Regulation and control method of multi-energy coupling system and multi-energy coupling system
By constructing a multi-objective optimization and control model, and combining it with grid demand, the energy flow conversion of the power, heat and natural gas systems in the multi-energy coupled system is optimized. This solves the problem of insufficient control of multi-energy coupled systems in the existing technology, and realizes efficient response and economic coordination for grid peak shaving.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-12
- Publication Date
- 2026-04-14
AI Technical Summary
Existing multi-energy coupled system control technologies face shortcomings in supporting power grid peak shaving, such as in flexibility characteristic analysis, optimized control modeling, and solution algorithm adaptation. These shortcomings make it difficult to efficiently tap the cross-domain peak shaving potential of multi-energy coupled systems, resulting in a disconnect between peak shaving resource potential modeling and actual power grid needs. Furthermore, traditional decomposition and coordination algorithms struggle to balance global peak shaving optimization with the autonomy of subsystem operation.
A multi-objective optimization and control model for a multi-energy coupled system is constructed. Combining the time-domain characteristics and amplitude requirements of the power grid, the operation strategy of the multi-energy coupled system is determined through flexibility analysis and cost-benefit model, including energy flow conversion of the power, heat and natural gas systems, and optimizing peak-shaving response deviation, net operating cost and total carbon emissions.
It achieves efficient response of multi-energy coupled systems to grid peak shaving, takes into account both peak shaving costs and system economy, provides an efficient solution path, strengthens the support capability of multi-energy systems for the grid, and promotes the consumption of renewable energy.
Smart Images
Figure CN121860280A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of integrated energy system optimization planning, and in particular to a control method for a multi-energy coupled system and a multi-energy coupled system. Background Technology
[0002] Multi-energy coupled systems, as important carriers for integrating multiple types of energy resources, can unleash cross-energy domain flexibility potential through energy conversion, storage, and complementary optimization, providing key support for grid peak shaving. Currently, energy systems exhibit characteristics of multi-energy flow interaction and intensified random fluctuations on both the source and load sides. Single-energy system control models are no longer sufficient to meet the grid's demands for rapid response and multi-scale coordinated peak shaving. Against this backdrop, constructing multi-energy coupled systems for grid peak shaving to regulate energy flow and achieve flexible utilization and optimized allocation of various energy subsystems has become an inevitable requirement for ensuring the safe and economical operation of the energy system and promoting the consumption of new energy sources.
[0003] Existing multi-energy coupled system control technologies face shortcomings in supporting power grid peak shaving, including inflexibility analysis, optimized control modeling, and algorithm adaptation. These shortcomings hinder the efficient exploitation of the cross-domain peak shaving potential of multi-energy coupled systems and restrict the accurate response to power grid peak shaving demands. Specifically: related technologies fail to effectively quantify the flexibility of coupled equipment such as cogeneration units, electric boilers, and power-to-gas conversion devices, and fail to build a correlation model that combines the time-domain characteristics and amplitude requirements of power grid peak shaving, resulting in a disconnect between peak shaving resource potential modeling and actual power grid needs. Current centralized algorithms struggle to cope with the computational complexity of large-scale coupling of multiple energy subsystems, and the coordination variables of traditional decomposition and coordination algorithms do not focus on key power grid peak shaving indicators, making it difficult to balance global peak shaving optimization with the autonomy of subsystem operation. Summary of the Invention
[0004] This application addresses at least one of the aforementioned technical problems by providing a control method for a multi-energy coupling system and a multi-energy coupling system.
[0005] In a first aspect, this application provides a control method for a multi-energy coupled system, wherein the multi-energy coupled system is connected to a power grid, and the architecture of the multi-energy coupled system includes a power system, a heating system, a natural gas system, and coupling equipment, wherein energy flow conversion is performed between the power system, the heating system, and the natural gas system through the coupling equipment; the control method includes: Based on the system parameters of the multi-energy coupled system, a multi-objective optimization control model for the multi-energy coupled system is constructed. The system parameters include the flexibility parameters of the multi-energy coupled system. The multi-objective optimization control model has multiple optimization objectives, including peak-shaving impact deviation, net operating cost of the multi-energy coupled system, and total carbon emissions. The multi-objective optimization control model includes the functional relationship between the operating strategy of the multi-energy coupled system and the system parameters and the peak-shaving demand of the power grid. The peak-shaving demand includes the time-domain characteristics and amplitude requirements of the power grid. Obtain the actual peak-shaving demand of the power grid; The actual peak-shaving demand is input into the multi-objective optimization control model to obtain the optimized operation strategy of the multi-energy coupling system; the operation strategy includes the power generation and power consumption of the power system, the heat consumption and heat storage power of the thermal system, and the energy flow conversion power between the natural gas system and the power system and the thermal system. The operation of the multi-energy coupling system is controlled according to the operating strategy.
