An electric heavy truck optimal scheduling method considering green certificate transaction and time-of-use electricity price

Abstract

The present application belongs to the technical field of power system and power market, and relates to an electric heavy truck optimal scheduling method considering green certificate transaction and time-of-use electricity price, comprising: establishing an electric heavy truck operator as an aggregator to participate in a green electricity day-ahead market transaction mechanism, electric heavy truck cluster behavior modeling and schedulable domain analysis, and establishing an electric vehicle optimal scheduling model considering green certificate transaction and time-of-use electricity price; the present application not only provides an economic cost and green consumption credible authentication comprehensive solution for the electric heavy truck operator, but also provides a new idea for the collaborative optimization of traffic and power systems under high proportion of renewable energy access. Therefore, the present application promotes the evolution of electric heavy trucks from intelligent load to virtual power plant, and provides continuous power for the deep integration and low-carbon transformation of China's energy and transportation systems.

Description

Technical Field

[0001] This invention belongs to the field of power system electricity market technology, specifically relating to an optimized scheduling method for electric heavy trucks that takes into account green certificate trading and time-of-use pricing. Background Technology

[0002] Electric heavy-duty trucks, also known as electric heavy-duty trucks, are heavy-duty freight vehicles that use electricity as their primary power source, driving the wheels through electric motors. Compared to traditional fuel-powered heavy-duty trucks, electric heavy-duty trucks exhibit unique advantages in environmental protection, operating costs, and performance. With their zero emissions, high energy efficiency, and low operating costs, electric heavy-duty trucks are considered a core path to replace traditional fuel-powered heavy-duty trucks and achieve decarbonization of road freight. Electric heavy-duty trucks are generally equipped with large-capacity battery packs, requiring high-power, centralized charging. Their disorderly charging behavior will cause significant load impacts on the power distribution network and generate high electricity costs, becoming a major bottleneck restricting their large-scale promotion. At the same time, the installed capacity of renewable energy sources, represented by wind power and photovoltaics, continues to grow rapidly, and the market-based consumption demand for green electricity (hereinafter referred to as "green electricity") is becoming increasingly urgent. Electricity market reforms have spurred the development of green electricity trading mechanisms, providing a market-based channel for users to directly consume green energy. Electric heavy-duty trucks, as a dispatchable load with spatial and temporal flexibility, can not only reduce their own energy costs if optimized in conjunction with the characteristics of green electricity output, but also effectively mitigate the volatility of renewable energy, enhance the grid's capacity to absorb green electricity, and achieve coordinated emission reduction between the transportation and energy systems. Currently, research on the optimized dispatch of electric heavy-duty trucks has yielded significant results, mainly focusing on economic dispatch, participation in the ancillary services market, and coordinated planning with charging infrastructure.

[0003] However, current research on charging dispatching largely focuses on simple economic optimization, lacking the tracking and assurance of the green attributes of electricity, making it difficult to quantify and certify its environmental benefits. On the other hand, research on green electricity traceability and Green Electricity Certificate (GEC) trading focuses primarily on market mechanism design and traceability technologies such as blockchain. Traceability research often deviates from the specific, dynamic load dispatching process. Particularly for electric heavy-duty truck clusters, how to design an optimized dispatching strategy that deeply couples the economic guidance of time-of-use pricing with the credible traceability of green electricity consumption, enabling operators to clearly demonstrate the green source of their consumed electricity while pursuing cost minimization, remains a gap that urgently needs to be filled in current research. This is not only related to the direct economic benefits of operators, but also to their fulfillment of environmental responsibilities, accurate calculation of carbon emission reductions, and their ability to cope with future international carbon tariffs and other trade barriers.

[0004] Furthermore, due to limitations in computational complexity, current algorithmic foundations, and computer hardware, probabilistic maintenance scheduling optimization models for large-scale real-world systems are still immature. Their focus is on simultaneously considering load supply demand, renewable energy regulation demand, and computational complexity to quickly obtain a set of highly generalizable maintenance schedules. This results in existing technologies exhibiting insufficient generality and high computational costs in providing annual generator unit maintenance schedules. Summary of the Invention

[0005] This invention aims to provide an optimized scheduling strategy for electric heavy-duty truck clusters that integrates green electricity traceability and electricity price guidance, in order to solve the problem of economically and efficiently enabling electric heavy-duty trucks to participate in green electricity trading and achieve green traceability. This invention establishes an optimized scheduling model for electric heavy-duty truck clusters that considers green certificate trading and time-of-use pricing. The GEC purchase cost is incorporated as a key variable into the optimization model, and the integration of economic scheduling and green traceability is achieved through a mathematical model.

