Offshore platform group monthly time-sharing electricity purchasing quantity curve optimization decision-making method under power grid agent electricity purchasing mechanism

Abstract

The invention provides an offshore platform group monthly time-sharing electricity purchase curve optimization decision method, device and equipment under a power grid agent electricity purchase mechanism, and a medium, and the method comprises the steps: building an electricity purchase curve decision optimization model with the minimum typical daily operation cost of an offshore oil platform group as an objective function under the power grid agent electricity purchase mechanism; setting constraint conditions; and solving the power purchase curve decision optimization model based on set constraint conditions to obtain a monthly time-sharing power purchase curve of the offshore platform group on a typical day. Therefore, the time-sharing electricity purchasing curve is determined by adopting the electricity purchasing curve decision optimization model, and the monthly time-sharing electricity purchasing curve optimization decision of the shore power of the offshore oil platform group under the power grid agent electricity purchasing mechanism considering multiple factors is realized.

Description

Technical Field

[0001] The present invention relates to a method, device, equipment and medium for optimizing the decision-making of a monthly time-sharing power purchase amount curve of an offshore platform group under a power grid agency power purchase mechanism, and relates to the field of power purchase. Background Art

[0002] In order to reduce carbon emissions, lower power supply costs, and improve power supply quality, offshore oil fields are gradually implementing projects to replace sea power with shore power for power supply. After the existing platform is connected to shore power, some existing gas units will still be retained for necessary heating, power supply and standby. At the same time, in order to further increase the green power supply rate, it is also planning to connect to offshore wind power, so as to form a multi-platform network, shore power, sea power and wind power collaborative power supply situation. After the market-oriented reform of electricity, industrial and commercial users will gradually enter the market. Therefore, after the shore power is connected, the offshore oil platform group needs to purchase electricity through the power sales company or the power grid company. Considering that the current power price of the power grid company is relatively cheap, the offshore oil platform currently mainly purchases electricity through the power grid company. After the shore power is connected, the offshore oil platform group needs to submit the time-sharing power purchase curve to the State Grid on a monthly basis on the basis of considering the economic efficiency of gas power generation and wind power generation capacity, and purchase electricity from the power market through the State Grid to minimize its electricity cost.

[0003] However, there are few reports on research in this area. Existing research on large-scale power purchases mainly focuses on the power purchase strategies of large users or power sales companies from the power market, including multi-market power purchase combination optimization, single market bidding decision-making, bilateral game contract transactions, etc. The prior art discloses the use of value at risk (VaR) to describe risk, adopts the mean-VaR power purchase combination model, and further combines the concept of entropy risk or fuzzy optimization methods to improve and determine the power purchase combination. The prior art also discloses the use of conditional value at risk (CVaR) to describe risk and proposes an optimization strategy for power sales companies to declare electricity spot on the day before. The prior art also discloses the proposal of a monthly market transaction optimization strategy for power sales companies based on the decomposition of the annual contract power of thermal power and the annual contract power of clean energy consumption. The prior art also discloses the study of bilateral contract quantity and price trading strategies between large users and power generators based on bilateral game theory.

[0004] The above existing technologies have not been able to solve the problem of how the offshore oil platform group can make a month-ahead time-of-day electricity purchase curve decision in an environment where the power grid company purchases electricity on behalf of the offshore oil platform group after the shore power and wind power are connected. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, in view of the above problems, the purpose of the present invention is to provide a method, device, equipment and medium for optimizing the decision-making of the monthly time-sharing power purchase curve of the offshore platform group under the power grid proxy power purchase mechanism, which can realize the optimization decision-making of the monthly time-sharing power purchase curve of the shore power of the offshore oil platform group under the power grid proxy power purchase mechanism taking into account multiple factors.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is:

[0007] In the first aspect, the present invention provides a method for optimizing the decision-making of the monthly time-sharing power purchase curve of an offshore platform group under the power grid proxy power purchase mechanism, comprising:

[0008] Under the power grid proxy power purchasing mechanism, a power purchasing curve decision optimization model is constructed with the lowest typical daily operating cost of offshore oil platform groups as the objective function;

[0009] Set constraints;

[0010] Based on the set constraints, the power purchase curve decision optimization model is solved to obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day.

