Multi-time scale risk scheduling method for transmission and distribution network with high proportion of distributed energy access

Abstract

The application discloses a kind of multi-time scale risk scheduling methods of transmission and distribution network for high proportion distributed energy access, belong to transmission and distribution network technical field.The application considers the comprehensive operation cost of transmission and distribution network, constructs the day-ahead scheduling model considering the different operation risk limit values of transmission and distribution network, and optimizes the source network load storage multiple adjustable resources of transmission and distribution network under the constraint of risk limit value;Secondly, the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is constructed, and the day-ahead scheduling model is

Description

Technical Field

[0001] This invention belongs to the field of power transmission and distribution network technology. Background Technology

[0002] With the strategic goal of "carbon peaking and carbon neutrality" being proposed, connecting a high proportion of renewable energy sources such as photovoltaics and wind power to the power system in the form of distributed power sources has become a trend in the development of new energy systems. The large-scale integration of distributed power sources into the distribution network has led to a gradual shift from passive to active power sources in the distribution network. The power flow in the transmission and distribution network has gradually become bidirectional, with increasingly stronger coupling relationships, which has also impacted safe and stable operation.

[0003] As the power grid continues to expand, the interaction and connection between transmission and distribution networks are becoming increasingly close, requiring careful consideration of their coordination during dispatch. Traditional dispatch strategies typically treat transmission and distribution networks independently, equating the distribution network to a load during transmission network dispatch and the transmission network to a power source during distribution network dispatch. This fragmented and independent dispatching approach weakens flexibility, making it difficult to coordinate resources across system layers to fully absorb renewable energy. It easily leads to unnecessary grid congestion and power imbalances, and even significant fluctuations in feeder power between transmission and distribution networks, resulting in a series of system risks and additional operating costs. Especially with large-scale distributed power generation integrated into the grid, it is necessary to coordinate the interaction between transmission and distribution networks to improve renewable energy absorption, maximizing the use of control resources to achieve interactive coordination between the two networks and addressing system operational risks and economic issues. Therefore, how to introduce risk theory into the interactive dispatching of transmission and distribution networks, optimize resource allocation on the generation and consumption sides, and ensure the safe operation of the power grid is a crucial issue that urgently needs to be addressed in the future development of smart grids.

[0004] In response to the challenges posed by the integration of large-scale distributed power sources to power grid dispatch and security, most studies focus on resource optimization at the distribution network level, neglecting the impact of transmission network risks on the distribution network. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-timescale risk scheduling method for transmission and distribution networks with a high proportion of distributed energy access, which enables interactive coordination between transmission and distribution networks and reduces various operational risks of transmission and distribution networks through day-ahead and intraday two-stage scheduling.

[0006] The steps of this invention are:

[0007] This includes a risk dispatching framework for the power transmission and distribution network in two phases: day-ahead and intraday.

[0008] I. Current optimized scheduling model:

[0009] Considering the operational risks of the power transmission and distribution network, and taking the operating cost of the power transmission and distribution network as the objective, an optimal scheduling model is established:

[0010] S1 objective function:

[0011] The current model uses minimizing the sum of the operating costs of the transmission and distribution networks as the optimization objective:

[0012] (1)

[0013] In the formula: The study period is divided into time periods; For the operating costs of the power transmission network; For distribution network operating costs;

[0014] (1) Transmission network operating costs

[0015] (2)

[0016] In the formula: This represents the total number of thermal power units in the power transmission network. , thermal power units Start-up costs and downtime costs, , It is a 0 / 1 variable, with a value of 0 indicating that the unit is out of service and a value of 1 indicating that the unit is in operation; For thermal power units At any moment contribution; , , The cost characteristic function of the output power of thermal power units;

[0017] (2) Distribution network operating costs

[0018] (3)

[0019] In the formula: This represents the total operating cost of the gas turbine. Total operating cost of diesel engine; For users' electricity purchase costs; The total operating cost of the energy storage device; Cost of interruptible load operation; Cost of abandoning light; Cost of wind curtailment;

[0020] (3) Total operating cost of gas turbine

[0021] (4)

[0022] (5)

[0023] In the formula: This represents the total number of gas turbines; , , These are the operating and maintenance costs of the gas turbine, fuel costs, and emissions costs. Let d be the output power of the gas turbine at time t; This represents the gas turbine operation and maintenance cost coefficient. For the operating efficiency of the gas turbine; For natural gas prices; It has a low calorific value for natural gas; The cost coefficient for carbon emissions from gas turbines; Carbon emissions;

[0024] (4) Total operating cost of diesel engine

[0025] (6)

[0026] (7)

[0027] In the formula: This represents the total number of diesel engines. , , These are the operating and maintenance costs of the diesel engine, fuel costs, and carbon emission costs, respectively. Let e ​​be the output power of the diesel engine at time t; This is the diesel engine operation and maintenance cost coefficient; , , For diesel engines; The cost coefficient for carbon emissions from diesel engines; Carbon emissions;

[0028] (5) User electricity purchase cost

[0029] (8)

[0030] In the formula: Revenue generated in response to user demand; Indicates the power of user participation in demand response;

[0031] (6) Total operating cost of energy storage device

[0032] (9)

[0033] (10)

[0034] In the formula: This represents the total number of energy storage devices. , These are the operating cost and loss cost of the energy storage device, respectively. For energy storage Purchase cost, , Energy storage for time period t The charging and discharging power; This refers to the battery capacity cost coefficient for energy storage devices. For energy storage charging and discharging power; The number of charge-discharge cycles for energy storage; For energy storage charge and discharge depth;

[0035] (7) Cost of interruptible load operation

[0036] (11)

[0037] In the formula: Number of interruptible loads for users; The compensation cost for n units of interruptible load. The interruption amount for interruptible load n;

[0038] (8) Cost of abandoning light

[0039] (12)

[0040] In the formula: Number of interruptible loads for users; This is the cost coefficient for abandoned light. , Distributed photovoltaic power generation at time t Recent forecasts and scheduling of power output;

[0041] (9) Cost of wind curtailment

[0042] (13)

[0043] In the formula: This represents the total number of wind turbine units. This is the cost coefficient for wind curtailment. , These represent the predicted and scheduled power output of the wind turbines at time t, respectively, s-day ahead.

