Congestion scheduling method for distribution networks that takes into account congestion resistance

Abstract

This invention discloses a distribution network congestion scheduling method that considers congestion resistance, comprising the following steps: Step S1: Input system line parameters, load data, operating parameters and cost parameters of distributed power sources and flexible loads; Step S2: Obtain daily wind speed and solar intensity prediction data, and construct a set of wind power and photovoltaic output scenarios based on prediction errors; Step S3: Consider the original scheduling plan and the ramp-up capabilities of gas turbines and energy storage systems, and take into account the time scale constraints of call-up flexibility; Step S4: Consider the flexible resource layout, system line transmission capacity and actual power direction, and take into account the line transmission limitations of call-up flexibility, i.e., the spatial scale constraints of flexibility; Step S5: Substitute the output scenarios into the model, and calculate the congestion resistance index based on the multi-temporal and spatial scale characteristics, directionality and probability of flexibility; Step S6: Construct a distribution network congestion scheduling model and solve it.

Description

Technical Field

[0001] This invention relates to the field of distribution network congestion scheduling technology, and specifically to a distribution network congestion scheduling method that takes into account congestion resistance. Background Technology

[0002] The over-reliance of traditional power systems on primary energy sources such as coal has led to a series of problems, including environmental pollution and energy supply-demand imbalances. Accelerating the development, promotion, and application of distributed renewable energy generation (RDG) technologies such as wind power and photovoltaics has become an important development strategy for many countries. However, the randomness and volatility of RDG output cannot be ignored due to factors such as natural environment, climate conditions, and geographical location. Furthermore, RDG is geographically closer to the load side, which may cause large-scale accumulation of power flow in the distribution system lines in both time and space, potentially leading to distribution line congestion in severe cases.

[0003] Therefore, given the uncertainties of new energy power generation systems, conducting in-depth research on the congestion scheduling problem of power distribution systems is key to promoting the development and application of new energy power generation technologies and solving the aforementioned problems.

[0004] To adapt to congestion dispatching in distribution networks under uncertain environments, existing research often describes the uncertainty of renewable energy generation in probabilistic terms and conducts corresponding congestion risk assessments and management. In this process, to avoid incurring high economic costs due to low-probability events, opportunity constraint methods or conditional value-at-risk (VAT) theory are often used to characterize line congestion risk and reduce the conservatism of decision-making schemes. However, opportunity constraint methods only guarantee that the dispatching scheme meets the safety constraints within the confidence interval, and while VAT theory considers tail risk, the possibility of congestion still exists. Therefore, existing research, in its pursuit of maximizing the economic efficiency of dispatching schemes, may be overly aggressive in certain scenarios and lacks the ability to address congestion risks in other situations. Summary of the Invention

[0005] To address the shortcomings and deficiencies of existing technologies, this invention proposes a distribution network congestion scheduling method that considers congestion resistance. Building upon existing research on improving scheduling schemes, this invention additionally considers the line congestion risks that may exist outside the confidence interval in traditional methods and proposes to address these risks with the adjustability of flexible resources. This defines a congestion resistance evaluation index, enhances the ability of optimized scheduling schemes to cope with distribution network line congestion risks in certain scenarios, and improves the applicability of optimized solutions in practical engineering.

[0006] To achieve the above objectives, the present invention specifically adopts the following technical solution:

[0007] A distribution network congestion scheduling method considering congestion resistance, characterized by comprising the following steps:

[0008] Step S1: Input system line parameters, load data, operating parameters and cost coefficients of distributed power sources and flexible loads;

[0009] Step S2: Obtain the predicted wind speed and solar intensity data for the scheduling day, and construct a set of wind power and photovoltaic output scenarios based on the prediction error;

[0010] Step S3: Taking into account the original scheduling plan and the ramp-up capabilities of the micro gas turbine and energy storage system, and considering the time-scale constraints on dispatch flexibility;

[0011] Step S4: Consider the flexible resource layout, system line transmission capacity and actual power direction, and consider the line transmission limitations for utilizing flexibility, i.e., the spatial scale constraints of flexibility.

[0012] Step S5: Substitute the output scenario into the model, and calculate the blocking resistance index by combining the flexible multi-temporal and spatial scale characteristics, directionality and probability.

[0013] Step S6: Construct and solve the power distribution network congestion scheduling model.

