Real-time electricity price and v2g partition charging and discharging collaborative scheduling method and system

Abstract

The present application relates to the field of charging and discharging technology, in order to solve the problem of limiting the adjustment potential of demand side resources in the prior art, a charging and discharging collaborative scheduling method and system considering real-time electricity price and V2G partition are provided, including generating dynamic electricity price based on marginal cost and average cost of renewable energy units, in order to maximize the profit of the integrated energy system, seek the balance between economy and carbon emission as the target, build a charging agent optimization model to determine the optimal energy sales price; taking the minimum charging cost as the target, building an electric vehicle user charging decision model; taking the charging agent optimization model as the upper model, the electric vehicle user charging decision model as the lower model, establishing the master-slave game framework between the charging agent and the user, adopting Lagrange relaxation efficient solution, under the balance of economy and carbon emission, obtaining the optimal energy sales price and the charging and discharging scheduling scheme with the minimum charging cost, realizing the multi-party benefit collaboration, and improving the economy and low carbon of the system.

Description

Technical Field

[0001] This invention relates to the field of charging and discharging technology, and in particular to a charging and discharging collaborative scheduling method and system that takes into account real-time electricity prices and V2G partitioning. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The rapid growth in EV (Electric Vehicle) ownership has brought new opportunities to IES (Integrated Energy System). However, traditional time-of-use pricing mechanisms, based on fixed peak-valley periods, struggle to dynamically respond to fluctuations in renewable energy output and real-time changes in EV charging demand. This disconnect between price signals and the actual system generation cost limits the potential for demand-side resource regulation. Existing charging and discharging resource scheduling schemes suffer from the following problems: most models only support orderly charging and lack a V2G (Vehicle-to-Grid) two-way interaction mechanism, failing to fully unleash the system's peak-shaving potential; existing strategies treat all EV users as a homogeneous group, ignoring differences in charging urgency and spatial distribution. Real-time prices are mostly generated based on load forecasts, resulting in a lack of targeted pricing, low user response rates, and a tendency to cause localized distribution network overload. Summary of the Invention

[0004] To address the aforementioned technical issues, this invention provides a charging and discharging collaborative scheduling method and system that considers real-time electricity prices and V2G partitioning. It generates dynamic electricity prices based on a weighted average of marginal and average costs, constructs a hierarchical partitioning management model for EV users with multi-dimensional characteristics, and establishes a master-slave game framework between charging agents and users. It employs Lagrange relaxation for efficient solution to achieve synergy among multiple parties and improve the system's economic efficiency and low carbon footprint.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a charging and discharging coordinated scheduling method that takes into account real-time electricity prices and V2G partitioning.

[0006] In one or more embodiments, a charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning is provided, including: Based on the weighted average of the marginal cost of conventional units and the average cost of renewable energy units, a dynamic electricity price is generated. Then, with the goal of maximizing the profit of the integrated energy system and seeking a balance between economic benefits and carbon emissions, a charging agent optimization model is constructed to determine the optimal energy sales price. The electric vehicle hierarchical and zonal management model based on user behavior characteristics extracts multidimensional features and divides users into groups by weighted clustering and taking into account the urgency of charging. It then manages user groups in a refined manner and implements differentiated pricing, thereby constructing an electric vehicle user charging decision model with the goal of minimizing charging costs. Using the charging agent optimization model as the upper-level model and the electric vehicle user charging decision model as the lower-level model, a master-slave game framework is established between the charging agent and the user. The Lagrange relaxation is used for efficient solution. Under the balance between economy and carbon emissions, the optimal energy sales price and the charging and discharging scheduling scheme with minimum charging cost are obtained.

[0007] A second aspect of the present invention provides a charging and discharging coordinated scheduling system that takes into account real-time electricity prices and V2G partitioning.

[0008] In one or more embodiments, a charge-discharge coordinated scheduling system considering real-time electricity pricing and V2G partitioning includes: The charging agent optimization model construction module is used to generate dynamic electricity prices based on the weighted average of the marginal cost of conventional units and the average cost of renewable energy units. It aims to maximize the profit of the integrated energy system while seeking a balance between economic benefits and carbon emissions, and to construct a charging agent optimization model to determine the optimal energy sales price. The electric vehicle user charging decision model construction module is used for a hierarchical and zonal management model of electric vehicles based on user behavior characteristics. By extracting multi-dimensional features and dividing them into weighted clusters, and taking into account the urgency of users' charging, it can carry out refined management and differentiated pricing of user groups, and thus build an electric vehicle user charging decision model with the goal of minimizing charging costs. The charging and discharging coordinated scheduling solution module is used to establish a master-slave game framework between the charging agent and the user, with the charging agent optimization model as the upper-level model and the electric vehicle user charging decision model as the lower-level model. It adopts Lagrange relaxation for efficient solution and obtains the optimal energy sales price and the charging and discharging scheduling scheme with the minimum charging cost while taking into account the balance between economy and carbon emissions.

[0009] A third aspect of the present invention provides a computer-readable storage medium.

