of optimizing virtual power plant scheduling based on a two-stage dynamic game. The specific implementation process of each step can be understood by referring to the relevant content in the aforementioned embodiments. A detailed description is omitted here. It is understood that the present invention is not limited to this.

[0130] Step 201: Obtaining gas consumption data, energy storage charging and discharging data, wind power generation power, photovoltaic power generation power, and total power generation power of the virtual power plant;

[0131] Step 202: constructing a gas turbine power output model based on the gas consumption data, and constructing an energy storage battery operation model based on the energy storage charge and discharge data;

[0132] Step 203: Based on the wind power generation power, photovoltaic power generation power and total power generation power, combined with the wind power reserve coefficient, photovoltaic reserve coefficient and user interruptible load, a system reserve constraint related to the system spinning reserve demand is constructed;

[0133] Step 204: Introduce a segmented incentive-based carbon emission mechanism. Based on the pre-set carbon trading volume interval length, incentive coefficient, and carbon price growth coefficient, combined with the benchmark carbon price and net carbon emissions, construct carbon cost calculation sub-models for different carbon trading volume intervals. Then, integrate each carbon cost calculation sub-model to obtain an incentive-based ladder carbon emission model.

[0134] Step 205: With minimizing total operating costs and maximizing revenue as the scheduling optimization objectives, an operator utility sub-model is constructed based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based step-by-step carbon emission model. Simultaneously, with minimizing electricity expenditure as the scheduling optimization objective, a user utility sub-model related to electricity costs is constructed.

[0135] Step 206: With the operator utility sub-model as the upper stage and the user utility sub-model as the lower stage, a scheduling equilibrium optimization model based on a two-stage dynamic game is constructed, and the scheduling equilibrium optimization model is optimized and solved to obtain the optimized scheduling result of the virtual power plant.

[0136] Reference Figure 3 , shows a structural block diagram of a virtual power plant optimization scheduling device based on two-stage dynamic game provided by an embodiment of the present invention, which may specifically include:

[0137] The data acquisition unit 301 is used to acquire the gas consumption data and energy storage charging and discharging data of the virtual power plant;

[0138] A basic operation model building unit 302 is configured to build a gas turbine power output model based on the gas consumption data, and to build an energy storage battery operation model based on the energy storage charge and discharge data;

[0139] The carbon emission constraint model construction unit 303 is used to combine the system reserve constraint, introduce the segmented incentive carbon emission mechanism, and construct a carbon emission constraint model;

[0140] The scheduling equilibrium optimization model construction unit 304 is used to construct a scheduling equilibrium optimization model based on a two-stage dynamic game based on the gas turbine power output model, the energy storage battery operation model and the carbon emission constraint model;

[0141] The optimization scheduling result solving unit 305 is used to optimize and solve the scheduling equilibrium optimization model to obtain the optimization scheduling result of the virtual power plant.

[0142] In an optional embodiment, the gas consumption data includes natural gas calorific value, power generation efficiency, and gas consumption of the gas turbine in the virtual power plant; the basic operation model building unit 302 includes:

[0143] The gas turbine power output model building unit is used to calculate the gas power generation power of the gas turbine according to the natural gas calorific value, the gas consumption and the power generation efficiency, so as to build a gas turbine power output model.

[0144] In an optional embodiment, the energy storage charging and discharging data includes the charging and discharging state, charging and discharging power, and charging and discharging efficiency of the energy storage battery in the virtual power plant; the basic operation model building unit 302 includes:

[0145] a charge and discharge state constraint constructing unit, configured to construct a charge and discharge state constraint of the energy storage battery according to the charge and discharge state and the charge and discharge power;

[0146] an energy storage battery state of charge model construction unit, configured to construct a state of charge model of the energy storage battery according to the charge and discharge state, the charge and discharge power, and the charge and discharge power, in combination with the self-discharge rate and the energy storage rated capacity;

[0147] The energy storage battery operation model construction unit is used to integrate the charge and discharge state constraints and the state of charge model to construct an energy storage battery operation model.

