Port energy station planning method and system for embedded operation simulation under energy flow-logistics coupling

Abstract

The invention relates to a port energy station planning method and system for embedded operation simulation under energy flow-logistics coupling, and the method comprises the following steps: obtaining port multi-energy load historical data, employing an improved DBSCAN clustering method to construct a port planning typical day set, and in the improved DBSCAN clustering method, a port planning typical day set is constructed; optimizing a neighborhood radius and a neighborhood sample number threshold value based on a kernel density estimation method; a port energy station planning model embedded with efficient operation simulation under energy flow-logistics coupling is established, the port energy station planning model takes the minimum annual comprehensive cost including the annual investment cost and the annual operation cost as the target, and constraint conditions considering the planning level and the operation simulation level are set; and based on the port planning typical day set, solving the port energy station planning model to obtain a port energy station energy equipment configuration scheme. Compared with the prior art, the method is beneficial to reducing the planning investment cost, improving the port energy structure, accelerating the replacement of clean fuel and improving the energy efficiency level of equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of port energy station planning, and in particular to a port energy station planning method and system with embedded operation simulation under energy flow-logistics coupling. Background Art

[0002] With the deepening development of global economic integration and the continuous growth of international trade, the status and role of ports as important hubs connecting sea and land are becoming increasingly prominent. However, the traditional port energy supply model often relies on fossil fuels, which not only has high energy consumption and large emissions, but also faces the dual pressures of resource depletion and environmental pollution. Therefore, planning and developing port energy stations to achieve clean, efficient and sustainable energy supply are of great significance to promoting the green development of ports and promoting the transformation and upgrading of regional economies. Port energy stations integrate various energy resources, optimize energy allocation, improve energy utilization efficiency, and provide stable and reliable energy guarantees for ports and surrounding areas. They are a key link in building green ports and smart ports.

[0003] As the degree of interaction and integration between port energy flow and logistics gradually deepens, the planning characteristics of port energy stations will undergo fundamental changes. The planning methods of port energy stations include the following two difficulties: First, the 8760-hour operation simulation will bring massive decision variables and constraints to the model. In order to reduce the dimension and complexity of the problem, how to construct a suitable typical day set to represent the characteristics of wind power, photovoltaics, and conventional multi-energy loads throughout the year with high fidelity is one of the keys to determining whether the planning results of the port energy station are accurate; second, this problem not only needs to consider the equipment selection and capacity configuration at the planning and design level, but also needs to deeply explore the operating characteristics and coupling relationship of energy flow and logistics at the operation simulation level, especially the logistics operation involves multiple links and multiple equipment. It is difficult to embed the operation simulation into the planning model and achieve fast and efficient solution.

[0004] Most of the existing studies have not considered the impact of the adjustable energy characteristics of all logistics links on the selection of equipment types and capacity configuration within the station. For example, the document "Integrated port energy system considering integrated demand response and energy interconnection (, International Journal of Electrical Power & Energy Systems, Southeast University)" discloses a multi-port area collaborative planning method for a port integrated energy system considering energy interconnection, constructs a port integrated energy system framework and a multi-port area energy station interconnection model considering energy interconnection and transmission constraints, and constructs a multi-regional port integrated energy system collaborative optimization model taking into account demand response, and converts it into a 0-1 mixed integer linear programming problem through linearization. The method proposed in this document only considers the interconnection of power between ports and conventional load demand response, and does not consider the impact of the adjustable energy characteristics of all logistics links. Therefore, there is an urgent need for a new port energy station planning method that can effectively take into account the energy flow-logistics coupling and the adjustable energy characteristics of all logistics links. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a port energy station planning method and system with embedded operation simulation under energy flow-logistics coupling, which can fully tap the potential of adjustable energy timing in all links of port logistics, help reduce planning investment costs, improve port energy structure, accelerate clean fuel substitution, and improve equipment energy efficiency.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A port energy station planning method with embedded operation simulation under energy flow-logistics coupling includes the following steps:

[0008] The historical data of multi-energy load of the port is obtained, and the typical day set of port planning is constructed by using the improved DBSCAN clustering method, in which the neighborhood radius and the neighborhood sample number threshold are optimized based on the kernel density estimation method;

[0009] Establishing a port energy station planning model with embedded efficient operation simulation under energy flow-logistics coupling, wherein the port energy station planning model aims to minimize the annual comprehensive cost including annual investment cost and annual operation cost, and sets constraints taking into account the planning level and the operation simulation level;

[0010] Based on the typical day set of the port planning, solving the port energy station planning model, and obtaining the energy equipment configuration plan of the port energy station;

[0011] Among them, when solving the port energy station planning model, the port energy station planning model is decomposed into main and sub-problems. The main problem corresponds to the planning stage, which is used to determine the investment plans for various equipment of the port energy station. The sub-problems correspond to the operation simulation stage, which is used to determine the optimal operating state of the port energy station under a known planning scheme.

[0012] Furthermore, the optimization formula for determining the neighborhood radius and the neighborhood sample number threshold is expressed as:

[0013]

[0014] Where r and m are the neighborhood radius and the neighborhood sample number threshold, respectively; h' is the optimal bandwidth of the kernel density estimation; K(·) is the kernel function; n is the data set x = [x1, x2, ..., x n ] data length.

[0015] Furthermore, the expression of the optimal bandwidth h' of the kernel density estimation is:

[0016]

[0017] Where: σ is the sample variance.

[0018] Furthermore, the annual operating costs include annual energy purchase costs and annual carbon emission costs.