[0010] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the charge-discharge coordinated scheduling method considering real-time electricity prices and V2G partitions as described above.

[0011] A fourth aspect of the present invention provides an electronic device.

[0012] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the charge-discharge coordinated scheduling method considering real-time electricity prices and V2G partitions as described above.

[0013] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention significantly improves the economic efficiency and environmental friendliness of the integrated energy system by introducing a real-time pricing mechanism and a zoning management model based on user behavior characteristics. The real-time pricing mechanism dynamically prices based on marginal and average costs, taking into account the low marginal cost characteristics of renewable energy power generation. While ensuring the maximization of profits of the integrated energy system, it effectively balances carbon emission costs and wind and solar curtailment costs, and realizes flexible response to electricity and heat loads. The zoning management model uses multi-dimensional feature clustering (such as urgency, spatial zoning, and historical response rate) to divide large-scale electric vehicles into subgroups with similar scheduling needs. Through rolling updates and local overload constraints, it avoids distribution network transformer overload and improves the safety and scheduling accuracy of system operation. This mechanism can significantly reduce the cost of purchasing electricity and gas and carbon trading costs of the integrated energy system, while increasing the renewable energy absorption rate, and has obvious economic and environmental benefits.

[0014] (2) The electric vehicle participation master-slave game construction and Lagrange relaxation solution method constructed in this invention realizes the balanced optimization of the interests of both charging agents and users. The upper-level agent aims to maximize electricity sales revenue, while the lower-level user makes decisions based on minimizing charging costs. Through Lagrange relaxation, the complex coupling constraints are decoupled into independent subproblems, and the multipliers are iteratively updated using the subgradient method to efficiently obtain the near-optimal solution. This method not only reduces the user's charging cost and improves the user's response enthusiasm, but also ensures the agent's reasonable profit. At the same time, the generated charging plan can effectively smooth out load fluctuations and reduce the impact on the upper-level power grid. Compared with the traditional centralized optimization, this invention significantly reduces the computational complexity and is suitable for large-scale electric vehicle real-time scheduling scenarios. It has strong engineering practical value and promotion prospects. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0016] Figure 1 This is a flowchart of the charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitions according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the integrated energy system structure according to an embodiment of the present invention; Figure 3This is a schematic diagram of the master-slave game architecture of the integrated energy system electric vehicle according to an embodiment of the present invention; Figure 4 This is the power balance under a fixed price in this embodiment of the invention; Figure 5 This refers to the power balance under time-of-use pricing in this embodiment of the invention; Figure 6 This refers to the power balance under real-time pricing in this embodiment of the invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0020] Figure 1 A schematic diagram of the charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning in this invention embodiment is provided. The IES (Environmental Engineering System) of this invention embodiment mainly includes renewable energy generation units, gas turbines, gas boilers, energy storage systems, and electric vehicle charging loads. The gas turbines not only provide electricity, but their residual heat can be supplied to the heat load through a waste heat recovery device, achieving combined heat and power (CHP). The gas boilers serve as auxiliary heat sources, supplementing heating during peak heat load periods. Prioritizing renewable energy access and the flexible adjustment of the energy storage system help reduce the system's dependence on the upper-level grid and improve the capacity for renewable energy absorption. Electric vehicles, as adjustable loads, possess the potential for orderly charging and V2G. Through coordinated interaction with IES operators, the system load curve can be further optimized, reducing operating costs and carbon emissions. The overall structure is as follows: Figure 2 As shown, the coupling relationships between the various energy flows provide a foundation for subsequent modeling and game optimization.

[0021] according to Figure 1 The charging and discharging coordinated scheduling method considering real-time electricity price and V2G partitioning provided in this embodiment may include the following steps S101 to S103.

[0022] The specific implementation process of steps S101 to S103 is as follows: Step S101: Generate a dynamic electricity price based on the weighted average of the marginal cost of conventional units and the average cost of renewable energy units. Then, with the goal of maximizing the profit of the integrated energy system and seeking a balance between economic benefits and carbon emissions, construct a charging agent optimization model to determine the optimal energy sales price.

[0023] Existing time-of-use pricing mechanisms use fixed peak-valley periods, making it difficult to dynamically track the random fluctuations in renewable energy output and the real-time changes in EV charging demand. Price signals fail to accurately reflect the system's power adequacy and marginal generation costs, resulting in a lack of precise guidance for EV charging and discharging behavior. This leads to increased wind and solar curtailment, a widening of the peak-valley load difference, and higher system carbon emissions. Furthermore, it makes it difficult to reconcile the conflicting interests between charging agents and users. The real-time pricing mechanism proposed in this invention aims to solve these problems by using dynamic pricing weighted by marginal and average costs to achieve real-time coupling between electricity prices and system status.

[0024] An optimal price model is used to determine the optimal energy sales price in order to maximize IES profits while seeking a balance between economic benefits and carbon emissions.