[0006] This invention provides the following technical solution: an optimized scheduling method for electric heavy-duty trucks that takes into account green certificate trading and time-of-use pricing, comprising the following steps: Step 1: Establish a mechanism for electric heavy-duty truck operators to participate in the day-ahead market transaction of green electricity as aggregators. Divide the overall architecture of electric heavy-duty trucks participating in green electricity transactions into three parts: physical layer, information layer and market layer, and achieve closed-loop collaboration through three types of flows.

[0007] Step 2: Modeling and Scheduling Domain Analysis of Electric Heavy Truck Cluster Behavior. The charging behavior characteristics of electric heavy trucks under disordered charging mode are modeled, and on this basis, a schedulable domain of electric heavy trucks is constructed to characterize the cluster's flexible adjustment capability in the spatiotemporal dimension.

[0008] Step 3: Establish an electric vehicle optimization scheduling model that takes into account green certificate trading and time-of-use pricing. Through economic incentives, coordinate and optimize green electricity procurement, grid interaction and user response behavior. Under the multiple constraints of electric heavy truck transportation tasks, grid operation and green electricity traceability, with the goal of minimizing the total cost of electric heavy truck operators, construct a day-ahead optimization scheduling model; realize the optimized allocation of charging load in the spatiotemporal dimension.

[0009] Preferably, in step one: the physical layer consists of renewable energy output, distribution network / park power grid, centralized charging stations, and electric heavy truck fleets; based on the actual supply and demand balance and metering of electricity, the physical layer forms the carrier for subsequent green electricity physical consumption and cost accounting. The information layer uses the load aggregator / operator platform as a key hub to perform fleet charging demand forecasting, vehicle status aggregation, renewable energy output forecasting, time-of-use pricing signal access, and day-ahead optimization scheduling calculations; the information layer is also used to issue charging power instructions and electricity purchase and sale strategies to charging stations. The market layer consists of the electricity market / grid settlement mechanism and the green electricity trading green certificate market.

[0010] More preferably, in step one, the green certificate transaction cost for: (3) Electricity sales price for: (5) In the formula: This indicates the market price of green certificates. This indicates the initial green certificate quota of the system. This indicates the actual number of green certificates obtained by the system; This indicates the cost difference in the grid's acceptance of distributed power sources. This indicates the electricity purchase price from the power grid.

[0011] Preferably, in step two, the Latin hypercube sampling method is used to simulate the charging behavior of electric heavy trucks in disordered charging mode, and the daily mileage and charging start time of electric heavy trucks are described by probability density function to construct the relationship between the state of charge of the vehicle when it returns and the mileage and battery capacity.

[0012] More preferably, in step two: The probability density function at the start of charging is: (7) in: and Let V be the variance and mean at the start of charging, respectively. Indicates the first The return time of the electric heavy truck.

[0013] State of charge of the vehicle upon return The relationship between driving range and battery capacity is as follows: (8) In the formula: This indicates the vehicle's range on a full charge. Indicates the first Daily mileage of a pure electric heavy-duty truck.

[0014] Preferably, in step two, the Minkowski method is introduced to construct a cluster schedulable domain based on individual vehicle constraints and to construct a schedulable domain model for the electric heavy-duty truck cluster, describing the range of charging power and energy states that the cluster may achieve under various operating constraints.

[0015] More preferably, the schedulable domain model of the electric heavy-duty truck cluster is as follows: (12) In the formula: This indicates the total charging power of the cluster. , These represent the upper and lower limits of the total charge state of the cluster, respectively. Indicates the total energy storage of the cluster. Indicates charging efficiency. This indicates the energy changes caused by vehicles going off-grid. Indicates the first The grid connection status of the electric heavy-duty truck at time t. Indicates the first The charging power of an electric heavy-duty truck. Indicates charging time.

[0016] Preferably, in step three, the objective function for optimizing the scheduling model is: (13) (14) (15) in: This represents the total cost of the scheduling cycle. This represents the net interaction cost with the power grid. This indicates the costs or benefits associated with green certificate transactions. This represents the cost of charging response compensation paid to electric heavy-duty truck users; This represents the power purchased from the grid during time period t. This represents the amount of electricity sold to the grid during time period t. , These represent the corresponding electricity purchase and sales prices, respectively. This indicates the price compensation per unit of charging power. Indicates the optimized vehicle The charging power during time period t This indicates its charging power in disordered charging scenarios. Indicates taking a positive value. Indicates charging time.

