Comprehensive energy system resource robust planning method considering low-carbon benefits of new energy vehicles

Abstract

The invention provides an integrated energy system resource robust planning method considering the low-carbon benefit of a new energy automobile, and the method comprises the steps: constructing an integrated energy system frame on the basis of analyzing the electricity-heat-cold-hydrogen energy conversion, flow and equipment operation characteristics; the new energy automobile is researched to replace a fuel automobile to run, the energy charging requirement of the new energy automobile is simulated, and a new energy automobile carbon-green certificate quota model is constructed; the uncertainty of new energy output and multi-energy load requirements is considered, and a min-max-min two-stage robust planning model is established by taking system planning economy optimization as a target. According to the comprehensive energy system resource robust planning method considering the low-carbon benefit of the new energy automobile provided by the invention, the IES resource robust planning is realized while the system planning-operation economy is optimized.

Description

Technical Field

[0001] This invention relates to the field of robust resource planning technology for integrated energy systems, and in particular to a robust resource planning method for integrated energy systems that takes into account the low-carbon benefits of new energy vehicles. Background Technology

[0002] In terms of clean and low-carbon energy transformation, integrated energy systems break away from the traditional separate energy flows of electricity, heat, cooling, hydrogen, and transportation. Through multi-energy complementarity and synergy, they improve system efficiency and stability, possessing the capacity for large-scale grid integration and consumption of new energy sources. This is a major pathway to promoting clean and low-carbon transformation in the energy sector. Integrated energy systems can supply energy to new energy vehicles by coupling charging piles and hydrogen refueling stations, promoting joint low-carbon transformation and integration between the transportation and energy sectors. Therefore, it is urgent to conduct planning research on integrated energy systems that include the charging needs of new energy vehicles, optimizing system resource allocation to achieve low-carbon transformation of energy and transportation.

[0003] Domestic and international scholars have conducted extensive research on hydrogen energy utilization, new energy vehicle operation, and integrated energy system resource allocation. However, the following issues still need to be addressed: 1. The operational potential of integrated energy systems connecting upstream hydrogen production from new energy sources with downstream hydrogen-powered vehicle energy consumption has not been fully explored; 2. The low-carbon benefits of new energy vehicles participating in carbon-green certificate trading have not been thoroughly investigated; 3. Given the interplay of the randomness of renewable energy output and the uncertainty of multi-energy load demand, the impact of system source-load uncertainty needs to be considered in system planning. Therefore, it is essential to design a robust resource planning method for integrated energy systems that takes into account the low-carbon benefits of new energy vehicles. Summary of the Invention

[0004] The purpose of this invention is to provide a robust planning method for integrated energy system resources that takes into account the low-carbon benefits of new energy vehicles, and can achieve robust planning of IES resources.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A robust resource planning method for integrated energy systems that takes into account the low-carbon benefits of new energy vehicles includes the following steps:

[0007] Step 1: Based on the analysis of the energy conversion, flow, and equipment operation characteristics of the integrated energy system (electricity-heat-cold-hydrogen), construct the framework of the integrated energy system;

[0008] Step 2: Based on the replacement of fuel vehicles with new energy vehicles, simulate the charging demand of new energy vehicles, and construct a carbon-green certificate quota model for new energy vehicles using the baseline method;

[0009] Step 3: Considering the uncertainty of new energy output and multi-energy load demand, and with the goal of optimizing the economic efficiency of system planning, establish a two-stage robust planning model of min-max-min.

[0010] Step 4: Solve the two-stage robust programming model based on the column and constraint generation algorithm and the commercially efficient solver Gurobi.

[0011] Optionally, in step 1, a comprehensive energy system framework is constructed, specifically as follows:

[0012] Based on the analysis of the energy conversion, flow, and equipment operation characteristics of the integrated energy system (IE) involving electricity, heat, cooling, and hydrogen, a framework for the integrated energy system is constructed. Specifically, this involves analyzing the specific structure and energy flow of the IE including the charging needs of new energy vehicles, encompassing multiple energy supply, conversion, and utilization stages. The electrical load in the system is supplied by wind power, photovoltaics, the external power grid, and hydrogen fuel cells; the heat load is supplied by electric heaters and hydrogen fuel cells; the cooling load is mainly supplied by electric chillers; and the hydrogen load is supplied by an electrolyzer combined with a hydrogen storage tank. The energy coupling equipment mainly includes electrolyzers, hydrogen fuel cells, electric heaters, and electric chillers. The upper-level energy supply, primarily from wind and photovoltaics, combined with electrolyzers, electric heaters, and electric chillers, breaks down the barriers between electrical energy and other heterogeneous energy sources, meeting the needs of lower-level hydrogen, heat, and cooling users, and achieving real-time matching of the supply and demand relationship between the system's upper-source and lower-load sources.

[0013] Optionally, a hydrogen energy utilization and multi-energy load demand model can be established based on the IES operating framework that includes the charging demand of new energy vehicles, specifically:

[0014] The electrolyzer generates hydrogen energy by electrolyzing water, dividing the produced hydrogen energy into three parts: one part is supplied to the hydrogen fuel cell to convert it into electricity and heat energy; another part is supplied to the hydrogen refueling station to provide hydrogen refueling services for hydrogen-powered vehicles; and the excess part is stored in the hydrogen storage tank to alleviate the system's energy supply pressure during periods of high load demand.

[0015] Electrolyzers produce hydrogen by electrolyzing water, which is key to achieving the "green electricity to green hydrogen" transition. The model is as follows:

[0016]

[0017] In the formula, η represents the hydrogen energy output by the electrolyzer during time period t; ec The energy conversion efficiency of the electrolytic cell; The energy consumption power of the electrolytic cell during time period t; and These are the upper and lower limits of energy consumption for electrolytic cells; and The upper and lower limits of the energy consumption power ramp-up of the electrolytic cell; The energy consumption power of the electrolytic cell during the t-1 time period is 1.

[0018] Hydrogen fuel cells with adjustable thermoelectric ratios use hydrogen as fuel, produce no carbon emissions, and have high electrothermal energy conversion efficiency. They are an important coupling element for realizing the "green hydrogen to green electricity" transition. The model is as follows:

[0019]

[0020] In the formula, The hydrogen energy consumed by the hydrogen fuel cell during time period t; and The hydrogen fuel cell outputs electrical and thermal energy during time period t; and To improve the electro-thermal conversion efficiency of hydrogen fuel cells; and and and For hydrogen fuel cells, the input of hydrogen energy, ramp-up, lower and upper limits of the thermoelectric ratio; The hydrogen energy consumed by the hydrogen fuel cell during the t+1 period.

[0021] Hydrogen refueling stations, as nodes for hydrogen refueling hydrogen-powered vehicles, are crucial hubs connecting hydrogen production via electrolyzers with system-level hydrogen consumption. Their model is as follows:

[0022]

[0023] In the formula, The demand for hydrogen refueling for hydrogen-powered vehicles during period t; Energy consumption for hydrogen refueling stations during time period t; This represents the energy density of hydrogen gas.

[0024] When the supply of hydrogen energy in an integrated energy system exceeds the demand, hydrogen storage tanks can store excess hydrogen energy for backup, providing a stable and time-shiftable hydrogen energy supply for the integrated energy system, thereby improving the complementarity and flexibility of the integrated energy system.

[0025]

[0026] In the formula, and The hydrogen storage capacity of the hydrogen storage tanks during time period t and time period t-1; Hydrogen is supplied to the hydrogen storage tank for the electrolyzer during time period t; The amount of hydrogen consumed by the hydrogen storage tank during time period t; and The initial hydrogen energy of the hydrogen storage tank and the hydrogen capacity at the end of the scheduling cycle; and These are the upper and lower limits of the hydrogen storage capacity of the hydrogen storage tank.

[0027] On the energy demand side, conventional electrical loads, electric vehicles, and hydrogen fuel cell vehicles all possess a degree of dispatchability. The system can respond to renewable energy output by adjusting load demand during different scheduling periods, thereby alleviating system energy supply pressure while expanding the space for renewable energy to connect to the grid. The specific model is shown below:

[0028]

[0029] In the formula, and These represent the electrical load, energy consumption of electric vehicles, and energy-powered vehicles during time period t before and after demand response; Let t represent the energy consumption of electrical load, electric vehicles, and hydrogen fuel cell vehicles in demand response during time period t; λ represent the load participation in demand response constraints; and T represent the total dispatch period.

[0030] The heat and cooling loads determined by indoor and outdoor temperatures can be flexibly adjusted to meet heat and cooling load demands by regulating indoor temperature, due to the ambiguity of human temperature perception and the slow changes in heat energy in heated buildings. Taking heat load as an example, the specific model is shown below:

[0031]

[0032] In the formula, and For heat load power demand before and after participating in demand response; T in The optimal indoor temperature; and Let t represent the indoor and outdoor temperatures during time period t; K, F, and V represent the heat transfer coefficient, building surface area, and volume, respectively; c air and ρ air For the specific heat capacity and density of indoor air; I min and I max The upper and lower limits of the temperature range that the human body can accept for comfort.

