Port-oriented virtual power plant incentive coordination optimization method and related equipment

Abstract

Embodiments of the present application provide a port-oriented virtual power plant incentive coordination optimization method and related equipment. The method comprises obtaining charging demand, charging preference parameters and market electricity price distribution of a plurality of electric ships; a charging scheduling incentive model is constructed based on the charging demand, the charging preference parameters, the market electricity price distribution, a buyout incentive variable and a demand incentive variable, the buyout incentive variable being a unit incentive variable for the schedulable capacity of each electric ship, and the demand incentive variable being a unit incentive variable for the actual scheduling power of each electric ship; the charging scheduling incentive model is linearized based on selection variables corresponding to the buyout incentive variable and the demand incentive variable to obtain a linear model; and a target incentive price is determined based on the linear model. The method can improve the operation efficiency of the virtual power plant in charging scheduling of the electric ships.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a virtual power plant incentive coordination optimization method for a port and related equipment. BACKGROUND

[0002] Under the promotion of global energy structure transformation, port energy systems are accelerating towards clean, intelligent and flexible development. As an important support means for port low-carbon transformation, virtual power plant technology has become an important part of smart port construction due to its characteristics of multi-energy collaboration, source-load interaction and unified scheduling. Virtual power plant aggregates distributed renewable energy, electrochemical energy storage devices, electric devices and controllable loads, etc. to realize flexible scheduling and optimized operation, so as to meet the port's own load demand and have the ability to participate in power market transactions and auxiliary services.

[0003] In recent years, the development of electric ships has become an important force to promote the green shipping system of the port. Compared with traditional internal combustion engine powered ships, electric ships have the advantages of zero emissions, low noise and low operation and maintenance costs, which meet the green development goal of the port. With the gradual maturity of shore-based power infrastructure and ship-shore power interaction technology, electric ships can be charged during berthing, participate in the charging scheduling of the virtual power plant and improve the charging flexibility under the incentive of the virtual power plant.

[0004] However, as a mobile, heterogeneous and task-driven load type, the charging behavior of electric ships has significant uncertainty and volatility, which leads to unstable incentive effect of the virtual power plant when mobilizing electric ships, affecting the operation efficiency of the virtual power plant in charging scheduling of electric ships. SUMMARY

[0005] The main purpose of the present application is to provide a virtual power plant incentive coordination optimization method for a port and related equipment to improve the operation efficiency of the virtual power plant in charging scheduling of electric ships.

[0006] To achieve the above purpose, the present application provides a virtual power plant incentive coordination optimization method for a port, comprising:

[0007] obtaining charging demand, charging preference parameters and market electricity price distribution of a plurality of electric ships;

[0008] constructing a charging scheduling incentive model based on the charging demand, the charging preference parameters, the market electricity price distribution, a buyout incentive variable and a demand-based incentive variable, the buyout incentive variable being a unit incentive variable for the dispatchable capacity of each electric ship, and the demand-based incentive variable being a unit incentive variable for the actual dispatch power of each electric ship;

[0009] linearize the charging scheduling incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model;

[0010] determine the target incentive price based on the linear model.

[0011] Optionally, in a possible implementation, the linearizing the charging scheduling incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model includes:

[0012] linearize the charging scheduling incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable, and the scheduling variables corresponding to the actual scheduling power to obtain a linear model.

[0013] Optionally, in a possible implementation, the determining the target incentive price based on the linear model includes:

[0014] divide the plurality of electric ships into a plurality of electric ship sets based on the berthing time of each electric ship;

[0015] calculate a first incentive price for the linear model based on the charging demand and the charging preference parameter in each electric ship set;

[0016] adjust a plurality of first incentive prices corresponding to the plurality of electric ship sets based on the penalty factor and the dual variable to obtain a target incentive price.

[0017] Optionally, in a possible implementation, the adjusting the plurality of first incentive prices corresponding to the plurality of electric ship sets based on the penalty factor and the dual variable to obtain a target incentive price includes:

[0018] when the number of adjustments does not reach a preset number, update the dual variable based on the plurality of first incentive prices corresponding to the plurality of electric ship sets and the penalty factor;

[0019] update the parameters of the linear model based on the dual variable;

[0020] calculate a second incentive price corresponding to each electric ship set for the updated linear model based on the penalty factor and the updated dual variable;

[0021] when the number of adjustments reaches the preset number, calculate the target incentive price for the updated linear model based on the penalty factor and the updated dual variable.

[0022] Optionally, in a possible implementation, after the calculating the second incentive price corresponding to each electric ship set for the updated linear model based on the penalty factor and the updated dual variable, the method further includes:

[0023] The scheduling gain for each electric vessel set is determined based on the second incentive price and the market electricity price distribution.

[0024] The adaptive weights for each set of electric ships are determined based on the scheduling gain;

[0025] The penalty factor for each electric ship set is adjusted based on the adaptive weights of each electric ship set.

[0026] Optionally, in one possible implementation, the dispatch gain corresponding to each electric vessel set is determined based on the second incentive price and the market electricity price distribution, including:

[0027] The electricity purchase reduction cost for each electric vessel group is determined based on the berthing time of each electric vessel and the distribution of market electricity prices;

[0028] The scheduling gain for each electric vessel group is determined based on the second incentive price and the cost reduction of electricity purchases.

[0029] Optionally, in one possible implementation, the dispatch gain corresponding to each electric vessel set is determined based on the second incentive price and the market electricity price distribution, including:

[0030] The maximum and minimum scheduling gains among multiple electric ship assemblies are determined based on the second incentive price and the market electricity price distribution.

[0031] When the relative quality difference between the maximum and minimum scheduling gain is less than or greater than a preset threshold, the scheduling gain corresponding to each electric ship set is determined based on the second incentive price and the market electricity price distribution.

[0032] Another aspect of this application provides an incentive coordination optimization device for a port virtual power plant, comprising:

[0033] The acquisition unit is used to acquire the charging needs, charging preference parameters, and market electricity price distribution of multiple electric ships;

[0034] The building unit is used to construct a charging dispatch incentive model based on charging demand, charging preference parameters, market electricity price distribution, buyout incentive variables and on-demand incentive variables. The buyout incentive variable is a unit incentive variable for the dispatchable capacity of each electric vessel, and the on-demand incentive variable is a unit incentive variable for the actual dispatched power of each electric vessel.

[0035] The linearization unit is used to linearize the charging scheduling incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable, so as to obtain a linear model;

[0036] The determination unit is used to determine the target incentive price based on a linear model.

[0037] Another aspect of this application provides an electronic device, comprising:

[0038] a memory, a transceiver, a processor, and a bus system;

[0039] The memory is configured to store a program.

[0040] The processor is configured to execute the program in the memory, including executing the method of each aspect described above.

[0041] The bus system is configured to connect the memory and the processor, so that the memory and the processor communicate.

[0042] Another aspect of the present application provides a computer-readable storage medium, which stores instructions, when the instructions are executed on a computer, causing the computer to execute the method of each aspect described above.

