Harbor ship multi-interest subject cooperative operation method and system considering new energy output uncertainty

Abstract

The invention discloses a port ship multi-interest subject cooperative operation method and system considering new energy output uncertainty. The method comprises the following steps: preprocessing collected information data; according to the preprocessed information data, determining an optimal parking space distribution scheme through a constructed parking space distribution model; a polyhedral uncertainty set is obtained through the constructed new energy output uncertainty model, and the polyhedral uncertainty set is adopted to describe wind and light output fluctuation; and based on the price corresponding to the optimal berth allocation scheme and the polyhedral uncertainty set, energy scheduling is carried out through the constructed port energy collaborative scheduling model, collaborative optimization is realized through upper and lower layer strategy interaction and bidirectional iteration, and an optimal operation strategy is obtained. According to the method, efficient consumption of new energy and coordination and balance of multi-subject benefits can be realized, the stability and independent operation capability of a port energy system are remarkably improved, and a feasible technical support is provided for low-carbon transformation of a port.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of port energy optimization scheduling, and relates to new energy comprehensive utilization and port multi-stakeholder collaborative optimization technology, in particular to a port-ship multi-stakeholder collaborative operation method and system considering new energy output uncertainty. BACKGROUND

[0002] The port can effectively alleviate the energy crisis and accelerate the low-carbon transformation process by developing new energy due to the geographical location advantage. The port-ship collaborative operation helps to realize the efficient matching of port energy supply and ship demand, and the berth allocation and shore power use are important links. Scientific and reasonable berth allocation can reduce the waiting time of the ship in the port and reduce energy consumption. The application of shore power technology enables the use of shore power system when the ship is in port, thereby replacing the traditional power supply mode relying on the ship's fuel generator, which has important significance for improving the energy utilization efficiency of the port.

[0003] The existing port-ship collaborative optimization operation technology mainly focuses on the economic optimization scheduling of the port micro-grid level, and fails to fully consider the benefit pursuit of the port operator, the ship operator and the energy supplier and other multiple subjects, and lacks in-depth exploration of the energy trading and collaborative interaction mechanism among multiple subjects. In addition, the output of new energy represented by photovoltaic and wind power is affected by natural factors, and has significant intermittency and uncertainty, which affects the stability of port energy supply, and may cause imbalance between supply and demand of port energy system, delay of operation due to insufficient shore power and other problems. The existing technology fails to fully consider the uncertain factors such as fluctuation of new energy output in the port energy system, and has not formed a systematic quantitative analysis and flexible response strategy for uncertainty. SUMMARY

[0004] The present application provides a port-ship multi-stakeholder collaborative operation method and system considering new energy output uncertainty, which takes new energy output uncertainty as the breakthrough point, combines game theory and robust optimization method, and solves the benefit equilibrium solution of the port and the ship, so as to realize efficient consumption of new energy and coordination and balance of multi-stakeholder benefits, significantly improve the stability and independent operation ability of the port energy system, and provide practical technical support for the low-carbon transformation of the port.

[0005] Technical scheme: In order to achieve the above purpose, the present application provides a port-ship multi-stakeholder collaborative operation method considering new energy output uncertainty, which comprises the following steps:

[0006] S1: collecting berth information data;

[0007] S2: determining the optimal berth allocation scheme according to the collected berth information data through the constructed berth allocation model;

[0008] S3: Obtain the polyhedral uncertainty set through the constructed new energy output uncertainty model, and use the polyhedral uncertainty set to depict the wind and light output fluctuation;

[0009] S4: Based on the price corresponding to the optimal berth allocation scheme and the polyhedral uncertainty set, perform energy scheduling through the constructed port energy collaborative scheduling model, realize collaborative optimization through upper and lower strategy interaction and bidirectional iteration, and obtain the optimal operation strategy.

[0010] Further, the berth allocation model in step S2 is expressed as follows:

[0011] Berth allocation is a key link in port logistics operation. The port operator allocates berths for arriving ships based on ship arrival time within the scheduling period, berth demand, service plan and charging plan, with the goal of minimizing the waiting time and service time of all ships, and is specifically expressed as follows:

[0012]

[0013] Where S, B and O are the sets of ships, berths and port services respectively; f mnk is the time for ship n to complete service as the kth ship at the mth berth; a n is the arrival time of the nth ship.

[0014] Further, the berth allocation model in step S2 includes constraints on the number of berths allocated when the ship arrives, the state of the berth, the arrival time of the ship and the service time, and is specifically as follows:

[0015]

[0016] Where x mnk is a 0-1 variable, which is 1 when the nth ship is docked at the mth berth as the kth ship, and 0 otherwise; d n is the number of berths required by the nth ship; b mnk represents the time when the nth ship starts service; C mn is the length of time when the nth ship is docked; H is a very large constant.

[0017] The berth allocation model takes into account the length of time when the ship is docked, the number of berths required and the ship size parameter, focuses on the special needs of large ships for consecutive berths, and ensures the continuity and overall coordination of berth resource allocation. By prioritizing early docking of ships, the model realizes the rapid connection of loading and unloading operations and charging and discharging operations, and promotes the timely departure of ships after completing the operation demand, thereby improving the port logistics turnover efficiency and energy utilization efficiency.