[0019] Furthermore, the constraints taken into account at the planning level and the operation simulation level include upper and lower limit constraints on the number of energy equipment, power balance constraints, energy equipment operation constraints, logistics equipment operation constraints and port daily carbon emission limit constraints.

[0020] Furthermore, the power balance constraint is established by taking into account the adjustable time sequence characteristics of energy consumption in all links of port logistics.

[0021] Furthermore, the energy equipment considered in the energy equipment operation constraints include cogeneration units, electric boilers, gas boilers, electric refrigeration, absorption refrigeration and electric energy storage.

[0022] Furthermore, the logistics equipment operation constraints are established based on the operational characteristics of all links of port logistics and the energy consumption characteristics of all links of port logistics.

[0023] Furthermore, the entire port logistics process includes ship berthing, quay crane loading and unloading, truck transportation, yard crane stacking and cold chain storage.

[0024] The present invention also provides a port energy station planning system with embedded efficient operation simulation under energy flow-logistics coupling, characterized in that it includes one or more processors, a memory and one or more programs stored in the memory, and the one or more programs include instructions for executing the method described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention optimizes the neighborhood radius and neighborhood sample number threshold of the DBSCAN clustering algorithm based on the kernel density estimation method, overcomes the limitations brought by human subjective settings, and aims to improve the accuracy and stability of the clustering results; and adopts the improved DBSCAN clustering method to construct a typical day set with high generalization and representativeness, which provides strong support for the establishment of subsequent planning models.

[0027] 2. The present invention constructs a port energy station planning model with embedded efficient operation simulation under energy flow-logistics coupling. The embedded operation simulation, at the operation simulation level, can give full play to the adjustable potential of flexible resources on the logistics side in the planning process of the port energy station by controlling the operating conditions of each equipment in each link on the logistics side and the output power of each equipment on the energy flow side, thereby improving the economy of the port energy station planning scheme, helping to improve the port energy structure, accelerate the replacement of clean fuels, improve the energy efficiency of equipment, and promote the green and efficient development of ports. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the planning process of a port energy station corresponding to an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of a typical day construction process for port planning according to an embodiment of the present invention;

[0030] Figure 3 A flow chart of solving a port energy station planning model based on Benders decomposition according to an embodiment of the present invention;

[0031] Figure 4 This is a historical data curve of port wind power, photovoltaic power, and multi-energy conventional loads according to an embodiment of the present invention;

[0032] Figure 5 A typical day set construction result graph based on improved DBSCAN clustering according to an embodiment of the present invention;

[0033] Figure 6 A result graph of a typical day set constructed based on K-means clustering according to an embodiment of the present invention;

[0034] Figure 7 It is a result diagram of the equipment configuration of the port energy station in different scenarios of the embodiment of the present invention;

[0035] Figure 8 It is a curve diagram of energy consumption of port logistics in all links under different scenarios of the embodiment of the present invention;

[0036] Fig. 9 This is a diagram of the equipment configuration results of the port energy station under different natural gas prices according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0038] Based on the research of the prior art, the applicant found that: on the one hand, it is necessary to consider the complementary and mutual assistance of multiple energy types such as electricity / gas / heat / cold during planning, and improve energy utilization efficiency through scientific and reasonable configuration of various energy equipment to meet the growing and diversified energy needs of ports; on the other hand, it is necessary to make full use of the adjustable time sequence of energy consumption in all links of logistics to reduce peaks and fill valleys, improve the matching degree of energy supply and demand in ports, and enhance the flexibility of energy station operation. The present invention is innovatively proposed based on the above findings.

[0039] like Figure 1 As shown, this embodiment provides a port energy station planning method with embedded efficient operation simulation under energy flow-logistics coupling, and the method includes the following steps:

[0040] Step S1: Based on the kernel density estimation method, the neighborhood radius and neighborhood sample number threshold of the DBSCAN clustering algorithm are optimized, and an improved DBSCAN clustering method is used to construct a typical day set with high generalization and representativeness.

[0041] The typical day construction process of port planning described in step S1 is as follows Figure 2 As shown, the specific steps are:

[0042] (1) Using the historical per-unit values ​​of the port's wind power, photovoltaic power, and conventional multi-energy loads throughout the year as input samples, the core point set and cluster set are initialized;

[0043] (2) Based on kernel density estimation, the neighborhood radius r and the neighborhood sample number threshold m are determined, and the distances between all data points are calculated to form a data distance matrix;

[0044] In step (2), for an input data set x = [x1, x2, ..., x n ], its probability density function is f(x), then the kernel density estimation of f(x) is shown in formula (1).

[0045]

[0046] Where: K(x) is the kernel function. Common kernel functions include Gaussian kernel function, Epanechnikov kernel function, etc. Selecting a suitable kernel function has a decisive influence on the smoothing effect and computational efficiency of kernel density estimation; h is the bandwidth.

[0047] The effect of bandwidth on kernel density estimation is consistent with the effect of neighborhood radius on DBSCAN clustering. The neighborhood radius value can be optimized by determining the size of bandwidth h.

[0048] First, the approximate value of the mean square error function is used to reflect the quality of the kernel density estimation, as shown in formula (2).

[0049]

[0050] Where: σ is the sample variance, which reflects the average value of the square of the deviation between the actual data distribution and the constructed kernel density estimation curve; the smaller σ is, the higher the degree of fit between the kernel density estimation and the actual data distribution, that is, the better the estimation result.

