Distribution optimization method for low-carbon energy and traffic system under dynamic hydrogen pricing

Abstract

The invention belongs to the technical field of joint optimization of energy-traffic coupling, and particularly relates to a distribution optimization method of a low-carbon energy and traffic system under dynamic hydrogen pricing, which comprises the following steps: S1, establishing a dynamic hydrogen pricing mechanism related to the proportion of renewable energy to energy supply; s2, establishing an operation optimization model of the distributed energy station based on a dynamic hydrogen pricing mechanism; s3, establishing a hydrogenation optimization model of the hydrogen-powered vehicle under the dynamic hydrogen price; s4, integrating the operation optimization model and the hydrogenation optimization model to form a Starberg game model; s5, solving a double-layer optimization problem in a balanced manner based on a Starberg game model; the distributed energy station serves as a leader to solve an operation strategy of the distributed energy station, and the operation strategy comprises a resource management scheme of the distributed energy station and a formulated dynamic hydrogen price; and the hydrogen-powered vehicle is used as a follower to determine the optimal refueling strategy.

Description

Technical Field

[0001] The present invention belongs to the technical field of combined optimization of energy - transportation coupling, and particularly relates to an allocation optimization method for a low - carbon energy and transportation system under dynamic hydrogen pricing. Background Art

[0002] Facing the increasing popularity of renewable energy and hydrogen - powered vehicles, it is necessary to coordinate and optimize the energy and transportation systems to improve economic efficiency and reduce carbon emissions. Generally speaking, green hydrogen production is related to the planning and operation of energy stations. Through reasonable capacity configuration and economic operation, the construction and operation costs of energy stations can be minimized.

[0003] However, the impact of dynamic hydrogen pricing on the optimization performance of energy and transportation systems has rarely been studied. From the perspective of energy stations, a reasonable hydrogen price can increase the income of energy stations and promote the use of renewable energy for water electrolysis to produce hydrogen. From the perspective of hydrogen - powered vehicles, price signals can guide the refueling strategies of hydrogen - powered vehicles to meet predetermined service quality, such as cost minimization and emission reduction. Considering that the energy transaction between energy stations and hydrogen - powered vehicles is a competitive interaction, it can be described as a game problem.

[0004] At present, the game optimization between electric vehicles and charging stations can provide a reference. However, the existing game optimization of energy and transportation systems does not involve the dynamic pricing mechanism of hydrogen and the balanced hydrogen refueling strategy. Facing the random behavior and increasing penetration of high - pressure vehicles, it is unrealistic for the dispatching agency to uniformly manage each vehicle.

[0005] Therefore, how to reasonably guide the optimization performance of energy and transportation systems and rationally optimize the allocation of resources in combination with the operating characteristics of distributed energy stations and hydrogen - powered vehicles has become an urgent problem to be solved at present. Summary of the Invention

[0006] In view of the deficiencies of the above - mentioned prior art, the present invention provides an allocation optimization method for a low - carbon energy and transportation system under dynamic hydrogen pricing, which can reasonably guide the optimization performance of energy and transportation systems and rationally optimize the allocation of resources in combination with the operating characteristics of distributed energy stations and hydrogen - powered vehicles.

[0007] To solve the above - mentioned technical problems, the present invention adopts the following technical solutions:

[0008] The allocation optimization method for a low - carbon energy and transportation system under dynamic hydrogen pricing includes the following steps:

[0009] S1. Establish a dynamic hydrogen pricing mechanism related to the proportion of renewable energy in the energy supply;

[0010] S2. Combing the structure of the distributed energy station, establish an operation optimization model of the distributed energy station based on the dynamic hydrogen pricing mechanism; the objective function of the operation optimization model is to minimize the total cost; the operation constraint conditions include the operation constraints of the electro-thermal boiler equipment, the operation constraints of the electric cooling equipment, the electrolysis equipment constraints, the energy storage equipment constraints, and the operation constraints with the public power grid;

[0011] S3. Combining with the road model of the transportation system, establish a hydrogen refueling optimization model for hydrogen-powered vehicles under dynamic hydrogen prices;

[0012] S4. Integrate the operation optimization model of the distributed energy station in S2 and the hydrogen refueling optimization model of the hydrogen-powered vehicle in S3 to form a Stackelberg game model;

[0013] S5. Based on the Stackelberg game model, solve the two-layer optimization problem by equilibrium; the distributed energy station, as the leader, obtains its operation strategy, including the resource management plan of the distributed energy station and the formulated dynamic hydrogen price; the hydrogen-powered vehicle, as the follower, determines its optimal refueling strategy.

