Power system multi-scale flexibility margin evaluation method based on multi-agent simulation

Abstract

The invention relates to a power system multi-scale flexibility margin evaluation method based on multi-agent simulation, and belongs to the technical field of power system dispatching and control. The method comprises the following steps: respectively setting agencies for each distribution network and each main network of each power system; performing multi-year multi-agent time sequence simulation on each power system based on each agent of each power system to obtain an optimal scheduling scheme of each power system in each year, the optimal scheduling scheme including output of each unit of each power system in each year and a flexibility demand which is not satisfied by each distribution network; determining the weight of each flexibility margin index based on all the optimal scheduling schemes of each power system; and determining a flexibility margin evaluation result of each power system based on the optimal scheduling scheme of each power system in each year and the weight of each flexibility margin index. According to the method, the multi-scale flexibility margin of the power system is accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching and control, and particularly to a method for evaluating the multi-scale flexibility margin of a power system based on multi-agent simulation. Background Art

[0002] In the process of building a new power system, the high-proportion grid connection of renewable energy represented by wind power and photovoltaic power increases the randomness of power supply. At the same time, large-scale electric energy substitution makes the power load structure more diversified, and the peak load change will be more complex, causing a huge impact on power balance and safe and stable operation. Accurately depicting the overall adequacy of the power system plays an important role in the development planning and layout of new energy and promoting the transformation of the power system. Therefore, how to evaluate the multi-scale flexibility margin of the power system has become an urgent problem to be solved.

[0003] Currently, the flexibility margin evaluation indexes can be divided into two categories: probabilistic and deterministic. Probabilistic evaluation indexes include the expected value of insufficient ramping resources, the probability and expected value of insufficient upward flexibility, the probability and expected value of insufficient downward flexibility, the probability and its expected value when flexibility is insufficient, the expected value and probability of the margin when flexibility is sufficient, etc. Deterministic indexes include the system flexibility interval index based on the random interval analysis method, etc. However, with the continuous advancement of the construction of the new power system, the grid connection of new energy has become the main factor affecting the flexibility margin of the power system, and its consideration is lacking in the existing research.

[0004] Regarding the evaluation method of the flexibility margin of the power system, some studies conduct flexibility assessment on different types of flexibility resources or demands by formulating a scoring mechanism; however, it does not consider the uncertainties existing in the supply side and demand side of the power system and is only applicable to the rough assessment of the flexibility adequacy in the initial stage of planning. On the basis of the above research, some scholars measure whether the system flexibility is sufficient by comparing whether the deterministic flexibility resources can match the uncertain net load demand. However, the flexibility of the power system has the characteristics of directionality, state dependence, multi-time scale, and probability. The flexibility resources of the power system can participate in the power regulation of multiple time scales to maintain the real-time power balance between power generation and consumption. This makes it a key problem to be solved urgently to study the flexibility margin of the new power system under multiple time scales and construct a new evaluation method for the flexibility margin of the new power system. Summary of the Invention

[0005] In view of the above analysis, the present invention aims to disclose a method for evaluating the multi-scale flexibility margin of a power system based on multi-agent simulation. Through the main grid agent and the distribution grid agent, the power system is subjected to multi-time scale time series simulation, considering the power regulation of new energy on the power system at multiple time scales, and the flexibility margin of the power system is accurately evaluated through the flexibility margin evaluation index system.

[0006] The present invention provides a multi-scale flexibility margin evaluation method for a power system based on multi-agent simulation, which specifically includes the following steps: Set up agents for each distribution network and main network of each power system; Based on the agents of each power system, a multi-agent time series simulation is performed on each power system for multiple years to obtain an optimal dispatching plan for each power system in each year, wherein the optimal dispatching plan includes the output of each unit of each power system in each year and the unmet flexibility demand of each distribution network; Determine the weight of each flexibility margin indicator based on all optimal dispatching schemes of each power system; The flexibility margin evaluation result of each power system is determined based on the optimal dispatch plan of each power system in each year and the weight of each flexibility margin indicator.

