A method for generating day-ahead electricity prices with the participation of vehicle-to-everything (V2X) network aggregators

Abstract

This invention discloses a day-ahead electricity price generation method under the participation of vehicle-grid interaction aggregators, belonging to the technical field of power system economic dispatch and electricity market modeling. It includes constructing a multi-energy-source generation and consumption model of vehicle-grid interaction and power system operation constraints with the participation of vehicle-grid interaction aggregators. Based on the multi-energy-source generation and consumption model of vehicle-grid interaction, and with the optimization objective of minimizing the total system operating cost, a day-ahead electricity market clearing model is established under the power system operation constraints with the participation of vehicle-grid interaction aggregators. This model is solved to obtain the processing plans for each generating unit and the system wholesale electricity price. The system wholesale electricity price is then corrected based on correction coefficients. This invention, by constructing an electricity market clearing model, comprehensively considers the characteristics of different types of generating units, energy storage devices, and regional loads. Under the premise of ensuring power supply and demand balance and system security constraints, it generates wholesale and retail electricity prices, achieving reasonable transmission of electricity price signals and improving market operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of power system economic dispatch and power market modeling technology, specifically to a method for generating day-ahead electricity prices with the participation of vehicle-grid interaction aggregators. Background Technology

[0002] With the advancement of energy decarbonization and electricity market reform, vehicle-to-grid (V2G) technology is gradually becoming an important means to improve system flexibility and promote the consumption of renewable energy. As key players connecting electric vehicles (EVs) and the electricity market, V2G aggregators participate in the day-ahead market and ancillary services by integrating flexible resources such as battery energy storage systems (BESS) to achieve peak shaving and load regulation. Existing electricity pricing models mostly focus on the traditional generation side, lacking bidirectional interaction modeling for aggregators. The resources aggregated by aggregators have non-convex characteristics, involving complex conditions such as start-up and shutdown costs, state of charge (SOC) constraints, and energy balance. The current electricity pricing mechanism fails to reflect their role in price linkage and distributed response, leading to a deviation between price signals and system flexibility requirements.

[0003] Patent CN111820892A proposes a price prediction method based on multi-energy collaborative optimization, focusing on energy balance optimization, but does not incorporate the time-varying charging and discharging behavior of vehicle-grid interaction resources; Patent CN116028729A discloses a day-ahead price simulation method for power systems, which, although considering generator constraints, ignores the demand response of aggregators and distributed flexibility adjustment; US Patent US20090200988A1 proposes an aggregation system for distributed power resources, which can centrally manage the charging behavior of multiple resources, but does not incorporate a market clearing mechanism; while US20090066287A and US9630511B2, although exploring the aggregation control and power compensation of vehicle-grid interaction systems, still remain at the physical layer power dispatch level and have not formed a complete market price generation framework.

[0004] Therefore, existing technologies have not yet established a method that can comprehensively characterize the multi-entity market clearing mechanism under the participation of vehicle-to-grid aggregators. There is an urgent need for a day-ahead electricity price generation method that can simultaneously consider the non-convex cost constraints of generating units, the energy balance relationship of energy storage units, the distributed regulation characteristics of aggregators, and the price linkage mechanism on the retail side within a unified electricity market model, so as to achieve accurate transmission of electricity price signals and an overall improvement in market operating efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a day-ahead electricity price generation method with the participation of vehicle-to-grid (V2G) aggregators. By constructing a unified electricity market clearing model, and comprehensively considering the characteristics of different types of generator sets, energy storage devices and regional loads, the method accurately generates wholesale and retail electricity prices while ensuring the balance of electricity supply and demand and system security constraints, thereby realizing the rational transmission of electricity price signals and improving market operation efficiency.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for generating day-ahead electricity prices with the participation of vehicle-to-grid (V2G) aggregators includes: Step 1: Construct a multi-energy source power generation and consumption model for vehicle-grid interaction, specifically including a generator set model, a new energy power generation model, a regional load model, and a vehicle-grid interaction aggregator model; Step 2: Construct power system operation constraints with the participation of vehicle-to-grid (V2G) interaction aggregators; Step 3: Based on the multi-energy source power generation and consumption model of vehicle-grid interaction, and with the optimization objective of minimizing the total system operating cost, establish a day-ahead power market clearing model with the participation of vehicle-grid interaction aggregators under the power system operation constraints. Step 4: Solve the day-ahead electricity market clearing model to obtain the processing plan for each unit and the system wholesale electricity price; Step 5: Adjust the system wholesale electricity price based on the correction factor.

