Low-carbon park power grid staged planning method and system

Abstract

The invention discloses a low-carbon park power grid stage planning method and system, and relates to the technical field of energy management and optimization, and the method comprises the steps: maximizing the overall operation income of a park according to distributed energy production and load consumption data in a day-ahead planning stage; in the real-time optimization scheduling stage, the overall economic benefit is maximized through an internal electricity price mechanism reflecting the market supply and demand relationship; and determining an equilibrium solution of the real-time stage game according to the benefits of the operator and the subject, and solving based on a white shark algorithm. The low-carbon park power grid staged planning method provided by the invention reduces the operation cost of the park, improves the energy utilization efficiency, improves the economic benefit, improves the response speed and adaptive capacity of a power system by constructing the interactive planning model with the master-slave game concept as the core, and improves the power grid performance of the park. The method has better effects in the aspects of operation cost, energy utilization efficiency and economic benefit.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and optimization, and in particular to a low-carbon park power grid phased planning method. Background Art

[0002] As the global climate change problem becomes increasingly serious, all countries are gradually achieving the goals of carbon emission peak and carbon neutrality, and promoting the energy system to develop in the direction of low carbon and intelligence. As a key platform for the industrialization process and urbanization process, the energy management and optimization of industrial parks have become an important link in achieving this goal. In recent years, with the rapid development of distributed energy technology, various distributed energy sources such as wind power, photovoltaics, micro gas turbines, fuel cells, etc. have been widely used in industrial parks. At the same time, the advancement of energy storage technology and the reduction of costs have made the application of large-scale energy storage systems in industrial parks feasible and economical. However, how to coordinate and optimize these diversified energy resources and improve the overall operation efficiency and economic benefits of the park is still a complex and challenging topic. Traditional power grid planning methods are mostly based on centralized power supply and single load demand, which is difficult to adapt to the complex interactive relationship between multiple energy sources and multiple loads in modern low-carbon parks. In addition, with the development of smart grid technology, the real-time monitoring and dynamic optimization capabilities of the power system have been significantly improved, providing new technical support and implementation paths for the power grid planning of low-carbon parks.

[0003] Although the application prospects of distributed energy and energy storage technology in low-carbon parks are broad, the existing related technologies still have many shortcomings in practical applications. First, the traditional power grid planning method mainly starts from the perspective of operators, focusing on maximizing the overall benefits, and often ignores the interests and response behaviors of end users. As a result, the user's electricity demand and behavior cannot be fully considered and guided, resulting in unsatisfactory actual results of power grid planning. Secondly, the existing distributed energy scheduling methods are mostly based on static optimization models, which fail to make full use of real-time monitoring data and dynamic optimization methods, and are difficult to cope with the complex and changeable energy supply and demand environment in the park. In addition, although some advanced intelligent algorithms (such as genetic algorithms, particle swarm algorithms, etc.) have achieved certain results in power grid planning, these algorithms still have problems such as easy to fall into local optimal solutions and slow convergence when dealing with large-scale complex problems, which affects the reliability and efficiency of the optimization effect. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing grid phase planning method has poor interest balance among stakeholders, poor intelligent algorithm optimization, and optimization problems on how to improve the response speed and adaptability of the power system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for phased planning of a low-carbon park power grid, including maximizing the overall operating benefits of the park in the day-ahead planning stage based on distributed energy production and load consumption data; maximizing the overall economic benefits through an internal electricity price mechanism that reflects market supply and demand in the real-time optimization and scheduling stage; determining the equilibrium solution of the real-time stage game based on the benefits of operators and entities, and solving it based on the White Shark algorithm.