[0044] S2, Constraints:

[0045] (1) Transmission network constraints

[0046] The active power balance constraints and nodal power balance constraints are as follows:

[0047] (14)

[0048] (15)

[0049] In the formula: For distribution network aggregation; For load sets; Power is exchanged between the transmission and distribution networks via interconnecting lines. For transmission network load During the period The predicted value; , For power transmission network nodes;

[0050] (2) Constraints of thermal power units

[0051] The output limit constraints and start-up constraints of thermal power units are as follows:

[0052] (16)

[0053] (17)

[0054] In the formula, , These represent the maximum and minimum output values ​​of the thermal power unit, respectively.

[0055] (3) Distribution network constraints

[0056] The active power balance constraints and nodal power balance constraints of the distribution network are as follows:

[0057] (18)

[0058] (19)

[0059] In the formula: For the distribution network load during the time period The predicted value; , For distribution network nodes;

[0060] (4) Gas turbine output limiting constraints

[0061] (20)

[0062] In the formula: , These represent the maximum and minimum output values ​​of the gas turbine, respectively.

[0063] (5) Diesel engine output limiting constraints

[0064] (twenty one)

[0065] In the formula: , These represent the maximum and minimum output power of the diesel engine, respectively.

[0066] (6) Constraints on Energy Storage Operation

[0067] The energy storage power balance constraint and the storage power limit constraint are as follows:

[0068] (twenty two)

[0069] (twenty three)

[0070] In the formula: for The amount of electricity stored in the energy storage device at any time; , The charging / discharging efficiency of the energy storage device; for Time-of-use energy storage The discharge power;

[0071] (7) Interruption limit of interruptible load

[0072] (twenty four)

[0073] In the formula, , These are the maximum and minimum values ​​of the interruptible load interruption amount, respectively.

[0074] (8) Constraints of distributed photovoltaic and wind turbine units

[0075] The output limits for distributed photovoltaic and wind turbine units are as follows:

[0076] (25)

[0077] (26)

[0078] In the formula, , They are respectively Distributed photovoltaic power generation Wind turbine Maximum output;

[0079] (9) Adjustment constraints of space frame structure

[0080] The constraints on the position variables of interconnecting and sectionalizing switches, the constraints on the action variables, and the constraints on the radial structure of the distribution network are as follows:

[0081] (27)

[0082] (28)

[0083] (29)

[0084] In the formula, for Time period The position variable of the switch has a value of 1 indicating that the switch is closed and a value of 0 indicating that the switch is open. for Time period The action variable of the switch takes a value of 1 to indicate that the switch is activated and 0 to indicate that the switch position remains unchanged. For the first The maximum number of daily operations allowed for each switch; The network topology of the distribution network in time period t. A collection of radial network structures for power distribution networks;

[0085] (10) Limitation constraints on transmission and distribution networks

[0086] The limits for node voltage, branch power flow, and unit capacity exceeding the limit risk values ​​are as follows:

[0087] (30)

[0088] (31)

[0089] (32)

[0090] In the formula: Calculated value for node voltage over-limit risk index; The calculated value for the risk index of power flow exceeding the limit in the branch line; This is the calculated value for the unit capacity over-limit index;

[0091] II. Intraday Optimized Scheduling Model:

[0092] The optimization objective is to minimize the combined operational risk of the transmission and distribution networks. The objective function is:

[0093] (33)

[0094] In the formula, The weighting coefficient represents the degree of importance that the scheduler places on each objective. Weights can be calculated based on different risk levels. ;

[0095] S1, the node voltage over-limit risk indicator is:

[0096] (34)

[0097] (35)

[0098] In the formula: For node importance, The severity of the loss due to node voltage exceeding the limit. The probability of node voltage exceeding the limit; , and These represent the upper and lower limits of the voltage value at time t and the voltage per unit value, respectively.

[0099] S2, the risk indicator for branch power flow exceeding the limit is:

[0100] (36)

[0101] (37)

[0102] (38)

[0103] In the formula: Importance of branch roads; The severity of the risk of loss due to branch voltage exceeding the limit. The probability of branch voltage exceeding the limit; Let i be the load rate of the distribution network line i;

[0104] S3, the risk indicators for unit capacity exceeding limits are:

[0105] (39)

[0106] (40)

[0107] In the formula: Importance of capacity; The severity of the risk of losses due to exceeding unit capacity limits; The probability of a branch power flow exceeding the limit; , These represent the maximum and minimum unit capacity.