[0007] Furthermore, the generator set model includes the unit type, maximum capacity, and marginal cost.

[0008] Furthermore, the new energy power generation modeling includes the marginal cost of wind power and photovoltaic units and the base load cost of hydropower and nuclear power units. The marginal cost of wind power and photovoltaic units is determined by annualizing the equipment construction cost, while the base load cost of hydropower and nuclear power units is jointly determined by fuel prices and equipment depreciation costs.

[0009] Furthermore, the regional load model includes residential electricity consumption, industrial electricity consumption, and dispatchable load.

[0010] Furthermore, the vehicle-to-grid interaction aggregator model includes the charging and discharging state and the energy storage state of the energy storage unit of the vehicle-to-grid interaction aggregator. The charging and discharging state of the energy storage unit of the vehicle-to-grid interaction aggregator is characterized by energy input and energy output, and the energy storage state of charge is calculated based on the energy flow balance relationship.

[0011] Furthermore, the power system operation constraints specifically include vehicle-to-grid interaction power balance constraints, aggregator status and power constraints, and generator set capacity and power constraints.

[0012] Furthermore, in step 4, mixed-integer linear programming is used to solve the day-ahead electricity market clearing model.

[0013] Furthermore, in step 5, the system wholesale electricity price is adjusted based on the correction factor, specifically as follows: The system wholesale electricity price is corrected based on the correction coefficient, and the ratio between the historical daily retail electricity price and the system wholesale electricity price is calibrated. At the same time, three modes of electricity price are generated: fixed price, time-of-use price and real-time price. The fixed electricity price remains unchanged throughout the billing cycle, the time-of-use price is set at different levels according to peak, flat and valley periods, and the real-time price is dynamically updated according to the real-time fluctuations of the wholesale market.

[0014] In summary, the present invention has at least one of the following beneficial technical effects: 1. The unified electricity market clearing model constructed in this invention takes vehicle-grid interaction aggregators as one of the core entities, and performs collaborative modeling and optimization with various generating units such as thermal power, gas power, and new energy, as well as regional loads including flexible loads. This model can more accurately depict the complex interactive relationship between power generation, energy storage, and electricity consumption, thereby obtaining unit output plans, power allocation results, and electricity price signals that are more consistent with the actual system operation status during the market clearing stage. This fundamentally improves the accuracy of market clearing and the overall efficiency of power dispatch.

[0015] 2. This invention overcomes the limitations of traditional methods that separate wholesale and retail markets. By using a correction coefficient λ based on historical data calibration, it constructs a direct conversion channel from day-ahead wholesale electricity prices to retail electricity prices. It can simultaneously generate three modes: fixed price, time-of-use price, and real-time price. This linkage mechanism ensures that price fluctuations in the wholesale market can be quickly and accurately transmitted to the retail side, providing end users with clear and diverse price options. In particular, time-of-use and real-time prices can effectively incentivize users to charge during off-peak hours, discharge during peak hours, or reduce electricity consumption, thereby fully stimulating the demand-side response potential of vehicle-grid interaction aggregators and a large number of electricity users, and improving the peak-shaving and valley-filling capabilities of the power grid.