[0007] As a preferred solution of the low-carbon park power grid phased planning method described in the present invention, wherein: the maximization of the overall operation efficiency of the park includes the park comprehensive operation benefit objective function, the user's electricity consumption benefit reflects the electricity demand of each subject in different time periods, the operator formulates the energy storage equipment charging and discharging plan based on the market electricity price and the user's electricity consumption behavior forecast to maximize the electricity consumption benefit, the power sales revenue in the park is based on the net load and electricity price strategy of each subject selling electricity to the power grid at different times, the operator dynamically adjusts the power purchase strategy according to the market electricity price fluctuation to reduce the power purchase cost, and the objective function is expressed as:

[0008] F=min(C A +C B -C D -C E )

[0009]

[0010] Among them, C A For the user's electricity efficiency, C B is the electricity sales revenue of the park, C D is the cost of electricity purchased from the power grid by the park, C E The operating costs of energy storage and distributed generation, is the electricity efficiency coefficient of the park, is the power consumption of the ith subject at time h, is the electricity purchase price of the distribution network at time h, is the net electricity sales load of the ith entity at time h, is the electricity price of the distribution network at time h, is the net electricity purchase load of the ith subject at time h. The environmental pollution cost is calculated based on the carbon emissions and pollutant control coefficients of each subject, expressed as:

[0011]

[0012] Among them, C wr is the carbon emission control cost of the park, k is the type of environmental pollutant, β k is the control coefficient of pollutant k, α ik is the carbon emission coefficient generated when the i-th entity is running.

[0013] As a preferred solution of the low-carbon park power grid phased planning method described in the present invention, wherein: the maximization of the overall operation efficiency of the park also includes constraints, the total power generation power in the park is equal to the total load power, the sum of the output power of all distributed energy sources, and the power demand of each entity in the park and the interactive power between the park and the power grid match, the power output of each distributed power source and the interactive power between the park and the power grid are within the preset rated range, and the constraints are expressed as:

[0014] P wt +P pv +P mt +P fc +P ess +P grid =P l

[0015] P i,min ≤P i ≤P i,max

[0016] P grid,min ≤P grid ≤P grid,max

[0017] R i,down Δt≤P i,t -P i,t-1 ≤R i,up Δt

[0018] SOC min ≤SOC≤SOC max

[0019] SOC start =SOC end

[0020] Among them, P wt is the wind power output power, P pv is the photovoltaic output power, P mt is the output power of the micro gas turbine, P fc is the fuel cell output power, P ess is the output power of the energy storage system, P grid is the interaction power between the park and the power grid, P l is the load power in the park, P i is the power output of the ith distributed generation, P i,min is the minimum power output of the ith distributed generation, P i,max is the maximum power output of the i-th distributed generation, P grid is the interaction power between the park and the power grid, P grid,min is the minimum energy exchange power between the park and the power grid, Pgrid,max is the maximum energy exchange power between the park and the power grid, P i,t and P i,t-1 are the output power of the i-th distributed generation at time t and t-1, R i,down is the lower limit of output active power, R i,up is the upper limit of output active power, SOC is the energy storage charge state, SOC min SOC is the minimum state of charge for energy storage. max SOC is the maximum state of charge of energy storage. start is the initial state of charge of energy storage, SOC end End the state of charge for energy storage.

[0021] As a preferred solution of the low-carbon park power grid phased planning method described in the present invention, wherein: the maximization of the overall economic benefit includes the park subject according to the operator's price strategy and its own demand response mechanism, the park subject's profit function reflects the electricity cost and profit at different times, the power purchase subject and the power sales subject determine the purchase and sales amount at each time according to the operator's purchase and sales prices, respectively, at each time, the purchase and sales amount of the park subject reaches a balance, and according to the real-time electricity price signal, the power demand is dynamically adjusted, and the objective function is expressed as:

[0022]

[0023]

[0024] Among them, ΔE h >0 is the electricity purchasing entity, ΔE h ≤0 is the electricity sales entity, is the electricity selling price of the main body within the park, The price of electricity purchased by the main body within the park. The power sold by the park to the operator at time h, is the amount of electricity purchased from the park entity to the operator at time h, and the electricity balance constraint is expressed as:

[0025]

[0026] in, is the amount of electricity purchased by each park entity at time h, The electricity sales of each park entity at time h.