[0014] Furthermore, in step S1, the operating parameters involved include:

[0015] (1) System line and node load parameters

[0016] Let the upper limit of the transmission capacity of line l in the system be . The system has a total of n nodes, and the load of each node is represented as follows: Where P i L This represents the active load at node i. This represents the reactive load at node i;

[0017] (2) Distributed power source operating parameters

[0018] Including the maximum output value of the gas turbine at node i Minimum output value Maximum uphill climbing rate Maximum downhill climbing rate Minimum continuous boot time and minimum continuous downtime The initial state of charge (SOC) of the energy storage system at node j j,0 Maximum State of Charge (SOC) j,max Minimum State of Charge (SOC) j,min Maximum charging rate Maximum discharge rate Charging efficiency and discharge efficiency Maximum disconnectable active power of interruptible load

[0019] (3) Distributed power generation cost coefficient

[0020] Including the gas turbine fuel cost coefficient at node i and operation and maintenance cost coefficient Energy storage unit cost coefficient and operation and maintenance cost coefficient Compensation coefficient for interruptible load shedding per unit of active load

[0021] Furthermore, step S2 specifically includes:

[0022] Based on the predicted values ​​of wind speed and light intensity, and considering the corresponding prediction biases, the uncertainty of the random variable is characterized by the actual wind speed v at time t. t and light intensity I t Characterized as:

[0023]

[0024] In the formula, v t,f and I t,f Let Δv represent the predicted values ​​of wind speed and light intensity at time t, respectively. t,f and ΔI t,f These represent the prediction deviations for wind speed and light intensity at time t, respectively.

[0025] The uncertainty of the prediction error is characterized by a normal distribution with a mean of 0 and a standard deviation proportional to the current predicted value. The uncertainty of the prediction error is represented as follows:

[0026]

[0027] Where σ v,t and ε v Let σ represent the standard deviation and prediction error coefficient of the wind speed prediction error at time t, respectively. I,t and ε I These represent the standard deviation and prediction error coefficient of the light intensity prediction error, respectively.

[0028] Subsequently, the Monte Carlo method was used to sample and generate prediction bias scenarios, and a set of output scenarios was constructed by combining the output characteristics of wind turbines and photovoltaic units. The intraday output curve of distributed renewable energy generation (RDG) corresponding to a certain scenario in the set can be expressed as follows:

[0029]

[0030] In the formula, Let represent the output value of the i-th wind turbine at time t. This represents the output value of the j-th photovoltaic unit at time t.

[0031] Furthermore, step S3 specifically includes the following:

[0032] Based on the directionality of flexibility, the adjustment capability of flexible resources is divided into two types: upward adjustable output and downward adjustable output, and their corresponding scheduling flexibility margins are taken into account respectively:

[0033]

[0034] In the formula, and These represent the adjustable flexibility margin (upward and downward) for line l, respectively. and Let represent the up-adjustment flexibility and down-adjustment flexibility of MT at node i at time t, respectively. and Let represent the up-adjustment flexibility and down-adjustment flexibility of ESS at node j at time t, respectively. This represents the amount of load removed by the IL at node k at time t;

[0035] in:

[0036] The constraint of gas turbine output scheduling on flexibility is specifically manifested in the time-dimensional coupling relationship between the output plan and its maximum upward and downward ramp rates. From the perspective of time t-1, the gas turbine output at time t should be within the range of ramp rate variation of the output at the previous time. From the perspective of time t+1, its output at time t should ensure that, considering the maximum ramp rate, it can meet the output plan at time t+1. Specifically:

[0037]

[0038] In the formula, This represents the active power output of MT at node i at time t, after considering adjustments for flexibility. and Let represent the active power output plans of MT at node i at time t-1 and t+1, respectively;

[0039] Therefore, the output of the gas turbine is arranged as follows: When the constraint on flexibility is expressed as follows:

[0040]

[0041] In the formula, Let represent the active power output plan of MT at node i at time t. and These represent the constraints on the upward adjustment flexibility of the gas turbine output plans at times t-1 and t+1, respectively. and These represent the constraints on the downward adjustment flexibility of the gas turbine output plans at times t-1 and t+1, respectively.

[0042] When the output of the gas turbine is arranged in other cases, its constraint on flexibility can be expressed as consistent with equation (6);

[0043] The constraints on flexibility imposed by the output scheduling of energy storage systems manifest in two aspects: 1) the need to consider the charging and discharging state of the previous moment, taking into account both the already occupied adjustment capacity and the additional adjustment capacity that can be released; 2) reserving sufficient charging and discharging margin for subsequent moments, specifically:

[0044] When SOC j,t-1 <SOC j,t At that time, the energy storage system is in a charging state, and its flexibility deployment constraints are:

[0045]

[0046] In the formula, SOC j,t-1 SOC j,t and SOC j,t+1 Let represent the charge state of the ESS at node j at times t-1, t, and t+1, respectively;

[0047] When SOC j,t-1 >SOC j,t At that time, the energy storage system is in a discharging state, and its flexibility deployment constraints are:

[0048]

[0049] Further, in step S4:

[0050] Considering the limitations imposed on flexibility allocation by factors such as the layout of flexibility resources, the direction of line congestion, and the line transmission capacity during congestion scheduling, flexibility capabilities are calculated by region, specifically as follows:

[0051]

[0052] In the formula, m represents the downstream region of line l. and Let m represent the set of nodes connected to MT, ESS, and IL, respectively.

