Stepped carbon transaction and low-carbon economy optimization scheduling system and method based on master-slave game

Abstract

The invention discloses a stepped carbon transaction mechanism and low-carbon economic optimization scheduling system based on a multi-agent master-slave game. A regional integrated energy system is used for constructing a regional integrated energy system based on source-load-storage complementation; the multi-agent master-slave game module establishes a multi-agent master-slave game model; the reward and punishment stepped carbon transaction module is used for obtaining carbon emission cost; the dual-excitation comprehensive demand response module is used for adjusting the load demand according to the carbon compensation signal and the energy market price; and the scheduling optimization module is used for solving the multi-main-body master-slave game model and performing scheduling optimization on the regional integrated energy system based on source-load-storage complementation according to a self-adaptive differential evolution algorithm to generate an optimal energy scheduling scheme. According to the invention, through source-load-storage multi-party collaborative optimization scheduling, the operation efficiency and the renewable energy consumption capability of the regional energy system are improved, the carbon emission and the operation cost are effectively reduced, and the greenness, the economy and the sustainable operation capability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatch optimization and energy management, and specifically refers to a ladder-type carbon trading and low-carbon economic optimization dispatching system and method based on master-slave game. Background Art

[0002] With the continuous growth of global energy demand and the increasingly serious environmental problems, how to reduce carbon emissions while improving energy efficiency has become the focus of attention of countries around the world. As a form of energy management with multi-energy complementarity and efficient utilization, the regional energy system has played an important role in promoting a low-carbon economy and reducing carbon emissions. However, the current energy dispatch model faces problems such as conflicts of interest among multiple market players, increased complexity of the energy system, and low efficiency of renewable energy consumption, and more effective solutions are urgently needed.

[0003] Carbon emission management is an important part of the low-carbon economy. In recent years, the international community has proposed carbon neutrality goals through the Paris Agreement and other agreements, requiring countries to gradually reduce greenhouse gas emissions through policy guidance, carbon trading mechanisms and technological innovation. As one of the world's largest energy consumers, my country faces major challenges in promoting the low-carbon transformation of regional energy systems. my country's energy structure is still dominated by fossil energy, and the utilization rate of renewable energy needs to be improved. The optimization of regional energy systems and carbon emission management also require effective mechanisms to achieve.

[0004] Existing carbon trading and demand response mechanisms mostly focus on single entities or local dispatch, and fail to fully consider the coordinated optimization of multiple entities in regional energy systems. Traditional energy dispatch methods cannot effectively solve the coordination problems between the source side, energy storage side and load side, resulting in energy waste and increased carbon emissions.

[0005] Therefore, it is urgent to establish a set of coordinated optimization methods for multiple subjects in an integrated regional energy system to resolve the problem of conflicts of interest among multiple market subjects, coordinate the interests of various subjects, improve energy utilization efficiency, reduce system carbon emissions through carbon trading and demand response mechanisms, and provide technical support for the green and economic operation of the system. Summary of the invention

[0006] The purpose of the present invention is to provide a ladder-type carbon trading and low-carbon economy optimization scheduling system and method based on master-slave game, aiming to improve the absorption rate of renewable energy and reduce carbon emissions. The present invention introduces a reward-penalty ladder-type carbon trading mechanism and a comprehensive demand response strategy of dual incentives to coordinate the management of carbon emissions and load response in energy production and consumption, and constructs a multi-subject master-slave game model with energy management companies as leaders, energy supply operators, energy storage operators and users as followers, and optimizes the energy production and use strategies of each subject through game interaction. The model effectively coordinates various energy forms such as wind energy, photovoltaics, and gas turbines to improve the energy utilization efficiency of the system; and through the dual incentive mechanism, users dynamically adjust energy demand according to price signals and carbon compensation incentives to achieve peak shaving and valley filling, optimize energy supply and demand balance, and further reduce system operating costs. Through the optimized scheduling of the game model, combined with the reward-penalty ladder-type carbon trading mechanism and the demand response strategy of dual incentives, the low-carbon economic operation of the industrial park is realized, carbon emissions are effectively reduced, and the green and efficient development of the system is promoted.

[0007] To achieve this purpose, the present invention designs a ladder-type carbon trading and low-carbon economic optimization scheduling system based on master-slave game, including:

[0008] The regional integrated energy system is used to build a regional integrated energy system based on source-load-storage complementarity and obtain data in the regional integrated energy system, including historical carbon emission data, energy supply prices, and energy market prices of energy managers and energy supply operators;

[0009] The multi-agent master-slave game module is used to construct the upper-level leaders in the multi-agent master-slave game model with the goal of maximizing the profits of energy managers, and to construct the lower-level followers in the multi-agent master-slave game model with the goals of maximizing the profits of energy supply operators, maximizing the economic benefits of energy storage operators, and maximizing the economic benefits and satisfaction of users;

[0010] The reward-penalty ladder carbon trading module is used to obtain the carbon emission costs of energy managers and energy supply operators based on historical carbon emission data and energy supply prices;

[0011] The dual incentive integrated demand response module generates carbon compensation signals for carbon trading incentives or penalties based on the carbon emission costs of energy managers and energy supply operators. Users adjust the demand for power load, thermal load and cooling load based on the carbon compensation signals and energy market prices to obtain demand response costs.

[0012] The scheduling optimization module is based on the reward and punishment ladder carbon trading module and the dual incentive comprehensive demand response module. It aims to maximize economic and environmental benefits, and establishes a multi-objective optimization function for the scheduling of regional integrated energy systems based on source-load-storage complementarity. With the constraints of carbon emission costs and demand response costs, the adaptive differential evolution algorithm is used. The upper leaders and lower followers in the multi-agent master-slave game model optimize the regional integrated energy system with source-load-storage complementarity, and generate the optimal energy scheduling plan based on the optimized regional integrated energy system with source-load-storage complementarity.