[0051] Secondly, the derivative of equation (2) is derived and the first-order derivative is set to 0 to obtain the optimal bandwidth, as shown in equation (3).

[0052]

[0053] Then, for the Gaussian kernel function, the optimal bandwidth expression is shown in formula (4):

[0054]

[0055] Finally, the neighborhood radius and the neighborhood sample number threshold are determined as shown in formula (5):

[0056]

[0057] (3) Randomly select a point as the starting point and mark it as visited. If the point is judged to meet the core point conditions, all data points in the point and its neighborhood are added to the same core sample set;

[0058] (4) Repeat step (3) until all data points are traversed to ensure that the core sample set contains all data points that meet the requirements;

[0059] (5) Randomly select a core point as a new cluster and mark it as visited. Find all the direct density reachable points and density reachable points of the core point in the core sample set and add them to the cluster.

[0060] (6) Repeat step (5) until all core points are traversed to ensure that all data points in the high-density area are completely covered;

[0061] (7) Data points that are not added to any cluster are recorded as noise and removed, indicating that the data belongs to a low-density area or an isolated area in the original sample set;

[0062] (8) The mean of all data points in each cluster is selected as the cluster center, and the clustering results are output to obtain the typical day set of port energy station planning.

[0063]

[0064] Where: μ i is the cluster center value; i is the value of all data points in the i-th cluster; D is the cluster set; P(μ i ) is the probability of cluster center appearing; N, N i 、N noise is the total number of data points, the number of data points in the i-th cluster, and the number of noise points.

[0065] The accuracy of the DBSCAN clustering method is closely related to the setting of the two hyperparameters, the neighborhood radius and the neighborhood sample number threshold. If the neighborhood radius is too small, the data points that are originally clustered in the same cluster may be missed, and if it is too large, the noise may also be classified into the cluster; if the neighborhood sample number threshold is too small, all data points may be clustered into a single cluster, and if it is too large, the number of core points may be reduced. Therefore, choosing the appropriate neighborhood radius and neighborhood sample number threshold is of great significance to improving the accuracy of clustering results. In step S1, the kernel density estimation method is used to find the optimal neighborhood radius and neighborhood sample number threshold, which overcomes the limitations of subjective settings, in order to improve the accuracy and stability of clustering results.

[0066] Step S2: Taking the minimization of annual comprehensive costs as the objective function and taking into account various constraints at the planning level and operation simulation level, a port energy station planning model with embedded efficient operation simulation under energy flow-logistics coupling is established.

[0067] In the port energy station planning model with embedded efficient operation simulation under the energy flow-logistics coupling, the equipment on the energy flow side supplies the load on the logistics side, and the power required for each link on the logistics side needs to be analyzed and modeled one by one.

[0068] The planning model of the port energy station with embedded efficient operation simulation under the energy flow-logistics coupling described in step S2 is established as follows:

[0069] a. Objective function

[0070] The planning model of the port energy station takes the minimization of the annual comprehensive cost as the optimization goal, where the annual comprehensive cost mainly includes the annual investment cost and the annual operating cost.

[0071] (1) Annual investment cost

[0072] The annual investment cost refers to the purchase cost of various energy equipment required to be selected in the planned energy station. It is obtained by calculating the sum of the annual values ​​of the investment costs of all energy equipment to be selected in the planning scheme, as shown in formula (7):

[0073]

[0074] Where: C inv is the annual investment cost of the port energy station; ω and W are the type index and set of the energy equipment to be selected, including CHP (Combined Heat and Power), GB (Gas Boiler), EB (Electric Boiler), EC (Electronic Commutation), AC (Alternating Current), EES (Energy Storage System), WT (Wind Turbine), and PV (Photovoltaic); m and M are the number index and set of a certain type of energy equipment to be selected; c ω,m is the investment cost of the mth energy equipment to be selected; is the configuration status of the mth energy device to be selected, a 0-1 variable, which is 1 when the energy device to be selected is configured, otherwise it is 0; R ω,m is the service life of the mth energy equipment to be selected; r is the discount rate.

[0075] (2) Annual operating costs

[0076] The annual operating cost includes the annual energy purchase cost and the annual carbon emission cost, as shown in formula (8).

[0077] C ope =C pur +C co2 (8)

[0078] Among them, the annual energy purchase cost C pur The calculation method of is shown in formula (9).

[0079]

[0080] Where: d, Typical day numbers and corresponding sets constructed for clustering; t, is the time period number and corresponding set within the scheduling period; ξ d is the number of days with typical days of type d throughout the year; Δt is the duration of the time period; are the electricity price and gas price during period t; is the amount of electricity and gas purchased during period t.

[0081] Annual carbon emission costs The calculation method of is shown in formula (10).

[0082]

[0083] Where: is the unit carbon price; The carbon emission factor of electricity purchased from the upper power grid; It is the carbon emission factor of the combustion of natural gas purchased from the upstream gas grid.

[0084] b. Constraints

[0085] The constraints of the port energy station operation simulation model that takes into account the adjustable timing characteristics of energy consumption in all aspects of logistics include: upper and lower limits on the number of energy equipment, power balance constraints, upper and lower limits on energy purchases from the superior power grid and superior gas network, upper and lower limits on clean energy output, energy equipment operation constraints, logistics equipment operation constraints, and port daily carbon emission limit constraints.