[0018] Further, in the present application, the port uses wind energy and photovoltaic as the main new energy power generation form. Since the power output of wind power generation and photovoltaic generation is affected by natural factors such as seasonal changes and weather conditions, it presents significant random volatility and intermittent characteristics, and needs to be described using appropriate mathematical tools.

[0019] The new energy output uncertainty model in the step S3 quantitatively models the wind and light output fluctuation range using a polyhedral uncertainty set, and is specifically expressed as follows:

[0020]

[0021] Wherein, U is a set of uncertain variables; is the actual power of wind power generation and photovoltaic generation, k∈{WT,PV}; is the predicted power of wind power generation and photovoltaic generation; and is an auxiliary variable; is the maximum fluctuation of wind power generation and photovoltaic generation power; Γ k is a budget parameter.

[0022] Compared with the box set, the polyhedral uncertainty set is more detailed, and compared with the ellipsoid set, it is easier to linearize. When using the polyhedral uncertainty set to describe the wind and light output fluctuation, the degree of conservatism can be flexibly adjusted by the budget parameter to balance the system robustness and economy.

[0023] Further, the port energy collaborative scheduling model in the step S4 is expressed as follows:

[0024] The port energy collaborative scheduling model is based on the Stackelberg game, and a multi-level optimization method is designed for the port-ship collaborative scenario, with the port operator as the leader and the new energy supplier and the ship as the follower. The proposed game decision is specifically as follows:

[0025]

[0026] Wherein, N is a set of all game participants; S is a set of strategies of each participant; U is a utility function of each participant; Ω C and Ω A are the sets of traditional ships and all-electric ships respectively; EP is a new energy supplier, and M is a port operator.

[0027] Further, the port energy collaborative scheduling model in the step S4 includes the following constraints:

[0028] The traditional ship needs to be provided with loading and unloading services by the port when it is in port, and the objective of the decision model thereof is to maximize the satisfaction level when the service is accepted, considering the loading and unloading power constraint and the cargo level constraint, which is specifically as follows:

[0029]

[0030] where a i and b i are the price sensitivity parameters of the ship; C s,i is the service price; P s,i is the handling power; E s,i is the cargo quantity level on the ship; and Δt is the time interval. is the maximum handling power; and are the lower and upper limits of the cargo quantity level on the i-th ship, respectively. is the initial cargo quantity level of the cargo on the i-th ship.

[0031] An all-electric ship needs handling and charging services provided by the port when it is at the port, and can actively support the energy system of the port through ship-to-grid technology. The objective of the decision-making model of the ship is to maximize the satisfaction level when the services are accepted, considering the handling power constraint, the charging and discharging power constraint, and the state of charge constraint, as follows:

[0032]

[0033] where c i and d i are the price sensitivity parameters of the ship; C e,i and C g,i are the selling and buying prices of electricity traded with the ship, respectively; P s,i is the handling power; P e,i and P g,i are the charging and discharging powers of the ship, respectively. and are the upper limits of the handling power and the charging and discharging power, respectively. and are the lower and upper limits of the energy of the on-board battery, respectively. is the initial energy of the on-board battery; η c and η dc are the charging and discharging efficiencies of the on-board battery, respectively.

[0034] A new energy supplier optimizes the controllable device output according to the buying price of electricity formulated by the port operator. The objective of the decision-making model of the new energy supplier is to maximize the net energy supply benefit, considering the selling power constraint, the charging and discharging power and state constraint of the energy storage device, as follows:

[0035]

[0036] where p is the buying price of electricity; is the traded power between the new energy supplier and the port operator. and respectively are actual output power of wind power and photovoltaic power; θ WT , θ PV and θ es respectively are operation and maintenance coefficients of wind power, photovoltaic power and energy storage device; and respectively are charging and discharging power of energy storage device; δ ep is carbon emission cost coefficient; is electricity purchased from main grid; ESS t+1 and ESS t respectively are electricity of energy storage device at t+1 and t time; ESS min and ESS max are lower limit and upper limit of electricity of energy storage device; T is a running period; η ch and η dis respectively are charging and discharging efficiency of energy storage device; and are 0-1 variables, respectively representing charging state and discharging state of energy storage device;

[0037] The port operator as a leader promotes the collaborative operation between the port and the ship and realizes the maximization of its own economic benefits through the purchase and sale of electricity prices and service prices. The decision model considers the purchase of electricity price constraints, the sale of electricity price constraints, the service price constraints, the purchase of electricity power constraints from the main grid, and the specific as follows:

[0038]

[0039] Wherein, ε is the power consumption rate of each unit of loading and unloading service.

[0040] Further, the nonlinear term in the new energy supplier decision model in the port energy collaborative scheduling model is linearized by using the big M method, and the maximum benefit objective of the utility function is converted into a minimum cost problem. At the same time, based on the polyhedral uncertainty set, the fluctuation of wind and light power generation is described, and a robust optimization model is constructed. The specific matrix form is as follows:

[0041]

[0042] Wherein, x is the decision variable of the new energy supplier; C is the coefficient matrix in the utility function; A and d are respectively the coefficient matrix and constant vector in the inequality constraint; B and e are respectively the coefficient matrix and constant vector in the equality constraint; u is an uncertain variable, F is the coefficient matrix related to u.