[0108] S4. The comprehensive risk assessment index result R can be calculated from the above three types of risks exceeding the limit, namely:

[0109] (41)

[0110] In the formula, The weighting coefficient represents the degree of importance that the scheduler places on each objective. Weights can be calculated based on different risk levels. ;

[0111] III. Solution Methods for Multi-Time-Scale Risk Dispatch Strategies in Power Transmission and Distribution Networks:

[0112] Assuming the total number of particles is Then the first Particles in dimension The position and velocity in the middle are represented as follows:

[0113] (42)

[0114] Each particle adjusts its velocity and position by tracking its previous individual best position and the group best position, which are denoted as:

[0115] (43)

[0116] In the formula: yes The optimal position for each individual particle; It is the optimal position of the group obtained from all particles in the previous iteration;

[0117] The formulas for velocity and position are expressed as follows:

[0118] (44)

[0119] In the formula: It is the inertia weighting factor; and These are learning factors, which respectively reflect the particle's self-learning ability and social learning ability; and It is a random number uniformly distributed in [0,1].

[0120] The particle swarm optimization algorithm was improved in two aspects: inertia weight factor and learning factor.

[0121] (45)

[0122] (46)

[0123] In the formula: This is the current iteration number; It is the maximum number of iterations; and These are the initial and final values ​​of the inertia weight factor; in the early stages of iteration, the larger values... This prevents the algorithm from getting trapped in local optima and facilitates global search; in the later stages of iteration, smaller... It is beneficial for local search and for algorithm convergence; and yes initial and stopping values, Greater than ; and yes initial and stopping values, Less than .

[0124] This invention proposes a multi-timescale risk scheduling strategy for transmission and distribution networks with a high proportion of distributed energy access, which reduces various operational risk values ​​of the transmission and distribution network and enables the transmission and distribution network to operate economically, safely and reliably. Attached Figure Description

[0125] Figure 1 This is a framework diagram of risk scheduling across multiple time scales in the power transmission and distribution network.

[0126] Figure 2 This is a flowchart of the improved particle swarm optimization algorithm;

[0127] Figure 3a This is a partial topology diagram of a transmission network designed for high-proportion distributed energy access;

[0128] Figure 3b This is a partial topology diagram of a distribution network oriented towards high-proportion distributed energy access;

[0129] Figure 4 This is a chart showing the day-ahead power forecast trends for wind power, solar power, and load.

[0130] Figure 5a This is the optimized topology diagram of distribution network A a few days ago;

[0131] Figure 5b This is the optimized topology diagram of distribution network B days ago;

[0132] Figure 6a This is a diagram showing the power output of thermal power units during scheduling.

[0133] Figure 6b This is a diagram showing the start-up and shutdown status of the generating units;

[0134] Figure 7a This is the risk dispatch strategy diagram for distribution network A;

[0135] Figure 7b This is a risk dispatching strategy diagram for distribution network B;

[0136] Figure 8a These are distribution network A loss curves under different scenarios;

[0137] Figure 8b These are distribution network B loss curves under different scenarios;

[0138] Figure 9a These are voltage curves of distribution network A under different scenarios;

[0139] Figure 9b These are voltage curves of distribution network B under different scenarios;

[0140] Figure 10 This is a comparison chart of the convergence curves of the three algorithms. Detailed Implementation

[0141] A day-ahead scheduling model is constructed with the objective function being the total operating cost of the transmission and distribution network. This model considers different operating risk limits for the transmission and distribution network. Under the constraint of risk limits, the model collaboratively optimizes various adjustable resources of the transmission and distribution network, including sources, grids, loads, and storage. An intraday scheduling model is then constructed with the objective function being the minimum comprehensive risk value of the transmission and distribution network. This model considers the magnitude of different operating risk values ​​of the transmission and distribution network and modifies the day-ahead scheduling plan to enable interaction and coordination between the transmission and distribution networks, reduce the operating risk of the transmission and distribution network, and improve the economy and operational reliability of the transmission and distribution network.

[0142] As the timescale approaches, the accuracy of new energy forecasts gradually improves, resulting in less uncertainty and disturbance to the system. Therefore, various controllable resources can be adjusted in a multi-timescale, hierarchical, and refined manner to enhance the safe operation capability of the power transmission and distribution network. This invention considers multiple types of operational risks in the power transmission and distribution network under distributed new energy grid integration, and comprehensively considers the functions and dynamic response characteristics of various scheduling resources, proposing a risk scheduling framework for the power transmission and distribution network based on two phases: day-ahead and intraday.

[0143] First, a day-ahead dispatch is conducted 24 hours in advance, with a duration of 1 hour. Based on the day-ahead forecasts of wind power, photovoltaic power, and transmission and distribution network loads, and comprehensively considering the operational risks of the transmission and distribution network, as well as the operating costs and characteristics of various distributed renewable energy sources within the distribution network, an optimized dispatch scheme is formulated with the goal of minimizing operating costs. This includes determining the start-up and shutdown plans of thermal power units in the transmission network, updating the distribution network structure, and determining the charging and discharging power of distributed renewable energy sources and energy storage devices in the distribution network, as well as the power of interruptible loads.

[0144] After the day-ahead dispatch is completed, based on the updated distribution network structure, the unit start-up and shutdown plans, thermal power unit output, gas turbine output, and diesel engine response are input into the intraday dispatch model as determinants, followed by intraday rolling optimization dispatch. The rolling optimization dispatch lasts for 15 minutes, with the objective of minimizing the overall risk value of the transmission and distribution system. While ensuring the effectiveness of the day-ahead dispatch plan, this approach effectively reduces the impact of day-ahead forecast errors on intraday optimization dispatch and promptly adjusts the output of energy storage devices and the response of interruptible loads.

[0145] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0146] The purpose of this invention is to design a multi-timescale risk scheduling strategy for transmission and distribution networks with a high proportion of distributed energy access. Its feature is that it enables interaction and coordination between transmission and distribution networks, thereby improving the economy and operational reliability of the transmission and distribution networks.