[0011] In some possible implementations, under the power grid proxy power purchase mechanism, a power purchase curve decision optimization model is constructed with the lowest typical daily operating cost of the offshore oil platform group as the objective function, including:

[0012] The objective function of the power purchase decision is to minimize the operating cost on a typical day while meeting the power and heat loads of the platform group. The cost includes three parts: shore power purchase cost, gas turbine fuel cost, and deviation penalty cost:

[0013]

[0014] In the formula, C represents the minimum energy supply cost of the offshore oil platform group; S represents different wind power output scenarios, s is the index of the wind power scenario; T represents the time period in a typical day, t represents the index of the time period; θ s represents the probability corresponding to wind power scenario s; F t m represents the shore power purchase cost at time period t; represents the fuel cost of the gas turbine in wind power scenario s at time period t; It represents the penalty cost of deviation electricity in wind power scenario s at time period t; D is the number of days in a month.

[0015] In some possible implementations, the cost of shore power purchase is:

[0016] F t m =λ t m ×P tm ×Δt

[0017] In the formula, λ t m P represents the shore power purchase price at time period t; t m It represents the shore power purchase power at time period t; Δt represents time.

[0018] In some possible implementations, the gas turbine fuel cost is:

[0019]

[0020] Where: represents the opportunity cost of using gas during time period t; It represents the natural gas consumption of gas turbine in wind power scenario s at time period t.

[0021] In some possible implementations, the deviation penalty cost is the purchase fee for the deviation electricity: according to the rules, when there is a deviation between the actual electricity consumption and the purchased electricity, a penalty settlement is required for the deviation electricity, and the fee is:

[0022]

[0023] Where U1 is the electricity price adjustment coefficient when the actual electricity demand is greater than the purchased electricity, and U2 is the electricity price adjustment coefficient when the actual electricity demand is less than the purchased electricity; ΔP t,s It represents the deviation power in wind power scenario s at time period t.

[0024] In some possible implementations, the set constraints include:

[0025] 1) Constraints on power purchase in different time periods: For any time period t on a typical day, the sum of the shore power purchase power and the deviation power under wind power scenario s should be equal to the actual power consumption of the platform group minus the power generation power of the platform itself:

[0026]

[0027] In the formula, Indicates the actual power consumption in wind power scenario s at time period t; represents the actual grid-connected wind power in wind power scenario s at time period t; K represents the number of gas turbines, k is the index of the number of gas turbines; I represents the number of platforms, i represents the index of the number of platforms; represents the power generation of the kth gas turbine on the ith platform at time period t under wind power scenario s;

[0028] 2) Gas turbine unit power constraints:

[0029]

[0030]

[0031] In the formula, Respectively represent the maximum and minimum power generation of the i-th platform and the k-th gas turbine unit; α i,k represents the electric-to-heat ratio of the kth gas turbine on the ith platform; H i represents the heat load of the ith platform;

[0032] 3) Natural gas quantity constraints:

[0033]

[0034] Where η k represents the gas consumption rate of the kth gas turbine unit, represents the gas consumption of the kth gas turbine unit in scenario s at time period t, V t gas It represents the gas production of associated gas at time period t;

[0035] 4) Transfer load constraints:

[0036]

[0037] Where: P min , P max Respectively represent the minimum and maximum power of actual electricity consumption; represents the original platform load power consumption: γ1 and γ2 represent the minimum and maximum transfer percentages of the actual load, respectively;

[0038] 5) Wind power output constraints:

[0039]

[0040] In the formula, It represents the maximum wind power generation in wind power scenario s at time period t.

[0041] In some possible implementations, the electricity purchase curve decision optimization model is solved based on the set constraint conditions by using MATLAB to call the YALMIP toolbox for programming modeling and calling the CPLEX solver for solving.

[0042] In a second aspect, the present invention provides a device for optimizing the monthly time-sharing power purchase curve of an offshore platform group under a power grid proxy power purchase mechanism, comprising:

[0043] The model building unit is configured to build a power purchase curve decision optimization model under the power grid proxy power purchase mechanism with the lowest typical day operation cost of the offshore oil platform group as the objective function;

[0044] A constraint condition setting unit, configured to set a constraint condition;

[0045] The model solving unit is configured to solve the power purchase curve decision optimization model based on the set constraint conditions to obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day.