[0007] Furthermore, based on the agents of each power system, a multi-agent time series simulation is performed on each power system for multiple years, and the optimal dispatching scheme of each power system in each year is obtained, including: The agent of each distribution network of each power system determines the flexibility demand of the corresponding distribution network based on the power supply and load within the distribution network; The agent of each power system main grid makes decisions on the output of each unit based on the flexibility requirements of each distribution grid and the goal of minimizing the operating cost of the power system to which it belongs, based on the set constraints; Each agent of each power system obtains the optimal dispatching plan of the power system in each year by simulating the power system year by year.

[0008] Furthermore, the weights of the flexibility margin indicators determined based on all optimal dispatching schemes of each power system include: Based on all the optimal dispatching schemes of each power system, the flexibility margin index values of each power system in each year are obtained; The average value of each flexibility margin index of each power system is calculated based on each flexibility margin index value of each power system in each year; Based on the average value of each flexibility margin index of each power system, the objective weight of each flexibility margin index is determined by using the entropy weight method; The analytic hierarchy process is used to determine the subjective weight of each flexibility margin indicator; The game theory method is used to determine the comprehensive weight of each flexibility margin indicator based on the objective weight and the subjective weight.

[0009] Furthermore, the flexibility margin evaluation result of each power system is determined based on the optimal dispatching plan of each power system in each year and the weight of each flexibility margin index, including: Calculate the values of various flexibility margin indicators for each power system in each year based on the optimal scheduling plan for each power system in each year; Obtain the cloud model digital characteristic values of various flexibility margin indicators for each power system based on the values of various flexibility margin indicators for each power system in each year; Calculate the comprehensive digital characteristic values of the flexibility margin for each power system based on the comprehensive weights of various flexibility margin indicators and the corresponding cloud model digital characteristic values; Determine the evaluation results of the flexibility margin for each power system based on the comprehensive digital characteristic values of the flexibility margin for each power system.

[0010] Furthermore, the cloud model digital characteristic values include the expected value, entropy value, and hyperentropy value; The calculation method of the cloud model digital characteristic values of various flexibility margin indicators is as follows: ; where, is the expected value of the th flexibility margin indicator; is the indicator value of the th year and the th flexibility margin indicator; is the mean value of the th flexibility margin indicator; is the total number of years of electrical simulation; is the entropy value of the th flexibility margin indicator; is the hyperentropy value of the th flexibility margin indicator; is the variance of the th flexibility margin indicator value, ; The calculation method of the comprehensive digital characteristic value of the flexibility margin is as follows: ; where, is the number of flexibility margin indicators; is the corresponding weight of the flexibility margin indicator.

[0011] Furthermore, the flexibility margin indicators include the new energy consumption rate, average line capacity margin, probability of insufficient flexibility, and expected value of flexibility margin.

[0012] Furthermore, with the goal of minimizing the operating cost of the power system, it is expressed by the objective function: ; ; ; ; Among them, is the distribution network set; , , , , , , are the power generation costs of coal-fired, oil-fired, gas-fired, hydropower, energy storage, centralized wind power, and photovoltaic units at time; is the penalty cost for curtailment of new energy, i.e., wind power and photovoltaic power; and are respectively the curtailment power of photovoltaic and wind turbine units at time; is the penalty cost for the system not meeting the interactive flexibility requirements of the distribution network ; is the flexibility requirement of the distribution network not met by the system; the superscript and represent the generator set types, , , represent coal-fired, oil-fired, and gas-fired units respectively; , , represent energy storage, centralized wind power, and photovoltaic units respectively; represents the output of each unit; is the function between the unit operation cost and the output; , are the start-up and shutdown states of the unit at time; , are the start-up and shutdown costs of the unit; the superscript represents hydropower.