[0147] The day-ahead-intraday risk dispatching model for power transmission and distribution networks of the present invention is described in detail below:

[0148] The scheduling model has been optimized recently:

[0149] Distributed renewable energy sources exhibit significant uncertainties, increasing the operational risks of the integrated transmission and distribution power grid. Therefore, it is necessary to consider the impact of the overall operational risks of the transmission and distribution grid on the scheduling plan. To balance the relationship between system risk and operating costs and resolve conflicting issues, this invention, considering the operational risks of the transmission and distribution grid, establishes an optimized scheduling model with the operating costs of the transmission and distribution grid as the objective.

[0150] Objective function:

[0151] Considering the costs of thermal power units in the transmission network, gas turbines and diesel engines in the distribution network, electricity purchase costs, energy storage costs, interruptible load costs, and wind and solar curtailment costs, the current model takes minimizing the sum of the operating costs of the transmission and distribution networks as the optimization objective, as shown in equation (1).

[0152] (1)

[0153] In the formula: The study period is divided into time periods; For the operating costs of the power transmission network; This refers to the operating costs of the power distribution network.

[0154] (1) Transmission network operating costs

[0155] (2)

[0156] In the formula: This represents the total number of thermal power units in the power transmission network. , thermal power units Start-up costs and downtime costs, , It is a 0 / 1 variable, with a value of 0 indicating that the unit is out of service and a value of 1 indicating that the unit is in operation; For thermal power units At any moment contribution; , , This is the cost characteristic function of the output power of thermal power units.

[0157] (2) Distribution network operating costs

[0158] (3)

[0159] In the formula: This represents the total operating cost of the gas turbine. Total operating cost of diesel engine; For users' electricity purchase costs; The total operating cost of the energy storage device; Cost of interruptible load operation; Cost of abandoning light; Cost of wind curtailment.

[0160] (3) Total operating cost of gas turbine

[0161] Gas turbines generate electricity by consuming fuel. The output power of a gas turbine is freely adjustable and has a fast response time.

[0162] (4)

[0163] (5)

[0164] In the formula: This represents the total number of gas turbines; These are the operating and maintenance costs of the gas turbine, fuel costs, and emissions costs. Let d be the output power of the gas turbine at time t; This represents the operating and maintenance cost coefficient for gas turbines. For the operating efficiency of the gas turbine; For natural gas prices; It has a low calorific value for natural gas; The cost coefficient for carbon emissions from gas turbines; This refers to carbon emissions.

[0165] (4) Total operating cost of diesel engine

[0166] Diesel engines are a common type of fuel generator, and their operation incurs fuel costs, maintenance costs, and carbon emission costs.

[0167] (6)

[0168] (7)

[0169] In the formula: This represents the total number of diesel engines. These are the operating and maintenance costs of the diesel engine, fuel costs, and carbon emission costs, respectively. Let e ​​be the output power of the diesel engine at time t; This is the coefficient for diesel engine operation and maintenance costs;

[0170] For diesel engines; The cost coefficient for carbon emissions from diesel engines; This refers to carbon emissions.

[0171] (5) User electricity purchase cost

[0172] (8)

[0173] In the formula: Revenue generated in response to user demand; This indicates the power of user participation in demand response.

[0174] (6) Total operating cost of energy storage device

[0175] Users control transferable load based on time-of-use pricing and response pricing.

[0176] (9)

[0177] (10)

[0178] In the formula: This represents the total number of energy storage devices. These are the operating cost and loss cost of the energy storage device, respectively. The purchase cost of energy storage m, Let i be the charging and discharging power of energy storage during time period t; This refers to the battery capacity cost coefficient for energy storage devices. For energy storage charging and discharging power; The number of charge-discharge cycles for energy storage; This refers to the depth of charge and discharge for energy storage.

[0179] (7) Cost of interruptible load operation

[0180] (11)

[0181] In the formula: Number of interruptible loads for users; The compensation cost for n units of interruptible load. Let n be the interruption amount of the interruptible load.

[0182] (8) Cost of abandoning light

[0183] (12)

[0184] In the formula: Number of interruptible loads for users; This is the cost coefficient for abandoned light. These represent the predicted and scheduled power output of distributed photovoltaic systems k days prior to time t.

[0185] (9) Cost of wind curtailment

[0186] (13)

[0187] In the formula: This represents the total number of wind turbine units. This is the cost coefficient for wind curtailment. These represent the predicted and scheduled power output of the wind turbines s days prior to time t.

[0188] Constraints:

[0189] Power transmission network constraints

[0190] Equations (14) and (15) represent the active power balance constraint and the nodal power balance constraint, respectively.

[0191] (14)

[0192] (15)

[0193] In the formula: k is the distribution network set; D is the load set; Power is exchanged between the transmission and distribution networks via interconnecting lines. d represents the predicted value of the transmission network load d in time period t; a and b represent the nodes of the transmission network.

[0194] thermal power unit constraints

[0195] Equations (16) and (17) are the output limit constraint and start-up constraint of thermal power units, respectively.

[0196] (16)

[0197] (17)

[0198] In the formula, These represent the maximum and minimum output values ​​of the thermal power unit, respectively.

[0199] Distribution network constraints

[0200] Equations (18) and (19) represent the active power balance constraint and the nodal power balance constraint of the distribution network, respectively.

[0201] (18)

[0202] (19)

[0203] In the formula: denoted as , where i and j are the predicted values ​​of the distribution network load during time period t; i and j are the distribution network nodes.

[0204] Gas turbine output limits

[0205] (20)

[0206] In the formula: These represent the maximum and minimum output values ​​of the gas turbine, respectively.

