Collaborative optimization scheduling method and system for integrated energy system containing electric heavy truck

Abstract

The invention provides a collaborative optimization scheduling method and system for an integrated energy system comprising an electric heavy truck. The method comprises the following steps: randomly generating key driving characteristics of the electric heavy truck; by analyzing the key features, the charging demand, the schedulable time period and the charging power range of the electric heavy truck are evaluated so as to construct an adjustable potential model of the electric heavy truck; based on the adjustable potential model and the current running state of the electric heavy truck, a daily charging load curve is generated by establishing a load prediction model of the electric heavy truck; establishing a power output model of photovoltaic power generation and a charging and discharging model of an energy storage system; an upper-layer optimization model of electric heavy truck charging scheduling based on IGDT and a lower-layer optimization model of energy storage system and power grid combined scheduling are established to generate a double-layer optimization model; and solving the double-layer optimization model by adopting an iterative solving method so as to generate a collaborative optimization scheduling strategy of the integrated energy system. According to the invention, the energy supplement problem of the new energy heavy truck is effectively solved, and the peak load shifting capability of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy regulation and control, and more specifically to a coordinated optimization scheduling method and system for an integrated energy system including an electric heavy truck. Background Art

[0002] As a high-energy-consuming transport load, electric heavy trucks (EV heavy trucks) are gradually becoming an important part of the power system, especially under the trend of large-scale electrification, their energy demand is increasing. As the main source of pollution in road transportation, the electrification of heavy trucks is of great significance to the development of green transportation and the construction of low-carbon logistics. However, the market penetration rate of new energy heavy trucks is currently low, and one of the main reasons for the limited promotion is the lack of charging infrastructure.

[0003] Specifically, due to the high-intensity use of heavy trucks, the large endurance requirements, and their large size and weight, existing charging facilities are unable to meet their frequent energy replenishment needs, resulting in low charging efficiency for electric heavy trucks. In addition, the charging demand of heavy trucks easily overlaps with the peak hours of the power grid load, increasing the operating pressure of the power grid, and the need for peak load reduction is more prominent.

[0004] The existing dispatching system usually handles a single type of load (such as electric vehicles or photovoltaic power generation) independently, lacking a collaborative optimization method for multiple flexible loads, especially to achieve coordinated response of loads at different time scales. As a high-energy-consuming load, electric heavy trucks lack an effective coordination strategy for their charging demand and the dispatching of other flexible loads, which easily leads to low energy utilization efficiency and cannot fully participate in the peak-shaving and valley-filling of the power grid. Summary of the invention In view of the problems existing in the prior art, the purpose of the present invention is to provide a collaborative optimization scheduling method and system for an integrated energy system including electric heavy trucks. By introducing new energy power generation and flexible loads for collaborative optimization, the problem of energy replenishment for new energy heavy trucks can be effectively solved. At the same time, through flexible load scheduling methods, the peak-shaving and valley-filling capabilities of the power grid can be improved, thereby promoting the efficient use of renewable energy. In order to achieve the above object, the present invention is implemented through the following technical solutions: A method for collaborative optimization and scheduling of an integrated energy system including an electric heavy truck comprises the following steps: S1: Randomly generate key driving characteristics of electric heavy trucks based on the Monte Carlo method; the key characteristics include driving distance, load, driving time, and frequency of use; S2: By analyzing the key features, the charging demand, schedulable time period and charging power range of the electric heavy truck are evaluated to construct an adjustable potential model of the electric heavy truck; S3: Based on the adjustable potential model and the current operating status of the electric heavy truck, a load prediction model for the electric heavy truck is established to predict the future charging load demand and generate a daily charging load curve; S4: Based on historical weather data, light intensity and the operating constraints of the energy storage system, establish a power output model for photovoltaic power generation and a charging and discharging model for the energy storage system; S5: Establish an upper-level optimization model for charging scheduling of electric heavy trucks based on IGDT; S6: Establish a lower-level optimization model for the joint dispatch of the IGDT-based energy storage system and the power grid to generate a two-level optimization model; S7: An iterative solution method is used to solve the two-level optimization model to generate a coordinated optimization scheduling strategy for the integrated energy system.

[0005] Further, the step S1 comprises: Get the driving information and load information of each electric heavy truck, and calculate the driving distance of the electric heavy truck using the following formula , Load , driving time , Frequency of use :

[0006]

[0007]

[0008]

[0009] in, is the distance traveled per trip, Is electric heavy truck in time The number of trips within is the load per trip, is the time of a single trip, is the driving time of the electric heavy truck, It is the total usage time of the electric heavy truck.