[0013] The beneficial effects of the present invention are:

[0014] 1) By real-time monitoring and optimizing the data of various entities through the regional integrated energy system, resources can be allocated more efficiently, energy waste can be reduced, and overall energy efficiency can be improved;

[0015] 2) Introduce a reward-and-punishment tiered carbon trading mechanism. By setting dynamic carbon emission quotas and tiered trading rules, energy managers and energy supply operators will be encouraged to pay more attention to their own carbon emission management, effectively reduce carbon emissions, and thus promote the transformation of the entire society to a low-carbon economy;

[0016] 3) The carbon emission costs obtained by the reward-penalty ladder carbon trading mechanism generate corresponding incentives or penalties. The carbon compensation signal formed encourages users to adjust their electricity, heat and cooling load demands, further promoting energy conservation and emission reduction;

[0017] 4) Users can flexibly adjust their energy consumption behavior based on carbon compensation signals and energy market prices, minimizing costs while ensuring quality of life, thereby improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the ladder-type carbon trading and low-carbon economic optimization scheduling system based on master-slave game of the present invention;

[0019] Figure 2 Schematic diagram of the regional integrated energy system structure and energy flow in the present invention

[0020] Figure 3 This is a framework diagram of the multi-agent master-slave game in the present invention;

[0021] Figure 4 The internal transaction energy price curve for electricity price optimization in the embodiment of the present invention;

[0022] Figure 5 It is the internal transaction energy price curve for heat price optimization in the embodiment of the present invention;

[0023] Figure 6 It is the internal transaction energy price curve for cooling price optimization in the embodiment of the present invention;

[0024] Figure 7 The power optimization scheduling result in the embodiment of the present invention;

[0025] Figure 8 The thermal energy optimization scheduling result in the embodiment of the present invention;

[0026] Fig. 9 This is the cooling energy optimization scheduling result in the embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0028] Embodiment 1:

[0029] like Figures 1 to 9 A ladder-type carbon trading and low-carbon economic optimization scheduling system based on master-slave game is shown, comprising:

[0030] The regional integrated energy system is used to build a regional integrated energy system based on source-load-storage complementarity and obtain data in the regional integrated energy system, including historical carbon emission data, energy supply prices, and energy market prices of energy managers and energy supply operators;

[0031] The multi-agent master-slave game module is used to construct the upper-level leaders in the multi-agent master-slave game model with the goal of maximizing the profits of energy managers, and to construct the lower-level followers in the multi-agent master-slave game model with the goals of maximizing the profits of energy supply operators, maximizing the economic benefits of energy storage operators, and maximizing the economic benefits and satisfaction of users, thus establishing a multi-agent master-slave game model.

[0032] The reward-penalty ladder carbon trading module is used to obtain the carbon emission costs of energy managers and energy supply operators based on historical carbon emission data and energy supply prices;

[0033] The dual incentive integrated demand response module generates carbon compensation signals for carbon trading incentives or penalties based on the carbon emission costs of energy managers and energy supply operators. Users adjust the demand for power load, thermal load and cooling load based on the carbon compensation signals and energy market prices to obtain demand response costs.

[0034] The scheduling optimization module is based on the reward and punishment ladder carbon trading module and the dual incentive comprehensive demand response module. It aims to maximize economic and environmental benefits, and establishes a multi-objective optimization function for the scheduling of regional integrated energy systems based on source-load-storage complementarity. With the constraints of carbon emission costs and demand response costs, the adaptive differential evolution algorithm is combined with the solver toolbox. The upper leaders and lower followers in the multi-agent master-slave game model optimize the regional integrated energy system with source-load-storage complementarity, and generate the optimal energy scheduling plan based on the optimized regional integrated energy system with source-load-storage complementarity.

[0035] The present invention proposes a ladder-type carbon trading mechanism and low-carbon economic optimization scheduling system of multi-agent master-slave game, which aims to optimize the energy management and carbon emission control of each subject by integrating multiple modules. By introducing a reward-and-punishment ladder-type carbon trading mechanism and a dual-incentive comprehensive demand response mechanism into the multi-agent master-slave game model, a reasonable carbon emission quota is set according to historical carbon emissions and energy prices, and economic incentives or penalties are imposed on each energy manager and energy supply operator to promote emission reduction. And by generating corresponding incentives or penalties based on carbon emission costs, users are guided to adjust their energy consumption patterns, thereby achieving more efficient resource allocation. Finally, through the adaptive differential evolution algorithm, the upper leaders and lower followers in the multi-agent master-slave game model optimize the regional integrated energy system with complementary sources, loads and storage, optimize the balance of interests of all parties, ensure the maximization of profits of major energy managers, and take into account the economic benefits and satisfaction of energy supply, energy storage operators and users, thereby generating the optimal energy scheduling plan.

[0036] This system not only promotes the efficient use of energy in the region and reduces carbon emissions, but also encourages all parties to participate in energy conservation and emission reduction through market mechanisms, while ensuring the economy and fairness of the system. This approach helps to build a greener, smarter and more efficient energy management system, which is of great value in promoting sustainable development. Through this systematic approach, a win-win situation of environmental and economic benefits can be achieved, and it also provides a reference model for future urban energy planning.

[0037] Regional integrated energy system based on source-load-storage complementarity: A regional integrated energy system built based on the equipment data of the source side (energy managers and energy operators), load side (users), and energy storage side (energy storage operators). By coordinating the relationship between source, load, and storage, it improves energy utilization efficiency and optimizes system operation.

[0038] Regional integrated energy system dispatch model based on source-load-storage complementarity: Combine the dynamic data and optimization objectives of the source side, load side, and energy storage side, and use the multi-objective optimization function of regional integrated energy system dispatch based on source-load-storage complementarity to dispatch energy production, consumption, and storage, ensuring low-carbon and efficient operation of the system and balancing economic and environmental benefits.

[0039] In the above technical solution, in the regional integrated energy system, the main equipment includes: the source side is the energy manager and the energy supply operator, the source side includes renewable energy devices and traditional energy devices, the energy storage side is the energy storage operator and the load side is the user. Through the equipment on the source side, the energy storage side and the load side, a regional integrated energy system with complementary sources, loads and storage is constructed. The data collected by each main equipment is:

[0040] Obtain historical carbon emission data, power generation data of renewable energy devices, and consumption data of traditional energy devices;

[0041] Obtain user's load demand data on electricity, heat and cooling energy;

[0042] Obtain energy supply prices published by energy supply operators, energy market prices and real-time price information published by energy managers;

[0043] Obtain the energy storage operator's energy storage charging cost, energy discharge income, and energy storage operation and maintenance cost.

[0044] Among them, real-time price information affects the demand response and adjustment of load-side users.

