Coordinated optimization method considering hydrogen energy production-storage-transportation system

Abstract

The invention belongs to the technical field of power grid dispatching, and particularly relates to a hydrogen energy generation-storage-transportation system coordinated optimization method, which comprises the following steps of: 1, establishing a hydrogen energy generation-storage-transportation coordinated optimization model; 2, establishing a hydrogen energy transportation path optimization model; 3, establishing a hydrogen energy system optimization model; 4, designing a two-stage robust model considering the wind and light uncertainty; 5, example analysis is conducted, the structure is reasonable, in the using process, the system operation cost is reduced by 13.98%, the carbon emission is reduced by 20.95%, the total operation cost of the mobile hydrogen energy system can be effectively reduced through the collaborative optimization method, and the flexibility of scheduling decision making is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a coordinated optimization method for a hydrogen energy production-storage-transportation system. Background Art

[0002] Accelerate the energy transformation and consumption revolution, and build a clean, low-carbon, safe and efficient modern energy system. As a clean, low-carbon, scalable, flexible and efficient secondary energy carrier, hydrogen energy will play a key role in the low-carbon transformation of the future energy system. On the one hand, the use of wind, light and other new energy sources to electrolyze water to produce hydrogen in areas with surplus new energy can effectively reduce the cost of hydrogen production, improve the local consumption capacity of new energy, and support the flexible peak and frequency regulation of high-proportion new energy power systems; on the other hand, hydrogen energy has broad application prospects in transportation, chemical industry, construction and other fields. For example, hydrogen fuel vehicles have the advantages of high energy density, low noise, clean and environmental protection, and fast fuel filling, and will become one of the most important development directions in the future. The popularization and utilization of hydrogen energy in the terminal field can promote the deep decarbonization of society and play an important supporting role in the green transformation and development of energy.

[0003] As a new type of energy storage technology, hydrogen energy storage has the advantages of low carbon and clean, high energy density, long storage time and space transportation, and is regarded as a large-capacity energy storage with great potential. In the prior art, a nonlinear operation model of the electrolyzer and hydrogen fuel cell is constructed, and then a low-carbon optimization operation model of the hydrogen-containing integrated energy system is proposed. In the prior art, a mixed integer linear model of the electrolyzer taking into account the start-stop state switching of the variable load is also proposed to achieve fine control of the electric hydrogen production process. In the prior art, for the electric-hydrogen hybrid energy storage system, an optimization operation method considering the dynamic efficiency characteristics of the electrolyzer is proposed to give full play to the complementary advantages of electric-hydrogen energy storage and promote the efficient and economic consumption of new energy. The above studies show that considering the dynamic characteristics of the electrolyzer can effectively improve the economy of the system and promote the consumption of new energy. However, the above studies mainly focus on modeling and optimizing the hydrogen production and storage process in the hydrogen energy system, without considering the transportation process of hydrogen. In the actual hydrogen energy system, the two-stage robust optimization of the hydrogen production station and the hydrogen users are often located in different geographical locations. Therefore, it is necessary to consider hydrogen transportation during the optimization operation of the hydrogen energy system.

[0004] The transportation of hydrogen by hydrogen tube trailer (HT) deeply couples the traditional hydrogen energy system with the transportation network to form a mobile hydrogen energy system (MHES) with spatiotemporal dispatchability. In MHES, hydrogen is prepared by water electrolysis at the hydrogen production station and then filled into a high-pressure hydrogen storage tank. It is then transported to various hydrogen demand points, such as hydrogen refueling stations (HRS), via HT, forming a hydrogen supply chain that includes the entire process of hydrogen production, storage, and transportation. In the prior art, the mobility of hydrogen energy is utilized to establish and solve the EH-IES optimization model that includes the HT transportation cost, thereby reducing the total operating cost and improving the operational flexibility of the power system. In the prior art, a model and method for the coordinated operation of HES and EPS is proposed in the integrated power and hydrogen system. The optimal scheduling of the hydrogen transportation network is achieved by optimizing the scheduling of the hydrogen transportation network considering cost and capacity constraints.

[0005] Existing research has not explored the potential and analyzed the benefits of the coordinated optimization of hydrogen production, storage and transportation in MHES. In actual MHES operation, the hydrogen demand of HRS is highly uncertain. Considering the coordination of hydrogen production, storage and transportation can improve the economy and stability of the system. In the prior art, a combined heat and power microgrid system was constructed, and a two-stage optimization scheduling method was proposed to achieve efficient coordinated utilization of electricity, heat and hydrogen energy, and improve energy utilization and system economy. In the prior art, an optimization scheduling method for an electric-hydrogen integrated energy system considering the uncertainty of hydrogen energy in multiple scenarios was proposed. By establishing a mathematical model including hydrogen storage and transportation, the goal of minimizing system operating costs and maximizing renewable energy consumption was achieved. In order to improve the power quality of the distribution network while taking into account the economy, the electric-hydrogen hybrid energy storage system was optimized with the goal of minimizing the annual operating cost of the system, and the voltage stability index was introduced for evaluation. However, the traditional optimization methods currently studied are often based on deterministic assumptions, ignoring the impact of these uncertainties, which may lead to poor performance of the optimization results in practical applications. The hydrogen energy system involves multiple links, including the production, storage, transportation and application of hydrogen. There are varying degrees of uncertainty in each link, such as the volatility of renewable energy, changes in market demand, risks in transportation routes, etc. To address this problem, a two-stage robust optimization method for dealing with uncertainty is proposed, which is of great significance for improving the operating efficiency and reliability of hydrogen energy systems.