[0010] Further, the step S2 comprises: Based on the key features, an adjustable potential model of electric heavy trucks is constructed according to driving characteristics and driving patterns to evaluate the charging needs of electric heavy trucks. , Scheduling time period and charging power range ; The adjustable potential models of the electric heavy truck include:

[0011]

[0012]

[0013] in, is a function of the charging demand, is the energy consumption coefficient of the vehicle; assuming that the driving time of the electric heavy truck in a day is , is the total time of the day; is the minimum charging power, is the maximum charging power.

[0014] Furthermore, the load prediction model of the electric heavy truck includes:

[0015] in, For the moment The charging load of electric heavy trucks, It's time The charging needs For the moment The schedulable time period, For the moment Charging power range. Further, the step S3 comprises: According to the adjustable potential model of the electric heavy truck, the daily charging load curve of the electric heavy truck is generated using the following formula:

[0016] in, Every moment of the day The charging load is N, and N is the number of charging tasks in different time periods of a day.

[0017] Further, the step S4 comprises: The power output model of photovoltaic power generation is specifically as follows:

[0018] in, For photovoltaic systems at all times The photovoltaic output power, For the moment The solar radiation intensity, is the effective area of ​​the photovoltaic panel, is the conversion efficiency of the photovoltaic system; The charging and discharging model of the energy storage system is as follows:

[0019]

[0020]

[0021]

[0022] in, For the moment The remaining capacity of the energy storage system; For the moment The remaining capacity of the energy storage system; For the moment Charging power of the energy storage system; For the moment Discharge power of the energy storage system; , are the charging efficiency and discharging efficiency of the energy storage system, is the time interval; is the minimum power of the energy storage system, The maximum capacity of the energy storage system; The maximum charging power of the energy storage system, Maximum discharge power of the energy storage system; The charging and discharging strategy of the energy storage system is optimized through the following formula:

[0023] .

[0024] Further, the step S5 comprises: The upper optimization model objective function Set to: =

[0025] in, is the average value of system load, It's time Total load; , for or , is the fluctuation of photovoltaic power generation, is the risk aversion degree when photovoltaic power generation is insufficient, the extent to which higher returns can be sought when PV generation exceeds expectations; For electric heavy truck users at all times The user cost, based on charging costs and electricity prices, is defined as:

[0026] in, It's time Time-of-use electricity prices; is the consumption rate of photovoltaic power generation, which is used to measure the degree to which photovoltaic power generation is effectively utilized and is defined as:

[0027] The charging power constraint of the electric heavy truck is set as: ; Set the schedulable time window to: ; Set the charging power requirement to:

[0028] in, is the total charging power requirement of an electric heavy truck over a period of time; The photovoltaic power constraint is set as:

[0029] in, is the maximum power generation capacity of the photovoltaic system.

[0030] Further, the step S6 comprises: The objective function of the lower optimization model Set to:

[0031] in, is the adjusted capacity of the energy storage system; When the available power of the energy storage system is lower than expected:

[0032] in, It is the risk aversion coefficient for dealing with insufficient capacity of the energy storage system; When the charging or discharging amount of the energy storage system exceeds expectations:

[0033] in, It is the risk seeking factor when the energy storage system exceeds the expected charging and discharging capacity; Set the constraints as: , , .

[0034] Further, the step S7 comprises: S71: Initialize the solution of the upper optimization model; S72: Given the upper-level decision, determine the optimal charging and discharging strategy of the energy storage system and the power dispatching plan of the power grid by using the lower-level optimization model response solution; S73: According to the feedback solution obtained by the lower optimization model, the upper layer will update the decision variables and the objective function of the upper optimization model; S74: Determine whether the objective function of the upper-level optimization model has reached the convergence condition; if not, continue to execute step S72; if so, obtain the final global optimal solution and generate a coordinated optimization scheduling strategy for the integrated energy system.

[0035] Correspondingly, the present invention also discloses a coordinated optimization dispatching system for an integrated energy system including an electric heavy truck, comprising: A feature extraction module, used to randomly generate key features of electric heavy truck driving based on the Monte Carlo method; the key features include driving distance, load, driving time, and frequency of use; A first model building module is used to evaluate the charging demand, schedulable time period and charging power range of the electric heavy truck by analyzing the key features to build an adjustable potential model of the electric heavy truck; A prediction module is used to predict future charging load demand and generate a daily charging load curve by establishing a load prediction model for electric heavy trucks based on an adjustable potential model and the current operating status of the electric heavy trucks; The second model building module is used to establish a power output model of photovoltaic power generation and a charging and discharging model of the energy storage system based on historical weather data, light intensity and operation constraints of the energy storage system; The third model building module is used to establish an upper-level optimization model for charging scheduling of electric heavy trucks based on IGDT; The fourth model building module is used to establish a lower-level optimization model for the joint dispatch of the IGDT-based energy storage system and the power grid to generate a two-level optimization model; The calculation module is used to solve the two-level optimization model using an iterative solution method to generate a coordinated optimization scheduling strategy for the integrated energy system.