[0053] Furthermore, in step S5:

[0054] When line congestion leads to load loss in the congested area, congestion resistance is the ratio of the load-carrying capacity that can be increased by the available upward flexibility to the amount of load loss; while when the consequence of line congestion is wind and solar power curtailment, congestion resistance is the ratio of the curtailed power absorbed by the downward flexibility, as shown in the following formula:

[0055]

[0056] In the formula, D l,t This represents the blocking resistance of line l at time t. and These represent the power shortage and power abandonment within region m, respectively.

[0057] Furthermore, step S6 specifically includes:

[0058] A multi-objective optimization model is established with the objectives of minimizing active power loss PLoss, maximizing the overall benefits of distributed generation, and maximizing congestion resistance.

[0059] (1) Minimum active power loss PLoss minF1

[0060]

[0061] In the formula, This represents the active power loss of the system at time t in the i-th scenario; mean() represents calculating the mean.

[0062] (2) The maximum comprehensive benefit of distributed power generation is maxF2

[0063]

[0064] In the formula, and These represent the electricity sales revenue, electricity purchase cost, and operation and maintenance cost of the distributed power source at time t, respectively.

[0065] (3) MaxF3 has the strongest blocking resistance.

[0066]

[0067] In the formula, and Z S Let represent the number and set of blocking risks outside the confidence interval, respectively. This represents the blocking resistance of the downstream region of the blocked line l in scenario s at time t.

[0068] The constraints include equality constraints and inequality constraints of the power flow equations.

[0069] (1) Power flow equation equality constraints

[0070]

[0071]

[0072] In the formula, P i RDG , P represents the active and reactive power injection of RDG at node i; i L , G represents the active and reactive power of the load at node i; ij B ij For the conductance and susceptance between lines, U i U j Let θ represent the voltages at nodes i and j, respectively. ij Indicates the phase angle difference of node power;

[0073] (2) Inequality constraints

[0074] 1) Line congestion risk constraints

[0075] Z α ≤γ (16)

[0076] In the formula, Z α γ and γ represent the real-time blocking risk and the allowable blocking risk of the system, respectively;

[0077] 2) Node voltage constraints

[0078] U i,min ≤U i,t ≤U i,max (17)

[0079] In the formula, U i,min U i,max and U i,t Let represent the lower and upper limits of the allowed voltage at node i and the actual voltage value at time t, respectively.

[0080] 3) Distributed power source constraints

[0081]

[0082]

[0083]

[0084] In the formula, N MT N ESS and N IL These represent the sets of MT, ESS, and IL access nodes, respectively. and These represent the start / stop status of the MT at node i at time t-1 and time t, respectively, where 1 indicates power-on and 0 indicates power-off; SOC j,T This represents the charge state of the ESS at node j at the end of the scheduling process; This represents the active power removed by the IL at node k at time t.

[0085] Compared with existing technologies, this invention and its preferred solution are based on the characteristic that flexible resources can quickly respond to power fluctuations on both sides of the source and load. They consider the congestion risk and response capability outside the confidence interval and improve upon the shortcomings of traditional congestion risk assessment and scheduling methods such as opportunity constraint methods and conditional value of risk theory, which have blind spots in risk response. At the same time, in terms of flexibility capability assessment, it fully combines the multi-temporal and spatial scale characteristics of flexibility to form two types of constraints: the original output arrangement constrains flexibility and the line transmission capacity limits the flexibility call-up, ensuring the feasibility of calling up the flexibility resource adjustment capability. Its optimized solution has a stronger congestion risk response capability and is more suitable for distribution network line congestion management in actual engineering. Attached Figure Description

[0086] Figure 1 This is a schematic diagram of the implementation scheme of an embodiment of the present invention. Detailed Implementation

[0087] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0088] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0089] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0090] like Figure 1 As shown, this embodiment provides a distribution network congestion scheduling strategy that takes into account congestion resistance, including the following steps:

[0091] Step S1: Input system line parameters, load data, operating parameters and cost coefficients of distributed power sources and flexible loads;

[0092] Step S2: Obtain the predicted wind speed and solar intensity data for the scheduling day, and construct a set of wind power and photovoltaic output scenarios based on the prediction error;

[0093] Step S3: Taking into account the original scheduling plan and the ramp-up capabilities of the micro gas turbine and energy storage system, and considering the time-scale constraints on dispatch flexibility;

[0094] Step S4: Consider the flexible resource layout, system line transmission capacity and actual power direction, and consider the line transmission limitations for utilizing flexibility, i.e., the spatial scale constraints of flexibility.