[0033] Optionally, based on the replacement of gasoline vehicles with new energy vehicles, the charging demand of new energy vehicles is simulated, and a baseline method is used to construct a carbon-green certificate quota model for new energy vehicles, specifically:

[0034] In an integrated energy system, hydrogen refueling stations use electrolyzers to consume electrical energy to produce hydrogen for hydrogen fuel cell vehicles, while charging stations directly consume electrical energy to meet the charging needs of electric vehicles, both of which support the utilization of new energy sources in the integrated energy system (IES). To further illustrate the advantages of new energy vehicles, considering their replacement of gasoline vehicles for travel, the charging load of gasoline vehicles is quantified into the vehicle's driving demand using the mileage per unit of fuel. Then, considering the mileage per unit of energy of new energy vehicles, a portion of the gasoline vehicle load is equivalent to the new energy vehicle load, as shown below:

[0035]

[0036] In the formula, and The charging demand for electric vehicles and hydrogen fuel cell vehicles on a typical day during period t; The equivalent refueling demand for gasoline vehicles on a typical day during period t; For the equivalent preload demand of fuel-powered vehicles; L ev L hv and L oil This refers to the driving range per unit of energy for electric vehicles, hydrogen fuel cell vehicles, and gasoline-powered vehicles.

[0037] However, considering the slow charging characteristics of electric vehicles and whether users urgently need to charge, the load of gasoline vehicles is divided into two categories: one category is equivalent to hydrogen fuel cell vehicles with very short charging times, and the other category is equivalent to electric vehicles that require a certain charging time. Equation (9) can be modified as follows:

[0038]

[0039] In the formula, and These represent the refueling demand for gasoline vehicles after the equivalent of hydrogen-powered vehicles and electric vehicles, respectively, with α and β representing the proportions of the equivalent of hydrogen-powered vehicles and electric vehicles, respectively.

[0040] To deepen the positive role of new energy vehicles in reducing carbon emissions in transportation systems and promoting the high-proportion consumption of new energy, this section constructs a carbon trading model for electric vehicles and hydrogen fuel cell vehicles based on the charging demand of new energy vehicles and using gasoline vehicles as a reference. Economic incentives are used to promote the replacement of gasoline vehicles with new energy vehicles, achieving low-carbon synergistic optimization between new energy vehicles and the integrated energy system on the load side.

[0041] However, considering that the system's energy sources include the external power grid and new energy sources, this paper uses the power generation ratio K to represent the external power grid purchase of new energy vehicles, and assumes that the output of the external power grid is generated by thermal power units.

[0042]

[0043] In the formula, and This refers to the power generation of wind and solar power during period t. Purchase energy from the external power grid during time period t.

[0044] This article uses the baseline method, taking the carbon emissions of gasoline-powered vehicles as a benchmark, and uses the difference between the carbon emissions of new energy vehicles and traditional gasoline-powered vehicles for the same driving distance as the initial carbon emission allowance obtained by the vehicle owner, as shown below:

[0045]

[0046] In the formula, and For carbon emission allowances for electric vehicles and hydrogen fuel cell vehicles during period t; E tra Carbon emissions per unit distance traveled by a gasoline-powered vehicle.

[0047] Although new energy vehicles use electricity and hydrogen as fuel during operation and emit no carbon dioxide, the carbon emissions from charging new energy vehicles need to be considered, as shown below:

[0048]

[0049] In the formula, and Carbon emissions generated during the charging of electric vehicles and hydrogen fuel cell vehicles in time period t; E represents the electricity consumed by a hydrogen fuel cell vehicle during time period t. c It is the carbon emission factor per unit of electricity generated by thermal power units.

[0050] The green certificate trading mechanism is my country's certification of renewable energy electricity fed into the grid, and also serves as proof for load-side users to consume green electricity. The government stipulates that systems that consume renewable energy can obtain green certificate quotas; similarly, new energy vehicles can obtain government-allocated green certificates by consuming renewable energy.

[0051] This article, referencing the renewable energy quota system and green certificate trading mechanism in the electricity market, constructs a green certificate trading model for new energy vehicles, as shown below:

[0052]

[0053] In the formula, and These are the system's tradable green electricity certificates for electric vehicles, the required quota of green electricity certificates, and the number of green electricity certificates obtained by electric vehicles for consuming new energy sources; and The system separately addresses the tradable green electricity certificates for hydrogen fuel cell vehicles, the required green electricity certificate quotas, and the number of green electricity certificates obtained by hydrogen fuel cell vehicles for consuming renewable energy; α ge and w ge This refers to the green electricity certificate quota factor and the quantitative factor for the number of renewable energy sources converted into green electricity certificates.

[0054] Optionally, considering the uncertainties of new energy output and multi-energy load demand, a two-stage robust programming model (min-max-min) is established with the goal of optimizing the system planning economy. The objective function for resource allocation in the integrated energy system is the annualized total cost C during the system planning period. total Minimum, C total Annualized investment cost C inv Fixed maintenance costs C fixCompared with typical daily total operating cost C ope It consists of two parts.

[0055] C total =C inv +C fix +C ope (19)

[0056] Investment cost C inv This mainly includes the annualized investment cost of electrolyzers, hydrogen storage tanks, hydrogen fuel cells, charging piles, and hydrogen refueling stations:

[0057]

[0058] In equation (15), Where E represents the type of resource planning. For wind power investment capacity; For photovoltaic investment capacity; For photovoltaic investment capacity; Investment capacity for hydrogen storage tanks; Investment capacity for hydrogen fuel cells; For the investment capacity of charging piles; Investment capacity for hydrogen refueling stations; Investment capacity for heating equipment; The investment capacity for the refrigeration unit; d = r(1 + r) m / (1+r) m -1 represents the resource investment recovery coefficient; r and m represent the equipment interest rate and service life, respectively; c e and For resource e, the unit investment cost and investment capacity; c main,e Fixed maintenance cost per unit capacity of equipment e;

[0059] Operating cost C ope This mainly includes the operation and maintenance costs of each unit (C). fix External grid energy purchase cost C ep Car charging cost C vc Load demand response cost C dr And the carbon trading cost of new energy vehicles C carbon Green certificate transaction cost C green :

[0060] C ope =C fix +C ep +C vc +C dr +C carbon +C green (16)

[0061]

[0062]

[0063] In the formula, and The unit operation and maintenance cost for electrolyzers, hydrogen fuel cells, wind power, and photovoltaics; and q represents the operating power of the electrolyzer, hydrogen fuel cell, wind power, and photovoltaic power during a typical day t period in season s; S is the season number, s=1; s=2; s=3 represent the cooling season, transitional season, and heating season, respectively; s w represents the number of days in season s; e For system time-of-use pricing; The system purchases power from the grid during a typical daytime period of season s; w h and w oil The system's hydrogen and oil prices for the specified time period; and The loads of electric vehicles, hydrogen fuel cell vehicles, and gasoline vehicles during a typical daily time period t in season s are respectively; c ela , and These are the unit response power compensation prices for transferable load, heat load, and cooling load, respectively. and These represent the electricity load, electric vehicle, and hydrogen fuel cell vehicle demand response energy consumption during a typical daily t-hour period in season s. and The heat load demand before and after the demand response during a typical daytime period t in season s; and The cooling load demand before and after the demand response during a typical daytime period t in season s; and c gre The base price for carbon trading and green certificate trading for new energy vehicles; and These are the carbon emission allowances for electric vehicles and hydrogen fuel cell vehicles during a typical daytime period t in season s, respectively. and Carbon emissions from charging electric vehicles and hydrogen fuel cell vehicles during a typical daytime period of season s; and These are tradable green electricity certificates for electric vehicles and hydrogen fuel cell vehicles, respectively.

[0064] To fully utilize the natural resources surrounding the system, the capacity of distributed renewable energy configuration should not be less than the maximum predicted renewable energy power per typical day, while allowing for a certain margin, but is limited by installation site and grid-connected power:

[0065]

[0066] In equation (23), kwt and k pv For wind power and solar power margin; and The maximum predicted power output for wind and solar power on typical days; and Wind power and solar power are limited by the maximum configuration capacity of the installation site;

[0067] Similar to distributed renewable energy deployments, the capacity of charging piles and hydrogen refueling stations is limited by installation site constraints, but the total capacity should not be less than the typical daily charging demand for new energy vehicles.

