A logistics-driven approach to the collaborative operation of energy suppliers and industrial parks.

Abstract

This invention provides a method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling, belonging to the technical field of multi-energy collaborative operation between energy suppliers and industrial parks. To address the current technical problems of high energy consumption and high carbon emissions in logistics parks, this invention constructs a multi-energy demand model for the park driven by logistics scheduling, including a truck-forklift collaborative scheduling model and a park AGV transfer scheduling model. It further constructs an energy cost optimization model for the park, including hydrogen, electricity, gas, and cooling load models, and a comprehensive energy cost minimization model. The invention also constructs a hydrogen, electricity, gas, and cooling energy conversion model for the energy hub of the park's energy supplier, and an economic operation model for energy suppliers that jointly optimizes energy hubs and prices. Finally, it constructs a collaborative optimization operation model between energy suppliers and the park based on Stackelberg game theory. This invention is applied to the multi-energy collaborative operation of energy suppliers and industrial parks.

Description

Technical Field

[0001] This invention provides a method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling, belonging to the technical field of multi-energy collaborative operation between energy suppliers and industrial parks. Background Technology

[0002] Under the dual-carbon strategy, the logistics industry is accelerating its development towards green and intelligent manufacturing. As a multi-equipment collaborative operation scenario integrating warehousing, sorting, loading and unloading, and transshipment, logistics parks are facing increasingly prominent issues of high energy consumption and high carbon emissions. Among them, the energy demand of equipment such as electric heavy trucks, forklifts, automated guided vehicles (AGVs), and cold chain warehousing continues to grow, increasing the pressure on park energy supply. At the same time, the increasing penetration rate of renewable energy sources such as wind power and photovoltaics has exacerbated the time mismatch between energy output and logistics park load. Integrated Energy Systems (IES) can achieve multi-energy synergy optimization and flexible scheduling of electricity, cooling, heating, hydrogen, etc., and can improve the park's renewable energy absorption capacity and enhance system operation flexibility. Therefore, how to apply integrated energy systems to logistics parks to achieve low-carbon economic collaborative operation has become a key issue that urgently needs to be addressed.

[0003] Current research on energy management in logistics parks has included the construction of integrated energy system models incorporating electric heavy trucks and cold chain equipment, achieving multi-entity collaborative operation through shared energy storage mechanisms. In terms of multi-energy coupling and demand response, there are also integrated energy system models combining electricity, hydrogen, gas, and cooling, achieving optimal energy allocation through demand response and energy storage synergy. However, existing research often simplifies logistics loads to rigid or fixed loads, failing to fully consider the coupling between scheduling decisions and energy demand under the collaborative operation of multiple types of logistics equipment. It lacks refined modeling of the various types of energy loads caused by logistics operations, making it difficult to accurately depict the intrinsic relationship between logistics scheduling and energy demand.

[0004] Furthermore, game theory is gradually becoming an important tool in energy coordination and dispatch research. Related research focuses on issues such as energy pricing strategies, resource allocation schemes, and demand response mechanisms. This includes the proposal of an IES operator-user operation model based on master-slave game theory under the carbon trading mechanism, which reduces carbon emissions and improves the absorption of new energy sources. It also includes the proposal of a master-slave interaction model between the energy system and the load side based on Stackelberg game theory, which realizes source-load coordinated optimization. Although game theory has been widely applied to energy dispatch, existing research focuses on single dimensions such as price mechanisms or equipment dispatch, lacking a unified model for the coordinated processes of multi-energy flow, price, and operation. Logistics dispatch and energy optimization are usually decided by different entities, and an effective framework has not yet been formed to characterize the dynamic coupling relationship between energy demand, price signals, and logistics load.

[0005] In summary, while preliminary progress has been made in the research of integrated energy systems related to logistics, existing studies mainly focus on multi-energy coupling modeling such as electricity, heat, and gas, while the modeling and scheduling research of hydrogen energy, as a new type of energy carrier, remains relatively insufficient. In terms of energy management in logistics parks, existing studies often simplify logistics loads to rigid or fixed loads, failing to fully consider the coupling between scheduling decisions and energy demand under the coordinated operation of multiple types of logistics equipment. There is a lack of refined modeling of the various types of energy loads caused by logistics operations, making it difficult to accurately depict the intrinsic relationship between logistics scheduling and energy demand. Furthermore, although game theory has been widely applied to energy scheduling, existing studies mostly focus on single dimensions such as price mechanisms or equipment scheduling, lacking unified modeling of the coordinated processes of multi-energy flows, prices, and operations. Logistics scheduling and energy optimization are usually decided by different entities, and an effective framework has not yet been formed to characterize the dynamic coupling relationship between energy demand, price signals, and logistics load. Summary of the Invention

[0006] To address the technical problems existing in the background art, the present invention adopts the following technical solution: a method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling, comprising the following collaborative operation steps:

[0007] Step 1: Construct a logistics scheduling-driven multi-energy demand model for the park, including a truck-forklift collaborative scheduling model and a park AGV transfer scheduling model, to obtain the timing of energy demand such as forklift hydrogen refueling and AGV charging;

[0008] Step 2: Based on the multi-energy demand model of the park obtained in Step 1, further construct the park energy cost optimization model for logistics scheduling, including the park hydrogen, electricity, gas, and cooling load models, and the park comprehensive energy cost minimization model.

[0009] Step 3: Construct a joint optimization model of energy supplier EA for energy hub EH and price, including the energy conversion model of energy hub EH and the economic operation optimization model of energy supplier EA;

[0010] Step 4: Based on the energy cost optimization model of the park and the economic operation optimization model of the energy supplier EA, construct a collaborative optimization operation model between the energy supplier EA and the park based on the Stackelberg game.

[0011] Step 5: Based on the collaborative optimization operation model of energy supplier EA and park obtained in Step 4, the lower-level park optimization problem is transformed into complementary constraints using KKT conditions. This is then integrated with the upper-level energy supplier EA optimization problem into a single-level mixed integer programming model and solved to obtain the optimal game equilibrium solution that satisfies the operational objectives of both parties. The time-of-use energy sales price and multi-energy flow equipment operation strategy of energy supplier EA are output respectively, as well as the optimal charging and load response curves on the logistics park side, realizing collaborative optimization of the park in the dimensions of logistics scheduling, energy sales pricing, energy system operation, and demand response.

[0012] The specific method for constructing the truck-forklift collaborative scheduling model in step one is as follows:

[0013] After trucks arrive at the port, forklifts perform loading and unloading operations. Considering truck arrival times, berth allocation, and forklift scheduling, a weighted objective function is constructed to minimize total daily loading and unloading time, load fluctuations, and service time conflicts. The expression is:

[0014] (1);

[0015] in, For the weighted objective function, , The total number of trucks and forklifts, respectively. Loading and unloading time of the i-th truck The standard deviation of forklift load. The time overlap between forklift k serving trucks i and j. This is the load balancing penalty coefficient. This represents the penalty coefficient for time conflicts.

[0016] The constraints of the truck-forklift collaborative scheduling model include:

[0017] Arrival time constraints: To ensure orderly operations, trucks must arrive within a preset time window and the overall operation cycle must not exceed 24 hours, expressed as follows:

[0018] (2);

[0019] in, For the arrival time of truck i, and These are the earliest and latest allowed arrival times, respectively. For the truck i's departure time, This represents the maximum permissible cycle time for forklift operation, in minutes.

[0020] Arrival location constraints: Truck parking locations must meet boundary and safety distance requirements, expressed as:

[0021] (3);

[0022] in, For the parking location of truck i, Truck j is parked at a location where W is the width of the parking area. For truck width; Minimum safe interval;

[0023] Forklift resource allocation constraints: To avoid operational delays due to insufficient forklift configuration or resource waste due to excessive configuration, upper and lower limits are set for the number of forklifts allocated to each task. The expression is:

[0024] (4);

[0025] in, Assign a number of forklifts to each truck. and These represent the upper and lower limits of the number of forklifts required for each truck. Forklift k is a binary decision variable. If forklift k serves truck i, the value is 1; otherwise, the value is 0. The time overlap constraint is non-positive, meaning that the same forklift cannot serve multiple trucks simultaneously.

[0026] The specific method for constructing the AGV transfer scheduling model in step one is as follows:

[0027] Based on truck-forklift collaborative scheduling, AGVs are responsible for warehouse transfer after goods are allocated. A weighted summation objective function is constructed, with the expression:

[0028] (5);

[0029] in, Let M be the weighted objective function, and M be the total number of tasks. The total number of AGVs. Let i be the execution duration. The standard deviation of AGV load. Let i be the time overlap between service tasks i and j of the k-th AGV. For load balancing penalty coefficient, This represents the penalty coefficient for time conflicts.

[0030] The constraints of the AGV transfer scheduling model in the park include:

[0031] Time constraint: Start after truck unloading is complete and end within 24 hours, expressed as:

[0032] (6);

[0033] in, Let be the actual start time of the i-th task. Let be the earliest start time of the i-th task. For task completion time, This refers to the maximum permissible cycle time for AGV operations.