[0014] Preferably, in S1, the expression of the dynamic hydrogen pricing mechanism is:

[0015]

[0016] In the formula, β h (t) represents the unit price of selling hydrogen at time t; is the minimum allowable unit price of selling hydrogen; β h,adj (t) is the additional slack variable allowed for the unit price of selling hydrogen at time t.

[0017] Preferably, the calculation formula of the additional slack variable β h,adj (t) of the unit price of selling hydrogen at time t is:

[0018]

[0019] P Re (t) = P WT (t) + P PV (t);

[0020] In the formula, η h is the efficiency coefficient of the slack variable of the unit price of selling hydrogen; P Ele,in (t) is the input power of electrolytic hydrogen production at time t; P Re (t) is the total renewable energy output power at time t; P WT (t), P PV (t) are the wind power and photovoltaic power of the distributed energy station at time t respectively.

[0021] Preferably, in S2, the objective function for running the optimization model is:

[0022] min f DES = f ope + f env + f tra ;

[0023] In the formula, f DES is the total cost of the distributed energy station; f ope , f env and f tra are the operation and maintenance cost, environmental cost, and transaction cost of the distributed energy station, respectively.

[0024] Preferably, the calculation formula for the operation and maintenance cost f ope is:

[0025]

[0026] Ω k = {EB, EC, Ele, ES, TS, HS};

[0027] In the formula, is the unit price of the operation and maintenance cost of the equipment; P k (t) is the output power of equipment k at time t; Ω k represents the equipment set; EB, EC, Ele, ES, TS, and HS represent the electric heating boiler equipment, electric cooling equipment, electrolysis equipment, electric energy storage equipment, thermal energy storage equipment, and hydrogen energy storage equipment, respectively;

[0028] The calculation formula for the environmental cost f env is:

[0029]

[0030] In the formula, γ is the unit carbon emission cost; α is the carbon emission intensity of power purchase; P eb (t) is the power of purchasing electricity at time t;

[0031] The calculation formula for the transaction cost f tra is:

[0032]

[0033] In the formula, f eb and f es are the power purchase cost and power sale cost, respectively; f h is the transaction cost of hydrogen sale cost; β b (t) and β s (t) are the unit price of power purchase and the unit price of power sale at time t, respectively; P es (t) is the power of selling electricity at time t; βh (t) represents the unit price of hydrogen sales at time t; P HL (t) is the hydrogen refueling demand of hydrogen-powered vehicles at time t.

[0034] Preferably, in S2, the operation constraints of the electric boiler equipment in the operation optimization model are:

[0035] P EB (t) = η EB P EB,in (t);

[0036]

[0037] In the formula, P EB (t) is the output power of the electric boiler at time t; η EB is the heating efficiency; P EB,in (t) is the input power of the electric boiler at time t; and are the minimum power factor and the maximum power factor of the electric boiler respectively; C EB is the capacity of the electric boiler at time t;

[0038] The operation constraints of the electric cooling equipment are:

[0039] P EC (t) = η EC P EC,in (t);

[0040]

[0041] In the formula, P EC (t) is the output power of the electric cooling device at time t; η EC is the refrigeration efficiency; P EC,in (t) is the input power of the electric cooling device at time t; and are the minimum power factor and the maximum power factor of the electric cooling device respectively; C EC is the capacity of the electric cooling device at time t;

[0042] The constraints of the electrolysis equipment are:

[0043] P Ele (t) = η Ele P Ele,in (t);

[0044]

[0045] In the formula, P Ele (t) is the output power of the electrolysis device at time t; η Ele is the electrolysis efficiency; PEle,in P(t) is the input power of the electrolysis device at time t; and are the minimum power factor and the maximum power factor of the electrolysis device respectively; C Ele is the capacity of the electrolysis device at time t.

[0046] Preferably, the constraints of the energy storage device for the operation optimization model are:

[0047]

[0048] C k (t) = (1 - ζ k )C k (t - 1)+η k,c P k,c (t)-P k,d (t) / η k,d ;

[0049]

[0050]

[0051] P k,c (t)·P k,d (t)=0;

[0052] C k (1)=C k (T);

[0053] In the formula, C k is the installed capacity of energy storage device k; and are the minimum power factor and the maximum power factor of energy storage device k respectively; C k (t) is the available capacity of energy storage device k at time t; ζ k is the self-loss coefficient of energy storage device k; η k,c and η k,d are the charging efficiency and the discharging efficiency of energy storage device k respectively; P k,c (t) and P k,d (t) are the charging power and the discharging power of energy storage device k at time t respectively; and are the minimum power factor and the maximum power factor during charging of energy storage device k respectively; and are the minimum power factor and the maximum power factor during discharging of energy storage device k respectively; T is the time period scale of concern;

