Carbon emission management and control system based on regional comprehensive energy coupling characteristics

Abstract

The invention provides a carbon emission management and control system based on regional comprehensive energy coupling characteristics, which relates to the technical field of energy coordination, and comprises a central processing system, an information acquisition module, an energy concentrator module and a distribution regulation module, the information acquisition module is used for acquiring the electric energy consumption load of the specified area in different time periods and converting the electric energy into electric power in a centralized manner to supply energy to the specified area; based on the concept of an energy concentrator, electric energy consumption loads of a designated area in different time periods and cost consumption and energy supply proportions of a plurality of energy sources in an energy concentrator module are analyzed, a dynamic electricity price mechanism of the designated area is combined, and under regional electric loads in different time periods, the lowest carbon emission is taken as a benchmark, and the energy consumption of the designated area is calculated. Energy supply proportions of different energy sources are switched, targets of efficiency maximization, cost minimization and carbon emission index minimization are considered, a scheduling scheme is provided, and the concept of low carbon and environmental protection is combined.

Description

Technical Field

[0001] The present invention relates to the field of energy coordination technology, and in particular to a carbon emission control system based on regional comprehensive energy coupling characteristics. Background Art

[0002] An energy hub is a device that collects energy from multiple energy sources and concentrates it in a single location. The technology can be used to collect energy from energy sources such as solar panels, wind turbines, turbine generators, and thermal power generation, and then convert it into electricity to meet the energy needs of homes, businesses, and even entire cities. The principle of an energy hub is to use the superposition effect of multiple energy sources. This technology takes advantage of the physical properties of energy, such as energy cannot be destroyed, but can be converted into other forms. Therefore, by integrating the energy generated by multiple energy sources, a more powerful energy output can be generated. Energy hubs usually use efficient electronic components, such as switching capacitors and rectifiers, to manage the energy output of multiple energy sources. These components are able to convert energy into appropriate voltage and current for output to the power grid or directly powering devices. In addition, energy hubs can also use intelligent control systems to optimize the use and distribution of energy to ensure that waste and losses are minimized. In general, energy hub technology offers great potential for the development of sustainable energy. Through the application of energy hubs, people can use multiple energy sources more efficiently, thereby reducing dependence on limited resources and reducing the impact of the environment on the ecology. Today, as energy crisis and climate warming issues receive more and more attention, low-carbon and environmental protection have become one of the indicators that must be achieved in energy application. In the application of energy hubs, the ratio of clean energy to non-clean energy is generally fixed, and the application distribution of various energy sources is also fixed. The power load in different time periods in the power supply area is different. Carbon emissions can be changed by adjusting the energy coupling ratio. For example, when the power load is small, clean energy is used as the main distribution ratio. When the load is large, thermal power generation with more work is used as the main energy distribution ratio. However, there is a lack of research in this direction in the prior art. Therefore, the present invention proposes a carbon emission control system based on regional comprehensive energy coupling characteristics to solve the problems existing in the prior art. Summary of the invention

[0003] In response to the above problems, the present invention proposes a carbon emission control system based on the regional comprehensive energy coupling characteristics. The carbon emission control system based on the regional comprehensive energy coupling characteristics takes the lowest carbon emissions as the benchmark, switches the energy supply proportions of different energy sources, considers the goals of maximizing efficiency, minimizing costs, minimizing carbon emission indicators, etc., and proposes a scheduling plan to implement the concept of low-carbon and environmental protection.

[0004] To achieve the purpose of the present invention, the present invention is implemented through the following technical solutions: a carbon emission control system based on regional comprehensive energy coupling characteristics, including a central processing system, an information collection module, an energy hub module and a distribution and adjustment module, the energy hub is used to collect energy from multiple energy sources, and centrally convert it into electricity to supply energy to a designated area, the information collection module is used to collect the power consumption load of the designated area in different time periods, and collect the proportion data of multiple energy sources in the energy hub module; The central processing system is used to analyze the power consumption load of the designated area in different time periods, as well as the proportion data of multiple energy sources in the energy hub module, calculate the cost consumption and energy supply proportion of different energy sources, and combine the dynamic electricity price mechanism of the designated area to output switching plans for the energy supply proportions of different energy sources based on the lowest carbon emissions under the power load in different time periods. The allocation and adjustment module outputs different switching plans to the energy hub module based on the time period for practical application.

