Adjustable resource optimization regulation and control method and system considering energy conversion

Abstract

The invention relates to an adjustable resource optimization regulation and control method and system considering energy conversion, and relates to the technical field of power grid regulation and control. The method comprises the following steps: S1, constructing a regulation and control demand decomposition model, and decomposing a regulation and control target value delta P scene; s2, determining that an upper-layer optimization target is maximized new energy consumption; s3, determining that a lower-layer optimization target is minimum regulation and control cost; s4, constructing a system operation constraint boundary; and S5, generating an optimized regulation and control strategy based on the models constructed in S1 to S4, the regulation and control target and the constraint. According to the method and the system, on the basis of fully considering the adjustable resources of the user side, the correlation conversion relation between the electric power and other energy varieties and the system is considered, in the power grid regulation and control process, the regulation potential with economy is mined in a larger range, and the regulation requirements of the electric power system under different time scales are met.

Description

Technical Field

[0001] The present application relates to the technical field of power grid control, and in particular to a method and system for optimizing and controlling adjustable resources taking energy conversion into consideration. Background Art

[0002] Under the "dual carbon" goal, the development of new power systems is accelerating. The operation of the power grid is accompanied by adjustments in the power structure and changes in power load. These changes have led to increased volatility and uncertainty in the power system. At the same time, large-scale access to the power grid by new energy sources has put forward higher requirements for the regulation capacity of the power system. With the development of communication and control technology, the rich adjustable resources in the power system have been widely explored, and the regulation potential has been gradually released. At present, domestic and foreign scholars have conducted a lot of research on the development and utilization of power system regulation resources and the construction of regulation systems. Many countries have carried out many engineering practice applications and achieved relatively good results.

[0003] At present, the research and practical results on the operation and regulation of new power systems and flexible resource regulation are focused on the construction and exploration of adjustable resources within the power system, mainly in the form of "flexible load demand response + flexible regulation of energy storage" to meet the regulation needs. The research on the regulation capacity of power systems is mainly focused on short-term regulation demand scenarios such as frequency regulation and peak regulation, which have already appeared in large numbers, while there is still a lack of in-depth research and practice on the flexible regulation of scenarios with different time scales.

[0004] In terms of resource utilization, existing technologies focus on adjustable resources on the load side and energy storage side, without considering the correlation and conversion relationship between different energy types, and have certain limitations in resource selection. In terms of time scale, research on the regulation capacity of power systems mainly focuses on short-term regulation needs such as frequency regulation and peak regulation, which have already appeared in large numbers, while there is still a lack of in-depth research on the overall situation at different time scales.

[0005] In fact, by utilizing the coupling conversion between electricity and other energy types and considering the adjustable resources on the source, grid, load and storage sides of the new power system, we can further tap into a large amount of economical regulation potential, which is an important direction for building a richer regulation system in the future. At present, there is relatively little research on this direction, and no effective technical solution has yet been formed. Summary of the invention

[0006] The purpose of this application is to provide a method and system for optimizing and controlling adjustable resources taking into account energy conversion. This method and system can tap the economical regulation potential over a larger range and meet the regulation needs of the power system at different time scales.

[0007] In order to achieve the above objectives, in a first aspect, the present invention provides an adjustable resource optimization control method considering energy conversion, the method comprising:

[0008] S1: Construct a regulation demand decomposition model to calculate the regulation target value ΔP 场景 To decompose;

[0009] S2: Determine the upper optimization goal to maximize the consumption of new energy;

[0010] S3: Determine the lower-level optimization goal as minimizing the control cost;

[0011] S4: Construct system operation constraint boundaries;

[0012] S5: Generate an optimization control strategy based on the model, control objectives and constraints constructed in S1 to S4.

[0013] In an optional embodiment, in S1, the control target value ΔP 场景 Decompose it as shown below:

[0014] ΔP 场景 (t) = ΔP 源 (t)+ΔP 网 (t)+ΔP 荷 (t)+ΔP 储 (t)

[0015] In the formula, ΔP 场景 (t) is the control target value of the demand scenario at time t, ΔP 源 (t), ΔP 网 (t), ΔP 荷 (t), ΔP 储 (t) are the control quantities of the power supply side, grid side, load side and energy storage side at time t respectively.