[0043] From the above technical solutions, the embodiments of the present application have the following advantages:

[0044] The method comprises: acquiring charging demand, charging preference parameters and market electricity price distribution of a plurality of electric ships; constructing a charging dispatching incentive model based on the charging demand, the charging preference parameters, the market electricity price distribution, a buyout incentive variable and an on-demand incentive variable, the buyout incentive variable being a unit incentive variable for a dispatchable capacity of each electric ship, and the on-demand incentive variable being a unit incentive variable for an actual dispatching power of each electric ship; linearizing the charging dispatching incentive model based on selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model; and determining a target incentive price based on the linear model.

[0045] The method can linearize the non-linear constraint or objective function of the charging dispatching incentive model for calculating the incentive for the electric ships by dividing the incentive for the electric ships into a buyout incentive and an on-demand incentive, and then introducing selection variables of the buyout incentive and the on-demand incentive. The linearized model can more accurately describe the charging demand and the preference parameters of the electric ships, so as to obtain a more accurate incentive scheme, reduce the fluctuation and uncertainty of the incentive scheme, and calculate the lowest incentive meeting the charging demand and the preference parameters of the electric ships based on the linearized model, thereby improving the operation benefit of the charging dispatching of the virtual power plant for the electric ships. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a system architecture diagram to which a port virtual power plant incentive coordination optimization method provided by the embodiments of the present application is applied;

[0047] Figure 2 is a flowchart of a port virtual power plant incentive coordination optimization method provided by the embodiments of the present application;

[0048] Figure 3is a structural schematic diagram of a device for port virtual power plant incentive coordination optimization provided by an embodiment of the present application.

[0049] Figure 4 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0051] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a manner different from the module division in the device or the order in the flowchart. The terms "first", "second", and the like in the specification and claims and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0052] The word "exemplary" is used herein in the sense of being an example, illustration, or demonstration. Any embodiment described herein as "exemplary" is not necessarily to be construed as being superior to or better than other embodiments.

[0053] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined target, and can be implemented entirely or partially by using software, hardware (such as a processing circuit or a memory) or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the functions of the module or unit.

[0054] In addition, in order to better illustrate the present application, numerous specific details are given in the specific embodiments below. Those skilled in the art should understand that the present application can also be implemented without some specific details. In some examples, methods, means, elements and circuits that are well known to those skilled in the art are not described in detail, in order to highlight the main idea of the present application.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0056] First, the terms involved in the present application are analyzed:

[0057] A virtual power plant (VPP) is a system that aggregates and optimizes the operation of distributed energy resources (DERs) such as distributed generation (DG), energy storage systems, controllable loads, and electric vehicles, and participates in the power market and grid operation as a special power plant. The core of a VPP can be summarized as "communication" and "aggregation". It does not change the way each DG is connected to the grid, but aggregates different types of distributed energy through advanced control and communication technologies, and realizes the coordinated optimization of multiple DERs through a higher-level software framework. The most advantageous function of a VPP is to aggregate energy and directly participate in the operation of the power market, realizing the integrated management of distribution and transmission networks. It can ensure the smooth operation of the grid through "peak shaving and valley filling", and promote the consumption of new energy. On the other hand, it can also help end users to save electricity costs by participating in grid interaction and adjusting power consumption habits.

[0058] An electric ship refers to a ship that uses electricity as the main power source and can interact with a VPP to participate in peak shaving, frequency regulation, and other auxiliary services of the grid through demand response and other mechanisms, thereby improving the flexibility and reliability of the power system. Such ships may be equipped with energy storage devices such as batteries, fuel cells, and supercapacitors, and may include renewable energy technologies such as solar photovoltaics as auxiliary energy sources.

[0059] Incentives can refer to a mechanism or strategy to encourage distributed energy resources such as electric ships, solar photovoltaics, wind energy, and energy storage devices to adjust their behavior according to the demand of the grid or market signals. Such incentives can be economic incentives, price discounts, priority use rights, or other forms of rewards.

[0060] In the current electricity market, the market clearing period is usually one hour. As a price taker in the electricity market, the port needs to purchase electricity to meet the shore power load at the market clearing price. However, due to the limitation of market access threshold, the port charging system often cannot directly participate in the electricity wholesale market and cannot directly benefit from competitive prices. Therefore, the help of a power aggregation platform (such as a virtual power plant with integration capabilities) is needed as an intermediary agent to represent the port in the energy procurement process. Since the port cannot influence the electricity price, its main scheduling goal is to shift the use of electricity during peak periods to low-price periods by regulating the charging load to minimize the overall electricity cost.

[0061] In recent years, the development of electric ships has become an important force in promoting green shipping systems in ports. Compared with traditional internal combustion engine-powered ships, electric ships have advantages such as zero emissions, low noise, and low operating and maintenance costs, which aligns with the green development goals of ports. With the gradual maturation of shore-based power infrastructure and ship-to-shore power interaction technologies, electric ships can be charged by the grid during berthing and participate in the charging scheduling of virtual power plants, thereby improving charging flexibility.

[0062] Within the framework of virtual power plants, electric ships have the potential to be integrated into flexible resource units. By establishing flexible incentive mechanisms to stimulate their adjustment potential in areas such as charging rate, timing, and power reduction, it is possible not only to help alleviate peak port loads and enhance the local consumption capacity of renewable energy, but also to strengthen the port's energy system's ability to cope with external disturbances.

[0063] Electricity market prices are time-varying, with significant differences between peak and off-peak periods. If virtual power plants purchase electricity during high-price periods to charge electric ships without regulation, operating costs will increase significantly. Therefore, controlling the charging time of electric ships and scheduling charging during low-price periods helps to minimize costs.

[0064] As a flexible resource aggregation and scheduling platform, virtual power plants can improve overall profitability by optimizing the charging time window of electric vessels, participating in the electricity spot market and ancillary service market, and obtaining peak-shaving revenue or load migration incentives.

[0065] In port settings, the simultaneous high-power charging of multiple electric vessels can impact the local power grid or shore power system. Regulating charging times helps to stagger electricity consumption during off-peak hours, reduce system load fluctuations, and improve energy security and resource utilization efficiency.

[0066] However, the behavior of electric ships is subject to significant uncertainties, such as changes in berthing time and fluctuations in energy demand, which can lead to biases in the incentive effect and affect the operational efficiency of virtual power plants.

[0067] Based on this, embodiments of this application provide an incentive coordination optimization method for port virtual power plants. This method includes: acquiring the charging demand, charging preference parameters, and market electricity price distribution of multiple electric vessels; constructing a charging scheduling incentive model based on the charging demand, charging preference parameters, market electricity price distribution, buyout incentive variables, and on-demand incentive variables, where the buyout incentive variable is a unit incentive variable for the dispatchable capacity of each electric vessel, and the on-demand incentive variable is a unit incentive variable for the actual dispatched power of each electric vessel; linearizing the charging scheduling incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model; and determining the target incentive price based on the linear model.