[0006] Optionally, constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters of the multi-energy coupled system includes: Based on the system parameters, determine the flexibility model and cost-effectiveness model of the multi-energy coupling system; the flexibility model is the upper limit capacity, lower limit capacity, and load demand response capability of the multi-energy coupling system; the cost-effectiveness model is the functional relationship between the peak-shaving demand of the power grid and the net peak-shaving revenue of the multi-energy coupling system. Based on the flexibility model and the cost-benefit model, a multi-objective optimization control model for the multi-energy coupling system is constructed.
[0007] Optionally, the system parameters of the multi-energy coupling system include power system flexibility parameters, thermal system flexibility parameters, and natural gas system flexibility parameters; The step of determining the flexibility model and cost-effectiveness model of the multi-energy coupled system based on the system parameters includes: Based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, flexibility analyses are performed on the power system, the heating system, and the natural gas system, respectively. Based on the flexibility analysis of the power system, the heating system, and the natural gas system, a flexibility model of the multi-energy coupled system is constructed.
[0008] Optionally, the power system flexibility parameters include the maximum technical output, minimum technical output, uphill ramp rate, and downhill ramp rate of conventional units, as well as the maximum discharge power and maximum charging power of the electrical energy storage of the conventional units and the uphill and downhill flexibility capacity for load response requirements. The process of performing flexibility analysis on the power system, the heating system, and the natural gas system based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, respectively, includes: Based on the power system flexibility parameters, the upward and downward flexibility capacities of the power system are determined.
[0009] Optionally, the flexibility parameters of the thermal system include the maximum and minimum heating power of the thermal system, the response time constant of the thermal system, and the maximum and minimum allowable temperatures of the thermal system. The process of performing flexibility analysis on the power system, the heating system, and the natural gas system based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, respectively, includes: Based on the flexibility parameters of the thermal system, determine the upward and downward flexibility capacities of the thermal system.
[0010] Optionally, the natural gas system flexibility parameters include the maximum and minimum flow rates of the natural gas system, the maximum and minimum allowable pressures of the natural gas system, pipeline constants, and the response time constants of the natural gas system. The process of performing flexibility analysis on the power system, the heating system, and the natural gas system based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, respectively, includes: Based on the natural gas system flexibility parameters, determine the upward and downward flexibility capacities of the natural gas system.
[0011] Optionally, constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters of the multi-energy coupled system includes: Based on the time-domain characteristics, amplitude characteristics, and response requirements of the power grid's peak-shaving demand, multiple peak-shaving scenarios are determined. Based on the peak-shaving scenario, determine the peak-shaving benefits and peak-shaving costs; wherein, the peak-shaving benefits include at least one of peak-shaving compensation benefits, energy-saving benefits, and environmental benefits, and the peak-shaving costs include at least one of energy procurement costs, equipment operating costs, peak-shaving response costs, and penalty costs; Based on the peak-shaving benefits and the peak-shaving costs, the net peak-shaving revenue is determined as the output of the cost-benefit model.
[0012] Optionally, constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters includes: Based on the peak-shaving scenario, determine the respective weights of multiple optimization objectives of the multi-energy coupling system; Based on the weights, multiple optimization objectives are weighted to obtain the operating strategy of the multi-energy coupled system.
[0013] Optionally, the peak-shaving impact deviation is the sum of the relative deviations between the actual peak-shaving amount of the multi-energy coupling system and the demand peak-shaving amount of the power grid.
[0014] Optionally, the net operating cost is the difference between the peak-shaving cost and the peak-shaving benefit.
[0015] Optionally, the total carbon emissions are the carbon emissions generated by the multi-energy coupling system consuming fossil fuels.
[0016] Secondly, this application provides a multi-energy coupling system that performs the control method described in the first aspect.