[0046] In a third aspect, the present invention further provides an electronic device, comprising:

[0047] at least one processor; and

[0048] A memory communicatively connected to the processor; wherein,

[0049] The memory stores instructions executable by the processor, and the instructions are executed by the processor to enable the processor to perform any one of the methods described above.

[0050] In a fourth aspect, the present invention further provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include computer instructions, and the computer instructions are used to enable a computer to execute any one of the methods described.

[0051] The present invention adopts the above technical solution, and has the following characteristics:

[0052] 1. In view of the way that offshore oil platforms purchase electricity through power grid agents, the shore power adopts peak and valley time-of-use electricity prices; the economic benefits of shore power and platform power generation at different time periods are considered in a compromise; various factors of the platform power system operation are considered, including: gas turbine power generation capacity, heat-based power load, shore power demand response capability and power generation volatility of connected wind power; penalties when there is a deviation between electricity consumption and power purchase are considered; an optimization model is used to determine the time-of-use power purchase curve, and the optimization decision of the monthly time-of-use power purchase curve of the shore power of the offshore oil platform group is realized under the power grid agent power purchase mechanism taking into account multiple factors.

[0053] 2. Considering the peak power generation of the platform gas turbines during the price peak period, the overall energy supply cost of the offshore oil platform group has dropped significantly; considering the platform group's power load demand response capability, the overall energy supply cost of the offshore oil platform group has dropped significantly; using multiple scenarios to describe the volatility of wind power generation will significantly reduce the cost of considering electricity deviations compared to using a single average wind power generation curve for power purchase decisions.

[0054] 3. The present invention realizes the combination of power purchase in multiple markets, reduces the risk of power purchase; realizes the bidding decision in a single market, reduces the cost of power purchase and increases the probability of winning the bid; realizes the contract transaction decision with the power generator, determines the contract transaction price and the transaction volume. The present invention also takes into account the controllable supply capacity of the user's internal gas generator set, the uncontrollable supply level of the wind turbine set, the power load response capability and the peak and valley time-of-use electricity price of the system, and aims at how large users make decisions on the amount of power to be purchased in the power grid agent power purchase environment before marketization.

[0055] In summary, when making electricity purchase decisions, the present invention takes into account the technical and economic characteristics of gas turbine power generation of offshore oil platform users, the power generation volatility of wind power connected to offshore oil platforms in different time periods within the month, and the monthly time-divided electricity consumption deviation penalty, and can be widely used in offshore oil platform group electricity purchase decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0057] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention.

[0058] Figure 2 FIG. 4 is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0060] Although the terms first, second, third, etc. can be used in the text to describe multiple elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates, terms such as "first", "second" and other numerical terms do not imply order or sequence when used in the text. Therefore, the first element, component, region, layer or section discussed below can be referred to as the second element, component, region, layer or section without departing from the teaching of the example embodiments.

[0061] For ease of description, spatially relative terms may be used herein to describe the relationship of one element or feature relative to another element or feature as shown in the figures, such as "inside", "outside", "inner side", "outer side", "below", "above", etc. Such spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0062] The present invention comprehensively considers factors such as the opportunity cost of gas turbine generators of offshore oil platform groups and their thermal load constraints, wind power generation capacity and its volatility, shore power peak and valley electricity prices, demand response, and power consumption deviation penalties, and provides a method, device, equipment, and medium for optimizing the monthly time-sharing power purchase curve of offshore platform groups under the power grid proxy power purchase mechanism, including: under the power grid proxy power purchase mechanism, taking the lowest typical day operation cost of the offshore oil platform group as the objective function to construct a power purchase curve decision optimization model; setting constraints; solving the power purchase curve decision optimization model based on the set constraints to obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day. Therefore, the present invention effectively supports the power purchase decision of the offshore oil platform group power grid after shore power and wind power are connected, and reduces its comprehensive power consumption cost. Considering the stability of the load, the volatility of wind power, and the reliability of gas-fired power generation, the present invention adopts a typical day optimization operation model based on multiple wind power generation scenarios to formulate power purchase decisions, and determines the shore power purchase amount in peak, valley, and flat periods with the lowest expected operating cost under multiple wind power scenarios on a typical day.