[0068] The method can linearize the nonlinear constraint or objective function of the charging scheduling incentive model for calculating the incentive of the electric ship by dividing the incentive of the electric ship into the buyout incentive and the on-demand incentive, introducing selection variables of the buyout incentive and the on-demand incentive, and linearizing the model after linearization, so that the linearized model can more accurately describe the charging demand and the preference parameter of the electric ship, thereby obtaining a more accurate incentive scheme, reducing the fluctuation and uncertainty of the incentive scheme, and calculating the minimum incentive meeting the charging demand and the preference parameter of the electric ship based on the linearized model, thereby improving the operation benefit of the virtual power plant for the charging scheduling of the electric ship.

[0069] System architecture and scenario to which the embodiments of the present disclosure are applied

[0070] Figure 1 Fig. 1 is a system architecture diagram to which a port-oriented virtual power plant incentive coordination optimization method according to an embodiment of the present application is applied. It includes a virtual power plant 110, an electric ship 120 and a power plant 130.

[0071] The electric ship 120 docks at the shore for maintenance and charging.

[0072] The virtual power plant 110 can purchase electricity from the power plant 130 to charge the electric ship 120, and can schedule the charging process of the electric ship 120 based on the price change of the power plant 130 to reduce the cost required for charging the electric ship 120.

[0073] The port-oriented virtual power plant incentive coordination optimization method provided by the embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0074] Please refer to Figure 2 As Figure 2 Fig. 2 is a flowchart of a port-oriented virtual power plant incentive coordination optimization method provided by the embodiments of the present application. The method includes:

[0075] Step 201, obtaining the charging demand, charging preference parameter and market electricity price distribution of a plurality of electric ships.

[0076] The electric ship can be a ship using electricity as the main power source, which can include an inland sightseeing ship, a public ferry, a passenger ferry, a short-distance passenger ship, a tourist sightseeing ship, a small work boat, a noise-requiring work boat, etc. The electric ship, as a component part of the virtual power plant, can interact with the power grid and participate in the peak shaving, frequency modulation and other auxiliary services of the power grid through demand response and other mechanisms.

[0077] The charging demand can refer to the total amount of electricity that the electric ship must obtain from the power grid or other power sources to meet its operation needs. This includes charging the battery to store energy for ship propulsion, on-board equipment operation, etc., which can include the current electricity, battery capacity, docking time window and maximum charging power, etc.

[0078] The charging preference parameter can refer to the personal preference settings of the user of the electric ship or electric vehicle when charging, which can include the acceptance of incentives provided by the virtual power plant.

[0079] The market electricity price distribution can refer to the fact that in the electricity market, the electricity price can vary according to different time periods in a day (such as peak hours, off-peak hours) or different types of power demand (such as residential electricity, industrial electricity). For example, the peak-valley time-of-use electricity price divides the time of each day into peak, high, flat, and low periods according to the user's electricity demand, and different electricity price levels are set for each period.

[0080] It can be understood that the charging demand and the charging preference parameter can be actively sent by the electric ship for shore charging, and the virtual power plant can actively obtain the charging demand and the charging preference parameter from the electric ship for shore charging. The market electricity price distribution can be the real-time or predicted electricity price information obtained by the virtual power plant through the electricity market trading platform.

[0081] Step 202, constructing a charging dispatch incentive model based on the charging demand, the charging preference parameter, the market electricity price distribution, the buyout incentive variable, and the on-demand incentive variable.

[0082] The charging dispatch incentive model is a mathematical model for optimizing the charging dispatch of the electric ship and determining a reasonable incentive amount, and the purpose is to optimize the efficiency of the power grid and reduce the incentive cost while meeting the charging demand and the charging preference parameter of the electric ship. That is, by introducing the buyout incentive variable and the on-demand incentive variable, combining the charging demand, the charging preference parameter, the market electricity price distribution, and other factors of the electric ship, an optimization model is constructed, and the objective function of the model is usually to minimize the incentive income while meeting a series of constraint conditions, such as the charging demand constraint, the market electricity price constraint, and the power grid load balance constraint.

[0083] The buyout incentive variable can be a unit incentive variable for the dispatchable capacity of each electric ship, and the on-demand incentive variable can be a unit incentive variable for the actual dispatch power of each electric ship. The buyout incentive variable can be a unit incentive variable based on the buyout incentive scheme, and the buyout incentive scheme can refer to the fixed amount paid by the virtual power plant to the resource provider related to the amount of flexible resources in order to ensure that a certain amount of flexible resources is obtained within a certain period of time, and the virtual power plant can arbitrarily dispatch the charging process of the electric ship based on the flexible resources.

[0084] The on-demand incentive variable can be based on a unit incentive variable under an on-demand incentive scheme, the on-demand incentive scheme can be based on adjusting the charging power of the electric ship according to the market electricity price distribution to adapt to the fluctuation of the market electricity price, and the incentive amount is calculated according to the actual scheduling difference between the actual charging power of the electric ship and the charging reference power. The charging reference power can refer to the power level of the electric ship under the condition that no special charging strategy (such as demand response, peak-valley electricity price adjustment, etc.) is implemented, and the charging power changes according to the normal charging program and demand, which can be the charging power change reference from the current power to full power based on the maximum charging power within the berthing time window.

[0085] In one example, from the perspective of the port virtual power plant, increasing the incentive price can encourage more electric ship operators to release their scheduling flexibility resources, which helps the virtual power plant reduce the energy market procurement cost in the port environment. However, as the incentive price increases and the amount of flexibility resources obtained increases, the incentive cost paid by the port virtual power plant to the electric ship will also increase accordingly. Therefore, the reasonable setting of the buyout incentive variable and the on-demand incentive variable is crucial to the overall benefit of the proposed hybrid incentive mechanism.

[0086] To achieve the goal of maximizing the overall operation benefit of the port virtual power plant, an optimal price decision model for the electric ship incentive strategy can be constructed. In this model, the goal is to minimize the sum of the electricity purchase expenditure of the port virtual power plant in the wholesale electricity market and the incentive cost paid to the electric ship. Therefore, before constructing the optimization model, the electric ship response payment based on the two types of incentive mechanisms is first calculated. Specifically as follows:

[0087] (1)

[0088] (2)

[0089] Wherein, is the incentive paid to the i-th electric ship in the buyout incentive scheme, is the incentive paid to the j-th electric ship in the on-demand incentive scheme. represents the potential flexible capacity that the i-th ship can provide; represents the downward component of power change decomposition; represents the scheduling resolution conversion factor to 1 hour.