[0045] Regional Integrated Energy System, abbreviated as RIES.

[0046] Collect power generation data of renewable energy such as photovoltaics and wind power in renewable energy devices, and consumption data of traditional energy such as gas turbines and gas boilers in traditional energy devices to realize the supply of multiple energy forms.

[0047] The load side includes power load, heat load and cooling load, and the conversion and scheduling between various energy forms are realized through the energy hub.

[0048] In the above regional integrated energy system, multi-dimensional data collection is used to ensure that the real-time data records of various energy sources can reflect the roles of energy managers, energy supply operators and energy storage operators in energy consumption, and provide complete input data, including not only conventional carbon emissions, but also by considering energy efficiency and demand response, to help identify the contributions of different market players (energy managers, energy supply operators, energy storage operators, users) in carbon emissions. This provides basic data support for the dispatch optimization of the multi-agent game model, ensuring that the carbon emission behavior of each subject can be reasonably modeled and adjusted in the dispatch optimization.

[0049] In the above technical solution, energy consumption and carbon emission data can be preprocessed:

[0050] Eliminate outliers: Eliminate values ​​that deviate too much from the actual values. That is, compare the carbon emission data of the current month with the carbon emission data of the previous month and the same month of the previous year. If the current data point is significantly higher or lower than these two reference values, it may be regarded as an outlier and be eliminated.

[0051] For outlier data points, the data from the previous month and the same month of the previous year are used to fill the gaps, which is called interpolation.

[0052] Data filling: Use the interpolation method based on historical data to fill in the data to ensure data integrity. The specific filling formula is:

[0053]

[0054] Among them, P t Represents the carbon emission value at the current moment, P t-1 and P t-12 Represents the carbon emission values ​​of the previous month and the same month of the previous year respectively.

[0055] The cleaned and filled data are standardized. The purpose of standardization is to ensure that different types of energy consumption and carbon emission data are comparable on the same scale. The standardization formula is as follows:

[0056]

[0057] Among them, X is the original value in the data, X min and X max are the minimum and maximum values ​​in the data set, respectively. The standardized data range is [0,1], which is convenient for subsequent analysis.

[0058] In the above technical solution, in the regional integrated energy system, the energy storage operator is used to balance short-term load fluctuations to ensure that the demand on the load side is met. The power generation data of renewable energy devices and the consumption data of traditional energy are used to obtain the total power output of electricity and heat. The power output formula of electricity and heat during system scheduling is as follows:

[0059] P(t)=P PV (t)+P WT (t)+P CHP (t)+P GF (t)+P Grid (t);

[0060] H(t)=H GB (t)+H GF (t)+H CHP (t)+H Grid (t);

[0061] Where P(t) and H(t) represent the total power output of electricity and heat, respectively. PV (t), P WT (t), P CHP (t), P GF (t), P Grid (t) are the power output of photovoltaic, wind power, cogeneration device, gas turbine and power grid, i.e. the power generation data of renewable energy devices; H GB (t), H GF (t), H CHP (t), H Grid(t) are the thermal power outputs of boilers, gas turbines, cogeneration units and heat networks, i.e., the consumption data of traditional energy.

[0062] The energy system balances short-term load fluctuations through the energy storage devices of energy storage operators. The above power output formulas for electricity and heat show how different energy devices can coordinate their output to meet the demand for electricity and heat. During the system dispatch process, by adjusting the output power of these devices, it can ensure that the power and heat demand on the load side is met.

[0063] In the above technical solution, in the reward and punishment ladder carbon trading module, the carbon emission quota is calculated based on the historical carbon emission data of energy managers and energy supply operators. The specific carbon emission quotas of energy managers and energy supply operators are calculated as follows:

[0064]

[0065]

[0066] in, Carbon emission credits for outsourced electricity to energy managers, The total carbon emission quota of the energy supply operator, δ e and δ h are the carbon emission factors per unit of electricity and per unit of heat, P buy (t) is the amount of electricity purchased from the external power grid, H GB (t) is the heat output of the gas boiler, H WHB , Q AR (t), P GT (t) are the heating, cooling and power generation of the waste heat boiler, absorption chiller and gas turbine respectively.

[0067] Energy storage operators indirectly affect carbon emissions through optimizing energy storage, while users are more involved in optimizing energy efficiency and carbon compensation mechanisms. Therefore, we select two types of entities that directly produce carbon emissions, energy managers and energy supply operators, for carbon trading incentives and penalties.

[0068] If the actual carbon emissions of the subject are lower than the quota, it will receive carbon trading incentives. If the subject exceeds the quota, it will pay excess fees and receive carbon trading penalties. The specific carbon trading incentives and penalties are calculated based on the following formula:

[0069]

[0070] Among them, F c is the carbon trading cost, including carbon trading incentives and penalties, D p is the actual carbon emissions, D cis the carbon emission quota, λ1 and λ2 are the reward and penalty coefficients respectively, and μ is the benchmark unit cost of carbon trading.

[0071] The above process first obtains the historical carbon emission data of energy managers and energy supply operators from the regional integrated energy system, and calculates the carbon emission quotas of energy managers and energy supply operators through quotas, and compares them with the actual carbon emissions of energy managers and energy supply operators in the regional integrated energy system. The carbon trading costs of energy managers and energy supply operators are calculated using carbon trading incentive and penalty functions. The specific method is: if the actual carbon emissions of the subject exceed the quota, it is necessary to purchase additional carbon emission rights and pay a tiered carbon trading fee based on the excess emissions. If the carbon emissions of the subject are lower than the quota, it can obtain carbon emission rewards, which will encourage each subject to optimize carbon emission strategies and achieve low-carbon operation.

[0072] The above-mentioned reward-and-penalty ladder carbon trading module calculates the carbon emission quotas of energy managers and energy supply operators, and gives economic incentives or penalties based on actual carbon emissions, aiming to encourage entities to optimize energy use strategies and improve the system's low-carbon operation capabilities. This mechanism uses historical carbon emission data to calculate the carbon emission quotas of each entity. When actual emissions are lower than the quota, economic rewards are provided to encourage continued emission reductions; conversely, excess fees are imposed as penalties, thereby effectively controlling the total amount of carbon emissions. This module not only helps to optimize resource allocation internally and reduce operating costs, but also guides companies to actively reduce their carbon footprint and improve environmental awareness through market means. In the long run, it provides strong support for building a low-carbon economic system, promotes green transformation and sustainable development, and ensures that while meeting energy needs, a win-win situation of environmental and economic benefits is achieved.