[0054] The operating constraints with the public power grid are:

[0055]

[0056]

[0057] P es (t)·P eb (t) = 0;

[0058]

[0059] P eb (t) - P es (t) + P WT (t) + P PV (t) + P ES,d (t) = P EB,in (t) + P EC,in (t) + P Ele,in (t) + P EL (t) + P ES,c (t);

[0060] P EB (t) + P TS,d (t) = P TL (t) + P TS,c (t);

[0061] P EC (t) = P CL (t);

[0062] P Ele (t) + P HS,d (t) = P HL (t) + P HS,c (t);

[0063]

[0064] Wherein, P eb (t) is the power of purchasing electricity at time t; is the maximum allowable power of purchasing electricity; P es (t) is the power of selling electricity at time t; is the maximum allowable power of selling electricity; β h (t) represents the unit price of selling hydrogen at time t; is the maximum allowable unit price of selling hydrogen; P eb (t) is the power of purchasing electricity at time t; P es (t) is the power of selling electricity at time t; P WT (t) is the wind power of the distributed energy station at time t; P PV (t) is the photovoltaic power of the distributed energy station at time t; P EB,in(t) is the input power of the electric boiler at time t; P EC,in (t) is the input power of the electric cooling device at time t; P Ele,in (t) is the input power of the electrolytic hydrogen production at time t; P EL (t) is the electrical load at time t; P ES,d (t) is the discharge power of the electrical energy storage device at time t; P ES,c (t) is the charging power of the electrical energy storage device at time t; P EB (t) is the output power of the electric boiler equipment at time t; P TS,d (t) is the discharge power of the thermal energy storage device at time t; P TS,c (t) is the charging power of the thermal energy storage device at time t; P EC (t) is the output power of the electric cooling equipment at time t; P CL (t) is the cooling load demand at time t; P Ele (t) is the output power of the electrolysis equipment at time t; P HS,d (t) is the discharge power of the hydrogen energy storage device at time t; P HS,c (t) is the charging power of the hydrogen energy storage device at time t; P HL (t) is the hydrogen refueling demand of the hydrogen-powered vehicle at time t; N m is the total number of hydrogen-powered vehicles; P m (t) is the hydrogen refueling demand of hydrogen-powered vehicle m at time t.

[0065] Preferably, in S3, the optimization objective function of the hydrogen refueling optimization model is:

[0066]

[0067] In the formula, represents the transportation cost of hydrogen-powered vehicle m on the path R: b → s; is the transaction cost for hydrogen-powered vehicle m to refuel from the distributed energy station; N m is the set of all paths.

[0068] Preferably, the transaction cost The calculation formula of is:

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, is the transaction cost for hydrogen-powered vehicle m to refuel at the distributed energy station, is the storage capacity of hydrogen-powered vehicle m, is the initial capacity of hydrogen-powered vehicle m, is the minimum / maximum refueling power of hydrogen-powered vehicle m, T m is the available refueling time for hydrogen-powered vehicle m; β h (t) represents the unit price of selling hydrogen at time t; P m (t) is the hydrogen refueling demand of hydrogen-powered vehicle m at time t.

[0074] Preferably, the transportation cost is calculated as:

[0075]

[0076] f (i,j) (t) = β0(L (i,j) / v (i,j) )(t));

[0077]

[0078]

[0079]

[0080]

[0081] In the formula, represents the transportation cost of hydrogen-powered vehicle m on the path R: b → s; f (i,j) (t) represents the transportation cost of hydrogen-powered vehicle m on the road (i, j) at time t; β0 is the transportation cost per unit time; L (i,j) is the distance of the road (i, j); v (i,j) (t) is the speed of hydrogen-powered vehicle m on the road (i, j) at time t; is the speed at zero vehicle flow on the road; ρ (i,j) (t) is the traffic density of the road (i, j) at time t; is the congestion density of the road (i, j) at time t; is the minimum remaining mileage of hydrogen-powered vehicle m; SL m (t) is the remaining mileage of hydrogen-powered vehicle m at time t, L R:b→s represents the minimum cost route of hydrogen-powered vehicle m under the path R: b → s.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] Using this method, after establishing a dynamic hydrogen pricing mechanism related to the proportion of renewable energy in the energy supply, an operation optimization model of a distributed energy station based on the dynamic hydrogen pricing mechanism is established; then, a hydrogen refueling optimization model of a hydrogen-powered vehicle under dynamic hydrogen prices is established in combination with the road model of the transportation system; and then, the operation optimization model of the distributed energy station and the hydrogen refueling optimization model of the hydrogen-powered vehicle are integrated to form a Stackelberg game model; in this way, a hierarchical game is formed between the distributed energy station in the energy layer and the hydrogen-powered vehicle in the traffic flow layer. Then, the two-layer optimization problem is solved by calculating the Stackelberg equilibrium of the game. While minimizing the cost of the entire system, a dynamic hydrogen pricing mechanism aiming to promote hydrogen production from renewable energy is realized. At the same time, a balanced refueling strategy for hydrogen-powered vehicles is realized.