[0005] A further improvement is that the information collection module includes a regional electricity consumption end collection module and an energy source collection module. The regional electricity consumption end collection module is used to connect to the electricity consumption control center of the specified area, firstly collect the monthly electricity load changes in the area in the year in units of years as the main set, then collect the daily electricity load changes in the area in the month in units of months as the secondary set, then collect the electricity load changes in different time periods of each day as subsets, merge the subsets into the corresponding secondary sets, merge the secondary sets into the corresponding main sets, and construct a regional electricity load change collection package.

[0006] A further improvement is that the energy source collection module is used to connect to the energy hub module to collect the proportion data of multiple energy sources in the energy hub module, including the change in the proportion data of multiple energy sources in different months, as a dynamic set.

[0007] A further improvement is that the energy hub module collects energy from multiple energy sources, which include various clean energy and non-clean energy, and uses the superposition effect of multiple energy sources to generate energy output and supply electricity to a designated area.

[0008] A further improvement is that the central processing system includes a proportional allocation model module, a calculation and analysis module and a big data database. The big data database has a built-in search framework, which retrieves and stores various clean energy and non-clean energy costs, power supply and carbon emissions, as well as a dynamic electricity price mechanism for designated areas based on the search framework.

[0009] A further improvement is that the proportion allocation module constructs an energy source allocation model according to the changes in the proportion data of multiple energy sources in different months, and based on the model and the data in the big data database, dynamically simulates the change plans of different energy source proportions under specified electricity demand, as well as the power supply costs and carbon emissions under different plans.

[0010] Further improvement is that: the calculation and analysis module includes an algorithm module and a solution output module. The algorithm module is based on the energy load algorithm, the regional power load change amount set package and the energy source allocation model. Under the power consumption load in different time periods in the specified area, according to the change schemes under different energy source proportions, the power supply cost and carbon emissions under different schemes are calculated, and the lowest carbon emission is used as the standard to obtain the optimal multiple change schemes. 1 clean energy a% + 2 clean energy b% + 1 non-clean energy c% + 2 non-clean energy d% = energy supply demand A, b, c, and d represent the proportions of different energy sources, and the proportions are allocated based on the lowest carbon emissions.

[0011] Further improvements are: the algorithm module combines the dynamic electricity price mechanism of the specified area to calculate the ratio of dynamic electricity price to power supply cost under the optimal multiple schemes, as well as the ratio of dynamic electricity price to power supply cost under other schemes, and then screens again, taking power supply cost, carbon emissions, and dynamic electricity price as equilibrium targets, and again screens out the scheme with the lowest carbon emissions and the economic match between power supply cost and real-time electricity price. Power supply cost: dynamic electricity price = coefficient B Coefficient B is the economic matching coefficient between power supply cost and real-time electricity price, which reflects the income level of the functional enterprise based on the real-time electricity price.

[0012] A further improvement is that the solution output module is used to summarize the optimal solution screened by the algorithm module into an electronic draft, output it to the allocation and adjustment module, and display it based on the management terminal. The solution output module is used to record other solutions simultaneously. When the optimal solution cannot be implemented, the next-level optimization solution is used as a substitute.

[0013] Further improvements are: the distribution adjustment module connects the solution output module and the energy hub module, and the distribution adjustment module is used to convert the optimal solution into a control instruction and send it to the energy hub module, and the distribution adjustment module has a built-in log module, which records each adjustment according to the timestamp and provides a log retrieval function.