[0148] In an optional embodiment, the carbon emission constraint model building unit 303 includes:

[0149] A power acquisition subunit, configured to acquire the wind power generation power, photovoltaic power generation power and total power generation power of the virtual power plant;

[0150] a system reserve constraint construction unit, configured to construct a system reserve constraint related to a system spinning reserve demand based on the wind power generation power, the photovoltaic power generation power, and the total power generation power, in combination with a wind power reserve coefficient, a photovoltaic reserve coefficient, and a user interruptible load;

[0151] An incentive-type tiered carbon emission model construction unit is used to introduce a segmented incentive-type carbon emission mechanism. Based on the pre-set carbon trading volume interval length, incentive coefficient and carbon price growth coefficient, combined with the benchmark carbon price and net carbon emissions, it constructs carbon cost calculation sub-models for different carbon trading volume intervals, and integrates each of the carbon cost calculation sub-models to obtain an incentive-type tiered carbon emission model.

[0152] The carbon emission constraint model integration unit is used to integrate the system standby constraint and the incentive-type step-by-step carbon emission model to construct a carbon emission constraint model.

[0153] In an optional embodiment, the carbon emission constraint model includes a system reserve constraint and an incentive-type step-by-step carbon emission model; the scheduling equilibrium optimization model construction unit 304 includes:

[0154] An operator utility sub-model construction unit is configured to construct an operator utility sub-model based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based stepped carbon emission model, with minimizing total operating costs and maximizing revenue as scheduling optimization objectives;

[0155] A user utility sub-model construction unit is used to construct a user utility sub-model related to electricity cost with minimizing electricity expenditure as the scheduling optimization goal;

[0156] The scheduling equilibrium optimization model construction subunit is used to construct a scheduling equilibrium optimization model based on a two-stage dynamic game with the operator utility submodel as the upper stage and the user utility submodel as the lower stage.

[0157] In an optional embodiment, the operator utility sub-model construction unit includes:

[0158] a gas turbine fuel cost model building unit, configured to build a gas turbine fuel cost model based on the gas turbine power output model;

[0159] An energy storage operation cost model construction unit, configured to construct an energy storage operation cost model based on the energy storage battery operation model;

[0160] A system standby cost model building unit, configured to build a system standby cost model based on the system standby constraint;

[0161] A user electricity fee revenue model construction unit is used to simultaneously consider the user's interruptible load, the interruptible load subsidy price, the user's actual electricity load and the internal electricity price set by the operator to construct the user electricity fee revenue model;

[0162] The power market transaction revenue model construction unit is used to simultaneously consider the external power grid time-of-use electricity price, the amount of electricity sold to the market, and the amount of electricity purchased from the market to construct the power market transaction revenue model;

[0163] The operator utility sub-model constructs a sub-unit, which is used to minimize the gas turbine fuel cost, energy storage operation cost, system backup cost and carbon trading cost, and maximize the user electricity bill income and electricity market transaction income as the scheduling optimization goal. The operator utility sub-model is constructed according to the gas turbine fuel cost model, the energy storage operation cost model, the system backup cost model, the incentive-based ladder carbon emission model, the user electricity bill income model and the electricity market transaction income model.

[0164] In an optional embodiment, the user utility sub-model construction unit includes:

[0165] A user electricity cost model building unit is used to simultaneously consider the user's response electricity load, the user's interruptible load, and the user's electricity price to build a user electricity cost model;

[0166] A user electricity utility model construction unit is used to simultaneously consider the electricity marginal utility coefficient, the electricity preference coefficient, the user's response electricity load and the user's interruptible load to construct the user electricity utility model;

[0167] An interruptible load subsidy revenue model construction unit is used to simultaneously consider the user's additional subsidy, the external grid peak benchmark subsidy and the user's interruptible load to construct an interruptible load subsidy revenue model;

[0168] The user utility submodel construction subunit is used to construct a user utility submodel related to electricity cost based on the user electricity cost model, the user electricity utility model and the interruptible load subsidy benefit model, with minimizing electricity expenditure as the scheduling optimization goal.