[0063] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0064] Embodiment 1: Figure 1As shown, the method for optimizing the monthly time-of-use electricity purchase curve of an offshore platform group under the power grid proxy power purchase mechanism provided in this embodiment includes:

[0065] S1. Analyze the energy system structure of the offshore oil platform group and the offshore platform power purchase rules to determine the power purchase method of the offshore oil platform.

[0066] In this embodiment, the electricity purchasing method of the offshore oil platform is determined to be the power grid proxy power purchasing policy. Under the power grid proxy power purchasing policy, the core issue of the monthly power purchasing decision of the offshore oil platform is to optimize and determine the power purchase amount in each peak, valley and flat period of the next month. The power purchase amount in each peak and valley period mainly depends on the power demand, gas turbine power generation and wind power generation. Among them: the power demand of the platform is relatively stable, and the intraday peak and valley periods and the daytime volatility are very small, which can be predicted and determined based on the production allocation data, so it is used as a deterministic known quantity; the wind power volatility is relatively large, and the power generation in each peak and valley period of each day is very different, so the scenario method is used to describe its power generation volatility; gas turbine power generation depends on the thermal load to determine the power situation, but if the shore power price is high, the power generation output can also be increased to replace the shore power, so it is used as a decision variable.

[0067] S2. Construct an optimization model for electricity purchase curve decision making with the lowest typical daily operating cost of offshore oil platform groups as the objective function.

[0068] In this embodiment, the objective function of the power purchase decision is the lowest operating cost on a typical day (since the load fluctuations between days and between different time periods of the offshore oil platform are very small, the daily power characteristic curve with a certain objectivity obtained through adjustment based on a large amount of measured data is used as the typical daily load curve) under the conditions of meeting the platform group power and heat load. The cost includes three parts: shore power purchase cost, gas turbine fuel cost and deviation penalty cost:

[0069]

[0070] In the formula, C represents the minimum energy supply cost of the offshore oil platform group; S represents different wind power output scenarios, s is the index of the wind power scenario; T represents the time period in a typical day, t represents the index of the time period; θ s represents the probability corresponding to wind power scenario s; F t m represents the shore power purchase cost at time period t; represents the fuel cost of the gas turbine in wind power scenario s at time period t; It represents the penalty cost of deviation electricity in wind power scenario s at time period t; D is the number of days in a month.

[0071] Furthermore, the cost of purchasing electricity from shore power is:

[0072]

[0073] Where:t m P represents the shore power purchase price at time period t; t m It represents the shore power purchase power at time period t; Δt represents time.

[0074] Further, the gas turbine fuel cost:

[0075]

[0076] Where: represents the opportunity cost of using gas at time period t (i.e., the natural gas selling price); It represents the natural gas consumption of gas turbine in wind power scenario s at time period t.

[0077] Furthermore, the deviation penalty cost - the purchase fee for the deviation electricity: According to the rules, when there is a deviation between the actual electricity consumption and the purchased electricity, a penalty settlement is required for the deviation electricity, and the fee is:

[0078]

[0079] Where: U1 is the electricity price adjustment coefficient when the actual electricity demand is greater than the purchased electricity amount (also called the over-use coefficient, which is a value greater than 1), U2 is the electricity price adjustment coefficient when the actual electricity demand is less than the purchased electricity amount (also called the under-use coefficient, which is a value less than 1); ΔP t,s represents the deviation power in wind power scenario s at time period t, which is determined by formula (5).

[0080] S3. Comprehensively consider the cost of gas turbine generators of offshore oil platform groups and their thermal load constraints, wind power generation capacity and its volatility, peak and valley electricity prices of shore power, demand response, electricity deviation penalties and other constraints.

[0081] In this embodiment, the constraints include:

[0082] S31. Constraints on electricity purchase quantity in different time periods.

[0083] For any period t of a typical day, the sum of the shore power purchase power and the deviation power under wind power scenario s should be equal to the actual power consumption of the platform group minus the platform's own power generation power:

[0084]

[0085] Where: Indicates the actual power consumption in wind power scenario s at time period t; represents the actual grid-connected wind power in wind power scenario s at time period t; K represents the number of gas turbines, k is the index of the number of gas turbines; I represents the number of platforms, i represents the index of the number of platforms; It represents the power generation of the kth gas turbine on the ith platform in the wind power scenario s at time period t.