[0043] Further, the game strategy of the energy scheduling in the step S4 comprises: for a given game problem Θ={N, S, U}, the strategy x * ∈S constitutes an equilibrium solution, i.e. an optimal solution, of the problem only when the following inequality is satisfied.

[0044]

[0045] Wherein, and are the decision of the ith ship and the decision of other participants except the ith ship respectively; and are the decision of the new energy supplier and the decision of other participants respectively; and are the decision of the port operator and the decision of other participants except the port operator.

[0046] Further, the self-adaptive differential evolution algorithm is used to solve the energy scheduling in the step S4, and the flow is as follows:

[0047] A1: the upper layer initializes algorithm parameters, generates an initial population POP_a and sets the maximum iteration number K max , and the iteration number K=0;

[0048] A2: the upper layer transmits decision variables such as the electricity purchase price, the electricity sale price and the service price to the lower layer ship and the new energy supplier;

[0049] A3: the lower layer optimizes the energy supply plan, the charging strategy and the like based on the received price signal, and feeds back the optimal decision;

[0050] A4: the upper layer calculates the own revenue U M1 based on the feedback of the lower layer, executes mutation and crossover operations on POP_a to generate a new population, and triggers the lower layer optimization again, and obtains the own revenue U M2 based on the returned result;

[0051] A5: selection and judgment, if U M2 >U M1 , then POP_a=POP_b, U M1 =U M2 , otherwise, the same is kept, and further judgment is made on whether the K reaches K max , if not, the iteration is continued, otherwise, the optimal strategy is output.

[0052] The application also provides a port-ship multi-benefit subject collaborative operation method considering new energy output uncertainty, comprising:

[0053] A preprocessing module is used for preprocessing collected information data;

[0054] a berth allocation module configured to determine an optimal berth allocation scheme by using a constructed berth allocation model;

[0055] an output uncertainty module configured to obtain a polyhedral uncertainty set by using a constructed new energy output uncertainty model, and use the polyhedral uncertainty set to depict wind and light output fluctuation;

[0056] an energy scheduling module configured to perform energy scheduling by using a constructed port energy collaborative scheduling model, and realize collaborative optimization through upper and lower layer strategy interaction and bidirectional iteration to obtain an optimal operation strategy.

[0057] The application adopts a two-stage optimization method. In the first stage, a berth allocation model is constructed to determine an optimal berth allocation scheme by comprehensively considering ship arrival time and demand, and the optimal berth allocation scheme is used as an input parameter of the second stage. In the second stage, energy scheduling is performed based on a Stackelberg game framework, and collaborative optimization is realized through upper and lower layer strategy interaction and bidirectional iteration. Based on this, the application has the following innovative points:

[0058] 1. Based on the Stackelberg game, a hierarchical decision-making framework is constructed with the port operator as the leader and the new energy supplier and the ship as the followers, and multi-party benefit balance is realized through dynamic pricing and strategy interaction.

[0059] 2. The new energy output uncertainty is considered, and robust optimization is combined with the game framework to provide a new method for port low carbonization with economic and robustness.

[0060] 3. Two-stage optimization of berth allocation-energy scheduling is realized, which not only can effectively allocate berths for all arriving ships in the scheduling period and improve the service efficiency of the port, but also can significantly reduce the power demand of the main power grid and improve the reliability and adjustment capacity of the port energy system.

[0061] Advantages: Compared with the prior art, the application has the following advantages:

[0062] 1. The ship berthing time, berth quantity demand and special demand of large ships for continuous berths are fully considered, the total waiting time of the ship is reduced, and the ship can timely leave the port after meeting its own demand, which significantly improves the overall operation efficiency and resource utilization rate of the port.

[0063] 2. A multi-agent collaborative optimization mechanism is constructed based on the game theory to realize multi-party benefit balance and optimization of the port operator-new energy supplier-ship, stimulate the enthusiasm of all parties to participate in collaborative operation, and further promote the sustainable development of the shipping service supply chain and the port energy system.

[0064] 3. The energy scheduling scheme combines robust optimization and game framework. In the case of new energy output fluctuation, it can guarantee the ship load demand, fully use new energy power generation to reduce the dependence on the main power grid, and enhance the reliability and regulation ability of the port energy system.

[0065] 4. The port-ship collaborative operation method has wide applicability, can adapt to different scale port operation scenarios, supports various combinations of renewable energy, is compatible with traditional ship and all-electric ship mixed scheduling, and can flexibly adjust parameter settings according to specific port conditions. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 A framework diagram of the two-stage optimization method provided by the present application is shown.

[0067] Figure 2 A port-ship collaborative operation flowchart is shown.

[0068] Figure 3 A berth optimization allocation result schematic diagram is shown.

[0069] Figure 4 A ship strategy and price schematic diagram is shown.

