CN-116316812-B - Multi-time-scale risk scheduling method for high-proportion distributed energy access transmission and distribution network

CN116316812BCN 116316812 BCN116316812 BCN 116316812BCN-116316812-B

Abstract

A multi-time scale risk scheduling method for a transmission and distribution network for high-proportion distributed energy access belongs to the technical field of transmission and distribution networks. The method comprises the steps of considering comprehensive operation cost of a transmission and distribution network, constructing a daily scheduling model considering different operation risk limit values of the transmission and distribution network, cooperatively optimizing a source network load of the transmission and distribution network under the constraint of the risk limit values to store various adjustable resources, constructing an intra-daily scheduling model with a minimum objective function as the comprehensive risk value of the transmission and distribution network, considering the magnitude of the different operation risk values of the transmission and distribution network, and finally correcting a scheduling plan arranged in the daily by solving the intra-daily model and a prediction error. The invention can realize interactive coordination between the transmission and distribution networks, reduce the running risk of the transmission and distribution networks and improve the economy and the running reliability of the transmission and distribution networks.

Inventors

BIAN JING
WANG HE
LIU ZIYUE
YU HUANAN
LI GUOQING
WANG ZHENHAO
LI SHIQIANG

Assignees

东北电力大学

Dates

Publication Date: 20260508
Application Date: 20230208

Claims (1)