[0068]

[0069] In the formula, k evc and k jqz Power margin for charging electric vehicles and hydrogen fuel cell vehicles; and This refers to the maximum permissible construction capacity for charging piles and hydrogen refueling stations. and To meet the maximum charging needs of electric vehicles and hydrogen fuel cell vehicles;

[0070] Multi-energy power supply and demand balance constraints:

[0071]

[0072] In the formula, Energy consumption for electric heaters; Energy consumption of electric refrigeration unit; The electric heater outputs heat power; It outputs cooling power to the electric chiller.

[0073] Considering the site constraints of the integrated energy system, there are also certain installation limits for the remaining planned resource capacity.

[0074]

[0075] In the formula, For the investment capacity of resource e; Resource e is limited by the maximum configuration capacity of the installation site.

[0076] Due to limitations such as the power capacity of the tie line, the system's power purchase from the external power grid is subject to certain restrictions.

[0077]

[0078] In the formula, The limit for the power exchange between the service area and the power grid.

[0079] In actual operation, IES (Environmental Engineering Systems) face many random factors. Considering the uncertainty of new energy output and load, the resource allocation obtained by the above deterministic optimization model of IES appears too risky. This paper introduces robust optimization into the above deterministic model to construct a box-shaped uncertainty set U that takes into account source-load uncertainty:

[0080]

[0081] In the formula: and These are the predicted outdoor temperatures for wind power, solar power, gasoline vehicles, conventional electrical load, heating season, and cooling season, respectively; Δu wt,max , Δu pv,max , Δu oil,max , Δu el,max , Δu hl,max and Δu cl,max The maximum fluctuation deviation of outdoor temperature for wind power, photovoltaic, gasoline vehicles, conventional electrical loads, heating season, and cooling season; These are the outdoor temperatures during the heating and cooling seasons, respectively. and For binary variables, a value of 1 indicates that the indeterminate invariant has reached the boundary value of the interval; Γ wt ,Γ pv ,Γ oil ,Γ el ,Γ hl and Γ cl The adjustment parameter for each uncertainty source represents the total number of time periods during which the corresponding uncertainty source takes the boundary value of the fluctuation range within the scheduling period. The larger the adjustment parameter, the more conservative the obtained parameter, and vice versa.

[0082] Taking into account the uncertainty of the system source load, the deterministic objective function of equation (16) is reconstructed into a two-stage robust optimization model, where the first-stage objective function is to minimize the investment cost, and the second-stage objective function is to minimize the system operating cost under adverse scenarios, as shown below:

[0083]

[0084] In the formula: y represents the continuous variable in the first-stage optimization problem, namely the resource allocation capacity of wind power, photovoltaics, etc. in the system; x is the continuous variable in the second-stage optimization problem, representing the operating power of the electrolyzer and the external power grid; u represents the uncertain source variable of wind power, photovoltaic output and multi-energy load demand in the system, as specifically expressed below:

[0085]

[0086] The above analysis shows that the two-stage robust optimization model shown in equation (32) can be expressed as:

[0087]

[0088] In the formula, A, B, C, D, E, F, G, Q1, Q2, Q3, and Q4 represent constant coefficient matrices or column vectors, respectively.

[0089] Since the model in equation (36) is a multi-level problem, it is decomposed into the main problem shown in equation (37) and the subproblems shown in equation (38). First, given an initial scenario variable u, MP is solved to obtain the lower bound L of the optimal value of the original problem. out First, we use the initial resource allocation capacity set E1; then, we substitute E1 into the subproblem SP to obtain the upper bound U of the optimal value of the original problem. out The value of u is then used as the basis for solving MP, along with new constraints and variables x. This process involves relaxation followed by pursuit, alternating between MP and SP to form a C&CG loop. The specific form is as follows:

[0090]

[0091] In the formula: the superscript * indicates a known quantity; the subscript k n k represents the current iteration number of the model; m η1 represents the upper limit of the number of iterations; η1 is an auxiliary variable introduced, representing the optimal value of the objective function in the second stage; λ1, λ2, λ3, and λ4 are the dual variables corresponding to the constraints in the subproblem.

[0092] As the C&CG algorithm iterates, the worst-case scenario generated after each SP solution is retained and passed to the current MP. The number of variables, constraints, and cutting planes in the MP continuously increases. out It will become increasingly larger; at the same time, the resource allocation capacity provided by MP to SP is also changing, optimizing U out Changes occur. If the convergence criterion (40) is satisfied, the loop exits, the optimal solution is returned, and the resource allocation capacity and the operation plan of each typical daily system are obtained.

[0093]

[0094] In the formula, ξ is a very small positive real number, representing the convergence gap.

[0095] The technical effects of this invention are as follows: It introduces charging piles and hydrogen refueling stations into the traditional Energy Storage and Engineering (IES) framework, broadening the energy demand on the load side, improving hydrogen energy utilization and source-side renewable energy consumption, analyzing the flow of multiple energy sources such as electricity, heat, cooling, and hydrogen, and constructing an IES operation framework that includes new energy vehicles. This achieves a supply-demand matching relationship between the upper-level renewable energy output and the lower-level multi-energy load demand. It quantifies the driving demand of gasoline vehicles, constructs an operation model for new energy vehicles to replace gasoline vehicles, and introduces a carbon-green certificate trading mechanism at the new energy vehicle scheduling level to leverage the low-carbon advantages of new energy vehicles through economic incentives. Facing the uncertainty of new energy output and multi-energy load, it constructs a two-stage robust planning model (min-max-min) with the optimal economic efficiency of system planning as the objective function, enhancing the system's ability to cope with uncertainty risks. Attached Figure Description

[0096] Figure 1 This is a schematic diagram of the IES runtime framework structure;

[0097] Figure 2 This is a schematic diagram of hydrogen energy utilization in IES;

[0098] Figure 3 The per-unit values ​​are the wind and solar power forecast information for each typical day;

[0099] Figure 4 Forecast information on typical daily electricity load, outdoor temperature, and fuel load;

[0100] Figure 5 This is a schematic diagram of the scheduling results for a typical day during the heating season.

[0101] Figure 6 This is a schematic diagram of the scheduling results for a typical day during the heating season.

[0102] Figure 7 This is a schematic diagram of the scheduling results for a typical day during the heating season.

[0103] Figure 8 This is a schematic diagram illustrating the analysis of uncertain operating results of the IES load-side electrical load.

[0104] Figure 9 This is a schematic diagram illustrating the analysis of uncertain operating results of wind power on the IES source side. Detailed Implementation

[0105] The purpose of this invention is to provide a robust planning method for integrated energy system resources that takes into account the low-carbon benefits of new energy vehicles, and can achieve robust planning of IES resources.

[0106] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0107] This invention proposes a highway roadway IES planning method that takes into account hydrogen energy storage and demand response, specifically including the following steps:

[0108] Step 1: Based on the analysis of the energy conversion, flow, and equipment operation characteristics of the integrated energy system (electricity-heat-cold-hydrogen), construct the framework of the integrated energy system;

[0109] Step 2: Based on the replacement of fuel vehicles with new energy vehicles, simulate the charging demand of new energy vehicles, and construct a carbon-green certificate quota model for new energy vehicles using the baseline method;

[0110] Step 3: Considering the uncertainty of new energy output and multi-energy load demand, and with the goal of optimizing the economic efficiency of system planning, establish a two-stage robust planning model of min-max-min.

[0111] Step 4: Solve the two-stage robust programming model based on the column and constraint generation algorithm and the commercially efficient solver Gurobi.

[0112] In step 1, the specific structure and energy flow of the IES containing the charging demand of new energy vehicles are analyzed to obtain the IES operating framework and equipment model, specifically as follows:

[0113] The specific structure and energy flow of the constructed IES that includes the charging needs of new energy vehicles are as follows: Figure 1 As shown, the system comprises three parts: energy supply, energy conversion and storage, and multi-energy load demand. The energy supply side mainly consists of wind power, photovoltaic power, and the external power grid, meeting the electricity needs of the energy conversion equipment while providing power to conventional electrical loads and electric vehicles. On the energy conversion and storage side, the system, in conjunction with electrolyzers, electric heaters, and electric chillers, breaks down barriers between electrical energy and other heterogeneous energy sources, achieving real-time matching of the supply and demand relationship between the system's source and load. Hydrogen storage tanks and hydrogen fuel cells, combined with electrolyzers, achieve energy time-shifting, alleviating the system's energy supply pressure caused by source-load mismatch. On the multi-energy load demand side, in addition to conventional electrical, thermal, and cooling load demands, to further broaden the load-side energy demand and promote the green and low-carbon transformation of the transportation sector, this paper establishes charging piles and hydrogen refueling stations on the load side to provide energy services for new energy vehicles.

[0114] The modeling process is based on the IES operating framework that includes the charging needs of new energy vehicles.