[0095] Step S5: Substitute the output scenario into the model, and calculate the blocking resistance index by combining the flexible multi-temporal and spatial scale characteristics, directionality and probability.

[0096] Step S6: Construct a power distribution network congestion scheduling model and solve it using an intelligent optimization algorithm.

[0097] In this embodiment, Figure 1 Execution box 1 in the flowchart corresponds to step S1 in the implementation scheme, which specifically includes the following:

[0098] The main operating parameters involved in the blocking scheduling strategy proposed in this invention are as follows:

[0099] (1) System line and node load parameters

[0100] The maximum transmission capacity of line l in the system is The system has a total of n nodes, and the load of each node is represented as follows: Where P i L This represents the active load at node i. This represents the reactive load at node i;

[0101] (2) Distributed power source operating parameters

[0102] Maximum output of the gas turbine at node i Minimum output value Maximum uphill climbing rate Maximum downhill climbing rate Minimum continuous boot time and minimum continuous downtime The initial state of charge (SOC) of the energy storage system at node j j,0 Maximum State of Charge (SOC) j,max Minimum State of Charge (SOC) j,min Maximum charging rate Maximum discharge rate Charging efficiency and discharge efficiency Maximum disconnectable active power of interruptible load

[0103] (3) Distributed power generation cost coefficient

[0104] Gas turbine fuel cost coefficient at node i and operation and maintenance cost coefficient Energy storage unit cost coefficient and operation and maintenance cost coefficient Compensation coefficient for interruptible load shedding per unit of active load

[0105] In this embodiment, Figure 1 Execution block 2 in the flowchart corresponds to step S2 in the implementation scheme, which specifically includes the following:

[0106] This invention characterizes the uncertainty of random variables by considering the prediction deviations of wind speed and light intensity predictions, thereby representing the actual wind speed v at time t. t and light intensity I t It can be characterized as:

[0107]

[0108] In the formula, v t,f and I t,f Let Δv represent the predicted values ​​of wind speed and light intensity at time t, respectively. t,f and ΔI t,f These represent the prediction errors for wind speed and light intensity at time t, respectively.

[0109] The uncertainty of the prediction error can be characterized by a normal distribution with a mean of 0 and a standard deviation proportional to the current predicted value. The uncertainty of the prediction error can be represented as follows:

[0110]

[0111] Where σ v,t and ε v Let σ represent the standard deviation and prediction error coefficient of the wind speed prediction error at time t, respectively. I,t and ε I These represent the standard deviation and prediction error coefficient of the light intensity prediction error, respectively.

[0112] Subsequently, the Monte Carlo method was used to sample and generate prediction bias scenarios, and a set of output scenarios was constructed by combining the output characteristics of wind turbines and photovoltaic units. The intraday output curve of distributed renewable energy (RDG) corresponding to a certain scenario in the set can be expressed as:

[0113]

[0114] In the formula, Let represent the output value of the i-th wind turbine at time t. This represents the output value of the j-th photovoltaic unit at time t.

[0115] In this embodiment, Figure 1 Execution block 3 in the flowchart corresponds to step S3 in the implementation scheme, which specifically includes the following:

[0116] Based on the directionality of flexibility, the adjustment capability of flexible resources can be divided into two types: upward adjustable output and downward adjustable output, with their corresponding scheduling flexibility margins taken into account respectively:

[0117]

[0118] In the formula, and These represent the adjustable flexibility margin (upward and downward) for line l, respectively. and Let represent the up-adjustment flexibility and down-adjustment flexibility of MT at node i at time t, respectively. and Let represent the up-adjustment flexibility and down-adjustment flexibility of ESS at node j at time t, respectively. This represents the amount of load that can be cut off at node k (Interruptible Load, IL) at time t.

[0119] (1) The constraint effect of gas turbine output arrangement on flexibility is specifically manifested in the coupling relationship between the output plan and its maximum upward speed and maximum downward ramp speed in the time dimension. From the perspective of time t-1, the gas turbine output value at time t should be within the range of ramp capability variation of the output value at the previous time; while from the perspective of time t+1, its output value at time t should ensure that it can meet the output plan at time t+1 after taking into account the maximum ramp capability, which can be specifically expressed as:

[0120]

[0121] In the formula, This represents the active power output of MT at node i at time t, after considering adjustments for flexibility. and Let represent the active power output plans of MT at node i at times t-1 and t+1, respectively. and Let represent the maximum upward and downward climbing rates of MT at node i, respectively.