[0084] In summary, this method can reasonably guide the optimization performance of the energy and transportation systems, and reasonably optimize and allocate resources in combination with the operating characteristics of distributed energy stations and hydrogen-powered vehicles. Brief Description of the Drawings

[0085] In order to make the objectives, technical solutions, and advantages of the invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings, where:

[0086] Figure 1 is a flowchart of the present invention. Detailed Embodiments

[0087] The following is a further detailed description through specific embodiments:

[0088] Embodiment:

[0089] As Figure 1 shown, an allocation optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing is disclosed in this embodiment, including the following steps:

[0090] S1. Establish a dynamic hydrogen pricing mechanism related to the proportion of renewable energy in the energy supply;

[0091] Specifically, when implemented, the expression of the dynamic hydrogen pricing mechanism is:

[0092]

[0093] In the formula, β h (t) represents the unit price of selling hydrogen at time t; is the minimum allowable unit price of selling hydrogen; β h,adj (t) is the additional slack variable allowed for the unit price of selling hydrogen at time t.

[0094] Among them, the calculation formula of the additional slack variable β h,adj (t) of the unit price of selling hydrogen at time t is:

[0095]

[0096] P Re (t) = P WT (t)+P PV (t);

[0097] Where η h is the efficiency coefficient of the slack variable of the unit price of hydrogen sold; P Ele,in (t) is the input power of hydrogen production by electrolysis at time t; P Re (t) is the power output of total renewable energy at time t; P WT (t), P PV (t) are the wind power and photovoltaic power of the distributed energy station at time t.

[0098] S2. Sort out the structure of distributed energy stations and establish an operation optimization model of distributed energy stations based on a dynamic hydrogen pricing mechanism; the objective function of the operation optimization model is to minimize the total cost; the operation constraints include electric boiler equipment operation constraints, electric cooling equipment operation constraints, electrolysis equipment constraints, energy storage equipment constraints, and operation constraints with the public power grid.

[0099] In specific implementation, the objective function of running the optimization model is:

[0100] min f DES =f ope +f env +f tra ;

[0101] In the formula, f DES is the total cost of the distributed energy station; f ope 、f env and f tra They are the operation and maintenance cost, environmental cost and transaction cost of distributed energy stations respectively.

[0102] Among them, the operation and maintenance cost f ope The calculation formula is:

[0103]

[0104] Ω k ={EB,EC,Ele,ES,TS,HS};

[0105] In the formula, P is the unit price of equipment operation and maintenance cost; k (t) is the output power of device k at time t; Ω kIndicates a set of devices; EB, EC, Ele, ES, TS, and HS represent an electric heating boiler device, an electric cooling device, an electrolysis device, an electric energy storage device, a thermal energy storage device, and a hydrogen energy storage device, respectively;

[0106] Environmental cost f env The calculation formula is:

[0107]

[0108] In the formula, γ is the unit carbon emission cost; α is the carbon emission intensity of power purchase; P eb (t) is the power of purchasing electricity at time t;

[0109] Transaction cost f tra The calculation formula is:

[0110]

[0111] In the formula, f eb and f es are the power purchase cost and the power sale cost, respectively; f h is the transaction cost of hydrogen sale cost; β b (t) and β s (t) are the unit power purchase price and the unit power sale price at time t, respectively; P es (t) is the power of selling electricity at time t; β h (t) represents the unit price of selling hydrogen at time t; P HL (t) is the hydrogen refueling demand of the hydrogen-powered vehicle at time t.

[0112] The operation constraint of the electric heating boiler device in the operation optimization model is:

[0113] P EB (t) = η EB P EB,in (t);

[0114]

[0115] In the formula, P EB (t) is the output power of the electric heating boiler at time t; η EB is the heating efficiency; P EB,in (t) is the input power of the electric heating boiler at time t; and are the minimum power factor and the maximum power factor of the electric heating boiler, respectively; C EB is the capacity of the electric heating boiler at time t.