[0207] Diesel engine output limits

[0208] (twenty one)

[0209] In the formula: These represent the maximum and minimum output values ​​of the diesel engine, respectively.

[0210] Energy storage operation constraints

[0211] Equations (22) and (23) represent the energy storage balance constraint and the energy storage limit constraint, respectively.

[0212] (twenty two)

[0213] (twenty three)

[0214] In the formula: Let t be the energy level of the energy storage device. The charging / discharging efficiency of the energy storage device; Let m be the discharge power of the stored energy during time period t.

[0215] Interruptible load interruption amount constraint

[0216] (twenty four)

[0217] In the formula, These represent the maximum and minimum values ​​of the interruptible load interruption amount, respectively.

[0218] Constraints of Distributed Photovoltaics and Wind Turbines

[0219] Equations (25) and (26) are the output limits for distributed photovoltaic and wind turbine units, respectively.

[0220] (25)

[0221] (26)

[0222] In the formula, Let K and S be the maximum output of distributed photovoltaic power generation k and wind turbine s, respectively, at time t.

[0223] Space frame structure adjustment constraints

[0224] Equations (27) and (28) represent the constraints on the position variables of the tie and sectionalizing switches, the constraints on the action variables, and the constraints on the radial structure of the distribution network, respectively.

[0225] (27)

[0226] (28)

[0227] (29)

[0228] In the formula, Let be the position variable of the u-th switch in time period t, with a value of 1 indicating that the switch is closed and a value of 0 indicating that the switch is open; Let be the action variable of the u-th switch in time period t, with a value of 1 indicating that the switch is activated and 0 indicating that the switch position remains unchanged. Let u be the maximum number of per day allowed actions for the u-th switch; The network topology of the distribution network in time period t. It is a set of radial network structures for power distribution networks.

[0229] Limitation of transmission and distribution network over-limit risks

[0230] Equations (30), (31), and (32) represent the risk limits for node voltage, branch power flow, and unit capacity exceeding limits, respectively.

[0231] (30)

[0232] (31)

[0233] (32)

[0234] In the formula: Calculated value for the node voltage over-limit risk index; The calculated value for the risk index of power flow exceeding the limit in the branch line; This is the calculated value for the unit capacity exceeding the limit.

[0235] ② Intraday Optimized Scheduling Model:

[0236] During the intraday phase, in order to further reduce the operational risk value of the power transmission and distribution network, an optimized scheduling model is established with the goal of minimizing the comprehensive operational risk value of the power transmission and distribution network. The aim is to minimize the various operational risks of the power transmission and distribution network and improve the reliability of its safe operation while minimizing the operating cost of the power transmission and distribution network.

[0237] Considering the risks of overcapacity of generating units in the transmission network, overvoltage of distribution network nodes, and overcurrent of branch power flow in the distribution network, an optimization objective is established with the minimum comprehensive operational risk value of the transmission and distribution networks as shown in equation (33).

[0238] (33)

[0239] In the formula, The weighting coefficient represents the degree of importance that the scheduler places on each objective. Weights can be calculated based on different risk levels. .

[0240] The formula for calculating the node voltage over-limit risk index is:

[0241] (34)

[0242] (35)

[0243] In the formula: For node importance, The severity of the loss due to node voltage exceeding the limit. The probability of node voltage exceeding the limit; These represent the upper and lower limits of the voltage value and the voltage per unit value at time t, respectively.

[0244] The formula for calculating the risk index of branch power flow exceeding the limit is:

[0245] (36)

[0246] (37)

[0247] (38)

[0248] In the formula: Importance of branch roads; The severity of the risk of loss due to branch voltage exceeding the limit. The probability of branch voltage exceeding the limit; Let i be the load rate of distribution network line i.

[0249] The formula for calculating the risk index of unit capacity exceeding limits is:

[0250] (39)

[0251] (40)

[0252] In the formula: This is the calculated value for the unit capacity over-limit index; Importance of capacity; The severity of risk and loss due to the tidal current exceeding the limit in the branch line; The probability of a branch power flow exceeding the limit; These represent the maximum and minimum unit capacity.

[0253] The comprehensive risk assessment index result R can be calculated from the above three types of risks exceeding the limit, namely:

[0254] (41)

[0255] In the formula, The weighting coefficient represents the degree of importance that the scheduler places on each objective. Weights can be calculated based on different risk levels. .

[0256] The method for solving multi-timescale risk scheduling strategies for power transmission and distribution networks according to the present invention is described in detail below:

[0257] In the Particle Swarm Optimization (PSO) algorithm, each particle is described by a position and velocity vector, where the position vector represents a possible solution to the problem, and the velocity vector represents the direction and magnitude of the position change. Assume the total number of particles is... Then the first Particles in dimension The position and velocity in the middle are represented as follows:

[0258] (42)

[0259] Each particle adjusts its velocity and position by tracking its previous individual best position and the group best position, which are denoted as:

[0260] (43)

[0261] In the formula: It represents the optimal individual position of n particles; It is the optimal position of the group obtained from all particles in the previous iteration.

[0262] The speed and position update formulas of the traditional PSO algorithm are expressed as follows:

[0263] (44)

[0264] In the formula: It is the inertia weighting factor; and These are learning factors, which respectively reflect the particle's self-learning ability and social learning ability; It is a random number that is uniformly distributed in [0,1].

[0265] The performance of the PSO algorithm is affected by the choice of parameters. Traditional PSO algorithms use fixed inertia weight factors and learning factors, making them prone to getting trapped in local optima. To address this drawback, improvements have been made to the PSO algorithm in terms of both inertia weight factors and learning factors. In the standard PSO algorithm, , The flowchart of the improved PSO algorithm is as follows: Figure 2 As shown.