[0013] Furthermore, the set constraint conditions include a power balance constraint, and the power balance constraint is expressed as: ; ; Among them, , , , , , are the power generation powers of coal-fired, oil-fired, gas-fired, hydropower, wind power, and photovoltaic units at time respectively; , are the energy storage units at The charging and discharging power at the moment; is the power transmission demand of the system at the moment; is the flexibility demand of the distribution network at the moment; is the interaction power between the system and the distribution network ; and are respectively the curtailment power of the photovoltaic and wind turbine units at the moment; is the unmet flexibility demand of the distribution network of the system ;

[0014] Furthermore, the set constraint conditions also include the substation feed-in power constraint, and the substation feed-in power constraint is expressed as: ; where is the interaction power between the system and the distribution network ; , are respectively the minimum and maximum feed-in powers of the distribution network substation.

[0015] Furthermore, the set constraint conditions also include the power transmission quantity constraint, and the power transmission quantity constraint is expressed as: ; where is the power transmission demand of the system at the moment; is the power transmission channel capacity at the moment; is the power transmission channel capacity of the power receiving area ; is the set of power receiving areas.

[0016] The present invention can at least achieve one of the following beneficial effects: By dividing each power system into a main grid and multiple distribution networks respectively, setting agents for each distribution network and the main grid respectively, performing multi-agent time-series simulation on each power system based on each agent, splitting the global optimization problem into two-level sub-problems of the main grid and the distribution network, and achieving decoupling through information interaction between agents, it avoids directly dealing with the high-dimensional non-linear model of the whole network, and solves the problem in the traditional time-series production simulation model that due to the existence of a hierarchical scheduling mode, each level of the power grid is responsible for different control centers, and it is necessary to ensure relative independence and clear responsibilities and powers, resulting in it being difficult for a single entity to obtain a large amount of system global information in real time.

[0017] By constructing a multi-dimensional evaluation system covering indicators such as new energy consumption rate, average margin of line capacity, probability of insufficient flexibility, and expected flexibility margin, and adopting a subjective and objective weighted evaluation method for flexibility margin based on the cloud model, the flexibility margin of the power system is evaluated from multiple dimensions, improving the credibility of the evaluation.

[0018] Through multi-year simulations of multiple power systems, multiple data samples with strong enough differences are obtained, providing reliable data support for determining the weights of each flexibility margin indicator and further improving the credibility of the evaluation.

[0019] By using the cloud model, the ambiguity and randomness of the flexibility margin caused by the uncertainty of new energy output are effectively processed, providing intuitive decision support for the flexibility margin evaluation.

[0020] Other features and advantages of the present invention will be described in the subsequent specification, and some advantages will be obvious from the specification or understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained from the content specifically pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings are only for the purpose of showing specific embodiments and are not considered as a limitation of the present invention. Throughout the drawings, the same reference signs denote the same components; Figure 1 is a flowchart of the method of the present invention; Figure 2 is a multi-agent time-series simulation framework for power systems. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will specifically describe the preferred embodiments of the present invention in conjunction with the drawings, where the drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, not for limiting the scope of the present invention.

[0023] An embodiment of the present invention discloses a multi-scale flexibility margin evaluation method for power systems based on multi-agent simulation, which can synchronously evaluate the flexibility margins of multiple power systems. As Figure 1 shown, it specifically includes steps S1-S4.

[0024] S1. Set agents for each distribution network and main network of the power system respectively.

[0025] Each power system refers to a power system divided according to a specified geographical area, with independent points and clear responsibilities and powers. Exemplarily, each provincial power system or municipal power system.

[0026] In the existing technology, time-series production simulation can model the flexibility of the power system by balancing the power of the power system in each period over a long and fine time scale, taking into account the timing characteristics of renewable energy power generation output and the relevant constraints of unit operation, thereby simulating the power and electricity balance process and becoming an important simulation and analysis tool for the power industry.

[0027] In the present invention, agents are respectively set for each distribution network and main network of the power system, and the global optimization problem is divided into two-level sub-problems of the main network and the distribution network. Information interaction is achieved between the agents of the main network and the distribution network to perform time-series production simulation.

