Pure electric heavy truck double-layer economic low-carbon dispatching based on electric-carbon coupling mechanism

Abstract

The invention discloses a pure electric heavy truck double-layer economic low-carbon scheduling method based on an electricity-carbon coupling mechanism. The method comprises the following steps: (1) establishing a carbon emission flow model based on the charging rate of a pure electric heavy truck; (2) calculating a flexible deployable class load of the pure electric heavy truck cluster; (3) calculating dynamically adjustable class loads of the pure electric heavy truck cluster; (4) calculating unparticipated scheduling loads of the pure electric heavy truck cluster; (5) calculating an adjustable capacity margin participating in low-carbon scheduling based on the operation characteristics of the pure electric heavy truck; (6) establishing an electricity-carbon coupling price model; (7) establishing an upper-layer economic dispatching model; (8) establishing a thermal power generating unit carbon emission transaction carbon price model; (9) establishing a power consumption model based on the weight of the transported goods and the running speed of the pure electric heavy truck; and (10) solving the master-slave game model by using a genetic algorithm and by means of a Yalmip tool and a CPLEX solver. The defects in the prior art can be overcome, and the stability and economical efficiency of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a double-layer economic and low-carbon scheduling of pure electric heavy trucks based on the electro-carbon coupling mechanism. Background Art

[0002] The popularization of new energy vehicles with the carbon emission flow model has become a key measure for energy conservation and carbon reduction in the road transportation field recognized internationally. Aiming at the problems that existing research does not consider the difference between ordinary loads and the charging loads of pure electric heavy trucks (ET), nor does it study in detail the price model of pure electric heavy trucks participating in the electro-carbon coupling market, and lacks pertinence. A carbon emission flow model considering the charging characteristics of pure electric heavy trucks is proposed; then, based on the influence of carbon potential-carbon price and time-of-use electricity price on the adjustable capacity of pure electric heavy truck clusters, an electro-carbon coupling electricity price model is used to guide pure electric heavy truck users to participate in low-carbon economic operation, but there are still deficiencies in the existing research on the electro-carbon coupling electricity price model. The large-scale popularization of pure electric heavy trucks and their participation in the electro-carbon market contribute to promoting the transformation of energy consumption in the transportation field to clean electric energy, and then optimizing the overall energy structure. As an important source of carbon emissions, the carbon emission problem of pure electric heavy trucks is particularly prominent due to their long driving mileage and large load capacity. With the incentive mechanism of the electro-carbon market, it can further prompt enterprises to accelerate the elimination of high-emission fuel heavy trucks. Thus, it can be seen that the new energy transformation of heavy trucks is an irresistible trend. Therefore, it is of great significance to reasonably guide ET to participate in economic and low-carbon scheduling. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a double-layer economic and low-carbon scheduling of pure electric heavy trucks based on the electro-carbon coupling mechanism in view of various deficiencies of the prior art.

[0004] To solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] A double-layer economic and low-carbon scheduling of pure electric heavy trucks based on the electro-carbon coupling mechanism includes the following steps:

[0006] (1) Establish a carbon emission flow model based on the charging rate of pure electric heavy trucks to dynamically describe the actual carbon emissions in electricity;

[0007] (2) Calculate the flexible dispatchable loads of the pure electric heavy truck cluster;

[0008] (3) Calculate the dynamically adjustable loads of the pure electric heavy truck cluster;

[0009] (4) Calculate the non-dispatchable loads of the pure electric heavy truck cluster;

[0010] (5) Calculate the adjustable capacity margin for participating in low-carbon scheduling based on the operating characteristics of pure electric heavy trucks;

[0011] (6) Based on the time-of-use electricity price mechanism, the peak-valley electricity price is uniformly expressed using the flat electricity price. Based on the carbon emission flow model of pure electric heavy trucks, an electricity-carbon coupling price model is established;

[0012] (7) An upper-layer economic dispatch model based on the start-stop cost of thermal power units, the curtailment cost of wind power generation, the operation cost of energy storage, and the carbon emission cost is established;

[0013] (8) A carbon price model for carbon emission trading of thermal power units based on the high-carbon penalty coefficient and the carbon reduction reward coefficient of carbon emissions is established;

[0014] (9) An electricity consumption model based on the weight of goods transported by pure electric heavy trucks and their driving speed is established;

[0015] (10) The genetic algorithm is used, and with the help of the Yalmip tool and the CPLEX solver, the master-slave game model is solved.

[0016] As a preferred technical solution of the present invention, in step (1), establishing a carbon emission flow model based on the charging rate of pure electric heavy trucks includes the following steps.

[0017] Define the carbon emission corresponding to the energy flow passing through a network node or branch per unit time as the carbon flow rate, expressed as

[0018]

[0019] where C is the carbon flow of the network node or branch; t is time;

[0020] Define the carbon emission corresponding to unit electric energy in the network as the carbon flow density ρ, expressed as

[0021]

[0022] where P is the active power flow in the corresponding network;

[0023] Estimate the actual carbon emission of the electric energy of the pure electric heavy truck through the charging rate characteristics of the pure electric heavy truck; the node carbon potential during the charging of the pure electric heavy truck is expressed as

[0024]

[0025] where e j is the carbon potential magnitude of load node j; P i,j is the power flowing from slave node i to node j; ρ i,j is the carbon flow density of the branch connecting node i and node j; P G,j is the power generation of the generator; N is the number of network nodes; the electric energy consumed by the pure electric heavy truck during charging is P k,t , after considering the battery charging efficiency η, the actual electric energy obtained by the pure electric heavy truck from the power grid is The carbon emission factor of the power source is β, so the equivalent carbon emission power brought by charging the pure electric heavy truck is

[0026] As a preferred technical solution of the present invention, in step (2), the calculation method of the flexibly deployable load is

[0027]

[0028] In the formula, M represents the set of the number of flexibly deployable loads, P k,t is the charging power of the pure electric heavy truck; η k is the charging efficiency of the pure electric heavy truck in the fast charging mode; is a switching variable, which is 1 when turned on and 0 when turned off.

[0029] As a preferred technical solution of the present invention, in step (3), the calculation method of the dynamically adjustable load is

[0030]

[0031] In the formula, N is the set of dynamically adjustable loads, η is the battery charging efficiency, is a switching variable, which is 1 when turned on and 0 when turned off; α represents the reduction coefficient in the slow charging mode, and 0 < α < 1 reflects the reduction degree of the load.

[0032] As a preferred technical solution of the present invention, in step (4), the calculation method of the non-schedulable load is

[0033]

[0034] In the formula, I is the set of non-adjustable loads, is a 0-1 variable, which is 1 when turned on and 0 when turned off.

[0035] As a preferred technical solution of the present invention, in step (5), the calculation method of the adjustable capacity margin participating in low-carbon scheduling is

[0036] ΔE s,t =ΔE bct +ΔE nt

[0037] In the formula, ΔE st represents the total amount of electricity change of the pure electric heavy truck cluster participating in the regulation; ΔE bct represents the electricity change amount of the dynamically adjustable load; ΔE nt represents the electricity change amount of the flexibly deployable load.