[0086] S32. Power constraint of gas turbine unit.

[0087]

[0088]

[0089] Where: Respectively represent the maximum and minimum power generation of the i-th platform and the k-th gas turbine unit; α i,k represents the electric-to-heat ratio of the kth gas turbine on the ith platform; H i represents the heat load of the ith platform. Formula (8) indicates that the total heat supply of the gas turbine of each platform should meet the heat load demand of the platform.

[0090] S33. Natural gas quantity constraints.

[0091] Considering that the electricity consumed by the platform gas turbine comes from its own associated natural gas, the consumption is lower than the associated gas consumption constraint.

[0092]

[0093] Where: η k represents the gas consumption rate of the kth gas turbine unit, represents the gas consumption of the kth gas turbine unit in scenario s at time period t, V t gas It represents the gas production of associated gas in time period t.

[0094] S34. Transfer load constraints.

[0095] By reasonably implementing operational adjustments, the offshore oil platform has a certain ability to respond to load adjustments. However, considering that the production allocation task remains unchanged, this corresponding ability can be expressed by a transferable load model, which is as follows:

[0096]

[0097] Where: P min , P max Respectively represent the minimum and maximum power of actual electricity consumption; Represents the power consumption of the original platform load: γ1 and γ2 represent the minimum and maximum transfer percentages of the actual load, respectively.

[0098] S35. Wind power output constraints.

[0099]

[0100] Where: It represents the maximum wind power generation in wind power scenario s at time period t.

[0101] S4. Solve the power purchase curve decision optimization model based on the set constraints, obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day, and determine the shore power purchase amount in peak, valley and flat periods.

[0102] In this embodiment, the optimization model constructed by formulas (1) to (13) is essentially a mixed integer linear programming model, so MATLAB can be used to call the yalmip toolbox for programming modeling and the cplex solver can be called for solving.

[0103] The following describes in detail the construction of a calculation example output power purchase decision structure based on actual data of a certain offshore oil platform group through an embodiment, and conducts comparative analysis.

[0104] After the market-oriented reform of electricity, all industrial and commercial users will gradually enter the market. Therefore, after the shore power is connected, the offshore oil platform group needs to purchase electricity through the power sales company or the power grid company. For industrial and commercial users, unless extreme situations occur, the price of power purchased by the power grid in the next month is basically stable. Therefore, the users of the offshore oil platform group can determine the amount of electricity purchased in each peak and valley period of the next month based on the forecast results of the peak and valley electricity prices of the next month, as well as the power generation cost of their own gas turbines and the platform load forecast results, and obtain the power purchase curve. In this example, the users of the offshore oil platform group can optimize the determination of the amount of electricity purchased in each peak and valley period of the next month based on the forecast results of the peak and valley electricity prices of the next month, as well as the power generation cost of their own gas turbines and the thermal power output, wind turbine power generation capacity forecast, power deviation penalty, platform load response capability and other factors, with the goal of minimizing operating costs, and set the following two scenarios for comparative analysis:

[0105] Scenario A: Wind power construction is not considered and is applicable to the current stage. It is divided into: Scenario A-1: ​​The gas turbine only meets the thermal load, and the remaining electrical load is all met by shore power; Scenario A-2: The power purchase curve is determined by the model of the present invention, but the platform load transferability is not considered; Scenario A-3: The model of the present invention is used and the platform load transferability is considered.

[0106] Scenario B: Considering 40MW wind power grid connection, it is suitable for the future stage. It is divided into: Scenario B-1: The gas turbine only meets the thermal load, and the remaining electrical load is first met by wind power (wind power adopts a single average output curve), and the shortfall is met by shore power; Scenario B-2: The power purchase curve is determined by the model of the present invention, and wind power adopts a single average output curve; Scenario B-3: The power purchase curve is determined by the model of the present invention, and wind power adopts a multi-scenario output curve.