[0006] In summary, the current research on hydrogen production, storage and transportation has achieved important results, but the following problems still exist: 1) The dynamic characteristics of the electrolyzer are an important factor in the operation of the electric hydrogen system. The current research on the hydrogen energy system has not comprehensively considered the effect of its dynamic characteristics on the system. 2) The current research on the collaborative optimization of MHES has not considered the improvement of system flexibility by the collaborative participation of hydrogen transportation. 3) The uncertainty of MHES requires a more appropriate optimization method to deal with the system. The optimization methods of many current studies ignore the uncertainty of MHES.

[0007] Based on the above problems, we propose a coordinated optimization method taking into account the hydrogen production, storage and transportation system. Summary of the invention

[0008] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0009] In view of the problems existing in the prior art, the present invention is proposed.

[0010] Therefore, the purpose of the present invention is to provide a coordinated optimization method taking into account the hydrogen energy production, storage and transportation system, which can reduce the system operating cost by 13.98% and the carbon emissions by 20.95% during use. The proposed collaborative optimization method can effectively reduce the total operating cost of the mobile hydrogen energy system and improve the flexibility of scheduling decisions.

[0011] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0012] A method for coordinated optimization of a hydrogen production-storage-transportation system, comprising the following steps:

[0013] Step 1: Establish a coordinated optimization model for hydrogen production, storage and transportation;

[0014] 1. Establish a hydrogen production station model;

[0015] 2. Establish a hydrogen storage tank model;

[0016] 3. Establish HRS model;

[0017] 4. Hydrogen energy transportation model;

[0018] Step 2: Establish a hydrogen transportation route optimization model;

[0019] 1. Establish a hydrogen transportation network model;

[0020] 2. Design an equivalent optimal path search algorithm for hydrogen transportation considering congestion;

[0021] Step 3: Establish a hydrogen energy system optimization model;

[0022] 1. Design objective function;

[0023] 2. Design constraints;

[0024] Step 4: Design a two-stage robust model considering the uncertainty of wind and solar power;

[0025] 1. Design a two-stage standard form;

[0026] 2. Design C&CG solution algorithm;

[0027] Step 5: Example analysis:

[0028] 1. Analysis of wind power curtailment and consumption results;

[0029] 2. Analysis of the impact of dynamic characteristics of electrolytic cells;

[0030] 3. Coordinated analysis of production, storage and transportation;

[0031] 4. Robust analysis of uncertainty.

[0032] As a preferred scheme of the method for coordinated optimization of hydrogen production, storage and transportation system taking into account the present invention, wherein: in the step one, establishing the hydrogen production station model also includes establishing a dynamic operation model of the electrolyzer taking into account Faraday efficiency loss.

[0033] As a preferred solution of the method for coordinated optimization of hydrogen production, storage and transportation system described in the present invention, in step three, the design constraints include energy balance constraints and constraints on each unit.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The proposed optimization of system operation by considering the dynamic efficiency characteristics of the electrolyzer can make hydrogen energy storage better follow the fluctuation of new energy and effectively improve the consumption of new energy. At the same time, the regulation of electric energy storage can be used to avoid PEMEL running in a low-efficiency and high-energy consumption state. After considering the dynamic characteristics of the electrolyzer, the system's new energy consumption increased by 3.37%, and the consumption rate increased by 3.02%. After considering the dynamic characteristics of the electrolyzer in typical summer / winter scenarios, the PEMEL energy consumption cost decreased by 23.6% and 45.7% respectively, and the cost decreased by 1.26% and 1.09% accordingly. It effectively improves the utilization rate of new energy, reduces the total operating cost of the system, and meets the energy needs of the system more economically and efficiently.

[0036] (2) The present invention proposes a production, storage and transportation coordination model that takes into account the optimal path selection. Through the coordinated scheduling of hydrogen production and storage at new energy hydrogen production and storage stations and HT hydrogen transportation, the total operating cost of MHES is reduced by about 17.21%, effectively reducing the system operating cost; after considering the optimal path selection, the penalty cost for abandonment of new energy power is reduced by about 35.08%, effectively matching the uncertainty of wind and solar power output and improving the level of new energy consumption.

[0037] (3) Model uncertainty greatly affects the economic efficiency of the system. When the uncertainty parameters are small, the system benefits are high, the cost is low, but the model is robust. When the uncertainty parameters are large, the system benefits are low, the cost is high, but the model is robust. Therefore, in a specific system, the economy, robustness, and conservatism of the model should be comprehensively considered to select appropriate uncertainty parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below in combination with the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0039] Figure 1 This is the structural diagram of EHH-IES of the present invention;

[0040] Figure 2 The working principle diagram of PEMEL of the present invention is shown in FIG.

[0041] Figure 3 The efficiency and hydrogen production curve of the system of the present invention;

[0042] Figure 4 This is the flow chart of the C&CG algorithm of the present invention;

[0043] Figure 5 This is a diagram of the operation of electric-hydrogen hybrid energy storage according to different schemes of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0048] The present invention provides the following technical solutions: a coordinated optimization method for hydrogen production, storage and transportation system, which reduces the system operating cost by 13.98% and carbon emissions by 20.95% during use. The proposed coordinated optimization method can effectively reduce the total operating cost of the mobile hydrogen energy system and improve the flexibility of scheduling decisions;

[0049] Example 1

[0050] Step 1: Hydrogen production, storage and transportation synergistic optimization model;

[0051] In order to solve the problem of power balance and difficulty in absorbing electricity brought about by large-scale grid connection of new energy, the present invention constructs an electric-hydrogen hybrid energy storage system, the schematic diagram of which is shown in Figure 1 The power supply side is composed of thermal power, photovoltaic power and wind power, which provide energy for the system; the load side includes electric load and hydrogen load; the energy storage part is an electric-hydrogen hybrid energy storage system composed of battery energy storage and hydrogen energy storage, among which hydrogen energy storage is mainly composed of key equipment such as electrolyzers, hydrogen storage tanks and fuel cells, and electric energy storage is batteries.