[0116] The operation constraint of the electric cooling device is:

[0117] P EC (t) = ηEC P EC,in (t);

[0118]

[0119] Wherein, P EC (t) is the output power of the electric cooling device at time t; η EC is the refrigeration efficiency; P EC,in (t) is the input power of the electric cooling device at time t; and are the minimum power factor and the maximum power factor of the electric cooling device respectively; C EC is the capacity of the electric cooling device at time t.

[0120] The electrolysis equipment constraint is:

[0121] P Ele (t) = η Ele P Ele,in (t);

[0122]

[0123] Wherein, P Ele (t) is the output power of the electrolysis device at time t; η Ele is the electrolysis efficiency; P Ele,in (t) is the input power of the electrolysis device at time t; and are the minimum power factor and the maximum power factor of the electrolysis device respectively; C Ele is the capacity of the electrolysis device at time t.

[0124] The energy storage equipment constraint is:

[0125]

[0126] C k (t) = (1 - ζ k )C k (t - 1) + η k,c P k,c (t) - P k,d (t) / η k,d ;

[0127]

[0128]

[0129] P k,c (t)·P k,d (t) = 0;

[0130] C k(1) = C k (T);

[0131] Wherein, C k is the installed capacity of energy storage device k; and are respectively the minimum power factor and the maximum power factor of energy storage device k; C k (t) is the available capacity of energy storage device k at time t; ζ k is the self-loss coefficient of energy storage device k; η k,c and η k,d are respectively the charging efficiency and the discharging efficiency of energy storage device k; P k,c (t) and P k,d (t) are respectively the charging power and the discharging power of energy storage device k at time t; and are respectively the minimum power factor and the maximum power factor during charging of energy storage device k; and are respectively the minimum power factor and the maximum power factor during discharging of energy storage device k; T is the time period scale of concern.

[0132] The operating constraints with the public power grid are:

[0133]

[0134]

[0135] P es (t) · P eb (t) = 0;

[0136]

[0137] P eb (t) - P es (t) + P WT (t) + P PV (t) + P ES,d (t) = P EB,in (t) + P EC,in (t) + P Ele,in (t) + P EL (t) + P ES,c (t);

[0138] P EB (t) + P TS,d (t) = P TL (t) + P TS,c (t);

[0139] P EC (t) = P CL (t);

[0140] P Ele (t) + P HS,d (t) = P HL (t) + P HS,c (t);

[0141]

[0142] In the formula, P eb (t) is the power of purchasing electricity at time t; is the maximum allowable power of purchasing electricity; P es (t) is the power of selling electricity at time t; is the maximum allowable power of selling electricity; β h (t) represents the unit price of selling hydrogen at time t; is the maximum allowable unit price of selling hydrogen; P eb (t) is the power of purchasing electricity at time t; P es (t) is the power of selling electricity at time t; P WT (t) is the wind power of the distributed energy station at time t; P PV (t) is the photovoltaic power of the distributed energy station at time t; P EB,in (t) is the input power of the electric boiler at time t; P EC,in (t) is the input power of the electric cooling device at time t; P Ele,in (t) is the input power of electrolytic hydrogen production at time t; P EL (t) is the electricity load at time t; P ES,d (t) is the discharge power of the electrical energy storage device at time t; P ES,c (t) is the charging power of the electrical energy storage device at time t; P EB (t) is the output power of the electric boiler equipment at time t; P TS,d (t) is the discharge power of the thermal energy storage device at time t; P TS,c (t) is the charging power of the thermal energy storage device at time t; P EC (t) is the output power of the electric cooling equipment at time t; P CL (t) is the refrigeration load demand at time t; P Ele (t) is the output power of the electrolysis equipment at time t; P HS,d (t) is the discharge power of the hydrogen energy storage device at time t; P HS,c (t) is the charging power of the hydrogen energy storage device at time t; P HL (t) is the hydrogen refueling demand of the hydrogen-powered vehicle at time t; N m is the total number of hydrogen-powered vehicles; P m (t) is the hydrogen refueling demand of hydrogen-powered vehicle m at time t.

[0143] S3. Combine with the road model of the transportation system to establish a hydrogen refueling optimization model for hydrogen-powered vehicles under dynamic hydrogen prices.

[0144] In specific implementation, when constructing the traffic road network model, a graph G = {V, E} with weights and directions is used to describe the traffic road model. Road intersections constitute the vertex set V = {1, 2,..., i, j,...}, and road segments constitute the edge set E = {(1, 2),...,(i, j),...}. The edge weight f (i,j) represents the transportation cost of the hydrogen-powered vehicle on the road (i, j), and the edge direction represents the road traffic direction.