[0017] By establishing a multi-objective optimization control model for multi-energy coupled systems, the operating strategy of the multi-energy coupled system can be calculated for multiple optimization objectives. In this embodiment, optimization can be performed on the peak-shaving response deviation between the actual peak-shaving capacity of the multi-energy coupled system and the peak-shaving capacity required by the power grid, the net operating cost of the multi-energy coupled system, and the total carbon emissions, to obtain the optimal operating strategy. When the multi-energy coupled system operates according to the operating strategy calculated by the multi-objective optimization control model, it can be controlled according to the actual time-domain characteristics and amplitude requirements of the power grid, effectively responding to the time-domain characteristics and amplitude requirements of power grid peak-shaving, and providing an efficient solution path for the optimization of large-scale multi-energy coupled systems.
[0018] The above-mentioned control methods enhance the support capability of multi-energy coupled systems for grid peak shaving, and can balance the grid peak shaving cost with the operating energy consumption of multi-energy coupled systems, and coordinate peak shaving benefits with system economy. The above-mentioned control methods have engineering practicality and promotion value, and provide key technical support for the scientific control and efficient operation of multi-energy coupled systems under grid peak shaving demand. Attached Figure Description
[0019] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0020] Figure 1The diagram shown is a flowchart of an embodiment of the control method for a multi-energy coupled system according to this application.
[0021] Figure 2 The diagram shown is a detailed flowchart of the control method for the multi-energy coupling system of this application.
[0022] Figure 3 The diagram shown is a detailed flowchart of the control method for the multi-energy coupling system of this application.
[0023] Figure 4 The diagram shown is a detailed flowchart of the control method for the multi-energy coupling system of this application. Detailed Implementation
[0024] The technical solutions in the embodiments (or "implementations") of this application will be clearly and completely described herein with reference to the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements.
[0025] If the embodiments of this application contain terms relating to directional indications or positional relationships (such as up, down, left, right, front, back, inside, outside, top, bottom, center, vertical, horizontal, longitudinal, transverse, length, width, counterclockwise, clockwise, axial, radial, circumferential, etc.), such terms are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the attached figures); if the specific posture changes, the directional indications or positional relationships will also change accordingly. Furthermore, the terms "first" and "second" used in the embodiments of this application are only for descriptive convenience and should not be construed as indicating or implying relative importance.
[0026] This application provides a control method for a multi-energy coupling system and the multi-energy coupling system itself. The control method and the multi-energy coupling system of this application will be described in detail below with reference to the accompanying drawings. Unless otherwise specified, the features in the following embodiments and implementations can be combined with each other.
[0027] This application provides a multi-energy coupling system. The multi-energy coupling system is connected to the power grid. The architecture of the multi-energy coupling system includes an electric system, a thermal system, and a natural gas system, and coupling equipment. Energy flow conversion between the electric system, the thermal system, and the natural gas system is achieved through the coupling equipment.
[0028] The power system includes generators, loads, and energy storage to ensure power balance; the thermal system includes heat sources, heating networks, and heat loads to ensure thermal balance; the natural gas system includes gas sources, gas pipelines, and gas loads to ensure gas balance; and coupling equipment is used to achieve energy flow conversion, such as combined heat and power, electric boilers, and gas boilers.
[0029] The multi-energy coupling system implements the control method of the multi-energy coupling system of this application.
[0030] See Figure 1 As shown, the control method includes steps S10, S20, S30 and S40.
[0031] In step S10, a multi-objective optimization control model for the multi-energy coupled system is constructed based on the system parameters. The system parameters include the flexibility parameters of the multi-energy coupled system. The multi-objective optimization control model has multiple optimization objectives, including peak-shaving response deviation, net operating cost of the multi-energy coupled system, and total carbon emissions. The multi-objective optimization control model includes the functional relationship between the operating strategy of the multi-energy coupled system and the system parameters and the peak-shaving demand of the power grid. Peak-shaving demand includes the time-domain characteristics and amplitude requirements of the power grid. The time-domain characteristics of the power grid peak-shaving demand include the division of peak and valley periods and response delay requirements. Power grid load typically exhibits daily cyclical fluctuations; during peak periods (such as morning and evening peak electricity consumption), output needs to be rapidly increased, while during valley periods (such as nighttime), output needs to be reduced. The amplitude requirements of the power grid peak-shaving demand include peak-shaving depth (i.e., power adjustment amplitude) and capacity limit (the maximum peak-shaving capacity the system can provide). Peak-shaving depth is usually expressed as power change, while the capacity limit is limited by the physical characteristics of the equipment.
[0032] In step S20, the actual peak-shaving demand of the power grid is obtained.