[0016] 3. This invention possesses high versatility and applicability, effectively supporting the stable operation of electricity markets with a high proportion of renewable energy integration. The model architecture of this invention is not designed for a specific energy structure; its unified multi-energy entity model is naturally applicable to scenarios involving high proportions of highly volatile renewable energy sources such as wind and solar power. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2a This is a schematic diagram of the power output curves of each unit in January; Figure 2bThis is a diagram illustrating wholesale electricity prices prior to January. Figure 2c This is a schematic diagram of the power output curves of each unit in April; Figure 2d This is a diagram illustrating wholesale electricity prices before April. Figure 2e This is a schematic diagram of the power output curves of each unit in July; Figure 2f This is a diagram illustrating wholesale electricity prices before July. Figure 2g This is a schematic diagram of the power output curves of each unit in November; Figure 2h This is a diagram illustrating wholesale electricity prices before November. Figure 3 This is a diagram illustrating the results of the current day's market retail electricity price generation. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0019] like Figure 1 As shown, this invention provides a method for generating day-ahead electricity prices with the participation of vehicle-to-grid (V2G) aggregators, comprising: Step 1: Construct a multi-energy source power generation and consumption model for vehicle-grid interaction, specifically including a generator set model, a new energy power generation model, a regional load model, and a vehicle-grid interaction aggregator model; Step 2: Construct power system operation constraints with the participation of vehicle-to-grid (V2G) interaction aggregators; Step 3: Based on the multi-energy source power generation and consumption model of vehicle-grid interaction, and with the optimization objective of minimizing the total system operating cost, establish a day-ahead power market clearing model with the participation of vehicle-grid interaction aggregators under the power system operation constraints. Step 4: Solve the day-ahead electricity market clearing model to obtain the processing plan for each unit and the system wholesale electricity price; Step 5: Adjust the system wholesale electricity price based on the correction factor.

[0020] Step 1 involves constructing a multi-energy source power generation and consumption model for vehicle-to-grid interaction, specifically including a generator set model, a new energy power generation model, a regional load model, and a vehicle-to-grid interaction aggregator model. The construction process for each model is described below: 1. Generator set model A regional generator unit information matrix is ​​established to describe the capacity and marginal cost of different types of power sources (including thermal power, gas-fired power, wind power, photovoltaic power, hydropower, and nuclear power). The matrix includes three types of parameters: unit type, maximum capacity K, and marginal cost p. The cost functions corresponding to different unit types are different. The marginal cost of thermal power units includes fuel cost, carbon emission cost, and start-up and shutdown cost. The cost of this unit can be calculated in the following form: (1) In the formula, It is the cost of the thermal power unit at time t. It is the average cost of equipment construction. It's the cost of fuel. It's about energy efficiency. It is the cost of carbon emissions. It's the start-up and shutdown cost.

[0021] 2. New Energy Power Generation Model The marginal cost of wind and solar power units is mainly determined by the annualized equipment construction cost, and its functional form is: (2) In the formula, It is the cost of the new energy unit at time t. It is the average cost of equipment construction.

[0022] For baseload units such as hydropower and nuclear power, their cost function is determined by both fuel prices and equipment depreciation costs, specifically: (3) In the formula, This refers to the cost of a baseload type unit at time t. It is the average cost of equipment construction. It's the cost of fuel. It's about energy efficiency.

[0023] 3. Regional load model A regional load model is constructed, including residential electricity consumption, industrial electricity consumption, and dispatchable load. The total load is represented as Dem(t) with time t as the index. It includes a flexible load item that can respond to changes in electricity prices, which is used to achieve the coupling response of wholesale and retail electricity prices in the future.