[0025] (1); The cost model for each part in the above formula can be specifically described as follows: (2); In the formula: This is the net revenue function of IES; Revenue from electricity sales to IES; Let IES be the electricity price during time period t; For electrical load following demand response; For IES heat sales revenue; Let IES be the electricity price during time period t; This refers to the heat load following the demand response; The cost of electricity purchased from the grid by IES operators; Cost coefficient for purchasing electricity from the large power grid; The power purchased by the IES operator from the main grid during time period t; This refers to the cost of purchasing gas from the natural gas network for IES operators; This represents the cost coefficient for purchasing natural gas online. This represents the amount of gas purchased by the IES operator from the natural gas network during time period t. Costs associated with curtailing wind and solar power; This refers to the cost coefficient for wind and solar power curtailment. This refers to the power of wind and solar power that has been curtailed. For equipment operating costs; For CCS (Carbon Capture and Storage) efficiency; Let t be the electrical power consumed by the CCS during time period t; The energy harvesting costs of electric vehicles paid by IES operators to electric vehicle agents; This is the discharge compensation cost coefficient for electric vehicles; For electric vehicles The charging power at time t; For electric vehicles The discharge power at time t; Cost of carbon emissions; For carbon trading prices; , , These are the equivalent carbon emissions from GT (Gas Turbine), GB (Gas Boiler), and coal-fired power plants that supply electricity to customers. and For time period t, the carbon emission allowances for thermal power units and gas turbine units are respectively. To take into account the overall demand response cost; The coefficient for the quadratic term of user dissatisfaction with heat load; The interruptible heat load power during time period t; The unit compensation cost coefficient for interruptible heat load; The coefficient of the quadratic term of user dissatisfaction with electricity load; The interruptible electrical load power during time period t; The unit compensation cost coefficient for interruptible electrical loads; The unit compensation cost coefficient for time-shiftable discrete electrical loads; This is a discrete electrical load removal state variable; a value of 1 indicates that a discrete electrical load has been removed during that period. Let t be the power of the discrete electrical load during time period t; The unit compensation cost coefficient for heat dissipation loads that can be moved away at any time; This is a state variable for the movement away from the heat dissipation load. A value of 1 indicates that there is movement away from the heat dissipation load during this period. t represents the power of the heat dissipation load during time period t; T is the scheduling period.

[0026] The actual carbon emissions calculation formulas for each unit are as follows: (3); In the formula: , , , , , , , For GT, GB, and carbon emission coefficients of coal-fired power units; The power generation capacity of thermal power units in the purchased electricity period t; This represents the power generation of the gas turbine during time period t. This represents the heating power of the gas-fired boiler during time period t. This represents the power supply from the upstream power grid during time period t.

[0027] A pricing method based on the marginal cost analysis of conventional generating units provides a benchmark for real-time electricity prices. The general idea is to estimate the marginal cost of different conventional generating units and add a reasonable profit. The marginal cost of conventional generating units is shown below: (4); (5); (6); (7); In the formula: , , , These represent the marginal cost of the gas turbine, the marginal cost of electricity purchase, the marginal cost of the gas boiler, and the marginal cost of heat purchase, respectively. It is the gas cost of GT power generation; For GT's operation and maintenance costs; Refers to electricity price; It is the heat production cost of GB. Maintenance cost per GB; It's the hot price.

[0028] WT (Wind Turbine) and PV (Photovoltaic) installations incur only fixed generation costs, with negligible marginal costs. Marginal cost pricing offers little benefit to IES (Engineering Industries) when applied to renewable energy generators. The pricing mechanism for renewable energy generation should be based on average cost pricing. The average costs of PV and WT are shown below: (8); (9); In the formula: and These represent the average costs of PV and WT, respectively. For photovoltaic power generation installed capacity, The unit installation cost of photovoltaic power generation, This represents the average annual power generation from photovoltaic power generation. For the lifespan of PV, For wavelet transform, the installed capacity, The unit cost per WT is the installation cost. This represents the average annual electricity generation of WT. This refers to the service life of WT.

[0029] The final reference price formulas for the electricity and heat sides are as follows: (10); (11); In the formula: , These represent the actual power generation of photovoltaic and wind turbine units during time period t, respectively. This represents the actual power generation of the gas turbine during time period t. The electrical power purchased from the upstream power grid during time period t. The waste heat power recovered from the gas turbine during time period t. Let t be the heat output of the gas-fired boiler during time period t. The heat power purchased from the external heating network during time period t.

[0030] This model dynamically calculates the marginal cost of each power generation unit in the system and weights it according to scheduling priority to obtain a real-time electricity price signal that changes with supply and demand, output, and load, thereby realizing a pricing mechanism that reflects the real-time state of the system. The real-time energy price set by IES is within a range of 25% above and below the reference price.

[0031] The reference price model allows prices to fluctuate within a specific range for each period, while also imposing constraints on the average price. To encourage customers to choose IES as their energy supplier, rather than their upstream network, the average energy price within IES must remain at a reasonable level. The constraints of the optimal price model are shown in the following equation: (12); (13); In the formula: The time-of-use electricity price of the upper-level power grid during time period t; This is the electricity price reduction coefficient; The heat price of the superior heating network during time period t; This is the coefficient for reducing heat prices.