[0266] The improved algorithm is as follows:

[0267] (45)

[0268] (46)

[0269] In the formula: This is the current iteration number; It is the maximum number of iterations; These are the initial and final values ​​of the inertia weight factor; in the early stages of iteration, the larger values... This prevents the algorithm from getting trapped in local optima and facilitates global search; in the later stages of iteration, smaller... It is beneficial for local search and for algorithm convergence; yes initial and stopping values, Greater than ; yes initial and stopping values, .

[0270] In the early stages of iteration, large and small This gives particles good self-learning ability but poor social learning ability, which is beneficial for global search. In the later stages of iteration, small... Heda This gives particles strong social learning ability and poor self-learning ability, which is beneficial to the convergence of the algorithm.

[0271] Scenario and parameter settings

[0272] ①Calculation System

[0273] The transmission network adopts the IEEE 30-bus system, with nodes 8 and 18 extended into a distribution network, such as... Figure 3a , Figure 3bAs shown. The distribution network structure adopts the IEEE 33-node system, with gas turbines, diesel engines, photovoltaic systems, energy storage devices, and wind turbines connected at nodes 12, 15, 20, 25, and 30. The base voltage for the distribution network section of the example is 12.66 kV, and the base power is 10 MW. It is assumed that the node voltages of the transmission and distribution networks and the branch power flow over-limit risk weights are the same and higher than the unit capacity over-limit risk index, with the following values: Day-ahead forecasts were performed on the load and active power output of the distribution network, and the forecast results are as follows: Figure 4 As shown.

[0274] ②Scheduling Result Analysis

[0275] To verify that the scheduling method proposed in this invention can improve the economic efficiency and safety stability of system operation and promote the absorption of wind and solar resources, a comparative analysis of the power transmission and distribution network scheduling results under different operating scenarios is conducted. The scenarios are as follows:

[0276] Scenario 1: Ignoring the operational risks of the power transmission and distribution network, but considering the controllable resources of the distribution network;

[0277] Scenario 2: Considering the operational risks of the power transmission and distribution network, but not the controllable resources of the distribution network;

[0278] Scenario 3 (Method presented in this paper): Considering the operational risks of the power transmission and distribution network, and the controllable resources of the distribution network.

[0279] The day-ahead to intraday scheduling models for three scenarios are solved, and the total cost, transmission network operating cost, and distribution network operating cost are shown in Table 1. Table 1 shows that by considering the operating conditions of the transmission and distribution networks and taking operational risks into account, and incorporating quantified operational risks into the constraints, the potential risks to the power grid can be reduced while maintaining a relatively low total cost for the scheduling plan. The reason why the operating cost of Scenario 3 is slightly higher than that of Scenario 1 is that, considering potential operational risks, the system cannot arrange generator output in the most economical way, increasing the overall operating cost. However, this sacrifice of some economic efficiency improves the system's operational safety. The operating cost of Scenario 2 is slightly higher than that of Scenario 1 because distributed power sources such as energy storage and interruptible loads in Scenario 1 can alleviate the pressure on transmission network units, enabling the system to obtain scheduling results even with less reserve, reducing frequent unit start-ups and shutdowns.

[0280] Table 1. Day-to-day and intraday operating costs for different scenarios

[0281]

[0282] The model was solved for each scenario, and the occurrence frequency and calculated values ​​of various risks in the power transmission and distribution network were compared and analyzed. The results are shown in Table 2.

[0283] Table 2 Intraday Risk Analysis for Different Scenarios

[0284]

[0285] As shown in Table 2, Scenario 3 has the lowest operational risk and the fewest instances of voltage and power flow violations, thus improving the operational safety of the transmission and distribution network. Comparing Tables 1 and 2 reveals that considering the comprehensive risk value of the transmission and distribution network reduces the overall risk value of the system, but relatively increases the system's operating cost. Therefore, while considering the comprehensive risk of the transmission and distribution network increases the total system operating cost, it promotes the absorption of distributed power sources, reduces voltage and power flow violation rates, and improves the system's safety and stability.

[0286] Considering risk constraints, network structure optimization was performed on the distribution network, adjusting the interconnection switch states between nodes to obtain the final distribution network topology that satisfies the objective function and constraints, such as... Figure 5a , Figure 5b As shown.

[0287] The response status and start-up / shutdown status of thermal power units in the power transmission network are as follows: Figure 6a , Figure 6b As shown, from 10:00 to 17:00, the overall output of new energy sources is at a relatively high level. Gas turbines and diesel engines can reduce their output, which can improve the local consumption of wind and solar power while reducing the economic losses caused by the shutdown and peak shaving of thermal power units. Therefore, the overall output of thermal power units is relatively stable without significant fluctuations.

[0288] Depend on Figure 7a , Figure 7b As shown in the power grid dispatch strategy, energy storage can effectively perform peak shaving and valley filling for the power grid. It can charge the energy storage when the load power drops rapidly at night or the photovoltaic output increases rapidly, thus relieving the downward pressure on the generator set. It can also discharge the energy storage when the load increases rapidly in the morning or evening, thus relieving the upward pressure on the generator set.

[0289] Because distributed power generation units in operation have the characteristic of fast adjustment speed, the period from 01:00 to 20:00 is the main period for diesel engines, gas turbines, interruptible loads, and energy storage to participate in power system regulation. From 21:00 to 24:00, wind power generation is high, and diesel units are shut down to make room for wind power to be connected to the grid. However, the power system loses its corresponding regulation capacity. At this time, by calling on gas turbines, interruptible loads, and energy storage resources, the scheduling resources of different periods can be complemented and utilized.