[0024] 4. Vehicle-to-Everything (V2X) Interactive Aggregator Model Through energy input With energy output The state of charge and discharge of the energy storage unit, and the state of charge of the energy storage, characterize the energy storage unit of the vehicle-to-everything (V2X) aggregator. Calculated based on the energy flow balance relationship: (4) In the formula, It refers to the state of charge of the energy storage system. It's about energy efficiency. It is the charging power of the energy storage system per unit time. It is the discharge power of the energy storage system per unit time. It is the total energy storage capacity of the system. The power system operation constraints specifically include vehicle-grid interaction power balance constraints, aggregator status and power constraints, and generator unit capacity and power constraints, which will be described in detail below: 1. Power balance constraints in vehicle-to-grid interaction At any given time, the total power generation of all generator sets, minus the difference between the charging and discharging power of the energy storage units, and plus the net power interaction of the electric vehicle group controlled by the vehicle-to-grid aggregator, should be equal to the total system load demand. This constraint ensures the dynamic balance between power supply and consumption among the generator sets, energy storage devices, and aggregator groups. The total power balance constraint of the system is expressed as: (5) In the formula, This refers to the real-time power output of the generating unit. It is the power of external system interconnection. This is the system's required power. It is the real-time power of the energy storage system.

[0025] 2. Aggregator status and power constraints The electric vehicle fleet managed by the aggregator is considered a distributed energy storage resource as a whole. Its charging and discharging behavior is dynamically adjusted based on market electricity price signals and vehicle availability, realizing bidirectional energy flow of charging and discharging. During periods of high market prices, the aggregator supports the power grid by discharging; during periods of low prices or off-peak load, the aggregator absorbs excess electricity by charging, thereby achieving peak shaving and valley filling and price arbitrage without affecting the normal use of vehicles. Specifically: (6) In the formula, These are the upper and lower limits of the energy storage system's power. These are the upper and lower limits of the state of charge of the energy storage system.

[0026] 3. Generator set capacity and power constraints The output of generator sets is limited by their rated capacity, and the actual power generation of each unit must not exceed its maximum allowable capacity, specifically: (7) In the formula, It is the upper limit of generator set capacity. The following is an introduction to the day-ahead electricity market clearing model: During the day-ahead electricity market clearing process, based on the multi-energy source generation and consumption model of vehicle-to-grid interaction, all generating units submit bids based on their marginal cost curves. The market operator uses mixed-integer linear programming (MILP) to solve for the optimal clearing scheme. The optimization objective is to minimize the sum of the total system generation cost and energy storage operating cost. Constraints include power balance constraints, unit capacity limitations, energy storage charging and discharging range, and SOC limitations. The objective function of the day-ahead electricity market clearing model can be expressed as: (8) In the formula, It is the energy storage system's per-kilowatt-hour regulation cost.

[0027] In step 4, the day-ahead electricity market clearing model is solved to obtain the processing plan for each generating unit and the system wholesale electricity price, specifically: After solving the above model, we obtain the output Pgen_idx(t) of each unit and the system wholesale electricity price.

[0028] In step 5, the system wholesale electricity price is adjusted based on the correction factor, specifically as follows: Retail electricity prices are calculated based on the day-ahead system wholesale electricity price adjusted by a correction factor λ. Calibration is performed using the historical ratio between retail and wholesale electricity prices. This allows for the simultaneous generation of three pricing models: fixed price, time-of-use price, and real-time price. The calculation relationship for retail electricity prices is as follows: (9) (10) In the formula, It is the electricity price fluctuation factor. It is the real-time retail electricity price at time t. The system's market wholesale electricity price at time t is the date of issue t.

[0029] Fixed electricity prices remain unchanged throughout the billing cycle; time-of-use prices are set at different levels for peak, flat, and valley periods; and real-time prices are dynamically updated based on real-time fluctuations in the wholesale market.

[0030] To verify the effectiveness of the method of this invention, modeling and simulation analysis were conducted using the UK electricity market as an example. The input data came from actual statistical data from the UK National Grid in 2023, covering 120 independent gas turbine units, 15 coal-fired units, and various power sources such as wind power, photovoltaic power, hydropower, nuclear power, and biomass.

[0031] In the simulation, typical daily data for each month were used, setting four representative periods: January, April, July, and November, each corresponding to different renewable energy output levels. The model was solved through MILP optimization to obtain the day-ahead wholesale electricity price and the output curves of each unit for each period.