[0032] Through the aforementioned real-time pricing mechanism, IES operators determine the electricity sales price for each time period. and heating prices This price will serve as the electricity purchase cost for the charging agents, and also as the benchmark for agents to set charging prices for users.

[0033] To further verify the mechanism by which real-time electricity pricing guides orderly charging of EVs, Figure 4 , Figure 5 and Figure 6 The scheduling plans for IES under three pricing mechanisms are presented. By comparing the three schemes, the impact of different pricing signals on EV user charging behavior and system operation can be analyzed.

[0034] Figure 4 The IES scheduling plan under the first scheme was shown. Due to the lack of price signals, the EV charging load is mainly concentrated during the midday and evening peak hours. This, combined with the base load, increases the peak load. In order to meet the power supply gap during the peak hours, the system is forced to increase the output of gas turbines, resulting in a curtailment of up to 1253kW of wind and solar power.

[0035] Figure 5 The presentation showcased the IES dispatch plan under Scheme Two. Incentives from time-of-use pricing shifted some EV charging load to off-peak hours at night, effectively alleviating daytime power supply pressure. This demonstrates that the time-of-use pricing signal guided user behavior, reducing wind and solar curtailment to 520kW. However, because the time-of-use pricing period is fixed, it cannot dynamically respond to real-time fluctuations in renewable energy output. While the actual system power supply cost is very low during peak daytime solar power generation, the relatively low charging frequency during this period, coupled with the fact that time-of-use pricing designates this time as peak time, results in 250kW of solar curtailment at midday.

[0036] Figure 6 The IES dispatch plan under Scheme 3 is demonstrated. The real-time electricity price dynamically changes based on the actual output and marginal cost of each power generation unit within the system. During the peak photovoltaic (PV) generation period from 11:00 to 14:00, the real-time price drops to around RMB 0.2 / kWh, effectively incentivizing EV users to charge during this period. During the evening peak period from 17:00 to 20:00, PV output decreases, increasing the system's power supply pressure, and the real-time price rises to around RMB 1.1 / kWh. Under this high-price signal, EV users proactively reduce or postpone charging, avoiding load aggregation. This mechanism reduces wind and solar curtailment to 384kW.

[0037] Step S102: The electric vehicle hierarchical and zonal management model based on user behavior characteristics is constructed by extracting multidimensional features and dividing the user group by weighted clustering, taking into account the urgency of charging, and implementing refined management and differentiated pricing for the user group. In order to minimize charging costs, the electric vehicle user charging decision model is constructed.

[0038] Existing technologies neglect the differences among electric vehicle users in terms of charging urgency and price distribution, resulting in a lack of targeted pricing strategies for charging agents, low user response rates, inefficient dispatching, and a tendency to cause localized power distribution network overload. This invention proposes a hierarchical and zoned management model for electric vehicles based on user behavior characteristics. Through multi-dimensional feature extraction and weighted clustering, it achieves refined management and differentiated pricing for user groups, thereby improving dispatching efficiency and system economy.

[0039] Before the start of the scheduling cycle, the charging agent collects data from each EV user. The real-time and historical characteristics are shown in Table 1. Table 1. Real-time and historical characteristics of each electric vehicle user i;

[0040] Define charging urgency for: (14); in, The battery level at which the user expects to leave. This indicates that regular charging cannot meet the user's needs, classifying them as a high-urgent user. This indicates ample time, which can be adjusted flexibly; The state of charge when the user expects to leave; Battery capacity when the user leaves; For users Maximum charging power; This is the estimated time to reach the charging station; This is the estimated departure time.

[0041] Spatial partitioning The zones are divided according to the location of the charging stations. Different zones have different base load curves and renewable energy penetration rates, and zoned management can avoid local distribution network overload.

[0042] Suppose there are N electric cars in total, and each user... The feature vectors are: (15); Normalize continuous variables: (16); in, These are the normalized eigenvalues; These are the original eigenvalues; It is the minimum value of the characteristic; This represents the maximum value of the characteristic.

[0043] Discrete variables One-hot encoding is used. Weighted K-means clustering is used to divide users into K subgroups. The clustering objective function is: (17); in, This represents minimizing the sum of squared weighted distances from all electric vehicle users to their respective cluster centers; Indicates user Assigned to a subgroup by the clustering algorithm middle; This is the weight vector; For the first Feature vectors of each user; Let K be the cluster center of the k-th class. The number of clusters K is determined by the silhouette coefficient. (18); in, The optimal number of clusters, i.e., the number of partitions; To maximize the objective function Value; N is the total number of electric vehicle users; The average distance within the sample. The average distance to the nearest other cluster, For the sample and The maximum value of is used for normalization, so that the silhouette coefficient of each sample falls within [ ]. Within the interval [1,1]. After clustering, each subgroup... It has the following clustering parameters: average urgency Total schedulable capacity Maximum total discharge power Adjustable time window interval .