[0034] AGV resource allocation constraints: To avoid job delays due to insufficient AGV configuration or resource waste due to excessive configuration, upper and lower limits are set for the number of AGVs allocated to each task. The expression is:

[0035] (7);

[0036] in, Let be the number of AGVs assigned to the i-th task. and These represent the upper and lower limits of the forklifts required for each task. For binary decision variables, the value is 1 if the k-th AGV serves task j, and 0 otherwise. The time overlap constraint is a non-positive value, meaning that the same AGV cannot serve multiple tasks simultaneously.

[0037] The specific method for constructing the hydrogen, electricity, gas, and cooling load model of the park in step two is as follows:

[0038] Constructing a hydrogen load model: The hydrogen load in the park consists of the park's fixed hydrogen load and the hydrogen refueling load of hydrogen-powered forklifts. The hydrogen load model is constructed as follows:

[0039] (8);

[0040] in, The total power demand for hydrogen refueling in hour h is... For the park's fixed hydrogen load, The rated power for a single forklift when refueling with hydrogen. The number of forklifts refueling in the h-hour period;

[0041] Constructing an electrical load model: The park's electrical load consists of fixed electrical load and AGV charging load. An electrical load model is established, with the following expression:

[0042] (9);

[0043] in, The total charging power requirement for hour h. For the park's fixed power load, This represents the number of AGVs currently charging in hour h. Charging power for a single AGV;

[0044] Gas load model: The gas load of the industrial park consists of fixed gas consumption load and truck refueling load. A gas load model is established, expressed as follows:

[0045] (10);

[0046] in, The total power demand for gas refueling in hour h. For the park's fixed gas load, Rated power output for refueling a single truck. The number of trucks refueling in the h-hour period;

[0047] Cooling load model: The cooling load consists of the base load for maintaining the basic temperature and the task-related load during loading and unloading, including insulation and pre-cooling requirements. The cooling load model is established as follows:

[0048] (11);

[0049] in, The total cooling load for hour h is... Based on the basic cooling load, The precooling power for the hth hour is... The power is maintained for the h-th hour.

[0050] The specific method for constructing the comprehensive energy cost minimization model of the park in step two is as follows:

[0051] Based on the constructed multi-energy load demand, the charging and cooling timing of logistics equipment is optimized, and the expression of the comprehensive energy cost minimization model of the park is as follows:

[0052] (12);

[0053] in, The total energy cost during the scheduling period. , , , The prices for electricity, natural gas, cooling energy, and hydrogen energy are respectively for the h-th hour. , , , These represent the total loads of the charging station, gas station, refrigeration system, and hydrogen refueling station at hour h.

[0054] The constraints of the comprehensive energy cost minimization model for the industrial park include:

[0055] Energy demand constraint: Each device must complete energy replenishment within the feasible time window, expressed as:

[0056] (13);

[0057] in, , , This includes all AGV equipment, trucks, and forklifts. , , They are the i-th AGV, the j-th truck, and the k-th forklift, respectively. , , Let be variables of 0 and 1 respectively: whether the i-th AGV is charging, whether the j-th truck is refueling with gas, and whether the k-th forklift is refueling with hydrogen during time period h. , , These represent the rated charging power of a single AGV, the rated gas refueling flow rate of a single truck, and the rated hydrogen refueling flow rate of a single forklift. , , Let be the total amount of electricity that the i-th AGV needs to replenish throughout the entire cycle, the total amount of gas that the j-th truck needs to replenish throughout the entire cycle, and the total amount of hydrogen that the k-th forklift needs to replenish throughout the entire cycle.

[0058] Operation-Charging Mutual Exclusion Constraint: Logistics equipment cannot be charged simultaneously during the operation period, expressed as:

[0059] (14);

[0060] in, , , The variables are 0 and 1 respectively: whether the i-th AGV is in operation during the h-th time period, whether the j-th truck is in operation during the h-th time period, and whether the k-th forklift is in operation during the h-th time period.

[0061] Charging trigger constraint: Each device must meet the condition that its remaining energy is below a safety threshold before charging. For AGVs, charging will be triggered when the remaining power is insufficient to cover the energy consumption and safety margin of the next task. The expression is:

[0062] (15);

[0063] in, This is the current remaining battery power of the AGV. The amount of electricity required to complete the current task, This is to ensure a safe margin of power.

[0064] For forklifts, hydrogen refueling is triggered when the remaining hydrogen supply is insufficient to meet the current operation and safety margin. The optimal refueling time is selected during the interval between operations, expressed as:

[0065] (16);

[0066] in, The remaining hydrogen amount before the k-th forklift performs its task. Hydrogen consumption per forklift operation. This is a safety margin for hydrogen.

[0067] The specific method for constructing the energy conversion model of the energy hub EH in step three is as follows:

[0068] Establish energy conversion and operating cost models for each device in the energy hub (EH), including:

[0069] The energy conversion model for the P2Hy device is established, and the expression is as follows:

[0070] (17);

[0071] in, and These are the input and output power of the electrolytic cell, respectively. The energy conversion efficiency of the electrolyzer;

[0072] An energy conversion model for electrical energy storage is established, expressed as follows:

[0073] (18);

[0074] in, The state of charge of electrical energy storage. , These are the power and capacity of the electrical energy storage, respectively. For charge and discharge efficiency;

[0075] The energy conversion model for the CHP unit is established, and the expression is as follows:

[0076] (19);

[0077] in, This refers to the input power of the CHP unit; , These are the output electrical and thermal power of the CHP unit, respectively. , These are the gas-to-electricity efficiency and gas-to-heat efficiency of the CHP unit, respectively.

[0078] An energy conversion model for an absorption chiller is established, expressed as follows:

[0079] (20);

[0080] in, This refers to the cooling power output of the absorption chiller. The thermal power output of the CHP unit; The coefficient of performance (COP) of an absorption chiller represents the amount of cooling that can be converted from a unit of input heat.

[0081] The energy conversion model for the methane reactor is established, and the expression is as follows:

[0082] (twenty one);

[0083] in, To obtain the methane power; , These are hydrogen and CO2, respectively, input to the methane reactor; , These are the energy consumption conversion coefficients corresponding to unit methane production;

[0084] The equivalent state-of-charge models for different types of energy storage systems are established, and the expressions are as follows:

[0085] (twenty two);

[0086] in, These are the equivalent states of charge for electric energy storage, hydrogen storage tanks, thermal storage tanks, and gas storage tanks, respectively. These are the energy conversion efficiencies for electricity, hydrogen, heat, and gas energy storage, respectively. These are the capacities for energy storage in electricity, hydrogen, heat, and gas, respectively. These represent the charging and discharging power of electric, hydrogen, thermal, and gas energy storage, respectively.

[0087] The specific method for constructing the economic operation optimization model of energy supplier EA in step three is as follows:

[0088] Establishing net income for energy supplier EA The objective function is expressed as:

[0089] (twenty three);

[0090] in:

[0091] The expression for revenue from energy sales is:

[0092] (twenty four);

[0093] In the formula , , , These are the unit prices for electricity, hydrogen, cooling, and gas, respectively. , , , These represent the electricity, hydrogen, heat, and gas power sold to the park at time t, respectively.

[0094] The environmental benefits, including carbon credit revenue, green certificate trading revenue, and carbon tax on CO2 emissions from equipment, are expressed as follows:

[0095] (25);

[0096] In the formula Carbon credit revenue, including carbon emission reduction credit revenue obtained by EH through CO2 absorption via methanation equipment, is expressed as:

[0097] (26);

[0098] In the formula To utilize the carbon credits earned per unit of CO2, CO2 absorbed by the methanation equipment;

[0099] In the formula The revenue from green certificate trading, including green certificates obtained by EH using green and clean energy, is expressed as:

[0100] (27);

[0101] In the formula Green electricity for consumption, that is, electricity sold. The revenue per green certificate;

[0102] In the formula The carbon tax, including the carbon tax generated by CHP units exceeding the rated carbon emissions of energy supplier EA, is expressed as:

[0103] (28);

[0104] in, The price per unit of carbon tax. , CHP units Actual and rated CO2 emissions within the facility;

[0105] The energy purchase cost to the upstream mainnet is expressed as:

[0106] (29);

[0107] in, and The unit price for electricity and gas purchased by the energy hub EH from the upstream energy network. and The energy hub EH represents the power capacity for purchasing electricity and gas from the upstream energy network.

[0108] The operating cost of the energy hub EH, including the depreciation cost of energy storage and electrolyzer equipment, as well as the operation and maintenance costs of each piece of equipment, is expressed as:

[0109] (30);

[0110] in, The depreciation cost of energy storage, The depreciation cost of the electrolytic cell equipment. For the operation and maintenance costs of the CHP unit, For the operation and maintenance costs of the electric-to-heat conversion equipment, This refers to the operation and maintenance costs of the methane reactor.