[0016] In an optional embodiment, in S1, the calculation formula of each control amount is as follows:

[0017]

[0018] In the formula, are the power generation of thermal power, hydropower, photovoltaic power, wind power and other power sources at time t; They are the control quantities of flexible power transfer of regional power grid and regional autonomy participating in power grid control at time t respectively; are the power requirements of temperature control load, electric vehicle and other loads at time t respectively; They are the adjustable capacities of pumped storage, hydrogen energy storage and other energy storage at time t; is the regulation coefficient of resource i at time t.

[0019] In an optional implementation, in S2, the goal is to maximize the consumption of new energy, as shown in the following formula:

[0020]

[0021] In the formula, C 新能源 (%) is the consumption rate of new energy, Q 光伏 , Q 风电 are photovoltaic power generation and wind power generation respectively; ΔQ 弃光 , ΔQ 弃风 They are the amount of abandoned solar power and wind power during the scene control period, which are directly affected by the amount of new energy control.

[0022] In an optional implementation, in S3, the control scheme that minimizes the control cost is as shown in the following formula:

[0023]

[0024] In the formula, S 场景 is the overall control cost to meet the scene target; N is the total scene control time; S 场景 (t) is the control cost required to complete the scene control target at time t.

[0025] In an optional implementation, in S3, the calculation of the comprehensive control cost of resources on each side is as shown in the following formula:

[0026]

[0027] In the formula, S 源 (t), S 网 (t), S 荷 (t), S 储 (t) are the comprehensive control costs of the power supply side, grid side, energy storage side, and load side at time t; They are the comprehensive control costs of thermal power, hydropower, photovoltaic power, wind power and other power sources; They are the comprehensive control costs of temperature control load, electric vehicles and other loads; They are the comprehensive regulation costs of pumped storage, hydrogen energy storage and other energy storage.

[0028] In an optional implementation, in S4, the system operation constraint boundaries include power balance constraints, traditional power supply operation constraints, new energy operation constraints, adjustable load constraints, energy storage operation constraints and system steady-state operation constraints.

[0029] In a second aspect, the present invention provides an adjustable resource optimization control system considering energy conversion, the system comprising:

[0030] An optimization control strategy formulation module is used to execute the method of the aforementioned implementation mode and generate an optimization control strategy;

[0031] The control strategy output module is used to output the optimization control strategy.

[0032] Through the above technical scheme, the method and system, on the basis of fully considering the adjustable resources on the user side, consider the correlation and conversion relationship between electricity and other energy types and systems, and in the process of power grid regulation, explore the economical regulation potential in a larger range to meet the power system regulation needs under different time scales.

[0033] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A flow chart of an adjustable resource optimization control method considering energy conversion provided in an embodiment of the present application;

[0036] Figure 2 Flowchart for generating an optimal control strategy using the particle swarm algorithm. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0038] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "disposed" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0039] This embodiment provides an adjustable resource optimization control method considering energy conversion (hereinafter referred to as: method), which is mainly based on the following working principles:

[0040] (1) Consider energy conversion relationships and capacity analysis

[0041] Energy includes thermal energy, electrical energy, mechanical energy, chemical energy, hydrogen energy and other types. There are certain coupling conversion relationships between different types of energy. Through the flexible conversion of energy, these relationships have a great impact on the selection of grid control resources and control boundaries, and provide more options for grid control. The conversion relationships of different types of energy mainly include electric-heat coupling, electric-cooling coupling, electric-hydrogen coupling and electrical coupling.

[0042] Electricity-heat coupling: mainly includes technologies such as cogeneration, electric heating and solar thermal power generation, and has the ability to climb, peak and balance medium- and short-term electricity. In combination with the actual regional development, the resource types, available resource scale and regulation costs related to electricity-heat coupling in the region are analyzed.

[0043] Electric-cooling coupling: mainly includes air conditioning cooling load and corresponding cold storage facilities. According to the application field, refrigeration equipment can be divided into two categories: industrial and commercial refrigeration equipment and household refrigeration equipment, which have peak-shifting and peak-peaking capabilities. Analyze the resource types, available resource scale and regulation costs related to electric-cooling coupling in the region.

[0044] Electricity-hydrogen coupling: including "one-way coupling" of electricity and hydrogen and "two-way coupling" of electricity, hydrogen and electricity, with the ability to regulate peak loads within a day, ensure peak supply, and balance across cycles. Combined with the application of regional electricity-hydrogen coupling technology, the scale of available resources and the cost of regulation of electricity-hydrogen coupling are analyzed.