[0017] The constraints include: (16) (17) (18) (19) (20) (twenty one) In the formula: This represents the base load other than electric heavy trucks during time period t. This indicates the power output of renewable energy procured or owned by the operator. , These represent the predicted upper limits of wind power and photovoltaic power output for time period t, respectively. This indicates the vehicle's maximum charging power. Indicates the battery's energy state. , These represent the charging and discharging efficiencies, respectively. Indicates driving energy consumption power. , Indicates the upper and lower limits of battery energy. Indicates the time the vehicle leaves the site. Indicates the minimum energy required to leave the field; This represents the green electricity purchased during time period t. This indicates the amount of electricity corresponding to a single green certificate. This represents the system's power purchase at time t. Represents the system's power output at time t. For the battery energy at the moment the vehicle leaves the site, Indicates the number of green certificates.

[0018] The beneficial effects of this invention are: 1. This invention enables traceable and verifiable consumption of green electricity. Through a two-dimensional stacked area diagram, this invention clearly shows the changes in the energy composition of the charging load over time. The charging load consists of two parts: direct power supply from wind and solar power and power supply from the grid. Optimized scheduling makes the charging load curve more closely match the total output curve of wind and solar power. Especially at night and in the early morning when wind power output is high, the charging load increases, realizing the active consumption of fluctuating green electricity. Quantitatively, this invention increases the local on-site real-time consumption rate of wind and solar power from 22% to 92%.

[0019] 2. Based on the Minkowski algorithm, this invention plots the dispatchable domain diagram of the cluster at typical moments in a three-dimensional space of total charging power, total energy storage state, and responsive wind and solar power fluctuations. This three-dimensional convex polyhedron represents the set of all feasible combinations of total charging power and total energy storage state of the cluster under all individual vehicle operation constraints. Its geometric volume and shape intuitively reflect the upper limit of the cluster's ability to participate in system regulation. Therefore, the scheduling process of this invention can achieve a smooth transition from low-power state to high-power state within this feasible domain, while maintaining the ability to track renewable energy fluctuations. The electric heavy-duty truck cluster of this invention has significant flexible adjustment potential, thus providing a quantitative decision-making basis for load aggregators to participate in a wider range of electricity market services. Attached Figure Description

[0020] Figure 1 This is a diagram illustrating the overall architecture of an electric heavy-duty truck participating in green electricity trading, based on an optimized scheduling method for electric heavy-duty trucks that incorporates green certificate trading and time-of-use pricing, according to the present invention. Figure 2 This is a topology diagram of the improved IEEE 33-node system of the present invention; Figure 3 This is a time-series comparison chart of the total charging power of electric heavy-duty truck clusters with time-of-use electricity price and total wind and solar power output under different scenarios of the present invention. Figure 4 This is a timing stack diagram of the energy composition of the charging load in scenario S3 of the present invention. Figure 5 This invention presents a three-dimensional schedulable domain diagram of an electric heavy-duty truck cluster based on the Minkowski algorithm. Figure 6 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0021] The related technologies of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figures 1-6 As shown, the electric heavy-duty truck optimization scheduling method considering green certificate trading and time-of-use pricing in this embodiment includes: Electric heavy-duty trucks participate in green electricity trading mechanisms; Modeling the charging behavior of electric heavy-duty trucks in disordered charging mode; Modeling of the schedulable domain for electric heavy-duty truck clusters; An optimized scheduling model for electric heavy-duty trucks that takes into account green certificate trading and time-of-use electricity pricing.

[0023] As a preferred embodiment of the electric heavy-duty truck optimal scheduling model that takes into account green certificate trading and time-of-use pricing in this invention, the mechanism for electric heavy-duty trucks to participate in green electricity trading includes: The overall architecture for electric heavy-duty trucks participating in green electricity trading is divided into three parts: the physical layer, the information layer, and the market layer, and closed-loop coordination is achieved through three types of flows. The physical layer consists of renewable energy output—distribution network / industrial power grid—centralized charging stations—electric heavy-duty truck fleet. The information layer is responsible for load aggregators / operator platforms as key hubs, including fleet charging demand forecasting, vehicle status aggregation, renewable energy output forecasting, time-of-use pricing signal access, and day-ahead optimization scheduling calculations, as well as issuing charging power instructions and power purchase and sale strategies to charging stations. The market layer consists of the electricity market / grid settlement mechanism and the green electricity trading—green certificate market.