[0111] The constraints for operating the energy conversion model of the energy hub EH include:

[0112] At each time t, different types of energy should satisfy their respective power balances, expressed as:

[0113] (31);

[0114] in, The purchased power input from the external power grid to the energy hub EH. For new energy power, The electrical power output of the CHP unit The charging and discharging power of electrical energy storage. For the electrical load power of users within the energy hub EH, This refers to the power consumption of the electro-hydrogen conversion equipment. This refers to the power consumption of the electric-to-heat conversion equipment; The hydrogen energy output of the electro-hydrogen conversion equipment. The charging and discharging power of hydrogen storage equipment, For the hydrogen load power of users within the energy hub EH, The hydrogen energy consumed by the methane reactor; This refers to the thermal power output of the CHP unit. The heat charge and discharge power of thermal energy storage The heat output of the electro-thermal conversion equipment. For the heat load power of users within the energy hub EH, This refers to the gas purchase capacity input from the external gas network to the energy hub EH. The power output of the methane reactor from the natural gas. The charging and discharging power of gas storage. For the gas load power of users within the energy hub EH, The gas power consumed by the CHP unit;

[0115] Different types of energy conversion equipment should meet their own power capacity constraints, as expressed in the following formula:

[0116] (32);

[0117] in, This refers to the power consumption of the electrolytic cell. This is the maximum power of the electrolytic cell. This refers to the power consumption of the electro-thermal conversion equipment. This refers to the maximum permissible power consumption of the electro-thermal conversion equipment. This refers to the gas consumption power of the CHP unit. This refers to the maximum permissible gas consumption power of the CHP unit. The power of the gas generated by the methane reactor. This represents the maximum permissible gas generation power of the methane reactor;

[0118] Different types of energy storage systems should meet their respective power constraints, expressed as follows:

[0119] (33);

[0120] in, The power of electrical energy storage, power , These are the minimum and maximum power of electrical energy storage, respectively. For the power of the hydrogen storage tank, , These are the minimum and maximum power ratings of the hydrogen storage tank, respectively. For the power of the thermal storage tank, , These are the minimum and maximum power of the thermal storage tank, respectively. The power of the gas storage tank. , These are the minimum and maximum power of the gas storage tank, respectively.

[0121] Each energy storage system should meet its own energy capacity constraint, expressed as:

[0122] (34);

[0123] in, For the capacity of electrical energy storage, , These are the minimum and maximum capacity constraints for electrical energy storage, respectively. For the capacity of the hydrogen storage tank, , These are the minimum and maximum capacity constraints for the hydrogen storage tank, respectively. For the capacity of the thermal storage tank, , These are the minimum and maximum capacity constraints for the thermal storage tank, respectively. For the capacity of the gas storage tank, , These are the minimum and maximum capacity constraints for the gas storage tank, respectively.

[0124] Since daily operating conditions vary, the energy storage system's energy at the end of each day must be kept within a reasonable range to meet the needs of the following day's operation. The expression is as follows:

[0125] (35);

[0126] in, , The constraint coefficient for electrical energy storage is... , The constraint factor for the hydrogen storage tank. , For the cold storage tank, , This represents the constraint coefficient of the gas storage tank;

[0127] The power constraint for purchasing energy from the superior energy grid is expressed as:

[0128] (36);

[0129] in, In order to purchase electricity from higher authorities, , To constrain the amount of electricity purchased from higher levels, In order to purchase gas from higher authorities, To constrain the power required for purchasing gas from higher authorities.

[0130] The specific method for step four is as follows:

[0131] The energy supplier EA and the integrated energy logistics park are modeled using a Stackelberg game model, with the expression as follows:

[0132] , , , ;

[0133] Where M stands for leader, namely energy supplier EA; and N stands for follower, namely the industrial park. Let p be the strategy set of energy aggregators, and p be the set of unit prices for different types of energy. Power sets for all equipment in the energy hub EH; A strategy set for the park; , These are the utility functions for leaders and followers, respectively.

[0134] In the Stackelberg game model, the leader, energy supplier EA, aims to maximize its net profit by providing multiple types of energy, as expressed in the following expression:

[0135] (37);

[0136] The follower's goal is to minimize its own energy cost through charging time, expressed as:

[0137] (38).

[0138] The specific method for step five is as follows:

[0139] Based on the KKT conditions, construct the Lagrange functions for both players in the game, with the following expressions:

[0140] (39);

[0141] in, (), () represent the Lagrangian functions of energy supplier EA and the industrial park, respectively, g Let λ be a constraint condition for the park, and let λ be a subset of Lagrange multipliers.

[0142] Leading energy supplier EA engages in a master game by optimizing the energy flow and unit prices of various energy types within its own energy hub EH. Decision variables include unit prices of various energy types. , , , Energy Hub EH Equipment Power , , , , , , and the power purchased from higher levels ;

[0143] The park optimizes its energy costs through price-driven demand-side response, with decision variables including the energy consumption time set. , , , ).

[0144] The beneficial effects of this invention compared to existing technologies are as follows: To address the current problems of high energy consumption and high carbon emissions in logistics parks, this invention proposes a multi-energy collaborative operation method between energy suppliers and parks based on logistics scheduling. It constructs a multi-equipment collaborative scheduling model for truck-forklift collaboration and AGV allocation on the logistics side, and further establishes a park energy cost optimization model based on logistics scheduling results. On this basis, it establishes a joint optimization economic model of energy supplier energy consumption (EH) and energy prices, and a master-slave game model between energy suppliers and logistics parks. By coupling energy prices, EH operation, and demand response for joint optimization, it achieves synergistic optimization of logistics scheduling, energy flow, and economic efficiency, thereby improving the system's economic efficiency and environmental benefits. Attached Figure Description

[0145] The present invention will be further described below with reference to the accompanying drawings:

[0146] Figure 1 This is a schematic diagram of the energy supplier and the industrial park of the present invention;

[0147] Figure 2 This is a flowchart illustrating the steps of the energy supplier and multi-energy collaborative operation method in the park according to the present invention;

[0148] Figure 3 This is a comparison chart of the scheduling results of the traditional method and the intelligent optimization in this embodiment of the invention;

[0149] Figure 4 This is a diagram showing the supply and demand balance of a park under the multi-energy collaborative operation method of energy suppliers and parks in this embodiment of the invention;

[0150] Figure 5 This is a diagram illustrating the effect of the charging time of the logistics equipment before optimization in an embodiment of the present invention.

[0151] Figure 6 This is a diagram showing the effect of optimized charging time for logistics equipment in an embodiment of the present invention.

[0152] Figure 7 This is a comparison chart of the price and load linkage optimization results for scenarios 3 and 4 in this embodiment of the invention;

[0153] Figure 8 This is a curve comparison chart of scenario 3 and scenario 4 before and after load optimization in the embodiments of the present invention. Detailed Implementation

[0154] This invention provides a method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling. It obtains the energy demand range of logistics equipment through logistics scheduling within the logistics park, and then achieves collaborative operation between the energy supplier and the park through a master-slave game theory. The invention constructs a logistics operation collaborative scheduling model on the logistics side, including a truck-forklift collaborative scheduling model and an AGV transfer scheduling model for the park. Based on this, it establishes a park energy cost optimization model based on logistics scheduling. Furthermore, the invention establishes a joint optimization model of energy suppliers' energy hubs and prices, further modeling the energy demand of energy suppliers and the park into a master-slave game theory model. By jointly optimizing the energy supplier's energy pricing strategy and energy hub operation mode, and collaboratively optimizing the park's energy demand response based on logistics scheduling, it achieves coordinated optimization of park logistics scheduling, energy flow allocation by energy suppliers, and system economy.

[0155] like Figure 1 As shown, this invention addresses the multi-dimensional collaborative and logistics-energy coupled optimization operation problem of logistics parks. By coordinating truck scheduling, forklift and AGV operations, energy supply and park load response, the logistics park achieves efficient logistics operation and multi-energy collaborative operation. The energy hub (EH) in EA includes a combined heat and power unit (CHP), an electric hydrogen production unit (P2H), a hydrogen-to-natural gas production unit (P2G), and various types of energy storage devices such as electricity, hydrogen, cooling, and gas. The logistics park includes operations such as truck arrival, loading and unloading, AGV transfer, cargo sorting and equipment charging.

[0156] To coordinate the operation of multiple entities in the park, a logistics-energy collaborative operation strategy based on Stackelberg game theory was constructed. The strategy comprehensively considers the dynamic operation and charging needs of various types of logistics equipment and the differentiated energy consumption characteristics of different types of goods. The EA guides the logistics park to achieve flexible response to energy demand through EH operation and dynamic pricing scheduling. On this basis, an energy supply and demand game model was constructed to improve the economy and efficiency of park operation.

[0157] The main steps of this invention include: First, addressing the issue of multi-equipment collaborative operation in logistics parks, a truck-forklift collaborative scheduling model and a park AGV transfer scheduling model are constructed to achieve collaborative optimization and rational resource allocation for multiple types of logistics equipment. Based on this, a park energy cost optimization model based on logistics scheduling is established, achieving demand response and load timing adjustment by optimizing charging timing. Furthermore, a master-slave game collaborative operation strategy between energy suppliers and logistics parks is constructed. The energy supplier (EA) guides demand-side response by jointly optimizing electricity, cooling, hydrogen, and gas sales prices and equipment operation strategies. The park optimizes its load by adjusting logistics operations and charging behavior, achieving synergistic optimization of energy flow and economy, and improving the overall efficiency of logistics and energy use in the park.

[0158] Logistics scheduling, as the core of park collaboration, directly determines the size and distribution of multi-energy loads through its timing arrangement. At the same time, energy performance also has a reciprocal effect on logistics operation efficiency. This invention constructs a multi-energy demand model for parks driven by logistics scheduling, clarifies operational rules, and derives load characteristics, providing data support for subsequent energy cost optimization and game theory analysis.