[0045] Electrical coupling: Gas turbines are supplied by the natural gas network and can be used as both a load and a power source for the gas grid; P2G (Power to Gas, Power to Fuel Gas) technology can be used as both a load and a gas source for the gas grid. Combined with the application of regional electrical coupling technology, the scale of available resources and the cost of regulation for electrical coupling are analyzed.

[0046] (2) Grid operation analysis

[0047] Combined with the resource conversion relationship and capacity across energy types, the operation of the regional power grid is analyzed, and the resource types and scales on the source, grid, load, and storage sides are analyzed. On the power supply side, the installed capacity and adjustable capacity of traditional power sources such as thermal power, hydropower, and biomass power generation, as well as new energy sources such as wind power and photovoltaic power are analyzed; on the grid side, the grid conditions and operating status of each level in the region are analyzed, and the load transfer capacity and balance autonomy capacity are analyzed; on the load side, the level of electricity demand and load characteristics in the region are analyzed, and the adjustable capacity of various adjustable resources such as air conditioning cooling load, electric heating load, electric vehicle load, and industrial load in the region are analyzed; on the energy storage side, the installed capacity and adjustable capacity of energy storage equipment such as pumped storage, hydrogen energy storage, electrochemical energy storage, and heat storage boilers in the region are analyzed. Combined with the historical operation and development plans of various resources, the operating characteristics and development trends of various resources are analyzed. At the control level, the modes of various resources participating in grid regulation (including demand response, ancillary services, etc.) and the control costs are analyzed.

[0048] (3) Grid operation status prediction and scenario analysis

[0049] Based on the historical load demand and energy output of the region, combined with the growth of regional load and new energy, and taking into account the impact of holidays, weather, time-of-use electricity prices, policy mechanisms and other factors, load demand forecasts and energy output forecasts are carried out. Combined with the grid planning and energy storage development layout, the forecast results of the source, grid, load and storage operation status of the regional power system are obtained, and the regulation potential of the adjustable resources on each side is further analyzed, as well as the scale of resource coordination across energy types.

[0050] Combined with the forecast results, the operating status of the power grid is analyzed, and demand scenarios such as rapid ramping, intraday peak regulation, peak supply guarantee and cross-cycle power balance are divided, and the regulation needs of each scenario are quantitatively analyzed.

[0051] Rapid ramping scenario: occurs during periods such as sunset when photovoltaic power generation is greatly reduced. In the evening, photovoltaic power generation output decreases and power demand increases, and the net load rises rapidly (growth rate ΔP / P0 ≥ 50%), which brings short-term rapid adjustment requirements to the power system. The time period of the rapid ramping scenario is generally hourly or less.

[0052] Intraday peak-shaving scenario: According to the grid operation specifications, the peak-to-valley changes in system load and the changes in renewable energy output are tracked, and the grid-connected entity needs to adjust the power generation and consumption or start and stop the equipment according to the dispatch instructions. The time period of the intraday peak-shaving scenario is daily.

[0053] Peak supply guarantee scenario: mainly occurs during periods of power shortage (usually during high temperatures in summer or severe cold in winter), and requires the adjustment of various resources to improve load supply capacity and ensure safe and stable operation of the power grid. The time period of the peak supply guarantee scenario is generally from days to weeks.

[0054] Long-term power balance scenario: The fluctuation of renewable energy output leads to the coexistence of power supply shortage and wind and solar power abandonment. In order to maintain the power balance over a long period of time, there is a need for large-scale space-time transfer of renewable energy power generation. The time period of the cross-cycle power balance scenario is from weekly to graded.

[0055] Based on the above analysis of the working principle, the method provided in this embodiment combines the differentiated scenario regulation needs, takes the safe and stable operation of the power grid as a prerequisite, takes the maximization of new energy consumption and economic optimization as the regulation goal, considers the mode and cost of multiple adjustable resources participating in power grid regulation, and considers the synergistic relationship of cross-energy resources. A two-layer decision-making method is adopted. The upper layer aims to maximize the consumption of new energy, and the lower layer aims to minimize the regulation cost. It optimizes the selection of multiple adjustable resources, formulates power grid operation regulation strategies, and meets the power grid regulation needs in different scenarios.

[0056] For details, please refer to Figure 1 , the method comprises the following steps:

[0057] S1: Construct a regulation demand decomposition model to calculate the regulation target value ΔP 场景 Decompose.