[0107] In summary, the power purchase curve decision optimization model is used to determine the time-of-use power purchase curve, which realizes the optimization decision of the monthly time-of-use power purchase curve of the offshore oil platform group under the power grid agent power purchase mechanism taking into account multiple factors.

[0108] Embodiment 2: The above-mentioned embodiment 1 provides a method for optimizing the time-sharing power purchase curve of an offshore platform group under the power grid proxy power purchase mechanism. Correspondingly, this embodiment provides a device for optimizing the time-sharing power purchase curve of an offshore platform group under the power grid proxy power purchase mechanism. The device provided in this embodiment can implement the method for optimizing the time-sharing power purchase curve of an offshore platform group under the power grid proxy power purchase mechanism of embodiment 1, and the device can be implemented by software, hardware, or a combination of software and hardware. For the convenience of description, the functions of this embodiment are divided into various units and described separately. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware during implementation. For example, the device may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the device of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple, and the relevant parts can refer to the partial description of embodiment 1. The embodiment of the device for optimizing the time-sharing power purchase curve of an offshore platform group under the power grid proxy power purchase mechanism provided by the present invention is only illustrative.

[0109] Specifically, the present invention provides a device for optimizing the monthly time-sharing power purchase amount curve of an offshore platform group under a power grid proxy power purchase mechanism, comprising:

[0110] The model building unit is configured to build a power purchase curve decision optimization model under the power grid proxy power purchase mechanism with the lowest typical day operation cost of the offshore oil platform group as the objective function;

[0111] A constraint condition setting unit, configured to set a constraint condition;

[0112] The model solving unit is configured to solve the power purchase curve decision optimization model based on the set constraint conditions to obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day.

[0113] Embodiment 3: This embodiment provides an electronic device corresponding to the method for optimizing the decision-making of the monthly time-of-use electricity purchase curve of an offshore platform group under the power grid agency electricity purchase mechanism provided in Embodiment 1. The electronic device may be an electronic device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of Embodiment 1.

[0114] like Figure 2As shown, the electronic device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program that can be run on the processor, and the processor executes the method of embodiment 1 when running the computer program. The implementation principle and technical effect are similar to those of embodiment 1, and will not be repeated here. Those skilled in the art can understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computing device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0115] In a preferred embodiment, the logic instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), optical disk and other media that can store program codes.

[0116] In a preferred embodiment, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors of various types, which are not limited here.

[0117] Embodiment 4: This embodiment provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include computer instructions. When the computer instructions are executed by a computer, the computer executes the method provided in the above-mentioned embodiment 1.

[0118] Embodiment 5: This embodiment provides a computer program product. The computer program product may include a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method provided in the above-mentioned embodiment 1. Its implementation principle and technical effects are similar to those of embodiment 1 and will not be repeated here.

[0119] In a preferred embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device, such as but not limited to an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer-readable storage medium stores computer program instructions that cause the computer to execute the method provided in the first embodiment.

[0120] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In the description of this specification, the description of reference terms "a preferred embodiment", "further", "specifically", "in the present embodiment", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradiction.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the monthly time-sharing power purchase curve of an offshore platform group under a power grid proxy power purchase mechanism, characterized in that: include: Under the power grid proxy power purchasing mechanism, a power purchasing curve decision optimization model is constructed with the lowest typical daily operating cost of offshore oil platform groups as the objective function; Set constraints; Based on the set constraints, the power purchase curve decision optimization model is solved to obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day.

2. The method for optimizing the monthly time-sharing power purchase amount curve of offshore platform groups under the power grid agency power purchase mechanism according to claim 1 is characterized in that: Under the power grid proxy power purchasing mechanism, the power purchase curve decision optimization model is constructed with the lowest typical daily operating cost of the offshore oil platform group as the objective function, including: The objective function of the power purchase decision is to minimize the operating cost on a typical day while meeting the power and heat loads of the platform group. The cost includes three parts: shore power purchase cost, gas turbine fuel cost, and deviation penalty cost: In the formula, C represents the minimum energy supply cost of the offshore oil platform group; S represents different wind power output scenarios, s is the index of the wind power scenario; T represents the time period in a typical day, t represents the index of the time period; θ s represents the probability corresponding to wind power scenario s; F t m represents the shore power purchase cost at time period t; represents the fuel cost of the gas turbine in wind power scenario s at time period t; It represents the penalty cost of deviation electricity in wind power scenario s at time period t; D is the number of days in a month.