[0052] 1Hydrogen production station model

[0053] As the core equipment of the hydrogen energy system, the operation model of the electrolyzer is the basis for optimizing the system operation. The proton exchange membrane electrolyzer has the advantages of fast response speed and rapid start and stop, which can effectively cope with wind and solar fluctuations. Therefore, the present invention establishes its dynamic operation model based on the dynamic operation characteristics and mechanism of PEMEL.

[0054] 1.1 Dynamic operation model of electrolyzer taking into account Faraday efficiency loss

[0055] The working principle of water electrolysis to produce hydrogen is as follows

[0056] 2H 2 O+electricity+heat→2H 2 +O 2 (1)

[0057] The working principle diagram of PEMEL is as follows Figure 2 As shown;

[0058] PEMEL can quickly respond to changes in the surplus electricity of renewable energy by adjusting the current, and realize flexible control of hydrogen production. The relationship between PEMEL operating voltage and current density is shown below.

[0059]

[0060] Where: U сеll,t is the operating voltage of PEMEL at time t; t is the operating interval; U rev is the reversible voltage; T is the operating temperature; R and F are the ideal gas constant and Faraday constant respectively; P H2 and P O2 are the partial pressures of hydrogen and oxygen respectively; R cell is the internal equivalent ohmic resistance; α an and α cat are the anode and cathode charge transfer coefficients respectively; i an and i cat are the anode and cathode exchange current densities, respectively; i cell,t is the current density.

[0061] According to the working principle of PEMEL and the relationship between hydrogen production and operating current, PEMEL power consumption and hydrogen production As shown below

[11] :

[0062]

[0063] Where: is the hydrogen production of PEMEL at time t; I cell,t is the PEMEL operating current, where N E k is the number of PEMELs connected in series, which is related to the maximum capacity of PEMEL; 1 Indicates the conversion coefficient; η f,t is the Faraday efficiency at time t, which is related to the generation of hydrogen and oxygen in the PEMEL, as shown in formula (5).

[0064] The Faraday efficiency represents the ideal charge Q required to produce a given amount of hydrogen. id With the actual charge Q re The specific ratios are as follows.

[0065]

[0066] Ideally, the Faraday efficiency is 1. However, PEMEL is permeable, and hydrogen and oxygen diffuse during the mass transfer process. Especially at low current density, the gas crossover phenomenon is more obvious, which will cause Faraday loss and significantly reduce the Faraday efficiency. However, in existing studies, the Faraday efficiency is often regarded as a constant, which is obviously inconsistent with the actual operation of PEMEL.

[0067] The Faraday efficiency can be further expressed as a function of membrane flux density and hydrogen production density as shown below.

[0068]

[0069] Where: is the hydrogen production density, as shown in formula (7); is the permeation flux density of hydrogen; is the oxygen permeation flux density.

[0070]

[0071] The diffusion of hydrogen on the membrane during PEMEL electrolysis is caused by the thermal motion of molecules and the concentration difference in the anode and cathode catalyst layers. Therefore, the permeation flux density of hydrogen is the sum of the diffusion flux density and the permeation flux density caused by the pressure difference. Based on Fick's law, the permeation flux density function of hydrogen is as follows.

[0072]

[0073] Where: is the hydrogen permeability in the diffusion mechanism; is the hydrogen permeability caused by the pressure difference; are the cathode hydrogen partial pressure and the anode oxygen partial pressure in the catalyst layer; d is the membrane thickness. Assuming that the hydrogen permeation from the cathode catalyst layer to the cathode gas outlet is affected by the pressure difference, since the gas production density is related to the current, the hydrogen partial pressure in the cathode catalyst layer can be approximated as:

[0074]

[0075] Where: A H2 is the fitting parameter for the increase in hydrogen partial pressure in the cathode catalyst layer, which depends on the structure, thickness and permeability of the catalyst layer.

[0076] Since the pressure difference has little effect on the permeation of oxygen and can be ignored, the oxygen partial pressure in the anode catalyst layer is as follows:

[0077]

[0078] Where: A O2 is the fitting parameter for the increase in oxygen partial pressure in the anode catalyst layer.

[0079] The oxygen permeation in PEMEL mainly comes from the diffusion mechanism. According to Fick's law, the oxygen permeation flux density is as follows:

[0080]

[0081] Where: is the oxygen permeability in the diffusion mechanism. Substituting equations (7)-(9) into equation (6), we can obtain:

[0082]

[0083] The operating efficiency of PEMEL varies with the input power of the electrolyzer. Figure 3 The relationship between the operating efficiency, Faraday efficiency and hydrogen production and power of the PEMEL with a maximum capacity of 9MW is shown.

[0084] Depend on Figure 3 It can be seen that the Faraday efficiency of PEMEL increases rapidly with the increase of input power. When the input power is small, the Faraday efficiency is small. The efficiency of PEMEL increases with the increase of input power until it reaches a certain input power, when the efficiency of PEMEL begins to decrease. The operating efficiency of PEMEL is low at high input power. Low efficiency will lead to an increase in the marginal cost of hydrogen production, but the system hydrogen production is relatively high at this time. Therefore, it is necessary to comprehensively consider the operating efficiency and hydrogen production of PEMEL to optimize its operation plan, so as to find the optimal operation strategy that meets the energy demand of the system and balances the system cost and efficiency.