[0169] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the aforementioned method embodiment.

[0170] An embodiment of the present invention further provides an electronic device, the device including a processor and a memory:

[0171] The memory is used to store program codes and transmit the program codes to the processor;

[0172] The processor is used to execute the virtual power plant optimization scheduling method based on two-stage dynamic game according to any embodiment of the present invention according to the instructions in the program code.

[0173] An embodiment of the present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the virtual power plant optimization scheduling method based on two-stage dynamic game according to any embodiment of the present invention.

[0174] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0175] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0178] If the integrated unit is implemented as a software functional unit 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 the present invention, or the portion that contributes to the prior art, or all or 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0179] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A virtual power plant optimization scheduling method based on two-stage dynamic game, characterized in that: include: Obtain gas consumption data and energy storage charging and discharging data of virtual power plants; Based on the gas consumption data, a gas turbine power output model is constructed, and based on the energy storage charge and discharge data, an energy storage battery operation model is constructed; Combined with system reserve constraints, a segmented incentive-based carbon emission mechanism is introduced to build a constraint model containing carbon emissions; Based on the gas turbine power output model, the energy storage battery operation model and the carbon emission constraint model, a scheduling equilibrium optimization model based on a two-stage dynamic game is constructed; The scheduling equilibrium optimization model is optimized and solved to obtain the optimized scheduling result of the virtual power plant.

2. The virtual power plant optimization scheduling method based on two-stage dynamic game according to claim 1 is characterized in that: The gas consumption data includes natural gas calorific value, power generation efficiency, and gas consumption of the gas turbine in the virtual power plant; and constructing a gas turbine power output model based on the gas consumption data includes: The gas power generation power of the gas turbine is calculated according to the calorific value of the natural gas, the gas consumption and the power generation efficiency, so as to construct a gas turbine power output model.

3. The virtual power plant optimization scheduling method based on two-stage dynamic game according to claim 1 is characterized in that: The energy storage charge and discharge data includes the charge and discharge status, charge and discharge power, and charge and discharge efficiency of the energy storage battery in the virtual power plant; and constructing the energy storage battery operation model based on the energy storage charge and discharge data includes: Constructing a charge and discharge state constraint of the energy storage battery according to the charge and discharge state and the charge and discharge power; Constructing a state of charge model of the energy storage battery according to the charge and discharge state, the charge and discharge power, and the charge and discharge power, combined with the self-discharge rate and the energy storage rated capacity; The charge and discharge state constraints and the state of charge model are integrated to construct an energy storage battery operation model.

4. The virtual power plant optimization scheduling method based on two-stage dynamic game according to claim 1 is characterized in that: The above-mentioned system reserve constraint is combined with the introduction of a segmented incentive carbon emission mechanism to construct a constraint model containing carbon emissions, including: Obtaining wind power generation power, photovoltaic power generation power and total power generation power of the virtual power plant; According to the wind power generation power, the photovoltaic power generation power and the total power generation power, combined with the wind power reserve coefficient, the photovoltaic reserve coefficient and the user's interruptible load, a system reserve constraint related to the system spinning reserve demand is established; Introducing a segmented incentive-based carbon emission mechanism, based on the pre-set carbon trading volume interval length, incentive coefficient and carbon price growth coefficient, combined with the benchmark carbon price and net carbon emissions, to construct carbon cost calculation sub-models for different carbon trading volume intervals, and integrating each of the carbon cost calculation sub-models to obtain an incentive-based ladder carbon emission model; The system standby constraint and the incentive-based step-by-step carbon emission model are integrated to construct a constraint model containing carbon emissions.