[0086] Among them, power balance constraints, upper and lower limits of energy purchases from the upper power grid and upper gas grid, upper and lower limits of clean energy output, energy equipment operation constraints, logistics equipment operation constraints, and port daily carbon emission limit constraints are as follows:

[0087] 1) Power balance constraints

[0088] Based on the rule that the sum of input power is equal to the sum of output power, power balance constraints for different types of energy buses can be established, as shown in equations (11) to (14).

[0089]

[0090] Where: To purchase electricity and gas from the superior network; P e,WT , P e,PV To contribute to the wind and light; The gas input power, electrical output power and thermal output power of CHP; is the gas input power and thermal output power of GB; Input electrical power and output thermal power for EB; Input heating power and output cooling power for AC; Input electrical power and output cooling power for EC; is the EES charging and discharging power; L e , L g , L h , L c It is the conventional multi-energy load of the port, including electricity, gas, heat and cold; SPS , PQC , P GC , P ECT , P RC They are shore power power, quay crane power, yard crane power, electric container truck and refrigerated container power respectively.

[0091] The right side of equation (11) in the above power balance constraint includes the energy power consumed by all logistics links, and it is adjustable.

[0092] 2) Energy stations purchase energy from the upper and lower limits of the network

[0093] The upper and lower limits of the power and gas purchased by the port energy station from the upper network are constrained, as shown in equations (15) to (16):

[0094]

[0095] Where: It is the maximum value of the purchased electricity power and purchased gas power.

[0096] 3) Upper and lower limits on clean energy output

[0097] The upper and lower limits of wind and solar power output at the port are constrained, as shown in equations (17) to (18):

[0098]

[0099] Where: It is the maximum value of wind and solar output.

[0100] 4) Energy equipment operation constraints

[0101] As the core hub of energy production, conversion and storage, the port energy station can realize the mutual conversion and cascade utilization of different energy sources by integrating various energy equipment, and promote the mutual complementarity of multi-energy in the port. It takes the upper power grid and gas grid as input, integrates wind turbine (WT), photovoltaic (PV) and other clean energy power generation, mainly including combined heat and power unit (CHP), electric boiler (EB), gas boiler (GB), electric chiller (EC), absorption chiller (AC), electric energy storage (EES) and other typical energy equipment to realize the coupling and interconnection of multiple energy sources such as electricity / gas / heat / cold, and provide efficient and reliable energy for port logistics loads and conventional multi-energy loads. Based on the above energy equipment model, the operation constraints of energy equipment are constructed.

[0102] 5) Operational constraints in all aspects of logistics

[0103] The operation constraints of all links of logistics are established based on the operational characteristics of all links of port logistics and the energy consumption characteristics of all links of port logistics.

[0104] a) Modeling of operational characteristics of all aspects of port logistics

[0105] The entire process of port logistics mainly includes ship berthing, quay crane loading and unloading, container truck transportation, yard crane stacking and cold chain storage. The port logistics equipment operation characteristic model is established from these five aspects.

[0106] (1) Ship berthing

[0107] Formula (19) to (21) describe the berthing time constraints of the ship, where Formula (19) indicates that the berthing time of the ship should be between the planned arrival time and the planned latest departure time; Formula (20) indicates that the actual departure time of the ship does not exceed the scheduled latest departure time limit; Formula (21) indicates that the berthing time of the ship is determined by the cargo capacity and the number of allocated quay cranes, and is the shore power connection time.

[0108] t arrive ≤t berth ≤t leave_latest (19)

[0109] t leave ≤t leave_latest (20)

[0110]

[0111] Where: t arrive is the planned arrival time of the ship, t leave_latest is the latest scheduled departure time of the ship, t berth is the ship berthing time, t leave is the actual departure time of the ship; t SPS is the shore power connection time; TEU is the ship's cargo capacity; r QC The number of quay cranes allocated to the ship; η QC For the loading and unloading efficiency of the quay crane.

[0112] Equations (22) to (26) describe the constraints on the continuous berth allocation of ships, where Equations (22) to (23) indicate that the berthing position of a ship should be within the shoreline of the berthing terminal; Equations (24) to (26) indicate that any two ships do not overlap in time and space.

[0113] b s ≥L left (twenty two)

[0114] bs +l s ≤L right (twenty three)

[0115]

[0116]

[0117] Where: b s 、b s' is the berthing starting position of ships s and s'; S is the set of ships arriving at the port; l s is the length of the ship; L left , L right The starting and ending positions of the wharf shoreline; is a 0-1 variable, which is 1 when ship s berths before ship s', otherwise it is 0; is a 0-1 variable, which is 1 when ship s is docked on the left side of ship s', otherwise it is 0; M is a sufficiently large positive number.

[0118] (2) Quay crane loading and unloading

[0119] Formula (27) to (36) describe the allocation and task constraints of the quay crane, where Formula (27) indicates that each quay crane only serves one ship in each period; Formula (28) indicates that the number of quay cranes in working state in each period does not exceed its total number; Formula (29) indicates that the number of quay cranes allocated to a ship does not exceed its upper and lower limits; Formula (30) to (32) indicate that the quay cranes should meet the constraint of not crossing each other during operation; Formula (33) to (34) indicate that the quay crane numbers operating on ship s must remain continuous; Formula (35) indicates that the amount of cargo handled by the quay crane must meet the total amount of the ship's loading and unloading tasks; Formula (36) indicates the upper and lower limits of the quay crane loading and unloading efficiency at time t.