[0090] Secondly, the optimization objective function of minimizing the total system cost can be constructed:

[0091] After obtaining the incentive payment of the electric ship, the minimization cost problem can be expressed as:

[0092] (3)

[0093] Next, the system builds a system operation constraint model, including power balance constraints, electrical ship charging and discharging limit constraints, energy balance constraints, incentive price boundary constraints, etc.:

[0094] (4)

[0095] (5)

[0096] (6)

[0097] (7)

[0098] (8)

[0099] (9)

[0100] (10)

[0101] (11)

[0102] wherein, represents the market clearing price at time t, represents the energy purchased from the market at time t. The time interval of one charging scheduling period is . The objective function contains the energy procurement cost and the incentive payment. The parameters and are the upper limits of the incentive price, which are chosen as the highest minimum acceptable price of the electrical ships to ensure the optimality of the problem. The constraint condition (4) is the power balance constraint, represents the charging power of the i-th ship at time t in the buyout incentive scheme, represents the charging power of the j-th ship at time t in the on-demand incentive scheme, represents the power variation of the i-th ship at time t in the buyout incentive scheme, represents the upward component of the power variation of the j-th ship at time t in the on-demand incentive scheme. The constraint conditions (5) to (8) represent the battery charging rate limits under the participation state limits of the electrical ships, represents the binary variable of whether the i-th electrical ship participates in the buyout incentive scheme, represents the binary variable of whether the j-th electrical ship participates in the on-demand incentive scheme. The value of 1 indicates consent to authorized scheduling, and 0 indicates refusal. The constraint conditions (9) and (10) ensure that the charging demand of the electrical ships is met during the scheduling period. The constraint condition (11) provides a reasonable range for the incentive price to reduce the search domain and ensure the convergence of the problem.

[0103] Step 203, linearize the charging scheduling incentive model based on the selection variable corresponding to the buyout incentive variable and the on-demand incentive variable, to obtain a linear model.

[0104] The selection variable can be a decision variable, which is used to represent whether the electric ship participates in a certain specific incentive mechanism or scheduling mode. Specifically, the selection variable can be used to represent whether the electric ship participates in the buyout incentive or the on-demand incentive. For each electric ship, the buyout incentive variable and the on-demand incentive variable correspond to a selection variable respectively. If the electric ship chooses to participate in the buyout incentive, the value of the corresponding selection variable is 1; if the electric ship chooses not to participate in the buyout incentive, the value of the corresponding selection variable is 0; if the electric ship chooses to participate in the on-demand incentive, the value of the corresponding selection variable is 1; if the electric ship chooses not to participate in the on-demand incentive, the value of the corresponding selection variable is 0.

[0105] The selection variable can be selected based on the charging preference parameter, that is, the value of the corresponding selection variable can only be equal to 1 (i.e., allowed to participate) if the buyout or on-demand incentive meets the charging preference parameter.

[0106] Linearization refers to the process of converting a nonlinear mathematical model or constraint condition into a linear form. After linearization, the objective function and constraint conditions of the model are all linear expressions, which makes the model can be solved using a linear programming solver. In the charging scheduling incentive model, there may be some constraints involving the product of selection variables, such constraints are nonlinear. Based on the fact that the selection variable is a binary variable, the original nonlinear constraint involving the selection variable can be converted into two linear constraints through logical conditions. The originally complex nonlinear constraint is decomposed into two simple linear constraints, thereby realizing linearization. In the entire charging scheduling incentive model, all constraints involving the selection variable can be processed in this way, and finally the entire charging scheduling incentive model is converted into a linear model.

[0107] In one possible implementation, the charging scheduling incentive model is linearized based on the selection variable corresponding to the buyout incentive variable and the on-demand incentive variable, to obtain a linear model, including:

[0108] The charging scheduling incentive model is linearized based on the selection variable corresponding to the buyout incentive variable and the on-demand incentive variable, and the scheduling variable corresponding to the actual scheduling power, to obtain a linear model.

[0109] In this embodiment, the dispatch variable can be the actual dispatch power of the electric ship, which is a continuous variable, representing the charging power of the electric ship that has a dispatch change from the charging reference power in a certain time period. The dispatch variable here can be a binary variable, i.e., it can be 0 or the maximum charging power. In combination with the selection variable, in order to linearize these nonlinear constraints, an auxiliary variable can be introduced to represent the value of the actual dispatch power. When the value of the selection variable is 1, the auxiliary variable is equal to the actual dispatch power; when the value of the selection variable is 0, the auxiliary variable is equal to 0. In order to realize this logic, several linear constraint conditions can be added to ensure that the auxiliary variable is equal to the actual dispatch power when the selection variable is 1, and is equal to 0 when the selection variable is 0. Then the original variable can be replaced by the auxiliary variable in the charging dispatch incentive model, so that the original nonlinear constraint is converted into a linear constraint.

[0110] Without introducing the dispatch variable, the nonlinear constraints in the model are mainly concentrated on the product of the selection variable and other variables, which makes the linearization process relatively complex. After introducing the dispatch variable, these nonlinear constraints can be approximately represented by simple linear constraints. For example, for the on-demand incentive part, the original nonlinear constraint is the product of the incentive amount and the actual charging power. After introducing the dispatch variable, it can be split into two linear constraints: one is the upper limit constraint of the dispatch variable, and the other is the linear relationship between the incentive amount and the dispatch variable. This splitting makes the linearization process more simple and intuitive, reduces the complexity of the model, and improves the solving efficiency.

[0111] In one example, the optimization model of the charging dispatch incentive model includes a bilinear term and a bilinear term In addition, as the number of electric ships increases, the electric ship charging dispatch problem faces a dimension disaster. Therefore, the optimization model can be linearized.

[0112] First, a linearization model of the product of a continuous variable and a binary variable can be constructed: the bilinear term is the product of a bounded continuous variable and a binary variable This term can be modeled by introducing a new continuous variable and the following constraints:

[0113] (12)

[0114] (13)

[0115] (14)

[0116] wherein is a large enough positive number.

[0117] Second, a linear equivalent model for the product of two continuous variables can be constructed: the bilinear term is the product of two bounded continuous variables and . To address this issue, the variable can be transformed into a binary variable and a constant using the optimality conditions, and then model the new term .

[0118] When , it follows from the objective function that

[0119] (15)

[0120] where is the market price when the load is moved out, and is the market price when the load is moved in. In this case, the profit improvement from load shifting is

[0121] (16)

[0122] where the profit is an increasing function of . Therefore, in the optimal solution, the value of is either 0 or its maximum possible value . To this end, the continuous variable can be transformed into a binary variable and a constant .

[0123] The new term is a bilinear product of the bounded continuous variable , the binary variable , and the constant . Similarly, this term can be modeled by introducing a new continuous variable and the following constraints:

[0124] (17)

[0125] (18)

[0126] (19)

[0127] where the bilinear term is replaced by the auxiliary variable ​and are limited by constraints (18) and (19).

[0128] Then, the linearized optimal incentive price optimization model can be constructed: reconstruct the optimization objective function, and the original problem can be re-expressed as:

[0129] (20)

[0130] s.t.

[0131] (4)-(19)(21)

[0132] Step 204, determining the target incentive price based on the linear model.

[0133] The target incentive price can be a reasonable incentive amount that needs to be paid to encourage the electric ship to participate in the grid scheduling, that is, the incentive amount actually provided by the virtual power plant to the electric ship, which can be a fixed incentive provided based on the dispatchable capacity of the electric ship, or an incentive provided based on the actual dispatch power of the electric ship. The target incentive price is determined through a linear model, which simplifies the complex nonlinear constraints into linear constraints, so that the entire model can be solved by a linear programming solver, and the target incentive price that can reduce the virtual power plant incentive expenditure as much as possible under the premise of meeting the charging demand and charging preference parameters of the electric ship is obtained.