[0115] A hydrogen energy utilization model is established based on the IES operating framework, which includes the charging needs of new energy vehicles. Specifically:

[0116] Based on the hydrogen energy flow and IES operation framework, a schematic diagram of hydrogen energy flow is shown below. Figure 2As shown, firstly, the electrolyzer generates hydrogen energy by electrolyzing water, dividing the produced hydrogen energy into three parts: one part is supplied to the hydrogen fuel cell to convert it into electricity and heat energy; another part is supplied to the hydrogen refueling station to provide hydrogen refueling services for hydrogen fuel cell vehicles; and the excess part is stored in the hydrogen storage tank to alleviate the system's energy supply pressure during periods of high load demand.

[0117] Electrolyzers produce hydrogen by electrolyzing water, which is key to achieving the "green electricity to green hydrogen" transition. The model is as follows:

[0118]

[0119] In the formula, η represents the hydrogen energy output by the electrolyzer during time period t; ec The energy conversion efficiency of the electrolytic cell; The energy consumption power of the electrolytic cell during time period t; and These are the upper and lower limits of energy consumption for electrolytic cells; and The upper and lower limits of the energy consumption power ramp-up of the electrolytic cell; The energy consumption power of the electrolytic cell during the t-1 time period is 1.

[0120] Hydrogen fuel cells with adjustable thermoelectric ratios use hydrogen as fuel, produce no carbon emissions, and have high electrothermal energy conversion efficiency. They are an important coupling element for realizing the "green hydrogen to green electricity" transition. The model is as follows:

[0121]

[0122] In the formula, The hydrogen energy consumed by the hydrogen fuel cell during time period t; and The hydrogen fuel cell outputs electrical and thermal energy during time period t; and To improve the electro-thermal conversion efficiency of hydrogen fuel cells; and and and For hydrogen fuel cells, the input of hydrogen energy, ramp-up, and upper and lower limits of the thermoelectric ratio; The hydrogen energy consumed by the hydrogen fuel cell during the t+1 period.

[0123] Hydrogen refueling stations, as nodes for hydrogen refueling hydrogen-powered vehicles, are crucial hubs connecting hydrogen production via electrolyzers with system-level hydrogen consumption. Their model is as follows:

[0124]

[0125] In the formula, The demand for hydrogen refueling for hydrogen-powered vehicles during period t; Energy consumption for hydrogen refueling stations during time period t; This represents the energy density of hydrogen gas.

[0126] When the supply of hydrogen energy in an integrated energy system exceeds the demand, hydrogen storage tanks can store excess hydrogen energy for backup, providing a stable and time-shiftable hydrogen energy supply for the integrated energy system, thereby improving the complementarity and flexibility of the integrated energy system.

[0127]

[0128] In the formula, and The hydrogen storage capacity of the hydrogen storage tanks during time period t and time period t-1; Hydrogen is supplied to the hydrogen storage tank for the electrolyzer during time period t; The amount of hydrogen consumed by the hydrogen storage tank during time period t; and The initial hydrogen energy of the hydrogen storage tank and the hydrogen capacity at the end of the scheduling cycle; and These are the upper and lower limits of the hydrogen storage capacity of the hydrogen storage tank.

[0129] A multi-energy load demand response model is established based on the IES operation framework, which includes the charging demand of new energy vehicles. Specifically:

[0130] On the energy demand side, conventional electrical loads, electric vehicles, and hydrogen fuel cell vehicles all possess a degree of dispatchability. The system can respond to renewable energy output by adjusting load demand during different scheduling periods, thereby alleviating system energy supply pressure while expanding the space for renewable energy to connect to the grid. The specific model is shown below:

[0131]

[0132] In the formula, and These represent the electrical load, energy consumption of electric vehicles, and energy-powered vehicles during time period t before and after demand response; Let t represent the energy consumption of electrical load, electric vehicles, and hydrogen fuel cell vehicles in demand response during time period t; λ represent the load participation limit in demand response; and T represent the total dispatch period.

[0133] The heat and cooling loads determined by indoor and outdoor temperatures can be flexibly adjusted to meet heat and cooling load demands by regulating indoor temperature, due to the ambiguity of human temperature perception and the slow changes in heat energy in heated buildings. Taking heat load as an example, the specific model is shown below:

[0134]

[0135]

[0136] In the formula, and For heat load power demand before and after participating in demand response; T in The optimal indoor temperature; and Let t represent the indoor and outdoor temperatures during time period t; K, F, and V represent the heat transfer coefficient, building surface area, and volume, respectively; c air and ρ air For the specific heat capacity and density of indoor air; I min and I max The upper and lower limits of the temperature range that the human body can accept for comfort.

[0137] In step 2, based on the premise that new energy vehicles are replacing gasoline vehicles, the charging demand of new energy vehicles is simulated, and a baseline method is used to construct a carbon-green certificate quota model for new energy vehicles, specifically:

[0138] In an integrated energy system, hydrogen refueling stations use electrolyzers to consume electrical energy to produce hydrogen for hydrogen fuel cell vehicles, while charging stations directly consume electrical energy to meet the charging needs of electric vehicles, both of which support the utilization of new energy sources in the integrated energy system (IES). To further illustrate the advantages of new energy vehicles, considering their replacement of gasoline vehicles for travel, the charging load of gasoline vehicles is quantified into the vehicle's driving demand using the mileage per unit of fuel. Then, considering the mileage per unit of energy of new energy vehicles, a portion of the gasoline vehicle load is equivalent to the new energy vehicle load, as shown below:

[0139]

[0140] In the formula, and The charging demand for electric vehicles and hydrogen fuel cell vehicles on a typical day during period t; The equivalent refueling demand for gasoline vehicles on a typical day during period t; For the equivalent preload demand of fuel-powered vehicles; L ev L hv and L oil This refers to the driving range per unit of energy for electric vehicles, hydrogen fuel cell vehicles, and gasoline-powered vehicles.

[0141] However, considering the slow charging characteristics of electric vehicles and whether users urgently need to charge, the load of gasoline vehicles is divided into two categories: one category is equivalent to hydrogen fuel cell vehicles with very short charging times, and the other category is equivalent to electric vehicles that require a certain charging time. Equation (9) can be modified as follows:

[0142]

[0143] In the formula, and These represent the refueling demand for gasoline vehicles after the equivalent of hydrogen-powered vehicles and electric vehicles, respectively, with α and β representing the proportions of the equivalent of hydrogen-powered vehicles and electric vehicles, respectively.

[0144] To deepen the positive role of new energy vehicles in reducing carbon emissions in transportation systems and promoting the high-proportion consumption of new energy, this section constructs a carbon trading model for electric vehicles and hydrogen fuel cell vehicles based on the charging demand of new energy vehicles and using gasoline vehicles as a reference. Economic incentives are used to promote the replacement of gasoline vehicles with new energy vehicles, achieving low-carbon synergistic optimization between new energy vehicles and the integrated energy system on the load side.

[0145] However, considering that the system's energy sources include the external power grid and new energy sources, this paper uses the power generation ratio K to represent the external power grid purchase of new energy vehicles, and assumes that the output of the external power grid is generated by thermal power units.

[0146]

[0147] In the formula, and This refers to the power generation of wind and solar power during the t-period. Purchase energy from the external power grid during time period t.

[0148] This article uses the baseline method, taking the carbon emissions of gasoline-powered vehicles as a benchmark, and uses the difference between the carbon emissions of new energy vehicles and traditional gasoline-powered vehicles for the same driving distance as the initial carbon emission allowance obtained by the vehicle owner, as shown below:

[0149]

[0150] In the formula, and For carbon emission allowances for electric vehicles and hydrogen fuel cell vehicles during period t; E tra Carbon emissions per unit distance traveled by a gasoline-powered vehicle.

[0151] Although new energy vehicles use electricity and hydrogen as fuel during operation and emit no carbon dioxide, the carbon emissions from charging new energy vehicles need to be considered, as shown below:

[0152]

[0153] In the formula, and Carbon emissions generated during the charging of electric vehicles and hydrogen fuel cell vehicles in time period t; E represents the electricity consumed by a hydrogen fuel cell vehicle during time period t. c It is the carbon emission factor per unit of electricity generated by thermal power units.

[0154] The green certificate trading mechanism is my country's certification of renewable energy electricity fed into the grid, and also serves as proof for load-side users to consume green electricity. The government stipulates that systems that consume renewable energy can obtain green certificate quotas; similarly, new energy vehicles can obtain government-allocated green certificates by consuming renewable energy.

[0155] This article, referencing the renewable energy quota system and green certificate trading mechanism in the electricity market, constructs a green certificate trading model for new energy vehicles, as shown below:

[0156]

[0157] In the formula, and These are the system's tradable green electricity certificates for electric vehicles, the required quota of green electricity certificates, and the number of green electricity certificates obtained by electric vehicles for consuming new energy sources; and The system separately addresses the tradable green electricity certificates for hydrogen fuel cell vehicles, the required green electricity certificate quotas, and the number of green electricity certificates obtained by hydrogen fuel cell vehicles for consuming renewable energy; α ge and w ge This refers to the green electricity certificate quota factor and the quantitative factor for the number of renewable energy sources converted into green electricity certificates.