5. The virtual power plant optimization scheduling method based on two-stage dynamic game according to any one of claims 1 to 4, characterized in that: The carbon emission constraint model includes a system reserve constraint and an incentive-type stepped carbon emission model; the scheduling equilibrium optimization model based on the gas turbine power output model, the energy storage battery operation model and the carbon emission constraint model is constructed based on a two-stage dynamic game, including: Taking minimizing total operating costs and maximizing revenue as scheduling optimization goals, an operator utility sub-model is constructed based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based step-by-step carbon emission model; Taking minimizing electricity expenditure as the scheduling optimization goal, a user utility sub-model related to electricity cost is constructed; With the operator utility sub-model as the upper stage and the user utility sub-model as the lower stage, a scheduling equilibrium optimization model based on two-stage dynamic game is constructed.

6. The virtual power plant optimization scheduling method based on two-stage dynamic game according to claim 5 is characterized in that: The scheduling optimization objective is to minimize total operating costs and maximize revenue. Based on the gas turbine power output model, the energy storage battery operation model, the system backup constraint, and the incentive-based step-by-step carbon emission model, an operator utility sub-model is constructed, including: constructing a gas turbine fuel cost model based on the gas turbine power output model; Based on the energy storage battery operation model, construct an energy storage operation cost model; Based on the system backup constraints, a system backup cost model is constructed; At the same time, the user's interruptible load, the interruptible load subsidy price, the user's actual electricity load and the internal electricity price set by the operator are considered to build the user's electricity fee income model; At the same time, the time-of-use electricity price of the external power grid, the electricity sold to the market and the electricity purchased from the market are considered to build a power market transaction revenue model; The scheduling optimization objectives are to minimize the gas turbine fuel cost, energy storage operation cost, system backup cost and carbon trading cost, and to maximize the user electricity bill income and electricity market transaction income. An operator utility sub-model is constructed based on the gas turbine fuel cost model, the energy storage operation cost model, the system backup cost model, the incentive-based tiered carbon emission model, the user electricity bill income model and the electricity market transaction income model.

7. The virtual power plant optimization scheduling method based on two-stage dynamic game according to claim 5 is characterized in that: The scheduling optimization goal is to minimize electricity expenditure and build a user utility sub-model related to electricity cost, including: At the same time, the user's response power load, user's interruptible load and user's electricity price are considered to build the user's electricity cost model; At the same time, the marginal utility coefficient of electricity consumption, electricity preference coefficient, user response electricity load and user interruptible load are considered to construct the user electricity utility model; At the same time, the user's additional subsidy, the external grid peak benchmark subsidy and the user's interruptible load are taken into account to build an interruptible load subsidy benefit model; Taking minimizing electricity expenditure as the scheduling optimization goal, a user utility sub-model related to electricity cost is constructed according to the user electricity cost model, the user electricity utility model and the interruptible load subsidy benefit model.

8. A virtual power plant optimization scheduling device based on two-stage dynamic game, characterized in that: include: A data acquisition unit, used to acquire the gas consumption data and energy storage charging and discharging data of the virtual power plant; a basic operation model construction unit, configured to construct a gas turbine power output model based on the gas consumption data, and to construct an energy storage battery operation model based on the energy storage charge and discharge data; The carbon emission constraint model construction unit is used to combine the system reserve constraint, introduce a segmented incentive carbon emission mechanism, and construct a carbon emission constraint model; a dispatch equilibrium optimization model construction unit, configured to construct a dispatch equilibrium optimization model based on a two-stage dynamic game based on the gas turbine power output model, the energy storage battery operation model, and the carbon emission constraint model; The optimization scheduling result solving unit is used to optimize and solve the scheduling equilibrium optimization model to obtain the optimization scheduling result of the virtual power plant.

9. An electronic device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the virtual power plant optimization scheduling method based on two-stage dynamic game as described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the virtual power plant optimization scheduling method based on two-stage dynamic game as described in any one of claims 1-7.

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