[0122] Therefore, the output of the gas turbine can be arranged as follows: When the constraint on flexibility is expressed as follows:

[0123]

[0124] In the formula, Let represent the active power output plan of MT at node i at time t. and These represent the constraints on the upward adjustment flexibility of the gas turbine output plans at times t-1 and t+1, respectively. and These represent the constraints on the downward adjustment flexibility of the gas turbine output plans at times t-1 and t+1, respectively.

[0125] When the output of the gas turbine is arranged as in other cases, its constraint on flexibility can be expressed mathematically in the same way as equation (6), so it will not be elaborated further.

[0126] (2) The constraints on flexibility of the energy storage system’s output arrangement are manifested in two aspects: 1) The charging and discharging state of the previous moment needs to be considered, taking into account the adjustment capacity that has been occupied or the adjustment capacity that can be released additionally; 2) Sufficient charging and discharging margin should be reserved for subsequent moments.

[0127] When SOC j,t-1 <SOC j,t At that time, the energy storage system is in a charging state, and its flexibility deployment constraints are:

[0128]

[0129] In the formula, and Represent the maximum discharge and charge rates of the ESS at node j, respectively, and the State of Charge (SOC). j,t-1 SOC j,t and SOC j,t+1 Let represent the charge states of the ESS at node j at times t-1, t, and t+1, respectively. and Let SOC represent the charging efficiency and discharging efficiency of the ESS at node j, respectively. j,min and SOC j,max These represent the lowest and highest charge states of the ESS at node j, respectively.

[0130] When SOC j,t-1 >SOC j,t At that time, the energy storage system is in a discharging state, and its flexibility deployment constraints are:

[0131]

[0132] In this embodiment, Figure 1 Execution block 4 in the flowchart corresponds to step S4 of the implementation scheme, which specifically includes the following:

[0133] This invention considers the congestion risk of multiple lines. Therefore, in actual scheduling, the transmission capacity of flexibility is comprehensively limited by the transmission capacity of distribution lines and the layout of flexibility resources. When a distribution line is congested, if its active power transmission direction is from upstream to downstream, the upward flexibility of the upstream area should not be taken into account when dealing with the risk of load shedding in the downstream area. Similarly, when its active power transmission direction is from downstream to upstream, the system can only call the downward flexibility of the downstream area to deal with the curtailment of wind and solar power caused by the line congestion. Therefore, in the congestion scheduling process, the limitations of flexibility resource layout, line congestion direction, and line transmission capacity on flexibility call should be considered, and flexibility capacity should be calculated by region. Specifically, it can be described as follows:

[0134]

[0135] In the formula, m represents the downstream region of line l. and Let m represent the set of nodes connected to MT, ESS, and IL, respectively.

[0136] In this embodiment, Figure 1 Execution box 5 in the flowchart corresponds to step S5 of the implementation scheme, which specifically includes the following:

[0137] The congestion resistance index aims to measure the level of flexibility resources' ability to cope with congestion risks outside the confidence interval. Therefore, when congestion leads to load loss in the congested area, congestion resistance is expressed as the ratio of the load-bearing capacity that can be increased by adjusting available flexibility to the amount of load loss. When the consequence of congestion is wind and solar power curtailment, congestion resistance can be expressed as the proportion of curtailed power absorbed by adjusting flexibility, as shown in the following formula:

[0138]

[0139] In the formula, D l,t This represents the blocking resistance of line l at time t. and These represent the power shortage and power abandonment within region m, respectively.

[0140] In this embodiment, Figure 1 Execution block 6 in the flowchart corresponds to step S6 of the implementation scheme, which specifically includes the following:

[0141] A multi-objective function optimization model is established with the objectives of minimizing active power loss PLoss, maximizing the overall benefits of distributed power sources, and maximizing congestion resistance.

[0142] (1) Minimum active power loss PLoss minF1

[0143]

[0144] In the formula, This represents the active power loss of the system at time t in the i-th scenario; mean() represents calculating the mean.

[0145] (2) The maximum comprehensive benefit of distributed power generation is maxF2

[0146]

[0147] In the formula, and These represent the electricity sales revenue, electricity purchase cost, and operation and maintenance cost of the distributed power source at time t, respectively.

[0148] (3) MaxF3 has the strongest blocking resistance.