[0158] In step 3, considering the uncertainty of new energy output and multi-energy load demand, a two-stage robust programming model of min-max-min is established with the goal of optimizing the economic efficiency of system planning.

[0159] Considering the uncertainties of renewable energy output and multi-energy load demand, a two-stage robust programming model (min-max-min) is established with the goal of optimizing system planning economy. The objective function for resource allocation in the integrated energy system is the annualized total cost C during the system planning period. total Minimum,

[0160] C total Annualized investment cost C inv Fixed maintenance costs C fix Compared with typical daily total operating cost C ope

[0161] It consists of two parts.

[0162] C total =C inv +C fix +C ope (19)

[0163] Investment cost C inv This mainly includes the annualized investment cost of electrolyzers, hydrogen storage tanks, hydrogen fuel cells, charging piles, and hydrogen refueling stations:

[0164]

[0165] In equation (15), Where E represents the type of resource planning. For wind power investment capacity; For photovoltaic investment capacity; For photovoltaic investment capacity; Investment capacity for hydrogen storage tanks; Investment capacity for hydrogen fuel cells; For the investment capacity of charging piles; Investment capacity for hydrogen refueling stations; Investment capacity for heating equipment;

[0166] The investment capacity for the refrigeration unit; d = r(1 + r) m / (1+r) m -1 represents the resource investment recovery coefficient; r and m represent the equipment interest rate and service life, respectively; c e and For resource e, the unit investment cost and investment capacity; c main,e Fixed maintenance cost per unit capacity of equipment e;

[0167] Operating cost C ope This mainly includes the operation and maintenance costs of each unit (C). fix External grid energy purchase cost C ep Car charging cost C vc Load demand response cost C dr And the carbon trading cost of new energy vehicles C carbon Green certificate transaction cost C green :

[0168] C ope =C fix +C ep +C vc +C dr +C carbon +C green (56)

[0169]

[0170] In the formula, and The unit operation and maintenance cost for electrolyzers, hydrogen fuel cells, wind power, and photovoltaics; and q represents the operating power of the electrolyzer, hydrogen fuel cell, wind power, and photovoltaic power during a typical day t period in season s; S is the season number, s=1; s=2; s=3 represent the cooling season, transitional season, and heating season, respectively; s w represents the number of days in season s; e For system time-of-use pricing; The system purchases power from the grid during a typical daytime period of season s; w h and w oil The system's hydrogen and oil prices for the specified time period; and The loads of electric vehicles, hydrogen fuel cell vehicles, and gasoline vehicles during a typical daily time period t in season s are respectively; c ela , and These are the unit response power compensation prices for transferable load, heat load, and cooling load, respectively. and These represent the electricity load, electric vehicle, and hydrogen fuel cell vehicle demand response energy consumption during a typical daily t-hour period in season s. and The heat load demand before and after the demand response during a typical daytime period t in season s; and The cooling load demand before and after the demand response during a typical daytime period t in season s; and c gre The base price for carbon trading and green certificate trading for new energy vehicles; and These are the carbon emission allowances for electric vehicles and hydrogen fuel cell vehicles during a typical daytime period t in season s, respectively. and Carbon emissions from charging electric vehicles and hydrogen fuel cell vehicles during a typical daytime period of season s; and These are tradable green electricity certificates for electric vehicles and hydrogen fuel cell vehicles, respectively.

[0171] To fully utilize the natural resources surrounding the system, the capacity of distributed renewable energy configuration should not be less than the maximum predicted renewable energy power per typical day, while allowing for a certain margin, but is limited by installation site and grid-connected power:

[0172]

[0173] In equation (23), k wt and k pv For wind power and solar power margin; and The maximum predicted power output for wind and solar power on typical days; and Wind power and solar power are limited by the maximum configuration capacity of the installation site;

[0174] Similar to distributed renewable energy deployments, the capacity of charging piles and hydrogen refueling stations is limited by installation site constraints, but the total capacity should not be less than the typical daily charging demand for new energy vehicles.

[0175]

[0176] In the formula, k evc and k jqz Power margin for charging electric vehicles and hydrogen fuel cell vehicles; and This refers to the maximum permissible construction capacity for charging piles and hydrogen refueling stations. and To meet the maximum charging needs of electric vehicles and hydrogen fuel cell vehicles;

[0177] Multi-energy power supply and demand balance constraints:

[0178]

[0179] In the formula, Energy consumption for electric heaters; Energy consumption of electric refrigeration unit; The electric heater outputs heat power; It outputs cooling power to the electric chiller.

[0180] Considering the site constraints of the integrated energy system, there are also certain installation limits for the remaining planned resource capacity.

[0181]

[0182] In the formula, For the investment capacity of resource e; Resource e is limited by the maximum configuration capacity of the installation site.

[0183] Due to limitations such as the power capacity of the tie line, the system's power purchase from the external power grid is subject to certain restrictions.

[0184]

[0185] In the formula, The limit for the power exchange between the service area and the power grid.

[0186] In actual operation, IES (Environmental Engineering Systems) face many random factors. Considering the uncertainty of new energy output and load, the resource allocation obtained by the above deterministic optimization model of IES appears too risky. This paper introduces robust optimization into the above deterministic model to construct a box-shaped uncertainty set U that takes into account source-load uncertainty:

[0187]

[0188] In the formula: and These are the predicted outdoor temperatures for wind power, solar power, gasoline vehicles, conventional electrical load, heating season, and cooling season, respectively; Δu wt,max , Δu pv,max , Δu oil,max , Δu el,max , Δu hl,max and Δu cl,max The maximum fluctuation deviation of outdoor temperature for wind power, photovoltaic, gasoline vehicles, conventional electrical loads, heating season, and cooling season; These are the outdoor temperatures during the heating and cooling seasons, respectively. and For binary variables, a value of 1 indicates that the indeterminate invariant has reached the boundary value of the interval; Γ wt ,Γ pv ,Γ oil ,Γ el ,Γ hl and Γ cl The adjustment parameter for each uncertainty source represents the total number of time periods during which the corresponding uncertainty source takes the boundary value of the fluctuation range within the scheduling period. The larger the adjustment parameter, the more conservative the obtained parameter, and vice versa.

[0189] Taking into account the uncertainty of the system source load, the deterministic objective function of equation (16) is reconstructed into a two-stage robust optimization model, where the first-stage objective function is to minimize the investment cost, and the second-stage objective function is to minimize the system operating cost under adverse scenarios, as shown below:

[0190]

[0191] In the formula: y represents the continuous variable in the first-stage optimization problem, namely the resource allocation capacity of wind power, photovoltaics, etc. in the system; x is the continuous variable in the second-stage optimization problem, representing the operating power of the electrolyzer and the external power grid; u represents the uncertain source variable of wind power, photovoltaic output and multi-energy load demand in the system, as specifically expressed below:

[0192]

[0193] The above analysis shows that the two-stage robust optimization model shown in equation (32) can be expressed as:

[0194]

[0195] In the formula, A, B, C, D, E, F, G, Q1, Q2, Q3, and Q4 represent constant coefficient matrices or column vectors, respectively.

[0196] In step 4, the two-stage robust programming model is solved based on the column and constraint generation algorithm and the commercially efficient solver Gurobi. Specifically:

[0197] Since the model in equation (36) is a multi-layered problem, it is difficult to solve and needs to be decomposed to obtain the main problem as shown in equation (37) and the sub-problems as shown in equation (38). First, we solve MP with an initial scenario variable u to obtain the lower bound L of the optimal value of the original problem. out First, we use the initial resource allocation capacity set E1; then, we substitute E1 into the subproblem SP to obtain the upper bound U of the optimal value of the original problem. outThe value of u is then used as the basis for solving MP, along with new constraints and variables x. This process involves relaxation followed by pursuit, alternating between MP and SP to form a C&CG loop. The specific form is as follows:

[0198]

[0199] In the formula: the superscript * indicates a known quantity; the subscript k n k represents the current iteration number of the model; m η1 represents the upper limit of the number of iterations; η1 is an auxiliary variable introduced, representing the optimal value of the objective function in the second stage; λ1, λ2, λ3, and λ4 are the dual variables corresponding to the constraints in the subproblem.

[0200] As the C&CG algorithm iterates, the worst-case scenario generated after each SP solution is retained and passed to the current MP. The number of variables, constraints, and cutting planes in the MP continuously increases. out It will become increasingly larger; at the same time, the resource allocation capacity provided by MP to SP is also changing, and the optimized U out Changes occur. If the convergence criterion (40) is satisfied, the loop exits, the optimal solution is returned, and the resource allocation capacity and the operation plan of each typical daily system are obtained.