[0044] Because user travel behavior fluctuates throughout the day, this invention designs a rolling update strategy: after each scheduling cycle lasts for one hour, the feature vector is updated based on the actual charging data of the most recent 24 hours, and clustering is re-executed every 24 hours. For newly connected users, a nearest neighbor classifier is used to assign them to an existing subgroup.

[0045] Correspondence between zones and subgroups: Users within a subgroup can come from multiple spatial zones, but zone constraints must be added when pricing. If the load of a subgroup in zone z exceeds 80% of the transformer capacity of that zone, the charging price for that subgroup within that zone will be temporarily increased by 5%-10% to alleviate local overload.

[0046] After clustering, the features of each subgroup are aggregated into parameters that characterize the overall scheduling characteristics of the group. These aggregated parameters will be directly used as input to the charging agent optimization model for differentiated pricing and constraint modeling. The specific coupling method is as follows.

[0047] After subgrouping, the charging agent will no longer set a uniform charging price, but will instead set a price for each subgroup. Set charging prices separately and discharge price The pricing strategy considers the urgency of the subgroups: highly urgent subgroups can accept higher prices, while flexible subgroups enjoy lower prices. The charging and discharging prices for subgroup k are shown below: (19); (20); In the formula: Let $\frac{k}{k}$ be the charging price premium coefficient for subgroup $k$. Let k be the discharge price discount factor for subgroup k. The average charging urgency of subgroup k, The maximum average urgency across all subgroups. It represents the minimum average urgency among all subgroups.

[0048] The agent must meet the total energy requirements of each subgroup during scheduling: (twenty one); in: The adjustable start time for subgroup k; The adjustable termination time for subgroup k; For subgroups During the period Total charging power; The duration of each scheduling period; The total charging capacity available to all electric vehicles in subgroup k during the current scheduling period; For the first The initial state of charge of the vehicle; Let be the battery capacity of the i-th electric vehicle.

[0049] Subgroup charge / discharge power is constrained by clustering parameters: (twenty two); in: Let be the charging power of the electric vehicle in region k at time t; This represents the maximum charging power of electric vehicles in region k. Let be the discharge power of the electric vehicle in region k at time t; This represents the maximum discharge power of electric vehicles in region k.

[0050] Step S103: Using the charging agent optimization model as the upper-level model and the electric vehicle user charging decision model as the lower-level model, establish a master-slave game framework between the charging agent and the user. Employ Lagrange relaxation for efficient solution. While balancing economics and carbon emissions, obtain the optimal energy sales price and the charging / discharging scheduling scheme that minimizes charging costs, such as... Figure 3 As shown.

[0051] There is a conflict of interest between charging agents and EV users. Agents aim to maximize profits, while users aim to minimize charging costs. Traditional centralized optimization methods struggle to reconcile the interests of both parties. Furthermore, large-scale EV charging decisions involve complex time-coupled constraints, making direct solutions difficult. This invention proposes a master-slave game model that establishes a master-slave equilibrium through upper-level agent pricing and lower-level user responses. It employs the Lagrange relaxation method to decouple the lower-level coupling constraints into independent single-variable subproblems, achieving efficient solutions and thus reconciling the conflicting interests of both parties.

[0052] Within a scheduling cycle, the objective function of the electric vehicle agent is to maximize electricity sales profit, which can be expressed as: (twenty three); in: For electric vehicle agency revenue; , , Decision variables include real-time electricity market purchase volume and EV charging / discharging prices; , For the kth region The charging and discharging power of the electric vehicle in time period t; The real-time electricity market price is the integrated real-time electricity sales price of energy operators; this invention uses stochastic modeling based on the Monte Carlo method to simulate the start and end times of electric vehicle charging.

[0053] In addition to economic profit targets, the charging agent, as the coordination layer between IES and users, should also consider the following physical indicators: 1) The goal of maximizing the absorption of renewable energy; Charging agents should prioritize scheduling charging during periods of surplus renewable energy to reduce wind and solar curtailment and achieve the goal of minimizing such curtailment. Renewable energy consumption rate objective function: (twenty four); In the formula: This refers to the total amount of wind and solar power curtailed. , These represent the power of wind and solar power curtailed during time period t.

[0054] The goal of maximizing renewable energy consumption is transformed into the constraint of minimizing wind and solar curtailment: (25); Ensure that the total amount of wind and solar power curtailed within the scheduling period T does not exceed the threshold. .

[0055] 2) Targeting the smoothing of load peak-valley differences; Disorderly charging of electric vehicles will exacerbate the peak-valley load difference and threaten the safety of the power distribution network.

[0056] Objective: Minimize load variance (26); in: For load variance; The base load for time period t (excluding EV); Total charging power for EVs; This refers to the total discharge power of the EV. This represents the average load.

[0057] The physical index for load peak-valley difference mitigation is transformed into a load variance minimization constraint: (27); Ensure that the variance of the total load (base load + net EV load) does not exceed The smaller the variance, the smoother the load curve.