[0085] 2 Hydrogen storage tank model

[0086] In the hydrogen storage process, the hydrogen storage tank is a bridge connecting the system's hydrogen production and use, providing stable and time-shiftable hydrogen energy for hydrogen fuel cells and hydrogen fuel cell vehicles, and improving the system's complementary space and flexibility. The hydrogen storage tank model is shown below:

[0087]

[0088] Where: is the hydrogen storage capacity of the hydrogen storage tank at time t and t-1; The efficiency of filling and discharging hydrogen storage tanks; is the hydrogen demand of the hydrogen fuel cell at time t.

[0089] 3HRS Model

[0090] As an important link in hydrogen energy terminal applications, HRS is an important hub connecting upstream hydrogen production and downstream hydrogen use. On the one hand, it can provide fuel for hydrogen vehicles; on the other hand, when the supply of hydrogen energy in the system exceeds the demand, the hydrogen storage tank configured in HRS can store the excess hydrogen energy for backup, thus realizing the dual functions of hydrogen use and hydrogen storage in the station

[18] . The model is as follows:

[0091]

[0092] Where: is the hydrogen storage capacity of HRS in period t; is the hydrogen storage capacity of HRS in period t-1; is the hydrogen production of HRS supplied during period t; is the hydrogen consumption of HRS during period t; They are the initial hydrogen capacity of the HRS hydrogen storage tank and the hydrogen capacity at the end of the dispatch period; They are the upper and lower limits of the hydrogen storage capacity of the hydrogen storage tank respectively; is the hydrogen energy demand of HRS during period t; is the energy density of hydrogen.

[0093] 4 Hydrogen Energy Transportation Model

[0094] Hydrogen energy transportation is completed between various hydrogen energy subsystems through HT. In order to calculate the hydrogen energy transportation cost and reasonably formulate the HT scheduling plan, it is necessary to establish a HT transportation spatiotemporal model. The model determines the transportation status of HT and further determines its location, and finally obtains the transportation time and distance. Therefore, the model includes five 0-1 variables: transportation status variable, location status variable, departure variable, arrival variable and auxiliary variable, which are subject to three constraints: logical constraint, motion space constraint and motion time constraint. The same hydrogen trailer cannot be in motion and stationary state at the same time, and similarly, it cannot arrive at node m and leave node m at the same time

[19] .

[0095]

[0096] Where: α m,n,t is the motion variable between node m and node n at time t; β m,t is the stationary variable at node m at time t; m,t is the starting variable at node m at time t; m,t is the arrival variable at node m at time t; Ω is the set of all nodes; T is the set of scheduling cycle times.

[0097] Equations (17) to (19) are the motion space constraints of the model. The hydrogen trailer starting from node m must run on the transportation path to another arbitrary node n at the next moment, and the movement status is consistent with the position change status.

[0098]

[0099] Where: is the starting state auxiliary variable of node m at time t, which is a 0–1 variable.

[0100] Formulas (20) to (22) are the motion time constraints of the model. Considering that the gas hydrogen trailer has the necessary loading and unloading time at any node m, the time interval between arriving at node m and departing from node m must not be less than the minimum time.

[0101] The transportation time of gas hydrogen trailers shall not be less than the shortest transportation time after correction for the congestion factor.

[0102]

[0103]

[0104] T m,n =κ m,n l m,n / v (23)

[0105] Where: is the minimum transportation time from node m to node n; is the transportation time from node m to node n after considering the traffic congestion coefficient; is the road congestion coefficient from node m to node n, which represents the delay of transportation time under traffic congestion, and is taken as =1.2; l m,n is the distance from node m to node n; v is the average transportation speed. Step 2: Hydrogen energy transportation path optimization model;

[0106] The HT flexible scheduling of the present invention takes into account the impact of traffic congestion. In order to characterize the road traffic situation, a dynamic road traffic model considering traffic congestion is introduced, and the road topology structure is described using graph theory, and then an equivalent optimal path search algorithm considering congestion is proposed, based on which the optimal path planning of HT can be achieved.

[0107] 1 Hydrogen Energy Transportation Network Model

[0108] In order to describe the travel status of n HTs in a dynamic transportation network, the present invention constructs an n×10 order dynamic travel status matrix At the same time, in order to more realistically reflect the dynamic characteristics of the traffic network, the present invention updates the dynamic travel state matrix with 1 minute as the time unit

[20] . and HT n The state vector of (n=1,2,…,n) They are:

[0109]

[0110] Where: O HT(n) , D HT(n) ,t o,HT(n) HT n The starting point, end point and departure time; HT for the period t nThe range of the vehicle, the current road, and the next target road; HT is the period t n Boolean variable of driving status. n When driving When HT n When in stop state HT for the period t n Boolean variable of battery life. n When the driving range is insufficient When HT n When the driving range is sufficient t d,HT(n) HT n Arrival time; HT is the period t n Willingness to participate in flexible scheduling.

[0111] With dynamic travel status matrix per minute The state variables such as flow and speed in the real-time dynamic traffic network model are also constantly updated. The main physical quantities involved in the dynamic traffic network model are as follows:

[0112]

[0113] Where: N, L, Q, V are the road traffic network node set, road section set, real-time traffic flow set, and real-time speed set respectively; n is the i-th traffic network node; l ij ,q ij 、v ij are the length, traffic volume and driving speed of road section (i, j) respectively; n N is the number of nodes in the transportation network; T is the set of time periods of 1d; t D is the number of time periods of 1d; f(q ij ) is the function of the traffic speed with respect to the flow rate.