[0036] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention specifically targets the high-power charging needs of electric heavy trucks, and formulates an accurate optimization scheduling strategy based on the characteristics of large-capacity batteries and long driving distances. By reasonably arranging the charging time, it not only ensures the stability of the power grid, but also effectively improves the satisfaction rate of the high-load charging needs of electric heavy trucks, ensuring their efficient operation. 2. The present invention can coordinate multiple flexible loads such as electric heavy trucks, distributed photovoltaic power generation, and energy storage systems to perform comprehensive energy optimization scheduling. By making full use of the characteristics of each energy source, the system achieves more efficient resource scheduling under uncertain conditions and improves the operating efficiency and stability of the overall energy system.

[0037] 3. The present invention introduces new energy generation (distributed photovoltaic power generation), flexible load (energy storage system) and adopts a two-layer optimization scheduling model for collaborative optimization, and adopts information difference decision theory (IGDT) to deal with the uncertainty of photovoltaic power generation and energy storage system. In the face of uncertainty in photovoltaic power generation, IGDT is more robust than traditional methods. IGDT ensures stable operation of the system when photovoltaic power generation is insufficient through risk avoidance strategies, and optimizes resource utilization when power generation exceeds expectations through risk pursuit strategies, thereby maximizing economic benefits and improving the robustness of the system and the dispatch efficiency of electric heavy trucks.

[0038] 4. The present invention significantly improves the peak-shaving and valley-filling effect through the joint dispatch of energy storage system and photovoltaic power generation, and maximizes the absorption rate of local photovoltaic power generation. Compared with the traditional dispatching method, the present invention further balances the load fluctuation of the power grid while achieving economic benefit optimization, and reduces the dependence on traditional fossil energy. The dynamic electricity price mechanism is adopted, charging during the low electricity price period and discharging during the peak period, which reduces the overall energy cost and promotes the green and efficient operation of the power system.

[0039] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0041] Figure 1 It is a method flow chart of a specific implementation mode of the present invention.

[0042] Figure 2 It is an iterative solution flow chart of a specific implementation mode of the present invention.

[0043] Figure 3 It is a system structure diagram of a specific implementation mode of the present invention. DETAILED DESCRIPTION

[0044] The specific implementation of the present invention is described below with reference to the accompanying drawings.

[0045] like Figure 1 A coordinated optimization scheduling method for an integrated energy system including an electric heavy truck is shown, comprising the following steps: S1: Randomly generate key driving characteristics of electric heavy trucks based on the Monte Carlo method; the key characteristics include driving distance, load, driving time, and frequency of use.

[0046] In a specific implementation, the key feature data is calculated by the following formula: 1. Driving distance :Each electric heavy truck has a certain period of time The total distance travelled.

[0047]

[0048] in, is the distance traveled per trip, Is electric heavy truck in time Number of trips within.

[0049] 2. Load :The weight of cargo carried by an electric heavy truck during driving.

[0050]

[0051] in, is the load per trip.

[0052] 3. Driving time : Duration of each trip of the electric heavy truck.

[0053]

[0054] in, is the time for a single trip.

[0055] 4. Frequency of use : The frequency of use of electric heavy trucks in a certain period of time.

[0056]

[0057] in, is the driving time of the electric heavy truck, It is the total usage time of the electric heavy truck.

[0058] S2: By analyzing the key characteristics, the charging demand, dispatchable time period and charging power range of the electric heavy truck are evaluated to construct an adjustable potential model of the electric heavy truck.

[0059] In a specific implementation, based on the features extracted in step S1, the driving pattern of the electric heavy truck is analyzed and its adjustable potential model is constructed. The charging demand, schedulable time period and charging power range of the electric heavy truck are evaluated according to the driving characteristics and driving pattern. Specifically, it includes: 1. Charging requirements : The charging demand of electric heavy trucks is closely related to their driving distance, load, driving time, etc. The charging demand can be evaluated by the following model:

[0060] in: is a function of charging demand; , They are driving distance, load, driving time and frequency of use; It is the vehicle's energy consumption coefficient (kWh / km), which reflects the amount of electricity consumed per unit driving distance of an electric heavy truck. 2. Dispatchable time period model: The dispatchable time period refers to the time window in which the electric heavy truck can be dispatched and charged. Assume that the driving time of the electric heavy truck in a day is , then the schedulable time period can be expressed by the following formula:

[0061] in, is the total time of a day (e.g. 24 hours), It is the time the heavy truck travels on that day.