[0014] The beneficial effects of the present invention are: 1. The present invention is based on the concept of energy hub, analyzes the power consumption load of the designated area in different time periods, as well as the cost consumption and energy supply proportion of multiple energy sources in the energy hub module, and combines the dynamic electricity price mechanism of the designated area. Under the regional power load in different time periods, the energy supply proportion of different energy sources is switched based on the lowest carbon emissions. Considering the goals of maximizing efficiency, minimizing costs, minimizing carbon emissions, etc., a scheduling plan is proposed to implement the concept of low-carbon and environmental protection.

[0015] 2. The present invention collects the change in regional electric load in different time periods as an analysis set, and collects the change in the proportion of multiple energy sources at different times as a dynamic set, so as to construct an energy source allocation model, analyze the change plans under different energy source proportions under electricity demand in different time periods, and the power supply cost and carbon emissions under different plans, and uses power supply cost, carbon emissions, and dynamic electricity prices as balance targets to analyze the optimal plans for different time periods, dynamically adapt, so that the three-level regional comprehensive energy is coordinated with energy efficiency, environmental protection, and economy, to achieve high-energy, economical, and low-carbon operation of the system, and help achieve the dual carbon goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a composition diagram of the present invention. Implementation

[0017] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention. Example

[0018] according to Figure 1 As shown, this embodiment proposes a carbon emission control system based on regional comprehensive energy coupling characteristics, including a central processing system, an information collection module, an energy hub module and a distribution and adjustment module. The energy hub is used to collect energy from multiple energy sources and centrally convert it into electricity to supply energy to a designated area. The information collection module is used to collect the power consumption load of the designated area in different time periods, and collect the proportion data of multiple energy sources in the energy hub module; The central processing system is used to analyze the power consumption load of the designated area in different time periods, as well as the proportion data of multiple energy sources in the energy hub module, calculate the cost consumption and energy supply proportion of different energy sources, and combine the dynamic electricity price mechanism of the designated area. Under the power load in different time periods, based on the lowest carbon emissions, output the switching scheme of the energy supply proportion of different energy sources. The allocation and adjustment module outputs different switching schemes to the energy hub module based on the time period for practical application. The present invention is based on the concept of energy hub, analyzes the power consumption load of the designated area in different time periods, as well as the cost consumption and energy supply proportion of multiple energy sources in the energy hub module, and combines the dynamic electricity price mechanism of the designated area. Under the regional power load in different time periods, based on the lowest carbon emissions, the energy supply proportion of different energy sources is switched. Considering the goals of maximizing efficiency, minimizing costs, minimizing carbon emission indicators, etc., a scheduling scheme is proposed to combine the concept of low-carbon and environmental protection.

[0019] The information collection module includes a regional power consumption end collection module and an energy source collection module. The regional power consumption end collection module is used to connect to the power consumption control center of the specified area, firstly collect the monthly power load changes of the area in the year in units of years as the main set, then collect the daily power load changes of the area in the month in units of months as the secondary set, then collect the power load changes of different time periods in each day as subsets, merge the subsets into the corresponding secondary sets, merge the secondary sets into the corresponding main sets, and construct a regional power load change collection package. The energy source collection module is used to connect to the energy hub module, collect the proportion data of multiple energy sources in the energy hub module, including the changes in the proportion data of multiple energy sources in different months, and use this as a dynamic set. The changes in the electric load in the area in different time periods are collected as an analysis set, and the changes in the proportion of multiple energy sources at different times are collected as a dynamic set. This is used to build an energy source allocation model, analyze the change plans under different energy source proportions under electricity demand in different time periods, and the power supply costs and carbon emissions under different plans, making the data more diversified.

[0020] The energy hub module collects energy from multiple energy sources, including various clean energy and non-clean energy, and uses the superposition effect of multiple energy sources to generate energy output and supply electricity to designated areas. This makes it convenient to explore new models and promote steady transformation through coordinated development. Promote the resource reserves and development and construction of distributed wind power, distributed photovoltaics, small and medium-sized wind and solar, and wind, solar, and thermal storage complementary projects, innovate multi-energy complementary energy models, and coal-fired power plants, especially urban coal-fired power plants, can carry out a variety of services, explore local aggregation with substations, energy storage power stations, distributed photovoltaic power stations and data centers, and form a "one-stop platform" for comprehensive energy services for cities, parks, communities and residents.