[0134] In one possible implementation, the target incentive price is determined based on the linear model, including:

[0135] The plurality of electric ships are divided into a plurality of electric ship sets based on the docking time of each electric ship;

[0136] A first incentive price is calculated for the linear model based on the charging demand and the charging preference parameter in each electric ship set;

[0137] The plurality of first incentive prices corresponding to the plurality of electric ship sets are adjusted based on a penalty factor and a dual variable to obtain the target incentive price.

[0138] In this implementation, the docking time can refer to the time when each electric ship can access charging and the latest time when the access to charging is stopped, that is, the time range when the electric ship can charge, and the plurality of electric ships can be divided into a plurality of electric ship sets according to the time range. For example, some electric ships can be docked during the day, and others can be docked at night, so the electric ships can be grouped according to these different docking time periods, and each electric ship group can be referred to as an electric ship set.

[0139] Next, for each set of electric ships, a preliminary incentive price, i.e., a first incentive price, is calculated based on the charging demand and charging preference parameters of the electric ships in the set using a linear model. The linear model can take into account the charging demand and charging preference parameters and can be solved using a linear programming solver to obtain the first incentive price that minimizes the virtual power plant incentive expenditure while satisfying the charging demand and charging preference parameters of the electric ships.

[0140] The penalty factor is a penalty coefficient used to measure the deviation from the ideal state, indicating that the greater the deviation from the ideal state, the higher the penalty cost in the optimization process. The penalty factor can encourage the optimization result of the model to approach the ideal state as much as possible by increasing the cost of deviating behavior. For example, if the actual charging amount of the electric ship does not meet its charging demand, the penalty factor will increase the cost of this unmet demand, which will be reflected in the incentive price.

[0141] The dual variable is a variable corresponding to the constraint condition in the original problem. In the original problem, the constraint condition limits the range of feasible solutions. The dual variable is used to measure the "value" or "cost" of these constraint conditions, i.e., their influence on the objective function. In the charging dispatch incentive scenario, the dual variable can be the influence of the charging demand and preference parameters of each set of electric ships in the linear model on the total incentive cost under the optimal dispatch.

[0142] The penalty factor allows the model to penalize behavior that does not meet the charging demand or deviates from the charging preference. This mechanism increases the flexibility of the dispatch, and the dual variable provides important information about the efficiency of resource allocation, allowing a more accurate assessment of the demand and use of grid resources by each set of electric ships, thereby optimizing resource allocation and ensuring that resources are effectively used where they are most needed.

[0143] In one possible implementation, the first incentive prices corresponding to the multiple sets of electric ships are adjusted based on the penalty factor and the dual variable to obtain the target incentive price, including:

[0144] When the number of adjustments does not reach the preset number, the dual variable is updated based on the multiple first incentive prices corresponding to the multiple sets of electric ships and the penalty factor;

[0145] The parameters of the linear model are updated based on the dual variable;

[0146] The second incentive price corresponding to each set of electric ships is calculated based on the updated linear model and the penalty factor and the updated dual variable;

[0147] When the number of adjustments reaches the preset number, the updated linear model is calculated based on the penalty factor and the updated dual variable to obtain the target incentive price.

[0148] In this embodiment, the process of adjusting the plurality of first incentive prices corresponding to the plurality of ship sets based on the penalty factor and the dual variable to obtain the target incentive price is realized through multiple iterations. Each iteration can be referred to as an adjustment. The number of adjustments can be updated after each iteration.

[0149] The preset number of times can be a preset maximum number of iterations. In each iteration, a new dual variable value is calculated according to the current dual variable value, the adjusted first incentive price, and the penalty factor. This process is repeated until a stop condition is met, such as reaching the preset number of times or the change in the dual variable being less than a certain threshold.

[0150] After obtaining the updated dual variable, it can be analyzed how the dual variable affects the constraint conditions and the objective function of the model. According to the value of the updated dual variable, the parameters of the linear model are adjusted, such as adjusting the cost coefficient, the capacity limit, or other parameters related to the constraint conditions. After updating the model parameters, the model can be solved again to find a new optimal solution, which can be referred to as a second incentive price. In the next iteration, the second incentive price obtained from this adjustment can be combined with the penalty factor to continue updating the dual variable. When the number of adjustments reaches the preset number of times, the iteration stops. The incentive price calculated based on the penalty factor, the latest dual variable, and the latest linear model can be taken as the target incentive price.

[0151] By continuously updating the dual variable and the parameters of the linear model, the incentive price can be optimized, and the accuracy of the incentive price can be improved.

[0152] In one example, the linear model of the distributed electric ship scheduling problem can be modeled and solved by the Alternating Direction Method of Multipliers (ADMM).

[0153] First, the master coordination problem model of the linear model can be constructed as follows:

[0154] Since the electricity price scenario and the number of electric ships need to be large enough to obtain statistically significant results, the electric ship coordinated scheduling problem in the port virtual power plant inevitably faces the curse of dimensionality. To solve this challenge, the original problem is constructed in a distributed form based on ADMM. Under this framework, electric ships can be divided into multiple groups according to their docking time in the port virtual power plant. Electric ships docking on the same day are divided into a group.

[0155] In the ADMM method, the master problem is used to coordinate the optimal incentive prices generated by each group. In the first iteration, the master problem can be expressed as the following optimization form:

[0156] ​(22)

[0157] s.t.

[0158] (23)

[0159] (24)

[0160] (25)

[0161] (26)

[0162] (27)

[0163] where, denotes the total power curtailment of the jth amount of electric ships in the vth iteration. The buyout plan cost curtailment and the cost curtailment in the pay-as-bid plan is the value calculated by equations (24) and (25) according to the sub-problem scheduling result. The coordinated optimal incentive price set is denoted by , and the optimal incentive price set obtained by the gth group of electric ships in the vth iteration is denoted by , . is the penalty factor of the gth group in the vth iteration, and are the scaled dual variables in the ADMM method. The range of the coordinated optimal incentive price is given by equations (26) and (27). The treatment of the bilinear term in equation (22) is similar to that in equations (12)-(14).

[0164] Secondly, the local sub-problem model can be constructed as:

[0165] When the master problem returns the optimal value , , each group will recalculate the incentive price, considering the deviation penalty from the coordinated optimal incentive price set using the sub-problem optimization, which is specifically expressed as:

[0166] (28)

[0167] s.t.

[0168] (4)-(19)(29)

[0169] where, and are the gth group of incentive prices to be optimized. It should be noted that in the first iteration, the penalty term is not included in the sub-problem.

[0170] Then, a dual variable updating model can be constructed:

[0171] By solving the primary problem and the secondary problem, the scaled dual variable (x , ) can be updated:

[0172] (30)

[0173] (31)

[0174] Finally, a convergence judgment model can be constructed:

[0175] When the change of the scaled dual variable is less than a certain standard, the problem converges:

[0176] (32)

[0177] wherein, represents the Euclidean norm (L2 norm). wherein, is a component of the vector .