[0033] In step S30, the actual peak-shaving demand is input into the multi-objective optimization control model to determine the optimized operating strategy of the multi-energy coupled system. The operating strategy includes the power generation and power consumption of the power system, the heat consumption and heat storage power of the thermal system, and the energy flow conversion power between the natural gas system and the power and thermal systems.
[0034] In step S40, the operation of the multi-energy coupling system is controlled according to the operating strategy.
[0035] By establishing a multi-objective optimization control model for multi-energy coupled systems, the operating strategy of the multi-energy coupled system can be calculated for multiple optimization objectives. In this embodiment, optimization can be performed on the peak-shaving response deviation between the actual peak-shaving capacity of the multi-energy coupled system and the peak-shaving capacity required by the power grid, the net operating cost of the multi-energy coupled system, and the total carbon emissions, to obtain the optimal operating strategy. When the multi-energy coupled system operates according to the operating strategy calculated by the multi-objective optimization control model, it can be controlled according to the actual time-domain characteristics and amplitude requirements of the power grid, effectively responding to the time-domain characteristics and amplitude requirements of power grid peak-shaving, and providing an efficient solution path for the optimization of large-scale multi-energy coupled systems.
[0036] The above-mentioned control methods enhance the support capability of multi-energy coupled systems for grid peak shaving, and can balance the grid peak shaving cost with the operating energy consumption of multi-energy coupled systems, and coordinate peak shaving benefits with system economy. The above-mentioned control methods have engineering practicality and promotion value, and provide key technical support for the scientific control and efficient operation of multi-energy coupled systems under grid peak shaving demand.
[0037] In an optional embodiment, see Figure 2 As shown, step S10 involves constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters of the multi-energy coupled system, including steps S11 and S12.
[0038] In step S11, based on the system parameters, the flexibility model and cost-benefit model of the multi-energy coupled system are determined. The flexibility model represents the upper limit capacity, lower limit capacity, and load demand response capability of the multi-energy coupled system. The cost-benefit model represents the functional relationship between the grid's peak-shaving demand and the net peak-shaving revenue of the multi-energy coupled system.
[0039] In step S12, a multi-objective optimization control model for the multi-energy coupled system is constructed based on the flexibility model and the cost-benefit model.
[0040] Thus, by determining the flexibility and cost-effectiveness models of multi-energy coupled systems, the flexibility of multi-energy coupled systems can be accurately quantified. This facilitates the construction of a related multi-objective optimization control model that combines the time-domain characteristics and amplitude requirements of grid peak-shaving demand. This enables the multi-energy coupled system to be controlled according to the actual needs of the grid. Furthermore, it allows for optimization of the net operating cost of the multi-energy coupled system, taking into account both the grid peak-shaving cost and the operating energy consumption of the multi-energy coupled system, and coordinating peak-shaving benefits with system economy.
[0041] In an optional embodiment, the system parameters of the multi-energy coupled system include power system flexibility parameters, thermal system flexibility parameters, and natural gas system flexibility parameters.
[0042] See Figure 3 As shown, step S11 involves determining the flexibility model and cost-effectiveness model of the multi-energy coupled system based on the system parameters, including steps S111 and S112.
[0043] In step S111, flexibility analysis is performed on the power system, the heating system, and the natural gas system based on the flexibility parameters of the power system, the heating system, and the natural gas system, respectively. In step S112, a flexibility model of the multi-energy coupled system is constructed based on the flexibility analysis of the power system, heating system, and natural gas system.
[0044] By conducting flexibility analysis on the power system, thermal system, and natural gas system of a multi-energy coupled system, the flexibility of the multi-energy coupled system can be accurately quantified. This facilitates the construction of a multi-objective optimization control model that combines the time-domain characteristics and amplitude requirements of the power grid's peak-shaving demand, enabling the multi-energy coupled system to be controlled according to the actual needs of the power grid.
[0045] In optional embodiments, power system flexibility parameters include the maximum technical output, minimum technical output, uphill rate, and downhill rate of conventional generating units, the maximum discharge power and maximum charging power of the electrical storage of conventional generating units, and the uphill and downhill flexibility capacity for load response requirements.
[0046] Step S111 involves performing flexibility analysis on the power system, heating system, and natural gas system based on the flexibility parameters of the power system, heating system, and natural gas system, respectively.
[0047] In step S1111, the upward and downward flexibility capacities of the power system are determined based on the power system flexibility parameters.