[0114] 2 Hydrogen energy transportation equivalent optimal path search algorithm considering congestion

[0115] In order to optimize the HT driving path, a path search algorithm needs to be used. At present, the shortest path search algorithm is commonly used in research, such as the Floyd algorithm, the Dijkstra algorithm, etc., but this type of algorithm only considers the length of the road section and does not take into account the impact of traffic congestion. For the traffic network, the driving time of a car is related to the length of the road section and the driving speed. The driving speed is closely related to the road section capacity and traffic flow. For this reason, the present invention introduces a speed-flow practical model. According to the traffic flow, the driving speed v of HT on the road section (i, j) during time period t can be obtained ij (t) is:

[0116]

[0117] Where: v ij-0 is the free flow speed of HT on the road section (i, j); q ij (t) is the traffic volume of the road section (i, j) during period t; C ij is the capacity of the road section (i, j); θ 1 ,θ 2 , γ are the adaptive coefficients under different road grades; p is the intermediate parameter variable in the calculation process and has no physical meaning.

[0118] The origin-destination (OD) analysis method can be used to generate the starting point O and the end point D of HT, but there are generally multiple road sections between (O, D) to choose from. The present invention is based on the Floyd shortest path search algorithm and proposes an equivalent optimal path search algorithm that takes into account real-time congestion. The core idea of ​​the algorithm equivalence is that HT traveling through a congested road section at actual speed is equivalent to HT traveling through an equivalent road section without congestion but with a longer length at free flow speed

[21] .

[0119] 1) Based on the shortest path search algorithm, the next traffic node that HT intends to go to is preliminarily planned, and the real-time speed v of each section is calculated from the real-time flow of each section according to the speed-flow practical model shown in formula (26) ij (t). The relationship between the traffic volume of the road section (i, j) in period t and the traffic volume in the previous period is shown in formula (27).

[0120]

[0121] Where: is the set of HTs that enter the road section during period t; is the set of HTs that leave the road section during period t.

[0122] 2) According to the actual length l of each section ij The ratio of the speed to the real-time speed is used to obtain the actual time required to pass each section, and then the equivalent length l of each section in time period t is calculated. ij-eq (t), as shown in formula (28).

[0123]

[0124] 3) Form a road adjacency matrix of equivalent lengths of each road section in time period t, take the shortest equivalent length as the goal, traverse all road nodes, plan the shortest equivalent length between any two road nodes, and obtain the equivalent optimal path of each HT. It is worth noting that due to the fast calculation speed of the proposed algorithm, the solution time is in seconds, and the optimal path of the HT can be refreshed every minute. After the path optimization, the HT arriving at the station chooses whether to connect to the grid according to the user's wishes. If connected to the grid, the connection and disconnection time and the current battery life are provided to facilitate flexible scheduling on the grid side.

[0125] Step 3: Hydrogen energy system optimization model;

[0126] 1 Objective Function

[0127] Considering that the energy grid is the future development form, thermal power generators, wind power generators, and energy storage merchants cannot pursue their own interests alone. Instead, the country should stand at a higher level and formulate a system operation plan from the perspective of maximizing the energy utilization efficiency and benefits of the entire society. In order to achieve the low-carbon economic operation of the system, this section optimizes the system based on the above-mentioned electric-hydrogen hybrid energy storage coordinated operation mechanism with the goal of minimizing the system's monthly total operating cost and carbon emission cost. The total operating cost of the system includes the operating cost of thermal power units, the cost of hydrogen energy production, storage and transportation, the cost of wind power, and the penalty cost of wind abandonment, as shown below.

[0128] minC=C H +C HP +C HS +C HT +C wind +C ab (29)

[0129] Thermal power operation cost:

[0130]

[0131] Where: T is the operation simulation period; n is the number of the thermal power unit, N h is the total number of thermal power units; is the power generation capacity of the nth thermal power unit; is the power generation cost function of thermal power units; U n,t is the start / stop status of thermal power unit n at time t, U n,t-1 is the start / stop status of thermal power unit n at time t-1. If it is 0, it means it is off, and if it is 1, it means it is on.

[0132] Hydrogen production cost:

[0133]

[0134] Where: c su 、c cdThe start-up and shutdown cost of the hydrogen generator; Represent the startup and shutdown actions respectively. The quadratic term represents the penalty cost of hydrogen buffer tank storage state deviation, where β is a non-negative penalty coefficient.

[0135] Hydrogen energy storage costs:

[0136] Energy storage costs include energy storage operation and maintenance costs and energy storage energy consumption costs.

[0137]

[0138] Where: u E,i,t is the running status of the ith PEMEL, which is a 0-1 variable, 1 means start, and vice versa means shutdown; C E,i,inv is the investment and construction cost of the i-th PEMEL; T E,i,life is the maximum running time of the i-th PEMEL; N E is the number of PEMEL.

[0139] Hydrogen transportation costs:

[0140]

[0141] Where: c HT is the investment cost of a hydrogen transport vehicle; is the total number of hydrogen transport vehicles in cluster j; c j is the comprehensive HT operation cost of cluster j.

[0142] Wind power costs:

[0143]

[0144] Where: c wind is the unit operating cost of wind power, is the actual wind power output during period t.

[0145] Wind curtailment penalty cost:

[0146]

[0147] Where: w s is the number of days in season s; c ab is the unit energy abandonment cost; is the unit energy abandonment cost of wind power.

[0148] 2 Constraints

[0149] 2.1 Energy balance constraints

[0150] Power balance constraints:

[0151]

[0152] Where: is the system power load at time t.

[0153] Hydrogen energy balance constraints:

[0154]

[0155] Where: is the system hydrogen energy load at time t

[0156] 2.2 Constraints of each unit

[0157] Output constraints of thermal power units:

[0158]

[0159] Where: is the maximum power generation capacity of the gas turbine.

[0160] Wind turbine output constraints:

[0161]

[0162] Where: is the predicted value of wind power output.