[0058] Specifically, in response to the needs of differentiated grid operation scenarios, it is necessary to comprehensively consider the regulation capabilities of resources on the source, grid, load and storage sides, and set the regulation target value ΔP 场景 Decompose it as shown below:

[0059] ΔP 场景 (t) = ΔP 源 (t)+ΔP 网 (t)+ΔP 荷 (t)+ΔP 储 (t) (1)

[0060]

[0061] In the formula, ΔP 场景 (t) is the control target value of the demand scenario at time t, ΔP 源 (t), ΔP 网 (t), ΔP 荷 (t), ΔP 储 (t) are the control quantities of the power supply side, grid side, load side and energy storage side at time t respectively; are the power generation of thermal power, hydropower, photovoltaic power, wind power and other power sources at time t; They are the control quantities of flexible power transfer of regional power grid and regional autonomy participating in power grid control at time t respectively; They are the power requirements of temperature control load (mainly air conditioning cooling and electric heating load), electric vehicles and other loads at time t; They are the adjustable capacities of pumped storage, hydrogen energy storage and other energy storage at time t; is the regulation coefficient of resource i at time t. A positive value indicates positive regulation, and a negative value indicates reverse regulation. There is reverse regulation between the resources on the load side and the energy storage side.

[0062] S2: Determine the upper-level optimization goal to maximize the consumption of new energy.

[0063] Specifically, the power generation and abandoned wind and solar power of photovoltaic and wind power are comprehensively considered, and the power generation and consumption rate of new energy (photovoltaic and wind power are analyzed as representatives) is calculated, with the goal of maximizing the consumption of new energy, as shown in the following formula:

[0064]

[0065] In the formula, C 新能源 (%) is the consumption rate of new energy, Q 光伏 , Q 风电 are photovoltaic power generation and wind power generation respectively; N is the scene control time; are the abandoned solar and wind power at time t; ΔQ 弃光 , ΔQ 弃风 They are the amount of abandoned solar power and wind power during the scene control period, which are directly affected by the amount of new energy control.

[0066] S3: Determine the lower-level optimization goal to minimize the control cost.

[0067] Specifically, on the basis of maximizing the consumption of new energy, a control scheme that minimizes the control cost is selected, as shown in the following formula:

[0068]

[0069]

[0070] In the formula, S 场景 is the overall control cost to meet the scene target; N is the total scene control time; S 场景 (t) is the control cost required to complete the scene control target at time t; S 源 (t), S 网 (t), S 荷 (t), S 储 (t) are the comprehensive control costs of the power supply side, grid side, energy storage side, and load side at time t; They are the comprehensive control costs of thermal power, hydropower, photovoltaic power, wind power and other power sources; They are the comprehensive control costs of temperature control load, electric vehicles and other loads; They are the comprehensive regulation costs of pumped storage, hydrogen energy storage and other energy storage.

[0071] S4: Construct system operation constraint boundaries.

[0072] Specifically, the system operation constraint boundaries include power balance constraints, traditional power supply operation constraints, new energy operation constraints, adjustable load constraints, energy storage operation constraints and system steady-state operation constraints.

[0073] ①The power balance constraint is:

[0074]

[0075] In the formula, are the control quantities of thermal power, hydropower, photovoltaic power, wind power and other power sources participating in grid control at time t, They are the control quantities of other loads, temperature control loads, electric vehicles, pumped storage and other energy storage participating in grid control at time t.

[0076] Note: The energy storage power in the formula defaults to charging power. When the value is negative, it is in discharge state.

[0077] ②Constraints on operation of traditional power supply:

[0078] Power output constraints:

[0079]

[0080] In the formula, The output of the traditional power supply i, are the upper and lower limits of the output of traditional power source i respectively.

[0081] Climbing speed constraint:

[0082]

[0083] In the formula, is the climbing capability of traditional power source i at time t, They are the upper and lower limits of the climbing capability of the traditional power source i respectively.

[0084] ③ Constraints on new energy operation:

[0085] Photovoltaic power generation constraints:

[0086]

[0087] In the formula, is the power generation of photovoltaic power station i at time t, is the predicted power generation of photovoltaic power station i at time t.

[0088] Wind power generation constraints:

[0089]

[0090] In the formula, is the output of wind farm j at time t, is the predicted output of wind farm j at time t.