[0290] ③ Comparative Analysis

[0291] Based on the above three scenarios, the current-day distribution network loss comparison curve and voltage curve are as follows: Figure 8a , Figure 8b , Figure 9a , Figure 9bAs shown, the voltage values ​​are the average voltage values ​​of each node over 24 hours.

[0292] from Figure 8a , Figure 8b It can be seen that the distribution network loss value is the largest in scenario 1, followed by scenario 2, while the distribution network loss value is relatively small in scenario 3. By considering the operational risks of the transmission and distribution networks and coordinating the scheduling of flexible resources within the transmission and distribution networks, it is possible to optimize the active power fluctuations of the distribution network, reduce the reverse power flow problem, and reduce the distribution network loss value.

[0293] from Figure 9a , Figure 9b As can be seen, in Scenario 3, considering grid operation risks, by coordinating the scheduling of flexible resources within the distribution network and thermal power unit resources within the transmission network, the voltage curve variation amplitude of the distribution network nodes is reduced, the voltage variation is more stable, and the difference between the highest and lowest voltages is reduced. Furthermore, the voltage variation rate decreases between nodes 18 and 20. Compared to the scenario without considering grid operation risks, risk scheduling of the transmission and distribution networks can reduce large voltage fluctuations and minimize the number of times the transmission network's node voltage exceeds its limits.

[0294] To demonstrate the advantages of the proposed improved particle swarm optimization algorithm in terms of good convergence and strong optimization capability, it is compared with genetic algorithms and traditional particle swarm optimization algorithms. All three algorithms are set with a particle swarm size of 100 and a maximum number of iterations of 30. The best-performing algorithm is selected for comparative analysis, and the results are shown below. Figure 10 .

[0295] Depend on Figure 10 It can be seen that the genetic algorithm converges the fastest, after the 6th iteration, followed by the traditional PSO algorithm, which converges after the 8th iteration. Although the improved PSO algorithm converges more slowly, after the 18th iteration, its search efficiency is high. Table 3 shows that although the improved PSO algorithm has a longer average time, it has the smallest fitness function value and the highest optimization rate. This indicates that both the genetic algorithm and the traditional particle swarm optimization algorithm are prone to getting trapped in local optima when searching for the global optimum, while the improved PSO algorithm has better global exploration capabilities, continuing its global search even after finding a local optimum. Therefore, this demonstrates that the improved PSO algorithm proposed in this paper is superior to both the genetic algorithm and the traditional particle swarm optimization algorithm.

[0296] Simulation results show that, although the operating cost of the multi-timescale risk scheduling strategy for power transmission and distribution networks in this invention is higher than that of scheduling strategies that do not consider operational risks, the overall operational risk value of the power transmission and distribution network is significantly reduced, enabling the power transmission and distribution network to further improve operational reliability during economical operation.