[0028] Specifically, Figure 2 It is a schematic diagram of the multi-agent timing simulation framework of the power system of the present invention.

[0029] S2. Based on the agents of each power system, multi-year multi-agent time series simulation is performed on each power system to obtain the optimal dispatching plan of each power system in each year. The optimal dispatching plan includes the output of each unit of each power system in each year and the unmet flexibility requirements of each distribution network. Taking a power system as an example, the optimal dispatching plan for each year specifically includes: Each of the agents of each distribution network of the power system determines the corresponding distribution network flexibility requirements based on the power supply and load within the distribution network; that is, during the simulation process, the distribution network, as a priority autonomous balancing unit, seeks to achieve supply and demand balance within the node, calculates the interactive flexibility requirements of the distribution network nodes, and feeds back to the main network agent; The agent of the main power grid of the power system makes decisions on the output of each unit based on the flexibility requirements of each distribution grid and the goal of minimizing the operating cost of the power system to which it belongs, based on the set constraints; The agents of the power system main grid and each distribution grid simulate the power system year by year to obtain the optimal dispatching plan of the power system. Exemplarily, the simulation tool can use Matlab R2022a, and the Cplex solver is called through the Yalmip toolbox to obtain the optimal dispatching plan of the power system each year.

[0030] Furthermore, the goal is to minimize the operating cost of the power system, and the objective function is expressed as: ; ; ; ; in, It is a distribution network collection; , , , , , , are the power generation costs of coal-fired, oil-fired, gas-fired, hydropower, energy storage, centralized wind power, and photovoltaic units at time, respectively; is the penalty cost for curtailment of new energy, i.e., wind power and photovoltaic power; and are respectively the curtailment power of photovoltaic and wind turbine units at is the penalty cost for the system not meeting the interactive flexibility requirements of the distribution network ; is the flexibility requirement of the distribution network that the system fails to meet; the superscript and and represent the types of generator units, , , represent coal-fired, oil-fired, and gas-fired units, respectively; , , represent energy storage, centralized wind power, and photovoltaic units, respectively; represents the output of each unit; is the function between the operation cost and the output of the unit; , are the start-up and shutdown states of the unit at time; , are the start-up and shutdown costs of the unit; the superscript represents hydropower.

[0031] Specifically, the set constraint conditions include power balance constraint, thermal power unit constraint, hydropower unit constraint, energy storage constraint, reserve constraint, outbound power constraint, and substation feed-in power constraint.

[0032] Furthermore, the power balance constraint is expressed as: ; ; Among them, , , , , , are the power generation powers of coal-fired, oil-fired, gas-fired, hydropower, wind power, and photovoltaic units at time, respectively; , are the charging and discharging powers of the energy storage unit at time, respectively; is the system at Power transmission demand at a certain moment; For the distribution network At Flexibility demand at a certain moment; For the interaction power between the system and the distribution network ; And Are respectively Photovoltaic and wind turbine curtailment power at a certain moment; Is the unmet flexibility demand of the distribution network by the system ;

[0033] Furthermore, thermal power units include coal-fired, oil-fired, and gas-fired units.

[0034] The thermal power unit constraints are expressed as: ; Wherein, Is the operating state of the th type of thermal power unit at a certain moment; , Are the start-up and shutdown states of the th type of thermal power unit at a certain moment; , Are respectively the minimum continuous start-up and shutdown times of the th type of thermal power unit; , Are the minimum and maximum outputs of the th type of thermal power unit; , Are the maximum allowable upward and downward ramp rates of the th type of thermal power unit; , Are respectively the up / down reserve capacities of the th type of thermal power unit at a certain moment. The thermal power unit constraints describe the minimum start-stop time constraints, start-stop logic constraints, maximum and minimum unit output constraints, and ramp constraints that thermal power units need to meet.