[0163] Unit climbing power constraint:

[0164]

[0165] Where: is the minimum and maximum power of the i-th unit; is the up and down climbing power of the i-th unit.

[0166] Power purchase constraints:

[0167]

[0168] Where: It is the maximum power that the system can purchase from the upper grid.

[0169] Electric energy storage constraints:

[0170]

[0171] Where: S es,i,t With P es,i,t are the energy storage capacity and energy storage power of the i-th energy storage device respectively; α is the capacity loss rate of different energy storage devices; P ch,i With P dis,i are the energy storage and release powers of the i-th energy storage device respectively; η ch,i With η dis,i are the energy storage and release efficiencies of the i-th energy storage device;min,i With λ max,i are the minimum and maximum charge states of the i-th energy storage device respectively; v ch,i With v dis,i are the maximum and minimum charging rates of the i-th energy storage device respectively; are the lower and upper power limits of charging and discharging of the i-th energy storage device respectively; μ ch,t With μ dis,t It is the energy storage and release status of the energy storage device. are the lower and upper limits of energy storage for the i-th energy storage device.

[0172] Hydrogen storage tank constraints:

[0173]

[0174] Where: is the hydrogen storage capacity of the hydrogen storage tank; are the amount of hydrogen input and output from the hydrogen storage tank at time t respectively; They are the upper limits of the amount of hydrogen input and output from the hydrogen storage tank respectively; They are the hydrogen storage capacity of the hydrogen storage tank at the beginning and end of the scheduling cycle respectively.

[0175] Hydrogen transport constraints:

[0176]

[0177] Where: is the total amount of hydrogen produced at node i in set j; is the hydrogen load of node i in set j; is the hydrogen consumption of the hydrogen gas turbine at node i in set j; m k is the hydrogen transported by the kth HT from node i to the outside; Ω k is the set of hydrogen transport vehicles configured in the route from node i to j; is the amount of hydrogen transported from node i to node j at time t; Δt is the transportation time required from node i to node j.

[0178] Step 4: Two-stage robust model considering wind and solar uncertainty;

[0179] 1 Two-stage standard form

[0180] Taking into account the uncertainty of system source and load, the present invention adopts a robust optimization method divided into two stages:

[0181] The first stage is a day-ahead robust decision that does not change with uncertain variables. The energy storage unit has a multi-period timing regulation function, so the binary state variables such as electricity and hydrogen storage are used as the decision variables in the first stage.

[0182] The second stage is to consider the uncertainties of the system when the variables in the first stage are known, and seek the most economical output decision in the "worst" scenario. The real-time output of each unit is used as the decision variable in the second stage.

[0183] The two-stage robust standard form corresponding to the present invention is:

[0184]

[0185] Where: z 1 、z 2 are the decision variables of the first and second stages respectively; d is the coefficient column vector corresponding to the objective function in matrix form; r T ξ is a constant term that is independent of the variables decomposed from the objective function; Y(z 1 ,0) is the feasible domain of the variables in the second stage after the decision variables and uncertain variables in the first stage are determined; E, F, G, H, J are the coefficient matrices of the variables corresponding to the constraints organized into matrix form according to the decision variables, and e, f, g, o are the constant column vectors corresponding to the constraints organized into matrix form. Ω is independent of z 1 、z 2 The spatial polyhedron uncertain set is as follows:

[0186]

[0187] Where: are the maximum deviations between the actual and predicted values ​​of wind power, photovoltaic power and electric load at time t respectively; It is a binary variable. When it is 1, the uncertain variable in the corresponding period takes the boundary value of the fluctuation range. When it is 0, the actual value of the corresponding uncertain variable is the predicted value. are the uncertain adjustment parameters corresponding to the uncertain variables of wind power, photovoltaic and load respectively.

[0188] 2C&CG solution algorithm

[0189] The present invention uses the C&CG decomposition algorithm to decompose the model into a master problem (MP) and a subproblem (SP), and iterates repeatedly to obtain the optimal solution. MP seeks min and relaxes some constraints, thus providing a lower bound for the optimal value; SP seeks max and provides a feasible solution for the original problem, which can be used as the upper bound of the optimal solution. MP and SP are continuously updated and converged, and finally the optimal solution is obtained.

[0190] The MP form is:

[0191]

[0192] Where: γ is an auxiliary variable; z 2,iReturns the solution of the subproblem after the i-th iteration; o i The worst scenario value returned for the i-th subproblem; i t is the current iteration number; L B is the lower bound of the optimal total cost of the system; γ* i It is the objective function value of the main problem obtained by solving the worst scenario after the i-th iteration.

[0193] The SP form is:

[0194]

[0195] Where: z* 1 The first stage decision variable value obtained by solving the main problem is the determined value; U B is the upper limit of the optimal total cost of the system; z* 2,i Solve the subproblem for the i-th time and obtain the value of the decision variable in the second stage.

[0196] Formula (47) MP is a single-layer problem involving only integers, which is easy to solve; Formula (49) SP is a max-min double-layer problem, which is difficult to solve directly. The present invention adopts the duality principle to transform the inner layer min into max, and merges it with the outer layer max, transforming the double-layer problem of the second stage into a single-layer problem for easy solution.

[0197] The SP dual form is:

[0198]

[0199] Where: ε, θ, ω, μ are the dual variables corresponding to the constraints in the second stage atomic problem; a is a vector whose elements are all 0. T μ, which is a non-deterministic polynomial problem, is difficult to solve directly, so the SP is linearized by the big M method:

[0200]

[0201] Where: o* is the column vector composed of the predicted values ​​of uncertain variables; To introduce auxiliary variables; M is a sufficiently large real number;

[0202] In summary, the C&CG algorithm cycle process is as follows Figure 5 shown.