[0091] ④Adjustable load constraint:

[0092]

[0093] In the formula, are the scale of load i participating in regulation at time t and the load forecast value respectively.

[0094] ⑤ Energy storage operation constraints:

[0095] Energy storage charging and discharging capacity constraints:

[0096] Power Constraints:

[0097] 0≤SOC i (t)≤E i (11)

[0098] In the formula, SOC i (t) is the energy storage state of energy storage i at time t, E i is the capacity of energy storage i.

[0099] Power Constraints:

[0100]

[0101] In the formula, are the charging and discharging power of energy storage i at time t respectively; are the rated charging and discharging powers of energy storage i respectively.

[0102] ⑥ System steady-state operation constraints:

[0103] The node voltage does not exceed the limit:

[0104]

[0105] In the formula, U, They represent the lower and upper limits of the node voltage respectively, and the upper and lower limits of the voltage of all nodes are the same; Represents the voltage of node i at time t in scheduling.

[0106] Branch current does not exceed the limit:

[0107]

[0108] In the formula, It represents the upper limit of the current of branch j. Affected by the line specifications, the upper limits of the currents of different branches in the system vary. Represents the current of branch j at time t during scheduling.

[0109] The power grids at all levels are not overloaded:

[0110] P i (t)≤Q i ×80% (16)

[0111] Among them, P i (t) is the load demand of the i-level power grid; Q i is the rated capacity, i is divided into 110 kV, 35 kV and 10 kV.

[0112] S5: Generate an optimization control strategy based on the model, control objectives and constraints constructed in S1 to S4.

[0113] Specifically, combined with the control target value of the power grid operation scenario, a two-layer decision-making method is used, and a particle swarm algorithm is adopted to carry out resource control optimization, generating a multi-adjustable resource optimization control strategy that takes into account both maximizing the new energy consumption and minimizing the control cost.

[0114] For details, please refer to Figure 2 , S5 comprises the following steps:

[0115] S51: Take the control coefficients of various resources as particles, the number of particles is 100, and initialize the particle swarm speed and position by combining the upper-level optimization objectives, the lower-level optimization objectives, and the system operation constraint boundaries.

[0116] S52: Set the target range for new energy consumption.

[0117] S53: Calculate the consumption rate of new energy.

[0118] S54: Select the optimal particles based on the new energy consumption rate and the new energy consumption target range.

[0119] S55: Determine whether the number of iterations has been reached.

[0120] If the number of iterations is not reached, S56 is executed: updating the particle swarm velocity and position and the inertia weight coefficient.

[0121] If the number of iterations is reached, S57 is executed: the comprehensive control cost of each side is calculated.

[0122] S58: Determine whether cost expectations are met.

[0123] If the cost expectation is not met, the process returns to S52 .

[0124] If the cost expectation is met, execute S59: calculate the maximum new energy consumption and the minimum control cost, and generate an optimized control strategy.

[0125] This embodiment also provides an adjustable resource optimization control system considering energy conversion (hereinafter referred to as: system), which is used to execute the above method and includes the following modules:

[0126] Basic data input module, used to input basic energy and power data in the region, including energy conversion technology application data, power supply data, grid data, user data, energy storage configuration data, resource participation regulation data, etc.;

[0127] Energy conversion capacity analysis module, which is used to analyze the conversion relationship and capacity of cross-variety energy resources in a region by combining basic energy and power data;

[0128] The power grid operation analysis module is used to analyze the power grid operation in the region by combining the conversion relationship and capacity of cross-variety energy resources. The power grid operation includes the type and scale of resources on the source, network, load and storage sides.

[0129] The state prediction and scenario analysis module is used to carry out load demand prediction and energy processing prediction, and obtain the operation state prediction results of the resources on the source, network, load and storage sides of the power system in the region. The operation state prediction results include the regulation needs of demand scenarios such as rapid ramping, intraday peak regulation, peak supply, and cross-cycle power balance, and further analyze the regulation capacity of resources on each side and the synergy scale of cross-variety energy resources;

[0130] The optimization control strategy formulation module is used to combine the control objectives, use the two-level decision-making method, and adopt the particle swarm algorithm to generate the optimization control strategy of multiple adjustable resources that takes into account both maximizing the consumption of new energy and minimizing the control cost, that is, to execute the above method;

[0131] The control strategy output module is used to output the optimized control strategy, which includes the scenario demand response, the time sequence control scale of various resources, the control cost, and the new energy consumption.