[0035] Specifically, the hydropower unit constraints are expressed as: ; Wherein, Is the operating state of the hydropower unit at a certain moment; , Are the minimum and maximum outputs of the hydropower unit; , Are respectively the up / down reserve capacities of the hydropower unit at a certain moment; The power generation of the hydropower unit at ; , are the minimum and maximum outputs of the hydropower unit; , are the maximum allowable upward and downward ramping rates of the hydropower unit; is the efficiency of converting water energy into electrical energy; is the density of water; is the acceleration due to gravity; is the net head height; The power generation flow rate of the hydropower unit at ; , are the minimum and maximum power generation flow rates of the hydropower unit respectively; , , are the inflow, outflow, and spillage flow rates of the reservoir at respectively; is the reservoir capacity at . The constraints of the hydropower unit describe that the hydropower unit needs to meet the maximum and minimum technical output constraints, ramping rate constraints, power generation capacity constraints, reservoir flow and capacity constraints, and consider that the unit can be started or stopped in a short time, without considering the start-stop time constraints.

[0036] Specifically, the energy storage constraint is expressed as: ; where is the charge-discharge power of the energy storage device at ; , are the charge and discharge state variables of the energy storage device; is the state of charge of the energy storage device at ; and are the charge and discharge efficiencies of the energy storage respectively; and are the rated charge and discharge powers of the energy storage device respectively; and are the minimum charge and discharge powers of the energy storage; and are the equivalent maximum and minimum energy storage capacities of the energy storage device.

[0037] Specifically, the reserve constraint is expressed as: ; where , are the total upward and downward reserve outputs of the power system at respectively; , and , are the up - and down - rotation reserve coefficients of the wind turbine generator set and the up - and down - rotation reserve coefficients of the photovoltaic generator set respectively; represents the serial number of the generator sets that can provide reserve capacity in the power system; is the total number of generator sets that can provide reserve capacity in the power system.

[0038] Specifically, the new - energy output constraint is expressed as: ; wherein, , are the maximum power generation of the wind power and photovoltaic generator sets respectively.

[0039] Specifically, the power transmission constraint is expressed as: ; wherein, is the power transmission demand of the power system at time; is the capacity of the transmission channel at time; is the capacity of the transmission channel of the power - receiving area ; is the set of power - receiving areas.

[0040] Specifically, the substation feed - in power is approximately expressed as: ; wherein, is the interactive power between the system and the distribution network ; , are the minimum and maximum feed - in powers of the distribution - network substation respectively.

[0041] The optimal scheduling plan for each year of the power system is obtained by simulation and solution based on the set constraint conditions. The optimal scheduling plan includes the output of each generator set and the unmet flexibility demand of each distribution network in each year of the power system.

[0042] Specifically, the Matlab R2022a simulation tool can be used. During the simulation process, the Cplex solver is called through the Yalmip toolbox to obtain the optimal scheduling plan for each year of the power system.

[0043] S3. Determine the weights of each flexibility margin index based on all the optimal scheduling plans of each power system. Specifically, it includes steps S31 - S35.

[0044] S31. Obtain the values of each flexibility margin index of each power system in each year based on all the optimal scheduling schemes of each power system.

[0045] The flexibility margin indexes include the new - energy consumption rate, the average line - capacity margin, the probability of insufficient flexibility, and the expected value of flexibility margin.

[0046] The calculation methods of the values of each flexibility margin index are as follows: ; is the value of the new - energy consumption rate; , are the generating powers of the photovoltaic units and wind turbine units at time respectively; is the generating power of all units at time; is the system - scheduling time within a year; ; is the value of the average line - capacity margin; is the line - capacity margin of line at time; is the maximum allowable transmission current of line ; is the transmission current of line at time; is the total number of lines; ; is the value of the probability of insufficient flexibility; is the period of insufficient flexibility; ; is the expected value of flexibility margin; , and are the flexibility margin, supply, and demand of the power system at time respectively.

[0047] S32. Calculate the average values of each flexibility margin index of each power system based on the values of each flexibility margin index of each power system in each year.