[0027] As a preferred solution of the low-carbon park power grid phased planning method described in the present invention, wherein: the maximization of the overall economic benefit also includes maximizing economic benefits by optimizing the electricity price strategy. The operator sets the internal purchase price and sales price according to the market supply and demand relationship, which affects the power consumption and sales decisions of the park subject. The operator adjusts the electricity price strategy according to the real-time load feedback of the park subject through the master-slave game theory to cope with the dynamic changes in the power market. The upper and lower limit constraints of the power consumption of the park subject stipulate the power consumption and sales range of each subject in different time periods, so that the power system can operate smoothly during peak and trough periods. The operator's profit objective function is expressed as:

[0028]

[0029] in, and They are respectively the upper and lower limits of the main electricity consumption in the park.

[0030] As a preferred solution of the low-carbon park power grid phased planning method described in the present invention, wherein: the equilibrium solution of the real-time phase game is determined, including that the park subject, in the process of implementing the master-slave game, makes adaptive adjustments to the individual load demand according to the electricity price guidance principle set by the superior operator. When the participants make self-interested choices according to the operator's optimization scheduling rules, and when the dominant party accepts the user's response strategy, the game state enters a balanced state. When the operator's income and the subject's income are maximized, there is an optimal solution set, and the equilibrium solution relationship is expressed as:

[0031]

[0032] in, and are the operator’s optimal electricity purchase price and electricity sales price at time h, E h* is the optimal load of all subjects, E i h* is the optimal load of the ith subject, is the optimal load solution set except for the i-th entity. When the equilibrium solution exists, the operator does not increase the profit by adjusting the controllable load alone, and the entities in the park do not increase the profit by adjusting the electricity price alone.

[0033] As a preferred solution of the low-carbon park power grid phased planning method described in the present invention, the solution based on the white shark algorithm includes a solution process, setting the number of great white shark populations and the total number of iterations, and setting the initial temperature and temperature attenuation coefficient of simulated annealing, setting the number of tempering to 2, initializing the initial position and initial speed of the white shark population by random numbers within the set range, calculating the fitness value of the white shark population, and retaining the optimal position and the optimal value, updating its own position through individual behavior, calculating the fitness function value, and saving the strategy, the great white shark updates its position according to the shark movement behavior to the optimal position, and adapts Calculate the value of the fitness function. If it is better than the value at the previous moment, update the position. If it is not better than the value at the previous moment, discard the current value and calculate the fitness difference. Calculate the probability of accepting the new position through the Metropolis criterion and compare it with a random number uniformly distributed in [0,1]. If the probability is greater than the random number, update the position. If the probability is not greater than the random number, discard it and determine whether the algorithm has reached the maximum number of iterations. If not, perform rapid annealing and determine whether it needs to be warmed up and return to search. If it needs to return, the algorithm returns to update the individual speed and continues the cycle until the algorithm reaches the termination condition and outputs the optimal value.

[0034] Another object of the present invention is to provide a low-carbon park power grid phased planning system, which can determine the game equilibrium solution according to the benefits of operators and various entities by determining the solution module, and solve the optimization problem through an improved White Shark algorithm, thereby solving the current problem of conflicts of interest among stakeholders.

[0035] As a preferred solution of the low-carbon park power grid phased planning system described in the present invention, it includes: an operation benefit module, an economic benefit module, and a determination solution module; the operation benefit module is used to maximize the overall operation benefit of the park according to the distributed energy production and load consumption data in the day-ahead planning stage; the economic benefit module is used to maximize the overall economic benefit through the internal electricity price mechanism that reflects the market supply and demand relationship in the real-time optimization scheduling stage; the determination solution module is used to determine the equilibrium solution of the real-time stage game according to the benefits of the operator and the subject, and solve it based on the White Shark algorithm.