[0159] This invention addresses the inefficiency of traditional manual scheduling by constructing a truck-forklift collaborative scheduling model and an AGV (Automated Guided Vehicle) transfer scheduling model for industrial parks. This model obtains the timing of energy demands such as forklift hydrogen refueling and AGV charging, providing a foundation for subsequent multi-energy load calculations. Specifically:

[0160] (1) Truck-forklift collaborative scheduling model.

[0161] After trucks arrive at the port, forklifts are used for loading and unloading. To address the issues of low loading and unloading efficiency and uneven distribution of forklift resources, a weighted objective function is constructed by considering truck arrival time, berth allocation, and forklift scheduling, aiming to minimize total daily loading and unloading time, load fluctuations, and service time conflicts.

[0162] (1);

[0163] in, For the weighted objective function, , The total number of trucks and forklifts, respectively. Loading and unloading time of the i-th truck The standard deviation of forklift load (a measure of load balance). The time overlap between forklift k serving trucks i and j. This is the load balancing penalty coefficient. This represents the penalty coefficient for time conflicts.

[0164] Truck-forklift collaborative scheduling needs to meet certain constraints:

[0165] 1) Arrival Time Constraints. To ensure orderly operations, trucks must arrive within the preset time window, and the overall operation cycle must not exceed 24 hours.

[0166] (2);

[0167] in, For the arrival time of truck i, and These are the earliest and latest allowed arrival times, respectively. For the truck i's departure time, This represents the maximum permissible cycle time for forklift operation, expressed in minutes.

[0168] 2) Arrival location constraints. Truck parking locations must meet boundary and safety distance requirements:

[0169] (3);

[0170] in, For the parking location of truck i, Truck j is parked at a location where W is the width of the parking area. For truck width; This is the minimum safe interval.

[0171] 3) Forklift resource allocation constraints. To avoid operational delays due to insufficient forklift allocation or resource waste due to excessive allocation, upper and lower limits are set for the number of forklifts allocated to each task:

[0172] (4);

[0173] in, Assign a number of forklifts to each truck. and These represent the upper and lower limits of the number of forklifts required for each truck. Forklift k is a binary decision variable. If forklift k serves truck i, the value is 1; otherwise, it is 0. The time overlap constraint is non-positive, meaning that the same forklift cannot serve multiple trucks simultaneously.

[0174] (2) Park AGV transfer scheduling model.

[0175] Based on truck-forklift collaborative scheduling, AGVs are responsible for warehouse transfer after goods are sorted. To achieve comprehensive optimization of operational efficiency, resource utilization, and scheduling feasibility, a weighted summation objective function is constructed:

[0176] (5);

[0177] in, Let M be the weighted objective function, and M be the total number of tasks. The total number of AGVs. Let i be the execution duration. The standard deviation of AGV load. Let i be the time overlap between service tasks i and j of the k-th AGV. For load balancing penalty coefficient, This represents the penalty coefficient for time conflicts.

[0178] Similarly, AGV transfer scheduling must meet the following constraints:

[0179] 1) Time Constraints. Each task must be executed within a reasonable time window, that is, it must start after the truck is unloaded and end within 24 hours:

[0180] (6);

[0181] in, Let be the actual start time of the i-th task. Let be the earliest start time of the i-th task. For task completion time, This is the maximum permissible cycle time for AGV operations.

[0182] 2) AGV resource allocation constraints. To avoid insufficient AGV configuration leading to job delays or excessive configuration causing resource waste, upper and lower limits are set for the number of AGVs allocated to each task:

[0183] (7);

[0184] in, Let be the number of AGVs assigned to the i-th task. and These represent the upper and lower limits of the forklifts required for each task. For binary decision variables, the value is 1 if the k-th AGV serves task j, and 0 otherwise. The time overlap constraint is a non-positive value, meaning that the same AGV cannot serve multiple tasks simultaneously.

[0185] This invention constructs a park energy cost optimization model based on logistics scheduling, including:

[0186] (1) Multi-energy load model based on logistics scheduling.

[0187] Based on the scheduling scheme in the previous step, the hydrogen, electricity, gas, and cooling load models for the park are constructed as follows:

[0188] 1) Hydrogen Load Model. The hydrogen load of the park consists of the park's fixed hydrogen load and the hydrogen refueling load of hydrogen-powered forklifts. A hydrogen load model is constructed as follows:

[0189] (8);

[0190] in, The total power demand for hydrogen refueling in hour h is... For the park's fixed hydrogen load, The rated power for a single forklift when refueling with hydrogen. This represents the number of forklifts that refueled with hydrogen in the h-hour period.

[0191] 2) Electrical Load Model. The park's electrical load consists of fixed electrical load and AGV charging load. The electrical load model is established as follows:

[0192] (9);

[0193] in, The total charging power requirement for hour h. For the park's fixed power load, This represents the number of AGVs currently charging in hour h. Charging power for a single AGV.

[0194] 3) Gas Load Model. The gas load of the industrial park consists of fixed gas consumption load and truck refueling load. Its gas load model is as follows:

[0195] (10);

[0196] in, The total power demand for gas refueling in hour h. For the park's fixed gas load, Rated power output for refueling a single truck. This represents the number of trucks that refueled in the h-hour period.

[0197] 4) Cooling Load Model. The cooling load consists of the base load for maintaining the basic temperature and the task-related load during loading and unloading, including insulation and pre-cooling requirements. The cooling load model is established as follows:

[0198] (11);

[0199] in The total cooling load for hour h is... Based on the basic cooling load, The precooling power for the hth hour is... The power is maintained for the h-th hour.

[0200] (2) Energy cost optimization model for the park.

[0201] To reduce energy costs for logistics in the park, and under the premise of meeting equipment charging requirements, this paper constructs a comprehensive energy cost minimization model for the park based on the constructed multi-energy load demand, optimizes the charging and cooling timing of logistics equipment, and performs the following:

[0202] (12);

[0203] in The total energy cost during the scheduling period. , , , The prices for electricity, natural gas, cooling energy, and hydrogen energy are respectively for the h-th hour. , , , These represent the total load of the charging station, gas station, refrigeration system, and hydrogen refueling station at hour h.

[0204] The following constraints must be met when purchasing energy for the industrial park:

[0205] 1) Energy demand constraint: Each device must complete energy replenishment within its feasible time window:

[0206] (13);

[0207] in, , , This includes all AGV equipment, trucks, and forklifts. , , They are the i-th AGV, the j-th truck, and the k-th forklift, respectively. , , The variables are 0 and 1 respectively: whether the i-th AGV is charged in the h-th time period (1 for charging, 0 for not charging), whether the j-th truck is refueled with gas, and whether the k-th forklift is refueled with hydrogen. , , These represent the rated charging power of a single AGV, the rated gas refueling flow rate of a single truck, and the rated hydrogen refueling flow rate of a single forklift. , , Let be the total electricity required to replenish the i-th AGV throughout the entire cycle, the total gas required to replenish the j-th truck throughout the entire cycle, and the total hydrogen required to replenish the k-th forklift throughout the entire cycle, respectively.

[0208] 2) Operation-charging mutual exclusion constraint: Logistics equipment cannot be charged simultaneously during operation periods.

[0209] (14);

[0210] in, , , Let each be a 0-1 variable, representing whether the i-th AGV is in operation during the h-th time period (1 in operation, 0 idle), whether the j-th truck is in operation during the h-th time period, and whether the k-th forklift is in operation during the h-th time period.

[0211] 3) Charging Trigger Constraints. Each device must meet the condition that its remaining energy is below a safety threshold before charging: For AGVs, charging will be triggered when the remaining power is insufficient to cover the energy consumption and safety margin of the next task.

[0212] (15);

[0213] in, This is the current remaining battery power of the AGV. The amount of electricity required to complete the current task, This is a safety margin for battery power.

[0214] For forklifts: When the remaining hydrogen level in the forklift is insufficient to meet the current operation and safety margin, hydrogen refueling is triggered. The optimal refueling time is selected during the interval between previous and subsequent operations.

[0215] (16);

[0216] in, The remaining hydrogen amount before the k-th forklift performs its task. Hydrogen consumption per forklift operation. This is a safety margin for hydrogen.

[0217] The joint optimization model of energy supplier EA's energy hub EH and price constructed in this invention includes:

[0218] (1) Energy conversion model of EH.

[0219] EA maximizes its own interests by jointly optimizing energy prices and energy flow in EH. To this end, the first step is to establish energy conversion and operating cost models for each device in EH. For ease of unified modeling and comparison, different types of energy are uniformly represented in the form of power.

[0220] 1) The energy conversion model of the P2Hy device (i.e., the electrolyzer, Elz) is as follows:

[0221] (17);

[0222] in, and These are the input and output power of Elz, respectively. This refers to the energy conversion efficiency of Elz.

[0223] 2) The energy conversion model for electrical energy storage is as follows:

[0224] (18);

[0225] in, This refers to the state of charge of electrical energy storage. , These represent the power and capacity of the electrical energy storage, respectively. This refers to the charge / discharge efficiency.

[0226] 3) The energy conversion model for the CHP unit is as follows:

[0227] (19);

[0228] in, This refers to the input power of the CHP unit; , These are the output electrical and thermal power of the CHP unit, respectively. , These are the gas-to-electricity efficiency and gas-to-heat efficiency of the CHP unit, respectively.

