Distributed port comprehensive energy-logistics scheduling method

Abstract

The invention discloses a distributed port comprehensive energy-logistics scheduling method, which realizes energy-logistics collaborative management with more economical energy consumption and less carbon emission under the condition that an energy system and a logistics system are independent. According to logistics parameters and energy parameters, after a global optimal scheduling problem is decomposed into a logistics scheduling problem and an energy scheduling problem, the energy scheduling problem and the logistics scheduling problem are solved in sequence through a data-driven embedded ADMM method, then the logistics parameters and the energy parameters are updated, and a global optimal solution is obtained after iteration is carried out until conditions are met.

Description

Technical Field

[0001] The present invention relates to a technology in the field of port control, specifically a distributed port integrated energy-logistics scheduling method, which realizes the coordinated management of energy system and logistics system through distributed scheduling strategy to improve energy efficiency and reduce carbon emissions. Background Art

[0002] As an important center of energy consumption and greenhouse gas emissions, ports account for about 3% of global carbon emissions. Integrating multiple energy systems in ports and developing electrified logistics systems are key steps to reduce carbon emissions and improve energy efficiency. With the electrification of logistics systems and the integration of multiple energy systems in ports, single logistics management is shifting to the coordinated management of integrated energy-logistics systems (IELS). Although the existing optimization methods have been widely studied for the coordinated scheduling of IELS, they focus on centralized scheduling strategies that require data sharing throughout the scheduling system. The shared data includes: parameters of logistics and energy equipment, ship arrival plans in the logistics system, energy forecasts, etc. However, the existing port energy system and logistics system are two independent systems with limited data interaction. The energy system schedules the power of each device to achieve real-time energy, mainly including: electricity, heat, and gas. The logistics system solves the problem of electrified quay crane allocation and berth allocation. The berth allocation plans the location of the ships arriving at the port and the number of allocated quay cranes. The energy system supplies energy to the logistics system, and the logistics system is a controllable flexible load for the energy system. The two systems are independent of each other: data management is independent, data privacy is protected, and decision-making is independent. IELS centralized scheduling based on complete data sharing is difficult to achieve. In addition, centralized scheduling amplifies local risks into global risks. Once a local system is attacked, the entire centralized scheduling system will be paralyzed. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention proposes a distributed integrated energy-logistics scheduling method for ports, which realizes energy-logistics collaborative management with more economical energy use and less carbon emissions when the energy system and the logistics system are independent. According to the logistics parameters and energy parameters, the global optimal scheduling problem is decomposed into the logistics scheduling problem and the energy scheduling problem, and then the energy scheduling problem and the logistics scheduling problem are solved in turn by the data-driven embedded ADMM method, and then the logistics parameters and energy parameters are updated, and then the global optimal solution is obtained after iterating until the conditions are met. Technical Effects

[0004] The present invention decomposes the global optimization problem into local optimization problems that are solved by alternating iterations, and adopts a distributed scheduling strategy to allow each system to operate independently. Compared with the prior art, the present invention simultaneously coordinates optimization, improves the flexibility and reliability of scheduling, and can better cope with emergencies and uncertainties; by rationally scheduling electricity, thermal energy and natural gas, it improves energy utilization efficiency while reducing the operating costs of the port's comprehensive energy-logistics system. Through the rational planning of berths, the utilization efficiency of berths and quay cranes is improved, the economic benefits of port operations are improved, and the sustainable development of ports is promoted. As a concentrated area of ​​port energy consumption and carbon emissions, the comprehensive utilization of energy and electrified logistics can improve energy efficiency and emissions, among which: cost is only one of the goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 This is a schematic diagram of the IELS framework for low-carbon ports;

[0006] Figure 2 It is a flow chart of the present invention;

[0007] Figure 3 This is a schematic diagram of the power dispatch results of the energy system;

[0008] Figure 4 It is a schematic diagram of the thermal scheduling results of the energy system;

[0009] Figure 5 Schematic diagram of berth allocation results in the logistics system. DETAILED DESCRIPTION

[0010] like Figure 2 As shown, this embodiment relates to a distributed port integrated energy-logistics scheduling method, including:

[0011] Step 1: Constructing the IELS framework for low-carbon ports

[0012] Structure as Figure 1 The IELS framework model of the low-carbon port shown in the figure includes: electricity, heat, natural gas and logistics systems, among which: the energy system includes: electricity, heat and natural gas subsystems; the power system includes: photovoltaic (PV) panels, energy storage system (ESS) and electric boiler (EB); the natural gas system includes: gas turbine (GT) and gas boiler (GB); the logistics system includes: power supply of the power system, which involves scheduling the arrival and departure of ships, berth allocation and allocation of quay crane (QC) tasks. Energy and logistics scheduling plans are usually developed on two independent platforms, and all equipment parameters and pre-scheduling reference information cannot be shared globally. Only limited information exchange is allowed, such as power values ​​of system interfaces. The energy system is to schedule the power of each device to achieve real-time energy, mainly including: electricity, heat and gas.

[0013] Step 2: Collect parameters

[0014] Logistics system parameters: available quay crane power, available number of quay cranes, available quay crane efficiency, ship arrival plan, available berth conditions, ship container conditions, and ship length.

[0015] Energy system parameters: maximum charging and discharging power, efficiency, and storage capacity of battery energy storage; maximum power, heating efficiency, and power generation efficiency of gas turbines; maximum power and heating efficiency of gas boilers; maximum power and heating efficiency of electric heat pumps.

[0016] Step 3: Establish logistics local equipment management issues

[0017] The optimization goal of logistics management problems is to minimize the energy cost and carbon emissions of logistics equipment.

[0018] Establish logistics management goals:

[0019] in: is the global communication volume, is the sth column vector of λ, and ρ is the penalty parameter of ADMM.

[0020] Formulate control objects: vessel arrival and departure schedules, berth allocation and QC task allocation.

[0021] Formulate the control domain: The control boundaries of ship arrival and departure include: entry and departure time constraints, and quay crane operation completion time constraints Where: t in,j and t out,j is the entry / exit time of vessel j, J is the total number of vessels; T arr,j and N is the arrival time and maximum departure time of vessel j; cargo,j is the number of cargoes on ship j; η QC For quay crane efficiency; and C QC,j is the maximum / minimum number of containers that the quay crane can handle. , where: u j and v j is an auxiliary variable, and M is a large constant.

[0022] Port entry and exit status constraints include: restrictions on ship entry and exit time Ship's entry and exit status Ship arrival constraints Where: y1,j,t ,y 2,j,t ,y 3,j,t , w j,t is an auxiliary variable. Before the ship arrives at the port and after it leaves the port, X j,t is the port entry status of ship j at time t, and its value is 0 before the ship arrives and after leaving the port, and 1 while the ship is in the port.

[0023] The berth allocation control boundary includes: the total length constraint of the ship when docking Ships do not overlap when docked Where: h i,j and z i,j is an auxiliary variable. If ship i docks before ship j, then h i,j =1, otherwise h i,j = 0. If ship i is docked on the left side of ship j, then z i,j =1, otherwise z i,j =0. berth , L ship,j is the total length of the wharf and the length of ship j, in meters (m ) . b i is the berth allocated to vessel i.

[0024] Quay crane (QC) task allocation control boundaries include: Quantity constraints of quay crane tasks Each gantry crane can only execute one task at a time. Constraints on the sequential execution of quay crane tasks Power Constraints of Container Terminal Cranes Power Constraints on Vessels Among them: C QC,i,j,t is the number of containers of ship i handled by quay crane j at time t, is the rated power of the quay crane, P B,j is the basic power requirement of ship j, in megawatts (MW).

[0025] Step 4: Establish the local energy management problem

[0026] The optimization goal of the energy management problem is to minimize the energy cost and carbon emissions of each flexible resource.

[0027] Setting energy management goals:

[0028] in: is the global communication volume, is the sth column vector of λ, and ρ is the penalty parameter of ADMM.

[0029] Establish control objects: energy storage power, photovoltaic power, gas turbine power, gas boiler power, and power purchased from the upper-level power grid.