[0201]

[0202] In the formula, ξ is a very small positive real number, representing the convergence gap.

[0203] This invention provides an implementation example to verify the rationality of the resource optimization allocation method described herein, in order to Figure 1 The integrated energy system shown is used as the test object to verify the effectiveness of the proposed model and solution algorithm. Programming was performed in the Matlab environment using the Yamlip toolbox, and the commercial solver Gurobi was called for solution. This paper sets the uncertainty adjustment parameters for wind power, photovoltaic output, conventional electricity-heat-cooling load, and fuel vehicle load demand to 12, 12, 12, and 8, respectively; the corresponding maximum fluctuation deviations are 15%, 15%, 10%, and 10% of the predicted values. Typical daily wind power, photovoltaic, and multi-energy load forecasts for each season are shown below. Figure 3-4 As shown.

[0204] To explore the impact of the proposed planning method on IES planning and operation, this section presents six planning schemes for comparative analysis:

[0205] Option 1: Only considers the construction of conventional units (wind power, photovoltaic, electrolyzer, hydrogen storage tank, hydrogen fuel cell), without considering the deterministic planning model of new energy vehicles replacing fuel vehicles for travel;

[0206] Option 2: Consider the construction of wind power, photovoltaic power, electrolyzers, hydrogen storage tanks, hydrogen fuel cells, charging piles and hydrogen refueling stations, and consider the replacement of fuel vehicles with new energy vehicles for travel;

[0207] Option 3: Based on Option 2, consider the comprehensive demand response of multi-energy load demand side;

[0208] Option 4: Based on Option 3, consider a fixed carbon-green certificate trading price mechanism;

[0209] Option 5: Consider a deterministic programming model for a tiered carbon-green certificate trading mechanism;

[0210] Option 6: Consider a two-stage robust programming model for a tiered carbon-green certificate trading mechanism.

[0211] Table 1 Results of road IES resource planning under different schemes

[0212]

[0213]

[0214] Table 2. Economic Analysis of IES under Different Schemes

[0215]

[0216] As shown in Tables 1 and 2, comparing Scheme 1 and Scheme 2, Scheme 2 considers the replacement of gasoline vehicles with new energy vehicles. The addition of charging piles and hydrogen refueling stations increases the demand for electricity and hydrogen on the load side. To meet the system's energy supply and demand balance, the installed capacity of wind power and photovoltaic power increased by 248.9kW and 841.1kW respectively, and the capacity of electrolyzers and hydrogen storage tanks increased by 136.3kW and 263.7kW respectively. The investment cost and fixed operation and maintenance cost of Scheme 2 increased by 21.6% and 23.7% respectively. Compared with refueling gasoline vehicles, electric vehicles and hydrogen fuel cell vehicles are cheaper to refuel, with a 38.67% reduction in refueling costs. Although the system equipment maintenance and electricity purchase costs have increased, the total system operating cost has still decreased by 32%, while carbon emissions in the transportation sector have decreased, with a 37.22% reduction in system carbon emissions.

[0217] Compared to Scheme 2, Scheme 3, the integrated demand response guides the load side to adjust its energy consumption according to different time periods, enabling the system to achieve peak shaving and valley filling capabilities, expanding the space for renewable energy consumption, and increasing wind power and photovoltaic installed capacity by 289.8kW and 30.4kW, respectively. Meanwhile, the demand response of electric vehicles and hydrogen fuel cell vehicles can reduce their own energy consumption during high-load periods, reducing the installed capacity of charging piles and hydrogen refueling stations, and also reducing the system's need to purchase energy from the external grid, resulting in a reduction of electricity purchase costs of 1.079 million yuan and a 10.4% reduction in the total system operating cost.

[0218] Comparing Schemes 3 and 4, with the addition of carbon-green certificate trading, the system can trade surplus carbon allowances with green certificates. The higher the proportion of renewable energy output on a typical day, the higher the number of green certificates available for trading in the system, as the introduction of green certificate trading is beneficial for increasing wind and solar power installed capacity. Simultaneously, the system's carbon trading mechanism constrains carbon emissions, preventing fluctuations in wind and solar power output from leading to increased electricity purchases and thus generating substantial carbon emissions, ensuring the system's low-carbon nature without sacrificing economic efficiency.

[0219] Comparing Schemes 4 and 5, Scheme 5 replaces fixed-price carbon trading with tiered carbon certificate trading. Firstly, tiered carbon trading allows for a tiered price increase in carbon emission allowances as the system's trading volume rises, guiding carbon emission sources to reduce emissions. Carbon emissions are reduced by 1.34% compared to Scheme 4, achieving low-carbon operation. Furthermore, the revenue from trading surplus carbon emission allowances is higher than the fixed-price revenue of Scheme 4, approximately 11.64%. Secondly, the inclusion of tiered carbon certificate trading in the system allows for revenue generation through the sale of more carbon certificates, further increasing the proportion of renewable energy output. This economic incentive encourages further increases in wind power and solar power installed capacity, reaching 17.8kW and 9.5kW respectively.

[0220] Comparing Schemes 5 and 6, given the uncertainty of system source and load, Scheme 6 necessitates increasing the installed capacity of energy supply units such as new energy sources and electric chillers to ensure energy supply and demand balance across different time periods, adding 1862.1kW and 107.7kW respectively. Simultaneously, addressing the mismatch between new energy output and the system's multi-energy load demand, hydrogen storage tanks, acting as energy time-shifting components, are combined with electrolyzers and hydrogen fuel cells to alleviate the pressure of multi-energy supply and demand balance. Their installed capacity increases by 188.8kW, 599.6kW, and 331.7kW compared to Scheme 5, respectively. Therefore, Scheme 6 increases investment costs and fixed maintenance costs by 20.22% and 20.64%, respectively. Furthermore, to mitigate source-load uncertainty, the external power grid increases electricity purchase costs by 521,000 yuan, and system carbon emissions increase by 1.861 million kilograms. Thus, Scheme 6 sacrifices economic efficiency and environmental friendliness to ensure the system's safe and stable operation under the worst-case scenario.

[0221] For the IES robust programming model proposed in this paper, taking into account the multi-energy flow of typical days in each season, and using the planning results of Scheme 5 as an example, the energy of electricity, heat, and hydrogen at different times during a typical day in the heating season is analyzed, and the results are as follows: Figure 5-7 As shown.

[0222] Depend on Figure 5-7It is known that during a typical heating season in the integrated energy system, wind and solar power primarily meet the electricity demands of conventional loads, electrolyzers, and electric vehicles. Between 24:00 and 6:00, solar power is not available, while wind power has superior output, resulting in overall output far exceeding conventional load demand. The electrolyzer absorbs excess electricity to supply hydrogen fuel cells or store it in hydrogen storage tanks, thus achieving wind power integration. Between 15:00 and 19:00, the electricity load is high, and with the increasing demand for electric vehicle charging, the combined output of wind and solar power cannot meet the demand. During this time, external grid power purchases increase, and hydrogen fuel cells utilize hydrogen to provide electricity. During the 24:00 period, to ensure the continuity of hydrogen storage tanks in the next scheduling period, the electrolyzer operates at its rated power to produce hydrogen, ensuring the safe and stable operation of the system.

[0223] For the integrated energy system to balance heat supply and demand at different times, due to the ambiguity of human perception of ambient temperature, the heat load, which is determined by indoor and outdoor temperatures, becomes adjustable. The system combines hydrogen fuel cells and electric heaters for heating. To alleviate the pressure on the system's heat supply, the indoor temperature is mostly kept at the minimum limit of 16 degrees Celsius. However, based on the combined heat and power characteristics of hydrogen fuel cells, during the peak electricity consumption period from 18:00 to 20:00, the heating capacity of hydrogen fuel cells increases, the indoor temperature rises, and the comfort of users with high heat loads is improved.

[0224] To explore the impact of source-load uncertainty on system planning and operation, this paper considers whether or not to construct a box-type uncertainty set as a factor when setting up the simulation examples (Schemes 5 and 6). Taking a typical day during the heating season as an example, the electrical load and wind power output after two-stage robust optimization are as follows: Figure 8-9 As shown.

[0225] from Figure 8-9 As can be seen, there are significant deviations between the robustly optimized wind power and electrical load power and the predicted power. Wind power output is lower than the predicted value in 12 time periods, while conventional electrical load is higher than the predicted value in 12 time periods. The periods of reduced wind power output are mainly concentrated between 11:00-15:00 and 18:00-24:00. During the 11:00-15:00 period, the electrical load is not only at its peak, but the actual demand is also higher than the predicted information. IES has to increase the output power of hydrogen fuel cells or increase grid power purchases to offset the uncertainty fluctuations between source and load and ensure system power balance. During the 18:00-24:00 period, photovoltaic output is close to zero, vehicle energy consumption is at its peak, and the electrolyzer's power demand is high to ensure the continuity of hydrogen storage tank scheduling within the dispatch cycle. The reduction in actual wind power output makes the system operating scenario more severe, directly increasing the pressure on the system's power supply and raising the system's energy purchase costs. The system maintains power balance in each time period at the expense of economic efficiency.