[0120]

[0121] m s ≤n s (30)

[0122]

[0123] Where: QC is the port quay crane collection, is a 0-1 variable, which is 1 when the quay crane q serves the ship s during period t, otherwise it is 0; r QC_total is the total number of quay cranes; is the upper and lower limits of the number of quay cranes that each ship can carry; j is the number of the quay crane operating on ship s; m s 、n s The starting and ending numbers of the quay cranes assigned to ship s; η QC,maxis the loading and unloading power and maximum loading and unloading efficiency of the quay crane at time t; T is the time period set.

[0124] (3) Truck transportation

[0125] Formulas (37) to (41) describe the allocation and task constraints of electric container trucks, where formula (37) indicates that each electric container truck is in an operating mode at each time period; formula (38) indicates that the sum of the time of the two operating modes is equal to the length of the scheduling cycle; formula (39) indicates that the sum of the number of electric container trucks in working state and charging state at each time period does not exceed their total number; formula (40) indicates that the amount of cargo handled by the electric container truck meets the port transportation demand; formula (41) represents the container truck transportation efficiency constraint at time t.

[0126]

[0127] Where: ECT is the electric container truck set, It is a 0-1 variable, which is 1 when the electric container truck e is in the transport working state, otherwise it is 0; is a 0-1 variable, which is 1 when the electric container truck e is in the state of being stopped for charging, otherwise it is 0; r ECT_total is the total number of electric container trucks; η ECT,max It is the transport efficiency and maximum transport efficiency of the electric container truck at time t.

[0128] (4) Yard bridge stacking process

[0129] Formulas (42) to (45) describe the allocation and task constraints of the yard crane, where formula (42) indicates that each yard crane only serves one arriving ship in each period; formula (43) indicates that the number of yard cranes in working state in each period does not exceed their total number; formula (44) indicates that the amount of cargo handled by the yard crane meets the port stacking demand; formula (45) represents the yard crane stacking efficiency constraint.

[0130]

[0131] Where: GC is the field bridge set, is a 0-1 variable, which is 1 when the bridge g serves the ship s during period t, otherwise it is 0; r GC_total is the total number of field bridges, η GC , η GC,max It is the stacking efficiency of the yard crane and its upper limit.

[0132] (5) Cold chain storage

[0133] Equations (46) to (48) describe the temperature change constraints of the refrigerated container, where Equation (46) indicates that the temperature of the refrigerated container satisfies its own temperature limit; Equation (47) indicates the temperature change when the refrigerated container is not powered; and Equation (48) indicates the temperature change when the refrigerated container is powered.

[0134]

[0135] Where: is the internal temperature change of the refrigerated container and the external ambient temperature; ΔT RC,max , ΔT RC,min are the upper and lower limits of the temperature change inside the refrigerated container; A is the surface area of ​​the refrigerated container; k is the heat transfer coefficient; M, C P Indicates the mass and specific heat capacity of the cargo in the refrigerated container; It is the refrigeration power of a single refrigerated container per unit time.

[0136] Based on the constraints of the above-mentioned links, we can construct the operational characteristic constraints of the entire logistics link.

[0137] b) Modeling of energy consumption characteristics of all aspects of port logistics

[0138] (1) Vessels at port

[0139] Usually, after a ship berths, it will shut down the main engine and use the ship's auxiliary engine fuel to generate electricity to meet its own load demand, thus generating a large amount of pollution emissions at the port. To avoid this phenomenon, countries have vigorously promoted the use of shore power instead of ship's auxiliary engine to supply power to ships, which brings a large amount of shore power load to the port. The berthing time of the ship will also determine the time distribution of the shore power load. Equations (49) to (50) describe the energy consumption model of the berthed ship, where Equation (49) represents the sum of the power supply power of the berthed ship connected to the shore power during the t period; Equation (50) represents the berth state of the ship, which is 1 when the ship is connected to the shore power at berth, and 0 before the ship berth and after leaving the port.

[0140]

[0141] Where: is the total shore power during period t; p SPS It is the shore power supply power of a single unit per unit time.

[0142] (2) Quay crane

[0143] The quay crane can move back and forth on the preset track arranged on the wharf shoreline, but different quay cranes cannot cross each other. It is mainly composed of several parts such as the gantry structure, lifting device and translation device. Equations (51) and (52) describe the energy consumption model of the quay crane, where Equation (51) represents the sum of the power of the quay cranes in loading and unloading work during the period t; Equation (52) represents the operating power of a single quay crane per unit time, which can be derived based on the relevant parameters of the gantry structure, lifting device and translation device through energy consumption mechanism analysis.

[0144]

[0145]

[0146] Where: H is the total power of the quay crane in time period t, the operating power of a single quay crane per unit time, the operating power of the lifting device of a single quay crane per unit time, and the operating power of the translation device of a single quay crane per unit time; QC , L QC , W QC It is the lifting height of the lifting device, the horizontal span and outreach of the translation device of a single quay crane; It is the lifting speed of the hoisting device and the moving speed of the translation device of a single quay crane.

[0147] (3) Electric container truck

[0148] Electric container trucks are important carriers for port cargo transportation. Under the premise of ensuring the efficient completion of cargo transfer tasks, the port dispatching center must formulate a reasonable charging plan for electric container trucks so that they can make full use of the working intervals for charging and switch between the working and charging states. Equations (53) to (58) describe the energy consumption model of electric container trucks, where Equation (53) indicates that the total power of electric container trucks in time period t is the sum of the charging powers of all electric container trucks in the shutdown charging state in this period; Equations (54) to (55) indicate that the charging / discharging states of electric container trucks are mutually exclusive; Equation (56) indicates the state transfer equation of electric container trucks in adjacent time periods; Equation (57) indicates the upper and lower limit constraints of the charge state of electric container trucks; Equation (58) indicates that the charge state of electric container trucks is the same at the beginning and end of the dispatch cycle.