[0229] 4) The energy conversion model of the absorption chiller (ARC) is as follows:

[0230] (20);

[0231] in, This refers to the cooling power output of the absorption chiller. The thermal power output of the CHP unit; The coefficient of performance (COP) of an absorption chiller represents the amount of cooling that can be converted from a unit of input heat.

[0232] 5) The energy conversion model for the methane reactor equipment is as follows:

[0233] (twenty one);

[0234] in, To obtain the methane power; , These are hydrogen and CO2, respectively, input to the methane reactor; , These are the energy conversion coefficients corresponding to unit methane production.

[0235] 6) The equivalent state of charge of different types of energy storage systems is as follows:

[0236] (twenty two);

[0237] in, These represent the equivalent states of charge for electric energy storage, hydrogen storage tanks, thermal storage tanks, and gas storage tanks, respectively. These are the energy conversion efficiencies for electricity, hydrogen, heat, and gas energy storage, respectively. These represent the storage capacities for electricity, hydrogen, heat, and gas, respectively. These represent the charging and discharging power of electric, hydrogen, thermal, and gas energy storage, respectively.

[0238] (2) EA’s economic operation optimization model.

[0239] EA's goal is to maximize net revenue by jointly optimizing energy prices and energy flows within the energy sector (EH). This includes revenue from energy sales. Environmental benefits Energy purchase costs from the upstream main network EH operating costs Therefore, EA net income can be established. The objective function is as follows:

[0240] (twenty three);

[0241] 1) Revenue from energy sales :

[0242] (twenty four);

[0243] in , , , These are the unit prices for electricity, hydrogen, cooling, and gas, respectively. , , , , , respectively, represent the power of electricity, hydrogen, heat, and gas sold to the park at time t.

[0244] 2) Environmental benefits This invention considers multiple types of environmental benefits during EH operation, including carbon credit revenue, green certificate trading revenue, and carbon tax on CO2 emissions from the equipment:

[0245] (25);

[0246] Carbon credit income This includes the carbon emission reduction credits that EH obtains by absorbing CO2 through methanation equipment:

[0247] (26);

[0248] in, To utilize the carbon credits earned per unit of CO2 (¥ / kg), CO2 (kg) absorbed by the methanation unit.

[0249] Green certificate trading profits This includes renewable energy certificates (RECs) obtained by EH (Environmental Health Organization) using green and clean energy. Note this. Green electricity for consumption, that is, electricity sold.

[0250] (27);

[0251] in, The revenue per green certificate.

[0252] carbon tax This includes carbon taxes generated from CHP units exceeding their rated carbon emissions from the EA:

[0253] (28);

[0254] in, The price per unit of carbon tax. , CHP units The actual and rated CO2 emissions.

[0255] 3) Energy purchase cost of EH :

[0256] (29);

[0257] in, and The unit price for EH to purchase electricity and gas from the upstream energy network. and EH represents the power purchased from the upstream energy network for electricity and gas, respectively.

[0258] 4) Operating costs of EH .

[0259] This mainly includes the depreciation costs of energy storage and Elz equipment, as well as the operation and maintenance costs of each piece of equipment.

[0260] (30);

[0261] in, The depreciation cost of energy storage, For the depreciation cost of Elz equipment, For the operation and maintenance costs of the CHP unit, For the operation and maintenance costs of the electric-to-heat conversion equipment, This refers to the operation and maintenance costs of the methane reactor.

[0262] To ensure the proper operation of EH, the following constraints must be met:

[0263] 1) At each time t, different types of energy should satisfy their respective power balance.

[0264] (31);

[0265] in, This refers to the purchased power input from the external power grid to EH. For new energy power, The electrical power output of the CHP unit The charging and discharging power of electrical energy storage. For the electrical load power of users within the EH, This refers to the power consumption of the electro-hydrogen conversion equipment. This refers to the power consumption of the electro-thermal conversion equipment. The hydrogen energy output of the electro-hydrogen conversion equipment. The charging and discharging power of hydrogen storage equipment, For the hydrogen load power of users within EH, The hydrogen energy consumed by the methane reactor. This refers to the thermal power output of the CHP unit. The heat charge and discharge power of thermal energy storage The heat output of the electro-thermal conversion equipment. The heat load power of users within EH, This refers to the gas purchase power input from the external gas network to EH. The power output of the methane reactor (M) is the natural gas output. The charging and discharging power of gas storage For the gas load power of users within the EH, The gas power consumed by the CHP unit.

[0266] 2) Different types of energy conversion equipment should meet the constraints of their own power capacity.

[0267] (32);

[0268] in, This refers to the power consumption of Elz. This is the maximum power of Elz. This refers to the power consumption of the electro-thermal conversion equipment. This refers to the maximum permissible power consumption of the electro-thermal conversion equipment. This refers to the gas consumption power of the CHP unit. This refers to the maximum permissible gas consumption power of the CHP unit. The power of the gas generated by the methane reactor. This represents the maximum permissible gas generation power of the methane reactor.

[0269] 3) Different types of energy storage systems should meet their respective power constraints.

[0270] (33);

[0271] in, The power of electrical energy storage, power , These are the minimum and maximum power of electrical energy storage, respectively. For the power of the hydrogen storage tank, , These are the minimum and maximum power ratings of the hydrogen storage tank, respectively. For the power of the thermal storage tank, , These are the minimum and maximum power of the thermal storage tank, respectively. The power of the gas storage tank. , These are the minimum and maximum power of the gas storage tank, respectively.

[0272] 4) Each energy storage system should meet its own energy capacity constraints.

[0273] (34);

[0274] in, For the capacity of electrical energy storage, , These are the minimum and maximum capacity constraints for electrical energy storage, respectively. For the capacity of the hydrogen storage tank, , These are the minimum and maximum capacity constraints for the hydrogen storage tank, respectively. For the capacity of the thermal storage tank, , These are the minimum and maximum capacity constraints for the thermal storage tank, respectively. For the capacity of the gas storage tank, , These are the minimum and maximum capacity constraints for the gas storage tank, respectively.

[0275] 5) Due to varying daily operating conditions, the energy storage system's energy level at the end of each day must be kept within a reasonable range to meet the needs of the following day's operation, i.e.:

[0276] (35);

[0277] in, , This is the constraint coefficient for electrical energy storage. , This represents the constraint factor for the hydrogen storage tank. , This represents the constraint coefficient of the cold storage tank. , This represents the constraint coefficient of the gas storage tank.

[0278] 6) Power constraints for purchasing energy from the superior energy grid.

[0279] (36);

[0280] in, In order to purchase electricity from higher authorities, , To constrain the amount of electricity purchased from higher levels. In order to purchase gas from higher authorities, To constrain the power required for purchasing gas from higher authorities.

[0281] In an integrated energy logistics park, both EA (Energy Provider) and the park aim to maximize their own interests. They are mutually constrained, forming a game theory relationship. Driven by energy prices, the revenues of EA and the park depend on their respective decision variables: EA's energy selling price and EH (Energy Harness) equipment power, and the park's energy consumption curve. Therefore, we model the relationship between EA and the park in the integrated energy logistics park using a Stackelberg game model. , , , Where M stands for Leader, or EA; and N stands for Follower, or Park. Let p be the strategy set of the energy aggregator, where p is the set of unit prices for different types of energy. For the power sets of each device in EH; A strategy set for the park; , These are the utility functions for the leader and the follower, respectively.

[0282] In the Stackelberg game model, the leader (EA) aims to maximize its net gain by providing multiple types of energy.

[0283] (37);

[0284] The goal of the follower (park) is to minimize its own energy costs by adjusting charging time.

[0285] (38).

[0286] The proposed master-slave game model involves nonlinear objective functions and constraints. Furthermore, complex nonlinear relationships exist between the master and slave strategies, typically requiring iterative solutions. KKT (Karush-Kuhn-Tucker) conditions can transform the nested optimization problem into a constrained single-layer optimization problem, effectively handling the interdependence and nonlinear characteristics of the proposed master-slave strategy model. Compared to traditional iterative solutions, the KKT-based method not only improves computational efficiency but also guarantees the optimality of the solution.

[0287] Based on the KKT conditions, the Lagrangian functions for both sides of the game can be constructed as follows:

[0288] (39);

[0289] in, (), () are the Lagrangian functions of EA and the park, respectively, g The constraints for the park are given. λ is a subset of Lagrange multipliers.

[0290] The leader, EA, engages in a master game by optimizing its own energy flow (EH) and the unit prices of various energy types. Its decision variables include the unit prices of various energy types (EH, EH, and EH). , , , EH equipment power , , , , , , and the power purchased from higher levels The park optimizes its energy costs through price-driven demand-side response. Its decision variables include the set of energy consumption times (…). , , , ).