[0132] The beneficial effects of the adjustable resource optimization control method and system considering energy conversion provided in this embodiment include:

[0133] This method is based on the regulation potential of multiple categories of adjustable resources in the region, combined with the regulation needs of typical scenarios such as rapid grid ramping, intraday peak regulation, peak supply guarantee and long-term power balance, with safe and stable grid operation, maximum consumption of new energy and optimal economy as the regulation goals, comprehensively considering the synergistic transformation relationship among different energy types, adopting a two-level decision-making method, proposing an optimized regulation method for adjustable resources that considers multiple energy conversions, building a regulation system, and formulating optimized regulation strategies to meet the grid regulation needs in different scenarios.

[0134] It should be noted that, in the absence of conflict, the features in the embodiments of this application may be combined with each other.

[0135] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing and controlling adjustable resources considering energy conversion, characterized in that: The method comprises: S1: Construct a regulation demand decomposition model to calculate the regulation target value ΔP 场景 To decompose; S2: Determine the upper optimization goal to maximize the consumption of new energy; S3: Determine the lower-level optimization goal as minimizing the control cost; S4: Construct system operation constraint boundaries; S5: Generate an optimization control strategy based on the model, control objectives and constraints constructed in S1 to S4.

2. The adjustable resource optimization and control method considering energy conversion according to claim 1 is characterized in that: In S1, the control target value ΔP 场景 Decompose it as shown below: ΔP scene (t) = ΔP source (t) + ΔP grid (t) + ΔP load (t) + ΔP storage (t) Where ΔP 场景 (t) is the control target value of the demand scenario at time t, ΔP 源 (t), ΔP 网 (t), ΔP 荷 (t), ΔP 储 (t) are the control quantities of the power supply side, grid side, load side and energy storage side at time t respectively.

3. The adjustable resource optimization and control method considering energy conversion according to claim 2 is characterized in that: In S1, the calculation formula of each control quantity is as follows: In the formula, are the power generation of thermal power, hydropower, photovoltaic power, wind power and other power sources at time t; They are the control quantities of flexible power transfer of regional power grid and regional autonomy participating in power grid control at time t respectively; are the power requirements of temperature control load, electric vehicle and other loads at time t respectively; They are the adjustable capacities of pumped storage, hydrogen energy storage and other energy storage at time t; is the regulation coefficient of resource i at time t.

4. The adjustable resource optimization and control method considering energy conversion according to claim 3 is characterized in that: In S2, the goal is to maximize the consumption of new energy, as shown in the following formula: In the formula, C 新能源 (%) is the consumption rate of new energy, Q 光伏 , Q 风电 They are photovoltaic power generation and wind power generation respectively; ΔQ 弃光 , ΔQ 弃风 They are the amount of abandoned solar power and wind power during the scene control period, which are directly affected by the amount of new energy control.

5. The adjustable resource optimization control method considering energy conversion according to claim 4 is characterized in that: In S3, the control scheme that minimizes the control cost is as follows: In the formula, S 场景 is the overall control cost to meet the scenario objectives; N is the total scenario control time; S 场景 (t) is the control cost required to complete the scene control target at time t.

6. The adjustable resource optimization and control method considering energy conversion according to claim 5 is characterized in that: In S3, the calculation of the comprehensive control cost of resources on each side is shown in the following formula: In the formula, S 源 (t), S 网 (t), S 荷 (t), S 储 (t) are the comprehensive control costs of the power supply side, grid side, energy storage side, and load side at time t; They are the comprehensive control costs of thermal power, hydropower, photovoltaic power, wind power and other power sources; They are the comprehensive control costs of temperature control load, electric vehicles and other loads; They are the comprehensive regulation costs of pumped storage, hydrogen energy storage and other energy storage.

7. The adjustable resource optimization and control method considering energy conversion according to claim 1 is characterized in that: In S4, the system operation constraint boundaries include power balance constraints, traditional power supply operation constraints, new energy operation constraints, adjustable load constraints, energy storage operation constraints and system steady-state operation constraints.

8. An adjustable resource optimization control system considering energy conversion, characterized in that: The system comprises: An optimization control strategy formulation module, used to execute the method described in claim 1 to generate an optimization control strategy; The control strategy output module is used to output the optimization control strategy.

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