[0038]

[0039] Where, ΔE st represents the total amount of electricity change when the pure electric heavy truck cluster participates in regulation; ΔE s,t+ represents the amount of electricity change when the response electricity is greater than 0 when the pure electric heavy truck cluster participates in regulation; ΔE s,t- represents the amount of electricity change when the response electricity is less than when the pure electric heavy truck cluster participates in regulation.

[0040] E DR,t,max = E DR,t,0 +ΔE s,t+

[0041] E DR,t,min = E DR,t,0 +ΔE s,t-

[0042] Where, E DR,t,max is the maximum response electricity when the pure electric heavy truck cluster participates in regulation at time t; E DR,t,min is the minimum load response electricity when the pure electric heavy truck cluster participates in regulation at time t; E DR,t,0 is the response electricity when the pure electric heavy truck cluster does not participate in regulation at time t; ΔE s,t+ represents the amount of electricity change when the response electricity is greater than 0 when the pure electric heavy truck cluster participates in regulation; ΔE s,t- represents the amount of electricity change when the response electricity is less than when the pure electric heavy truck cluster participates in regulation.

[0043] Considering that the electricity amount before and after demand response of the pure electric heavy truck cluster remains unchanged within one cycle, it can be expressed as:

[0044] ∑E DR = ∑E DR，0

[0045] Where, E DR is the total electricity amount when the pure electric heavy truck cluster participates in regulation, and E DR，0 is the total electricity amount when the pure electric heavy truck cluster does not participate in regulation.

[0046] As a preferred technical solution of the present invention, in step (6), the electricity-carbon coupling price model is

[0047]

[0048] Where, e j (t) represents the node carbon potential of the pure electric heavy truck at time t, q j,t is the electricity-carbon coupling price of the pure electric heavy truck at time t; P c (t) is the time-of-use electricity price, and q j,c (t) is the carbon potential-carbon price;

[0049]

[0050] Wherein, the normal electricity price is P p ; the peak electricity price is P f ; the valley electricity price is P g ;

[0051] Calculate the carbon potential of a pure electric heavy truck during charging at the current time t according to the carbon emission flow model, and refer to the peak, valley, and flat periods of the time-of-use electricity price to divide the carbon potential-carbon price into three grades: low, medium, and high.

[0052]

[0053] Wherein, λ is the carbon price difference between high, medium, and low; e min is the minimum value of the charging carbon potential at time t, and e max is the maximum value of the charging carbon potential at time t; e ave is the average value of all charging carbon potentials at time t.

[0054] As a preferred technical solution of the present invention, in step (7), the upper-layer economic dispatch model includes that the objective function on the grid side is

[0055] The start-stop cost of the thermal power unit is Wherein, C U,g represents the start-stop cost of the thermal power unit g; C g,t represents the start-stop state variable of the thermal power unit g in the t period, and the value is 0 or 1.

[0056] The energy storage operation cost is Wherein, b cha and b dis are the charging and discharging cost coefficients of the energy storage respectively; P cha,t and P dis,t are the charging and discharging powers of the energy storage in the t period respectively; X cha,t and X dis,t are 0-1 variables indicating whether the energy storage is in the charging or discharging state.

[0057] The curtailment penalty cost of wind power is Wherein, b q,w represents the curtailment cost coefficient of the wind power unit; P q,w,t represents the curtailment power of the wind power unit w in the t period.

[0058] The carbon emission cost generated by the thermal power unit during power generation is Wherein, q j,c represents the carbon emission trading price; Q t represents the carbon emission trading volume of the thermal power unit in the t period.

[0059] As a preferred technical solution of the present invention, in step (8), the carbon trading price is

[0060]

[0061] In the formula, Q high represents the carbon emissions of the thermal power unit in the high-carbon responsibility area, λ is the carbon trading base price; L is the threshold of carbon emissions; μ is the penalty coefficient; ξ is the reward coefficient,

[0062] Q t = Q F,t - Q H,av ,

[0063] In the formula, Q F,t represents the actual carbon emissions of the thermal power unit; P q,w,t represents the carbon emission quota of the thermal power unit. The carbon emission quota of the thermal power unit is,

[0064]

[0065] In the formula, T is the total number of time periods, P M,t is the active power of the coal-fired power unit in the t-th time period,; δ H is the carbon quota per unit power generation of the thermal power unit,

[0066] The constraint conditions are as follows,

[0067] Thermal power unit capacity and ramp constraint,

[0068] P g,min > P g,t > P g,max ,

[0069] In the formula: P g,max 、P g,min are the maximum and minimum active power outputs of the thermal power unit g respectively,

[0070] R down > P g,t - P g,t-1 > R up , t ≥ 2,

[0071] In the formula: P g,t represents the active power output of the thermal power unit g at time t; P g,t-1 represents the active power output of the thermal power unit g at time t - 1, R up 、R down are the maximum and minimum values of the ramp power respectively,

[0072] Wind power unit capacity constraint,

[0073] P w,min > P w,t > P w,max ,

[0074] In the formula: P w,max 、Pw,min are the maximum and minimum active power outputs of the wind turbine, respectively,

[0075] Power balance constraint,

[0076]

[0077] In the formula, P L,t is the charging power of the pure electric heavy truck, G represents the set of the number of thermal power units; W represents the set of the number of wind turbines; P dis，t represents the discharging power of the energy storage at time t; P cha，t represents the charging power of the energy storage at time t,

[0078] Electricity price constraint,

[0079] q j,min <q j <q j,max ,

[0080] In the formula, q j,min 、q j,max are the minimum and maximum electricity prices of the electricity-carbon coupling electricity price in a day, respectively,

[0081] The maximum power constraint is as follows,

[0082] P(i)≤P max ,

[0083] In the formula, P(i) is the total power at the i-th time period in a day, and P max is the maximum power limit of the charging station.

[0084] As a preferred technical solution of the present invention, in step (9), the power consumption model includes,

[0085] Taking the minimum charging cost of the pure electric heavy truck as the objective function, the objective function on the load side is minF = C d -C eh,t ,

[0086] The cost of purchasing electricity from the power grid when the pure electric heavy truck is charging is In the formula, q g,t represents the electricity-carbon coupling price corresponding to time t, and P eh,t represents the charging power of the pure electric heavy truck at time t,

[0087] The power consumption of the pure electric heavy truck is In the formula, is a decision variable, taking 1 if the k-th pure electric heavy truck travels from node i to j, otherwise taking 0; d ij represents the distance from node i to j; G k 、G MThey are the load capacity of the k-th pure electric heavy truck and the maximum load capacity of the pure electric heavy truck respectively; S max is the maximum battery power of the pure electric heavy truck; D max is the maximum driving range of the fully charged pure electric heavy truck; v k ij is the driving speed of the k-th pure electric heavy truck from node i to j; a, b, and c are the driving speed coefficients of the pure electric heavy truck;

[0088] Define the carbon quota obtained by the pure electric heavy truck in a certain area at time t as