[0291] The strategy process adopted in this invention is as follows: Figure 2 As shown in the diagram, firstly, to address the differentiated energy demands arising from truck arrivals, forklift and AGV operations, and general and cold chain cargo, the logistics scheduling module generates 24-hour load curves for electricity, hydrogen, cooling, and natural gas. The energy supply side (EA) then configures corresponding multi-energy flow equipment, including CHP, P2H, P2G, and various types of energy storage, based on this information, and dynamically adapts to logistics energy demands. Secondly, based on the Stackelberg game theory framework, an EA net revenue model based on EA energy prices and multi-energy flow path optimization, and a park-side energy cost model based on logistics operation demand response are constructed. Finally, using KKT conditions, the lower-level park optimization problem is transformed into complementary constraints, integrated with the upper-level EA optimization problem into a single-layer mixed-integer programming model, and solved to obtain the optimal game equilibrium solution that satisfies the operational objectives of both parties. This outputs the EA's time-of-use energy price and multi-energy flow equipment operation strategy, as well as the optimal charging and load response curves for the logistics park side, ultimately achieving collaborative optimization of the smart logistics park across logistics scheduling, energy pricing, energy system operation, and demand response dimensions.

[0292] To verify the effectiveness of the proposed logistics-driven multi-energy collaborative master-slave game theory method, an example analysis was conducted based on historical operational data of a modern integrated logistics park and new energy data of a wind and solar demonstration base. The proposed model and strategy were solved using MATLAB and the CPLEX optimization toolbox. The main parameter settings are shown in Table 1 below:

[0293] Table 1. Parameter definitions and values ​​for the embodiments.

[0294]

[0295] In an embodiment of the present invention, to verify the effectiveness of the park logistics optimization scheduling model, two comparison scenarios are specifically set up:

[0296] (1) Scenario A1: Traditional fixed scheduling method, that is, the arrival time of trucks is randomly distributed, each truck is fixedly assigned 3 forklifts, and the AGV adopts a single fixed configuration;

[0297] (2) Scenario A2: The logistics scheduling method proposed in this paper is adopted.

[0298] The scheduling results are as follows Figure 3 As shown, by Figure 3 It can be seen that, in terms of logistics unloading operations, the total unloading time under the proposed scheduling method is 1450 minutes, a reduction of 10.7% compared to the traditional scheduling method. Regarding AGV scheduling, the total AGV completion time under the proposed method is 1445 minutes, a reduction of 16.1% compared to the traditional scheduling method. This is mainly because the traditional method does not consider the load differences of different trucks and AGVs, leading to redundancy or idle waiting of logistics equipment. The proposed scheduling method can dynamically adjust the configuration of forklifts and AGVs according to truck load and AGV transfer needs, making the task load more balanced, thereby improving the overall system efficiency.

[0299] Depend on Figure 3 A comparison of the results from traditional scheduling reveals that, under traditional manual scheduling, truck parking locations are relatively concentrated, easily causing congestion in loading and unloading areas and increasing safety risks. The method presented in this paper uses dynamic planning of truck parking locations to maintain vehicle spacing above a safe threshold, ensuring safe operational intervals while avoiding passageway blockage.

[0300] In embodiments of the present invention, to verify the effectiveness of the proposed logistics-driven energy supplier-park multi-energy collaborative master-slave game strategy, four comparison scenarios are set up:

[0301] (1) Scenario B1: Traditional logistics scheduling strategy, logistics equipment adopts the "use and charge" mode, and energy prices are fixed.

[0302] (2) Scenario B2: The proposed logistics scheduling strategy adopts the "use and charge" mode for logistics equipment, and the energy price is fixed.

[0303] (3) Scenario B3: The proposed logistics scheduling strategy adopts the "use and charge" mode for logistics equipment, and the energy price is adjustable;

[0304] (4) Scenario B4: The proposed logistics scheduling strategy allows for adjustable timing of logistics equipment charging and adjustable energy prices.

[0305] Table 2 below shows the results for four scenarios. Figure 4 The energy balance results for the park's optimized scheduling in scenario 4.

[0306] Table 2 Comparison of results under different scenarios

[0307]

[0308] (1) Analysis of results under different operating methods.

[0309] According to the results in Table 2, compared to Scenario B1 (traditional scheduling, fixed energy price), Scenario B2, after adopting the proposed logistics scheduling strategy, saw a 4.5% reduction in the park's energy costs and a 9.9% increase in total EA net revenue. This is because the intelligent collaborative scheduling of forklifts and AGVs improved equipment configuration and operational efficiency, thereby enhancing overall energy utilization efficiency. Furthermore, after adopting a dynamic pricing mechanism in Scenario B3, compared to Scenario B2, the park's costs decreased by 2.7%, while the total EA net revenue further increased by 7.3%, achieving synergistic optimization of park costs and EA revenue.

[0310] Compared to the baseline scenario B1, scenario B4, under the proposed collaborative optimization strategy, reduced park costs by 7.0% while increasing EA's total net revenue by 20.0%. This is mainly due to the park's further optimization of charging timing based on logistics scheduling optimization, and EA's joint optimization of energy prices and EH energy flow. Through the coordinated adjustment of EH equipment operation, energy prices, and energy demand response, both parties in the game achieved a simultaneous improvement in the economics of EA and the logistics park.

[0311] Furthermore, regarding the balance of energy supply and demand, by Figure 4 It can be seen that the park's electricity, hydrogen, cooling, and natural gas systems achieve multi-energy synergy and supply-demand balance. On the electricity side, wind power, photovoltaic (PV), and CHP units provide energy collaboratively. PV handles peak daytime output, CHP provides stable base load, energy storage achieves peak shaving and valley filling through "off-peak charging-peak discharging," and the electrolyzer absorbs wind and solar power to produce hydrogen during periods of renewable energy surplus. On the hydrogen supply and demand side, the hydrogen production from the electrolyzer matches the output of wind and solar power. Furthermore, the produced hydrogen supplies the methane reactor for CO2 resource utilization. The hydrogen storage tank achieves a balance between supply and demand fluctuations. The results show that the proposed model achieves full absorption of wind and solar power and coordinated operation of multiple energy flows. It also verifies the effectiveness of the game theory strategy.

[0312] (2) Optimization analysis of the charging time sequence of the park under the master-slave game.

[0313] Figure 5 and Figure 6 Comparison results are presented before and after the optimization of the charging sequence in the park (Scenario 3 and Scenario 4). Figure 5 and Figure 6Before optimization, forklift hydrogen refueling and AGV charging were primarily driven by logistics operations, with charging demand passively distributed across different time periods based on loading, unloading, and transfer tasks, with little consideration given to time-of-use electricity pricing and equipment operating characteristics. For example, forklift hydrogen refueling was mainly concentrated between 6:00 and 8:00 and between 14:00 and 16:00, while AGV charging was concentrated between 8:00 and 10:00 and between 16:00 and 20:00. However, these charging periods overlapped significantly with peak energy price periods, lacking proactive response to price signals. This not only increased the electricity and hydrogen costs in the industrial park but also easily led to concentrated fluctuations in electricity and hydrogen loads, increasing the peak-valley difference in the energy efficiency (EA) and hindering the stable and economical operation of the integrated energy system.

[0314] In contrast, after optimization, the timing of forklift hydrogen refueling and AGV charging can be adaptively adjusted according to electricity and hydrogen prices. Specifically, the hydrogen refueling time for forklifts shifts later in the 10-15 hour low-hydrogen-price range, while the charging time for AGVs is dispersed between 10-16 hours, significantly reducing the overlap between charging activities and peak energy price periods, and achieving effective response to time-of-use price signals. This is mainly due to the constructed EA-logistics park master-slave game strategy. The park aims to minimize overall energy costs, and under the premise of ensuring continuous operation of logistics equipment and the safety constraints of hydrogen and electricity, it coordinates and optimizes the timing of various charging loads. After optimization, not only is the overall energy purchase cost of the park reduced, but the concentrated fluctuation of charging and hydrogen refueling loads is also alleviated, reducing the EA peak-valley difference. The demand response strategy based on the park not only improves the collaborative operation capability of multiple energy flows, but also achieves coordination and unity between logistics operation needs and supplier economic operation.

[0315] (3) Analysis of the results of price-user demand collaborative optimization.

[0316] further, Figure 7 and Figure 8 The energy price-load linkage curves after EA-park optimization and the comparison results before and after energy demand optimization are presented respectively.

[0317] Depend on Figure 5 and Figure 6 It is evident that in scenario B3, which does not consider demand response, centralized AGV scheduling and dense truck arrivals lead to a significant increase in charging and cooling loads during peak logistics periods such as 8h-12h and 18h-22h. This causes a rapid increase in localized loads within the park, increasing the energy supply pressure on EA and necessitating the purchase of additional external energy, thus raising operating costs. Simultaneously, due to user constraints imposed by logistics operations, their responsiveness to energy prices is limited, resulting in weak demand elasticity. Figure 7 and Figure 8 During certain periods, electricity and cooling prices reached their limits, and EA adjusted energy prices to their boundary values ​​in order to maximize profits.

[0318] In contrast, Scenario B4 further introduces a demand response mechanism on top of the proposed logistics scheduling. This allows the park to proactively adjust its energy consumption behavior based on changes in energy prices and, by combining AGV operation schedules with charging plans, shift some loads during specific periods. This effectively reduces peak loads and alleviates the pressure on EA's energy supply and equipment overload issues. Ultimately, the loads of electricity, cooling, hydrogen, and natural gas all exhibit significant "peak shaving and valley filling" characteristics, promoting a dynamic balance between energy supply and demand. This results in a 1.6% reduction in overall energy costs compared to Scenario B3, improving the stability and economy of the entire system.