[0058] 3) The goal of minimizing carbon emissions; The indirect carbon emissions from electric vehicle charging depend on the marginal emission factor of the power grid corresponding to the charging period. The objective function for carbon emissions is: (28); in: Carbon emissions from the integrated energy system; Marginal carbon emission factor for electricity purchased from the grid during time period t; This refers to the power purchased by the agent from the IES or the upstream grid during period t. This target encourages the agent to purchase and recharge during periods of low carbon emissions.

[0059] The above objective function for carbon emissions is transformed into a constraint for minimizing carbon emissions: (29); Ensure that the total carbon emissions during the scheduling cycle do not exceed the threshold. .

[0060] The above , , The optimal value can be obtained from a single objective using a solver to speed up the solution process.

[0061] Other constraints are: (30); (31); (32); (33); (34); in: , The charging and discharging power of the energy storage system; , For the first The charging and discharging power of the electric vehicle in time period t; and These are the upper limits for discharge and charging power, respectively. Variables for the charging and discharging states of energy storage devices; Indicates the initial capacity of the energy storage device; Indicates the final capacity of the energy storage device; Indicates the maximum capacity of the storage device; and These are discharge and charge efficiencies, respectively. This represents the decrease in electricity prices, and its value is less than 1.

[0062] The objective function for EV user decision-making is to minimize charging costs. The decision variable in the EV user optimization problem is the charging amount for each time period, and the sign of the decision variable is... For electric vehicles The objective function is to minimize the charging cost, and the objective function is as follows: (35); in: Let K be the charging price for subgroup K in time period t. For the k-th subgroup The charging power of an electric vehicle during time period t; This is the emergency penalty coefficient. The baseline penalty coefficient; urgency of the user Positive correlation; For the first The energy required to charge an electric vehicle.

[0063] When electric vehicles participate in V2G discharge, each deep charge-discharge cycle accelerates battery aging. To quantify this physical cost, a battery life loss penalty term is defined as follows: (36); in: Battery aging index; For the first The state of charge of an electric vehicle at time t; Depth of discharge; It is the convexity coefficient, used to penalize deep discharge.

[0064] The objective function described above is transformed into a constraint that limits the cumulative aging index of all vehicles throughout the entire scheduling cycle: (37); Ensure that the total aging of all EV batteries does not exceed the threshold. , The optimal value can be obtained from a single objective using a solver to speed up the solution process.

[0065] Other constraints: (38); (39); (40); (41); in: For the first The maximum battery capacity of a vehicle; This is the initial capacity of the battery; It is the Kth district The charging power of the electric vehicle in time period t. Equation (41) represents the user's desired charging power after charging. It will reach 100% charging power within a short period of time.

[0066] For the EV user charging decision problem, the charging price is set by an agent, and the user needs to formulate a charging plan based on this price. This invention uses the Lagrange relaxation method to relax the coupling constraints in the lower-level electric vehicle user charging decision model into the objective function, decomposing the original problem into multiple independent sub-problems, and using the subgradient method to iteratively update the Lagrange multipliers, thereby obtaining an approximate optimal solution to the original problem. The Lagrange function is constructed as shown in equation (42).

[0067] (42); in: For the Lagrange multiplier of the penalty coefficient for insufficient charging energy; For Lagrange multipliers constrained by the upper limit of charging energy; For the Lagrange multiplier constrained by the amount of charge within the time window.

[0068] Simplify the Lagrange function: (43); in: The overall charging coefficient, whether positive or negative, directly determines the user's charging decision; This represents the constant term in the Lagrange function that is independent of the charging power decision variable.

[0069] Since the relaxed objective function is completely decoupled between electric vehicles and time periods, the original problem can be decomposed into N*T independent univariate subproblems. For the i-th electric vehicle in the i-th time period, the subproblem is: (44); (45); when When charging during this period reduces the Lagrange function value, the user chooses to charge at maximum power; when At times, charging is either pointless or increases costs, so users will not charge their devices.

[0070] The update and constraints of the Lagrange multipliers are as follows: (46); (47); in: The updated Lagrange multiplier corresponds to the multiplier of the charging energy constraint for the i-th electric vehicle, used to penalize the degree to which the charging amount deviates from the demand; max is the maximum value; For the current Lagrange multipliers, the multiplier value from the previous iteration; For the updated Lagrange multipliers, the multipliers corresponding to the charging amount constraint of the time window of the i-th electric vehicle; The target power ratio coefficient; The window length is the number of time periods contained within the time window. The start time of the time window, i.e., the starting period of the emergency charging time window set by the user; and The iteration step size is used, and a decreasing sequence is taken to ensure convergence; T is the scheduling period. For the kth region The charging power of the electric vehicle in time period t; For the first The upper limit of battery capacity for electric vehicles; It is the first The initial capacity of the battery of an electric vehicle.

[0071] The overall solution process is as follows: Step a: Electric vehicle agents set initial electricity prices .