Claims

1. A multi-timescale risk scheduling method for transmission and distribution networks with a high proportion of distributed energy access, characterized in that: The steps are as follows: This includes a risk dispatching framework for the power transmission and distribution network in two phases: day-ahead and intraday. I. Current optimized scheduling model: Considering the operational risks of the power transmission and distribution network, and taking the operating cost of the power transmission and distribution network as the objective, an optimal scheduling model is established: S1 objective function: The current model uses minimizing the sum of the operating costs of the transmission and distribution networks as the optimization objective: （1） In the formula: The study period is divided into time periods; For the operating costs of the power transmission network; For distribution network operating costs; (1) Transmission network operating costs （2） In the formula: This represents the total number of thermal power units in the power transmission network. , thermal power units Start-up costs and downtime costs, , It is a 0 / 1 variable, with a value of 0 indicating that the unit is out of service and a value of 1 indicating that the unit is in operation; For thermal power units At any moment contribution; , , The cost characteristic function of the output power of thermal power units; (2) Distribution network operating costs （3） In the formula: This represents the total operating cost of the gas turbine. Total operating cost of diesel engine; For users' electricity purchase costs; The total operating cost of the energy storage device; Cost of interruptible load operation; Cost of abandoning light; Cost of wind curtailment; (3) Total operating cost of gas turbine （4） （5） In the formula: This represents the total number of gas turbines; , , These are the operating and maintenance costs of the gas turbine, fuel costs, and emissions costs. Let d be the output power of the gas turbine at time t; This represents the operating and maintenance cost coefficient for gas turbines. For the operating efficiency of the gas turbine; For natural gas prices; It has a low calorific value for natural gas; The cost coefficient for carbon emissions from gas turbines; Carbon emissions; (4) Total operating cost of diesel engine （6） （7） In the formula: This represents the total number of diesel engines. , , These are the operating and maintenance costs of the diesel engine, fuel costs, and carbon emission costs, respectively. Let e ​​be the output power of the diesel engine at time t; This is the coefficient for diesel engine operation and maintenance costs; , , For diesel engines; The cost coefficient for carbon emissions from diesel engines; Carbon emissions; (5) User electricity purchase cost （8） In the formula: Revenue generated in response to user demand; Indicates the power of user participation in demand response; (6) Total operating cost of energy storage device （9） （10） In the formula: This represents the total number of energy storage devices. , These are the operating cost and loss cost of the energy storage device, respectively. For energy storage Purchase cost, , Energy storage for time period t The charging and discharging power; This refers to the battery capacity cost coefficient for energy storage devices. For energy storage charging and discharging power; The number of charge-discharge cycles for energy storage; For energy storage charge and discharge depth; (7) Cost of interruptible load operation （11） In the formula: Number of interruptible loads for users; The compensation cost for n units of interruptible load. The interruption amount for interruptible load n; (8) Cost of abandoning light （12） In the formula: Number of interruptible loads for users; This is the cost coefficient for abandoned light. , Distributed photovoltaic power generation at time t Recent forecasts and scheduling of power output; (9) Cost of wind curtailment （13） In the formula: This represents the total number of wind turbine units. This is the cost coefficient for wind curtailment. , These represent the predicted and scheduled power output of the wind turbines at time t, respectively, s-day ahead. S2, Constraints: (1) Transmission network constraints The active power balance constraints and nodal power balance constraints are as follows: （14） （15） In the formula: K represents the distribution network set; For load sets; Power is exchanged between the transmission and distribution networks via interconnecting lines. For transmission network load During the period The predicted value; , For power transmission network nodes; (2) Constraints of thermal power units The output limit constraints and start-up constraints of thermal power units are as follows: （16） （17） In the formula, , These represent the maximum and minimum output values ​​of the thermal power unit, respectively. (3) Distribution network constraints The active power balance constraints and nodal power balance constraints of the distribution network are as follows: （18） （19） In the formula: For the distribution network load during the time period The predicted value; , For distribution network nodes; (4) Gas turbine output limiting constraints （20） In the formula: , These represent the maximum and minimum output values ​​of the gas turbine, respectively. (5) Diesel engine output limiting constraints （21） In the formula: , These represent the maximum and minimum output power of the diesel engine, respectively. (6) Constraints on Energy Storage Operation The energy storage power balance constraint and the storage power limit constraint are as follows: （22） （23） In the formula: for The amount of electricity stored in the energy storage device at any time; , The charging / discharging efficiency of the energy storage device; for Time-of-use energy storage The discharge power; (7) Interruption limit of interruptible load （24） In the formula, , These are the maximum and minimum values ​​of the interruptible load interruption amount, respectively. (8) Constraints of distributed photovoltaic and wind turbine units The output limits for distributed photovoltaic and wind turbine units are as follows: （25） （26） In the formula, , They are respectively Distributed photovoltaic power generation Wind turbine Maximum output; (9) Adjustment constraints of space frame structure The constraints on the position variables of interconnecting and sectionalizing switches, the constraints on the action variables, and the constraints on the radial structure of the distribution network are as follows: （27） （28） （29） In the formula, for Time period The position variables of the switches, with a value of 1 indicating that the switch is closed and a value of 0 indicating that the switch is open; for Time period The action variable of the switch takes a value of 1 to indicate that the switch is activated and 0 to indicate that the switch position remains unchanged. For the first The maximum number of daily operations allowed for each switch; The network topology of the distribution network in time period t. A collection of radial network structures for power distribution networks; (10) Limitation constraints on transmission and distribution networks The limits for node voltage, branch power flow, and unit capacity exceeding the limit risk values ​​are as follows: （30） （31） （32） In the formula: Calculated value for the node voltage over-limit risk index; The calculated value for the risk index of power flow exceeding the limit in the branch line; This is the calculated value for the unit capacity over-limit index; II. Intraday Optimized Scheduling Model: The optimization objective is to minimize the combined operational risk of the transmission and distribution networks. The objective function is: （33） In the formula, The weighting coefficients represent the degree of importance that the scheduler places on each objective. The weights are calculated based on different risk levels. ; S1, the node voltage over-limit risk indicator is: （34） （35） In the formula: Represented as node at time t Voltage over-limit operation risk index value; For node importance, The severity of the loss due to node voltage exceeding the limit. The probability of node voltage exceeding the limit; , and These represent the upper and lower limits of the voltage value at time t and the voltage per unit value, respectively. S2, the risk indicator for branch power flow exceeding the limit is: （36） （37） （38） In the formula: Importance of branch roads; The severity of the risk of loss due to branch voltage exceeding the limit. The probability of branch voltage exceeding the limit; Let i be the load rate of distribution network line i; S3, the risk indicator for unit capacity exceeding limits is: （39） （40） In the formula: Importance of capacity; The severity of the risk of losses due to exceeding unit capacity limits; The probability of the unit exceeding its capacity limit; , These represent the maximum and minimum unit capacity. S4. The comprehensive risk assessment index result R can be calculated from the above three types of risks exceeding the limit, namely: （41） In the formula, The weighting coefficients represent the degree of importance that the scheduler places on each objective. The weights are calculated based on different risk levels. ; III. Solution Methods for Multi-Time-Scale Risk Dispatch Strategies in Power Transmission and Distribution Networks: Assuming the total number of particles is Then the first Particles in dimension The position and velocity in the middle are represented as follows: （42） Each particle adjusts its velocity and position by tracking its previous individual best position and the group best position, which are denoted as: (43) In the formula: yes The optimal position for each individual particle; It is the optimal position of the group obtained from all particles in the previous iteration; The formulas for velocity and position are expressed as follows: （44） In the formula: It is the inertia weighting factor; and These are learning factors, which respectively reflect the particle's self-learning ability and social learning ability; and It is a random number uniformly distributed in [0,1]. The particle swarm optimization algorithm was improved in two aspects: inertia weight factor and learning factor. （45） （46） In the formula: This is the current iteration number; It is the maximum number of iterations; and These are the initial and final values ​​of the inertia weighting factor; In the early stages of the iteration, larger This prevents the algorithm from getting trapped in local optima and facilitates global search; in the later stages of iteration, smaller... It is beneficial for local search and for algorithm convergence; and yes initial and stopping values, Greater than ; and yes initial and stopping values, Less than .

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