[0149]

[0150] Where: p ECT , P ch,ECT,max , P dis,ECT,max The total power of the electric truck in time period t, the operating power of a single electric truck per unit time, the charging power of the electric truck, the discharging power of the electric truck, the maximum charging power of the electric truck, and the maximum discharging power of the electric truck; is the storage energy and rated value of the electric container truck in period t; η ECT Charging, discharging and transportation efficiency of electric container trucks; SoC ECT,min 、SoC ECT,max It is the minimum and maximum value of the state of charge of the electric container truck; It is the charge state at the initial and end time of the electric container truck scheduling cycle.

[0151] (4) Field bridge

[0152] The yard crane moves bidirectionally along the vertical track in the yard. Similar to the quay crane, it is also mainly composed of several parts such as the gantry structure, lifting device and translation device. However, the boom of the yard crane cannot be extended, so the influence of the outrigger arm does not need to be considered when calculating the energy consumption. Equations (59) to (60) describe the energy consumption model of the yard crane, where Equation (59) represents the sum of the power of the yard crane in the stacking work during the period t; Equation (60) represents the operating power of a single yard crane per unit time, which can be derived based on the relevant parameters of the gantry structure, lifting device and translation device through energy consumption mechanism analysis.

[0153]

[0154] Where: H is the total power of the field crane in time period t, the operating power of a single field crane per unit time, the operating power of the lifting device of a single field crane per unit time, and the operating power of the translation device of a single field crane per unit time; GC , L GC It is the lifting height of the hoisting device and the horizontal span of the translation device of a single field crane; It is the lifting speed of the hoisting device and the moving speed of the translation device of a single yard crane.

[0155] (5) Refrigerated Container

[0156] As a key equipment for cold chain transportation, refrigerated containers have good thermal insulation properties and are usually provided with cooling power by built-in or external refrigeration units. Equations (61) and (62) describe the energy consumption model of refrigerated containers, where Equation (61) represents the sum of the refrigeration power of all refrigerated containers in the power supply state during period t; Equation (62) represents the upper and lower limits of the refrigeration power of refrigerated containers.

[0157]

[0158] Where: is the total power of refrigerated containers in period t, the minimum refrigeration power of a single refrigerated container per unit time, and the maximum refrigeration power of a single refrigerated container per unit time; RC is the refrigerated container set; It is a 0-1 variable, which is 1 when the refrigerated container is in charging state, and 0 otherwise.

[0159] Based on the energy consumption models of the above-mentioned logistics equipment, energy consumption characteristic constraints for the entire logistics process can be constructed.

[0160] 6) Daily carbon emission limits at ports

[0161] In order to limit the port’s carbon emissions, a maximum constraint on the port’s daily carbon emissions is added, as shown in formula (63).

[0162]

[0163] Where: is the daily carbon emission limit of the port. Step S3: Use the Benders decomposition algorithm to establish an iterative solution framework for the above optimization model and achieve effective solution.

[0164] In step S3, the decomposition process of the main and sub-problems of the port energy station planning model based on the Benders decomposition algorithm is as follows:

[0165] The planning stage corresponding to the main problem is shown in equation (64), which is used to determine the investment plan for various types of equipment in the port energy station. Its objective function is to minimize the annual investment cost, and the constraint condition is the equipment investment constraint. The main problem does not explicitly contain the relevant variables of the sub-problem. By introducing auxiliary variables and the corresponding cut set constraints of the sub-problem, the optimization results of the sub-problem are returned to the main problem. In the iterative process, the upper and lower bounds of the original problem are constantly approached, and finally the original problem is solved. The optimal solution of the main problem is obtained by solving equation (64): Let the optimal value of the main problem at this time be the lower bound LB of the optimal value of the original problem.

[0166]

[0167]

[0168] Where: is the decision variable for the main problem; θ is the auxiliary variable that feeds back the result of the sub-problem.

[0169] The sub-problem corresponding to the operation simulation stage is shown in formula (66). The decision variable is represented by y, which is used to determine the optimal operation state of the port energy station under the premise that the planning scheme is known. Its objective function is to minimize the annual operation cost. The constraints include power balance constraints, energy equipment operation constraints, logistics equipment operation constraints, etc. Since each typical day is independent of each other, it can be solved in parallel, which greatly improves the solution efficiency.

[0170]

[0171] From formula (64), we can see that the main problem does not directly contain the constraints related to the sub-problem, so even if the main problem is solved, it has an optimal solution. There is no guarantee that the solution can satisfy all subproblem constraints. If the subproblem is infeasible, the corresponding infeasible cut set needs to be introduced into the main problem to exclude such solutions; if the subproblem is in the optimal solution If the subproblem is feasible but the main subproblem does not reach the convergence standard during the overall iteration, it is necessary to introduce the corresponding optimal cut set into the main problem to make the subproblem closer to a better solution.

[0172] (1) Feasibility sub-problem

[0173] Whether the subproblem is feasible can be determined by the optimization problem shown in formula (67):

[0174]

[0175] Where: η is the relaxation factor vector; is a series of constraints.