3. The method for optimizing the monthly time-sharing power purchase amount curve of offshore platform groups under the power grid agency power purchase mechanism according to claim 2 is characterized in that: Shore power purchase cost: In the formula, P represents the shore power purchase price at time period t; t m It represents the shore power purchase power at time period t; Δt represents time.

4. The method for optimizing the monthly time-sharing power purchase amount curve of offshore platform groups under the power grid agency power purchase mechanism according to claim 3 is characterized in that: Gas Turbine Fuel Cost: Where: represents the opportunity cost of using gas at time period t; It represents the natural gas consumption of gas turbine in wind power scenario s at time period t.

5. The method for optimizing the monthly time-sharing power purchase amount curve of offshore platform groups under the power grid agency power purchase mechanism according to claim 4 is characterized in that: The deviation penalty cost is the electricity purchase fee for the deviation electricity volume: According to the rules, when there is a deviation between the actual electricity consumption and the purchased electricity volume, a penalty settlement is required for the deviation electricity volume, and the fee is: Where U1 is the electricity price adjustment coefficient when the actual electricity demand is greater than the purchased electricity, and U2 is the electricity price adjustment coefficient when the actual electricity demand is less than the purchased electricity; ΔP t,s It represents the deviation power in wind power scenario s at time period t.

6. The method for optimizing the monthly time-sharing power purchase amount curve of offshore platform groups under the power grid agency power purchase mechanism according to claim 1 is characterized in that: The constraints set include: 1) Constraints on power purchase in different time periods: For any time period t on a typical day, the sum of the shore power purchase power and the deviation power under wind power scenario s should be equal to the actual power consumption of the platform group minus the power generation power of the platform itself: P t m ≥0 In the formula, Indicates the actual power consumption in wind power scenario s at time period t; represents the actual grid-connected wind power in wind power scenario s at time period t; K represents the number of gas turbines, k is the index of the number of gas turbines; I represents the number of platforms, i represents the index of the number of platforms; represents the power generation of the kth gas turbine on the ith platform at time period t under wind power scenario s; 2) Gas turbine unit power constraints: In the formula, Respectively represent the maximum and minimum power generation of the i-th platform and the k-th gas turbine unit; α i,k represents the electric-to-heat ratio of the kth gas turbine on the ith platform; H i represents the heat load of the ith platform; 3) Natural gas quantity constraints: Where η k represents the gas consumption rate of the kth gas turbine unit, represents the gas consumption of the kth gas turbine unit in scenario s at time period t, V t gas It represents the gas production of associated gas at time period t; 4) Transfer load constraints: Where: P min , P max Respectively represent the minimum and maximum power of actual electricity consumption; represents the original platform load power consumption: γ1 and γ2 represent the minimum and maximum transfer percentages of the actual load, respectively; 5) Wind power output constraints: In the formula, It represents the maximum wind power generation in wind power scenario s at time period t.

7. The method for optimizing the monthly time-sharing power purchase amount curve of offshore platform groups under the power grid agency power purchase mechanism according to claim 1 is characterized in that: Based on the set constraints, the electricity purchase curve decision optimization model is solved by using MATLAB to call the YALMIP toolbox for programming modeling and calling the CPLEX solver for solving.

8. A device for optimizing the monthly time-sharing power purchase curve of an offshore platform group under a power grid proxy power purchase mechanism, characterized in that: include: The model building unit is configured to build a power purchase curve decision optimization model under the power grid proxy power purchase mechanism with the lowest typical day operation cost of the offshore oil platform group as the objective function; A constraint condition setting unit, configured to set a constraint condition; The model solving unit is configured to solve the power purchase curve decision optimization model based on the set constraint conditions to obtain the monthly time-sharing power purchase curve of the offshore platform group on a typical day.

9. An electronic device, characterized in that: include: at least one processor; as well as A memory communicatively connected to the processor; wherein, The memory stores instructions executable by the processor, and the instructions are executed by the processor to enable the processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include computer instructions for causing a computer to execute the method according to any one of claims 1-7.

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