[0036] A computer device includes a memory and a processor, wherein the memory stores a computer program, and wherein the processor executes the computer program to implement a step of a method for phased planning of a low-carbon park power grid.

[0037] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method for phased planning of a low-carbon park power grid are implemented.

[0038] Beneficial effects of the present invention: The low-carbon park power grid phased planning method provided by the present invention reduces the operation cost of the park, improves energy utilization efficiency, and enhances economic benefits. By constructing an interactive planning model with the master-slave game concept as the core, the response speed and adaptability of the power system are improved. By introducing environmental pollution costs, the carbon emissions of the park are reduced. By adopting the improved White Shark algorithm, the reliability and efficiency of the global optimal solution of power grid planning are improved. Through the power balance and electricity balance constraints of the park, the safety and reliability of the power system are improved. The present invention achieves better results in terms of operation cost, energy utilization efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0040] Figure 1 An overall flow chart of a low-carbon park power grid phased planning method provided for the first embodiment of the present invention.

[0041] Figure 2 An overall module diagram of a low-carbon park power grid phased planning system provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] Example 1

[0044] Reference Figure 1 , is an embodiment of the present invention, and provides a low-carbon park power grid phased planning method, comprising:

[0045] S1: In the day-ahead planning stage, the overall operation benefits of the park are maximized based on the distributed energy production and load consumption data.

[0046] Furthermore, maximizing the overall operation efficiency of the park includes the objective function of the park's comprehensive operation benefit.

[0047] It should be noted that the user's electricity efficiency reflects the electricity demand of each entity in different time periods. The operator formulates a charging and discharging plan for energy storage equipment based on the market electricity price and the user's electricity consumption behavior forecast to maximize the electricity efficiency. The electricity sales revenue in the park is based on the net load and electricity price strategy of each entity selling electricity to the power grid at different times. The operator dynamically adjusts the electricity purchase strategy according to the market electricity price fluctuations to reduce the electricity purchase cost. The objective function is expressed as:

[0048] F=min(C A +C B -C D -C E )

[0049]

[0050] Among them, C A For the user's electricity efficiency, C B is the electricity sales revenue of the park, C D is the cost of electricity purchased from the power grid by the park, C E The operating costs of energy storage and distributed generation, is the electricity efficiency coefficient of the park, is the power consumption of the ith subject at time h, is the electricity purchase price of the distribution network at time h, is the net electricity sales load of the ith entity at time h, is the electricity price of the distribution network at time h, is the net electricity purchase load of the ith subject at time h. The environmental pollution cost is calculated based on the carbon emissions and pollutant control coefficients of each subject, expressed as:

[0051]

[0052] Among them, C wr is the carbon emission control cost of the park, k is the type of environmental pollutant, β k is the control coefficient of pollutant k, α ik is the carbon emission coefficient generated when the i-th entity is running.

[0053] Furthermore, maximizing the overall operational efficiency of the park also includes constraints.

[0054] It should be noted that the total power generation in the park is equal to the total load power, the sum of the output power of all distributed energy sources, and the power demand of each entity in the park and the interactive power between the park and the power grid match. The power output of each distributed power source and the interactive power between the park and the power grid are within the preset rated range. The constraints are expressed as:

[0055] P wt +P pv +Pmt +P fc +P ess +P grid =P l

[0056] P i,min ≤P i ≤P i,max

[0057] P grid,min ≤P grid ≤P grid,max

[0058] R i,down Δt≤P i,t -P i,t-1 ≤R i,up Δt

[0059] SOC min ≤SOC≤SOC max

[0060] SOC start =SOC end

[0061] Among them, P wt is the wind power output power, P pv is the photovoltaic output power, P mt is the output power of the micro gas turbine, P fc is the fuel cell output power, P ess is the output power of the energy storage system, P grid is the interaction power between the park and the power grid, P l is the load power in the park, P i is the power output of the ith distributed generation, P i,min is the minimum power output of the ith distributed generation, P i,max is the maximum power output of the i-th distributed generation, P grid is the interaction power between the park and the power grid, P grid,min is the minimum energy exchange power between the park and the power grid, P grid,max is the maximum energy exchange power between the park and the power grid, P i,t and P i,t-1 are the output power of the i-th distributed generation at time t and t-1, R i,down is the lower limit of output active power, R i,up is the upper limit of output active power, SOC is the energy storage charge state, SOC min SOC is the minimum state of charge for energy storage. max SOC is the maximum state of charge of energy storage. start is the initial state of charge of energy storage, SOCend End the state of charge for energy storage.

[0062] S2: In the real-time optimization and dispatching stage, the overall economic benefits are maximized through an internal electricity price mechanism that reflects the market supply and demand relationship.

[0063] Furthermore, maximizing the overall economic benefits includes the park entity’s pricing strategy based on the operator’s pricing strategy and its own demand response mechanism.

[0064] It should be noted that the revenue function of the park entity reflects the electricity cost and revenue at different times. The electricity purchase entity and the electricity sales entity determine the purchase and sales amount at each time according to the purchase and sales prices of the operator. At each time, the purchase and sales of the park entity reach a balance, and dynamically adjust the power demand according to the real-time electricity price signal. The objective function is expressed as:

[0065]

[0066]

[0067] Among them, ΔE h >0 is the electricity purchasing entity, ΔE h ≤0 is the electricity sales entity, is the electricity selling price of the main body within the park, The price of electricity purchased by the main body within the park. The power sold by the park to the operator at time h, is the amount of electricity purchased from the park entity to the operator at time h, and the electricity balance constraint is expressed as:

[0068]

[0069]

[0070] in, is the amount of electricity purchased by each park entity at time h, The electricity sales of each park entity at time h.

[0071] Furthermore, maximizing overall economic benefits also includes maximizing economic benefits by optimizing electricity price strategies.

[0072] It should be noted that the operator sets the internal purchase and sale prices of electricity according to the market supply and demand relationship, which affects the power consumption and sales decisions of the park entities. The operator adjusts the electricity price strategy according to the real-time load feedback of the park entities through the master-slave game theory to cope with the dynamic changes in the power market. The upper and lower limits of the power consumption of the park entities stipulate the power consumption and sales range of each entity in different time periods, so that the power system can operate smoothly during peak and trough periods. The operator's profit objective function is expressed as:

[0073]

[0074] in, and They are respectively the upper and lower limits of the main electricity consumption in the park.

[0075] S3: Determine the equilibrium solution of the real-time stage game based on the benefits of the operator and the subject, and solve it based on the White Shark algorithm.

[0076] Furthermore, determining the equilibrium solution of the real-time stage game includes the process in which the park entities implement the master-slave game.

[0077] It should be noted that the individual load demand is adaptively adjusted according to the electricity price guidelines set by the superior operator. When the participants make self-interested choices according to the operator's optimized dispatching rules and when the dominant party accepts the user's response strategy, the game state enters a balanced state. When the operator's profit and the subject's profit are maximized, there is an optimal solution set, and the equilibrium solution relationship is expressed as:

[0078]

[0079] in, and are the operator’s optimal electricity purchase price and electricity sales price at time h, E h* is the optimal load of all subjects, E i h* is the optimal load of the ith subject, is the optimal load solution set except for the i-th entity. When the equilibrium solution exists, the operator does not increase the profit by adjusting the controllable load alone, and the entities in the park do not increase the profit by adjusting the electricity price alone.

[0080] Furthermore, the solution based on the White Shark algorithm includes a solution process.