[0032] The results show that under low wind power output conditions in January, the proportion of thermal power unit output increased, and the wholesale electricity price was highly correlated with the load curve; while under high wind power output conditions in November, wind power output led to a significant decrease in nighttime electricity prices. During April and July, nighttime electricity prices remained relatively stable due to fluctuations in wind and solar power output and the participation of energy storage in charge and discharge regulation.

[0033] The generated wholesale electricity price curve is largely consistent with the actual price trend in the UK market, indicating that the electricity price generation model of this invention can effectively capture market fluctuation characteristics. The corresponding wholesale electricity price results are as follows: Figures 2a-2h As shown.

[0034] This invention generates retail electricity prices based on wholesale electricity prices, including three forms: fixed price, time-of-use price, and real-time price. The time-of-use price is lower during off-peak hours at night, while the real-time price fluctuates with market dynamics. The results are as follows: Figure 3 As shown.

[0035] The above embodiments demonstrate that the present invention can achieve high-precision electricity price generation and price linkage while ensuring power balance and system constraints, providing technical support for the transparent operation of the electricity market and demand-side response.

[0036] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0037] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0038] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0040] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.

Claims

1. A method for generating day-ahead electricity prices with the participation of vehicle-to-grid (V2G) aggregators, characterized in that, include: Step 1: Construct a multi-energy source power generation and consumption model for vehicle-grid interaction, specifically including a generator set model, a new energy power generation model, a regional load model, and a vehicle-grid interaction aggregator model; Step 2: Construct power system operation constraints with the participation of vehicle-to-grid (V2G) interaction aggregators; Step 3: Based on the multi-energy source power generation and consumption model of vehicle-grid interaction, and with the optimization objective of minimizing the total system operating cost, establish a day-ahead power market clearing model with the participation of vehicle-grid interaction aggregators under the power system operation constraints. Step 4: Solve the day-ahead electricity market clearing model to obtain the processing plan for each unit and the system wholesale electricity price; Step 5: Adjust the system wholesale electricity price based on the correction factor.

2. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators as described in claim 1, characterized in that, The generator set model includes the unit type, maximum capacity, and marginal cost.

3. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators as described in claim 2, characterized in that, The modeling of new energy power generation includes the marginal cost of wind power and photovoltaic units and the base load cost of hydropower and nuclear power units. The marginal cost of wind power and photovoltaic units is determined by the annualized equipment construction cost, while the base load cost of hydropower and nuclear power units is determined by fuel prices and equipment depreciation costs.

4. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators as described in claim 3, characterized in that, The regional load model includes residential electricity consumption, industrial electricity consumption, and dispatchable load.

5. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators according to claim 4, characterized in that, The vehicle-to-grid (V2G) interaction aggregator model includes the charge / discharge state and energy storage state of the energy storage unit of the V2G interaction aggregator. The charge / discharge state of the energy storage unit of the V2G interaction aggregator is characterized by energy input and energy output, and the energy storage state of charge is calculated based on the energy flow balance relationship.

6. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators according to claim 5, characterized in that, The power system operation constraints specifically include vehicle-grid interaction power balance constraints, aggregator status and power constraints, and generator set capacity and power constraints.

7. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators according to claim 6, characterized in that, In step 4, mixed-integer linear programming is used to solve the day-ahead electricity market clearing model.

8. The method for generating day-ahead electricity prices with the participation of vehicle-to-grid interactive aggregators according to claim 7, characterized in that, In step 5, the system wholesale electricity price is adjusted based on the correction factor, specifically as follows: The system wholesale electricity price is corrected based on the correction coefficient, and the ratio between the historical daily retail electricity price and the system wholesale electricity price is calibrated. At the same time, three modes of electricity price, time-of-use electricity price and real-time electricity price are generated. The fixed electricity price remains unchanged throughout the billing cycle, the time-of-use price is set at different levels according to peak, flat and valley periods, and the real-time price is dynamically updated according to the real-time fluctuations of the wholesale market.

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