[0024] As a preferred embodiment of the electric heavy-duty truck optimal scheduling model considering green certificate trading and time-of-use pricing of the present invention, the modeling of the charging behavior of electric heavy-duty trucks in disordered charging mode includes: The Latin hypercube sampling method was used to simulate the charging process of the electric heavy truck in disordered charging mode. The daily mileage and charging start time of the electric heavy truck were described by probability density function, and the relationship between the state of charge of the vehicle when it returns and the mileage and battery capacity was constructed.

[0025] As a preferred embodiment of the electric heavy-duty truck optimal scheduling model of the present invention, which considers green certificate trading and time-of-use pricing, the model includes: scheduling domain modeling of the electric heavy-duty truck cluster, comprising: By introducing the Minkowski method, a schedulable domain model of an electric heavy-duty truck cluster is constructed based on individual vehicle constraints to describe the range of possible charging power and energy states that the cluster can achieve under various operational constraints.

[0026] As a preferred embodiment of the electric heavy-duty truck optimal scheduling model considering green certificate trading and time-of-use pricing of the present invention, the electric heavy-duty truck optimal scheduling model considering green certificate trading and time-of-use pricing includes: Based on the established electric heavy-duty truck charging behavior model, cluster schedulable domain model, green certificate trading mechanism, and time-of-use electricity price signal, a day-ahead optimization scheduling model is constructed with the goal of minimizing the total cost of electric heavy-duty truck operators.

[0027] This invention provides an optimized scheduling model for electric heavy-duty trucks that takes into account green certificate trading and time-of-use pricing, including: Step 1: Design a mechanism for electric heavy-duty truck operators to participate as aggregators in the day-ahead market trading of green electricity; The overall architecture for electric heavy trucks to participate in green electricity trading can be divided into three parts: the physical layer, the information layer, and the market layer, and closed-loop coordination is achieved through three types of flows.

[0028] (1) Physical Layer. This layer consists of renewable energy output—distribution network / park power grid—centralized charging station—electric heavy truck fleet. Renewable energy sources such as wind power and photovoltaics are locally connected to the park or distribution network to provide green electricity that can be directly consumed by the charging station. When local green electricity is insufficient, the system can purchase electricity from the grid to supplement it. When local green electricity is surplus or conditions permit, it can also sell electricity to the grid to achieve two-way energy interaction. This layer is based on the actual supply and demand balance and metering of electricity, and constitutes the objective carrier for subsequent physical consumption and cost accounting of green electricity.

[0029] (2) Information Layer. The load aggregator / operator platform serves as a key hub, responsible for fleet charging demand forecasting, vehicle status aggregation, renewable energy output forecasting, time-of-use pricing signal access, and day-ahead optimization scheduling calculations. It also issues charging power instructions and electricity purchase and sale strategies to charging stations. This layer abstracts fleet transportation task constraints and charging schedulable capabilities into the available information boundary of the optimization model, thereby supporting subsequent calls to the mixed-integer linear programming solver for unified solution and plan output.

[0030] (3) Market Layer. This layer consists of the electricity market / grid settlement mechanism and the green electricity trading-green certificate market. The electricity market provides aggregators with time-of-use pricing and electricity metering settlement basis; the green certificate market is responsible for the registration, trading, and verification of green electricity certificates. Aggregators purchase green electricity in day-ahead transactions and simultaneously configure corresponding green certificates. Through the consistency constraints of green electricity procurement, charging consumption, and green certificate verification, a measurable, reportable, and verifiable green energy consumption certificate is formed, thus solving the problem that it is difficult to prove green attributes based solely on physical electricity consumption.

[0031] Under this three-layer architecture, the optimized scheduling strategy proposed in this invention can not only achieve cost optimization through time-of-use pricing, but also achieve full-process certification of the green attributes of charging power through physical consumption and voucher verification, providing auditable basis for subsequent green electricity consumption rate assessment and traceability verification.

[0032] Green electricity certificate trading is one of the important mechanisms for promoting the market-based consumption of renewable energy in my country. Electric heavy-duty truck operators, as load aggregators, can purchase green electricity through the day-ahead market and simultaneously obtain green electricity certificates for the corresponding amount of electricity. According to current policy, one green certificate unit corresponds to 1000 kWh of renewable energy. The system's green certificate quota and actual amount obtained can be expressed as: (1) (2) in, This is the initial green certificate quota for the system. This represents the actual number of green certificates obtained by the system. This refers to the green certificate quota coefficient for the system. For the system electrical load, , These represent the wind turbine power generation and photovoltaic power generation at time t, respectively.

[0033] Green certificate transaction costs for: (3) In the formula: This refers to the market price of green certificates. If... The system allows users to sell excess green certificates to generate revenue, while others must purchase them to fulfill their obligations.