[0048] Specifically, the upward and downward flexibility capacities of the power system can be calculated using formulas (1) and (2): (1) (2) In the formula: and These represent the upward and downward flexibility capacity provided by the power system during time period t, respectively. This is the number of conventional generating units; , and These represent the maximum technical output, minimum technical output, and output during time period t of conventional unit i, respectively. and Let represent the upward ramp rate and downward ramp rate of conventional unit i, respectively. For time step; and These represent the maximum discharge power and maximum charging power of the stored energy during time period t, respectively. and These represent the upward and downward flexibility capacity provided by the load demand response during time period t, respectively.
[0049] In an optional embodiment, the thermal system flexibility parameters include the maximum and minimum heating power of the thermal system, the response time constant of the thermal system, and the maximum and minimum allowable temperatures of the thermal system.
[0050] Step S111: Based on the flexibility parameters of the power system, the thermal system, and the natural gas system, perform flexibility analysis on the power system, the thermal system, and the natural gas system respectively, including step S1112.
[0051] In step S1112, the upward and downward flexibility capacities of the thermal system are determined based on the thermal system flexibility parameters.
[0052] Specifically, the upward and downward flexibility capacities of the thermal system can be calculated using formulas (3) and (4): (3) (4) In the formula: and These represent the upward and downward flexibility capacities provided by the thermal system during time period t, respectively. , and These represent the maximum and minimum heating power of the thermal system, as well as the heating power during time period t, respectively. , and These represent the highest and lowest permissible temperatures of the thermal system and the temperature over time period t, respectively. Heat capacity; is the response time constant of the thermodynamic system.
[0053] In an optional embodiment, the natural gas system flexibility parameters include the maximum and minimum flow rates of the natural gas system, the maximum and minimum allowable pressures of the natural gas system, pipeline constants, and the response time constant of the natural gas system.
[0054] Specifically, the upward and downward flexibility capacities of a natural gas system can be calculated using formulas (5) and (6): (5) (6) In the formula: and These represent the upward and downward flexibility capacity provided by the natural gas system during time period t, respectively. , and These represent the maximum and minimum flow rates of the natural gas system, as well as the flow rate during time period t, respectively. , and These represent the maximum and minimum allowable pressures of the natural gas system and the pressure over time period t, respectively. This refers to the pipeline constant of the natural gas system. is the response time constant of the natural gas system.
[0055] Thus, by solving for the upward and downward flexibility capacities of the power system, heating system, and natural gas system, the flexibility analysis of the power system, heating system, and natural gas system of the multi-energy coupled system can be realized, and the flexibility of the multi-energy coupled system can be accurately quantified. This is conducive to building a related multi-objective optimization control model by combining the time-domain characteristics and amplitude requirements of the grid peak-shaving demand, so that the multi-energy coupled system can be controlled according to the actual needs of the grid.
[0056] In an optional embodiment, see Figure 3 As shown, step S11 involves determining the flexibility model and cost-effectiveness model of the multi-energy coupled system based on the system parameters, including steps S113, S114, and S115.
[0057] In step S113, multiple peak-shaving scenarios are determined based on the time-domain characteristics, amplitude characteristics, and response requirements of the power grid's peak-shaving demand. These scenarios include, but are not limited to, conventional peak-shaving scenarios, deep peak-shaving scenarios, emergency peak-shaving scenarios, and renewable energy peak-shaving scenarios. Conventional peak-shaving scenarios refer to scenarios with moderate peak-shaving amplitude, such as a peak-shaving amplitude demand of 10%-30% of rated capacity, and a peak-shaving response time requirement of 15-30 minutes; typical scenarios are the morning and evening peak hours each day. Deep peak-shaving scenarios refer to scenarios that address deep load reduction during off-peak periods, with a peak-shaving amplitude demand of 30%-50% of rated capacity and a peak-shaving response time requirement of 30-60 minutes; typical scenarios are the nighttime off-peak period. Emergency peak-shaving scenarios refer to scenarios that address sudden power grid failures, with uncertain peak-shaving amplitude demand and a peak-shaving response time requirement of 1-5 minutes; typical scenarios include generator tripping and line faults. Renewable energy consumption and peak shaving refers to responding to peak wind / solar power output periods. The response time for peak shaving is required to be between 15 and 30 minutes, in order to absorb excess green electricity and reduce the curtailment rate.
[0058] In step S114, peak-shaving benefits and peak-shaving costs are determined based on the peak-shaving scenario. Peak-shaving benefits include at least one of peak-shaving compensation benefits, energy-saving benefits, and environmental benefits, while peak-shaving costs include at least one of energy procurement costs, equipment operating costs, peak-shaving response costs, and penalty costs.