[0089] In the formula, M eh,t represents the carbon emission quota owned by the pure electric heavy truck at time t; Δt represents the unit inspection period; P eh,t represents the charging power of the pure electric heavy truck at time t; E gas represents the carbon emission per kilometer when a fuel truck carrying the same cargo weight travels 1 km; P clear,t represents the new energy charging power at time t; P h,t represents the charging power of the thermal power unit at time t. By calculating the proportion of the thermal power unit in the total charging power at time t, the carbon emission of the pure electric heavy truck charging is calculated; L eh is the mileage that the pure electric heavy truck can travel per unit of electricity; E th is the carbon emission factor per unit of electricity of the thermal power unit;

[0090] The carbon emission quota income that the pure electric heavy truck can obtain at time t is C eh,t =q eh (M e,t -M eh,t ), in the formula, q eh represents the selling price of the carbon emission quota of the pure electric heavy truck, M e,t represents the free carbon emission quota of the pure electric heavy truck at time t,

[0091] The constraint conditions are as follows,

[0092] At any time, the SOC of the pure electric heavy truck battery cannot exceed the upper and lower limits of SOC, that is,

[0093] E SOC,min ≤E SOC,t ≤E SOC,max ,

[0094] In the formula, E soc,t is the SOC of the pure electric heavy truck battery at time t; E soc,max , E soc,min are the upper and lower limits of the battery SOC respectively,

[0095] The load constraint of the pure electric heavy truck is,

[0096] Gk∈K ≤70% G m ,

[0097] Wherein, G k is the actual cargo load of the kth pure electric heavy truck; G m is the rated load of each pure electric heavy truck;

[0098] When the total daily charging load of the pure electric heavy truck remains unchanged, there are the following constraints,

[0099]

[0100] Wherein, D is the total value of the daily charging load of the pure electric heavy truck.

[0101] As a preferred technical solution of the present invention, in step (10), solving the master-slave game model includes the following steps,

[0102] 1) Set the population size n, the maximum number of iterations M, the population crossover rate, the mutation rate, the convergence error ε, and m = 0;

[0103] 2) Initialize the population, randomly generate the electricity-carbon coupling price at the power grid center, and transmit the parameters to the pure electric heavy truck cluster;

[0104] 3) m = m + 1;

[0105] 4) The pure electric heavy truck cluster reports the regulation capacity to the power grid center according to the user's charging plan. The power grid center sends the coupling electricity price to the pure electric heavy truck cluster according to the regulation capacity reported by the pure electric heavy truck cluster and the carbon reduction demand, and encourages the pure electric heavy truck users to actively participate in the regulation. After receiving the coupling electricity price specified by the power grid center, the pure electric heavy truck cluster uses the CPLEX solver to calculate the low-carbon scheduling capacity, and at the same time reports the capacity with the minimum charging cost in the pure electric heavy truck cluster to the power grid center;

[0106] 5) After receiving the capacity reported by the pure electric heavy truck cluster, the power grid center calculates the price with the minimum operating cost on the power grid side and retains the current electricity-carbon coupling electricity price;

[0107] 6) If the reported capacity does not meet the cost requirements of the power grid center, use the genetic algorithm to select, cross, and mutate to generate a new electricity-carbon coupling electricity price, and repeat steps 3-5 to calculate the charging cost of the pure electric heavy truck cluster and the operating cost of the power grid center

[0108] 7) If Let Otherwise C m+1 = C m , F m+1 = F m ; C m+1 = C mIndicates that the results of the m-th and (m + 1)-th iterations of the upper layer are the same, F m+1 = F m Indicates that the results of the m-th and (m + 1)-th iterations of the lower layer are the same;

[0109] 8) If |C m+1 - C m | ≤ ε and |F m+1 - F m | ≤ ε, then it is determined that the game reaches equilibrium, the iteration ends, otherwise return to step 3).

[0110] The beneficial effects produced by adopting the above technical solutions are as follows: Aiming at the problems that existing research does not simultaneously consider the carbon reduction effect of reducing the output of thermal power units in the power grid and the problem of considering the income from selling carbon quotas of battery electric heavy trucks to reduce the charging cost, through the carbon emission flow theory, the dynamic description of the carbon emissions contained in unit electricity during the charging of battery electric heavy trucks and the construction of an electricity-carbon coupling price mechanism, a two-layer coordinated optimization scheduling strategy considering low-carbon economic operation is proposed. First, according to the carbon emission flow theory, the actual carbon emissions of battery electric heavy trucks are dynamically described, the carbon emission responsibility on the source side is transferred to the load side, and considering the charging rate during the charging of battery electric heavy trucks, a carbon emission flow model suitable for the charging characteristics of battery electric heavy trucks is established. Second, considering the electricity consumption of users, the battery electric heavy truck cluster is divided into load types that can be flexibly allocated, load types that can be dynamically adjusted, and load types that cannot participate in scheduling. The scheduling capacity is obtained. Third, an electricity-carbon coupling price mechanism of time-of-use electricity price and carbon potential-carbon price is established to guide battery electric heavy truck users to participate in the low-carbon economic scheduling strategy. On this basis, a two-layer economic low-carbon scheduling model is established. The upper layer takes the minimum grid operation cost as the objective function, and at the same time considers constraints such as thermal power capacity, wind turbine capacity, and energy storage equipment to establish the upper-layer economic scheduling model; the lower layer takes the minimum charging cost of battery electric heavy trucks as the objective function, considering the income from selling carbon emission quotas, to establish the lower-layer low-carbon scheduling model. Further, the genetic algorithm is used, and with the help of the Yalmip tool and the CPLEX solver, the master-slave game model constructed by the present invention is solved to achieve low-carbon economic scheduling. The present invention has a good economic low-carbon response effect, greatly reducing the grid-side operation cost and the user's charging cost. The innovation points of the present invention are mainly reflected in two points: one is to consider the charging characteristics of battery electric heavy trucks and improve the load carbon emission flow theory, making the dynamic description of the actual carbon emissions contained in the electricity of battery electric heavy trucks more reasonable and enhancing the carbon reduction potential of battery electric heavy trucks; the other is to propose an economic low-carbon two-layer coordinated optimization method based on the electricity-carbon coupling price mechanism, effectively reducing the charging cost of battery electric heavy trucks while realizing economic and low-carbon operation. Description of the Drawings

[0111] Figure 1 Is the framework flow chart of the present invention;

[0112] Figure 2 It is the dispatchable capacity diagram under three electricity price scenarios for a pure electric heavy truck cluster;

[0113] Figure 3 It is the grid load comparison curve; Specific implementation manners

[0114] The following embodiments illustrate the present invention in detail. In the description of the following embodiments, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application. It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0115] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" according to the context. Additionally, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0116] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure, or characteristic described in combination with the embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0117] See Figure 1 , the present invention includes the following steps:

[0118] (1) Establish a carbon emission flow model based on the charging rate of battery electric heavy trucks to dynamically describe the actual carbon emissions in electricity; specifically,

[0119] Define the carbon emission corresponding to the energy flow passing through a network node or branch per unit time as the carbon flow rate, expressed as,

[0120]

[0121] In the formula, C is the carbon flow of the network node or branch; t is time;

[0122] Define the carbon emission corresponding to unit electric energy in the network as the carbon flow density ρ, expressed as,

[0123]

[0124] In the formula, P is the active power flow in the corresponding network;