[0034] As an important market mechanism for guiding the optimal allocation of power resources and incentivizing user-side demand response, time-of-use pricing directly regulates users' electricity consumption behavior by setting differentiated purchase and sale price signals at different times, thereby achieving the goals of peak shaving and valley filling, and improving grid operation efficiency and economy. The grid purchase price can be expressed as: (4) In the formula: , , These represent the unit electricity purchase price during peak, flat, and valley periods, respectively.

[0035] When the system has surplus power, it can sell electricity to the grid, and the electricity price is usually designed as follows: (5) In the formula: This is to reflect the cost differences in grid acceptance of distributed generation, with the aim of encouraging local green energy consumption.

[0036] Step 2: Modeling and schedulable domain analysis of electric heavy truck cluster behavior. Model the charging behavior characteristics and construct the schedulable domain to characterize the cluster's ability to flexibly adjust in the spatiotemporal dimensions.

[0037] The charging behavior of electric heavy-duty trucks is influenced by multiple factors, including their transportation tasks, driving patterns, and driver habits, exhibiting significant randomness and spatiotemporal coupling characteristics. To accurately describe the spatiotemporal distribution of charging load in large-scale electric heavy-duty truck clusters, this invention employs the Latin hypercube sampling method to simulate the charging process under disordered charging conditions.

[0038] Assume that the daily mileage of electric heavy trucks approximately follows a log-normal distribution, with the following probability density function: (6) In the formula: and These represent the variance and mean of the daily mileage, respectively. For the first Daily mileage of a pure electric heavy-duty truck.

[0039] The probability density function at the start of charging is: (7) In the formula: and Let V be the variance and mean at the start of charging, respectively. For the first The return time of a pure electric heavy-duty truck.

[0040] State of charge of the vehicle upon return The relationship between driving range and battery capacity is as follows: (8) In the formula: The range of a vehicle on a full charge.

[0041] No. The charging time for a pure electric heavy-duty truck is: (9) In the formula: For the first The charging power of a pure electric heavy-duty truck. This refers to the rated capacity of the battery for pure electric heavy-duty trucks. For charging efficiency.

[0042] Based on the above probability model, Latin hypercube sampling is used to generate... The charging time series of electric heavy-duty trucks were collected and aggregated to obtain the total charging load of the cluster under the disordered charging scenario: (10) In the formula: A 0-1 variable, representing a vehicle. Whether it is in a charging state during time period t.

[0043] To effectively aggregate and schedule electric heavy-duty truck clusters, it is necessary to establish a schedulable domain model to describe the range of charging power and energy states that the cluster can achieve under various operational constraints. This invention introduces the Minkowski method to construct the cluster's schedulable domain based on individual vehicle constraints.

[0044] Define Boolean variables Indicates the first The grid connection status of the heavy trucks at time t. Consider the charging energy constraints that each truck must meet: (11) In the formula: For vehicles Energy stored in time period t , These represent its lower and upper limits of energy, respectively.

[0045] The schedulable domain model for constructing an electric heavy-duty truck cluster is as follows: (12) In the formula: For the total charging power of the cluster, , These are the lower and upper limits of the total charge state of the cluster, respectively. For the total energy storage of the cluster, For charging efficiency, This indicates the energy change caused by the vehicle going off-grid.

[0046] Step 3: Establish an electric vehicle optimization scheduling model that takes into account green certificate trading and time-of-use pricing. The aim is to coordinate and optimize green electricity procurement, grid interaction and user response behavior through economic incentives, and achieve optimized allocation of charging load in the spatiotemporal dimension under multiple constraints such as electric heavy truck transportation tasks, grid operation and green electricity traceability.

[0047] Optimize the scheduling model based on the scheduling cycle The objective is to minimize the total operating cost of the internal operator, and the objective function is as follows: (13) (14) (15) In the formula: The total cost of the scheduling cycle; The net interaction cost with the power grid includes expenditures on purchasing electricity from the grid and revenues from selling electricity to the grid; The costs or benefits related to green certificate transactions are represented by formula (3); The cost of charging response compensation paid to electric heavy-duty truck users; Let t be the power purchased from the grid during time period t. Let t be the amount of electricity sold to the grid during time period t. , These are the corresponding electricity purchase and sales prices, determined by the time-of-use pricing mechanism. Price compensation per unit of charging power. To optimize the vehicle The charging power during time period t Its charging power in disordered charging scenarios, This indicates that a positive value is taken, meaning that compensation is only provided for charging delays or power adjustments caused by scheduling.