[0203] Step 5: Case analysis;

[0204] The present invention conducts simulation analysis based on the improved IEEE39 node test system to verify the rationality and adaptability of the method proposed in this chapter. The total installed capacity of thermal power is 750.2MW, the total installed capacity of new energy is 300MW, the penetration rate of new energy is about 25%, the peak loads of electricity and hydrogen are 600MW and 1.5t respectively, and the hydrogen energy storage is configured according to 5% to 15% of the total installed capacity of the system. One wind farm, one photovoltaic power station and four electric-hydrogen hybrid energy storage systems are added to the system, and the electric-hydrogen hybrid energy storage is connected to the same node with the wind farm and photovoltaic power station for joint operation. Among them, the wind farm access nodes are 4 and 39, with installed capacities of 100MW each, and the corresponding electric-hydrogen hybrid energy storage numbers are 1-2; the photovoltaic power station access nodes are 8 and 15, with installed capacities of 110MW each, and the corresponding electric-hydrogen hybrid energy storage numbers are 3-4. All numerical simulations in the present invention are performed on a computer configured with an Intel Core i7-8700 CPU (3.20 GHz) and 16 GB of memory. The optimization problem is modeled based on MATLAB using the Yalmip toolbox and the commercial solver Gurobi 10.0.0 is called for solution.

[0205] In order to verify the effectiveness of the two-stage robust collaborative optimization of the hydrogen production-storage-transportation system for optimal hydrogen transportation route selection proposed in the present invention, the following four scenarios are set for comparative analysis, as shown in Table 1.

[0206] Table 1 Comparison of results under four scenarios

[0207]

[0208] 1 Analysis of wind power consumption results

[0209] The system operation results under the four operation models are shown in Table 2.

[0210] Table 2 Results of new energy consumption under different schemes

[0211]

[0212] As shown in Table 2, the new energy consumption in the four scenarios is 41.78GW, 43.19GW, 46.52GW and 47.60GW, respectively, and the consumption rates are 89.69%, 92.71%, 95.38% and 96.95%, respectively. The proportion of energy abandonment period is 43.5%, 31.3%, 24.2% and 11.6%, respectively. Compared with the basic scenario 1, the new energy consumption in scenario 2 considering the dynamic characteristics of the electrolyzer increased by 3.37%, the consumption rate increased by 3.02%, and the proportion of energy abandonment period decreased by 12.2%. Compared with scenario 2, considering the optimal path selection, the new energy consumption in scenario 3 with coordinated production, storage and transportation increased by 7.71%, the consumption rate increased by 2.67%, and the proportion of energy abandonment period decreased by 7.1%. Compared with scenario 3, considering uncertainty, the new energy consumption of scenario 4 with robust optimization increased by 2.32%, the consumption rate increased by 1.57%, and the proportion of energy abandonment period decreased by 12.6%. The above reason is that the electrolyzer can respond more flexibly to the load changes of the power system and the fluctuations of new energy power generation in dynamic scenarios. By adjusting the operating status of the electrolyzer in real time, the instantaneous new energy power generation can be better utilized; when considering the best path selection and the coordination of production, storage and transportation, through the coordination of hydrogen production and energy storage system, energy can be more effectively released at peak demand, balancing supply and demand, maximizing the use of produced electricity, and reducing energy abandonment; further considering uncertainty, after robust optimization, the integrated energy system can dynamically adjust the power generation and load scheduling, enhance the system's adaptability to fluctuations, make the scheduling of new energy power generation more flexible, further increase the consumption, and reduce new energy abandonment.

[0213] 2. Influence of dynamic characteristics of electrolytic cell

[0214] In order to study the impact of the dynamic characteristics of the electrolytic cell on the mid-term operation of the system, the present invention sets up scenario 1 and scenario 2 for comparative analysis:

[0215] The simulation results of different schemes for typical summer / winter scenarios are shown in Table 3.

[0216] Table 3 Simulation results of different schemes for summer / winter scenarios

[0217]

[0218]

[0219] As shown in Table 1, the total operating costs of scenario 1 in the typical summer / winter scenario are 4.195 billion yuan and 7.203 billion yuan respectively; the total operating costs of scenario 2 are 4.142 billion yuan and 7.124 billion yuan respectively. Compared with scenario 1, the total operating costs of scenario 2 in the typical summer / winter scenario are reduced by 53 million yuan and 79 million yuan respectively, and the total operating costs are reduced by 1.26% and 1.09% respectively. It can be seen that ignoring the dynamic characteristics of the electrolyzer for optimization operation will lead to a high total operating cost of the system. The reason is that in scenario 1, the efficiency of the electrolyzer is considered to be fixed and it is assumed that this value is the rated efficiency of the electrolyzer, that is, the efficiency corresponding to the rated power. However, in actual operation, when the input power reaches the rated power, the operating efficiency of the electrolyzer is not the highest, which results in more electricity consumption to produce the same quality of hydrogen, which increases the total operating cost of the system.

[0220] The PEMEL simulation results for different schemes in typical summer / winter scenarios are shown in Table 4.