[0125] Estimate the actual carbon emissions of the battery electric heavy truck's electricity through the charging rate characteristics of the battery electric heavy truck; the node carbon potential during the charging of the battery electric heavy truck is expressed as,

[0126]

[0127] In the formula, e j is the carbon potential magnitude of load node j; P i,j is the power flowing from node i to node j; ρ i,j is the carbon flow density of the branch connecting node i and node j; P G,j is the power generation of the generator; N is the number of network nodes; the electric energy consumed by the battery electric heavy truck for charging is P k,t , considering the battery charging efficiency η, the actual electric energy obtained by the battery electric heavy truck from the power grid is The carbon emission factor of the power source is β, so the equivalent carbon emission power brought by the battery electric heavy truck charging is

[0128] In the context of the coordinated development of smart grids and new energy transportation, the battery electric heavy truck cluster, as a new type of electricity-consuming entity, has an important impact on the optimal allocation of power resources. Specifically, according to the transportation characteristics of the orders undertaken by the battery electric heavy trucks, such as transportation time, transportation route, and cargo delivery deadline, etc., combined with the two charging modes of fast charging and slow charging possessed by the battery electric heavy trucks themselves, the load of the battery electric heavy truck cluster can be clearly divided into flexible allocation type load, dynamic adjustment type load, and non-participating scheduling type load.

[0129] (2) Calculate the flexibly adjustable load of the pure electric heavy truck cluster. The characteristic of this type of load cluster is that when the order transportation task has a certain degree of flexibility in time arrangement, the pure electric heavy truck can flexibly adjust the charging time period and location according to the real-time load condition of the power grid and the electricity price policy. For example, if the power grid is at a low load and the electricity price is low at night, and the order transportation task allows the vehicle to charge at night and depart early the next morning, then the pure electric heavy truck can transfer the charging load from daytime to night, thereby achieving the optimal allocation of power resources in the time dimension. The existing technology has a relatively simple modeling of the pure electric heavy truck cluster model, only dividing the pure electric heavy truck cluster into schedulable and non-schedulable loads, without considering the detailed classification and model construction of the schedulable loads.

[0130] The calculation method for the flexibly adjustable load is as follows:

[0131]

[0132] In the formula, M represents the set of the number of flexibly adjustable loads, P k,t is the charging power of the pure electric heavy truck; η k is the charging efficiency of the pure electric heavy truck in the fast charging mode; is a switching variable, which is 1 when it is turned on and 0 when it is turned off. η k = 0.892.5kW, and the power P k,t = 7kW during fast charging.

[0133] (3) Calculate the dynamically adjustable load of the pure electric heavy truck cluster. The existing technology has a relatively simple modeling of the pure electric heavy truck cluster model, only dividing the pure electric heavy truck cluster into schedulable and non-schedulable loads, without considering the detailed classification and model construction of the schedulable loads. On the basis of the existing technology, consider that the pure electric heavy truck adjusts its own charging power accordingly. Switch the charging mode from fast charging to slow charging to reduce the instantaneous power consumption load and assist the power grid to maintain stable operation. This right can model the charging electricity of the dynamically adjustable pure electric heavy truck to achieve the optimal allocation of power resources in the charging mode dimension. It solves the problems of the model construction of the dynamically adjustable load of the pure electric heavy truck and the refined classification of the flexibly adjustable load. The characteristic of this type of cluster means that the pure electric heavy truck can adjust its own charging power accordingly according to the electro-carbon coupling electricity price signal. For example, during the peak load period of the power grid, in order to avoid putting too much pressure on the power grid, the pure electric heavy truck can appropriately reduce the fast charging power or switch from the fast charging mode to the slow charging mode to reduce the instantaneous power consumption load and assist the power grid to maintain stable operation.

[0134] The calculation method for the dynamically adjustable load is as follows:

[0135]

[0136] In the formula, N is the set of dynamically adjustable loads, η is the battery charging efficiency, is a switching variable, which is 1 when turned on and 0 when turned off; α represents the reduction coefficient in the slow charging mode, and 0 < α < 1 reflects the degree of load reduction. The charging power is set to 2.5 kW during slow charging.

[0137] (4) Calculate the non-schedulable load of the pure electric heavy truck cluster. The existing technology has a relatively simple modeling of the pure electric heavy truck cluster model, only dividing the pure electric heavy truck cluster into schedulable and non-schedulable loads, without considering the detailed division and model construction of the schedulable load. Based on the existing technology, a model is built by considering the charging time allocation of the pure electric heavy truck cluster during charging, and a detailed model of the flexible load that can be adjusted in the pure electric heavy truck cluster is constructed to achieve the optimal allocation of electric power resources in the time dimension. Such clusters mainly originate from some orders with extremely urgent transportation tasks and strict time requirements. The pure electric heavy trucks must complete charging and be put into transportation within the specified time, and cannot adjust the charging time or power according to the operation requirements of the power grid. For example, some cold chain transportation orders with extremely high freshness requirements, the vehicles must complete charging and depart quickly within a short time, and their charging loads are non-adjustable, so the electricity volume of the pure electric heavy truck cluster that cannot participate in scheduling is

[0138]

[0139] In the formula, is a 0-1 variable, which is 1 when turned on and 0 when turned off.

[0140] (5) Based on the operating characteristics of the pure electric heavy truck, calculate the adjustable capacity margin for participating in low-carbon scheduling. The operating characteristics of the pure electric heavy truck can be used as a flexible load. Under the condition of ensuring that the total load remains unchanged within one operating cycle, the flexible load that can flexibly allocate the electricity consumption in each time period can transfer the charging time period with a higher electricity-carbon coupling price to the time period with a lower electricity price for charging. While ensuring that the total electricity consumption remains unchanged, the electricity consumption cost is reduced to achieve the low-carbon operation of the power grid. The refined modeling of the response capacity of the pure electric heavy truck cluster within one scheduling cycle effectively improves the problem of inaccurate measurement of the scheduling capacity in the existing technology during the scheduling process of the pure electric heavy truck cluster. This model can not only determine the upper and lower envelope lines of the schedulable response capacity of the pure electric heavy truck cluster, but also accurately constrain the electricity consumption of the pure electric heavy truck cluster before and after scheduling within one scheduling cycle.

[0141] The calculation method of the adjustable capacity margin for participating in low-carbon scheduling is

[0142] ΔE s,t = ΔE bct + ΔE nt

[0143] In the formula, ΔE st represents the total change in the electricity volume of the pure electric heavy truck cluster participating in the regulation; ΔE bctIndicates the variable quantity of the electricity consumption of the dynamically adjustable type of load; ΔE nt Indicates the variable quantity of the electricity consumption of the flexibly deployable type of load.

[0144]

[0145] In the formula, ΔE st Indicates the total variable quantity of the electricity consumption when the pure electric heavy truck cluster participates in the regulation; ΔE s,t+ Indicates the variable quantity of the electricity consumption when the response electricity of the pure electric heavy truck cluster participating in the regulation is greater than 0; ΔE s,t- Indicates the variable quantity of the electricity consumption when the response electricity of the pure electric heavy truck cluster participating in the regulation is less than.