[0149]

[0150] In the formula, and Z S Let represent the number and set of blocking risks outside the confidence interval, respectively. This represents the blocking resistance of the downstream region of the blocked line l in scenario s at time t.

[0151] The constraints include equality constraints and inequality constraints of the power flow equations.

[0152] (1) Power flow equation equality constraints

[0153]

[0154]

[0155] In the formula, P i RDG , P represents the active and reactive power injection of RDG at node i; i L , G represents the active and reactive power of the load at node i; ij B ij For the conductance and susceptance between lines, U i U j Let θ represent the voltages at nodes i and j, respectively. ij This represents the phase angle difference of node power.

[0156] (2) Inequality constraints

[0157] 1) Line congestion risk constraints

[0158] Z α ≤γ (16)

[0159] In the formula, Z α γ and γ represent the real-time blocking risk and the allowable blocking risk of the system, respectively.

[0160] 2) Node voltage constraints

[0161] U i,min ≤U i,t ≤U i,max (17)

[0162] In the formula, U i,min U i,max and U i,t Let represent the lower and upper limits of the allowed voltage at node i and the actual voltage value at time t, respectively.

[0163] 3) Distributed power source constraints

[0164]

[0165]

[0166]

[0167] In the formula, and Let N represent the minimum and maximum power generation of MT at node i, respectively. MT N ESS and N IL These represent the sets of MT, ESS, and IL access nodes, respectively. and Let be the continuous power-on time and continuous power-off time of MT at node i at time t-1. and Let represent the minimum consecutive uptime and minimum consecutive downtime of MT at node i, respectively. and These represent the start / stop status of the MT at node i at time t-1 and time t, respectively, where 1 indicates power-on and 0 indicates power-off. SOC j,min and SOC j,max Let SOC represent the lower and upper limits of the State of Charge (ESS) at node j, respectively. j,0 and SOC j,T These represent the charge states of the ESS at node j at the initial and final times of scheduling, respectively. and Let represent the active power that IL at node k can remove at time t and the maximum active power that can be removed, respectively.

[0168] Preferably, based on the above design, the scheme of this embodiment takes the remaining adjustment amount of flexibility resources in the scheduling plan as a reserve capacity to deal with the risk of line power flow exceeding the limit outside the confidence interval. Combining the original output arrangement, the layout of flexibility resources and the line transmission capacity, the scheme considers the multi-temporal and spatial characteristics of flexibility, defines the congestion resistance index, and establishes a distribution network congestion scheduling model that takes into account the congestion risk outside the confidence interval.

[0169] (1) Study the output limits and power adjustment constraints of flexible resources to lay the foundation for considering the use of the remaining adjustment capacity of flexible resources.

[0170] (2) Taking into account the time scale characteristics of flexibility, not changing the original output arrangement is one of the constraints on the remaining adjustment capacity of flexible resources.

[0171] (3) Consider the spatial scale characteristics of flexibility, and combine the layout of flexibility resources with the line transmission capacity to form regional transmission restrictions on the remaining adjustment capacity of flexibility resources.

[0172] (4) Propose a blockage resistance evaluation index to alleviate the load-bearing pressure in the blockage area by adjusting the upward adjustment capacity and to improve the absorption level of RDG power generation by adjusting the downward adjustment capacity.

[0173] (5) Based on the line transmission capacity security constraint, the scheduling scheme is improved by optimizing the congestion resistance index, which enhances the ability to respond to congestion risks outside the confidence interval and provides technical support for distribution network congestion scheduling.

[0174] Preferably, this embodiment leverages the adjustability of flexible resources to address congestion risks outside the confidence interval, fully utilizing the rapid response capabilities of distributed generator sets such as micro turbines (MT) and energy storage systems (ESS). Simultaneously, during congestion scheduling, the directional, probabilistic, and multi-temporal-scale characteristics of flexibility are fully considered to ensure the applicability of the scheduling scheme. In the optimization process, congestion resistance is incorporated into the optimization objective, improving the safety of the scheduling scheme. The technical effects of this invention, in conjunction with the embodiments, are shown in Table 1.

[0175] Table 1. Risk mitigation capabilities of different strategies outside the confidence interval for blocking

[0176] Table.1 Coping ability of congestion risk beyond the confidence interval of different strategies

[0177]

[0178] As shown in Table 1, under conditions of comparable congestion risk levels, the proposed solution controls the severity of line transmission power exceeding limits to a manageable range. While its line congestion risk count is higher than that of traditional methods, its average line congestion severity is lower. Furthermore, the proposed solution provides ample flexibility in resource adjustment, enabling it to more effectively address congestion risks outside the confidence interval compared to traditional congestion scheduling methods.