[0319] Furthermore, Figure 7 and Figure 8 The results show that, compared to scenario B3, the proposed game theory strategy has adjusted aspects such as AGV charging timing, cooling supply, and hydrogen consumption. Specifically, the park's load increases during the low-electricity-price period from 1 am to 8 am and the relatively quiet period from 10 pm to 11 am, indicating that some AGV charging load has been shifted to the low-price period. This allows for off-peak charging of AGVs while ensuring normal daytime logistics operations, effectively reducing the overall energy cost of the park and alleviating energy supply pressure during peak hours.

[0320] (4) Carbon emission results and analysis under different scenarios.

[0321] Table 3 below presents the carbon emission comparison results under different scenarios. Compared with logistics scheduling optimization or price optimization alone, the proposed method significantly improves the system's carbon emission reduction. Compared with the baseline scenario B1 (no logistics scheduling optimization, no game theory), the system's net carbon emissions in scenario B4 decreased from -8.2 t to -25.69 t (the negative sign indicates that the system uses methanation equipment to absorb CO2). This shows that the proposed collaborative optimization strategy can effectively improve the system's low-carbon operation level.

[0322] Table 3 Comparison of carbon emissions under different scenarios

[0323]

[0324] Furthermore, by introducing load response and master-slave game mechanisms based on price optimization in scenario B3, net carbon emissions in scenario B4 were further reduced by 38.1%. This is mainly because the master-slave game mechanism guides flexible energy use in the park through price signals, optimizing AGV operations and charging timing while increasing the conversion of hydrogen to methane and enhancing CO2 absorption. Simultaneously, it reduces the high-load operation requirements of the CHP unit, thereby reducing system carbon emissions.

[0325] Ultimately, through multi-energy flow synergistic optimization, the park's economic efficiency and environmental benefits were synergistically improved.

[0326] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling, characterized by: The following collaborative operation steps are included: Step 1: Construct a logistics scheduling-driven multi-energy demand model for the park, including a truck-forklift collaborative scheduling model and a park AGV transfer scheduling model, to obtain the timing of energy demand such as forklift hydrogen refueling and AGV charging; Step 2: Based on the multi-energy demand model of the park obtained in Step 1, further construct the park energy cost optimization model for logistics scheduling, including the park hydrogen, electricity, gas, and cooling load models, and the park comprehensive energy cost minimization model. Step 3: Construct a joint optimization model of energy supplier EA for energy hub EH and price, including the energy conversion model of energy hub EH and the economic operation optimization model of energy supplier EA; Step 4: Based on the energy cost optimization model of the park and the economic operation optimization model of the energy supplier EA, construct a collaborative optimization operation model between the energy supplier EA and the park based on the Stackelberg game. Step 5: Based on the collaborative optimization operation model of energy supplier EA and park obtained in Step 4, the lower-level park optimization problem is transformed into complementary constraints using KKT conditions. This is then integrated with the upper-level energy supplier EA optimization problem into a single-level mixed integer programming model and solved to obtain the optimal game equilibrium solution that satisfies the operational objectives of both parties. The time-of-use energy sales price and multi-energy flow equipment operation strategy of energy supplier EA are output respectively, as well as the optimal charging and load response curves on the logistics park side, realizing collaborative optimization of the park in the dimensions of logistics scheduling, energy sales pricing, energy system operation, and demand response.

2. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 1, characterized in that: The specific method for constructing the truck-forklift collaborative scheduling model in step one is as follows: After trucks arrive at the port, forklifts perform loading and unloading operations. Considering truck arrival times, berth allocation, and forklift scheduling, a weighted objective function is constructed to minimize total daily loading and unloading time, load fluctuations, and service time conflicts. The expression is: （1）； in, For the weighted objective function, , The total number of trucks and forklifts, respectively. Loading and unloading time of the i-th truck The standard deviation of forklift load. The time overlap between forklift k serving trucks i and j. This is the load balancing penalty coefficient. This is the penalty coefficient for time conflicts; The constraints of the truck-forklift collaborative scheduling model include: Arrival time constraints: To ensure orderly operations, trucks must arrive within a preset time window and the overall operation cycle must not exceed 24 hours, expressed as follows: （2）； in, For the arrival time of truck i, and These are the earliest and latest allowed arrival times, respectively. For the truck i's departure time, This represents the maximum permissible cycle time for forklift operation, expressed in minutes. Arrival location constraints: Truck parking locations must meet boundary and safety distance requirements, expressed as: （3）； in, For the parking location of truck i, Truck j is parked at a location where W is the width of the parking area. For truck width; Minimum safe interval; Forklift resource allocation constraints: To avoid operational delays due to insufficient forklift configuration or resource waste due to excessive configuration, upper and lower limits are set for the number of forklifts allocated to each task. The expression is: （4）； in, Assign a number of forklifts to each truck. and These represent the upper and lower limits of the number of forklifts required for each truck. Forklift k is a binary decision variable. If forklift k serves truck i, the value is 1; otherwise, the value is 0. The time overlap constraint is non-positive, meaning that the same forklift cannot serve multiple trucks simultaneously.

3. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 2, characterized in that: The specific method for constructing the AGV transfer scheduling model in step one is as follows: Based on truck-forklift collaborative scheduling, AGVs are responsible for warehouse transfer after goods are allocated. A weighted summation objective function is constructed, with the expression: （5）； in, Let M be the weighted objective function, and M be the total number of tasks. The total number of AGVs. Let i be the execution duration. The standard deviation of AGV load. Let i be the time overlap between service tasks i and j of the k-th AGV. For load balancing penalty coefficient, This is the penalty coefficient for time conflicts; The constraints of the AGV transfer scheduling model in the park include: Time constraint: Start after truck unloading is complete and end within 24 hours, expressed as: （6）； in, Let be the actual start time of the i-th task. Let be the earliest start time of the i-th task. For task completion time, This refers to the maximum permissible cycle time for AGV operations. AGV resource allocation constraints: To avoid job delays due to insufficient AGV configuration or resource waste due to excessive configuration, upper and lower limits are set for the number of AGVs allocated to each task. The expression is: （7）； in, Let represent the number of AGVs assigned to the i-th task. and These represent the upper and lower limits of the forklifts required for each task. For binary decision variables, the value is 1 if the k-th AGV serves task j, and 0 otherwise. The time overlap constraint is a non-positive value, meaning that the same AGV cannot serve multiple tasks simultaneously.

4. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 3, characterized in that: The specific method for constructing the hydrogen, electricity, gas, and cooling load model of the park in step two is as follows: Constructing a hydrogen load model: The hydrogen load in the park consists of the park's fixed hydrogen load and the hydrogen refueling load of hydrogen-powered forklifts. The hydrogen load model is constructed as follows: （8）； in, The total power demand for hydrogen refueling in hour h is... For the park's fixed hydrogen load, The rated power for a single forklift when refueling with hydrogen. The number of forklifts refueling in the h-hour period; Constructing an electrical load model: The park's electrical load consists of fixed electrical load and AGV charging load. An electrical load model is established, expressed as follows: （9）； in, The total charging power requirement for hour h. For the park's fixed power load, This represents the number of AGVs currently charging in hour h. Charging power for a single AGV; Gas load model: The gas load of the industrial park consists of fixed gas consumption load and truck refueling load. A gas load model is established, expressed as follows: （10）； in, The total power demand for gas refueling in hour h. For the park's fixed gas load, Rated power output for refueling a single truck. The number of trucks refueling in the h-hour period; Cooling load model: The cooling load consists of the base load for maintaining the basic temperature and the task-related load during loading and unloading, including insulation and pre-cooling requirements. The cooling load model is established as follows: （11）； in, The total cooling load for hour h is... Based on the basic cooling load, The precooling power for the hth hour is... The power is maintained for the h-th hour.

5. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 4, characterized in that: The specific method for constructing the comprehensive energy cost minimization model of the park in step two is as follows: Based on the constructed multi-energy load demand, the charging and cooling timing of logistics equipment is optimized, and the expression of the comprehensive energy cost minimization model of the park is as follows: （12）； in, The total energy cost during the dispatch period. , , , The prices for electricity, natural gas, cooling energy, and hydrogen energy are respectively for the h-th hour. , , , These represent the total loads of the charging station, gas station, refrigeration system, and hydrogen refueling station at hour h. The constraints of the comprehensive energy cost minimization model for the industrial park include: Energy demand constraint: Each device must complete energy replenishment within the feasible time window, expressed as: （13）； in, , , This includes all AGV equipment, trucks, and forklifts. , , They are the i-th AGV, the j-th truck, and the k-th forklift, respectively. , , Let be variables of 0 and 1 respectively: whether the i-th AGV is charging, whether the j-th truck is refueling with gas, and whether the k-th forklift is refueling with hydrogen during time period h. , , These represent the rated charging power of a single AGV, the rated gas refueling flow rate of a single truck, and the rated hydrogen refueling flow rate of a single forklift. , , Let be the total amount of electricity that the i-th AGV needs to replenish throughout the entire cycle, the total amount of gas that the j-th truck needs to replenish throughout the entire cycle, and the total amount of hydrogen that the k-th forklift needs to replenish throughout the entire cycle. Operation-Charging Mutual Exclusion Constraint: Logistics equipment cannot be charged simultaneously during the operation period, expressed as: （14）； in, , , The variables are 0 and 1 respectively: whether the i-th AGV is in operation during the h-th time period, whether the j-th truck is in operation during the h-th time period, and whether the k-th forklift is in operation during the h-th time period. Charging trigger constraint: Each device must meet the condition that its remaining energy is below a safety threshold before charging. For AGVs, charging will be triggered when the remaining power is insufficient to cover the energy consumption and safety margin of the next task. The expression is: （15）； in, This is the current remaining battery power of the AGV. The amount of electricity required to complete the current task, This is to ensure a safe margin of power. For forklifts, hydrogen refueling is triggered when the remaining hydrogen supply is insufficient to meet the current operation and safety margin. The optimal refueling time is selected during the interval between operations, expressed as: （16）； in, The remaining hydrogen amount before the k-th forklift performs its task. Hydrogen consumption per forklift operation. This is a safety margin for hydrogen.

6. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 5, characterized in that: The specific method for constructing the energy conversion model of the energy hub EH in step three is as follows: Establish energy conversion and operating cost models for each device in the energy hub (EH), including: The energy conversion model for the P2Hy device is established, and the expression is as follows: （17）； in, and These are the input and output power of the electrolytic cell, respectively. The energy conversion efficiency of the electrolyzer; An energy conversion model for electrical energy storage is established, expressed as follows: （18）； in, The state of charge of electrical energy storage. , These are the power and capacity of the electrical energy storage, respectively. For charge and discharge efficiency; The energy conversion model for the CHP unit is established, and the expression is as follows: （19）； in, This refers to the input power of the CHP unit; , These are the output electrical and thermal power of the CHP unit, respectively. , These are the gas-to-electricity efficiency and gas-to-heat efficiency of the CHP unit, respectively. An energy conversion model for an absorption chiller is established, expressed as follows: （20）； in, This refers to the cooling power output of the absorption chiller. The thermal power output of the CHP unit; The coefficient of performance (COP) of an absorption chiller represents the amount of cooling that can be converted from a unit of input heat. The energy conversion model for the methane reactor is established, and the expression is as follows: （21）； in, To obtain the methane power; , These are hydrogen and CO2, respectively, input to the methane reactor; , These are the energy consumption conversion coefficients corresponding to unit methane production; The equivalent state-of-charge models for different types of energy storage systems are established, and the expressions are as follows: （22）； in, These are the equivalent states of charge for electric energy storage, hydrogen storage tanks, thermal storage tanks, and gas storage tanks, respectively. These are the energy conversion efficiencies for electricity, hydrogen, heat, and gas energy storage, respectively. These are the capacities for energy storage in electricity, hydrogen, heat, and gas, respectively. These represent the charging and discharging power of electric, hydrogen, thermal, and gas energy storage, respectively.

7. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 6, characterized in that: The specific method for constructing the economic operation optimization model of energy supplier EA in step three is as follows: Establishing net income for energy supplier EA The objective function is expressed as: （23）； in: The expression for revenue from energy sales is: （24）； In the formula , , , These are the unit prices for electricity, hydrogen, cooling, and gas, respectively. , , , These represent the electricity, hydrogen, heat, and gas power sold to the park at time t, respectively. The environmental benefits, including carbon credit revenue, green certificate trading revenue, and carbon tax on CO2 emissions from equipment, are expressed as follows: （25）； In the formula Carbon credit revenue, including carbon emission reduction credit revenue obtained by EH through CO2 absorption via methanation equipment, is expressed as: （26）； In the formula To utilize the carbon credits earned per unit of CO2, CO2 absorbed by the methanation equipment; In the formula The revenue from green certificate trading, including green certificates obtained by EH using green and clean energy, is expressed as: （27）； In the formula Green electricity for consumption, that is, electricity sold. The revenue per green certificate; In the formula The carbon tax, including the carbon tax generated by CHP units exceeding the rated carbon emissions of energy supplier EA, is expressed as: （28）； in, The price per unit of carbon tax. , CHP units Actual and rated CO2 emissions within the facility; The energy purchase cost to the upstream mainnet is expressed as: （29）； in, and The unit price for electricity and gas purchased by the energy hub EH from the upstream energy network. and The energy hub EH represents the power capacity for purchasing electricity and gas from the upstream energy network. The operating cost of the energy hub EH, including the depreciation cost of energy storage and electrolyzer equipment, as well as the operation and maintenance costs of each piece of equipment, is expressed as: （30）； in, The depreciation cost of energy storage, The depreciation cost of the electrolytic cell equipment. For the operation and maintenance costs of the CHP unit, For the operation and maintenance costs of the electric-to-heat conversion equipment, This refers to the operation and maintenance costs of the methane reactor.

8. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 7, characterized in that: The constraints for operating the energy conversion model of the energy hub EH include: At each time t, different types of energy should satisfy their respective power balances, expressed as: （31）； in, The purchased power input from the external power grid to the energy hub EH. For new energy power, The electrical power output of the CHP unit The charging and discharging power of electrical energy storage, For the electrical load power of users within the energy hub EH, This refers to the power consumption of the electro-hydrogen conversion equipment. This refers to the power consumption of the electric-to-heat conversion equipment. The hydrogen energy output of the electro-hydrogen conversion equipment. The hydrogen charging and discharging power of hydrogen storage equipment, For the hydrogen load power of users within the energy hub EH, The hydrogen energy consumed by the methane reactor; This refers to the thermal power output of the CHP unit. The heat charge and discharge power of thermal energy storage The heat output of the electro-thermal conversion equipment. For the heat load power of users within the energy hub EH, This refers to the gas purchase capacity input from the external gas network to the energy hub EH. The power output of the methane reactor from the natural gas. The charging and discharging power of gas storage. For the gas load power of users within the energy hub EH, The gas power consumed by the CHP unit; Different types of energy conversion equipment should meet their own power capacity constraints, as expressed in the following formula: （32）； in, This refers to the power consumption of the electrolytic cell. This is the maximum power of the electrolytic cell. This refers to the power consumption of the electro-thermal conversion equipment. This refers to the maximum permissible power consumption of the electro-thermal conversion equipment. This refers to the gas consumption power of the CHP unit. This refers to the maximum permissible gas consumption power of the CHP unit. The power of the gas generated by the methane reactor. This represents the maximum permissible gas generation power of the methane reactor; Different types of energy storage systems should meet their respective power constraints, expressed as follows: （33）； in, The power of electrical energy storage, power , These are the minimum and maximum power of electrical energy storage, respectively. For the power of the hydrogen storage tank, , These are the minimum and maximum power ratings of the hydrogen storage tank, respectively. For the power of the thermal storage tank, , These are the minimum and maximum power of the thermal storage tank, respectively. The power of the gas storage tank. , These are the minimum and maximum power of the gas storage tank, respectively; Each energy storage system should meet its own energy capacity constraint, expressed as: （34）； in, For the capacity of electrical energy storage, , These are the minimum and maximum capacity constraints for electrical energy storage, respectively. For the capacity of the hydrogen storage tank, , These are the minimum and maximum capacity constraints for the hydrogen storage tank, respectively. For the capacity of the thermal storage tank, , These are the minimum and maximum capacity constraints for the thermal storage tank, respectively. For the capacity of the gas storage tank, , These are the minimum and maximum capacity constraints for the gas storage tank, respectively. Since daily operating conditions vary, the energy storage system's energy at the end of each day must be kept within a reasonable range to meet the needs of the following day's operation. The expression is as follows: （35）； in, , The constraint coefficient for electrical energy storage is... , The constraint factor for the hydrogen storage tank. , For the cold storage tank, , This represents the constraint coefficient of the gas storage tank; The power constraint for purchasing energy from the superior energy grid is expressed as: （36）； in, In order to purchase electricity from higher authorities, , To constrain the amount of electricity purchased from higher levels, In order to purchase gas from higher authorities, To constrain the power required for purchasing gas from higher authorities.

9. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 8, characterized in that: The specific method for step four is as follows: The energy supplier EA and the integrated energy logistics park are modeled using a Stackelberg game model, with the expression as follows: , , , ； Where M stands for leader, namely energy supplier EA; and N stands for follower, namely the industrial park. Let p be the strategy set of energy aggregators, and p be the set of unit prices for different types of energy. Power sets for all equipment in the energy hub EH; A strategy set for the park; , These are the utility functions for leaders and followers, respectively. In the Stackelberg game model, the leader, energy supplier EA, aims to maximize its net profit by providing multiple types of energy, as expressed in the following expression: （37）； The follower's goal is to minimize its own energy cost through charging time, expressed as: （38）。 10. The method for multi-energy collaborative operation between energy suppliers and industrial parks based on logistics scheduling as described in claim 9, characterized in that: The specific method for step five is as follows: Based on the KKT conditions, construct the Lagrange functions for both players in the game, with the following expressions: （39）； in, (), () represent the Lagrangian functions of energy supplier EA and the industrial park, respectively, g Let λ be a constraint condition for the park, and let λ be a subset of Lagrange multipliers. Leading energy supplier EA engages in a master game by optimizing the energy flow and unit prices of various energy types within its own energy hub EH. Decision variables include unit prices of various energy types. , , , Energy Hub EH Equipment Power , , , , , , and the power purchased from higher levels ; The park optimizes its energy costs through price-driven demand-side response, with decision variables including the energy consumption time set. , , , ).

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