[0062] 3. Charging power range :The charging power range of electric heavy trucks is related to the configuration of their charging stations and the battery capacity of the vehicle, and usually fluctuates within a fixed power range. Set the minimum charging power and maximum charging power , then its charging power range is:

[0063] S3: Based on the adjustable potential model and the current operating status of the electric heavy truck, a load prediction model for the electric heavy truck is established to predict the future charging load demand and generate a daily charging load curve.

[0064] In a specific implementation, based on the adjustable potential model constructed in step S2 and the current operating status of the electric heavy truck (such as remaining power, current driving status, etc.), the future charging load demand is predicted and the daily charging load curve of the electric heavy truck is generated. Specifically, it includes: 1. Charging demand prediction model: Assume that the charging demand of electric heavy trucks changes over time. The following time series model can be established:

[0065] in, For the moment The charging load of electric heavy trucks, It's time The charging needs For the moment The schedulable time period, For the moment Charging power range. 2. Based on the above model, the daily charging load curve of electric heavy trucks can be generated:

[0066] in, Every moment of the day The charging load, It is the number of charging tasks in different time periods of a day.

[0067] S4: Based on historical weather data, light intensity and the operating constraints of the energy storage system, a power output model of photovoltaic power generation and a charging and discharging model of the energy storage system are established.

[0068] In a specific implementation, by considering the output uncertainty of the photovoltaic power generation system and the dynamic characteristics of the charge and discharge of the energy storage system, based on the historical weather data, light intensity and the operation constraints of the energy storage system, a power output model of photovoltaic power generation and a charge and discharge model of the energy storage system are established. Specifically, it includes: 1. Photovoltaic power output model: The output of the photovoltaic power generation system mainly depends on factors such as solar radiation intensity, photovoltaic module area, and conversion efficiency. At the same time, photovoltaic power generation has obvious uncertainty and is affected by weather conditions. Its power output can be described by the following equation: Photovoltaic system at all times Output power It can be expressed by the following formula:

[0069] in, :time The photovoltaic output power; time The solar radiation intensity (unit: W / m2) can be obtained through historical weather data; The effective area of ​​the photovoltaic panel (unit: m2); The conversion efficiency of a photovoltaic system.

[0070] 2. Charging and discharging model of energy storage system: The energy storage system is Remaining power It can be expressed by the following state equation:

[0071] in, For the moment The remaining capacity of the energy storage system; For the moment The remaining capacity of the energy storage system; For the moment Charging power of the energy storage system; For the moment Discharge power of the energy storage system; , The charging efficiency and discharging efficiency of the energy storage system are usually between 0.85 and 0.95; : time interval.

[0072] 3. Capacity constraints:

[0073] in, : The minimum power of the energy storage system (usually 0, indicating full discharge); The maximum capacity of the energy storage system.

[0074] 4. Charge and discharge power limit:

[0075]

[0076] in, The maximum charging power of the energy storage system; The maximum discharge power of the energy storage system.

[0077] 5. Charge and discharge strategy: The charge and discharge strategy of the energy storage system can be optimized based on dynamic conditions such as electricity price signals, fluctuations in photovoltaic power generation, and load demand. The usual strategy is: when photovoltaic power generation exceeds the current load demand and the electricity price is low, the energy storage system will charge. When the load demand is high and the electricity price is high, the energy storage system will discharge to help smooth out the peaks and fill the valleys. The charge and discharge strategy optimization model is as follows:

[0078]

[0079] in, :time The load demand, :time of photovoltaic power generation.

[0080] S5: Establish an upper-level optimization model for charging scheduling of electric heavy trucks based on IGDT.

[0081] In a specific implementation, in order to deal with the uncertainty of photovoltaic output, an upper-level optimization scheduling model is established by introducing information-based decision theory (IGDT).

[0082] First, define the uncertainty of photovoltaic power generation: the fluctuation amount of photovoltaic power generation , which represents the uncertainty range of photovoltaic power generation.

[0083] IGDT handles uncertainty through two strategies: (1) Risk avoidance: If photovoltaic power generation is likely to be lower than expected, we use To describe the impact of reduced photovoltaic power generation.

[0084] Risk Aversion Model:

[0085] in, Reflects the degree of risk aversion when photovoltaic power generation is insufficient. (2) Risk seeking: If photovoltaic power generation exceeds expectations, we will use To describe the economic benefits of increased photovoltaic power generation.

[0086] Risk Seeking Model:

[0087] in, Indicates the degree to which higher returns are sought when PV power generation exceeds expectations.