[0059] In step S115, the net peak-shaving benefit is determined as the output of the cost-benefit model based on the peak-shaving benefits and peak-shaving costs.
[0060] Thus, a cost-benefit model with a general framework for different peak-shaving scenarios is constructed. The net benefit of peak shaving is the difference between the total benefit and the total cost, calculated using the following general formula: (7) (8) (9) in, , and These represent net peak-shaving revenue, peak-shaving benefits, and peak-shaving costs, respectively. , , and These represent the energy procurement cost, equipment operating cost, peak-shaving response cost, and penalty cost of a multi-energy coupled system, respectively. , and These represent the system's peak-shaving compensation benefits, energy-saving benefits, and environmental benefits, respectively.
[0061] Based on the cost-benefit model of the above general framework, it can be further refined for different peak-shaving scenarios. Taking the conventional peak-shaving scenario as an example, the peak-shaving cost mainly considers the peak-shaving response cost, which is the start-up, shutdown, and adjustment loss of fast-regulating equipment (such as electric boilers and gas turbines). The peak-shaving benefit is mainly the peak-shaving compensation benefit, which is directly linked to the peak peak-shaving amount. The specific calculation is shown in formulas (10) and (11): (10) (11) In the formula: and These represent the peak-shaving response cost and peak-shaving compensation benefits for conventional peak-shaving scenarios, respectively. To participate in the assembly of conventional peak-shaving equipment; and These represent the number of times device k is started and the unit start-up cost, respectively, during time period t; and Let represent the power adjustment amount and unit adjustment cost of device k during time period t, respectively. This is the standard peak-shaving compensation coefficient.
[0062] Thus, based on the time-domain characteristics and amplitude requirements of the power grid under different peak-shaving scenarios, the cost of peak-shaving for multi-energy coupled systems can be calculated. This facilitates the construction of a related multi-objective optimization control model that combines the time-domain characteristics and amplitude requirements of the power grid's peak-shaving demand. This enables the multi-energy coupled system to be controlled according to the actual needs of the power grid, and on this basis, the net operating cost of the multi-energy coupled system can be optimized, taking into account both the power grid's peak-shaving cost and the operating energy consumption of the multi-energy coupled system, and coordinating peak-shaving benefits with system economy.
[0063] In an optional embodiment, see Figure 4 As shown, step S10 involves constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters of the multi-energy coupled system, including steps S13 and S14.
[0064] In step S13, the weights of the various optimization objectives of the multi-energy coupled system are determined according to the peak-shaving scenario.
[0065] In step S14, multiple optimization objectives are weighted according to their weights to determine the operating strategy of the multi-energy coupled system.
[0066] In different peak-shaving scenarios, the impact deviation of peak shaving, the net operating cost of multi-energy coupled systems, and the total carbon emissions have varying degrees of importance. Therefore, a multi-objective optimization control model can take into account the different optimization needs under different peak-shaving scenarios, determine the optimal operating strategy, and coordinate peak-shaving benefits with system economy. This makes the control method engineering-practical and has promotional value, providing key technical support for the scientific control and efficient operation of multi-energy coupled systems under the peak-shaving demand of the power grid.
[0067] In practical implementation, multiple optimization objectives can be adaptively weighted according to different peak-shaving scenarios, so that multiple optimization objectives can be dynamically adapted to peak-shaving scenarios. The optimization results of multiple optimization objectives under different weights can be calculated according to formula (12): (12) in: Let represent the weighted overall objective function of multiple optimization objectives. For peak shaving response deviation, Net operating costs, Total carbon emissions Let be the target weight coefficient, and satisfy... .
[0068] In an optional embodiment, the peak-shaving impact deviation is the sum of the relative deviations between the actual peak-shaving amount of the multi-energy coupled system and the demand peak-shaving amount of the power grid. The specific calculation of the peak-shaving impact deviation is shown in formula (13): (13) in, This indicates the total number of control periods in a multi-energy coupled system. and These represent the actual peak-shaving amount of the multi-energy coupled system and the peak-shaving amount of the power grid demand during time period t, respectively.
[0069] In an optional embodiment, the net operating cost is the difference between peak shaving cost and peak shaving benefit, and the net operating cost is specifically calculated as shown in formula (14): (14) in, and These represent peak-shaving costs and peak-shaving benefits, respectively.