[0072] Step b: Based on the given current electricity price Initialize the Lagrange multipliers and calculate the comprehensive coefficients according to equation (43). Update the user charging power according to equation (45). Update the multipliers according to equations (46) and (47) until the constraints are satisfied or the iteration limit is reached, and output the user's optimal charging plan and the corresponding charging cost.

[0073] Step c: The upper layer receives the charging plan from the lower layer, substitutes it into the upper layer agent's revenue function, updates the electricity price strategy to maximize electricity sales revenue, and determines whether the results of the upper and lower layers converge.

[0074] If convergence is not achieved, return to step b; otherwise, output the optimal electricity price strategy and charging / discharging scheduling scheme to guide the real-time operation and scheduling of the IES.

[0075] In one or more embodiments, a charge-discharge coordinated scheduling system considering real-time electricity prices and V2G partitioning is provided, which can be implemented in software. The charge-discharge coordinated scheduling system considering real-time electricity prices and V2G partitioning includes the following software modules: The charging agent optimization model construction module is used to generate dynamic electricity prices based on the weighted average of marginal cost and average cost of renewable energy units. It aims to maximize the profit of the integrated energy system while seeking a balance between economic benefits and carbon emissions, and to construct a charging agent optimization model to determine the optimal energy sales price. The electric vehicle user charging decision model construction module is used for a hierarchical and zonal management model of electric vehicles based on user behavior characteristics. By extracting multi-dimensional features and dividing them into weighted clusters, and taking into account the urgency of users' charging, it can carry out refined management and differentiated pricing of user groups, and thus build an electric vehicle user charging decision model with the goal of minimizing charging costs. The charging and discharging coordinated scheduling solution module is used to establish a master-slave game framework between the charging agent and the user, with the charging agent optimization model as the upper-level model and the electric vehicle user charging decision model as the lower-level model. It adopts Lagrange relaxation for efficient solution and obtains the optimal energy sales price and the charging and discharging scheduling scheme with the minimum charging cost while taking into account the balance between economy and carbon emissions.

[0076] It should be noted that each module in the charging and discharging coordinated scheduling system considering real-time electricity price and V2G partition in this embodiment of the invention corresponds one-to-one with each step in the charging and discharging coordinated scheduling method considering real-time electricity price and V2G partition in the above embodiment, and their specific implementation process is the same, so it will not be repeated here.

[0077] The structure of the electronic device according to embodiments of the present invention will be described in detail below. The electronic device provided in the embodiments of the present invention includes: at least one processor, a memory, a user interface, and at least one network interface. Considering the various components in the real-time electricity price and V2G zone-based charging and discharging coordinated scheduling system, they are coupled together through a bus system. It can be understood that the bus system is used to realize the connection and communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The user interface may include a display, keyboard, mouse, trackball, click wheel, buttons, a touchpad, or a touch screen, etc.

[0078] It is understood that the memory can be volatile memory or non-volatile memory, or both. The memory in this embodiment of the invention is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0079] In some embodiments, the charge-discharge coordinated scheduling system considering real-time electricity prices and V2G partitions provided in this invention can be implemented using a combination of hardware and software. As an example, the charge-discharge coordinated scheduling system considering real-time electricity prices and V2G partitions provided in this invention can be a processor in the form of a hardware decoding processor, programmed to execute the charge-discharge coordinated scheduling method considering real-time electricity prices and V2G partitions provided in this invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0080] As an example, a processor can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where a general-purpose processor can be a microprocessor or any conventional processor, etc.

[0081] As an example of the hardware implementation of the charge-discharge coordinated scheduling system considering real-time electricity price and V2G partitioning provided in the embodiments of the present invention, the device provided in the embodiments of the present invention can be directly executed by a processor in the form of a hardware decoding processor. For example, it can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the charge-discharge coordinated scheduling method considering real-time electricity price and V2G partitioning provided in the embodiments of the present invention.

[0082] The memory in this embodiment of the invention is used to store various types of data to support the operation of a charge-discharge coordinated scheduling system that takes into account real-time electricity prices and V2G partitioning, or to store data for execution. Figure 1 The program code for the method shown. Examples of this data include: any executable instructions for operation on a charge-discharge coordinated scheduling system that takes into account real-time electricity prices and V2G partitions, such as executable instructions. A program implementing the charge-discharge coordinated scheduling method considering real-time electricity prices and V2G partitions according to embodiments of the present invention can be included in executable instructions.

[0083] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0084] 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, as well as 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. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning, characterized in that, include: Based on the weighted average of the marginal cost of conventional units and the average cost of renewable energy units, a dynamic electricity price is generated. Then, with the goal of maximizing the profit of the integrated energy system and seeking a balance between economic benefits and carbon emissions, a charging agent optimization model is constructed to determine the optimal energy sales price. The electric vehicle hierarchical and zonal management model based on user behavior characteristics extracts multidimensional features and divides users into groups by weighted clustering and taking into account the urgency of charging. It then manages user groups in a refined manner and implements differentiated pricing, thereby constructing an electric vehicle user charging decision model with the goal of minimizing charging costs. Using the charging agent optimization model as the upper-level model and the electric vehicle user charging decision model as the lower-level model, a master-slave game framework is established between the charging agent and the user. The Lagrange relaxation is used for efficient solution. Under the balance between economy and carbon emissions, the optimal energy sales price and the charging and discharging scheduling scheme with minimum charging cost are obtained.