[0178] In one possible implementation, after the second incentive price is calculated based on the charging demand in each set of electric ships and the charging preference parameter of the updated linear model, the method further includes:

[0179] determining a scheduling gain corresponding to each set of electric ships based on the second incentive price and the market electricity price distribution;

[0180] determining an adaptive weight of each set of electric ships based on the scheduling gain;

[0181] adjusting a penalty factor corresponding to each set of electric ships based on the adaptive weight of each set of electric ships.

[0182] In this implementation, the scheduling gain refers to the benefit that the virtual power plant realizes cost savings or income increases by intelligently scheduling the charging time of the electric ships and taking advantage of the fluctuation of the electricity market price. It can be understood that the charging price of the electric ships by the virtual power plant can be set as a unified price, which is not related to the market electricity price distribution, and then the scheduling gain can be obtained by increasing the charging amount when the electricity price is low and reducing the charging amount when the electricity price is high, and the virtual power plant can obtain the income based on the charging of the electric ships at the unified price.

[0183] The adaptive weight is a dynamically adjusted weight coefficient used to adjust the importance of different factors or variables according to changes in system performance or data characteristics. In the charging scheduling incentive model, the adaptive weight can reflect the relative value of different sets of electric ships in grid scheduling. On the basis of scheduling gain, which refers to the benefit obtained by intelligently scheduling electric ship charging, the set of electric ships with higher scheduling gain can be given a higher adaptive weight.

[0184] After determining the adaptive weight of each set of electric ships, the penalty factor corresponding to each set of electric ships can be adjusted based on the adaptive weight of each set of electric ships. The adjustment of the penalty factor can ensure that the charging behavior of the electric ships matches the demand of the grid better. For example, the set of electric ships with a high adaptive weight contributes more to grid scheduling, so stricter management is needed to ensure that its behavior conforms to the overall interests of the grid. The penalty factor can be increased to encourage these sets to be more cautious in charging scheduling to avoid adverse effects on the grid. Or a high weight may mean high risk, as the charging behavior of these sets may have a greater impact on grid stability. By increasing the penalty factor, the cost of these sets violating the scheduling rules can be increased, thereby reducing the potential risk.

[0185] By dynamically adjusting the penalty factor, the scheduling behavior of the set of electric ships can be ensured to be consistent with the demand and rules of the grid, improving the operating efficiency and stability of the grid.

[0186] In one possible implementation, determining the scheduling gain corresponding to each set of electric ships based on the second incentive price and the market electricity price distribution includes:

[0187] Determining the electricity purchase reduction cost of each set of electric ships based on the docking time of each electric ship and the market electricity price distribution;

[0188] Determining the scheduling gain corresponding to each set of electric ships according to the second incentive price and the electricity purchase reduction cost.

[0189] In this implementation, the electricity purchase reduction cost can refer to the cost saved by optimizing the electricity purchase strategy and reducing electricity purchase during peak periods. It can be the economic benefit obtained by the virtual power plant through intelligent scheduling of the electric ship charging process to reduce electricity purchase during high electricity price periods. For example, first, the docking time of each set of electric ships, i.e., their time window for charging at the port, needs to be analyzed. Then, based on the market electricity price distribution, the fluctuation of electricity price during these docking times is determined. Next, the cost saved if the electric ship charges during a period with lower electricity price compared to charging during a period with higher electricity price, i.e., the electricity purchase reduction cost, is calculated.

[0190] The scheduling gain can be the cost saved by the virtual power plant through adjusting the charging plan while meeting the charging demand, and the revenue obtained by the incentive expenditure of the electric ship based on the second incentive price. By determining the power purchase reduction cost and the scheduling gain, the scheduling revenue of the virtual power plant can be more accurately calculated.

[0191] In one example, the penalty factor can also be adaptively and dynamically updated. The traditional ADMM method usually allocates the same penalty factor to all optimization subgroups, but this method cannot reflect the differences in the coordination of different incentive price schemes. In order to improve the convergence efficiency of the port virtual power plant and electric ship incentive coordination model, an adaptive penalty mechanism is proposed in the embodiments of the present application. In the initial stage of the algorithm, the quality of each group of incentive price schemes is dynamically adjusted according to the quality of each group of incentive price schemes.

[0192] Firstly, an incentive price set quality evaluation model can be constructed. The quality of each price set is evaluated by calculating its final gain , which is obtained by the following formula:

[0193] (33)

[0194] The first and second terms in formula (33) represent the gains under the buyout plan and the pay-as-you-go plan, respectively. In formula (33), is the result of optimization of each group of secondary problems. Therefore, the port virtual power plant gain under each group of incentive price sets can be obtained through a simple calculation process, and the calculation process hardly requires additional calculation time.

[0195] Secondly, a quality normalization weight calculation model can be constructed. After obtaining the quality of the price set, the adaptive weight of each group is calculated by the following formula:

[0196] (34)

[0197] (35)

[0198] (36)

[0199] wherein and represent the maximum and minimum gains of the port virtual power plant under different price sets in the vth iteration. The adaptive weight is calculated based on the quality of each group using formula (36).

[0200] Then, an adaptive penalty factor dynamic updating model can be constructed, assuming is the initial penalty factor, and the penalty factor in each iteration for different groups can be obtained by the following formula:

[0201] (37)

[0202] wherein, is the threshold of iterations, after which the update of the adaptive penalty factor will stop.

[0203] In one possible implementation, the dispatching gain corresponding to each set of electric ships is determined based on the second incentive price and the distribution of market electricity prices, comprising:

[0204] The maximum dispatching gain and the minimum dispatching gain in the plurality of sets of electric ships are determined based on the second incentive price and the distribution of market electricity prices.

[0205] When the relative quality difference between the maximum dispatching gain and the minimum dispatching gain is less than the preset threshold, the dispatching gain corresponding to each set of electric ships is determined based on the second incentive price and the distribution of market electricity prices.

[0206] In this implementation, first, the dispatching gain of each set of electric ships is calculated based on the second incentive price and the distribution of market electricity prices of each set of electric ships. After the dispatching gains of all sets of electric ships are calculated, the maximum and minimum values among these gains are determined. The maximum dispatching gain represents the maximum economic benefit that can be obtained by scheduling in all sets, while the minimum dispatching gain represents the lowest economic benefit.

[0207] In the iterative calculation of the incentive price, due to the large difference in the incentive price at the beginning of the iteration, the dispatching gain of each set of electric ships in each iteration is significantly different, and the penalty factor can be adjusted to accelerate the convergence of the iteration. Here, a preset threshold based on the gain can be set to determine whether the dispatching gains of different sets of electric ships are sufficiently diverse, i.e., if the relative quality difference between the maximum dispatching gain and the minimum dispatching gain is less than the preset threshold, it may mean that the dispatching gains of all sets of electric ships are relatively close and not significantly different, so it may not be necessary to accelerate the convergence.

[0208] If the relative quality difference is greater than the preset threshold, it indicates that there is a significant difference in the dispatching gain, and the penalty factor can be dynamically adjusted according to the real-time performance of the algorithm to promote faster convergence.

[0209] When the system has approached the optimal solution, i.e., the quality difference between groups is not large, frequent adjustment may not bring significant improvement, but rather increase the computational burden. Adjusting the penalty factor only when necessary can reduce the consumption of computing resources.