[0145] The optimization objective function of the hydrogen refueling optimization model is:

[0146]

[0147] In the formula, represents the transportation cost of hydrogen-powered vehicle m on the path R: b → s; is the transaction cost for hydrogen-powered vehicle m to refuel from the distributed energy station; N m is the set of all paths;

[0148] Transaction cost The calculation formula of is:

[0149]

[0150]

[0151]

[0152]

[0153] In the formula, is the transaction cost for hydrogen-powered vehicle m to refuel from the distributed energy station, is the storage capacity of hydrogen-powered vehicle m, is the initial capacity of hydrogen-powered vehicle m, is the minimum / maximum refueling power of hydrogen-powered vehicle m, T m is the available refueling time of hydrogen-powered vehicle m; β h (t) represents the unit price of selling hydrogen at time t; P m (t) is the hydrogen refueling demand of hydrogen-powered vehicle m at time t.

[0154] Transportation cost The calculation formula of is:

[0155]

[0156] f (i,j) $(t) = \beta_0 (L (i,j) / v (i,j) $(t))$;

[0157]

[0158]

[0159]

[0160]

[0161] wherein, represents the transportation cost of the hydrogen-powered vehicle m on the path R: b → s; f (i,j) $(t)$ represents the transportation cost of the hydrogen-powered vehicle m on the road (i, j) at time t; $\beta_0$ is the transportation cost per unit time; L (i,j) is the distance of the road (i, j); v (i,j) $(t)$ is the speed of the hydrogen-powered vehicle m on the road (i, j) at time t; is the speed at zero vehicle flow on the road; $\rho$ (i,j) $(t)$ is the traffic density of the road (i, j) at time t; is the jam density of the road (i, j) at time t. Specifically, in implementation, it can be taken as 143 veh / h; is the minimum remaining mileage of the hydrogen-powered vehicle m; SL m $(t)$ is the remaining mileage of the hydrogen-powered vehicle m at time t; L R:b→s represents the minimum-cost route of the hydrogen-powered vehicle m under the path R: b → s.

[0162] S4. The operation optimization model of the distributed energy station in S2 and the hydrogen refueling optimization model of the hydrogen-powered vehicle in S3 constitute a Stackelberg game model.

[0163] S5. Solve the bi-level optimization problem based on the equilibrium of the Stackelberg game model; the distributed energy station, as the leader, obtains its operation strategy, including the resource management plan of the distributed energy station and the formulated dynamic hydrogen price; the hydrogen-powered vehicle, as the follower, determines its optimal refueling strategy.

[0164] Using this method, after establishing a dynamic hydrogen pricing mechanism related to the proportion of renewable energy in the energy supply, an operation optimization model of a distributed energy station based on the dynamic hydrogen pricing mechanism is established; then, a hydrogen refueling optimization model of a hydrogen-powered vehicle under dynamic hydrogen prices is established by combining with the road model of the transportation system; and then, a Stackelberg game model is constructed by integrating the operation optimization model of the distributed energy station and the hydrogen refueling optimization model of the hydrogen-powered vehicle; in this way, a hierarchical game is formed between the distributed energy station in the energy layer and the hydrogen-powered vehicle in the flow layer. Since the random behavior reflected in the hydrogen refueling demand of the hydrogen-powered vehicle will affect the operation optimization of the distributed energy station, including the formulation of hydrogen power pricing. Therefore, the hydrogen-powered vehicle reports the refueling demand (such as hydrogen refueling time) for the next day to the distributed energy station. Considering the random behavior of the hydrogen-powered vehicle, the distributed energy station completes equivalent aggregation based on the information reported by the hydrogen-powered vehicle. Then, the two-layer optimization problem is solved by calculating the Stackelberg equilibrium of the game. While minimizing the cost of the entire system, a dynamic hydrogen pricing mechanism aimed at promoting hydrogen production from renewable energy is realized. At the same time, a balanced refueling strategy for hydrogen-powered vehicles is realized. This method can reasonably guide the optimization performance of the energy and transportation systems, and reasonably optimize and allocate resources in combination with the operation characteristics of distributed energy stations and hydrogen-powered vehicles.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the technical solutions. Those of ordinary skill in the art should understand that any modifications or equivalent replacements made to the technical solutions of the present invention without departing from the purpose and scope of the present technical solution shall be covered by the scope of the claims of the present invention.