[0088] After the introduction of IGDT, the objective function of the upper optimization model is as follows: 1. Objective function: =

[0089] in, ,in Can be (risk aversion) or (Risk Seeking). :Electric heavy truck users are at the moment The user cost, based on charging costs and electricity prices, is defined as:

[0090] in, It's time The charging power, It is a time-of-use electricity price. : The consumption rate of photovoltaic power generation is used to measure the degree to which photovoltaic power generation is effectively utilized and is defined as:

[0091] in, is the photovoltaic power generation power, is the total load demand. : The average value of the system load, used to reduce peak loads and fill valleys, and reduce load fluctuations. Smoothing is achieved by reducing the square of load fluctuations:

[0092] in, It's time of the total load.

[0093] 2. The constraints are set as follows: Charging power constraints for electric heavy trucks:

[0094] in, It is the maximum charging power of the electric heavy truck charging station.

[0095] Scheduling time windows:

[0096] in, This is the time period when electric heavy trucks can be dispatched.

[0097] Charging power requirement:

[0098] in, It is the total charging power requirement of an electric heavy truck over a period of time.

[0099] Photovoltaic power generation constraints:

[0100] in, is the maximum power generation capacity of the photovoltaic system.

[0101] S6: Establish a lower-level optimization model for the joint dispatch of the IGDT-based energy storage system and the power grid to generate a two-level optimization model.

[0102] In the specific implementation, the lower optimization model mainly focuses on the coordinated scheduling of the energy storage system to ensure the economy and stability of the system. According to the charging scheduling decision of the upper layer, the charging and discharging behavior of the energy storage system is dynamically optimized, charging when the electricity price is low and discharging when the electricity price is high to reduce the system load.

[0103] At the same time, there is a certain degree of uncertainty in the charging and discharging capabilities of the energy storage system. Especially when the battery is aging or the charging and discharging efficiency fluctuates, the actual available power and power of the energy storage system may be different from expectations.

[0104] Combining the risk avoidance and risk seeking strategies of IGDT, the uncertainty of the energy storage system is defined as , indicating the time The available power or charging and discharging power of the energy storage system fluctuates.

[0105] Two risk strategies for energy storage systems: (1) Risk avoidance strategy: When the available power of the energy storage system is lower than expected, the system needs to dispatch more photovoltaic power generation or purchase electricity from the grid to ensure a stable supply for charging electric heavy trucks and other loads.

[0106] The formula is as follows:

[0107] in, It is the risk aversion coefficient when dealing with insufficient capacity of the energy storage system. (2) Risk-seeking strategy: When the charging or discharging amount of the energy storage system exceeds expectations, the system can prioritize storing electricity for use during subsequent peak loads, or discharge it during periods of higher electricity prices to obtain higher returns.

[0108] The formula is as follows:

[0109] in, It is the risk pursuit coefficient when the energy storage system exceeds the expected charging and discharging capacity.

[0110] After introducing the uncertainty of the energy storage system, the objective function of the lower optimization model can be expressed as: 1. Objective function:

[0111] in, is the adjustment amount of photovoltaic power generation, which depends on the volatility of photovoltaic power generation and the corresponding risk factors (risk aversion) or (Risk Seeking). For the moment The electricity price can be changed according to the time of the power grid under the time-of-use electricity price mechanism. For energy storage system at all times The discharge power, is the charging power of the energy storage system. is the adjusted capacity of the energy storage system, depending on the uncertainty and its risk factors or .

[0112] Risk Aversion Model: .

[0113] Risk Seeking Model: .

[0114] 2. The constraints are set as follows: Capacity constraints of energy storage systems:

[0115] in, It's time The energy storage system has remaining power, and the energy storage system needs to perform charging and discharging operations within the capacity range.

[0116] Charge and discharge power limit:

[0117] The charging and discharging power of the energy storage system needs to be within the physical limitations of the system.

[0118] S7: An iterative solution method is used to solve the two-level optimization model to generate a coordinated optimization scheduling strategy for the integrated energy system.

[0119] In a specific implementation, the iterative solution is based on the classic Stackelberg game theory and is an effective method for dealing with two-layer optimization problems. This method uses the "master-slave relationship" to optimize and solve the upper and lower layers. IGDT is introduced, and the iterative solution process is carried out between the upper (Leader) and lower (Follower) models through the "master-slave relationship". The upper layer is responsible for the charging scheduling decisions of electric heavy trucks, and the lower layer is responsible for the coordinated scheduling of the energy storage system, while considering the uncertainty of photovoltaic power generation. In each iteration process, the system will gradually adjust the decision according to different risk preferences (risk aversion or risk seeking), and finally converge to the global optimal solution. The two-layer optimization model can be expressed as follows: 1. Upper optimization model (Leader):

[0120] in, are the decision variables of the upper model (such as the charging period and power distribution of electric heavy trucks), The lower model is given The best response, is the objective function of the upper layer.