[0030] Formulate regulatory domains: Energy equipment operation regulatory boundaries include: Power output limits of photovoltaic panels Power output and energy capacity limitations of energy storage systems (ESS)

[0031] Energy state transition equation of ESS Initial energy state of ESS

[0032] Gas supply restrictions for gas turbines (GT) and boilers (GB)

[0033] GT's gas conversion equation for electricity and heat

[0034] GB gas converted into heat

[0035] Power output limitation of electric boiler (EB) EB's electrical energy is converted into heat energy Where: P PV,t and are the output power and predicted available power of photovoltaic at time t respectively; and are the charging / discharging power of the energy storage system at time t respectively; and are the maximum charge / discharge power of the energy storage system at time t; E ESS,t and are the capacity and maximum capacity of the energy storage system at time t respectively; and is the charge / discharge efficiency of the energy storage system; G GT,t , G GB,t and are the gas consumption and maximum gas input of the gas turbine and gas boiler at time t, both in km 3 / t;P GT,t , P GB,t , P EB,t and are the output power and maximum output power of the gas turbine, gas boiler and electric boiler at time t respectively; H GT,t , H GB,t and H EB,t are the heat outputs of the gas turbine, gas boiler and electric boiler at time t, in megawatts (MW); is the gas turbine power generation efficiency, and are the thermal efficiencies of gas turbines, gas boilers and electric boilers respectively; T is the total dispatching time; the units of the above powers are all megawatts (MW).

[0036] The power balance constraints include: upper grid power and natural gas supply restrictions Heat balance equation Natural Gas Balance Equation Where: P G,t and G is the electric power purchased from the upper power grid at time t and the maximum value of the purchased electric power, in megawatts (MW); G,t and is the gas purchase volume from the upper-level gas grid and the maximum supply volume from the upper-level gas grid at time t, is the natural gas load forecast value at time t, in km 3 / t;P SR,t and P QC,t are the shore power consumption and quay crane power consumption at time t, respectively, in megawatts (MW); and is the predicted power of the electric load and the predicted value of the thermal load at time t, in megawatts (MW);

[0037] Step 5: Establish common coupling regulatory boundaries

[0038] Establishing the Common Coupling Regulation Boundary: The Power Balance Equation

[0039]

[0040] Step 6: Adopt ADMM-based distributed scheduling

[0041] The global optimal scheduling model is as follows: minF LS (Φ LS )+F ES (Φ ES ),in:

[0042] The implementation process of ADMM is as follows: First, the augmented Lagrangian function is defined as:

[0043]

[0044] ,in: is the global communication volume, is the sth column vector of λ, and ρ is the penalty parameter of ADMM. For each iteration, the local scheduling problem will be solved alternately. The process of each iteration is: Update of real variable λ:

[0045] In this embodiment, Gurobi Optimizer is used for solving. Solvers that support mixed integer quadratic programming, including but not limited to GUROBI and CPLEX, can also be used to alternately solve energy scheduling and logistics scheduling problems to achieve alternating distributed scheduling.

[0046] Step 7: Determine the stop condition

[0047] To determine when the method should stop, the primal and dual residuals are defined as follows:

[0048] Solve logistics and energy problems alternately.

[0049] When both the primal residual and the dual residual are smaller than the threshold ε, the method stops.

[0050] After specific practical experiments, the simulation environment is built based on the operation mode and partial data of a port in Shanghai. The time-of-use electricity price is shown in Table 1. The parameter settings of the energy and logistics system equipment are shown in Table 2. The ship arrival plan in the test case is shown in Table 3. The time interval of the scheduling plan is half an hour. All optimization problems in the case are modeled using the cvxpy framework and include: Gurobi Optimizer for solution.