[0073] In the above technical solution, in the dual incentive integrated demand response module, the carbon compensation signal is generated according to the carbon trading incentive or penalty of the energy manager and the energy supply operator, and the user adjusts the power, heat and cooling load demand in combination with the real-time energy market price provided by the energy manager;

[0074] Among them, the carbon compensation signal reflects the reward and punishment ladder composed of carbon trading incentives and penalties in the reward and punishment ladder carbon trading module, as well as the implementation of carbon emission quotas. The cost calculation formula for incentive demand response is as follows:

[0075]

[0076] Among them, C IDR is the demand response cost, a i and b i are the compensation coefficients for power load and thermal load, respectively, Li (t) and H Li(t) are the electricity and heat loads participating in demand response, respectively. i represents the load adjustment category. i=1 represents load transfer, i=2 represents load reduction, and t represents the time.

[0077] Carbon compensation signals refer to signals generated by energy managers based on the comparison results of each entity's carbon emissions and quotas, real-time energy prices and other factors, which are used to encourage users to adjust their energy strategies, reduce carbon emissions, or prompt users to take measures to avoid penalties for excess emissions. Incentive demand response encourages users to participate in load shifting and reduction through compensation. Since users have different willingness to shift and reduce electric and thermal loads, the present invention adopts incentive response strategies with different compensation methods.

[0078] The above-mentioned dual incentive integrated demand response strategy of price and carbon compensation is adopted. Users obtain price signals and carbon compensation signals based on the energy market price provided by the energy manager and the carbon trading incentives or penalties of the energy manager and energy supply operator obtained in the reward and punishment ladder carbon trading module, adjust the power, heat and cooling loads, and achieve optimal load scheduling. By flexibly adjusting the load demand, users increase energy consumption during off-peak hours and reduce electricity demand during peak hours, achieving the effect of "peak shaving and valley filling", further reducing system operating costs and carbon emissions.

[0079] Through the interaction of the reward and punishment ladder carbon trading module and the dual incentive comprehensive demand response module, each entity conducts energy scheduling and load adjustment under the constraints of carbon emission quotas to minimize system carbon emissions, while reducing overall operating costs and improving the greenness and economic benefits of the system.

[0080] In the above technical solution, in the multi-agent master-slave game module, the objective function of each agent is as follows:

[0081] With the energy manager as the upper-level leader, the first objective function is constructed. Specifically, the first objective function is constructed to maximize its own net profit through the price difference between electricity sales and electricity purchases, that is, the price difference between the energy supply price of the energy supply operator and the energy market price released by the energy manager, as well as the carbon trading costs or benefits and demand response incentives:

[0082]

[0083] Wherein, the superscript t represents the period t; and The energy storage operator sells energy to users and the energy storage operator benefits from energy sales, which are dynamically calculated through optimization algorithms based on user demand and energy storage behavior; and The costs of purchasing energy from energy supply operators and power grids for energy managers depend on electricity prices and demand fluctuations, and can be obtained dynamically through real-time market prices and optimization models; Penalty costs for heating or cooling interruptions are dynamically calculated based on real-time user demand and system operating status; is the user's carbon trading incentive and penalty function, ε1 is the weight coefficient of the energy manager's carbon trading incentive and penalty; is the carbon trading cost of outsourced electricity for energy managers; T is the total time period, which is 24 hours.

[0084] With energy supply operators, energy storage operators and users as lower-level followers, the second objective function, the third objective function and the fourth objective function of the lower level are constructed;

[0085] Energy management operator (EMO) is the coordinator and leader in the RIES energy market. It is responsible for balancing the power of sources, loads and storage. It also considers the initiative and decision-making ability of the three parties, and sets the purchase and sale prices of energy with the goal of maximizing net profit. When the electricity purchased by EMO from EGO cannot meet the needs of users, it needs to purchase electricity from the external power grid and bear the carbon emission costs generated by the purchased electricity.

[0086] Energy supply operators: Based on the energy market price set by the energy manager, with the goal of maximizing energy sales revenue and minimizing fuel costs and carbon trading costs, optimize the output of controllable equipment of the energy supply operator and build a second objective function with the goal of maximizing the profit of the energy supply operator itself:

[0087]

[0088] in, The energy supply operator benefits from energy sales; ε2 represents the weight coefficient of the carbon compensation borne by the energy supply operator; represents the carbon trading cost of energy supply operators; represents the fuel cost of the CHP unit and gas boiler; Represents the start-up and shutdown cost of the gas turbine.

[0089] Energy supply operator is referred to as EGO. EGO takes CCHP as the core, considers the carbon emissions generated during the operation of CCHP, and optimizes the output of each controllable device with the maximum energy sales revenue and the minimum fuel cost and carbon trading cost as the objective function. The controllable devices include:

[0090] Combined heat and power (CCHP): Responsible for providing both electricity and heat, its operating output needs to be dynamically adjusted according to system needs and goals.

[0091] Gas boiler (GB): used for heating. Output optimization means adjusting the boiler's heat output according to changes in demand.

[0092] Gas turbine (GT): used for power generation, output optimization includes adjusting the start-up time and output power to reduce start-up costs and fuel consumption.

[0093] Photovoltaic (PV) and wind power (WT): Although they are uncontrollable devices, their utilization can be improved by co-optimizing them with energy storage systems.

[0094] Energy storage system (ESO): Achieve peak-valley electricity price arbitrage and balance system load by adjusting charging and discharging strategies.

[0095] Energy storage operators: Energy storage operators achieve arbitrage through valley charging and peak discharging, and build a third objective function with the goal of maximizing the economic benefits of energy storage operators:

[0096]

[0097] in, They are the charging cost and discharging benefit of energy storage respectively; It is the operation and maintenance cost of energy storage operators.

[0098] Energy storage operators are referred to as ESOs. Based on price information, ESOs optimize their own charging and discharging power through low charging and high discharging between EMOs and users to achieve arbitrage. Their equipment includes batteries (BT) and heat storage tanks (HST).