[0021] The central processing system includes a proportional allocation model module, a calculation and analysis module, and a big data database. The big data database has a built-in search framework, which retrieves and stores various clean energy and non-clean energy costs, power supply and carbon emissions, as well as dynamic electricity price mechanisms in designated areas based on the search framework. The search framework is deployed using the ElasticSearch+Logstash+Kibana framework, and the ElasticSearch search server is used to complete a distributed multi-user full-text search engine for data management. The search framework includes the Logstash log analysis engine and the Kibana visualization platform to implement a log analysis and data visualization platform, which is customized for business needs and assists in the development of business processes.

[0022] The proportion allocation module constructs an energy source allocation model according to the change in the proportion of multiple energy sources in different months. Based on the model and the data in the big data database, it dynamically simulates the change schemes under different energy source proportions under the specified electricity demand, and the power supply cost and carbon emissions under different schemes. The energy source allocation model of the comprehensive energy system is calculated by the alternating direction multiplier method to construct a complementary evaluation system; including: overall complementary indicators, specifically: energy supply cost complementary ratio, power source cost complementary ratio, carbon emission complementary ratio, and regional maximum carbon emission complementary ratio.

[0023] The calculation and analysis module includes an algorithm module and a solution output module. The algorithm module is based on an energy load algorithm, a regional power load change amount set package and an energy source allocation model. Under the power consumption load in different time periods in a specified area, according to the change schemes under different energy source proportions, the power supply cost and carbon emissions under different schemes are calculated, and the optimal multiple change schemes are obtained with the lowest carbon emissions as the standard. 1 clean energy a% + 2 clean energy b% + 1 non-clean energy c% + 2 non-clean energy d% = energy supply demand A, b, c, and d represent the proportions of different energy sources, and the proportions are allocated based on the lowest carbon emissions.

[0024] The algorithm module combines the dynamic electricity price mechanism in the specified area to calculate the ratio of dynamic electricity price to power supply cost under the optimal multiple schemes, as well as the ratio of dynamic electricity price to power supply cost under other schemes, and then screens again, taking power supply cost, carbon emissions, and dynamic electricity price as the equilibrium target, and screens again the scheme with the lowest carbon emissions and the economic match between power supply cost and real-time electricity price. Power supply cost: dynamic electricity price = coefficient B Coefficient B is the economic matching coefficient between power supply cost and real-time electricity price, which reflects the income level of functional enterprises based on real-time electricity price. With power supply cost, carbon emissions and dynamic electricity price as the equilibrium target, the optimal solution in different time periods is analyzed and dynamically adapted, so that the three-level regional comprehensive energy is coordinated, with energy efficiency, environmental protection and economy, and the system can be operated in a high-energy, economical and low-carbon manner, helping to achieve the dual carbon goals.

[0025] The solution output module is used to summarize the optimal solution selected by the algorithm module into an electronic draft, output it to the allocation and adjustment module, and display it based on the management terminal. The solution output module records other solutions synchronously. When the optimal solution cannot be implemented, the next-level optimization solution is used as a substitute. Ensure the normal output of the solution to avoid the situation where there is no solution to rely on. Example