[0048] The constraints include: (16) (17) (18) (19) (20) (twenty one) In the formula: For time period t, the base load excluding electric heavy trucks is... To contribute to the procurement of renewable energy by operators or their own renewable energy sources; , These represent the predicted upper limits of wind power and solar power output for time period t, respectively. Maximum charging power for the vehicle. Battery energy state, , These are the charging and discharging efficiencies, respectively. For driving energy consumption power, , These are the upper and lower limits of battery energy. The time when the vehicle leaves the site. This is the minimum energy required for departure; Green electricity purchased for time period t This represents the electricity consumption corresponding to a single green certificate.

[0049] Equation (16) is the system power balance constraint; Equation (17) is the line capacity constraint; Equation (18) is the constraint that the purchase and sale of electricity do not occur simultaneously; Equation (19) is the renewable energy output constraint; Equation (20) is the electric heavy truck cluster constraint, including: charging power limit, battery energy dynamics and travel demand; Equation (21) is the green electricity traceability and green certificate matching constraint, that is, the green electricity purchased by the operator should match the charging amount and meet the green certificate policy requirements.

[0050] Example This embodiment uses, as follows: Figure 3 The improved IEEE 33-node system shown is used as the simulation network. The system has a rated voltage of 10 kV. A campus photovoltaic power station with a peak power of 1.5 MW is connected to node 18, a wind turbine generator with a rated power of 1.0 MW is connected to node 22, and a centralized charging station for electric heavy trucks with a rated power of 2.5 MW is connected to node 25. The loads of the remaining nodes constitute the system's basic load. Standard parameters such as line impedance and transformer capacity are referenced from publicly available literature to ensure the physical realism and reproducibility of the model.

[0051] The electric heavy-duty truck fleet consists of 100 vehicles. Each vehicle has a battery capacity of 350 kWh, a maximum charging power of 150 kW, and a charge / discharge efficiency of 0.95. Daily mileage follows a log-normal distribution with a mean of 250 km and a standard deviation of 50 km. Vehicle return times are concentrated between 18:00 and 22:00. A minimum state of charge of 90% is required upon departure.

[0052] The electricity market and price signals adopt the typical industrial time-of-use electricity price in a certain region during summer. The purchase price is RMB 1.20 / kWh during peak hours (10:00-12:00, 14:00-19:00); RMB 0.80 / kWh during normal hours (8:00-10:00, 12:00-14:00, 19:00-22:00); and RMB 0.40 / kWh during off-peak hours (22:00-8:00 the next day). The price for selling electricity to the grid is set at 65% of the purchase price during the same period. The user response compensation price is RMB 0.10 / kWh.

[0053] The installed capacity of wind power is 1.0 MW, and the installed capacity of photovoltaic power is 1.5 MW. The total wind power generation on that day was 9.8 MWh, and the total photovoltaic power generation was 10.2 MWh, for a total of 20.0 MWh of green power generation. The price of a green electricity certificate is 150 yuan per certificate, and each certificate corresponds to 1 MWh of renewable energy electricity environmental rights.

[0054] The simulation included four comparison scenarios: Scenario S1: Baseline unordered charging scenario. Vehicles charge at maximum power immediately upon returning to base until they meet the next day's travel needs or the battery is fully charged. No optimized scheduling is performed, and the vehicle does not participate in the green certificate market.

[0055] Scenario S2: Time-of-use pricing scenario. Optimized scheduling aims to minimize grid interaction costs while meeting vehicle travel demands, but does not consider constraints related to green certificate purchases or green source tracking.

[0056] Scenario S3: The full-strategy scenario of this invention. The complete optimization model proposed in this invention is executed, with the objective function being to minimize the total cost while satisfying the green certificate matching constraint, aiming to achieve dual optimization of economic efficiency and green traceability.

[0057] Scenario S4: High Green Certificate Price Sensitivity Scenario. Based on the S3 strategy, the market price of green certificates is increased from 150 yuan / certificate to 300 yuan / certificate to analyze the marginal impact of green certificate costs on scheduling decisions and total costs.

[0058] The direct economic benefits brought about by optimized scheduling are the core evaluation indicator. Table 1 shows the detailed composition and rigorous calculation of operating costs under four scenarios. Compared with the completely disordered S1 scenario, the S2 scenario, which only considers electricity price guidance, reduces the total cost by 38.8% through load shifting and valley filling. The S3 strategy proposed in this invention further optimizes green electricity procurement and local wind and solar power consumption based on S2. Although it incurs green certificate costs, it achieves the lowest total cost by making fuller use of off-peak electricity and maximizing physical green electricity consumption, a decrease of 17.2% compared to S2, demonstrating the comprehensive advantage of coupling economic and environmental signals in a diversified green energy environment. When the price of green certificates doubles to 300 yuan / certificate, the cost of green certificates increases significantly, leading to a total cost of 35,760 yuan, but it is still significantly lower than the disordered charging scenario, showing that the strategy still has economic advantages under cost pressure. The comparison of daily operating costs under different scheduling scenarios is shown in Table 1 below.