Claims

1. An allocation optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing, characterized in that, It includes the following steps: S1. Establish a dynamic hydrogen pricing mechanism related to the proportion of renewable energy in the energy supply; S2. Sort out the structure of the distributed energy station and establish an operation optimization model of the distributed energy station based on the dynamic hydrogen pricing mechanism; the objective function of the operation optimization model is to minimize the total cost; the operation constraint conditions include the operation constraints of the electric heating boiler equipment, the operation constraints of the electric cooling equipment, the electrolysis equipment constraints, the energy storage equipment constraints, and the operation constraints with the public power grid; S3. Combine with the traffic system road model to establish a hydrogen refueling optimization model for hydrogen-powered vehicles under dynamic hydrogen prices; S4. Integrate the operation optimization model of the distributed energy station in S2 and the hydrogen refueling optimization model of the hydrogen-powered vehicle in S3 to form a Stackelberg game model; S5. Based on the Stackelberg game model, solve the two-layer optimization problem by equilibrium; the distributed energy station, as the leader, obtains its operation strategy, including the resource management plan of the distributed energy station and the formulated dynamic hydrogen price; the hydrogen-powered vehicle, as the follower, determines its optimal refueling strategy.

2. The distribution optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 1, wherein: In S1, the expression of the dynamic hydrogen pricing mechanism is: where β h (t) represents the unit price of hydrogen sold at time t; is the minimum allowable unit price of hydrogen sold; β h,adj (t) is the additional slack variable allowed for the unit price of hydrogen sold at time t.

3. The allocation optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 2, wherein: The additional slack variable β allowed for the unit price of hydrogen sales at time t h,adj (t) is calculated as follows: P Re P(t) = WT P(t) + PV P(t); where η h is the efficiency coefficient of the price slack variable of the hydrogen sales unit; P Ele,in (t) is the input power of electrolytic hydrogen production at time t; P Re (t) is the power output of the total renewable energy at time t; P WT (t), P PV (t) are the wind power and photovoltaic power of the distributed energy station at time t, respectively.

4. The distribution optimization method of a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 3, characterized in that: In S2, the objective function of the operation optimization model is: minf DES = f ope + f env + f tra ; where f DES is the total cost of the distributed energy station; f ope , f env and f tra are the operation and maintenance cost, environmental cost and trading cost of the distributed energy station, respectively.

5. The distribution optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 4, characterized in that: Operation and maintenance cost f ope The calculation formula is as follows: Ω k = {EB, EC, Ele, ES, TS, HS}; In the formula, is the unit price of the operation and maintenance cost of the equipment; P k (t) is the output power of equipment k at time t; Ω k represents the equipment set; EB, EC, Ele, ES, TS, and HS represent the electric heating boiler equipment, electric cooling equipment, electrolysis equipment, electric energy storage equipment, thermal energy storage equipment, and hydrogen energy storage equipment respectively; Environmental cost f env The calculation formula is as follows: where γ is the unit carbon emission cost; α is the carbon emission intensity of electricity purchase; P eb (t) is the power of purchasing electricity at time t; Transaction cost f tra The calculation formula is as follows: where f eb and f es are the electricity purchase cost and the electricity sale cost respectively; f h is the hydrogen sale cost transaction cost; β b (t) and β s (t) are the unit electricity purchase price and the unit electricity sale price at time t respectively; P es P(t) is the power of electricity sales at time t; β h β(t) represents the unit price of hydrogen sales at time t; P HL P(t) is the hydrogen refueling demand of hydrogen-powered vehicles at time t.

6. The distribution optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 5, characterized in that: In S2, the operation constraint of the electric heating boiler equipment of the operation optimization model is: P EB (t) = η EB P EB,in (t); Wherein, P EB (t) is the output power of the electric boiler at time t; η EB is the heating efficiency; P EB,in (t) is the input power of the electric boiler at time t; and are the minimum power factor and the maximum power factor of the electric boiler respectively; C EB is the capacity of the electric boiler at time t; The operation constraint of the electric cooling equipment is: P EC (t) = η EC P EC,in (t); where P EC (t) is the output power of the electric cooling device at time t; η EC is the refrigeration efficiency; P EC,in (t) is the input power of the electric cooling device at time t; and are the minimum power factor and the maximum power factor of the electric cooling device respectively; C EC is the capacity of the electric cooling device at time t; The electrolysis equipment constraint is: P Ele (t) = η Ele P Ele,in (t); Where P Ele (t) is the output power of the electrolysis device at time t; η Ele is the electrolysis efficiency; P Ele,in (t) is the input power of the electrolysis device at time t; and are the minimum power factor and the maximum power factor of the electrolysis device respectively; C Ele is the capacity of the electrolysis device at time t.