[0048] Specifically, for each flexibility margin index of each power system, calculate the average value of each flexibility margin index according to the corresponding index values of each year respectively.

[0049] S33. Determine the objective weights of each flexibility margin index by using the entropy weight method based on the average values of the flexibility margin indexes of each power system.

[0050] Specifically, when using the entropy weight method to determine the objective weights of each flexibility margin index, construct an initial evaluation matrix based on the average values of the flexibility margin indexes of each power system , expressed as: ; Among them, represents the number of objects to be evaluated, which is the number of power systems in the present invention, represents the number of evaluation indexes, which represents the number of flexibility margin indexes in the present invention, represents the th power system and the th average value of the flexibility margin index.

[0051] Furthermore, determine the objective weights of each flexibility margin index based on the initial evaluation matrix.

[0052] S34. Determine the subjective weights of each flexibility margin index by using the analytic hierarchy process.

[0053] Specifically, when using the analytic hierarchy process to determine the subjective weights, the primary index of the present invention, i.e., the overall goal, is the flexibility margin of the power system, and the secondary indexes include: new energy consumption rate, average line capacity margin, probability of insufficient flexibility, and expected flexibility margin. Use the analytic hierarchy process to determine the subjective weight values corresponding to each secondary index when evaluating the overall goal.

[0054] S35. Use the game theory method to determine the weights of each flexibility margin index based on the objective weights and the subjective weights.

[0055] Specifically, based on the objective weights and the subjective weights, construct two weight vectors. Furthermore, linearly combine them to construct the basic weight set of the flexibility margin evaluation index. Introduce the game theory idea to find the Nash equilibrium point, minimize the deviation between the comprehensive weight and the weights of each method, and solve the optimal linear combination coefficient and , calculate the comprehensive weight of each flexibility margin index , ; among them, is the th flexibility margin index.

[0056] S4. Determine the flexibility margin evaluation results of each power system based on the optimal scheduling scheme of each power system for each year and the weights of each flexibility margin index. Specifically, it includes S41 - S44.

[0057] S41. Obtain the values of each flexibility margin index for each power system in each year calculated based on the optimal scheduling plan for each power system in each year.

[0058] Specifically, directly use the calculation result of step S31.

[0059] S42. Obtain the digital characteristic values of the cloud model of each flexibility margin index for each power system based on the values of each flexibility margin index for each power system in each year.

[0060] The digital characteristic values of the cloud model include the expected value, entropy value, and hyperentropy value.

[0061] The calculation method for the digital characteristic values of the cloud model of each flexibility margin index for each power system in each year is as follows: ; Among them, is the expected value of the th flexibility margin index; is the index value of the th year and the th flexibility margin index; is the mean value of the th flexibility margin index; is the total number of years of electrical simulation; is the entropy value of the th flexibility margin index; is the hyperentropy value of the th flexibility margin index; is the variance of the th flexibility margin index value, .

[0062] S43. Calculate the comprehensive digital characteristic value of the flexibility margin of each power system based on the weights of each flexibility margin index and the corresponding digital characteristic values of the cloud model.

[0063] Among them, the weights of each flexibility margin index are obtained from step S3.

[0064] The calculation method for the comprehensive digital characteristic value of the flexibility margin is as follows: ; Among them, is the number of flexibility margin indices; is the corresponding weight of the flexibility margin index.

[0065] S44. Determine the evaluation result of the flexibility margin of each power system based on the comprehensive digital characteristic value of the flexibility margin of each power system.

[0066] Specifically, the evaluation levels of flexibility margin include: excellent, good, average, low, and poor.

[0067] Specifically, the given evaluation level classification criteria are the cloud digital characteristic values corresponding to each level.

[0068] Specifically, the cloud digital characteristic values corresponding to each level are calculated based on the following formula: ; where the subscript represents the th flexibility margin index; the subscript represents the th level; , are the upper and lower limits of the evaluation level interval of the flexibility margin index respectively; is a constant, which is adjusted according to the randomness of the index. Preferably, the new energy consumption rate is taken as 0.1, the average margin of line capacity is taken as 0.05, the probability of insufficient flexibility is taken as 0.1, and the expected value of flexibility margin is taken as 0.025.