[0210] In one example, in the early stage of the algorithm, due to the large difference in incentive prices, the quality of the price set of each group is obviously different. Therefore, the introduction of an adaptive penalty mechanism can accelerate convergence. However, as the iteration proceeds, the quality between the price sets gradually approaches, and adaptive adjustment is no longer necessary.

[0211] A stage division and switching criterion model can be constructed. In order to estimate the convergence speed without presetting, this paper proposes an event-triggered stage conversion mechanism. When the relative quality difference between the price sets is less than a set threshold ,

[0212] (38)

[0213] Once the condition is met, the algorithm switches from the "pre-event" stage to the "post-event" stage, and then uses the standard ADMM method to continue iteration, ensuring the stability and global convergence of the final solution.

[0214] The final ADMM-AP algorithm can be summarized as follows:

[0215] 1. Initialize the parameters: set the ADMM convergence precision threshold to , the initial penalty factor , and the adaptive stage conversion threshold ;

[0216] 2. Iterative solution process: when the convergence condition (formula (32)) is not met, perform the following steps:

[0217] 2.1. Update the penalty factor of each sub-population according to the adaptive penalty mechanism (formula (33)-(38)) ;

[0218] 2.2. Solve the global master problem (formula (22)-(27)) and obtain the master problem update variables , );

[0219] 2.3. Solve the local problem (formula (28)-(29)) respectively to obtain the corresponding incentive strategy variables , );

[0220] 2.4. Update the variables , ) according to formula (33)-(35);

[0221] 3. Terminate the iteration: when the convergence condition is met (i.e., formula (32) is met), terminate the iteration and output the final solution.

[0222] Device and equipment description

[0223] It can be understood that, although each step in each of the above flowcharts is shown in sequence according to the representation of the arrow, these steps are not necessarily executed in the order represented by the arrow. Unless otherwise specified in the present embodiment, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least part of the steps in the above flowcharts can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or steps or stages in other steps.

[0224] Please refer to Figure 3 As Figure 3 The device 300 provided by the embodiment of the present application comprises:

[0225] The acquisition unit 301 is configured to acquire the charging demand, the charging preference parameter and the market electricity price distribution of the plurality of electric ships.

[0226] The construction unit 302 is configured to construct a charging dispatch incentive model based on the charging demand, the charging preference parameter, the market electricity price distribution, a buyout incentive variable and an on-demand incentive variable. The buyout incentive variable is a unit incentive variable for the dispatchable capacity of each electric ship, and the on-demand incentive variable is a unit incentive variable for the actual dispatch power of each electric ship.

[0227] The linearization unit 303 is configured to linearize the charging dispatch incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable, to obtain a linear model.

[0228] The determination unit 304 is configured to determine a target incentive price based on the linear model.

[0229] Optionally, in a possible implementation, the linearization unit 303 is specifically configured to:

[0230] linearize the charging dispatch incentive model based on the selection variables corresponding to the buyout incentive variable and the on-demand incentive variable, and the dispatch variable corresponding to the actual dispatch power, to obtain the linear model.

[0231] Optionally, in a possible implementation, the determination unit 304 comprises:

[0232] The division sub-unit is configured to divide the plurality of electric ships into a plurality of electric ship sets based on the berthing time of each electric ship.

[0233] The calculating sub-unit is configured to calculate a first incentive price based on a charging demand and a charging preference parameter in each of the plurality of electrical ship sets.

[0234] The adjusting sub-unit is configured to adjust the first incentive prices corresponding to the plurality of electrical ship sets based on a penalty factor and a dual variable to obtain a target incentive price.

[0235] Optionally, the adjusting sub-unit comprises:

[0236] The first updating module is configured to update the dual variable based on the first incentive prices corresponding to the plurality of electrical ship sets and the penalty factor when the number of adjustments does not reach a preset number.

[0237] The second updating module is configured to update a parameter of the linear model based on the dual variable.

[0238] The first calculating module is configured to calculate a second incentive price corresponding to each of the plurality of electrical ship sets based on the penalty factor and the updated dual variable and the updated linear model.

[0239] The second calculating module is configured to calculate the target incentive price based on the penalty factor and the updated dual variable and the updated linear model when the number of adjustments reaches the preset number.

[0240] Optionally, in a possible implementation, the adjusting sub-unit further comprises:

[0241] The first determining module is configured to determine a dispatch gain corresponding to each of the plurality of electrical ship sets based on the second incentive price and a market electricity price distribution.

[0242] The second determining module is configured to determine an adaptive weight of each of the plurality of electrical ship sets based on the dispatch gain.

[0243] The adjusting module is configured to adjust the penalty factor corresponding to each of the plurality of electrical ship sets based on the adaptive weight of each of the plurality of electrical ship sets.

[0244] Optionally, in a possible implementation, the first determining module comprises:

[0245] The first determining sub-module is configured to determine a purchase electricity reduction cost of each of the plurality of electrical ship sets based on a docking time of each of the plurality of electrical ships and the market electricity price distribution.

[0246] The second determining sub-module is configured to determine the dispatch gain corresponding to each of the plurality of electrical ship sets based on the second incentive price and the purchase electricity reduction cost.

[0247] Optionally, in a possible implementation, the first determining module comprises:

[0248] The third determining sub-module is configured to determine a maximum scheduling gain and a minimum scheduling gain in the plurality of electrical ship sets based on the second incentive price and the market electricity price distribution.

[0249] The fourth determining sub-module is configured to determine a scheduling gain corresponding to each electrical ship set based on the second incentive price and the market electricity price distribution when a relative quality difference between the maximum scheduling gain and the minimum scheduling gain is less than a preset threshold.

[0250] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the port-oriented virtual power plant incentive coordination optimization method when executing the computer program. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0251] Please refer to Figure 4 , Figure 4 The hardware structure of the electronic device of another embodiment is illustrated, and the electronic device comprises:

[0252] The processor 401 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0253] The memory 402 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 402 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 402 and are called and executed by the processor 401 to implement the port-oriented virtual power plant incentive coordination optimization method.

[0254] The input / output interface 403 is used to realize information input and output.

[0255] The communication interface 404 is used to realize the communication interaction between the device and other devices, and can realize communication through a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0256] A bus 405 transmits information between various components (e.g., the processor 401, the memory 402, the input / output interface 403, and the communication interface 404) of the device.

[0257] The processor 401, the memory 402, the input / output interface 403, and the communication interface 404 are communicatively connected to each other within the device through the bus 405.

[0258] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the port-oriented virtual power plant incentive coordination optimization method.

[0259] The memory, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0260] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0261] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps, or different steps.

[0262] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0263] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0264] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, based on stated preference or the like, and that "default" or other orderings are thus permissible. Further, the terms "comprise", "comprising", "include", "including", and the like, are specifically intended to be open-ended. That is, references to individual steps and the like do not suhstantially exclude the presence of two or more of a recited step or its integral sub-steps or additional steps whether or not readily ascertainable from the description or the like. Further, the words "a" or "an", as used herein in the disclosure and elsewhere, are used indiscriminately and are to be interpreted in the same way, i.e. as meaning "one or more".