[0070] Figure 5 A port energy system scheduling schematic diagram is shown. DETAILED DESCRIPTION

[0071] The present application will be further illustrated below in conjunction with the drawings and specific embodiments, and it should be understood that these embodiments are only used to illustrate the present application and not to limit the scope of the present application. After reading the present application, those skilled in the art can make various equivalent modifications to the present application, which fall within the scope defined by the appended claims.

[0072] Example 1:

[0073] As shown in Figure 1 , the present embodiment provides a port-ship multi-benefit subject collaborative operation method considering new energy output uncertainty, including ship-to-port berth allocation and port energy collaborative scheduling. The berth scheduling optimizes the berth allocation to minimize the total waiting time and service time of the ship, so as to improve the port logistics efficiency; the energy collaborative scheduling maximizes the benefits of each benefit subject, through the interaction and game among the multi-benefit subjects, while guaranteeing the interests of each party in the port collaborative operation, effectively dealing with the volatility of new energy output, reducing the dependence on the main power grid, and realizing the stable operation of the port energy system.

[0074] Referring to Figure 1 and Figure 2 , a port-ship multi-benefit subject collaborative operation method considering new energy output uncertainty includes the following steps:

[0075] S1: Collecting berth information data such as ship arrival time, berth plan, etc.

[0076] S2: According to the collected berth information data, the optimal berth allocation scheme is determined through the constructed berth allocation model;

[0077] S3: Through the constructed new energy output uncertainty model, the polyhedral uncertainty set is obtained, and the polyhedral uncertainty set is used to describe the wind and light output fluctuation;

[0078] S4: Based on the price corresponding to the optimal berth allocation scheme and the polyhedral uncertainty set, the energy scheduling is carried out through the constructed port energy collaborative scheduling model, and the collaborative optimization is realized through the interaction of upper and lower strategies, and the optimal operation strategy is obtained.

[0079] The berth allocation model in step S2 is expressed as follows:

[0080] Berth allocation is a key link in port logistics operation. The port operator allocates berths for arriving ships based on ship arrival time, berth demand, service plan and charging plan within the scheduling period, with the goal of minimizing the waiting time and service time of all ships, which is specifically expressed as follows:

[0081]

[0082] Where S, B and O are the sets of ships, berths and port services respectively; f mnk is the time for ship n to complete service as the kth ship at the mth berth; a n is the arrival time of the nth ship.

[0083] The berth allocation model includes constraints on the number of berths allocated when the ship arrives, the state of the berth, the arrival time of the ship and the service time, which are as follows:

[0084]

[0085] Where x mnk is a 0-1 variable, which is 1 when the nth ship is the kth ship docked at the mth berth, and 0 otherwise; d n is the number of berths required by the nth ship; b mnk represents the time when the nth ship starts service; C mn is the length of time when the nth ship is docked; H is a very large constant.

[0086] The berth allocation model considers the berthing time of the ship, the number of berth demand and the volume parameter of the ship, pays attention to the special demand of large ship to continuous berth, and ensures the continuity and overall coordination of the berth resource allocation. By preferentially ensuring that the ship docks as soon as possible, the rapid connection of loading and unloading operation and charging and discharging operation is realized, the ship is prompted to leave the port in time after completing the operation demand, and the port logistics turnover efficiency and energy utilization efficiency are improved.

[0087] In the application, the port uses wind energy and photovoltaic as the main new energy power generation form. Since the power output of wind power generation and photovoltaic generation is affected by natural factors such as seasonal change and weather condition, it presents significant random fluctuation and intermittent characteristics, and needs to use appropriate mathematical tools for description.

[0088] In step S3, the new energy output uncertainty model quantitatively models the wind and light output fluctuation range by using a polyhedral uncertainty set, and is specifically expressed as follows:

[0089]

[0090] Wherein, U is a set of uncertain variables; is the actual power of wind power generation and photovoltaic generation, k∈{WT,PV}; is the predicted power of wind power generation and photovoltaic generation; and is an auxiliary variable; is the maximum fluctuation of wind power generation and photovoltaic generation power;Γ k is a budget parameter.

[0091] Compared with the box set, the polyhedral uncertainty set is more detailed, and compared with the ellipsoid set, it is easier to linearize. When the polyhedral uncertainty set is used to describe the wind and light output fluctuation, the degree of conservatism can be flexibly adjusted by the budget parameter, so as to balance the system robustness and economy.

[0092] The expression of the port energy collaborative scheduling model in step S4 is as follows:

[0093] The port energy collaborative scheduling model is based on Stackelberg game, and a multi-level optimization method in the port-ship collaborative scenario is designed, taking the port operator as the leader and the new energy supplier and the ship as the follower. The specific game decision is as follows:

[0094]

[0095] Wherein, N is a set of all game participants;S is a strategy set of each participant;U is a utility function of each participant;Ω C and Ω A are the set of traditional ships and all-electric ships respectively;EP is a new energy supplier, and M is a port operator.