[0099] User: Introduce adjustable load, comprehensively consider energy purchase cost, energy comfort and carbon compensation, and build the fourth objective function with the goal of maximizing the comprehensive benefits on the user side. Users adjust their energy demand according to the function. The fourth objective function is:

[0100]

[0101] in, For user satisfaction, is the energy purchase payment function, is the carbon compensation function.

[0102] On the user side, a certain proportion of adjustable loads of electricity, heat and cooling energy is introduced, and the energy purchase cost, energy comfort and carbon compensation are comprehensively considered to adjust the energy demand to maximize the comprehensive benefits of the user side. The adjusted actual energy demand will in turn affect the benefits of each stakeholder.

[0103] The user's demand response strategy not only optimizes the supply and demand balance of electricity, heat and cold energy, but also reduces dependence on the external power grid by shaving peaks and filling valleys, thereby improving the system's operating economy and environmental benefits. Based on the master-slave game model, the user's load response will reversely affect the strategies of the energy supplier and the energy storage party, realizing dynamic collaborative optimization among multiple subjects. Through this mechanism, each subject optimizes its own strategy in demand response, achieves low-carbon economic goals, and promotes the maximization of the overall benefits of the regional energy system.

[0104] In the above multi-agent master-slave game module, based on the master-slave game theory, a multi-agent game optimization model is constructed with energy managers as leaders and energy supply operators, energy storage operators and users as followers. Each subject optimizes its own energy production and consumption strategy based on energy supply prices, energy market prices, reward and punishment ladder carbon trading and dual incentive comprehensive demand response strategies, that is, optimizes the power generation data of renewable energy devices and the consumption data of traditional energy devices, and ultimately maximizes the overall benefits of the system.

[0105] In the scheduling optimization module, the multi-agent master-slave game model constructed in the multi-agent master-slave game module is solved by combining the adaptive differential evolution algorithm with the solver toolbox. In this scheduling process, carbon trading costs, operating costs and demand response are comprehensively considered. By solving the first objective function of the upper leader, the second objective function, the third objective function and the fourth objective function of the lower follower are influenced and optimized. The strategy generates the optimal energy scheduling plan, and dynamically adjusts the output of each main equipment in the regional integrated energy system to optimize the scheduling decision of each subject, and coordinately adjusts its energy supply, energy storage and load strategies to minimize the overall operating cost of the system and reduce carbon emissions, and ultimately maximize the overall benefits of the system, including the dual goals of economic benefits and environmental benefits, to ensure the green, economic and sustainable development of the regional energy system.

[0106] In the above technical solution, in the dispatch optimization module, multi-objective optimization is performed on the dispatch of the regional integrated energy system based on source-load-storage complementarity to maximize the economic and environmental benefits of the system. The optimization objective function of the system is as follows:

[0107] minf=C op +C CET +C IDR -C ps ;

[0108] Among them, C op is the basic operating cost of the system, C CET is the cost of carbon trading, C IDR is the demand response cost, C ps For peak load benefits.

[0109] Under the multi-objective optimization framework of the above regional integrated energy system, the dispatch optimization module combines the characteristics of source-load-storage complementarity, and combines the adaptive differential evolution algorithm with the solver toolbox to build a multi-objective optimization model with the goal of maximizing economic and environmental benefits for the dispatch model of the regional integrated energy system based on source-load-storage complementarity. Specifically, this module takes energy managers as leaders, energy supply operators, energy storage operators and users as followers, and optimizes energy supply and demand balance and carbon emission management based on a reward-punishment ladder-type carbon trading mechanism and a dual-incentive comprehensive demand response strategy. On this basis, the system's optimization objective function clarifies the mathematical expression of system operating costs, carbon trading costs, demand response costs and peak-shaving benefits, providing theoretical support for the final dispatch plan.

[0110] In the above technical solution, the regional integrated energy system also includes a monitoring module, which monitors the operating data of energy managers, energy supply operators and users in the regional integrated energy system in real time, optimizes the scheduling in real time by using a multi-agent master-slave game model, and makes dynamic adjustments;

[0111] Operational data include: energy market prices of energy managers; energy supply prices of energy supply operators; energy storage operators’ energy storage charging costs, energy discharge income, and energy storage operation and maintenance costs; real-time carbon emissions and energy production of energy managers and energy supply operators; user load demand and real-time electricity prices.

[0112] Real-time monitoring and dynamic adjustment of energy production on the source side and demand on the load side ensures the balance of energy supply and demand and efficient operation of the system, further reduces pressure during peak hours, and optimizes energy consumption during off-peak hours.

[0113] Embodiment 2:

[0114] A ladder-type carbon trading and low-carbon economic optimization scheduling method based on master-slave game, according to Example 1, further gives a specific example, the present invention constructs a RIES structure containing four energy forms of cold, heat, electricity and gas and an energy flow diagram as shown in Figure 2 The steps shown include:

[0115] Step 1: Obtain data from each entity, including historical carbon emission data, energy supply prices, and energy market prices from energy managers and energy supply operators, and manage the equipment of each entity and control the output of equipment.

[0116] Step 1.1: Obtain data sets from the industrial scenario of the combined heat and power RIES in a certain area of ​​Anhui, including the power generation of renewable energy (such as photovoltaic and wind power), the fuel consumption of traditional energy (such as gas turbines, gas boilers, etc.), the load demand of electricity, heat and cold energy, and the historical carbon emission data of enterprises. Assume that the user's adjustable electric load accounts for 20% of the total demand electric load, and the adjustable heat and cold load accounts for 10% of the total demand heat and cold load. The optimal scheduling time is 24 hours, and the step length is 1 hour.

[0117] Step 1.2: Preprocess the sample data:

[0118] Eliminate outliers: Eliminate values ​​that deviate too much from the actual values. That is, compare the carbon emission data of the current month with the carbon emission data of the previous month and the same month of the previous year. If the current data point is significantly higher or lower than these two reference values, it may be regarded as an outlier and be eliminated.

[0119] For outlier data points, the data from the previous month and the same month of the previous year are used to fill the gaps, which is called interpolation.

[0120] Data filling: Use the interpolation method based on historical data to fill in the data to ensure data integrity. The specific filling formula is:

[0121]

[0122] Among them, P t Represents the carbon emission value at the current moment, P t-1 and P t-12 Represents the carbon emission values ​​of the previous month and the same month of the previous year respectively.