[0081] It should be noted that the population size and total number of iterations of great white sharks are set, and the initial temperature and temperature attenuation coefficient of simulated annealing are set, and the number of tempering is set to 2. The initial position and initial speed of the white shark population are initialized by random numbers within the set range, the fitness value of the white shark population is calculated, and the optimal position and the optimal value are retained. The position of the great white shark is updated through individual behavior, the fitness function value is calculated, and the strategy is saved. The great white shark updates its position according to the shark movement behavior to the optimal position, calculates the fitness function value, and updates the position if it is better than the value at the previous moment. If it is not better than the value at the previous moment, the current value is discarded, and the fitness difference is calculated. The probability of accepting the new position is calculated by the Metropolis criterion and compared with a random number uniformly distributed in [0,1]. If the probability is greater than the random number, the position is updated. If the probability is not greater than the random number, it is discarded. It is determined whether the algorithm has reached the maximum number of iterations. If not, rapid annealing is performed, and it is determined whether it needs to be warmed up and returned to search. If it needs to return, the algorithm returns to update the individual speed and continues to cycle until the algorithm reaches the termination condition and outputs the optimal value.

[0082] Example 2

[0083] An embodiment of the present invention provides a method for phased planning of a low-carbon park power grid. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0084] A comprehensive experiment was conducted. The experimental objects included the traditional power grid planning method and the low-carbon park power grid planning method of the present invention. An industrial park was selected as the experimental scenario. The park has a variety of distributed energy sources such as wind power, photovoltaics, micro gas turbines, energy storage systems, and multiple electricity-consuming enterprises. The experimental data covered the distributed energy production, load consumption, electricity price fluctuations, etc. in different time periods.

[0085] During the test preparation stage, the historical production data of each distributed energy source in the park and the enterprise electricity demand data are first collected to develop a basic data set for the test. Then, the traditional power grid planning method and the planning method of the present invention are respectively applied to carry out day-ahead and real-time planning of the park power grid. In the day-ahead planning stage, the traditional method mainly relies on the static optimization model to develop a charging and discharging plan for the energy storage equipment, while the present invention develops a scientific and reasonable charging and discharging strategy based on the distributed energy production and load consumption data to maximize the overall operating benefits of the park. In the real-time planning stage, the traditional method fails to make full use of real-time monitoring data and lacks dynamic adjustment capabilities. The present invention sets an internal electricity price strategy through the master-slave game theory to guide user demand response and ensure the dynamic balance of the power grid and maximize economic benefits.

[0086] As shown in Table 1, the method of the present invention has a higher monthly operating income than the traditional method through a scientific and reasonable energy storage equipment charging and discharging strategy, which reflects the effectiveness and scientificity of optimizing energy storage scheduling and improves the overall energy utilization efficiency of the park. In the real-time planning stage, the method of the present invention dynamically adjusts the electricity price through the master-slave game theory and the internal electricity price strategy to guide the user demand response, so that the economic benefit of the park is higher than the traditional method, and makes full use of the real-time monitoring data to improve the allocation efficiency and economic benefits of power resources. The method of the present invention introduces environmental pollution costs into the objective function, and effectively reduces the use frequency of high-carbon emission energy by optimizing the electricity price mechanism and the energy storage scheduling strategy, and promotes the low-carbon operation of the park. Compared with the traditional method, it has significant environmental benefits.

[0087] Table 1 Experimental data table

[0088]

[0089] Example 3

[0090] Reference Figure 2 , which is an embodiment of the present invention, provides a low-carbon park power grid phased planning system, including: an operation income module, an economic benefit module, and a determination solution module.

[0091] The operation benefit module is used to maximize the overall operation benefit of the park in the day-ahead planning stage based on the distributed energy production and load consumption data; the economic benefit module is used to maximize the overall economic benefit in the real-time optimization scheduling stage through the internal electricity price mechanism that reflects the market supply and demand relationship; the determination solution module is used to determine the equilibrium solution of the real-time stage game based on the benefits of operators and entities, and solve it based on the White Shark algorithm.