[0121] 2. Lower layer optimization model (Follower):

[0122] The lower-level optimization model makes decisions at the upper level Under given conditions, optimize (such as the charging and discharging strategy of the energy storage system and the grid dispatching). The goal of the lower layer is to minimize , that is, the total operating cost of the energy storage system and the power grid.

[0123] like Figure 2 As shown, the steps for solving the substitution equation are as follows: Step 1: Initialize the solution of the upper model.

[0124] First, initialize the upper-level decision variables , including the dispatchable potential of electric heavy trucks, charging time periods and initial charging power allocation. These policy variables determine the initial plan based on historical data, photovoltaic power generation forecasts, user needs, etc.

[0125] Then, with the introduction of IGDT, the upper-level decision-making will be made by selecting different risk aversion factors Risk Seeking Factor To generate multiple possible charging scheduling schemes.

[0126] Among them, risk aversion: choose higher value, ensuring the system performance in the worst case; risk seeking: choose a higher value, pursuing maximum return under optimistic conditions.

[0127] The upper objective function is initially expressed as:

[0128] in, is the adjustment amount of photovoltaic power generation, based on the uncertainty factor or Make adjustments.

[0129] Step 2: Solve the lower layer response.

[0130] Given a high-level decision Under the condition of uncertainty, solve the lower optimization problem and determine the optimal charging and discharging strategy of the energy storage system and the power dispatching plan of the power grid. , giving different feedbacks under risk aversion and risk seeking.

[0131] The lower objective function is as follows:

[0132] in, It is expressed as:

[0133] In addition, photovoltaic power generation Adjust for uncertainty: For risk aversion: Photovoltaic uncertainty: , energy storage uncertainty: .

[0134] For risk seeking: Photovoltaic uncertainty: , energy storage uncertainty: .

[0135] The lower layer will give ,This is the optimal response of the energy storage system and grid dispatch to the upper-level charging dispatch decision.

[0136] Step 3: Feedback update of the upper solution.

[0137] Based on the feedback solution obtained by the lower layer optimization , the upper layer will update the decision variables Through the feedback mechanism, the upper layer can adjust the charging period and power distribution of the electric heavy truck according to the charging and discharging strategy of the energy storage system, the uncertainty of photovoltaic power generation and the feedback of the grid dispatch. At this time, the objective function of the upper layer is updated as follows:

[0138] By adjusting the uncertainty factor or ,The upper layer will modify its decision to achieve the optimal charging ,scheduling scheme and continue to optimize the economic benefits and load ,smoothing of the system.

[0139] Step 4: Iterative Convergence Repeat steps 2 and 3. In each iteration, the upper layer continues to optimize its charging scheduling strategy based on the feedback from the lower layer. The lower layer model re-optimizes the charging and discharging strategy of the energy storage system and the grid scheduling based on the new decision of the upper layer. After each iteration, it is determined whether the solutions of the upper and lower layers have converged. The convergence condition is: the change of the upper layer objective function is less than a certain threshold, that is:

[0140] When the upper and lower layer solutions meet the convergence conditions at the same time, the iteration process ends and the final global optimal solution is obtained.

[0141] It can be seen that the present invention discloses a method for collaborative optimization and scheduling of an integrated energy system including electric heavy trucks. First, the charging load of the electric heavy trucks is predicted based on their driving characteristics and charging characteristics, and a dynamic model of the photovoltaic power generation and energy storage system is constructed. Under the premise of considering the randomness of photovoltaic power generation output and the volatility of charging demand for electric heavy trucks, a two-layer optimization scheduling model of electric heavy trucks, photovoltaic power generation and energy storage systems based on information difference decision theory (IGDT) is proposed. By introducing IGDT, this method can make more robust scheduling decisions when dealing with uncertainty, achieve maximum economic benefits, peak shaving and valley filling, and efficient consumption of new energy.

[0142] In the present invention, the main goal of the upper optimization model is to optimize the charging scheduling of electric heavy trucks. According to the fluctuations in charging demand for electric heavy trucks, the present invention uses IGDT to evaluate the benefits of different charging time periods under uncertain conditions, and gives priority to the timing of charging to maximize economic benefits. In addition, the upper model reduces the pressure of peak load on the power grid and achieves grid load smoothing by reasonably arranging the charging plan. At the same time, the present invention gives priority to charging electric heavy trucks during periods with strong photovoltaic output to maximize the utilization rate of photovoltaic power generation. When dealing with the uncertainty of photovoltaic output, IGDT analyzes the possible worst-case scenarios in the future to ensure that even in the case of large photovoltaic fluctuations, reasonable charging arrangements can be made to avoid dependence on traditional electricity and improve the efficiency of new energy consumption.