[0226] This invention provides a robust resource planning method for integrated energy systems that considers the low-carbon benefits of new energy vehicles. First, based on the analysis of the energy conversion, flow, and equipment operation characteristics of electricity-heat-cold-hydrogen, a framework for an integrated energy system is constructed. Second, the replacement of gasoline vehicles with new energy vehicles is studied, and the charging demand of new energy vehicles is simulated to construct a carbon-green certificate quota model for new energy vehicles. Then, considering the uncertainties of new energy output and multi-energy load demand, a two-stage robust planning model (min-max-min) is established with the goal of optimizing system planning economy. Finally, the effectiveness of the proposed planning method is verified through numerical examples.

Claims

1. A robust resource planning method for a comprehensive energy system that takes into account the low-carbon benefits of new energy vehicles, characterized in that, Includes the following steps: Step 1: Based on the analysis of the energy conversion, flow, and equipment operation characteristics of the integrated energy system (electricity-heat-cold-hydrogen), construct the framework of the integrated energy system; Step 2: Based on the replacement of fuel vehicles with new energy vehicles, simulate the charging demand of new energy vehicles, and construct a carbon-green certificate quota model for new energy vehicles using the baseline method; Step 3: Considering the uncertainty of new energy output and multi-energy load demand, and with the goal of optimizing the economic efficiency of system planning, establish a two-stage robust planning model of min-max-min. Step 4: Solve the two-stage robust programming model based on the column and constraint generation algorithm and the commercially efficient solver Gurobi.

2. The robust resource planning method for a comprehensive energy system considering the low-carbon benefits of new energy vehicles according to claim 1, characterized in that, Step 1, the construction of the integrated energy system framework, specifically involves: The construction of the IES (Environmental Engineering System) structure and energy flow, which includes the charging needs of new energy vehicles, involves multiple energy supply, conversion, and utilization links. In the integrated energy system, the electrical load is supplied by wind power, photovoltaics, the external power grid, and hydrogen fuel cells; the heat load is supplied by electric heaters and hydrogen fuel cells; the cooling load is mainly supplied by electric chillers; and the hydrogen load is supplied by an electrolyzer combined with a hydrogen storage tank. The energy coupling equipment mainly includes an electrolyzer, a hydrogen fuel cell, an electric heater, and an electric chiller. Among them, the upper-level energy supply, mainly wind power and photovoltaics, combined with the electrolyzer, electric heater, and electric chiller, breaks down the barriers between electrical energy and other heterogeneous energy conversion, meets the needs of lower-level hydrogen, heat, and cold energy users, and realizes real-time matching of the supply and demand relationship between the upper-level source and the lower-level load of the system. Modeling is based on the IES operation framework that includes the charging demand of new energy vehicles, including hydrogen energy utilization and multi-energy load demand models.

3. The robust resource planning method for integrated energy systems considering the low-carbon benefits of new energy vehicles according to claim 2, characterized in that, The specific model of the IES operating framework, which includes the charging needs of new energy vehicles, is as follows: The electrolyzer generates hydrogen energy by electrolyzing water, and divides the produced hydrogen energy into three parts: one part is supplied to the hydrogen fuel cell to convert it into electricity and heat energy; another part is supplied to the hydrogen refueling station to provide hydrogen refueling services for hydrogen fuel cell vehicles; and the excess part is stored in the hydrogen storage tank to alleviate the system's energy supply pressure during periods of high load demand. The model for hydrogen production by electrolyzing water in an electrolyzer is as follows: In equation (1), This represents the hydrogen energy output by the electrolyzer during time period t. η ec The energy conversion efficiency of the electrolytic cell; The energy consumption power of the electrolytic cell during time period t; and These are the upper and lower limits of energy consumption for electrolytic cells; and The upper and lower limits of the energy consumption power ramp-up of the electrolytic cell; This represents the energy consumption power of the electrolytic cell during time period t-1. The electrothermal energy conversion model for hydrogen fuel cells is as follows: In equation (2), The hydrogen energy consumed by the hydrogen fuel cell during time period t; and The hydrogen fuel cell outputs electrical and thermal energy during time period t; and To improve the electro-thermal conversion efficiency of hydrogen fuel cells; and and and For hydrogen fuel cells, the input of hydrogen energy, ramp-up, lower and upper limits of the thermoelectric ratio; The hydrogen energy consumed by the hydrogen fuel cell during the t+1 period; The energy model for hydrogen refueling stations is as follows: In equation (3), The demand for hydrogen refueling for hydrogen-powered vehicles during period t; Energy consumption for hydrogen refueling stations during time period t; The energy density of hydrogen gas; When the hydrogen supply in an integrated energy system exceeds demand, the hydrogen storage tank stores the excess hydrogen for backup, providing a stable and time-shiftable hydrogen supply for the integrated energy system. In equation (4), and The hydrogen storage capacity of the hydrogen storage tanks during time period t and time period t-1; Hydrogen is supplied to the hydrogen storage tank for the electrolyzer during time period t; The amount of hydrogen consumed by the hydrogen storage tank during time period t; and The initial hydrogen energy of the hydrogen storage tank and the hydrogen capacity at the end of the scheduling cycle; and These are the upper and lower limits of the hydrogen storage capacity of the hydrogen storage tank; On the energy demand side, the energy models for conventional electrical loads, electric vehicles, and hydrogen fuel cell vehicles are as follows: In equation (5), and These represent the electrical load, energy consumption of electric vehicles, and energy-powered vehicles during time period t before and after demand response; Let t represent the energy consumption of electrical load, electric vehicles, and hydrogen fuel cell vehicles in demand response during time period t; λ represent the load participation limit in demand response; and T represent the total dispatch period. Heating and cooling loads are adjusted by regulating indoor temperature to control heating and cooling load demands. Taking heating load as an example, the specific model is as follows: In equations (6), (7), and (8), and For heat load power demand before and after participating in demand response; T in The optimal indoor temperature; and Let t represent the indoor and outdoor temperatures during time period t; K, F, and V represent the heat transfer coefficient, building surface area, and volume, respectively; c air and ρ air For the specific heat capacity and density of indoor air; I max and I min The upper and lower limits of the temperature range that the human body can accept for comfort.

4. The robust resource planning method for integrated energy systems considering the low-carbon benefits of new energy vehicles according to claim 2, characterized in that, Step 2, the construction of the carbon-green certificate quota model for new energy vehicles, is as follows: In a comprehensive energy system, the energy load of gasoline vehicles is quantified into vehicle driving demand by utilizing the driving range per unit of fuel. Considering the driving range per unit of energy of new energy vehicles, a portion of the gasoline vehicle load is equivalent to the new energy vehicle load, as shown below: In equation (9), and The charging demand for electric vehicles and hydrogen fuel cell vehicles on a typical day during period t; The equivalent refueling demand for gasoline vehicles on a typical day during period t; For the equivalent preload demand of fuel-powered vehicles; L ev L hv and L oil The driving range per unit energy for electric vehicles, hydrogen fuel cell vehicles, and gasoline-powered vehicles; Considering the slow charging characteristics of electric vehicles and whether users urgently need to charge, the load of fuel vehicles is divided into two categories: one category is equivalent to hydrogen fuel cell vehicles with very short charging times, and the other category is equivalent to electric vehicles that require a certain charging time. Equation (9) is then modified as follows: In equation (10), and These represent the refueling demand for gasoline vehicles after the equivalent of hydrogen fuel cell vehicles and electric vehicles, respectively, with α and β representing the proportions of the equivalent of hydrogen fuel cell vehicles and electric vehicles, respectively. Considering that the energy sources of the integrated energy system include the external power grid and new energy sources, the power generation ratio K is used to represent the energy purchased from the external power grid for new energy vehicles, and it is assumed that the output of the external power grid is all generated by thermal power units: In equation (11), and This refers to the power generation of wind and solar power during period t. Purchase energy from the external power grid during time period t; Using the baseline method, with the carbon emissions of gasoline-powered vehicles as the benchmark, the difference between the carbon emissions of new energy vehicles and traditional gasoline-powered vehicles for the same driving distance is used as the initial carbon emission allowance obtained by the vehicle owner, as shown below: In equation (12), and For carbon emission allowances for electric vehicles and hydrogen fuel cell vehicles during period t; E tra Carbon emissions per unit distance traveled by a gasoline-powered vehicle; Considering the carbon emissions caused by charging new energy vehicles, as shown below: In equation (13), and Carbon emissions generated during the charging of electric vehicles and hydrogen fuel cell vehicles in time period t; E represents the electricity consumed by a hydrogen fuel cell vehicle during time period t. c Carbon emission factor per unit of electricity generated by thermal power units; Based on the renewable energy quota system and green certificate trading mechanism in the electricity market, a green certificate trading model for new energy vehicles is constructed as follows: In equation (14), and These are the system's tradable green electricity certificates for electric vehicles, the required quota of green electricity certificates, and the number of green electricity certificates obtained by electric vehicles for consuming new energy sources; and The system separately addresses the tradable green electricity certificates for hydrogen fuel cell vehicles, the required green electricity certificate quotas, and the number of green electricity certificates obtained by hydrogen fuel cell vehicles for consuming renewable energy; α ge and w ge This refers to the green electricity certificate quota factor and the quantitative factor for the number of renewable energy sources converted into green electricity certificates.