[0092] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0094] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0095] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0096] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A low-carbon park power grid phased planning method, characterized in that: include: In the day-ahead planning stage, the overall operation benefits of the park are maximized based on the distributed energy production and load consumption data; In the real-time optimization and dispatching stage, the overall economic benefits are maximized through the internal electricity price mechanism that reflects the market supply and demand relationship; According to the benefits of operators and entities, the equilibrium solution of the real-time stage game is determined and solved based on the White Shark algorithm.

2. The low-carbon park power grid phased planning method according to claim 1, characterized in that: The maximization of the overall operation efficiency of the park includes the park's comprehensive operation benefit objective function. The user's electricity consumption benefit reflects the electricity demand of each subject in different time periods. The operator formulates a charging and discharging plan for energy storage equipment based on the market electricity price and the user's electricity consumption behavior forecast to maximize the electricity consumption benefit. The power sales revenue in the park is based on the net load and electricity price strategy of each subject selling electricity to the power grid at different times. The operator dynamically adjusts the power purchase strategy according to the market electricity price fluctuation to reduce the power purchase cost. The objective function is expressed as: F = min (C A +C B -C D -C E ) Among them, C A For the user's electricity efficiency, C B is the electricity sales revenue of the park, C D is the cost of purchasing electricity from the power grid, C E The operating costs of energy storage and distributed generation, is the electricity efficiency coefficient of the park, is the power consumption of the ith subject at time h, is the electricity purchase price of the distribution network at time h, is the net electricity sales load of the ith entity at time h, is the electricity price of the distribution network at time h, is the net electricity purchase load of the ith subject at time h. The environmental pollution cost is calculated based on the carbon emissions and pollutant control coefficients of each subject, expressed as: Among them, C wr is the carbon emission control cost of the park, k is the type of environmental pollutant, β k is the control coefficient of pollutant k, α ik is the carbon emission coefficient generated when the i-th entity is running.

3. The low-carbon park power grid phased planning method according to claim 2, characterized in that: The maximization of the overall operation efficiency of the park also includes constraints. The total power generation in the park is equal to the total load power. The sum of the output power of all distributed energy sources matches the power demand of each entity in the park and the interactive power between the park and the power grid. The power output of each distributed power source and the interactive power between the park and the power grid are within the preset rated range. The constraints are expressed as: P wt +P pv +P mt +P fc +P ess +P grid =P l P i,min ≤P i ≤P i,max P grid,min ≤P grid ≤P grid,max R i,down Δt≤P i,t -P i,t-1 ≤R i,up Δt SOC min ≤SOC≤SOC max SOCIETY start =SOC end Among them, P wt is the wind power output power, P pv is the photovoltaic output power, P mt is the output power of the micro gas turbine, P fc is the fuel cell output power, P ess is the output power of the energy storage system, P grid is the interaction power between the park and the power grid, P l is the load power in the park, P i is the power output of the ith distributed generation, P i,min is the minimum power output of the ith distributed generation, P i,max is the maximum power output of the i-th distributed generation, P grid is the interaction power between the park and the power grid, P grid,min is the minimum energy exchange power between the park and the power grid, P grid,max is the maximum energy exchange power between the park and the power grid, P i,t and P i,t-1 are the output power of the i-th distributed generation at time t and t-1, R i,down is the lower limit of output active power, R i,up is the upper limit of output active power, SOC is the state of charge of energy storage, SOC min SOC is the minimum state of charge for energy storage. max SOC is the maximum state of charge of energy storage. start is the initial state of charge of energy storage, SOC end End the state of charge for energy storage.