[0221] Table 4 PEMEL simulation results of different schemes for typical summer / winter scenarios

[0222]

[0223] It can be seen from Table 4 that in the typical summer / winter scenario, the energy consumption cost of PEMEL in Scenario 1 is 4.78 million yuan and 2.91 million yuan respectively, and the energy consumption cost of PEMEL in Scenario 2 is 3.65 million yuan and 1.58 million yuan respectively. Compared with Scenario 1, the energy consumption cost of PEMEL in Scenario 2 in the typical summer / winter scenario decreased by 23.6% and 45.7% respectively. Compared with Scenario 1, the operating time of PEMEL in Scenario 2 in the typical summer / winter scenario increased by 296h and 1119h respectively. It can be seen that the optimized operation considering the variable efficiency characteristics of PEMEL can reduce the energy consumption loss of PEMEL operation and improve the utilization rate of PEMEL. Combined with Figure 1 It can be seen that when the output of new energy is large and the system's forward power fluctuation exceeds the optimal operating range of PEMEL power, scenario 2 will give up part of the power to the battery, thereby ensuring that PEMEL is in an efficient operating state and reducing the large energy conversion losses caused by inefficient operation; when the output of new energy is small, the PEMEL dynamic operation model in scenario 2 is more sensitive to low power fluctuations, and can electrolyze water to produce hydrogen at a higher efficiency at a lower power, thereby improving the utilization rate of PEMEL, an expensive equipment.

[0224] In summary, compared with ignoring the change of PEMEL efficiency, finely considering the dynamic efficiency characteristics of PEMEL for system operation optimization can improve the overall operation efficiency of the system and the utilization rate of PEMEL, reduce the energy loss of the system, and economically and efficiently meet the energy demand of the system, thereby reducing the total operation cost of the system.

[0225] 3. Storage and transportation coordination

[0226] In order to analyze the impact of the hydrogen production-storage-transportation coordinated optimization model proposed in the present invention on the operation of MHES, summer scenario 2 and scenario 3 are set for comparative analysis:

[0227] Table 5 Operating costs of MHES under different scenarios

[0228]

[0229] The overall optimization operation results of MHES under the two scenarios are analyzed. Compared with scenario 2, scenario 3 reduces the total operating cost of MHES by about 17.71% through the coordinated scheduling of hydrogen production and storage at new energy hydrogen production and storage stations and HT hydrogen transportation. Among them, except for the HT cost increase of 42.38 million yuan, the sub-item costs of scenario 3 are all reduced compared with scenario 2, especially the electricity purchase cost is reduced the most, by 708.92 million yuan. This is because scenario 2 did not consider the constraints related to hydrogen production and storage at new energy hydrogen production and storage stations when planning the HT hydrogen transportation route. Therefore, although scenario 2 can obtain a better HT path planning result than scenario 3, it leads to higher operating costs of new energy hydrogen production and storage stations and new energy power abandonment costs. In addition, the penalty cost of new energy power abandonment in scenario 3 is reduced by about 35.08% compared with scenario 2, indicating that the proposed collaborative optimization model can effectively match the uncertainty of wind and solar output and improve the level of new energy consumption.

[0230] 4 Robust Analysis of Uncertainty

[0231] The robustness of the system constructed in scenario 4 can be changed by adjusting the uncertainty parameters. In this section, five different optimization models are constructed to analyze the impact of uncertain parameters on the economy and robustness of the system. The specific model parameter settings are shown in Table 6.

[0232] Table 6 Uncertainty parameter settings for different models

[0233]

[0234]

[0235] The day-ahead operating costs and system benefits of the system under different models are analyzed and compared, and the results are shown in Table 7. It can be seen from Table 7 that when formulating the day-ahead scheduling plan, the higher the uncertainty of the system setting, the worse the scenario, the more conservative the scheduling plan, and the higher the corresponding operating cost. Although the system considers the "worst" scenario and the system has good robustness, the probability of this scenario occurring is low and the model is too conservative. When the uncertainty set by the system is lower, the system benefit is higher, but the system robustness decreases at this time, and the ability to resist uncertain risks becomes worse. When the uncertain parameters are all 0, it means that the uncertain variables of the system are all taken as predicted values, and the system is a deterministic model. Therefore, it is necessary to select appropriate uncertain parameters based on the system economy, robustness and conservatism.

[0236] Table 7 System operation costs and benefits under different models

[0237]

[0238] When the uncertain parameters in the two-stage robust method proposed in Scenario 4 are small, the system cost is low and the benefits are high, but the model robustness is poor; when the uncertain parameters are large, although the system benefits are low and the cost is high, the model robustness is good. Through the above analysis, Model 4 has low costs, high benefits, and good robustness. When the uncertainty of Model 4 is used, the optimal operation result under Scenario 4 is obtained.

[0239] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for coordinated optimization of hydrogen production, storage and transportation system, characterized in that: The steps include: Step 1: Establish a coordinated optimization model for hydrogen production, storage and transportation; 1. Establish a hydrogen production station model; 2. Establish a hydrogen storage tank model; 3. Establish HRS model; 4. Hydrogen energy transportation model; Step 2: Establish a hydrogen transportation route optimization model; 1. Establish a hydrogen transportation network model; 2. Design an equivalent optimal path search algorithm for hydrogen transportation considering congestion; Step 3: Establish a hydrogen energy system optimization model; 1. Design objective function; 2. Design constraints; Step 4: Design a two-stage robust model considering the uncertainty of wind and solar power; 1. Design a two-stage standard form; 2. Design C&CG solution algorithm; Step 5: Example analysis:

1. Analysis of wind power curtailment and consumption results; 2. Analysis of the impact of dynamic characteristics of electrolytic cells; 3. Analysis of production, storage and transportation coordination; 4. Robust analysis of uncertainty.

2. A method for coordinated optimization of hydrogen production, storage and transportation system according to claim 1, characterized in that: In the step 1, establishing the hydrogen production station model also includes establishing a dynamic operation model of the electrolyzer taking into account the Faraday efficiency loss.

3. The method for coordinated optimization of hydrogen production, storage and transportation system according to claim 1, characterized in that: In the step three, the design constraints include energy balance constraints and constraints on each unit.

Images