[0265] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the relationship between the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0266] In several embodiments provided by the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above-mentioned units is only a logical functional division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed objects can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0267] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the application.

[0268] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0269] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause 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 methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various other media that can store programs.

[0270] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A port-oriented virtual power plant incentive coordination optimization method, characterized in that, The method comprises: obtaining charging demand, charging preference parameters and market electricity price distribution of a plurality of electric ships; constructing a charging scheduling incentive model based on the charging demand, the charging preference parameters, the market electricity price distribution, a buyout incentive variable and an on-demand incentive variable, the buyout incentive variable being a unit incentive variable for the schedulable capacity of each electric ship, and the on-demand incentive variable being a unit incentive variable for the actual scheduling power of each electric ship; linearizing the charging scheduling incentive model based on selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model; determining a target incentive price based on the linear model; a formula of the charging scheduling incentive model is where, is the incentive given to the ith electric vessel in the buyout incentive scheme; is the incentive given to the jth electric vessel in the on-demand incentive scheme; represents the potential flexible capacity available from the ith electric vessel; represents the downward component of the power variation disassembly; represents the scaling factor of the dispatch resolution to 1 hour; a is the buyout incentive variable; β is the on-demand incentive variable; represents the market clearing price at time t, represents the energy purchased from the market at time t; the charging scheduling incentive model further comprises a constraint formula: wherein, is a time interval of one charging scheduling period; represents the charging power of the i-th electric ship at time t in the buyout incentive scheme, represents the charging power of the j-th electric ship at time t in the on-demand incentive scheme, represents the power variation of the i-th electric ship at time t in the buyout incentive scheme, represents the upward component of the power variation of the j-th electric ship at time t in the on-demand incentive scheme; represents the maximum charging power of the i-th electric ship; represents a binary variable indicating whether the i-th electric ship participates in the buyout incentive scheme, represents a binary variable indicating whether the j-th electric ship participates in the on-demand incentive scheme; represents the upper limit of the buyout incentive price; represents the upper limit of the on-demand incentive price.

2. The method of claim 1, wherein, the linearization of the charging scheduling incentive model based on selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model comprises: linearizing the charging scheduling incentive model based on selection variables corresponding to the buyout incentive variable and the on-demand incentive variable, and scheduling variables corresponding to the actual scheduling power to obtain a linear model.

3. The method according to claim 1 or 2, characterized in that, the determination of the target incentive price based on the linear model comprises: dividing the plurality of electric ships into a plurality of electric ship sets based on the docking time of each electric ship; calculating a first incentive price for the linear model based on the charging demand and the charging preference parameters in each electric ship set; adjusting a plurality of first incentive prices corresponding to the plurality of electric ship sets based on a penalty factor and a dual variable to obtain a target incentive price.

4. The method of claim 3, wherein, the adjustment of the plurality of first incentive prices corresponding to the plurality of electric ship sets based on the penalty factor and the dual variable to obtain the target incentive price comprises: updating the dual variable based on the plurality of first incentive prices corresponding to the plurality of electric ship sets and the penalty factor when the number of adjustments does not reach a preset number; updating parameters of the linear model based on the dual variable; calculating a second incentive price corresponding to each electric ship set for the updated linear model based on the penalty factor and the updated dual variable; calculating a target incentive price for the updated linear model based on the penalty factor and the updated dual variable when the number of adjustments reaches the preset number.

5. The method of claim 4, wherein, after the calculation of the second incentive price corresponding to each electric ship set for the updated linear model based on the penalty factor and the updated dual variable, the method further comprises: determining a scheduling gain corresponding to each electric ship set based on the second incentive price and the market electricity price distribution; determining an adaptive weight of each electric ship set based on the scheduling gain; adjusting the penalty factor corresponding to each electric ship set based on the adaptive weight of each electric ship set.

6. The method of claim 5, wherein, the determination of the scheduling gain corresponding to each electric ship set based on the second incentive price and the market electricity price distribution comprises: determining a power purchase reduction cost of each electric ship set based on the docking time of each electric ship and the market electricity price distribution; The dispatch gain corresponding to each of the plurality of electric ships is determined based on the second incentive price and the market electricity price distribution.

7. The method of claim 5, wherein, The dispatch gain corresponding to each of the plurality of electric ships is determined based on the second incentive price and the market electricity price distribution. The maximum dispatch gain and the minimum dispatch gain in the plurality of electric ships are determined based on the second incentive price and the market electricity price distribution. The dispatch gain corresponding to each of the plurality of electric ships is determined based on the second incentive price and the market electricity price distribution when the relative quality difference between the maximum dispatch gain and the minimum dispatch gain is less than or greater than a preset threshold.

8. A port-oriented virtual power plant incentive coordination optimization apparatus, characterized by, Comprise: An acquisition unit is configured to acquire charging demands, charging preference parameters, and a market electricity price distribution of a plurality of electric ships; A construction unit is configured to construct a charging dispatch incentive model based on the charging demands, the charging preference parameters, the market electricity price distribution, a buyout incentive variable, and an on-demand incentive variable, the buyout incentive variable being a unit incentive variable for a dispatchable capacity of each electric ship, and the on-demand incentive variable being a unit incentive variable for an actual dispatch power of each electric ship; A linearization unit is configured to linearize the charging dispatch incentive model based on selection variables corresponding to the buyout incentive variable and the on-demand incentive variable to obtain a linear model; A determination unit is configured to determine a target incentive price based on the linear model; The formula of the charging dispatch incentive model is where, is the incentive given to the ith electric vessel in the buyout incentive scheme; is the incentive given to the jth electric vessel in the on-demand incentive scheme; represents the potential flexible capacity available from the ith electric vessel; represents the downward component of the power variation disassembly; represents the scaling factor of the dispatch resolution to 1 hour; a is the buyout incentive variable; β is the on-demand incentive variable; represents the market clearing price at time t, represents the energy purchased from the market at time t; The charging dispatch incentive model further comprises a constraint formula: wherein, is a time interval of one charging scheduling period; represents the charging power of the i-th electric ship at time t in the buyout incentive scheme, represents the charging power of the j-th electric ship at time t in the on-demand incentive scheme, represents the power variation of the i-th electric ship at time t in the buyout incentive scheme, represents the upward component of the power variation of the j-th electric ship at time t in the on-demand incentive scheme; represents the maximum charging power of the i-th electric ship; represents a binary variable indicating whether the i-th electric ship participates in the buyout incentive scheme, represents a binary variable indicating whether the j-th electric ship participates in the on-demand incentive scheme; represents the upper limit of the buyout incentive price; represents the upper limit of the on-demand incentive price.

9. An electronic device, comprising: Comprise: A memory, a transceiver, a processor, and a bus system; The memory is configured to store programs; The processor is configured to execute the programs in the memory, including executing the method in any one of claims 1 to 7; The bus system is configured to connect the memory and the processor to enable the memory and the processor to communicate.

10. A computer-readable storage medium, characterized in that, The instructions, when executed on a computer, cause the computer to perform the method in any one of claims 1 to 7.

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