4. The low-carbon park power grid phased planning method according to claim 3 is characterized in that: The maximization of the overall economic benefits includes that the park entity is based on the operator's price strategy and its own demand response mechanism. The park entity's revenue function reflects the electricity cost and revenue at different times. The power purchase entity and the power sales entity determine the power purchase and sales at each time according to the operator's power purchase price and power sales price respectively. At each time, the power purchase and sales of the park entity reach a balance, and according to the real-time power price signal, the power demand is dynamically adjusted. The objective function is expressed as: Where, ΔE h >0 is the electricity purchasing entity, ΔE h ≤0 is the electricity sales entity, is the electricity selling price of the main body within the park, The price of electricity purchased by the main body within the park. The power sold by the park to the operator at time h, is the amount of electricity purchased from the park entity to the operator at time h, and the electricity balance constraint is expressed as: in, is the amount of electricity purchased by each park entity at time h, The electricity sales of each park entity at time h.

5. The low-carbon park power grid phased planning method according to claim 4, characterized in that: The maximization of overall economic benefits also includes maximizing economic benefits by optimizing electricity price strategies. Operators set internal electricity purchase and sales prices based on market supply and demand relationships to influence the power consumption and sales decisions of park entities. Operators use master-slave game theory to adjust electricity price strategies based on real-time load feedback from park entities to respond to dynamic changes in the power market. The upper and lower limits of power consumption of park entities specify the power consumption and sales range of each entity in different time periods, so that the power system can operate smoothly during peak and trough periods. The operator's revenue objective function is expressed as: in, and They are respectively the upper and lower limits of the main electricity consumption in the park.

6. The low-carbon park power grid phased planning method according to claim 5, characterized in that: The equilibrium solution of the real-time stage game is determined by the main body of the park in the process of implementing the master-slave game, according to the electricity price guidance principle set by the superior operator, and when the participants make self-interested choices according to the operator's optimization scheduling rules, and when the dominant party accepts the user's response strategy, the game state enters a balanced state. When the operator's income and the main body's income are maximized, there is an optimal solution set, and the equilibrium solution relationship is expressed as: in, and are the operator’s optimal electricity purchase price and electricity sales price at time h, E h* is the optimal load of all subjects, E i h* is the optimal load of the ith subject, is the optimal load solution set except for the i-th entity. When the equilibrium solution exists, the operator does not increase the profit by adjusting the controllable load alone, and the entities in the park do not increase the profit by adjusting the electricity price alone.

7. The low-carbon park power grid phased planning method according to claim 6, characterized in that: The solution based on the white shark algorithm includes a solution process, setting the number of great white shark populations and the total number of iterations, setting the initial temperature and temperature attenuation coefficient of simulated annealing, setting the number of tempering times to 2, initializing the initial position and initial speed of the white shark population by random numbers within the set range, calculating the fitness value of the white shark population, and retaining the optimal position and the optimal value, updating its own position through individual behavior, calculating the fitness function value, and saving the strategy, the great white shark updates its position according to the shark movement behavior to the optimal position, calculates the fitness function value, and if it is better than the value at the previous moment , then update the position. If it is not better than the value at the previous moment, discard the current value and calculate the fitness difference. Calculate the probability of accepting the new position through the Metropolis criterion and compare it with a random number uniformly distributed in [0,1]. If the probability is greater than the random number, update the position. If the probability is not greater than the random number, discard it and determine whether the algorithm has reached the maximum number of iterations. If not, perform rapid annealing and determine whether it needs to be warmed up and return to search. If it needs to return, the algorithm returns to update the individual speed and continues the cycle until the algorithm reaches the termination condition and outputs the optimal value.

8. A system using the low-carbon park power grid phased planning method as claimed in any one of claims 1 to 7, characterized in that: It includes operation income module, economic benefit module and determination solution module; The operation benefit module is used to maximize the overall operation benefit of the park according to the distributed energy production and load consumption data in the day-ahead planning stage; The economic benefit module is used to maximize the overall economic benefits during the real-time optimization scheduling phase through an internal electricity price mechanism that reflects the market supply and demand relationship; The determination and solution module is used to determine the equilibrium solution of the real-time stage game according to the benefits of the operator and the subject, and solve it based on the White Shark algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the low-carbon park power grid phased planning method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the low-carbon park power grid phased planning method described in any one of claims 1 to 7 are implemented.

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