7. The allocation optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 6, characterized in that: The energy storage equipment constraint of the operation optimization model is: C k (t) = (1 - ζ k )C k (t - 1) + η k,c P k,c (t) - P k,d (t) / η k,d ; P k,c (t)·P k,d (t) = 0; C k (1) = C k (T); where, C k is the installed capacity of energy storage device k; and are the minimum power factor and the maximum power factor of energy storage device k respectively; C k (t) is the available capacity of energy storage device k at time t; ζ k is the self-loss coefficient of energy storage device k; η k,c and η k,d are the charging efficiency and discharging efficiency of energy storage device k respectively; P k,c (t) and P k,d (t) are the charging efficiency and discharging power of energy storage device k at time t respectively; and are the minimum power factor and maximum power factor during the charging of energy storage device k respectively; and are the minimum power factor and maximum power factor during the discharging of energy storage device k respectively; T is the time period scale of concern; The operation constraint with the public power grid is: P es (t)·P eb (t) = 0; P eb (t)-P es (t)+P WT (t)+P PV (t)+P ES,d (t)=P EB,in (t)+P EC,in (t)+P Ele,in (t)+P EL (t)+P ES,c (t); P EB (t) + P TS,d (t) = P TL (t) + P TS,c (t); P EC P(t) = CL P(t); P Ele (t) + P HS,d (t) = P HL (t) + P HS,c (t); Wherein, P eb (t) is the power of purchasing electricity at time t; is the maximum allowable power of purchasing electricity; P es (t) is the power of selling electricity at time t; is the maximum allowable power of selling electricity; β h (t) represents the unit price of selling hydrogen at time t; is the maximum allowable unit price of selling hydrogen; P eb (t) is the power of purchasing electricity at time t; P es (t) is the power of selling electricity at time t; P WT (t) is the wind power of the distributed energy station at time t; P PV (t) is the photovoltaic power of the distributed energy station at time t; P EB,in (t) is the input power of the electric boiler at time t; P EC,in (t) is the input power of the electric cooling device at time t; P Ele,in (t) is the input power of electrolytic hydrogen production at time t; P EL (t) is the electricity load at time t; P ES,d (t) is the discharge power of the electrical energy storage device at time t; P ES,c (t) is the charging power of the electrical energy storage device at time t; P EB (t) is the output power of the electric boiler equipment at time t; P TS,d (t) is the discharge power of the thermal energy storage device at time t; P TS,c (t) is the charging power of the thermal energy storage device at time t; P EC (t) is the output power of the electric cooling equipment at time t; P CL (t) is the refrigeration load demand at time t; P Ele (t) is the output power of the electrolysis equipment at time t; P HS,d (t) is the discharge power of the hydrogen energy storage device at time t; P HS,c (t) is the charging power of the hydrogen energy storage device at time t; P HL (t) is the hydrogen refueling demand of the hydrogen-powered vehicle at time t; N m is the total number of hydrogen-powered vehicles; P m (t) is the hydrogen refueling demand of hydrogen-powered vehicle m at time t.

8. The allocation optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 7, characterized in that: In S3, the optimization objective function of the hydrogen refueling optimization model is: In the formula, represents the transportation cost of the hydrogen-powered vehicle m on the path R: b → s; is the transaction cost for the hydrogen-powered vehicle m to refuel at the distributed energy station; N m is the set of all paths.

9. The allocation optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 8, characterized in that: Transaction cost The calculation formula is as follows: In the formula, is the transaction cost for the hydrogen-powered vehicle m to refuel from the distributed energy station, is the storage capacity of the hydrogen-powered vehicle m, is the initial capacity of the hydrogen-powered vehicle m, is the minimum / maximum refueling power of the hydrogen-powered vehicle m, T m is the available refueling time of the hydrogen-powered vehicle m; β h (t) represents the unit price of selling hydrogen at time t; P m (t) is the hydrogen refueling demand of the hydrogen-powered vehicle m at time t.

10. The distribution optimization method for a low-carbon energy and transportation system under dynamic hydrogen pricing according to claim 9, characterized in that: Transportation cost The calculation formula is as follows: f (i,j) (t) = β0(L (i,j) / v (i,j) (t)); In the formula, represents the transportation cost of the hydrogen-powered vehicle m on the path R: b → s; f (i,j) (t) represents the transportation cost of the hydrogen-powered vehicle m on the road (i, j) at time t; β0 is the transportation cost per unit time; L (i,j) is the distance of the road (i, j); v (i,j) (t) is the speed of the hydrogen-powered vehicle m on the road (i, j) at time t; is the speed at zero vehicle flow on the road; ρ (i,j) (t) is the traffic density of the road (i, j) at time t; is the congestion density of the road (i, j) at time t; is the minimum remaining mileage of the hydrogen-powered vehicle m; SL m (t) is the remaining mileage of the hydrogen-powered vehicle m at time t, L R:b→s represents the minimum-cost route of the hydrogen-powered vehicle m under the path R: b → s.

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