[0059]

[0060] Note: In Table 1, total cost = grid purchase cost + revenue from selling electricity to the grid + cost of purchasing green certificates + user response compensation cost. Revenue from selling electricity is negative and is subtracted in the calculation.

[0061] To further explain the mechanism of cost changes, Figure 3 The total charging power curves of the power clusters under three scenarios (S1, S2, and S3) were compared with the time-of-use pricing and the time-series relationship of total wind and solar power output. The S1 curve exhibits typical evening peak-hour overlap characteristics. The S2 curve successfully transferred a large amount of load to the off-peak hours at night. The S3 curve demonstrates more intelligent coordination characteristics, not only continuing the concentrated charging mode during off-peak hours but also appropriately increasing charging power during the daytime hours when wind and solar power output is higher, achieving immediate absorption of green electricity and further reducing the need to purchase electricity from the grid at high prices.

[0062] The strategy in this embodiment enables traceable, verifiable, and efficient consumption of green electricity. Figure 4 The two-dimensional stacked area diagram clearly illustrates the change in the energy composition of the charging load over time in the S3 scenario. The diagram shows that the charging load consists of two parts: direct power supply from wind and solar power and power supply from the grid. Optimized scheduling results in a higher degree of alignment between the charging load curve and the total wind and solar power output curve. Especially during nighttime and early morning when wind power output is higher, the charging load increases, achieving proactive absorption of fluctuating green electricity. Quantitatively, the S3 scenario increases the on-site real-time absorption rate of wind and solar power from 22% in S1 to 92%.

[0063] To intuitively characterize the adjustment potential of electric heavy-duty truck clusters as flexible aggregated resources, this embodiment, based on the Minkowski algorithm, plots the schedulable domain of the cluster at typical moments in a three-dimensional space of total charging power - total energy storage state - responsive wind and solar power fluctuations, as shown below. Figure 5 As shown.

[0064] This three-dimensional convex polyhedron represents the set of all feasible combinations of total charging power and total energy storage state of the cluster under all individual vehicle operating constraints. Its geometric volume and shape intuitively reflect the upper limit of the cluster's ability to participate in system regulation. A typical optimization trajectory in the figure shows that the scheduling process can achieve a smooth transition from low-power to high-power states within this feasible region, while maintaining the ability to track renewable energy fluctuations. This visualization result illustrates that electric heavy-duty truck clusters possess significant flexible adjustment potential, thus providing a quantitative decision-making basis for load aggregators to participate in broader electricity market services.

[0065] In summary, this invention achieves coordinated optimization of electric heavy-duty truck charging load across time and space by constructing a day-ahead market participation framework encompassing green certificate trading mechanisms and establishing a mixed-integer programming model aimed at minimizing the operator's total cost. Simulation analysis based on an improved IEEE 33-node system verifies the effectiveness of this strategy. The proposed strategy, by proactively responding to time-of-use pricing signals and optimizing green certificate procurement, can intelligently guide charging load to shift towards periods of low electricity prices and peak renewable energy output. While improving the renewable energy absorption rate, it also achieves traceable and verifiable authentication of the green attributes of charging electricity. This invention not only provides electric heavy-duty truck operators with a systematic solution that balances economic costs with credible authentication of green consumption but also offers new insights into the coordinated optimization of transportation and power systems under high-proportion renewable energy access.

[0066] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for optimizing the scheduling of electric heavy-duty trucks, taking into account green certificate trading and time-of-use pricing, characterized in that, Includes the following steps: Step 1: Establish a mechanism for electric heavy-duty truck operators to participate in the day-ahead market transaction of green electricity as aggregators. Divide the overall architecture of electric heavy-duty trucks participating in green electricity transactions into three parts: physical layer, information layer and market layer, and achieve closed-loop collaboration through three types of flows. Step 2: Modeling and schedulable domain analysis of electric heavy-duty truck cluster behavior. The charging behavior characteristics of electric heavy-duty trucks under disordered charging mode are modeled, and on this basis, the schedulable domain of electric heavy-duty trucks is constructed to characterize the cluster's flexible adjustment capability in the spatiotemporal dimension. Step 3: Establish an electric vehicle optimization scheduling model that takes into account green certificate trading and time-of-use pricing. Through economic incentives, coordinate and optimize green electricity procurement, grid interaction and user response behavior. Under the multiple constraints of electric heavy truck transportation tasks, grid operation and green electricity traceability, with the goal of minimizing the total cost of electric heavy truck operators, construct a day-ahead optimization scheduling model; realize the optimized allocation of charging load in the spatiotemporal dimension.

2. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 1, characterized in that, In step one: The physical layer consists of renewable energy output, distribution network / park power grid, centralized charging stations, and electric heavy truck fleets; the physical layer is based on the actual supply and demand balance and metering of electricity, and forms the carrier for subsequent green electricity physical consumption and cost accounting; The information layer uses the load aggregator / operator platform as a key hub to perform fleet charging demand forecasting, vehicle status aggregation, renewable energy output forecasting, time-of-use pricing signal access, and day-ahead optimization scheduling calculations; the information layer is also used to issue charging power instructions and electricity purchase and sale strategies to charging stations. The market layer consists of the electricity market / grid settlement mechanism and the green electricity trading and green certificate market.

3. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 2, characterized in that, In step one, the green certificate transaction cost for: （3） Electricity sales price for: （5） In the formula: This indicates the market price of green certificates. This indicates the initial green certificate quota of the system. This indicates the actual number of green certificates obtained by the system; This indicates the cost difference in the grid's acceptance of distributed power sources. This indicates the electricity purchase price from the power grid.

4. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 1, characterized in that, In step two, the Latin hypercube sampling method is used to simulate the charging behavior of electric heavy trucks in disordered charging mode. The daily mileage and charging start time of the electric heavy trucks are described by probability density functions, and the relationship between the state of charge when the vehicle returns and the mileage and battery capacity is constructed.

5. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 4, characterized in that, In step two: The probability density function at the start of charging is: （7） in: and Let V be the variance and mean at the start of charging, respectively. Indicates the first The return time of the electric heavy-duty truck; State of charge of the vehicle upon return The relationship between driving range and battery capacity is as follows: （8） In the formula: This indicates the vehicle's range on a full charge. Indicates the first Daily mileage of a pure electric heavy-duty truck.

6. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 1, characterized in that, In step two, the Minkowski method is introduced to construct the schedulable domain of the cluster based on individual vehicle constraints and to construct a schedulable domain model of the electric heavy truck cluster, which describes the range of charging power and energy state that the cluster may achieve under various operating constraints.

7. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 6, characterized in that, The schedulable domain model of the electric heavy-duty truck cluster is as follows: （12） In the formula: This indicates the total charging power of the cluster. , These represent the upper and lower limits of the total charge state of the cluster, respectively. Indicates the total energy storage of the cluster. Indicates charging efficiency. This indicates the energy changes caused by vehicles going off-grid. Indicates the first The grid connection status of the electric heavy-duty truck at time t. Indicates the first The charging power of an electric heavy-duty truck. Indicates charging time.

8. The method for optimizing the scheduling of electric heavy-duty trucks considering green certificate trading and time-of-use pricing as described in claim 1, characterized in that, In step three, the objective function of the optimized scheduling model is: （13） （14） （15） in: This represents the total cost of the scheduling cycle. This represents the net interaction cost with the power grid. This indicates the costs or benefits associated with green certificate transactions. This represents the cost of charging response compensation paid to electric heavy-duty truck users; This represents the power purchased from the grid during time period t. This represents the amount of electricity sold to the grid during time period t. , These represent the corresponding electricity purchase and sales prices, respectively. This indicates the price compensation per unit of charging power. Indicates the optimized vehicle The charging power during time period t This indicates its charging power in disordered charging scenarios. Indicates taking a positive value. Indicates charging time; The constraints include: （16） （17） （18） （19） （20） （21） In the formula: This represents the base load other than electric heavy trucks during time period t. This indicates the power output of renewable energy procured or owned by the operator. , These represent the predicted upper limits of wind power and photovoltaic power output for time period t, respectively. This indicates the vehicle's maximum charging power. Indicates the battery's energy state. , These represent the charging and discharging efficiencies, respectively. Indicates driving energy consumption power. , Indicates the upper and lower limits of battery energy. Indicates the time when the vehicle leaves the site. Indicates the minimum energy required to leave the field; This represents the green electricity purchased during time period t. This indicates the amount of electricity corresponding to a single green certificate. This represents the system's power purchase at time t. Represents the system's power output at time t. For the battery energy at the moment the vehicle leaves the site, Indicates the number of green certificates.

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