[0146] E DR,t,max = E DR,t,0 + ΔE s,t+

[0147] E DR,t,min = E DR,t,0 + ΔE s,t-

[0148] In the formula, E DR,t,max Is the maximum response electricity when the pure electric heavy truck cluster participates in the regulation at time t; E DR,t,min Is the minimum load response electricity when the pure electric heavy truck cluster participates in the regulation at time t; E DR,t,0 Is the response electricity when the pure electric heavy truck cluster does not participate in the regulation at time t; ΔE s,t+ Indicates the variable quantity of the electricity consumption when the response electricity of the pure electric heavy truck cluster participating in the regulation is greater than 0; ΔE s,t- Indicates the variable quantity of the electricity consumption when the response electricity of the pure electric heavy truck cluster participating in the regulation is less than.

[0149] Considering that the electricity quantity remains unchanged before and after the demand response of the pure electric heavy truck cluster in one cycle, it can be expressed as:

[0150] ∑E DR = ∑E DR，0

[0151] In the formula, E DR Is the total electricity quantity when the pure electric heavy truck cluster participates in the regulation, E DR，0 Is the total electricity quantity when the pure electric heavy truck cluster does not participate in the regulation.

[0152] (6) Based on the time-of-use electricity price mechanism, the peak-valley electricity prices are uniformly expressed using the flat electricity price. Based on the carbon emission flow model of pure electric heavy trucks, an electricity-carbon coupling price model is established. The existing technology expresses the time-of-use electricity price mechanism through peak, flat, and valley electricity prices, without considering the proportional relationship of the three electricity prices. To further guide the orderly charging behavior of pure electric heavy truck users, considering the time-of-use electricity price mechanism, in order to accurately describe the constructed electricity-carbon coupling electricity price mechanism, the peak-valley electricity prices are uniformly expressed using the flat electricity price. To guide users to participate in low-carbon scheduling, a method combining time-of-use electricity price, real-time electricity price, and demand response is proposed to guide the reasonable charging behavior of pure electric heavy trucks, effectively reducing the peak-valley difference and the electricity cost of users. At the same time, the time-of-use carbon price of the carbon trading mechanism is proposed to further guide users' reasonable charging behavior, but currently, the time-of-use electricity price, stepped carbon price, and the scheduling of pure electric heavy trucks are not associated.

[0153] The electricity-carbon coupling price model is as follows:

[0154]

[0155] In the formula, e j (t) represents the node carbon potential of the pure electric heavy truck at time t, and q j,t is the electricity-carbon coupling price of the pure electric heavy truck at time t; P c (t) is the time-of-use electricity price, and q j,c (t) is the carbon potential-carbon price;

[0156] To guide users' electricity consumption behavior, the power grid company adjusts the peak-valley-flat electricity prices, enabling part of the peak-hour load of users to be transferred to the valley hour, achieving the purpose of peak shaving and valley filling.

[0157]

[0158] In the formula, the flat electricity price is P p ; the peak-hour electricity price is P f ; the valley-hour electricity price is P g ;

[0159] The higher the carbon potential of each load node at the same moment, the greater the carbon emission value per unit of electricity consumed. Based on this, through the optimal scheduling of the load side, it can promote the load to consume more electricity when the node carbon potential is low and less electricity when the node carbon potential is high. After scheduling, the carbon emission value per unit of electric energy of the load within a cycle is reduced, achieving effective energy conservation and emission reduction.

[0160] According to the carbon emission flow model, calculate the carbon potential of the pure electric heavy truck during charging at the current time t. Referring to the time-of-use electricity price peak-valley period division, the carbon potential-carbon price is divided into three levels: low, medium, and high.

[0161]

[0162] In the formula, λ is the carbon price difference between high, medium, and low; e minis the minimum value of the charging carbon potential at time t, e max is the maximum value of the charging carbon potential at time t; e ave is the average value of all charging carbon potentials at time t.

[0163] (7) Establish an upper-layer economic dispatch model based on the start-stop cost of thermal power units, the curtailment cost of wind power generation, the operation cost of energy storage, and the carbon emission cost; the upper-layer economic dispatch model includes,

[0164] The objective function on the power grid side is

[0165] The start-stop cost of thermal power units is In the formula, C U,g represents the start-stop cost of thermal power unit g; C g,t represents the start-stop state variable of thermal power unit g in the t period, and the value is 0, 1,

[0166] The operation cost of energy storage is In the formula, b cha and b dis are the charge and discharge cost coefficients of energy storage respectively; P cha,t and P dis,t are the charge and discharge powers of energy storage in the t period respectively; X cha,t , X dis,t is a 0-1 variable indicating whether the energy storage is in the charge or discharge state,

[0167] The curtailment penalty cost is In the formula, b q,w represents the curtailment cost coefficient of wind turbine; P q,w,t represents the curtailment power of wind turbine w in the t period,

[0168] The carbon emission cost generated by thermal power unit power generation is In the formula, q j,c represents the carbon emission trading price; Q t represents the carbon emission trading volume of thermal power unit in the t period.

[0169] (8) Establish a carbon price model for carbon emission trading of thermal power units based on the high-carbon penalty coefficient and low-carbon reward coefficient of carbon emissions; the carbon price for carbon emission trading is,

[0170]

[0171] In the formula, Q high represents the carbon emission of thermal power unit in the high-carbon responsibility area, λ is the carbon trading base price; L is the threshold of carbon emission; μ is the penalty coefficient; ξ is the reward coefficient,

[0172] Q t =Q F,t -QH,av ,

[0173] In the formula, Q F,t represents the actual carbon emissions of the thermal power unit; P q,w,t represents the carbon emission quota of the thermal power unit. The carbon emission quota of the thermal power unit is

[0174]

[0175] In the formula, T is the total time period, and P M,t is the active power of the coal-fired power unit in the t-th time period,; δ H is the carbon quota per unit power generation of the thermal power unit,

[0176] The constraint conditions are as follows

[0177] Thermal power unit capacity and ramp rate constraints

[0178] P g,min > P g,t > P g,max ,

[0179] In the formula: P g,max and P g,min are the maximum and minimum active power outputs of the thermal power unit g, respectively

[0180] R down > P g,t - P g,t-1 > R up , t ≥ 2

[0181] In the formula: P g,t represents the active power output of the thermal power unit g at time t; P g,t-1 represents the active power output of the thermal power unit g at time t - 1, and R up and R down are the maximum and minimum ramp powers, respectively

[0182] Wind turbine capacity constraints

[0183] P w,min > P w,t > P w,max ,

[0184] In the formula: P w,max and P w,min are the maximum and minimum active power outputs of the wind turbine w, respectively

[0185] Power balance constraints

[0186]

[0187] In the formula, P L,tThe charging power of the pure - electric heavy - truck, \(G\) represents the set of the number of thermal power units; \(W\) represents the set of the number of wind power units; \(P\) dis，t represents the discharging power of the energy storage at time \(t\); \(P\) cha，t represents the charging power of the energy storage at time \(t\),

[0188] Electricity price constraint,

[0189] \(q\) j,min \(<q\) j \(<q\) j,max ,

[0190] In the formula, \(q\) j,min 、\(q\) j,max are respectively the minimum electricity price and the maximum electricity price of the electricity - carbon coupling electricity price in a day,

[0191] The maximum power constraint is as follows,

[0192] \(P(i)\leq P\) max ,

[0193] In the formula, \(P(i)\) is the total power at the \(i\) - th time period in a day, and \(P\) max is the maximum power limit of the charging station.