[0123] The cleaned and filled data are standardized. The purpose of standardization is to ensure that different types of energy consumption and carbon emission data are comparable on the same scale. The standardization formula is as follows:

[0124]

[0125] Among them, X is the original value in the data, X min and X max are the minimum and maximum values ​​in the data set, respectively. The standardized data range is [0,1], which is convenient for subsequent analysis.

[0126] Step 1.3: Build a regional integrated energy system based on source-load-storage synergy to manage the output of each main device and control equipment;

[0127] Step 2: Introduce a reward-and-penalty ladder-type carbon trading mechanism. Based on historical carbon emission data and energy supply prices, calculate the carbon emission quotas of energy managers and energy supply operators and compare them with actual carbon emissions. Based on the comparison results, charge energy managers and energy supply operators for excess emissions or issue carbon compensation incentives to obtain the carbon emission costs of energy managers and energy supply operators.

[0128] Step 3: Introduce a dual incentive integrated demand response mechanism. According to the carbon emission costs of energy managers and energy supply operators, generate carbon compensation signals for carbon trading incentives or penalties. Users adjust electricity, heat and cooling load demands based on carbon compensation signals and energy market prices to respond to demand. Combined with the multi-agent master-slave game model, implement a dual incentive integrated demand response mechanism based on price incentives and carbon compensation. The RIES multi-agent master-slave game interaction framework is as follows: Figure 2 shown.

[0129] Based on the carbon emission cost and demand response of the reward-penalty ladder carbon trading mechanism and the dual incentive comprehensive demand response mechanism, the first objective function is constructed with the goal of maximizing the profit of the energy manager as the upper leader, and the lower followers take the maximization of the profit of the energy supply operator, the maximization of the economic benefit of the energy storage operator, and the maximization of the economic benefit and satisfaction of the user as the second, third and fourth objective functions, respectively, to establish a multi-agent master-slave game model;

[0130] According to the combination of adaptive differential evolution algorithm and solver toolbox, the optimization strategy of the first objective function is solved, the strategies of the second objective function, the third objective function and the fourth objective function are coordinated and optimized to generate the optimal energy scheduling plan, and dynamically adjust the output of each main equipment in the regional integrated energy system.

[0131] Embodiment 3:

[0132] According to Example 1 and Example 2, in order to illustrate the economic and environmental benefits of multi-agent game optimization scheduling considering the dual incentive IDR strategy and the reward and punishment ladder carbon trading mechanism, the following four strategies are designed for comparison with the strategy in this article.

[0133] Scenario 1 considers EMO, EGO and users, and price-based IDR.

[0134] Scenario 2 considers EMO, EGO, ESO and users, but does not consider price-based IDR.

[0135] Scenario 3 considers EMO, EGO, ESO and users, and price-based IDR.

[0136] Scenario 4 considers EMO, EGO, ESO and users, and considers conventional carbon trading mechanism and dual incentive IDR strategy.

[0137] Results and comparative analysis of different strategies:

[0138] The results of the five scenarios are shown in Tables 1 and 2.

[0139] Table 1 Profits of each entity under different scenarios

[0140]

[0141] Table 2 Carbon emissions of EMO and EGO under different scenarios

[0142]

[0143]

[0144] The analysis is as follows:

[0145] In scenarios 1 and 3, as shown in Table 1, the benefits of EMO, EGO and users in scenario 3 increased by 1.87%, 5.28% and 2.65% respectively compared with those in scenario 1. Although the addition of energy storage devices in scenario 3 will occupy a small share of user energy purchases of EMO, ESO can not only relieve the output pressure of EGO equipment through low charging and high discharging, but also reduce the cost of direct interaction between EMO and the external power grid at load peaks, and enable users to obtain more favorable energy purchase prices than EMO, reducing users' energy purchase costs, thus reflecting the advantages of joining ESO.

[0146] In scenarios 2 and 3, as shown in Tables 1 and 2, the benefits of EGO and users in scenario 3 increased by 8.4% and 6.48% respectively compared with scenario 2, and the system carbon emissions decreased by 6.98%. Because scenario 3 takes into account the price-based IDR, it effectively smoothes the peak-to-valley difference of user load, reduces the user's energy purchase cost and the carbon emissions generated by EMO's purchased electricity. However, since the actual load of users in scenario 2 did not shift, EMO did not need to adjust its own energy price according to the user's load, and its energy sales price was close to the price of the large power grid, so the benefits of EMO in scenario 2 will increase slightly compared with scenario 3.

[0147] In scenarios 3 and 4, as shown in Tables 1 and 2, the profits of EGO and users in scenario 4 increased by 10.01% and 8.29% respectively compared with scenario 3, and the total carbon emissions of the system decreased by 13.76%. Since EMO will bear the carbon trading costs generated by purchasing electricity from outside, the profit of EMO decreased by 2.55% compared with scenario 3, but EGO actively increased the output of equipment after obtaining carbon trading income, reducing the cost of purchasing electricity from the external power grid and reducing carbon emissions.

[0148] Finally, compare scenario 4 and the method proposed in the present invention. As can be seen from Tables 1 and 2, the profit of EGO and the total carbon emissions of the system in the strategy of this paper increased by 3.32% and decreased by 2.11% respectively compared with scenario 4. This is because the carbon emissions generated by EGO are less than the prescribed carbon emission allocation, so it is rewarded in the carbon trading market. And due to the increase in the output of EGO equipment, EMO indirectly reduces the amount of electricity purchased from the external power grid and carbon emissions. The comparison shows that the reward and punishment ladder carbon trading cost model adopted by the present invention can better improve the emission reduction effect of the system.

[0149] Analysis of optimized scheduling results:

[0150] The EMO pricing strategy after Stackelberg game optimization is as follows: Figures 4 to 6 As shown in the figure, the dispatch results of electricity, heat and cold energy after game optimization are as follows: Figure 7 , Figure 8 and Fig. 9 As shown. Considering environmental protection, EMO gives priority to absorbing renewable energy wind turbines (WT). During the period of 0:00-7:00, the electric load and cooling load are low, and the electricity price is in the valley. The electric load is mainly provided by the wind turbine (WT), and the shortfall is supplemented by purchased electricity. The battery (BT) is charged at this time to store excess electricity and provide power reserves for high-load periods. The gas turbine (GT) has a low output, and the thermal energy demand is mainly met by price incentives for gas boilers (GB) and waste heat boilers (WHB). The heat storage tank (HST) also performs heat storage operations at this time. The cooling load demand is low, mainly provided by ice storage air conditioners (ISAC).