[0096] The port energy coordinated scheduling model includes the following constraints:

[0097] The traditional ship needs to be provided with loading and unloading services by the port when it is in port, and the decision model of the ship aims to maximize the satisfaction level when the services are accepted, considering the loading and unloading power constraint and the cargo level constraint, and the specific constraint is as follows:

[0098]

[0099] wherein x i is the decision of the i th ship; x -i is the decision of other ships; a i and b i are both sensitive parameters of the ship to price; C s,i is the service price; P s,i is the loading and unloading power; E s,i is the cargo quantity level on the ship; and Δt is the time interval; is the maximum value of the loading and unloading power; and are the lower limit and the upper limit of the cargo quantity level on the i th ship, respectively; is the initial cargo quantity level of the cargo on the i th ship;

[0100] The all-electric ship needs to be provided with loading and unloading services and charging services by the port when it is in port, and can also actively support the port energy system through the ship-to-grid technology, and the decision model of the ship aims to maximize the satisfaction level when the services are accepted, considering the loading and unloading power constraint, the charging and discharging power constraint and the state of charge constraint, and the specific constraint is as follows:

[0101]

[0102] wherein c i and d i are both sensitive parameters of the ship to price; C e,i and C g,i are the selling price and the buying price of electricity traded with the ship, respectively; P s,i is the loading and unloading power; P e,i and P g,i are the charging and discharging power of the ship, respectively; and are the upper limits of the loading and unloading power and the charging and discharging power, respectively; and are the lower limit and the upper limit of the energy of the on-board battery, respectively; is the initial energy of the on-board battery; η c and η dc are the charging efficiency and the discharging efficiency of the on-board battery, respectively;

[0103] The new energy supplier optimizes the controllable equipment output according to the electricity purchase price formulated by the port operator, and the decision model aims to maximize the net energy supply benefit, considering the energy selling power constraint, the energy storage device charging and discharging power and state constraint, as follows:

[0104]

[0105] wherein, is the electricity purchase price; is the transaction power between the new energy supplier and the port operator; and are the actual output powers of wind power and photovoltaic power respectively; θ WT , θ PV and θ es are the operation and maintenance coefficients of wind power, photovoltaic power and energy storage device respectively; and are the charging and discharging powers of the energy storage device respectively; δ ep is the carbon emission cost coefficient; is the electricity purchase from the main power grid; ESS t+1 and ESS t are the electric quantities of the energy storage device at t+1 and t respectively; ESS min and ESS max are the lower limit and upper limit of the electric quantity of the energy storage device; T is an operation cycle; η ch and η dis are the charging and discharging efficiencies of the energy storage device respectively; and are 0-1 variables, representing the charging state and discharging state of the energy storage device respectively;

[0106] The port operator as the leader promotes the collaborative operation between the port and the ship through the electricity purchase and sale price and the service price, and realizes the maximization of its own economic benefit, and the decision model considers the electricity purchase price constraint, the electricity sale price constraint, the service price constraint, and the electricity power constraint purchased from the main power grid, as follows:

[0107]

[0108] wherein, ε is the power consumption rate of each unit of loading and unloading service.

[0109] In the port energy collaborative scheduling model, the big M method is used for linearization treatment for the nonlinear term in the decision model of the new energy supplier, and the maximum benefit objective of the utility function is converted into a minimum cost problem; at the same time, based on the polyhedral uncertainty set, the fluctuation of wind and light power generation output is described, and a robust optimization model is constructed, and the specific matrix form is as follows:

[0110]

[0111] where x is the decision variable of the new energy supplier; C is the coefficient matrix in the utility function; A and d are the coefficient matrix and constant vector in the inequality constraint, respectively; B and e are the coefficient matrix and constant vector in the equality constraint, respectively; u is the uncertainty variable, corresponding to formula (3) is the actual power of wind power and photovoltaic power, k ∈ {WT, PV}; F is the coefficient matrix related to u.

[0112] The game strategy of energy scheduling in step S4 includes: for a given game problem Θ = {N, S, U}, the strategy x * ∈ S constitutes the equilibrium solution, i.e. the optimal solution, of the problem. Its specific representation is as follows:

[0113]

[0114] where, and are the decisions of the ith ship and other participants except the ith ship, respectively; and are the decisions of the new energy supplier and the remaining participants, respectively; and are the decisions of the port operator and other participants except it.

[0115] The adaptive differential evolution algorithm is used to solve the energy scheduling in step S4, referring to Figure 2 , and the process is as follows:

[0116] A1: The upper layer initializes the algorithm parameters, generates an initial population POP_a and sets the maximum number of iterations K max , the number of iterations K = 0;

[0117] A2: The upper layer passes the decision variables such as electricity purchase price, electricity sale price and service price to the lower layer of ships and new energy suppliers;

[0118] A3: The lower layer optimizes the energy supply plan, charging strategy, etc. based on the received price signal, and feeds back the optimal decision;

[0119] A4: The upper layer calculates its own revenue U M1 based on the feedback from the lower layer, performs mutation and crossover operations on POP_a to generate a new population, and triggers the lower layer optimization again to obtain its own revenue U M2 ;

[0120] A5: Selection judgment, if U M2 > U M1 , then POP_a = POP_b, U M1= U M2 , and further determining whether K reaches K max , if not, continue iteration, otherwise output the optimal strategy.