[0179] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be included in the scope of the present invention.

[0180] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0182] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0184] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0185] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0186] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0187] This patent is not limited to the above-described preferred embodiments. Anyone can derive other forms of distribution network congestion scheduling methods that take into account congestion resistance under the guidance of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A distribution network congestion scheduling method considering congestion resistance, characterized in that, Includes the following steps: Step S1: Input system line parameters, load data, operating parameters and cost coefficients of distributed power sources and flexible loads; Step S2: Obtain the daily wind speed and solar intensity prediction data for scheduling, and construct a set of wind power and photovoltaic output scenarios based on the prediction error; Step S3: Taking into account the original scheduling plan and the ramp-up capabilities of the micro gas turbine and energy storage system, and considering the time-scale constraints on dispatch flexibility; Step S4: Consider the flexible resource layout, system line transmission capacity and actual power direction, and consider the line transmission limitations for utilizing flexibility, i.e., the spatial scale constraints of flexibility. Step S5: Substitute the output scenario into the model, and calculate the blocking resistance index by combining the flexible multi-temporal and spatial scale characteristics, directionality and probability. Step S6: Construct and solve the distribution network congestion scheduling model; specifically: A multi-objective optimization model is established with the objectives of minimizing active power loss PLoss, maximizing the overall benefits of distributed generation, and maximizing congestion resistance. (1) Minimum active power loss PLoss minF1 In the formula, This represents the active power loss of the system at time t in the i-th scenario; mean() represents calculating the mean. (2) The maximum comprehensive benefit of distributed power generation is maxF2 Where, and These represent the electricity sales revenue, electricity purchase cost, and operation and maintenance cost of the distributed power source at time t, respectively. (3) MaxF3 has the strongest blocking resistance. Where, and Z S Let represent the number and set of blocking risks outside the confidence interval, respectively. This represents the blocking resistance of the downstream region of the blocked line l in scenario s at time t. The constraints include equality constraints and inequality constraints of the power flow equations. (1) Power flow equation equality constraints In the formula, P i RDG , P represents the active and reactive power injection of RDG at node i; i L , G represents the active and reactive power of the load at node i; ij B ij For the conductance and susceptance between lines, U i U j Let θ represent the voltages at nodes i and j, respectively. ij Indicates the phase angle difference of node power; (2) Inequality constraints 1) Line congestion risk constraints WITH α ≤γ (16) In the formula, Z α γ and γ represent the real-time blocking risk and the allowable blocking risk of the system, respectively; 2) Node voltage constraints IN i,min ≤U i,t ≤U i,max (17) In the formula, U i,min U i,max and U i,t These represent the lower and upper limits of the allowed voltage at node i, and the actual voltage value at time t, respectively. 3) Distributed power source constraints In the formula, N MT N ESS and N IL These represent the sets of MT, ESS, and IL access nodes, respectively. and These represent the start / stop status of the MT at node i at time t-1 and time t, respectively, where 1 indicates power-on and 0 indicates power-off; SOC j,T This represents the charge state of the ESS at node j at the end of the scheduling process; This represents the active power removed by the IL at node k at time t.

2. The distribution network congestion scheduling method considering congestion resistance according to claim 1, characterized in that: In step S1, the operating parameters involved include: (1) System line and node load parameters Let the upper limit of the transmission capacity of line l in the system be . The system has a total of n nodes, and the load of each node is represented as follows: Where P i L This represents the active load at node i. This represents the reactive load at node i; (2) Distributed power source operating parameters Including the maximum output value of the gas turbine at node i Minimum output value Maximum uphill speed Maximum downhill climbing rate Minimum continuous boot time and minimum continuous downtime The initial state of charge (SOC) of the energy storage system at node j j,0 Maximum State of Charge (SOC) j,max Minimum State of Charge (SOC) j,min Maximum charging rate Maximum discharge rate Charging efficiency and discharge efficiency Maximum disconnectable active power of interruptible load (3) Distributed power generation cost coefficient Including the gas turbine fuel cost coefficient at node i and operation and maintenance cost coefficient Energy storage unit cost coefficient and operation and maintenance cost coefficient Compensation coefficient for interruptible load shedding per unit of active load 3. The distribution network congestion scheduling method considering congestion resistance according to claim 1, characterized in that: Step S2 specifically includes: Based on the predicted values ​​of wind speed and light intensity, and considering the corresponding prediction biases, the uncertainty of the random variable is characterized by the actual wind speed v at time t. t and light intensity I t Characterized as: Where, v t,f and I t,f Let Δv represent the predicted values ​​of wind speed and light intensity at time t, respectively. t,f and ΔI t,f These represent the prediction deviations for wind speed and light intensity at time t, respectively. The uncertainty of the prediction error is characterized by a normal distribution with a mean of 0 and a standard deviation proportional to the current predicted value. The uncertainty is represented as follows: Where, σ v,t and ε v Let σ represent the standard deviation and prediction error coefficient of the wind speed prediction error at time t, respectively. I,t and ε I These represent the standard deviation and prediction error coefficient of the light intensity prediction error, respectively. Subsequently, the Monte Carlo method was used to sample and generate prediction bias scenarios, and a set of output scenarios was constructed by combining the output characteristics of wind turbines and photovoltaic units. The intraday output curve of distributed renewable energy generation (RDG) corresponding to a certain scenario in the set can be expressed as follows: Where, Let represent the output value of the i-th wind turbine at time t. This represents the output value of the j-th photovoltaic unit at time t.