1. A multi-time scale risk scheduling method for a transmission and distribution network for high-proportion distributed energy access is characterized by comprising the steps of a transmission and distribution network risk scheduling framework comprising two stages of the day before and the day in 1. Day-ahead optimization scheduling model: Under the condition of considering the running risk of the transmission and distribution network, taking the running cost of the transmission and distribution network as a target, establishing an optimized scheduling model: s1 objective function: the day-ahead model takes the minimum sum of the running cost of a transmission network and a distribution network as an optimization target: (1) Wherein: time periods divided within a study period; The running cost of the power transmission network is; the running cost of the power distribution network is; (1) Grid operation cost (2) Wherein: the total number of the thermal power generating units of the power transmission network is; 、 Respectively, thermal power generating unit Is characterized by that the starting cost and stopping cost of said machine, 、 A variable of 0/1, wherein a value of 0 indicates that the unit is stopped, and a value of 1 indicates that the unit is operated; is a thermal power generating unit At the moment of time Is a force of the (a); 、、 For outputting thermal power generating units a power cost characteristic function; (2) Running cost of distribution network (3) Wherein: The total operating cost of the gas turbine; the total running cost of the diesel engine; the electricity purchasing cost is for the user; the total operation cost of the energy storage device; operating costs for interruptible loads; The cost of discarding light; the cost of wind disposal is the cost; (3) Total cost of gas turbine operation (4) (5) Wherein: is the total number of gas turbines; 、、 The operation and maintenance costs, the fuel costs and the emission costs of the gas turbine, The output power of the gas turbine d at the time t; maintaining a cost coefficient for gas turbine operation; Is the operating efficiency of the gas turbine; Is the price of natural gas; Is natural gas with low calorific value; Cost factors for carbon emissions of gas turbines; Is carbon emission; (4) Total cost of diesel engine operation (6) (7) Wherein: Is the total number of diesel engines; 、、 the operation and maintenance cost, the fuel cost and the carbon emission cost of the diesel engine are respectively; the output power of the diesel engine e at the time t; Maintaining a cost coefficient for diesel engine operation; 、、 Is the coefficient of the diesel engine; Cost coefficients for carbon emissions from diesel engines; Is carbon emission; (5) Cost of electricity purchase for users (8) Wherein: Revenue for user demand response; Power representing user participation in demand response; (6) Total cost of operation of energy storage device (9) (10) Wherein: the total number of the energy storage devices; 、 the operation cost and the loss cost of the energy storage device are respectively; To store energy Is added to the purchase cost of (a) the product, 、 For t period of time to store energy Charging and discharging power of (a); The cost coefficient of the battery capacity of the energy storage device; The energy storage charge and discharge power; the number of charge and discharge cycles of the stored energy; the depth of energy storage charge and discharge is set; (7) Interruptible load operating costs (11) Wherein: the number of interruptible loads for the user; the cost of compensation for the amount of interruption in units of interruptible load n, An interruption amount which is an interruptible load n; (8) Cost of discarding light (12) Wherein: the number of interruptible loads for the user; In order to reject the cost factor of light, 、 Respectively t-moment distributed photovoltaic Predicting and dispatching output before the day; (9) Cost of wind disposal (13) Wherein: the total number of the wind turbine generators is the total number of the wind turbine generators; In order to discard the cost factor of the wind, 、 Predicting and dispatching output before the s days of the wind turbine generator at the moment t respectively; s2, constraint conditions: (1) Grid network constraints The active power balance constraint and the node power balance constraint are respectively as follows: (14) (15) k is a distribution network set; Is a load set; exchanging power for a tie line between a transmission network and a distribution network; For grid loads In the time period Is a predicted value of (2); 、 Is a power transmission network node; (2) Thermal power generating unit constraint The thermal power generating unit output limit constraint and the starting constraint are respectively as follows: (16) (17) in the formula, 、 Respectively the maximum value and the minimum value of the output of the thermal power unit; (3) Network constraints for power distribution network The active power balance constraint and the node power balance constraint of the power distribution network are respectively as follows: (18) (19) Wherein: For loading distribution network in time period Is a predicted value of (2); 、 The node is a power distribution network node; (4) Gas turbine output limit constraints (20) Wherein: 、 the maximum and minimum output values of the gas turbine are respectively; (5) Diesel engine output limiting constraint (21) Wherein: 、 the maximum and minimum output values of the diesel engine are respectively; (6) Energy storage operation constraint The energy storage electric quantity balance constraint and the storage electric quantity limit constraint are respectively as follows: (22) (23) Wherein: Is that The electric quantity of the time energy storage device; 、 charge/discharge efficiency for the energy storage device; Is that Time period energy storage Is set in the above range; (7) Interruptible load interrupt volume constraint (24) In the formula, 、 The maximum value and the minimum value of the interruption quantity of the interruptible load are respectively; (8) Distributed photovoltaic and wind turbine generator system constraint The output limit constraint of the distributed photovoltaic and wind turbine generator is as follows: (25) (26) in the formula, 、 Respectively is Time-of-day distributed photovoltaic Wind turbine generator system Maximum force of (2); (9) Grid structure adjustment constraint The position variable value constraint, the action variable value constraint and the radial structure constraint of the power distribution network of the interconnection and sectionalizing switch are respectively as follows: (27) (28) (29) in the formula, Is that Time period of Position variables of the switches, wherein a value of 1 indicates that the switches are closed and a value of 0 indicates that the switches are opened; Is that Time period of The action variable of each switch, wherein the value 1 represents the action of the switch, and the value 0 represents the unchanged switch position; Is the first The maximum daily allowable action times of the switches; for the network topology of the distribution network during period t, The method is a radial network structure set of the power distribution network; (10) Power transmission and distribution network out-of-limit risk constraint Node voltage, branch power flow and unit capacity out-of-limit risk value limiting constraint are respectively as follows: (30) (31) (32) Wherein: calculating a value for a node voltage out-of-limit risk index; Calculating a value for the branch power flow out-of-limit risk index; calculating a value for a unit capacity out-of-limit index; 2. day optimization scheduling model: establishing an objective function by taking the minimum comprehensive operation risk value of the transmission network and the distribution network as an optimization objective: (33) in the formula, The weight coefficient is the importance degree of scheduling on each target, the weight is calculated according to different risk levels, and ; S1, node voltage out-of-limit risk indexes are as follows: (34) (35) Wherein: represented as node at time t Is a voltage out-of-limit running risk indicator value; For the importance of the node(s), For node voltage out-of-limit risk loss severity, Probability of node voltage out-of-limit risk; , And The upper limit and the lower limit of the voltage value at the time t and the voltage per unit value are respectively expressed; s2, branch power flow out-of-limit risk indexes are as follows: (36) (37) (38) Wherein: The importance of the branch is; for branch voltage out-of-limit risk loss severity, Probability of occurrence of branch voltage out-of-limit risk; The load factor of the power distribution network line i; s3, the unit capacity out-of-limit risk index is as follows: (39) (40) Wherein: Is the importance of capacity; severity of loss of risk for out-of-limit unit capacity; probability of occurrence of unit capacity out-of-limit risk; 、 The maximum value and the minimum value of the unit capacity are set; S4, calculating the comprehensive risk assessment index result R by the three out-of-limit risks, namely: (41) in the formula, The weight coefficient is the importance degree of scheduling on each target, the weight is calculated according to different risk levels, and ; 3. Multi-time scale of transmission and distribution network the solving method of the risk scheduling strategy comprises the following steps: assuming that the total number of particles is Then (1) Individual particles in dimension The position and velocity of (c) are expressed as follows: (42) each particle adjusts its speed and position by tracking the individual best position and the population best position before them, expressed as: (43) Wherein: Is that Individual best positions of individual particles; Is the group best position obtained from all particles of the previous iteration; The speed and position formula is expressed as: (44) Wherein: is an inertial weight factor; And Are learning factors that reflect the self-learning ability and social learning ability of the particles, respectively; And Is a random number uniformly distributed in [0,1 ]; the particle swarm algorithm is improved in two aspects of inertia weight factors and learning factors: (45) (46) Wherein: is the current iteration number; Is the maximum number of iterations; And Is the initial value and the final value of the inertia weight factor, and is larger in the initial stage of iteration The algorithm is not suitable to be trapped in a local optimal value, is convenient for global search, and is smaller in the later period of iteration The method is favorable for local search and convergence of an algorithm; And Is that Is set to be equal to the initial value and the stop value of (c), Greater than ; And Is that Is set to be equal to the initial value and the stop value of (c), Less than 。