[0151] During the periods from 7:00 to 12:00 and from 14:00 to 17:00, as the electric load and cooling load gradually increased, the system completely absorbed the output of the wind turbine (WT) and photovoltaic (PV), and the output of the gas turbine (GT) increased, but the system still relied on some purchased electricity to meet the power demand. The battery (BT) continued to charge and was ready to be discharged during the peak load period. The cooling load increased with the increase in temperature, and the ice storage air conditioner (ISAC) and the gas turbine refrigeration equipment worked together to meet the demand. The demand for thermal energy also increased over time, and the gas boiler (GB) and waste heat boiler (WHB) continued to output, while the heat storage tank (HST) provided support for thermal energy supply through low charging and high discharging operations during high load periods.

[0152] During the period from 12:00 to 18:00, the electric load reaches its peak, and the system supplements the power shortage by discharging the battery (BT) and purchasing electricity. The cooling load reaches its peak at this time, and the output of the gas turbine cooling and ice storage air conditioner (ISAC) increases to ensure that the cooling energy demand is fully met. The thermal load is also maintained at a high level, and the output of the gas boiler (GB) and the waste heat boiler (WHB) increases, and continues to be adjusted through the heat storage tank (HST). At this time, the system dispatch must not only ensure that the user load is met, but also optimize the utilization rate of renewable energy.

[0153] During the period from 18:00 to 23:00, the electric load and cooling load gradually decreased, and the battery (BT) continued to discharge, reducing the system's dependence on purchased electricity. The load of the ice storage air conditioning (ISAC) system gradually decreased, and the gas turbine cooling also decreased accordingly. Although the demand for thermal energy has decreased, it still relies on the output of the gas boiler (GB) and the waste heat boiler (WHB) to balance it. The heat storage tank (HST) continues to store heat at low load to jointly meet the heat load demand of the next period.

[0154] Therefore, the ladder-type carbon trading mechanism and low-carbon economic optimization scheduling model based on multi-agent master-slave game constructed by the present invention can better tap the peak-shaving potential of incentive-based demand response, effectively reduce the operating pressure of related units, and realize low-carbon economic scheduling of regional energy systems.

[0155] Embodiment 4:

[0156] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0157] Embodiment 5:

[0158] A computer program product comprises a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0159] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A ladder-type carbon trading and low-carbon economic optimization scheduling system based on master-slave game, characterized in that: include: The regional integrated energy system is used to build a regional integrated energy system based on source-load-storage complementarity and obtain data in the regional integrated energy system, including historical carbon emission data, energy supply prices, and energy market prices of energy managers and energy supply operators; The multi-agent master-slave game module is used to construct the upper-level leaders in the multi-agent master-slave game model with the goal of maximizing the profits of energy managers, and to construct the lower-level followers in the multi-agent master-slave game model with the goals of maximizing the profits of energy supply operators, maximizing the economic benefits of energy storage operators, and maximizing the economic benefits and satisfaction of users; The reward-penalty ladder carbon trading module is used to obtain the carbon emission costs of energy managers and energy supply operators based on historical carbon emission data and energy supply prices; The dual incentive integrated demand response module generates carbon compensation signals for carbon trading incentives or penalties based on the carbon emission costs of energy managers and energy supply operators. Users adjust the demand for power load, thermal load and cooling load based on the carbon compensation signals and energy market prices to obtain demand response costs. The scheduling optimization module is based on the reward and punishment ladder carbon trading module and the dual incentive comprehensive demand response module. It aims to maximize economic and environmental benefits, and establishes a multi-objective optimization function for the scheduling of regional integrated energy systems based on source-load-storage complementarity. With the constraints of carbon emission costs and demand response costs, the adaptive differential evolution algorithm is used. The upper leaders and lower followers in the multi-agent master-slave game model optimize the regional integrated energy system with source-load-storage complementarity, and generate the optimal energy scheduling plan based on the optimized regional integrated energy system with source-load-storage complementarity.

2. The ladder-type carbon trading and low-carbon economy optimization scheduling system based on master-slave game according to claim 1 is characterized in that: In the regional integrated energy system, the main equipment includes: the source side is the energy manager and energy supply operator, which includes renewable energy devices and traditional energy devices; the energy storage side is the energy storage operator, and the equipment includes batteries and heat storage tanks; the load side is the user. Through the equipment on the source side, energy storage side and load side, a regional integrated energy system with complementary sources, loads and storage is constructed. The data collected by each main equipment is: Obtain historical carbon emission data, power generation data of renewable energy devices, and consumption data of traditional energy devices; Obtain user's load demand data on electricity, heat and cooling energy; Obtain energy supply prices published by energy supply operators and energy market prices published by energy managers; Obtain the energy storage operator's energy storage charging costs, energy discharge benefits, and energy storage operation and maintenance costs.

3. The ladder-type carbon trading and low-carbon economy optimization scheduling system based on master-slave game according to claim 2 is characterized in that: In the regional integrated energy system, energy storage operators are used to balance short-term load fluctuations to ensure that the demand on the load side is met. The power generation data of renewable energy devices and the consumption data of traditional energy are used to obtain the total power output of electricity and heat. The power output formula of electricity and heat during system scheduling is as follows: P(t)=P PV (t)+P WT (t)+P CHP (t)+P GF (t)+P Grid (t); H(t)=H GB (t)+H GF (t)+H CHP (t)+H Grid (t); Where P(t) and H(t) represent the total power output of electricity and heat, respectively. PV (t), P WT (t), P CHP (t), P GF (t), P Grid (t) are the power output of photovoltaic, wind power, cogeneration device, gas turbine and power grid, i.e. the power generation data of renewable energy devices; H GB (t), H GF (t), H CHP (t), H Grid (t) are the thermal power outputs of boilers, gas turbines, cogeneration units and heat networks, i.e., the consumption data of traditional energy.