[0070] In an optional embodiment, the total carbon emissions are the carbon emissions generated by the multi-energy coupled system consuming fossil fuels, and the total carbon emissions are specifically calculated as shown in formula (15): (15) In the formula: This represents the carbon emission factor of device k. Let k be the output of device k during time period t.
[0071] In this way, the optimization results of multiple optimization objectives are quantified, and the multi-objective optimization control model can take into account the different optimization needs under different peak-shaving scenarios, determine the optimized operation strategy, coordinate peak-shaving benefits and system economy, and make the control method have engineering practicality and promotion value, providing key technical support for the scientific control and efficient operation of multi-energy coupled systems under the peak-shaving demand of the power grid.
[0072] This application proposes a decomposition coordination algorithm that takes the peak-shaving demand of the power grid as the core coordination variable. The multi-objective optimization control model is decomposed into two relatively independent sub-problems: an upper-level coordination sub-problem, which is responsible for evaluating the global optimization objective and optimizing the allocation of peak-shaving tasks; and a lower-level optimization sub-problem, which optimizes multiple optimization objectives, including peak-shaving impact deviation, net operating cost of multi-energy coupled system and total carbon emissions, to obtain the operation strategy of multi-energy coupled system.
[0073] In this way, the upper-level coordination layer no longer intervenes in the specific power output of each sub-energy system through directives. Instead, it sends a signal indicating the "desired peak-shaving contribution" to the lower-level sub-systems to guide their orderly response to the grid's peak-shaving demands. Upon receiving this signal, the lower-level sub-systems autonomously make optimization and control decisions based on their own operating status, flexibility models, and cost-effectiveness to respond to the upper-level peak-shaving demands and report their "peak-shaving capacity range" back to the upper level. Through iterative communication between the upper and lower levels regarding "peak-shaving contribution" and "peak-shaving capacity," a dynamic balance is ultimately achieved between the globally optimized peak-shaving operating strategy and the autonomy of the sub-energy systems.
[0074] The regulation method for multi-energy coupled systems oriented towards grid peak-shaving demand provided in this application has significant beneficial effects: At the theoretical level, the constructed peak-shaving-oriented multi-energy coupled system optimization regulation framework systematically integrates the grid peak-shaving demand with the cross-energy-domain coupling architecture between electricity, heat, and natural gas, providing a theoretical foundation for the deep participation of multi-energy coupled systems in grid peak-shaving; the proposed flexibility model characterizes the peak-shaving response characteristics and cross-energy-domain complementary potential of the multi-energy coupled system, fully tapping the potential of multi-energy complementary peak-shaving; the multi-objective optimization model effectively coordinates peak-shaving benefits and system operating costs; and the proposed regulation method provides an efficient solution path for large-scale multi-energy coupled system optimization problems. At the technical application level, this method strengthens the support capability of multi-energy systems for grid peak-shaving, which is conducive to promoting the consumption of a high proportion of renewable energy; at the same time, it can take into account the grid peak-shaving cost and the energy consumption of multi-energy system operation, coordinating peak-shaving benefits and system economy; the decomposition-coordination algorithm has engineering practicality and promotion value, providing key technical support for the scientific regulation and efficient operation of multi-energy coupled systems under grid peak-shaving demand.
[0075] It should be noted that the technical solutions or features described in the above embodiments can be combined or supplemented with each other without conflict. The scope of protection of this application is not limited to the precise structures described in the above embodiments and shown in the accompanying drawings; all modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A control method for a multi-energy coupled system, characterized in that, This is used in a multi-energy coupling system, which is connected to the power grid. The architecture of the multi-energy coupling system includes a power system, a thermal system, a natural gas system, and coupling equipment. The power system, the thermal system, and the natural gas system exchange energy flow through the coupling equipment. The control method includes: Based on the system parameters of the multi-energy coupled system, a multi-objective optimization control model for the multi-energy coupled system is constructed. The system parameters include the flexibility parameters of the multi-energy coupled system. The multi-objective optimization control model has multiple optimization objectives, including peak-shaving impact deviation, net operating cost of the multi-energy coupled system, and total carbon emissions. The multi-objective optimization control model includes the functional relationship between the operating strategy of the multi-energy coupled system and the system parameters and the peak-shaving demand of the power grid. The peak-shaving demand includes the time-domain characteristics and amplitude requirements of the power grid. Obtain the actual peak-shaving demand of the power grid; The actual peak-shaving demand is input into the multi-objective optimization control model to obtain the optimized operation strategy of the multi-energy coupling system; the operation strategy includes the power generation and power consumption of the power system, the heat consumption and heat storage power of the thermal system, and the energy flow conversion power between the natural gas system and the power system and the thermal system. The operation of the multi-energy coupling system is controlled according to the operating strategy.