[0143] In the present invention, the lower-level optimization model focuses on the joint scheduling of the energy storage system and the power grid. The present invention gives priority to using unabsorbed photovoltaic power generation to charge the energy storage system, evaluates the volatility of photovoltaic power generation through IGDT, and determines the best time for energy storage charging in advance. Even in the case of large fluctuations in photovoltaic power generation, it can still ensure the maximum utilization of photovoltaic power generation. When photovoltaic power generation is insufficient, the energy storage system balances the grid load through reasonable discharge operations to reduce the pressure on the grid. At the same time, when facing load demand fluctuations, the energy storage system optimizes the charging and discharging strategy through IGDT to ensure that the overall operating cost of the system is minimized.

[0144] Correspondingly, such as Figure 3 As shown, the present invention also discloses a coordinated optimization scheduling system for an integrated energy system including an electric heavy truck, comprising: The feature extraction module 1 is used to randomly generate key features of the electric heavy truck based on the Monte Carlo method; the key features include driving distance, load, driving time, and frequency of use.

[0145] The first model building module 2 is used to evaluate the charging demand, schedulable time period and charging power range of the electric heavy truck by analyzing the key characteristics, so as to build an adjustable potential model of the electric heavy truck.

[0146] The prediction module 3 is used to predict the future charging load demand and generate a daily charging load curve by establishing a load prediction model for the electric heavy truck based on the adjustable potential model and the current operating status of the electric heavy truck.

[0147] The second model building module 4 is used to establish a power output model of photovoltaic power generation and a charging and discharging model of the energy storage system based on historical weather data, light intensity and operation constraints of the energy storage system.

[0148] The third model building module 5 is used to establish an upper-level optimization model for charging scheduling of electric heavy trucks based on IGDT.

[0149] The fourth model building module 6 is used to establish a lower-level optimization model for the joint dispatch of the IGDT-based energy storage system and the power grid to generate a two-level optimization model.

[0150] The calculation module 7 is used to solve the two-layer optimization model by adopting an iterative solution method to generate a coordinated optimization scheduling strategy for the integrated energy system.

[0151] The specific implementation of the coordinated optimization and scheduling system for an integrated energy system including electric heavy trucks in this embodiment is basically the same as the specific implementation of the coordinated optimization and scheduling method for an integrated energy system including electric heavy trucks described above, and will not be repeated here.

[0152] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, etc., which can store program codes, including several instructions to enable a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The same and similar parts between the various embodiments in this specification can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0153] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0154] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] The present invention will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the application.

Claims

1. A method for coordinated optimization and scheduling of an integrated energy system including electric heavy trucks, characterized in that: The steps include: S1: Randomly generate key driving characteristics of electric heavy trucks based on the Monte Carlo method; the key characteristics include driving distance, load, driving time, and frequency of use; S2: By analyzing the key features, the charging demand, schedulable time period and charging power range of the electric heavy truck are evaluated to construct an adjustable potential model of the electric heavy truck; S3: Based on the adjustable potential model and the current operating status of the electric heavy truck, a load prediction model for the electric heavy truck is established to predict the future charging load demand and generate a daily charging load curve; S4: Based on historical weather data, light intensity and the operation constraints of the energy storage system, establish a power output model for photovoltaic power generation and a charging and discharging model for the energy storage system; S5: Establish an upper-level optimization model for charging scheduling of electric heavy trucks based on IGDT; S6: Establish a lower-level optimization model for the joint dispatch of the IGDT-based energy storage system and the power grid to generate a two-level optimization model; S7: An iterative solution method is used to solve the two-level optimization model to generate a coordinated optimization scheduling strategy for the integrated energy system.

2. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 1 is characterized in that: The step S1 comprises: Get the driving information and load information of each electric heavy truck, and calculate the driving distance of the electric heavy truck using the following formula , Load , driving time , Frequency of use : in, is the distance traveled per trip, Is electric heavy truck in time The number of trips within is the load per trip, is the time of a single trip, is the driving time of the electric heavy truck, It is the total usage time of the electric heavy truck.

3. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 2 is characterized in that: The step S2 comprises: Based on the key features, an adjustable potential model of electric heavy trucks is constructed according to driving characteristics and driving patterns to evaluate the charging needs of electric heavy trucks. , Scheduling time period and charging power range ; The adjustable potential models of the electric heavy truck include: in, is a function of the charging demand, is the energy consumption coefficient of the vehicle; assuming that the driving time of the electric heavy truck in a day is , is the total time of the day; is the minimum charging power, is the maximum charging power.

4. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 3 is characterized in that: The load prediction model of the electric heavy truck includes: in, For the moment The charging load of electric heavy trucks, It's time Charging needs, For the moment The schedulable time period, For the moment Charging power range.

5. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 4 is characterized in that: The step S3 comprises: According to the adjustable potential model of the electric heavy truck, the daily charging load curve of the electric heavy truck is generated using the following formula: in, Every moment of the day The charging load is N, and N is the number of charging tasks in different time periods of a day.

6. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 5 is characterized in that: The step S4 comprises: The power output model of photovoltaic power generation is specifically as follows: in, For photovoltaic systems at all times The photovoltaic output power, For the moment The solar radiation intensity, is the effective area of ​​the photovoltaic panel, is the conversion efficiency of the photovoltaic system; The charging and discharging model of the energy storage system is as follows: in, For the moment The remaining capacity of the energy storage system; For the moment The remaining capacity of the energy storage system; For the moment Charging power of the energy storage system; For the moment Discharge power of the energy storage system; , are the charging efficiency and discharging efficiency of the energy storage system, is the time interval; is the minimum power of the energy storage system, The maximum capacity of the energy storage system; The maximum charging power of the energy storage system, Maximum discharge power of the energy storage system; The charging and discharging strategy of the energy storage system is optimized through the following formula: 。 7. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 6 is characterized in that: The step S5 comprises: The upper optimization model objective function Set to: = in, is the average value of system load, It's time Total load; , for or , is the fluctuation of photovoltaic power generation, is the risk aversion degree when photovoltaic power generation is insufficient, the extent to which higher returns can be sought when PV generation exceeds expectations; For electric heavy truck users at all times The user cost, based on charging costs and electricity prices, is defined as: in, It's time Time-of-use electricity prices; is the consumption rate of photovoltaic power generation, which is used to measure the degree to which photovoltaic power generation is effectively utilized and is defined as: The charging power constraint of the electric heavy truck is set as: ; Set the schedulable time window to: ; Set the charging power requirement to: in, is the total charging power requirement of an electric heavy truck over a period of time; The photovoltaic power constraint is set as: in, is the maximum power generation capacity of the photovoltaic system.

8. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 7 is characterized in that: The step S6 comprises: The objective function of the lower optimization model Set to: in, is the adjusted capacity of the energy storage system; When the available power of the energy storage system is lower than expected: in, It is the risk aversion coefficient for dealing with insufficient capacity of the energy storage system; When the charging or discharging amount of the energy storage system exceeds expectations: in, It is the risk seeking factor when the energy storage system exceeds the expected charging and discharging capacity; Set the constraints as: , , .

9. The coordinated optimization scheduling method for an integrated energy system including electric heavy trucks according to claim 8 is characterized in that: The step S7 comprises: S71: Initialize the solution of the upper optimization model; S72: Given the upper-level decision, determine the optimal charging and discharging strategy of the energy storage system and the power dispatching plan of the power grid by using the lower-level optimization model response solution; S73: According to the feedback solution obtained by the lower optimization model, the upper layer will update the decision variables and the objective function of the upper optimization model; S74: Determine whether the objective function of the upper-level optimization model has reached the convergence condition; if not, continue to execute step S72; if so, obtain the final global optimal solution and generate a coordinated optimization scheduling strategy for the integrated energy system.

10. A coordinated optimization and dispatching system for an integrated energy system including electric heavy trucks, characterized in that: The system adopts the coordinated optimization scheduling method of the integrated energy system containing electric heavy trucks according to any one of claims 1 to 9, and the system comprises: A feature extraction module, used to randomly generate key features of electric heavy truck driving based on the Monte Carlo method; the key features include driving distance, load, driving time, and frequency of use; A first model building module is used to evaluate the charging demand, schedulable time period and charging power range of the electric heavy truck by analyzing the key features to build an adjustable potential model of the electric heavy truck; A prediction module is used to predict future charging load demand and generate a daily charging load curve by establishing a load prediction model for electric heavy trucks based on an adjustable potential model and the current operating status of the electric heavy trucks; The second model building module is used to establish a power output model of photovoltaic power generation and a charging and discharging model of the energy storage system based on historical weather data, light intensity and operation constraints of the energy storage system; The third model building module is used to establish an upper-level optimization model for charging scheduling of electric heavy trucks based on IGDT; The fourth model building module is used to establish a lower-level optimization model for the joint dispatch of the IGDT-based energy storage system and the power grid to generate a two-level optimization model; The calculation module is used to solve the two-level optimization model using an iterative solution method to generate a coordinated optimization scheduling strategy for the integrated energy system.

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