[0194] (9) Establish a power consumption model based on the weight of the goods transported by the pure - electric heavy - truck and its driving speed; the power consumption model includes,

[0195] Taking the minimum charging cost of the pure - electric heavy - truck as the objective function, the objective function on the load side is \(\min F = C\) d -C\) eh,t ,

[0196] The cost of purchasing electricity from the power grid when the pure - electric heavy - truck is charging is In the formula, \(q\) g,t represents the electricity - carbon coupling price corresponding to time \(t\), and \(P\) eh,t represents the charging power of the pure - electric heavy - truck at time \(t\),

[0197] The power consumption of the pure - electric heavy - truck is In the formula, is a decision variable, which takes 1 when the \(k\) - th pure - electric heavy - truck travels from node \(i\) to \(j\), and 0 otherwise; \(d\) ij represents the distance from node \(i\) to \(j\); \(G\) k 、\(G\) M are respectively the load of the \(k\) - th pure - electric heavy - truck and the maximum load of the pure - electric heavy - truck; \(S\) max is the maximum battery capacity of the pure - electric heavy - truck; \(D\) max is the maximum driving range of the fully - charged pure - electric heavy - truck; \(v\) k ij is the driving speed of the \(k\) - th pure - electric heavy - truck from node \(i\) to \(j\); \(a\), \(b\), \(c\) are the driving speed coefficients of the pure - electric heavy - truck;

[0198] Define the carbon quota obtained by a battery electric heavy truck within a certain area during the period t as

[0199] In the formula, M eh,t represents the carbon emission quota owned by the battery electric heavy truck at time t; Δt represents the unit inspection period; P eh,t represents the charging power of the battery electric heavy truck at time t; E gas represents the carbon emission when a fuel truck carrying the same cargo weight travels 1 km; P clear,t represents the new energy charging power at time t; P h,t represents the charging power of the thermal power unit at time t. By calculating the proportion of the thermal power unit in the total charging power at time t, the carbon emissions of the battery electric heavy truck charging are calculated; L eh is the mileage that a battery electric heavy truck can travel per unit of electricity; E th is the carbon emission factor per unit of electricity of the thermal power unit;

[0200] The carbon emission quota income that the battery electric heavy truck can obtain at time t is C eh,t = q eh (M e,t - M eh,t ), in the formula, q eh represents the selling price of the carbon emission quota of the battery electric heavy truck, M e,t represents the free carbon emission quota of the battery electric heavy truck at time t,

[0201] The constraint conditions are as follows,

[0202] The SOC of the battery of the battery electric heavy truck at any time cannot exceed the upper and lower limits of the SOC, that is,

[0203] E SOC,min ≤ E SOC,t ≤ E SOC,max ,

[0204] In the formula, E soc,t is the SOC of the battery of the battery electric heavy truck during the period t; E soc,max , E soc,min are the upper and lower limits of the battery SOC respectively,

[0205] The load constraint of the battery electric heavy truck is,

[0206] G k∈K ≤ 70% G m ,

[0207] In the formula, G k is the actual loaded cargo volume of the kth battery electric heavy truck; G m is the rated load capacity of each battery electric heavy truck;

[0208] The total daily charging load of the battery electric heavy truck remains unchanged, and there are the following constraints,

[0209]

[0210] Wherein, D is the total daily charging load of battery electric heavy trucks.

[0211] (10) Use the genetic algorithm and, with the help of the Yalmip tool and the CPLEX solver, solve the master-slave game model, including the following steps

[0212] 1) Set the population size n, the maximum number of iterations M, the population crossover rate, the mutation rate, the convergence error ε, and m = 0;

[0213] 2) Initialize the population, randomly generate the electricity-carbon coupling price at the power grid center, and transmit the parameters to the battery electric heavy truck cluster;

[0214] 3) m = m + 1;

[0215] 4) The battery electric heavy truck cluster reports the regulation capacity to the power grid center according to the user's charging plan. The power grid center sends the coupling electricity price to the battery electric heavy truck cluster according to the regulation capacity reported by the battery electric heavy truck cluster and the carbon reduction demand, to encourage battery electric heavy truck users to actively participate in regulation. After receiving the coupling electricity price specified by the power grid center, the battery electric heavy truck cluster uses the CPLEX solver to calculate the low-carbon scheduling capacity, and at the same time reports the capacity with the minimum charging cost in the battery electric heavy truck cluster to the power grid center;

[0216] 5) After receiving the capacity reported by the battery electric heavy truck cluster, the power grid center calculates the price with the minimum operating cost on the power grid side and retains the current electricity-carbon coupling electricity price;

[0217] 6) If the reported capacity does not meet the cost requirements of the power grid center, use the genetic algorithm to select, cross, and mutate to generate a new electricity-carbon coupling electricity price, and repeat steps 3-5 to calculate the charging cost of the battery electric heavy truck cluster and the operating cost of the power grid center

[0218] 7) If Let Otherwise C m+1 = C m , F m+1 = F m ; C m+1 = C m indicates that the results of the m-th and (m + 1)-th iterations of the upper layer are the same, and F m+1 = F m indicates that the results of the m-th and (m + 1)-th iterations of the lower layer are the same;

[0219] 8) If |C m+1 - C m | ≤ ε and |F m+1 - Fm If |≤ε, it is determined that the game reaches equilibrium and the iteration ends; otherwise, return to step 3).

[0220] The principle of the genetic factor algorithm is simple and easy to understand, with a relatively fast convergence speed and fewer parameters to be adjusted. These characteristics enable it to show strong adaptability in various optimization problems. It does not need to rely on the specific mathematical properties of the problem and can effectively handle both continuously differentiable functions and complex discrete combinatorial problems. This algorithm has parallelism. During the iteration process, it can not only completely preserve the global information of each generation of factors, but also help the algorithm jump out of the local optimal solution to achieve global optimal search. Given the above advantages, when solving the game model, an improved genetic algorithm incorporating genetic factor weights is used.

[0221] The simulation environment is based on the IEEE 69-node distribution network, and the parameter settings of each entity are as follows: the base voltage is 12.66 kV, and the base power is 10,000 kW. The number of electric vehicles is 5,000, the charging electric power is 160 W, the battery charge-discharge efficiency is 95%, and the battery capacity is 320 kWh; the upper and lower limits of SOC are 0.95 and 0.1 respectively, the expected SOC is 0.85, and the upper and lower thresholds for triggering charging selection are 0.5 and 0.15 respectively; the participation response periods are for charging: 0:00 - 9:00 in summer; 1:00 - 6:00 and 12:00 - 15:00 in winter; during 10:00 - 13:00 and 18:00 - 21:00, the load of the pure electric heavy truck cluster increases the grid load. See the following table for details.