[0121] Embodiment 2:

[0122] Based on the method of embodiment 1, this embodiment provides a port-ship multi-stakeholder collaborative operation method considering new energy output uncertainty, comprising:

[0123] A preprocessing module is configured to preprocess the collected information data.

[0124] A berth allocation module is configured to determine an optimal berth allocation scheme through the constructed berth allocation model.

[0125] An output uncertainty module is configured to obtain a polyhedral uncertainty set through the constructed new energy output uncertainty model, and use the polyhedral uncertainty set to depict wind and light output fluctuations.

[0126] An energy dispatching module is configured to perform energy dispatching through the constructed port energy collaborative dispatching model, and realize collaborative optimization through bidirectional iteration of upper and lower layer strategies to obtain an optimal operation strategy.

[0127] Embodiment 3:

[0128] In order to verify the effectiveness and effect of the scheme, the following examples and analysis are carried out in this embodiment:

[0129] Taking a port with 7 ship berths as an example, 20 ships are introduced in a 24-hour dispatching cycle, including large, medium and small all-electric ships and traditional ships. After the first stage optimization, the berth allocation scheme makes the total waiting time of all ships 0 hours, as shown in Table 1. If the benchmark method of "no berth optimization" is used, the total waiting time of the ships is at least 4 hours, and there is a ship that cannot complete the berthing plan within the dispatching cycle. This shows that the proposed method significantly improves the overall operation efficiency and resource utilization of the port. Figure 3 The upper limit of the electricity price set by the port operator is the main grid electricity price, and the lower limit is 0.2 dollars / kWh. The main grid adopts time-of-use electricity price, and the peak, flat and valley periods are 0.825, 0.55 and 0.28 (dollars / kWh), respectively. The upper and lower limits of the service price set by the port operator are 0.14 and 0.10 (dollars / shipment unit), respectively. The maximum handling power of the ship is set to 100 shipment units / hour, and the maximum charging and discharging power is set to 20 kW. For new energy output uncertainty, the maximum power fluctuation range is set to 20%, and the robustness parameter is set to 15.

[0130]

[0131] ​As shown in Table 1, four cases are set to verify the effectiveness of the second-stage energy scheduling scheme. The economic benefits of each participant in different cases are shown in Table 2. According to Table 1 and Table 2, comparing cases 1 and 2, if the port operator does not conduct dynamic pricing, each participant makes independent decisions, and the traditional ship cost increases by at least 21.83%, the all-electric ship cost increases by at least 27.06%, and the new energy supplier's revenue decreases by at least 25.97%. Comparing cases 2 and 3, considering the discharging behavior of all-electric ships, their costs are further reduced. Case 4 considers the uncertainty of new energy output based on case 3, and the new energy supplier's revenue decreases by 9.21%, while the benefits of other participants remain relatively stable. Figures 4-5 The implementation effect of energy collaborative scheduling considering new energy output uncertainty and its mechanism are demonstrated.

[0132] Table 1 Comparison of cases

[0133]

[0134] Table 2 Economic benefits of each participant in different cases

[0135]

[0136] As shown in Table 1, four cases are set to verify the effectiveness of the second-stage energy scheduling scheme. The economic benefits of each participant in different cases are shown in Table 2. According to Table 1 and Table 2, comparing cases 1 and 2, if the port operator does not conduct dynamic pricing, each participant makes independent decisions, and the traditional ship cost increases by at least 21.83%, the all-electric ship cost increases by at least 27.06%, and the new energy supplier's revenue decreases by at least 25.97%. Comparing cases 2 and 3, considering the discharging behavior of all-electric ships, their costs are further reduced. Case 4 considers the uncertainty of new energy output based on case 3, and the new energy supplier's revenue decreases by 9.21%, while the benefits of other participants remain relatively stable. Figure 4 As shown in Table 1, four cases are set to verify the effectiveness of the second-stage energy scheduling scheme. The economic benefits of each participant in different cases are shown in Table 2. According to Table 1 and Table 2, comparing cases 1 and 2, if the port operator does not conduct dynamic pricing, each participant makes independent decisions, and the traditional ship cost increases by at least 21.83%, the all-electric ship cost increases by at least 27.06%, and the new energy supplier's revenue decreases by at least 25.97%. Comparing cases 2 and 3, considering the discharging behavior of all-electric ships, their costs are further reduced. Case 4 considers the uncertainty of new energy output based on case 3, and the new energy supplier's revenue decreases by 9.21%, while the benefits of other participants remain relatively stable.