4. The ladder-type carbon trading and low-carbon economy optimization scheduling system based on master-slave game according to claim 1 is characterized in that: In the reward-penalty ladder carbon trading module, the carbon emission quota is calculated based on the historical carbon emission data of energy managers and energy supply operators. The specific carbon emission quotas of energy managers and energy supply operators are calculated as follows: in, Carbon emission credits for outsourced electricity to energy managers, The total carbon emission quota of the energy supply operator, δ e and δ h are the carbon emission factors per unit of electricity and per unit of heat, P buy (t) is the amount of electricity purchased from the external power grid, H GB (t) is the heat output of the gas boiler, H WHB , Q AR (t), P GT (t) The heating, cooling and power generation capacity of waste heat boilers, absorption chillers and gas turbines respectively; If the actual carbon emissions of the subject are lower than the quota, it will receive carbon trading incentives. If the subject exceeds the quota, it will pay excess fees and receive carbon trading penalties. The specific carbon trading incentives and penalties are calculated based on the following formula: Among them, F c is the carbon trading cost, including carbon trading incentives and penalties, D p is the actual carbon emissions, D c is the carbon emission quota, λ1 and λ2 are the reward and penalty coefficients respectively, and μ is the benchmark unit cost of carbon trading.

5. The ladder-type carbon trading and low-carbon economy optimization scheduling system based on master-slave game according to claim 4 is characterized in that: In the dual incentive integrated demand response module, carbon compensation signals are generated based on carbon trading incentives or penalties from energy managers and energy supply operators. Combined with the real-time energy market prices provided by energy managers, users adjust their electricity, heating and cooling load demands. Among them, the carbon compensation signal reflects the reward and punishment ladder composed of carbon trading incentives and penalties in the reward and punishment ladder carbon trading module, as well as the implementation of carbon emission quotas. The cost calculation formula for incentive demand response is as follows: Among them, C IDR is the demand response cost, a i and b i are the compensation coefficients for power load and thermal load, respectively, Li (t) and H Li (t) are the electricity and heat loads participating in demand response, respectively. i represents the load adjustment category. i=1 represents load transfer, i=2 represents load reduction, and t represents the time.

6. The ladder-type carbon trading and low-carbon economy optimization scheduling system based on master-slave game according to claim 1 is characterized in that: In the multi-agent master-slave game optimization module, the objective function of each agent is as follows: With the energy manager as the upper-level leader, the first objective function is constructed. Specifically, the first objective function is constructed to maximize its own net profit through the price difference between electricity sales and electricity purchases, that is, the price difference between the energy supply price of the energy supply operator and the energy market price released by the energy manager, as well as the carbon trading cost and demand response incentives: Wherein, the superscript t represents the period t; and The benefits of energy sales to users and energy storage operators respectively; and are the costs of energy managers purchasing energy from energy supply operators and the grid respectively; Penalty costs for interruptions of heating or cooling; and ε1 are the user's carbon trading incentive and penalty functions, and the weight coefficient of the energy manager's carbon trading incentive and penalty; is the carbon trading cost of outsourcing electricity to the energy manager; T is the total time period; With energy supply operators, energy storage operators and users as lower-level followers, the second objective function, the third objective function and the fourth objective function of the lower level are constructed; Energy supply operators: Based on the energy market price set by the energy manager, with the goal of maximizing energy sales revenue and minimizing fuel costs and carbon trading costs, optimize the output of controllable equipment and build a second objective function with the goal of maximizing the energy supply operator's own profits: in, The energy supply operator benefits from energy sales; ε2 represents the weight coefficient of the carbon compensation borne by the energy supply operator; represents the carbon trading cost of energy supply operators; represents the fuel cost of the CHP unit and gas boiler; represents the start-up and shutdown cost of the gas turbine; Energy storage operators: Energy storage operators achieve arbitrage through valley charging and peak discharging, and build a third objective function with the goal of maximizing the economic benefits of energy storage operators: in, They are the charging cost and discharging benefit of energy storage respectively; Operation and maintenance costs for energy storage operators; User: Introduce adjustable load, comprehensively consider energy purchase cost, energy comfort and carbon compensation, and build the fourth objective function with the goal of maximizing the comprehensive benefits on the user side. Users adjust their energy demand according to the function. The fourth objective function is: in, For user satisfaction, is the energy purchase payment function, is the carbon compensation function.

7. The ladder-type carbon trading and low-carbon economy optimization scheduling system based on master-slave game according to claim 1 is characterized in that: In the dispatch optimization module, the multi-objective optimization of regional integrated energy system dispatch based on source-load-storage complementarity is carried out to maximize the economic and environmental benefits of the system. The optimization objective function of the system is as follows: minf=C op +C CET +C IDR -C ps ; Among them, C op is the basic operating cost of the system, C CET is the cost of carbon trading, C IDR is the demand response cost, C ps For peak load benefits.

8. A ladder-type carbon trading and low-carbon economic optimization scheduling method based on master-slave game, characterized in that: The following steps are involved: Build a regional integrated energy system based on source-load-storage complementarity, and obtain data in the regional integrated energy system, including historical carbon emission data, energy supply prices, and energy market prices of energy managers and energy supply operators; The upper-level leaders in the multi-agent master-slave game model are constructed with the goal of maximizing the profits of energy managers, and the lower-level followers in the multi-agent master-slave game model are constructed with the goals of maximizing the profits of energy supply operators, maximizing the economic benefits of energy storage operators, and maximizing the economic benefits and satisfaction of users. Based on historical carbon emission data and energy supply prices, the carbon emission costs of energy managers and energy supply operators are obtained to build a reward-penalty ladder carbon trading mechanism; Generate carbon compensation signals for carbon trading incentives or penalties based on the carbon emission costs of energy managers and energy supply operators. Users adjust the demand for power load, thermal load and cooling load based on the carbon compensation signals and energy market prices, obtain demand response costs, and build a dual incentive comprehensive demand response mechanism. Based on the reward-and-punishment ladder-type carbon trading mechanism and the dual-incentive comprehensive demand response mechanism, with the goal of maximizing economic and environmental benefits, a multi-objective optimization function for the scheduling of regional integrated energy systems based on source-load-storage complementarity is established. With the constraints of carbon emission costs and demand response costs, an adaptive differential evolution algorithm is used, and the upper leaders and lower followers in the multi-agent master-slave game model optimize the regional integrated energy system with source-load-storage complementarity. According to the optimized regional integrated energy system with source-load-storage complementarity, the optimal energy scheduling plan is generated.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in claim 8 are implemented.

Images