Description

Multi-time-scale risk scheduling method for high-proportion distributed energy access transmission and distribution network Technical Field The invention belongs to the technical field of transmission and distribution networks. Background Along with the proposal of the strategic targets of carbon peak, carbon neutralization, the connection of renewable energy sources such as high-proportion photovoltaics, wind power and the like to a power system in the form of a distributed power source has become a trend of development of a novel energy system. The distributed power supply is connected into the power distribution network in a large quantity, the power distribution network gradually realizes the conversion from passive to active, the power flow of the transmission and distribution network gradually shows bidirectional from fixed, the coupling relationship is increasingly enhanced, and the safe and stable operation is also influenced. With the continuous expansion of the power grid scale, the interaction and the connection of the power transmission network and the distribution network are more compact, and the coordination relationship between the power transmission network and the distribution network needs to be fully considered during the dispatching. In the traditional scheduling strategy, independent consideration is generally given to a power transmission network or a power distribution network, the power distribution network is equivalent to a load during power transmission network scheduling, and the power transmission network is equivalent to a power supply during power distribution network scheduling. The flexibility of the splitting and mutually independent scheduling method is weakened, the system resources of each layer are difficult to coordinate to fully consume renewable energy sources, unnecessary power grid blocking and power unbalance are easy to occur, even the feeder power between the transmission and distribution networks can have larger fluctuation, a series of system risk problems are caused, and additional operation cost is generated. Especially under the condition that a large-scale distributed power supply is connected into a power grid, the transmission grid and the power distribution network are required to be interactively coordinated to promote new energy consumption, the control resource is utilized as much as possible to realize interactive coordination of the transmission grid and the power distribution network, and the problems of system operation risk and economy are solved. Therefore, how to introduce a risk theory in interactive dispatching of a transmission and a distribution network, optimize resource allocation of a power generation side and a power utilization side, and ensure safe operation of a power grid is an important subject to be solved in the future of intelligent power grid development. Aiming at the challenges brought to power grid dispatching and safety by accessing large-scale distributed power sources, most researches focus on resource optimization of a distribution network layer, and influence of occurrence risks of a transmission network on a power distribution network is ignored. Disclosure of Invention The invention aims to provide a multi-time-scale risk scheduling method for a transmission and distribution network facing high-proportion distributed energy access, which can realize interactive coordination between transmission and distribution networks and reduce various running risk values of the transmission and distribution networks through day-day two-stage scheduling. The method comprises the following steps: power transmission and distribution network risk scheduling framework comprising two stages of day before and day in 1. Day-ahead optimization scheduling model: Under the condition of considering the running risk of the transmission and distribution network, taking the running cost of the transmission and distribution network as a target, establishing an optimized scheduling model: s1 objective function: the day-ahead model takes the minimum sum of the running cost of a transmission network and a distribution network as an optimization target: (1) Wherein: time periods divided within a study period; The running cost of the power transmission network is; the running cost of the power distribution network is; (1) Grid operation cost (2) Wherein: the total number of the thermal power generating units of the power transmission network is; 、 Respectively, thermal power generating unit Is characterized by that the starting cost and stopping cost of said machine,、A variable of 0/1, wherein a value of 0 indicates that the unit is stopped, and a value of 1 indicates that the unit is operated; is a thermal power generating unit At the moment of timeIs a force of the (a);、、 For outputting thermal power generating units a power cost characteristic function; (2) Running cost of distribution network (3) Wherein: The total operating cost of the gas t