[0137] As shown in Table 1, four cases are set to verify the effectiveness of the second-stage energy scheduling scheme. The economic benefits of each participant in different cases are shown in Table 2. According to Table 1 and Table 2, comparing cases 1 and 2, if the port operator does not conduct dynamic pricing, each participant makes independent decisions, and the traditional ship cost increases by at least 21.83%, the all-electric ship cost increases by at least 27.06%, and the new energy supplier's revenue decreases by at least 25.97%. Comparing cases 2 and 3, considering the discharging behavior of all-electric ships, their costs are further reduced. Case 4 considers the uncertainty of new energy output based on case 3, and the new energy supplier's revenue decreases by 9.21%, while the benefits of other participants remain relatively stable. Figure 5 As shown in Table 1, four cases are set to verify the effectiveness of the second-stage energy scheduling scheme. The economic benefits of each participant in different cases are shown in Table 2. According to Table 1 and Table 2, comparing cases 1 and 2, if the port operator does not conduct dynamic pricing, each participant makes independent decisions, and the traditional ship cost increases by at least 21.83%, the all-electric ship cost increases by at least 27.06%, and the new energy supplier's revenue decreases by at least 25.97%. Comparing cases 2 and 3, considering the discharging behavior of all-electric ships, their costs are further reduced. Case 4 considers the uncertainty of new energy output based on case 3, and the new energy supplier's revenue decreases by 9.21%, while the benefits of other participants remain relatively stable.

[0138] As shown in Table 1, four cases are set to verify the effectiveness of the second-stage energy scheduling scheme. The economic benefits of each participant in different cases are shown in Table 2. According to Table 1 and Table 2, comparing cases 1 and 2, if the port operator does not conduct dynamic pricing, each participant makes independent decisions, and the traditional ship cost increases by at least 21.83%, the all-electric ship cost increases by at least 27.06%, and the new energy supplier's revenue decreases by at least 25.97%. Comparing cases 2 and 3, considering the discharging behavior of all-electric ships, their costs are further reduced. Case 4 considers the uncertainty of new energy output based on case 3, and the new energy supplier's revenue decreases by 9.21%, while the benefits of other participants remain relatively stable.

[0139] Table 3 Economic benefits of each participant under different uncertainty parameters

[0140]

[0141]

Claims

1. A method for coordinated operation of port-ship multi-stakeholders considering uncertainty of new energy output, characterized in that, The method comprises the following steps: S1: collecting berth information data; S2: determining an optimal berth allocation scheme according to the collected berth information data through a constructed berth allocation model; S3: obtaining a polyhedral uncertainty set through a constructed new energy output uncertainty model, and using the polyhedral uncertainty set to depict wind and light output fluctuation; S4: performing energy scheduling through a constructed port energy collaborative scheduling model based on a price corresponding to the optimal berth allocation scheme and the polyhedral uncertainty set, and realizing collaborative optimization through upper and lower strategy interaction and bidirectional iteration to obtain an optimal operation strategy.

2. The method according to claim 1, wherein, The berth allocation model in the step S2 is expressed as follows: The port operator allocates berths for the arriving ships based on the ship arrival time, berth demand, service plan and charging plan in the scheduling period, and the target is to minimize the waiting time and service time of all ships, and is specifically expressed as follows: where S, B and O are sets of ships, berths and port services respectively; f mnk is the time for ship n to complete service at the mth berth as the kth ship; a n is the time of arrival of the nth ship.

3. The method according to claim 2, wherein, The berth allocation model in the step S2 comprises constraints on the number of berths allocated when the ship arrives, the berth state, the ship arrival time and the service time, and is specifically as follows: where x mnk is a 0-1 variable, which is 1 when the nth ship is berthing at the mth berth as the kth ship, otherwise 0; d n is the number of berths required by the nth ship; b mnk represents the time when the nth ship starts to serve; C mn is the berthing duration of the nth ship; and H is a constant.

4. The method according to claim 3, wherein, The new energy output uncertainty model in the step S3 uses a polyhedral uncertainty set to quantitatively model the wind and light output fluctuation range, and is specifically expressed as follows: where U is a set of uncertain variables; PWT, PV are the actual power of wind power and photovoltaic power, k ∈ {WT, PV}; PWT, PV are the predicted power of wind power and photovoltaic power; and are auxiliary variables; are the maximum fluctuation of wind power and photovoltaic power; Γ k are budget parameters.

5. The method according to claim 4, wherein, The port energy collaborative scheduling model in the step S4 is expressed as follows: The port energy collaborative scheduling model is based on a Stackelberg game, and a multi-level optimization method in a port-ship collaborative scenario is designed, in which the port operator is the leader and the new energy supplier and the ship are the followers. The proposed game decision is specifically as follows: Wherein, N is the set of all game participants; S is the strategy set of each participant; U is the utility function of each participant; Ω C and Ω A Respectively, the set of traditional ships, all-electric ships; EP is a new energy supplier, and M is a port operator.