5. The robust resource planning method for integrated energy systems considering the low-carbon benefits of new energy vehicles according to claim 1, characterized in that, The method for establishing the two-stage robust programming model of min-max-min in step 3 is as follows: Wherein, the objective function for resource allocation of the integrated energy system is the annualized total cost C during the system planning period. total Minimum, C total Annualized investment cost C inv Fixed maintenance costs C fix Compared with typical daily total operating cost C ope It consists of two parts. C total =C inv +C fix +C ope (19) Investment cost C inv This mainly includes the annualized investment cost of electrolyzers, hydrogen storage tanks, hydrogen fuel cells, charging piles, and hydrogen refueling stations: In equation (15), Where E represents the type of resource planning. For wind power investment capacity; For photovoltaic investment capacity; For photovoltaic investment capacity; Investment capacity for hydrogen storage tanks; Investment capacity for hydrogen fuel cells; For the investment capacity of charging piles; Investment capacity for hydrogen refueling stations; Investment capacity for heating equipment; The investment capacity for the refrigeration unit; d = r(1 + r) m / (1+r) m -1 represents the resource investment recovery coefficient; r and m represent the equipment interest rate and service life, respectively; c e and For resource e, the unit investment cost and investment capacity; c main,e Fixed maintenance cost per unit capacity of equipment e; Operating cost C ope This mainly includes the operation and maintenance costs of each unit (C). fix External grid energy purchase cost C ep Car charging cost C vc Load demand response cost C dr And the carbon trading cost of new energy vehicles C carbon Green certificate transaction cost C green : C ope =C fix +C ep +C vc +C dr +C carbon +C green (16) In the formula, and The unit operation and maintenance cost for electrolyzers, hydrogen fuel cells, wind power, and photovoltaics; and These represent the operating power of electrolyzers, hydrogen fuel cells, wind power, and photovoltaics during a typical day t period in season s, respectively; S is the season number, s = 1; s = 2; s = 3 represents the cooling season, transitional season, and heating season, respectively; q s w represents the number of days in season s; e For system time-of-use pricing; The system purchases power from the grid during a typical daytime period of season s; w h and w oil The system's hydrogen and oil prices for the specified time period; and The loads of electric vehicles, hydrogen fuel cell vehicles, and gasoline vehicles during a typical daily time period t in season s are respectively; c ela , and These are the unit response power compensation prices for transferable load, heat load, and cooling load, respectively. and These represent the electricity load, electric vehicle, and hydrogen fuel cell vehicle demand response energy consumption during a typical daily t-hour period in season s. and The heat load demand before and after the demand response during a typical daytime period t in season s; and The cooling load demand before and after the demand response during a typical daytime period t in season s; and c gre The base price for carbon trading and green certificate trading for new energy vehicles; and These are the carbon emission allowances for electric vehicles and hydrogen fuel cell vehicles during a typical daytime period t in season s, respectively. and Carbon emissions from charging electric vehicles and hydrogen fuel cell vehicles during a typical daytime period of season s; and These are tradable green electricity certificates for electric vehicles and hydrogen fuel cell vehicles, respectively. To fully utilize the natural resources surrounding the system, the capacity of distributed renewable energy configuration should not be less than the maximum predicted renewable energy power per typical day, while allowing for a certain margin, but is limited by installation site and grid-connected power: In equation (23), k wt and k pv For wind power and solar power margin; and The maximum predicted power output for wind and solar power on typical days; and Wind power and solar power are limited by the maximum configuration capacity of the installation site; Similar to distributed renewable energy deployments, the capacity of charging piles and hydrogen refueling stations is limited by installation site constraints, but the total capacity should not be less than the typical daily charging demand for new energy vehicles. In the formula, k evc and k jqz Power margin for charging electric vehicles and hydrogen fuel cell vehicles; and This refers to the maximum permissible construction capacity for charging piles and hydrogen refueling stations. and To meet the maximum charging needs of electric vehicles and hydrogen fuel cell vehicles; Multi-energy power supply and demand balance constraints: In the formula, Energy consumption for electric heaters; Energy consumption of electric refrigeration unit; The electric heater outputs heat power; To output cooling power to the electric chiller; Considering the site constraints of the integrated energy system, there are also certain installation limits for the remaining planned resource capacity: In the formula, For the investment capacity of resource e; Resource e is limited by the maximum configuration capacity of the installation site; Due to limitations in tie-line power, the integrated energy system has certain restrictions on the amount of electricity it can purchase from the external grid. In the formula, Limits on the power exchanged between the service area and the power grid; In actual operation, IES faces many random factors. Considering the uncertainty of new energy output and load, robust optimization is introduced into the above deterministic model to construct a box-shaped uncertainty set U that takes into account source-load uncertainty: In the formula: and These are the predicted outdoor temperatures for wind power, solar power, gasoline vehicles, conventional electrical load, heating season, and cooling season, respectively; Δu wt,max , Δu pv,max , Δu oil,max , Δu el,max , Δu hl,max and Δu cl,max The maximum fluctuation deviation of outdoor temperature for wind power, photovoltaic, gasoline vehicles, conventional electrical loads, heating season, and cooling season; These are the outdoor temperatures during the heating and cooling seasons, respectively. and For binary variables, a value of 1 indicates that the indeterminate invariant has reached the boundary value of the interval; Γ wt ,Γ pv ,Γ oil ,Γ el ,Γ hl and Γ cl The adjustment parameter for each uncertainty source represents the total number of time periods during which the corresponding uncertainty source takes the boundary value of the fluctuation range within the scheduling period. The larger the adjustment parameter, the more conservative the obtained parameter, and vice versa. Taking into account the uncertainty of the system source load, the deterministic objective function of equation (16) is reconstructed into a two-stage robust optimization model, where the first-stage objective function is to minimize the investment cost, and the second-stage objective function is to minimize the system operating cost under adverse scenarios, as shown below: In the formula: y represents the continuous variable in the first-stage optimization problem, namely the resource allocation capacity of wind power, photovoltaics, etc. in the system; x is the continuous variable in the second-stage optimization problem, representing the operating power of the electrolyzer and the external power grid; u represents the uncertain source variable of wind power, photovoltaic output and multi-energy load demand in the system, as specifically expressed below: The above analysis shows that the two-stage robust optimization model shown in equation (32) can be expressed as: In the formula, A, B, C, D, E, F, G, Q1, Q2, Q3, and Q4 represent constant coefficient matrices or column vectors, respectively; Since the model in equation (36) is a multi-level problem, it is decomposed into the main problem shown in equation (37) and the subproblems shown in equation (38). First, given an initial scenario variable u, MP is solved to obtain the lower bound L of the optimal value of the original problem. out First, we use the initial resource allocation capacity set E1; then, we substitute E1 into the subproblem SP to obtain the upper bound U of the optimal value of the original problem. out The value of u is then returned to MP, and new constraints and variables x are added accordingly. The process involves relaxation followed by pursuit, alternating between solving MP and SP to form a C&CG loop, as follows: In the formula: the superscript * indicates a known quantity; the subscript k n k represents the current iteration number of the model; m η1 represents the upper limit of the number of iterations; η1 is an introduced auxiliary variable representing the optimal value of the objective function in the second stage; λ1, λ2, λ3, and λ4 are the dual variables corresponding to the constraints in the subproblem. As the C&CG algorithm iterates, the worst-case scenario generated after each SP solution is retained and passed to the current MP. The number of variables, constraints, and cutting planes in the MP continuously increases. out It will become increasingly larger; at the same time, the resource allocation capacity provided by MP to SP is also changing, and the optimized U out If the changes occur and the convergence criterion (40) is satisfied, the loop exits, the optimal solution is returned, and the resource allocation capacity and the operating plan of each typical system are obtained: In the formula, ξ is a very small positive real number, representing the convergence gap.

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