[0026] according to Figure 1 As shown, this embodiment proposes a carbon emission control system based on regional comprehensive energy coupling characteristics, including a central processing system, an information collection module, an energy hub module and a distribution and adjustment module. The energy hub is used to collect energy from multiple energy sources and centrally convert it into electricity to supply energy to a designated area. The information collection module is used to collect the power consumption load of the designated area in different time periods, and collect the proportion data of multiple energy sources in the energy hub module; The central processing system is used to analyze the power consumption load of the designated area in different time periods, as well as the proportion data of multiple energy sources in the energy hub module, calculate the cost consumption and energy supply proportion of different energy sources, and combine the dynamic electricity price mechanism of the designated area. Under the power load in different time periods, based on the lowest carbon emissions, output the switching scheme of the energy supply proportion of different energy sources. The allocation and adjustment module outputs different switching schemes to the energy hub module based on the time period for practical application. The present invention is based on the concept of energy hub, analyzes the power consumption load of the designated area in different time periods, as well as the cost consumption and energy supply proportion of multiple energy sources in the energy hub module, and combines the dynamic electricity price mechanism of the designated area. Under the regional power load in different time periods, based on the lowest carbon emissions, the energy supply proportion of different energy sources is switched. Considering the goals of maximizing efficiency, minimizing costs, minimizing carbon emission indicators, etc., a scheduling scheme is proposed to combine the concept of low-carbon and environmental protection.

[0027] The calculation and analysis module includes an algorithm module and a solution output module. The algorithm module is based on an energy load algorithm, a regional power load change amount set package and an energy source allocation model. Under the power consumption load in different time periods in the specified area, according to the change schemes under different energy source ratios, the power supply cost and carbon emissions under different schemes are calculated, and the lowest carbon emission is used as the standard to obtain the optimal multiple change schemes. The algorithm module combines the dynamic electricity price mechanism in the specified area to calculate the ratio of dynamic electricity price and power supply cost under the optimal multiple schemes, as well as the ratio of dynamic electricity price and power supply cost under other schemes, and then screens again. With power supply cost, carbon emissions and dynamic electricity price as the equilibrium target, the scheme with the lowest carbon emission and economic matching of power supply cost and real-time electricity price is screened again. The solution output module is used to summarize the optimal solution screened by the algorithm module into an electronic draft, output it to the allocation and adjustment module, and display it based on the management terminal. The solution output module records other solutions synchronously. When the optimal solution cannot be implemented, the next-level optimization solution is used as a substitute. Ensure the normal output of the solution to avoid the situation where there is no solution to rely on.

[0028] The distribution and adjustment module is connected to the solution output module and the energy hub module, and is used to convert the optimal solution into a control instruction and send it to the energy hub module. The distribution and adjustment module has a built-in log module, which records each adjustment according to the timestamp and provides a log retrieval function, which is convenient for management personnel to query logs and troubleshoot.

[0029] The carbon emission control system based on regional comprehensive energy coupling characteristics is based on the concept of energy hub, analyzes the power consumption load of the designated area in different time periods, and the cost consumption and energy supply proportion of multiple energy sources in the energy hub module, and combines the dynamic electricity price mechanism of the designated area. Under the regional power load in different time periods, the lowest carbon emission is used as the benchmark to switch the energy supply proportion of different energy sources, considering the goals of maximizing efficiency, minimizing costs, and minimizing carbon emission indicators, and proposes a scheduling scheme to combine the concept of low-carbon and environmental protection. In addition, the present invention collects the change in the power load of the region in different time periods as an analysis set, collects the change in the proportion of multiple energy sources at different times as a dynamic set, and constructs an energy source allocation model to analyze the change schemes under different energy source proportions under the power demand in different time periods, and the power supply cost and carbon emissions under different schemes, and uses the power supply cost, carbon emissions, and dynamic electricity price as the balance target, analyzes the optimal scheme for different time periods, and dynamically adapts, so that the three-level regional comprehensive energy is coordinated, with energy efficiency, environmental protection, and economy, to achieve high-energy, economic, and low-carbon operation of the system, and help achieve the dual carbon goals.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A carbon emission control system based on regional comprehensive energy coupling characteristics, including a central processing system, an information collection module, an energy hub module and a distribution and adjustment module, characterized in that: The energy hub is used to collect energy from multiple energy sources and convert it into electricity to supply energy to a designated area. The information collection module is used to collect the power consumption load of the designated area in different time periods and collect the proportion data of multiple energy sources in the energy hub module. The central processing system is used to analyze the power consumption load of the designated area in different time periods, as well as the proportion data of multiple energy sources in the energy hub module, calculate the cost consumption and energy supply proportion of different energy sources, and combine the dynamic electricity price mechanism of the designated area to output switching plans for the energy supply proportions of different energy sources based on the lowest carbon emissions under the power load in different time periods. The allocation and adjustment module outputs different switching plans to the energy hub module based on the time period for practical application.

2. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 1 is characterized by: The information collection module includes a regional electricity consumption end collection module and an energy source collection module. The regional electricity consumption end collection module is used to connect to the electricity consumption control center of the specified area, firstly collect the monthly electricity load changes in the area in the year as the main set, then collect the daily electricity load changes in the area in the month as the secondary set, then collect the electricity load changes in different time periods of each day as subsets, merge the subsets into the corresponding secondary sets, merge the secondary sets into the corresponding main sets, and construct a regional electricity load change collection package.

3. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 2 is characterized by: The energy source collection module is used to connect to the energy hub module to collect the proportion data of multiple energy sources in the energy hub module, including the change in the proportion data of multiple energy sources in different months, as a dynamic set.

4. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 3 is characterized by: The energy hub module collects energy from multiple energy sources, which include various clean energy and non-clean energy, and uses the superposition effect of multiple energy sources to generate energy output and supply electricity to a designated area.

5. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 4 is characterized in that: The central processing system includes a proportional allocation model module, a calculation and analysis module and a big data database. The big data database has a built-in search framework, which retrieves and stores various clean energy and non-clean energy costs, power supply and carbon emissions, as well as dynamic electricity price mechanisms in designated areas based on the search framework.

6. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 5 is characterized by: The proportion allocation module constructs an energy source allocation model according to the changes in the proportion data of multiple energy sources in different months. Based on the model and the data in the big data database, it dynamically simulates the change plans of different energy source proportions under specified electricity demand, as well as the power supply costs and carbon emissions under different plans.

7. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 6 is characterized by: The calculation and analysis module includes an algorithm module and a solution output module. The algorithm module is based on an energy load algorithm, a regional power load change amount set package and an energy source allocation model. Under the power consumption load in different time periods in a specified area, according to the change schemes under different energy source proportions, the power supply cost and carbon emissions under different schemes are calculated, and the optimal multiple change schemes are obtained with the lowest carbon emissions as the standard. 1 clean energy a% + 2 clean energy b% + 1 non-clean energy c% + 2 non-clean energy d% = energy supply demand A, b, c, and d represent the proportions of different energy sources, and the proportions are allocated based on the lowest carbon emissions.

8. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 7 is characterized by: The algorithm module combines the dynamic electricity price mechanism in the specified area to calculate the ratio of dynamic electricity price to power supply cost under the optimal multiple schemes, as well as the ratio of dynamic electricity price to power supply cost under other schemes, and then screens again, taking power supply cost, carbon emissions, and dynamic electricity price as the equilibrium target, and screens again the scheme with the lowest carbon emissions and the economic match between power supply cost and real-time electricity price. Power supply cost: dynamic electricity price = coefficient B Coefficient B is the economic matching coefficient between power supply cost and real-time electricity price, which reflects the income level of the functional enterprise based on the real-time electricity price.

9. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 8 is characterized by: The solution output module is used to summarize the optimal solution screened by the algorithm module into an electronic draft, output it to the allocation and adjustment module, and display it based on the management terminal. The solution output module is used to record other solutions simultaneously. When the optimal solution cannot be implemented, the next-level optimization solution is used as a substitute.

10. The carbon emission control system based on regional comprehensive energy coupling characteristics according to claim 9 is characterized in that: The distribution and adjustment module is connected to the solution output module and the energy hub module, and is used to convert the optimal solution into a control instruction and send it to the energy hub module. The distribution and adjustment module has a built-in log module, which records each adjustment according to a timestamp and provides a log retrieval function.

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