[0069] Exemplarily, the upper and lower limits of the evaluation level interval of the flexibility margin index are shown in Table 1: Table 1 Upper and Lower Limits of Evaluation Level Interval of Flexibility Margin Index ; Furthermore, based on the comprehensive digital characteristic values of the flexibility margin of each power system and the cloud digital characteristic values corresponding to the given levels respectively, the evaluation results of the flexibility margin of each power system are obtained.

[0070] A multi-scale flexibility margin evaluation method for power systems based on multi-agent simulation disclosed in this embodiment divides each power system into a main grid and multiple distribution grids respectively, sets agents for each distribution grid and the main grid respectively, conducts multi-agent time-series simulation on each power system based on each agent, splits the global optimization problem into two-level sub-problems of the main grid and the distribution grid, and realizes decoupling through information interaction between agents, avoiding directly dealing with the high-dimensional non-linear model of the whole network, and solving the problem that in the traditional time-series production simulation model, due to the existence of a hierarchical scheduling mode, each level of power grid is responsible for different control centers, and it is necessary to ensure relative independence and clear responsibilities and powers, resulting in the difficulty for a single entity to obtain a large amount of system global information in real time.

[0071] By constructing a multi-dimensional evaluation system covering indicators such as new energy consumption rate, average margin of line capacity, probability of insufficient flexibility, and expected value of flexibility margin, and adopting a subjective and objective weighting evaluation method for flexibility margin based on the cloud model, the flexibility margin of the power system is evaluated from multiple dimensions, improving the credibility of the evaluation.

[0072] By conducting multi-year simulations on multiple power systems, a sufficient number of data samples with strong differences are obtained, providing reliable data support for determining the weights of various flexibility margin indicators and further enhancing the credibility of the evaluation.

[0073] By effectively processing the fuzziness and randomness of the flexibility margin caused by the uncertainties of new energy output and load demand through the cloud model, intuitive decision-making support is provided for the flexibility margin evaluation.

[0074] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating the multi-scale flexibility margin of a power system based on multi-agent simulation, characterized in that, The steps include: Set up agents for each distribution network and main network of each power system; Based on the agents of each power system, a multi-year multi-agent time series simulation is performed on each power system to obtain an optimal dispatching plan for each power system in each year, wherein the optimal dispatching plan includes the output of each unit of each power system in each year and the unmet flexibility demand of each distribution network; Determine the weight of each flexibility margin indicator based on all optimal dispatching schemes of each power system; The flexibility margin evaluation result of each power system is determined based on the optimal dispatch plan of each power system in each year and the weight of each flexibility margin indicator.

2. The flexibility margin evaluation method according to claim 1, wherein Based on the agents of each power system, a multi-agent time series simulation is performed on each power system for multiple years, and the optimal dispatching scheme of each power system in each year is obtained, including: The agent of each distribution network of each power system determines the flexibility demand of the corresponding distribution network based on the power supply and load within the distribution network; The agent of each power system main grid makes decisions on the output of each unit based on the flexibility requirements of each distribution grid and the goal of minimizing the operating cost of the power system to which it belongs, based on the set constraints; Each agent of each power system obtains the optimal dispatching plan of the power system in each year by simulating the power system year by year.

3. The flexibility margin evaluation method according to claim 2, wherein The weights of the flexibility margin indicators determined based on all optimal dispatching schemes of each power system include: Based on all the optimal dispatching schemes of each power system, the flexibility margin index values of each power system in each year are obtained; The average value of each flexibility margin index of each power system is calculated based on each flexibility margin index value of each power system in each year; Based on the average value of each flexibility margin index of each power system, the objective weight of each flexibility margin index is determined by using the entropy weight method; The analytic hierarchy process is used to determine the subjective weight of each flexibility margin indicator; The game theory method is used to determine the comprehensive weight of each flexibility margin indicator based on the objective weight and the subjective weight.