[0176] If 1 Τ If η is not 0, it means that the subproblem is infeasible, and the infeasible cut set corresponding to the dual optimal solution of equation (67) needs to be introduced into the main problem. Since equation (67) contains a 0-1 variable z, its dual problem cannot be solved directly. Therefore, z is first fixed to the optimal value z that satisfies equation (67). * , then its dual problem can be transformed into:

[0177]

[0178] Where m is the dual variable and Λ is a matrix whose diagonal elements are all 1.

[0179] Solving equation (68) yields the optimal solution m * , then the infeasible cut set added to the main problem is:

[0180]

[0181] Where: d is the typical day number, Φ d is the corresponding probability of a typical day.

[0182] (2) Optimal subproblem

[0183] If 1 Τ When η is 0, it indicates that the subproblem is feasible. Solving equation (66) yields its optimal solution: And update the upper bound UB of the optimal value of the original problem to the following formula:

[0184]

[0185] If |UB-LB| < ε, it indicates that the iterative solution of the main subproblem satisfies the convergence accuracy, so the iterative process can be terminated; otherwise, it is necessary to introduce the optimal cut set corresponding to the dual optimal solution represented by equation (66) into the main problem and continue the iteration. Since equation (66) contains a 0-1 variable z, it is impossible to directly convert and solve its dual problem, so z is fixed to the optimal value z that satisfies equation (66) * , then the dual problem of formula (66) can be transformed into:

[0186]

[0187] Where n is the dual variable.

[0188] Solving equation (72) yields the optimal solution n * , then the optimal cut set added to the main problem is:

[0189]

[0190] The port energy station planning model with embedded operation simulation is solved based on the Benders decomposition method. The solution process is as follows: Figure 3 shown.

[0191] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0192] In order to verify the effectiveness of the above method, this embodiment selects an improved example of a port in China for analysis. The port terminal has a shoreline of 750m long, with a total of 8 shore power supplies, 15 quay cranes, 35 electric container trucks, 20 yard cranes and 1,000 refrigerated containers deployed. The parameters of each logistics equipment are shown in Table 1. The port plans to plan an energy station, and the energy equipment to be planned includes CHP, GB, EB, AC, EC, EES, WT, and PV. Their main parameters are shown in Table 2. In addition, the service life of the energy station planning and the selected energy equipment are set to 20 years, and the discount rate is set to 5%. In order to analyze the impact of the adjustable timing characteristics of energy consumption in all aspects of logistics on the planning of port energy stations, this section sets two scenarios for comparison:

[0193] Scenario 1: The impact of the adjustable energy sequence characteristics of all logistics links on the energy station operation simulation is not considered;

[0194] Scenario 2: Consider the impact of the adjustable timing of energy consumption in all aspects of logistics on the energy station operation simulation.

[0195] Table 1 Logistics equipment parameters

[0196]

[0197]

[0198] Table 2 Parameters of energy equipment to be configured

[0199]

[0200] like Figure 4 As shown in Figure 1, the actual historical data of wind power, photovoltaic power, and conventional multi-energy loads in the port area throughout the year are used as sample input, and the DBSCAN clustering method based on kernel density estimation is used to construct typical days. The typical day construction results are shown in Figure 1. Figure 5 The probability of each typical day is shown in Table 3. In order to verify the advantages of the typical day construction method adopted in this embodiment, the K-means clustering method is introduced to construct typical days. The construction results are shown in Table 3. Figure 6 As shown. Figure 5 and Figure 6 It can be seen that the hierarchical characteristics of the typical day construction results based on the improved DBSCAN clustering method are more significant and the coverage of each level is more balanced. This is because K-means clustering has high requirements for the form of input data samples, and the actual historical data of wind power, photovoltaics, and conventional multi-energy loads throughout the year often have obvious periodicity, volatility, and non-uniformity, resulting in a decrease in the K-means clustering effect. However, the DBSCAN clustering method can generate clusters of any shape, and can still achieve good clustering results when the density of each area of ​​the data sample set varies greatly.

[0201] Table 3 Typical day probabilities based on improved DBSCAN

[0202]

[0203] The comparison of the planning costs of the port energy station under the two scenarios is shown in Table 4. The annual comprehensive cost of scenario 1 is 184.09 million yuan, of which the annual investment cost is 11.27 million yuan and the annual operating cost is 172.82 million yuan. Based on scenario 1, the annual comprehensive cost of scenario 2 is 173.90 million yuan, a decrease of 5.54%, of which the annual investment cost is 10.98 million yuan, a decrease of 2.57%, and the annual operating cost is 164.12 million yuan, a decrease of 5.03%. From the above comparison, it can be seen that after considering the adjustable characteristics of the energy sequence of all links in logistics, the annual comprehensive cost of the port energy station planning has been reduced, indicating that its investment and construction plan has been optimized. The cost of energy stations purchasing energy from the superior network has been greatly reduced, indicating that the energy station makes full use of the energy equipment in the station to efficiently supply energy and reduces the demand for external energy purchases.

[0204] Table 4 Comparison of planning costs of port energy stations under different scenarios

[0205]

[0206] The number of energy equipment configurations in the port energy station under different scenarios is shown in Table 5. The capacity and topological connection of energy equipment inside the energy station are shown in Table 5. Figure 7As shown. Scenario 1 configures CHP capacity of 6MW, GB capacity of 2MW, AC capacity of 6MW, EC capacity of 2.4MW, EES capacity of 1.25MW / 3MWh, WT capacity of 9MW, and PV capacity of 6MW to meet the multi-energy load demand of electricity / gas / heat / cold in the port. Scenario 2 considers the adjustable time sequence of energy consumption in all links of logistics, and the load distribution in the port is more balanced. At the same time, CHP and AC are deeply coordinated, so that the GB configuration capacity is reduced to 0, and the EC configuration capacity is reduced to 1.2MW. At the same time, the capacity configuration of wind and solar units remains unchanged. EB is not configured in both scenarios.