[0051] Table 1

[0052] Table 2

[0053] Table 3

[0054] The results of the power and heat dispatch of the energy system in the two-stage random distributed dispatch under the set parameters are as follows: Figure 3 , Figure 4 As shown in Table 4, the berth allocation results of the logistics system are as follows Figure 5 The power dispatch results of the energy system are shown in Figure 3As shown in the figure, it includes: Since photovoltaic power does not generate electricity at night and the electricity price is lower at night, nighttime operation mainly relies on purchasing electricity from the upper power grid to meet the energy needs of the energy system, while most other equipment remains inactive. During peak electricity price periods, the energy system will increase the purchase of natural gas to meet electricity demand. The energy storage system charges when the electricity price is low and discharges when the electricity price is high to achieve the most economical operation plan. The thermal scheduling results of the energy system are shown in the figure. Figure 4 As shown, in terms of heat dispatch, electric boilers are usually used when electricity prices are low, while gas turbines are used when electricity prices are high but natural gas prices are low, ensuring that heat demand is met at the lowest cost.

[0055] Table 4

[0056] In the berth allocation plan, the berth allocation results of the logistics system are as follows: Figure 5 As shown in the figure, all ships leave before their maximum departure time and complete their port operations. The allocation of berths is reasonable, and there is no berth conflict or cross-movement between quay cranes. This scheduling result ensures that the ship stays in the port for the shortest time and improves the efficiency of berth and quay crane use. And logistics operations are mainly concentrated in the period with lower electricity prices, that is, the low point of the day and at night. This method ensures that logistics operations are completed at the lowest electricity cost.

[0057] The above-mentioned specific implementation may include: technicians in this field may make local adjustments in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention shall be based on the claims and does not include: limited by the above-mentioned specific implementation, and each implementation scheme within its scope is subject to the constraints of the present invention.

Claims

1. A distributed port integrated energy-logistics scheduling method, characterized in that: According to the logistics parameters and energy parameters, the global optimal scheduling problem is decomposed into the logistics scheduling problem and the energy scheduling problem. The energy scheduling problem and the logistics scheduling problem are solved in turn through the data-driven embedded ADMM method, and then the logistics parameters and energy parameters are updated. Then, the global optimal solution is obtained after iterating until the conditions are met.