2. The control method according to claim 1, characterized in that, The step of constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters of the multi-energy coupled system includes: Based on the system parameters, determine the flexibility model and cost-effectiveness model of the multi-energy coupling system; the flexibility model includes the upper limit capacity, lower limit capacity and load demand response capability of the multi-energy coupling system; the cost-effectiveness model includes the functional relationship between the peak-shaving demand of the power grid and the net peak-shaving revenue of the multi-energy coupling system. Based on the flexibility model and the cost-benefit model, a multi-objective optimization control model for the multi-energy coupling system is constructed.
3. The control method according to claim 2, characterized in that, The system parameters of the multi-energy coupling system include power system flexibility parameters, thermal system flexibility parameters, and natural gas system flexibility parameters; The step of determining the flexibility model and cost-effectiveness model of the multi-energy coupled system based on the system parameters includes: Based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, flexibility analyses are performed on the power system, the heating system, and the natural gas system, respectively. Based on the flexibility analysis of the power system, the heating system, and the natural gas system, a flexibility model of the multi-energy coupled system is constructed.
4. The control method according to claim 3, characterized in that, The power system flexibility parameters include the maximum technical output, minimum technical output, uphill ramp rate, and downhill ramp rate of conventional generating units, the maximum discharge power and maximum charging power of the electrical energy storage of conventional generating units, and the uphill and downhill flexibility capacity of load response requirements. The process of performing flexibility analysis on the power system, the heating system, and the natural gas system based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, respectively, includes: Based on the power system flexibility parameters, the upward and downward flexibility capacities of the power system are determined.
5. The control method according to claim 3, characterized in that, The flexibility parameters of the thermal system include the maximum and minimum heating power of the thermal system, the response time constant of the thermal system, and the maximum and minimum allowable temperatures of the thermal system. The process of performing flexibility analysis on the power system, the heating system, and the natural gas system based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, respectively, includes: Based on the flexibility parameters of the thermal system, determine the upward and downward flexibility capacities of the thermal system.
6. The control method according to claim 3, characterized in that, The natural gas system flexibility parameters include the maximum and minimum flow rates of the natural gas system, the maximum and minimum allowable pressures of the natural gas system, pipeline constants, and the response time constants of the natural gas system. The process of performing flexibility analysis on the power system, the heating system, and the natural gas system based on the power system flexibility parameters, the heating system flexibility parameters, and the natural gas system flexibility parameters, respectively, includes: Based on the natural gas system flexibility parameters, determine the upward and downward flexibility capacities of the natural gas system.
7. The control method according to claim 2, characterized in that, The step of constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters of the multi-energy coupled system includes: Based on the time-domain characteristics, amplitude characteristics, and response requirements of the power grid's peak-shaving demand, multiple peak-shaving scenarios are determined. Based on the peak-shaving scenario, determine the peak-shaving benefits and peak-shaving costs; wherein, the peak-shaving benefits include at least one of peak-shaving compensation benefits, energy-saving benefits, and environmental benefits, and the peak-shaving costs include at least one of energy procurement costs, equipment operating costs, peak-shaving response costs, and penalty costs; Based on the peak-shaving benefits and the peak-shaving costs, the net peak-shaving revenue is determined as the output of the cost-benefit model.
8. The control method according to claim 7, characterized in that, The step of constructing a multi-objective optimization control model for the multi-energy coupled system based on the system parameters includes: Based on the peak-shaving scenario, determine the respective weights of multiple optimization objectives of the multi-energy coupling system; Based on the weights, multiple optimization objectives are weighted to obtain the operating strategy of the multi-energy coupled system.
9. The control method according to claim 8, characterized in that, The peak-shaving impact deviation is the sum of the relative deviations between the actual peak-shaving amount of the multi-energy coupling system and the demand peak-shaving amount of the power grid; and / or The net operating cost is the difference between the peak-shaving cost and the peak-shaving benefit; and / or The total carbon emissions refer to the carbon emissions generated by the multi-energy coupling system consuming fossil fuels.
10. A multi-energy coupling system, characterized in that, The multi-energy coupling system performs the control method as described in any one of claims 1-9.