4. The flexibility margin evaluation method according to claim 3, wherein The determination of the flexibility margin evaluation results of each power system based on the optimal dispatching scheme of each power system in each year and the weights of each flexibility margin index includes: Based on the optimal dispatching plan of each power system in each year, each flexibility margin index value of each power system in each year is calculated respectively; Based on the flexibility margin index values of each power system in each year, the cloud model digital characteristic values of each flexibility margin index of each power system are obtained; Based on the comprehensive weight of each flexibility margin index and the corresponding cloud model digital eigenvalue, the comprehensive digital eigenvalue of the flexibility margin of each power system is calculated; The flexibility margin evaluation results of each power system are determined based on the comprehensive digital characteristic values of the flexibility margin of each power system.

5. The flexibility margin evaluation method according to claim 4, wherein The digital characteristic values of the cloud model include expected value, entropy value and super entropy value; The calculation method of the cloud model digital eigenvalue of each flexibility margin index is as follows: ； Among them, is the expected value of the th flexibility margin index; is the index value of the th flexibility margin index in the th year; is the mean value of the th flexibility margin index; is the total number of years of electrical simulation; is the entropy value of the th flexibility margin index; is the hyperentropy value of the th flexibility margin index; is the variance of the th flexibility margin index value, ; The calculation method of the comprehensive digital eigenvalue of the flexibility margin is: ； Among them, is the quantity of flexibility margin indicators; is the corresponding weight of the flexibility margin indicator.

6. The flexibility margin evaluation method according to claim 3, wherein The flexibility margin indicators include new energy consumption rate, average line capacity margin, probability of insufficient flexibility, and flexibility margin expectation.

7. The flexibility margin evaluation method according to claim 2, characterized in that The objective is to minimize the operating cost of the power system, which can be expressed as: ； ； ； ； Among them, is the distribution network set; , , , , , , are the power generation costs of coal - fired, fuel - oil, gas - fired, hydropower, energy storage, centralized wind power, and photovoltaic units at moment respectively; is the curtailment penalty cost of new energy, namely wind power and photovoltaic; and are respectively the curtailment power of photovoltaic and wind turbine units at is the penalty cost for the system not meeting the interactive flexibility requirements of the distribution network ; is the flexibility requirement of the distribution network not met by the system; the superscripts and represent the generator set types, , , represent coal - fired, fuel - oil, and gas - fired units respectively; , , represent energy storage, centralized wind power, and photovoltaic units respectively; represents the output of each unit; is the function between the unit operation cost and the output; , are the start - up and shut - down states of the unit at moment; , are the start - up and shut - down costs of the unit; the superscript represents hydropower.

8. The flexibility margin evaluation method according to claim 7, characterized in that The set constraints include power balance constraints, which are expressed as: ； ； Among them, , , , , , are the power generation powers of coal-fired, oil-fired, gas-fired, hydropower, wind power and photovoltaic units at moment respectively; , are the charging and discharging powers of the energy storage unit at moment respectively; is the power transmission demand of the system at moment; is the flexibility demand of the distribution network at moment; is the interaction power between the system and the distribution network ; and are the curtailment powers of the photovoltaic and wind turbine units at moment respectively; is the flexibility demand of the distribution network that the system fails to meet.

9. The flexibility margin evaluation method according to claim 7, wherein The set constraints also include substation feed-in power constraints, which are expressed as: ； Among them, is the interaction power between the system and the distribution network ; , are respectively the minimum and maximum feed-in powers of the distribution network substation.

10. The flexibility margin evaluation method according to claim 7, wherein The set constraints also include power transmission quantity constraints, which are expressed as: ； Among them, is the power transmission demand of the system at moment; is the transmission channel capacity at is the transmission channel capacity of the power receiving area ; is the set of power receiving areas.

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