[0207] Table 5 Number of energy equipment configurations in port energy stations under different scenarios

[0208]

[0209] In order to further analyze the impact of the adjustable time series of energy consumption in all links of port logistics on the operation simulation of energy stations, the energy consumption time series curves of port logistics under two scenarios are analyzed based on typical day 1, such as Figure 8 As shown in the figure. In scenario 2, the port changes the logistics operation plan and migrates the logistics energy demand that was originally concentrated in the high electricity price period to the relatively low electricity price period, thus realizing the flexible translation of the logistics energy demand in the time dimension, and then effectively utilizing the time-of-use electricity price mechanism to significantly reduce the energy purchase cost of the energy station, playing the role of peak shaving and valley filling. Although the shapes of the port logistics energy time series curves in the two scenarios are different, the areas of the two curves and the figures enclosed by the horizontal axis are equal, that is, although the time distribution of logistics energy has changed, the demand for the total amount of port logistics operation tasks has not changed at all, indicating that this method can effectively improve the economic efficiency of system operation without sacrificing the satisfaction of port logistics services.

[0210] like Fig. 9 The figure shows the energy equipment configuration under different natural gas prices. When the natural gas price is 3 yuan / m 3 When the natural gas price is 4 yuan / m 3 When the energy station reduces the configuration of cogeneration units, it indirectly leads to the reduction of the capacity configuration of absorption refrigeration equipment, indicating that there is still redundancy in heating when the planned energy station is equipped with two cogeneration units. Therefore, when the natural gas price rises, the annual investment cost of the energy station decreases, the annual energy purchase cost increases significantly, and the annual comprehensive cost increases; when the natural gas price increases to 5 yuan / m 3 When the natural gas price further increases to 6 yuan / m 3At that time, the economic efficiency of purchasing electricity from the superior power grid is better than consuming natural gas to generate electricity. Therefore, the energy station is not equipped with cogeneration units and absorption refrigeration, but the capacity of gas boilers and electric boilers is increased, and the capacity of electric refrigeration is also increased.

[0211] Example 2

[0212] The present invention also provides a port energy station planning system with an embedded efficient operation simulation under energy flow-logistics coupling, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the port energy station planning method with an embedded efficient operation simulation under energy flow-logistics coupling as described in Example 1.

[0213] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0214] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0215] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0217] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0218] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0219] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

[0220] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A port energy station planning method with embedded operation simulation under energy flow-logistics coupling, characterized in that: The following steps are involved: The historical data of multi-energy load of the port is obtained, and the typical day set of port planning is constructed by using the improved DBSCAN clustering method, in which the neighborhood radius and the neighborhood sample number threshold are optimized based on the kernel density estimation method; Establishing a port energy station planning model with embedded efficient operation simulation under energy flow-logistics coupling, wherein the port energy station planning model aims to minimize the annual comprehensive cost including annual investment cost and annual operation cost, and sets constraints taking into account the planning level and the operation simulation level; Based on the typical day set of the port planning, solving the port energy station planning model, and obtaining the energy equipment configuration plan of the port energy station; Among them, when solving the port energy station planning model, the port energy station planning model is decomposed into main and sub-problems. The main problem corresponds to the planning stage, which is used to determine the investment plans for various equipment of the port energy station. The sub-problems correspond to the operation simulation stage, which is used to determine the optimal operating state of the port energy station under a known planning scheme.

2. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 1 is characterized in that: The optimization formula for determining the neighborhood radius and the neighborhood sample number threshold is expressed as: r＝h' Where r and m are the neighborhood radius and the neighborhood sample number threshold, respectively; h' is the optimal bandwidth of the kernel density estimation; K(·) is the kernel function; n is the data set x = [x1, x2, ..., x n ] data length.

3. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 2 is characterized in that: The expression of the optimal bandwidth h' of the kernel density estimation is: Where: σ is the sample variance.

4. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 1 is characterized in that: The annual operating costs include annual energy purchase costs and annual carbon emission costs.

5. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 1 is characterized in that: The constraints taken into account at the planning level and the operation simulation level include upper and lower limit constraints on the number of energy equipment, power balance constraints, energy equipment operation constraints, logistics equipment operation constraints and port daily carbon emission limit constraints.

6. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 5 is characterized in that: The power balance constraint is established by taking into account the adjustable time sequence characteristics of energy consumption in all links of port logistics.

7. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 5 is characterized in that: The energy equipment considered in the energy equipment operation constraints include cogeneration units, electric boilers, gas boilers, electric refrigeration, absorption refrigeration and electric energy storage.

8. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 5 is characterized in that: The logistics equipment operation constraints are established based on the operational characteristics of all links of port logistics and the energy consumption characteristics of all links of port logistics.

9. The port energy station planning method with embedded operation simulation under energy flow-logistics coupling according to claim 6 or 8, characterized in that: The entire port logistics process includes ship berthing, quay crane loading and unloading, container truck transportation, yard crane stacking and cold chain storage.

10. A port energy station planning system with embedded efficient operation simulation under energy flow-logistics coupling, characterized in that: The system comprises one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method according to any one of claims 1 to 9.

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