2. The distributed port integrated energy-logistics scheduling method according to claim 1 is characterized in that: Specifically include: Step 1: Construct the IELS framework model of the low-carbon port, including: electricity, heat, natural gas and logistics systems, where: the energy system includes: electricity, heat and natural gas subsystems; the power system includes: photovoltaic (PV) panels, energy storage system (ESS) and electric boiler (EB); the natural gas system includes: gas turbine (GT) and gas boiler (GB); the logistics system includes: power supply of the power system, which involves scheduling the arrival and departure of ships, berth allocation and allocation of quay crane (QC) tasks. Energy and logistics scheduling plans are usually developed on two independent platforms. All equipment parameters and pre-scheduling reference information cannot be shared globally, and only limited information exchange is allowed, such as the power value of the system interface. The energy system is to schedule the power of each device to achieve real-time energy, mainly including: electricity, heat, and gas; Step 2: Collect parameters, including: Logistics system parameters: available quay crane power, available quay crane quantity, available quay crane efficiency, ship arrival plan, available berths, ship container situation, ship length; Energy system parameters: maximum charge and discharge power, efficiency, and storage capacity of battery energy storage; maximum power, heating efficiency, and power generation efficiency of gas turbines; maximum power and heating efficiency of gas boilers; maximum power and heating efficiency of electric heat pumps; Step 3: Establish logistics local equipment management problem: The optimization goal of logistics management problem is to minimize the energy cost and carbon emissions of logistics equipment, including: Establish logistics management goals: in: is the global communication volume, is the sth column vector of λ, and ρ is the penalty parameter of ADMM; Formulate control objects: ship arrival and departure schedules, berth allocation and QC task allocation; Formulate the control domain: The control boundary of ship arrival and departure includes: entry and departure time constraints, and quay crane operation completion time constraints Where: t in,j and t out,j is the entry / exit time of vessel j, J is the total number of vessels; T arr,j and N is the arrival time and maximum departure time of vessel j; cargo,j is the number of cargoes on ship j; η QC For quay crane efficiency; and C QC,j is the maximum / minimum number of containers that the quay crane can handle, where: j and v j is an auxiliary variable, and M is a large constant; Port entry and exit status constraints include: restrictions on ship entry and exit time Ship's entry and exit status Ship arrival constraints Where: y 1,j,t ,y 2,j,t ,y 3,j,t , w j,t is an auxiliary variable. Before the ship arrives at the port and after it leaves the port, X j,t is the port entry status of ship j at time t, its value is 0 before the ship arrives and after leaving the port, and is 1 while the ship is in the port; The berth allocation control boundary includes: the total length constraint of the ship when docking Ships do not overlap when docked Where: h i,j and z i,j is an auxiliary variable. If ship i docks before ship j, then h i,j =1, otherwise h i,j = 0, if ship i is docked on the left side of ship j, then z i,j =1, otherwise z i,j =0,L berth , L ship,j is the total length of the wharf and the length of ship j, in meters (m ) , b i is the berth allocated to ship i; Quay crane (QC) task allocation control boundaries include: Quantity constraints of quay crane tasks Each gantry crane can only execute one task at a time. Constraints on the sequential execution of quay crane tasks Power Constraints of Container Terminal Cranes Power Constraints on Vessels Among them: C QC,i,j,t is the number of containers of ship i handled by quay crane j at time t, is the rated power of the quay crane, P B,j is the basic power requirement of ship j, in megawatts (MW); Step 4: Establish local energy management problem: The optimization goal of energy management problem is to minimize the energy cost and carbon emission of each flexible resource, including: Setting energy management goals: in: is the global communication volume, is the sth column vector of λ, and ρ is the penalty parameter of ADMM; Formulate control objects: energy storage power, photovoltaic power, gas turbine power, gas boiler power, and power purchased by the upper-level power grid; Formulate regulatory domains: Energy equipment operation regulatory boundaries include: Power output limits of photovoltaic panels Power output and energy capacity limitations of energy storage systems (ESS) Energy state transition equation of ESS Initial energy state of ESS Gas supply restrictions for gas turbines (GT) and boilers (GB) GT's gas conversion equation for electricity and heat GB gas converted into heat Power output limitation of electric boiler (EB) EB's electrical energy is converted into heat energy Where: P PV,t and are the output power and predicted available power of photovoltaic at time t respectively; and are the charging / discharging power of the energy storage system at time t respectively; and are the maximum charge / discharge power of the energy storage system at time t; E ESS,t and are the capacity and maximum capacity of the energy storage system at time t respectively; and is the charge / discharge efficiency of the energy storage system; G GT,t , G GB,t and are the gas consumption and maximum gas input of the gas turbine and gas boiler at time t, both in km 3 / t;P GT,t , P GB,t , P EB,t and are the output power and maximum output power of the gas turbine, gas boiler and electric boiler at time t respectively; H GT,t , H GB,t and H EB,t are the heat outputs of the gas turbine, gas boiler and electric boiler at time t, in megawatts (MW); is the gas turbine power generation efficiency, and are the thermal efficiencies of gas turbines, gas boilers and electric boilers respectively; T is the total dispatching time; the above power units are all megawatts (MW); The power balance constraints include: upper grid power and natural gas supply restrictions Heat balance equation Natural Gas Balance Equation Where: P G,t and G is the electric power purchased from the upper power grid at time t and the maximum value of the purchased electric power, in megawatts (MW); G,t and is the gas purchase volume from the upper-level gas grid and the maximum supply volume from the upper-level gas grid at time t, is the natural gas load forecast value at time t, in km 3 / t;P SR,t and P QC,t are the shore power consumption and the quay crane power consumption at time t, respectively, in megawatts (MW); and is the predicted power of electric load and predicted value of thermal load at time t, in megawatts (MW); Step 5: Establish the common coupling control boundary, specifically: power balance equation Step 6: Adopt ADMM-based distributed scheduling, including: Global optimal scheduling model: minF LS (Φ LS )+F ES (Φ ES ),in: The ADMM specifically includes: Augmented Lagrangian function: in: is the global communication volume, is the sth column vector of λ, ρ is the penalty parameter of ADMM, and for each iteration, the local scheduling problem will be solved alternately. The process of each iteration is: Update of real variable λ: Step 7: Determine the stopping condition: Determine the stopping condition based on the original residual and the dual residual, specifically: The logistics and energy problems are solved alternately, and the method stops when both the primal residual and the dual residual are less than the threshold ε.

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