[0222]

[0223]

[0224] As Figure 2 shown, the schedulable capacity of the pure electric heavy truck cluster will increase due to different incentive electricity price mechanisms. Under the electricity-carbon coupling electricity price mechanism, the regulation capacity of the pure electric heavy truck cluster is more significant than that of the time-of-use electricity price and the carbon potential-carbon price mechanism. By participating in the low-carbon dispatching of the power grid, the pure electric heavy truck cluster can not only effectively reduce the load impact on the power grid, increase the proportion of clean energy in the energy structure, but also reduce its own charging cost and achieve the benefits of both parties.

[0225] The power of the pure electric heavy truck grid before and after game optimization is as Figure 3 shown. Through the electricity-carbon coupling price mechanism, the pure electric heavy truck cluster is encouraged to carry out spatio-temporal transfer, cut peak loads, and increase valley loads, making the overall load distribution more balanced, thereby improving the stability and economy of the power system.

[0226] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial control computers, personal computers, smart phones, handheld single devices, floor-standing single devices, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memories, calculators, and controllers, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS networks, the World Wide Web, etc. Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0227] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented.

[0228] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A dual-layer economical low-carbon dispatching method for pure electric heavy trucks based on the electric-carbon coupling mechanism, characterized in that The following steps are involved: (1) Establish a carbon emission flow model based on the charging rate of pure electric heavy trucks to dynamically describe the actual carbon emissions in electricity; (2) Calculate the load that can be flexibly deployed by the pure electric heavy truck cluster; (3) Calculate the dynamically adjustable load of the pure electric heavy truck cluster; (4) Calculate the load that the pure electric heavy truck cluster cannot participate in dispatching; (5) Based on the operating characteristics of pure electric heavy trucks, calculate the adjustable capacity margin for low-carbon dispatch; (6) Based on the time-of-use electricity price mechanism, the peak and valley electricity prices are uniformly expressed as normal electricity prices, and an electricity-carbon coupling price model is established based on the carbon emission flow model of pure electric heavy trucks; (7) Establish an upper-level economic dispatch model based on the start-up and shutdown costs of thermal power units, the cost of wind power curtailment, the operating costs of energy storage, and the carbon emission costs; (8) Establish a carbon price model for carbon emissions trading of thermal power units based on a high carbon penalty coefficient and a carbon reduction reward coefficient; (9) Establish a power consumption model based on the cargo weight and driving speed of pure electric heavy trucks; (10) The master-slave game model is solved by using genetic algorithm, Yalmip tool and CPLEX solver.

2. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 1 is characterized by: In step (1), establishing a carbon emission flow model based on the charging rate of a pure electric heavy truck includes the following steps: The carbon emission corresponding to the energy flow through the network node or branch per unit time is defined as the carbon flow rate, which is expressed as: Where C is the carbon flow of the network node or branch; t is time; The carbon emission corresponding to unit electric energy in the network is defined as carbon flow density ρ, which is expressed as: Where, P is the active power flow in the corresponding network; The actual carbon emissions of pure electric heavy trucks are estimated through the charging rate characteristics of pure electric heavy trucks; the node carbon potential of pure electric heavy trucks during charging is expressed as: In the formula, e j is the carbon potential of load node j; P i,j is the power flowing from node i to node j; ρ i,j is the carbon flow density of the branch connecting node i and node j; P G,j is the power generated by the generator; N is the number of network nodes; the power consumed by charging a pure electric heavy truck is P k,t After considering the battery charging efficiency η, the actual power obtained by the pure electric heavy truck from the power grid is The carbon emission factor of the electricity source is β, so the equivalent carbon emission power brought by charging a pure electric heavy truck is 3. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 2 is characterized by: In step (2), the calculation method of the flexibly deployable load is: Where M represents the number of loads that can be flexibly deployed, P k,t Charging power for pure electric heavy trucks; η k Charging efficiency of pure electric heavy trucks in fast charging mode; is a switch variable, which is 1 when on and 0 when off.

4. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 2 is characterized in that: In step (3), the calculation method of the dynamically adjustable load is: Where N is the set of dynamically adjustable loads, η is the battery charging efficiency, It is a switch variable, which is 1 when turned on and 0 when turned off; α represents the reduction coefficient in the slow charging mode, and 0<α<1 reflects the degree of load reduction.

5. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 2 is characterized in that: In step (4), the calculation method of the non-schedulable load is: Where I is the set of non-adjustable loads. is a switch variable, which is 1 when on and 0 when off.

6. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 2 is characterized by: In step (5), the calculation method of the adjustable capacity margin participating in low-carbon dispatch is: ΔE s,t =ΔE bct +ΔE nt In the formula, ΔE st Indicates the total amount of electricity change in the pure electric heavy truck cluster participating in the regulation; ΔE bct Indicates the change in power of dynamically adjustable load; ΔE nt Indicates the change in load power that can be flexibly deployed. In the formula, ΔE st Indicates the total amount of electricity change in the pure electric heavy truck cluster participating in the regulation; ΔE s,t+ Indicates the change in power when the response power is greater than 0 when the pure electric heavy truck cluster participates in regulation; ΔE s,t- It indicates the amount of change in power when the pure electric heavy truck cluster participates in the regulation and control and the response power is less than that. E DR,t,max =E DR,t,0 +ΔE s,t+ E DR,t,min =E DR,t,0 +ΔE s,t- In the formula, E DR,t,max E is the maximum response power of the pure electric heavy truck cluster when participating in regulation during period t; DR,t,min E is the minimum load response power of the pure electric heavy truck cluster when participating in regulation during period t; DR,t,0 is the response power of the pure electric heavy truck cluster when it does not participate in regulation at time t; ΔE s,t+ Indicates the change in power when the response power is greater than 0 when the pure electric heavy truck cluster participates in regulation; ΔE s,t- It indicates the amount of change in power when the pure electric heavy truck cluster participates in the regulation and control and the response power is less than that. Considering that the power of a pure electric heavy truck cluster remains unchanged before and after demand response within a cycle, it can be expressed as: ∑E DR =∑E DR，0 In the formula, E DR is the total power of the pure electric heavy truck cluster when participating in regulation, E DR，0 It is the total power of the pure electric heavy-duty truck cluster when it does not participate in regulation.

7. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 2 is characterized by: In step (6), the electricity-carbon coupling price model is: In the formula, e j (t) represents the node carbon potential of the pure electric heavy truck at time t, q j,t is the electricity-carbon coupling price of pure electric heavy truck at time t; P c (t) is the time-of-use electricity price, q j,c (t) is carbon potential-carbon price; In the formula, the normal electricity price is P p ; Peak electricity price is P f ; Off-peak electricity price P g ; The carbon potential of a pure electric heavy truck when charging at the current time t is calculated based on the carbon emission flow model, and the carbon potential-carbon price is divided into three levels: low, medium, and high, with reference to the peak, flat, and valley periods of the time-of-use electricity price. Where λ is the carbon price difference between high, medium and low; e min is the minimum value of the charge carbon potential at time t, e max is the maximum value of the charging carbon potential at time t; e ave is the average value of the charge carbon potential at time t.

8. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 1 is characterized by: In step (7), the upper-level economic dispatch model includes: The objective function of the grid side is: The start-up and shutdown cost of thermal power units is In the formula, C U,g represents the start-up and shutdown cost of thermal power unit g; C g,t Indicates the start and stop state variable of thermal power unit g in period t, with values ​​of 0 and 1. The operating cost of energy storage is Where b cha and b dis are the energy storage charging and discharging cost coefficients respectively; P cha,t and P dis,t are the charging and discharging power of energy storage in the tth period respectively; X cha,t , X dis,t It is a 0-1 variable, indicating whether the energy storage is in the charging or discharging state. The penalty cost of wind curtailment is Where b q,w P represents the wind abandonment cost coefficient of the wind turbine; q,w,t represents the abandoned wind power of wind turbine w in period t, The carbon emission cost of thermal power generation is In the formula, q j,c represents the carbon emission trading price; Q t Represents the carbon emission trading amount of the thermal power unit during period t.

9. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 8 is characterized in that: In step (8), the carbon price of carbon emission trading is, In the formula, Q high represents the carbon emissions of thermal power units in the high-carbon responsibility area, λ is the carbon trading base price; L is the threshold of carbon emissions; μ is the penalty coefficient; ξ is the reward coefficient, Q t =Q F,t -Q H,av , In the formula, Q F,t Represents the actual carbon emissions of thermal power units; P q,w,t Represents the carbon emission quota of thermal power units. The carbon emission quota of thermal power units is, In the formula, T is the total number of time periods, P M,t is the active power of the coal-fired power unit during period t; δ H is the carbon quota per unit of electricity generated by thermal power units, The constraints are as follows, Thermal power unit capacity and ramp constraints, P g,min >P g,t >P g,max , Where: P g,max , P g,min are the maximum and minimum active output of thermal power unit g, R down >P g,t -P g,t-1 >R up ,t≥2, Where: P g,t represents the active power output of thermal power unit g at time t; P g,t-1 represents the active output of thermal power unit g at time t-1, R up , R down are the maximum and minimum values ​​of climbing power respectively, Wind turbine capacity constraints, P w,min >P w,t >P w,max , Where: P w,max , P w,min are the maximum and minimum active output of wind turbine w, respectively. Power balance constraints, Where P L,t is the charging power of pure electric heavy trucks, G represents the number of thermal power units; W represents the number of wind power units; P dis，t represents the discharge power of the energy storage in period t; P cha，t represents the charging power of the energy storage in period t, Electricity price constraints, q j,min <q j <q j,max , In the formula, q j,min ,q j,max are the lowest and highest electricity prices of the electricity-carbon coupling price in a day, The maximum power constraint is as follows, P(i)≤P max , Where P(i) is the total power in the i-th period of a day, P max is the maximum power limit of the charging station.

10. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 9 is characterized in that: In step (9), the power consumption model includes: The objective function is to minimize the charging cost of pure electric heavy trucks, and the objective function on the load side is minF = C d -C eh,t , The cost of purchasing electricity from the grid when charging a pure electric heavy truck is In the formula, q g,t It is expressed as the electricity-carbon coupling price corresponding to time t, P eh,t represents the charging power of the pure electric heavy truck at time t, The power consumption of pure electric heavy truck is In the formula, is a decision variable, which takes 1 if the kth pure electric heavy truck moves from node i to node j, otherwise it takes 0; d ij represents the distance from node i to j; G k , G M are the load capacity of the kth pure electric heavy truck and the maximum load capacity of the pure electric heavy truck respectively; S max The maximum power of the battery in a pure electric heavy truck; D max v is the maximum mileage of a fully-charged electric heavy-duty truck; k ij is the driving speed of the kth pure electric heavy truck from node i to j; a, b, c are the driving speed coefficients of the pure electric heavy truck; Define the carbon quota obtained by pure electric heavy trucks in a certain area during period t as Where M eh,t represents the carbon emission quota of pure electric heavy trucks at time t; Δt represents the unit inspection period; P eh,t represents the charging power of the pure electric heavy truck at time t; E gas It represents the carbon emission of a fuel truck carrying the same cargo weight when traveling 1 km; P clear,t P represents the charging power of new energy at time t; h,t represents the charging power of the thermal power unit at time t. By calculating the proportion of the thermal power unit in the total charging power at time t, the carbon emissions of charging a pure electric heavy truck are calculated; L eh E is the mileage that a pure electric heavy truck can travel per unit of electricity; th is the carbon emission factor per unit of electricity of thermal power units; At time t, the carbon emission quota benefit that a pure electric heavy truck can obtain is C eh,t =q eh (M e,t -M eh,t ), Where M e,t represents the free carbon emission quota of pure electric heavy trucks at time t, q eh It is expressed as the selling price of carbon emission quota for pure electric heavy trucks. The constraints are as follows, The SOC of the battery of a pure electric heavy truck cannot exceed the upper and lower limits of SOC at any time, that is, AND SOC,min ≤E SOC,t ≤E SOC,max , In the formula, E soc,t is the SOC of the battery of the pure electric heavy truck during period t; E soc,max 、E soc,min are the upper and lower limits of the battery SOC respectively, The load limit of pure electric heavy truck is: G k∈K ≤70%G m , In the formula, G k is the actual cargo load of the kth pure electric heavy truck; G m is the rated load capacity of each pure electric heavy truck; The total daily charging load of pure electric heavy-duty trucks remains unchanged, subject to the following constraints: Where D is the total daily charging load of pure electric heavy trucks.

11. The method for dual-layer economical low-carbon dispatching of pure electric heavy trucks based on the electric-carbon coupling mechanism according to claim 10 is characterized in that: In step (10), solving the master-slave game model includes the following steps: 1) Set the population size n, the maximum number of iterations M, the population crossover rate, the mutation rate, the convergence error ε, and m = 0; 2) Initialize the population, the power grid center randomly generates the electricity-carbon coupling price, and transmits the parameters to the pure electric heavy truck cluster; 3) m = m + 1; 4) The pure electric heavy truck cluster reports the control capacity to the power grid center according to the user's charging plan. The power grid center sends the coupling electricity price to the pure electric heavy truck cluster based on the control capacity and carbon reduction demand reported by the pure electric heavy truck cluster, encouraging pure electric heavy truck users to actively participate in the control. After receiving the coupling electricity price specified by the power grid center, the pure electric heavy truck cluster uses the CPLEX solver to calculate the low-carbon dispatch capacity, and at the same time reports the capacity with the lowest charging cost in the pure electric heavy truck cluster to the power grid center; 5) After receiving the capacity reported by the pure electric heavy truck cluster, the grid center calculates the price with the lowest grid-side operating cost and retains the current electricity-carbon coupling electricity price; 6) If the reported capacity does not meet the cost requirements of the power grid center, use genetic algorithm selection, crossover, and mutation to generate a new electricity-carbon coupling price, repeat steps 3-5, and calculate the charging cost of the pure electric heavy truck cluster and grid center operating costs 7) If make Otherwise C m+1 =C m 、F m+1 =F m ; C m+1 =C m Indicates that the results of the mth iteration and the m+1th iteration of the upper layer are the same, F m+1 =F m Indicates that the results of the mth and m+1th iterations of the lower layer are the same; 8) If |C m+1 -C m |≤ε and |F m+1 -F m |≤ε, the game is considered to have reached equilibrium and the iteration ends, otherwise return to step 3).

Images