2. The charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning as described in claim 1, characterized in that, The expression for the charging agent optimization model is: ； in: For electricity sales profit; K is the maximum value; K is the number of partitions; T is the scheduling period; , , The decision variables include the electricity purchase volume in the electricity market during period t, the charging price in area k during period t, and the discharging price in area k during period t. , For the kth region The charging power and discharging power of the electric vehicle in time period t; Let be the electricity market price for time period t.

3. The charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning as described in claim 1, characterized in that, The charging agent optimization model also includes: the constraint of minimizing wind and solar curtailment converted from the goal of maximizing renewable energy consumption, the constraint of minimizing load variance converted from the goal of smoothing load peak-valley difference, and the constraint of minimizing carbon emissions converted from the goal of carbon emissions.

4. The charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning as described in claim 1, characterized in that, The expression for the electric vehicle user charging decision model is as follows: ； Where min is the minimum value function; K is the number of partitions; and T is the scheduling period. For the first The charging price for a vehicle in time period t; For the kth region The charging power of the electric vehicle in time period t; For the first The expected battery level of an electric vehicle when it leaves; The duration of each scheduling period; For the first Emergency penalty coefficient for electric vehicles.

5. The charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning as described in claim 1, characterized in that, The expression for the degree of electrical urgency is: ； in, The level of emergency in terms of electricity supply; This indicates that regular charging cannot meet the user's needs, classifying them as a high-urgent user. If the time limit is less than the preset threshold, it indicates sufficient time; the preset threshold is less than 1. For the first The expected battery level of an electric vehicle when it leaves; The state of charge when the user expects to leave; Indicates the first The battery capacity of an electric vehicle; Indicates the first The maximum charging power of a vehicle; Indicates the first The estimated departure time of the electric vehicles; Indicates the first The estimated time for each electric vehicle to arrive at the charging station.

6. The charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning as described in claim 1, characterized in that, In the process of efficiently solving the problem using Lagrange relaxation, the Lagrange relaxation method is used to relax the coupling constraints in the lower-level electric vehicle user charging decision model into the objective function. The subgradient method is used to iteratively update the Lagrange multipliers, thereby obtaining the approximate optimal solution of the electric vehicle user charging decision model.

7. The charging and discharging coordinated scheduling method considering real-time electricity prices and V2G partitioning as described in claim 6, characterized in that, The update and constraints of the Lagrange multipliers are as follows: ； ； in: For the updated Lagrange multipliers, corresponding to the th The multiplier of the charging energy constraint for an electric vehicle is used to penalize the degree to which the charging amount deviates from the demand; max is the maximum value. For the current Lagrange multipliers, the multiplier value from the previous iteration; For the updated Lagrange multipliers, corresponding to the th The multiplier of the time window charging amount constraint for an electric vehicle; The target power ratio coefficient; The window length is the number of time periods contained within the time window. The start time of the time window, i.e., the starting period of the emergency charging time window set by the user; and The iteration step size is used, and a decreasing sequence is taken to ensure convergence; T is the scheduling period. For the kth region The charging power of the electric vehicle in time period t; For the first The upper limit of battery capacity for electric vehicles; It is the first The initial capacity of the battery of an electric vehicle.

8. A charging and discharging coordinated scheduling system considering real-time electricity pricing and V2G regionalization, characterized in that, The charging and discharging coordinated scheduling method based on any one of claims 1-7, considering real-time electricity prices and V2G partitioning, includes: The charging agent optimization model construction module is used to generate dynamic electricity prices based on the weighted average of marginal cost and average cost of renewable energy units. It aims to maximize the profit of the integrated energy system while seeking a balance between economic benefits and carbon emissions, and to construct a charging agent optimization model to determine the optimal energy sales price. The electric vehicle user charging decision model construction module is used for a hierarchical and zonal management model of electric vehicles based on user behavior characteristics. By extracting multi-dimensional features and dividing them into weighted clusters, and taking into account the urgency of users' charging, it can carry out refined management and differentiated pricing of user groups, and thus build an electric vehicle user charging decision model with the goal of minimizing charging costs. The charging and discharging coordinated scheduling solution module is used to establish a master-slave game framework between the charging agent and the user, with the charging agent optimization model as the upper-level model and the electric vehicle user charging decision model as the lower-level model. It adopts Lagrange relaxation for efficient solution and obtains the optimal energy sales price and the charging and discharging scheduling scheme with the minimum charging cost while taking into account the balance between economy and carbon emissions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the charge-discharge coordinated scheduling method considering real-time electricity prices and V2G partitions as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the charge-discharge coordinated scheduling method considering real-time electricity prices and V2G partitions as described in any one of claims 1-7.

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