4. The distribution network congestion scheduling method considering congestion resistance according to claim 1, characterized in that: Step S3 specifically includes the following: Based on the directionality of flexibility, the adjustment capability of flexible resources is divided into two types: upward adjustable output and downward adjustable output, and their corresponding scheduling flexibility margins are taken into account respectively: In the formula, and These represent the adjustable flexibility margin (upward and downward) for line l, respectively. and Let represent the up-adjustment flexibility and down-adjustment flexibility of MT at node i at time t, respectively. and Let represent the up-adjustment flexibility and down-adjustment flexibility of ESS at node j at time t, respectively. This represents the amount of load removed by the IL at node k at time t; in: The constraint of gas turbine output scheduling on flexibility is specifically manifested in the time-dimensional coupling relationship between the output plan and its maximum upward and downward ramp rates. From the perspective of time t-1, the gas turbine output at time t should be within the range of ramp rate variation of the output at the previous time. From the perspective of time t+1, its output at time t should ensure that, considering the maximum ramp rate, it can meet the output plan at time t+1. Specifically: Where, This represents the active power output of MT at node i at time t, after considering adjustments for flexibility. and Let represent the active power output plans of MT at node i at time t-1 and t+1, respectively; and Let represent the maximum upward and downward ramp rates of MT at node i, respectively; Therefore, the output of the gas turbine is arranged as follows: When the constraint on flexibility is expressed as follows: Where, Let represent the active power output plan of MT at node i at time t. and These represent the constraints on the upward adjustment flexibility of the gas turbine output plans at times t-1 and t+1, respectively. and These represent the constraints on the downward adjustment flexibility of the gas turbine output plans at times t-1 and t+1, respectively. When the output of the gas turbine is arranged in other cases, its constraint on flexibility is expressed in the same way as in equation (6); The constraints on flexibility imposed by the output scheduling of energy storage systems manifest in two aspects: 1) the need to consider the charging and discharging state of the previous moment, taking into account both the already occupied adjustment capacity and the additional adjustment capacity that can be released; 2) reserving sufficient charging and discharging margin for subsequent moments, specifically: When SOC j,t-1 <SOC j,t At that time, the energy storage system is in a charging state, and its flexibility deployment constraints are: Where, and Represent the maximum discharge and charge rates of the ESS at node j, respectively, and the State of Charge (SOC). j,t-1 SOC j,t and SOC j,t+1 Let represent the charge states of the ESS at node j at times t-1, t, and t+1, respectively. and Let SOC represent the charging efficiency and discharging efficiency of the ESS at node j, respectively. j,min and SOC j,max These represent the lowest and highest charge states of the ESS at node j, respectively. When SOC j,t-1 >SOC j,t At that time, the energy storage system is in a discharging state, and its flexibility deployment constraints are:

5. The distribution network congestion scheduling method considering congestion resistance according to claim 1, characterized in that: In step S4: Considering the limitations imposed on flexibility allocation by factors such as the layout of flexible resources, the direction of line congestion, and line transmission capacity during congestion scheduling, flexibility capabilities are calculated by region, specifically as follows: In the formula, m represents the downstream region of line l. and Let m represent the set of nodes connected to MT, ESS, and IL, respectively.

6. The distribution network congestion scheduling method considering congestion resistance according to claim 1, characterized in that: In step S5: When line congestion leads to load loss in the congested area, congestion resistance is the ratio of the load-carrying capacity that can be increased by the available upward flexibility to the amount of load loss; while when the consequence of line congestion is wind and solar power curtailment, congestion resistance is the ratio of the curtailed power absorbed by the downward flexibility, as shown in the following formula: In the formula, D l,t This represents the blocking resistance of line l at time t. and These represent the power shortage and power abandonment within region m, respectively.

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