6. The method according to claim 5, wherein, The port energy collaborative scheduling model in the step S4 comprises the following constraints: The traditional ship needs to be provided with loading and unloading services by the port when it arrives, and the target of the decision model is to maximize the satisfaction level when the service is accepted, considering the loading and unloading power constraint and the cargo level constraint, and is specifically as follows: where x i is the decision of the ith ship; x -i is the decision of the other ships; a i and b i are both the price sensitivity parameters of the ships; C s,i is the service price; P s,i is the handling power; E s,i is the cargo quantity level on the ship; Δt is the time interval; is the maximum value of the handling power; and are the lower and upper limits of the cargo quantity level on the ith ship, respectively; is the initial cargo quantity level of the cargo on the ith ship; The all-electric ship needs to be provided with loading and unloading services and charging services by the port when it arrives, and can also actively support the port energy system through the ship-to-grid technology, and the target of the decision model is to maximize the satisfaction level when the service is accepted, considering the loading and unloading power constraint, the charging and discharging power constraint and the state of charge constraint, and is specifically as follows: wherein c i and d i are both the price sensitivity parameters of the ship; C e,i and C g,i are the selling and buying electricity prices for the ship transaction; P s,i is the loading and unloading power; P e,i and P g,i are the charging and discharging power of the ship; P s max and P e max are the upper limits of the loading and unloading power and the charging and discharging power, respectively; and are the lower and upper limits of the ship-mounted battery energy; is the initial energy of the ship-mounted battery; η c and η dc are the charging and discharging efficiencies of the ship-mounted battery, respectively. The new energy supplier optimizes the controllable equipment output according to the electricity purchase price formulated by the port operator, and the target of the decision model is to maximize the energy supply net income, considering the power selling constraint, the energy storage device charging and discharging power and state constraint, and is specifically as follows: wherein, is the electricity purchase price; is the traded power between the new energy supplier and the port operator; and are the actual output powers of wind power and photovoltaic, respectively; θ WT , θ PV , and θ es are the operation and maintenance coefficients of wind power, photovoltaic, and energy storage device, respectively; and are the charging and discharging powers of the energy storage device, respectively; δ ep is the carbon emission cost coefficient; is the electricity purchase from the main power grid; ESS t+1 and ESS t are the electric quantities of the energy storage device at t+1 and t, respectively; ESS min and ESS max are the lower limit and upper limit of the electric quantity of the energy storage device; T is an operation cycle; η ch and η dis are the charging and discharging efficiencies of the energy storage device, respectively; and are 0-1 variables, respectively representing the charging state and discharging state of the energy storage device; The port operator as the leader promotes the collaborative operation between the port and the ship and realizes the maximization of its own economic benefits through the electricity purchase and sale price and the service price, and the decision model considers the electricity purchase price constraint, the electricity sale price constraint, the service price constraint and the power purchased from the main grid constraint, and is specifically as follows: Wherein, ε is the power consumption rate of each unit of loading and unloading service.

7. The method according to claim 6, wherein, In the port energy collaborative scheduling model, the nonlinear term in the new energy supplier decision model is linearized by using the big M method, and the maximum benefit target of the utility function is converted into a minimum cost problem; meanwhile, the wind and light power generation output fluctuation is depicted based on the polyhedral uncertainty set, and a robust optimization model is constructed, and the specific matrix form is as follows: where x is the decision variable of the new energy supplier; C is the coefficient matrix in the utility function; A and d are the coefficient matrix and constant vector in the inequality constraint, respectively; B and e are the coefficient matrix and constant vector in the equality constraint, respectively; u is the uncertainty variable, F is the coefficient matrix related to u.

8. The method according to claim 7, wherein, The game strategy of the energy scheduling in the step S4 includes: for a given game problem Θ={N, S, U}, the strategy x * ∈S constitutes the equilibrium solution, i.e. the optimal solution, of the problem, which is specifically represented as follows: wherein, and respectively are the decision of the ith ship and the decision of the other participants except the ith ship; and respectively are the decision of the new energy supplier and the decision of the other participants except the new energy supplier; and respectively are the decision of the port operator and the decision of the other participants except the port operator.

9. The method according to claim 8, wherein, The step S4 is solved by using an adaptive differential evolution algorithm, and a flowchart is as follows: A1: upper layer initialization algorithm parameter, generate initial population POP_a and set the maximum iteration number K max , iteration number K = 0; A2: The upper layer transmits decision variables such as power purchase price, power sale price and service price to the lower layer ship and new energy supplier; A3: The lower layer optimizes energy supply plan and charging strategy based on the received price signal, and feeds back the optimal decision; A4: The upper layer calculates its own benefit U according to the feedback of the lower layer M1 , and triggers the lower layer optimization again, and obtains its own benefit U according to the returned result M2 ; A5: If U M2 > U M1 , then POP_a = POP_b, U M1 = U M2 , otherwise, keep it unchanged, and further judge whether K reaches K max , if not, continue iteration, otherwise, output the optimal strategy.

10. A method for coordinated operation of port-ship multi-stakeholders considering the uncertainty of new energy output, characterized in that, For implementing the method of claim 1, the system comprises: a preprocessing module for preprocessing the collected information data; a berth allocation module for determining an optimal berth allocation scheme through the constructed berth allocation model; an output uncertainty module for obtaining a polyhedral uncertainty set through the constructed new energy output uncertainty model, and using the polyhedral uncertainty set to depict wind and light output fluctuation; an energy scheduling module for scheduling energy through the constructed port energy collaborative scheduling model, and realizing collaborative optimization through upper and lower layer strategy interaction and bidirectional iteration to obtain an optimal operation strategy.

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