CN105703369B - Optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network - Google Patents
Optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
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- H—ELECTRICITY
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Abstract
The invention relates to a modeling and solving method for an optimal energy flow of a multi-energy coupling transmission and distribution network, belonging to the research field of a day-ahead scheduling plan of an electric power system under energy interconnection and comprising the following steps: acquiring basic data in a system scheduling period, and acquiring a random fuzzy time-space sequence model of large-scale wind power, distributed power supplies and multi-energy loads through historical data mining; the power and voltage of the power transmission network and each active distribution network at the common node are used as shared variables, and a multi-target system engineering theoretical dynamic optimal energy flow model which gives consideration to economy, low carbon and renewable energy consumption and loss reduction under the static safety constraint is constructed; and (3) obtaining multi-energy source load prediction by random fuzzy simulation, and obtaining a Pareto solution set, an optimal compromise solution and an energy flow result by adopting an improved system engineering theory hierarchical optimization algorithm based on approximate dynamic programming and NSGA-II. The invention adapts to the development trend of energy interconnection and realizes the day-ahead scheduling comprehensive coordination optimization of transmission and distribution parties on the premise of meeting the static safety and stability of the system.
Description
Technical Field
The invention belongs to the field of research on day-ahead scheduling plans of power systems under energy interconnection, and relates to a method for modeling and solving optimal energy flows of a multi-energy coupling transmission and distribution network.
Background
Under the background of energy Internet, the development of distributed renewable energy sources and energy source hubs in active distribution networks is beneficial to realizing energy conservation and emission reduction and comprehensive and efficient utilization of multiple energy sources. The source-load side power injection of the multi-energy system has uncertainty and obvious space-time difference, and the traditional scheduling method prohibits the distribution network from transmitting power to the transmission network, so that part of renewable energy sources are difficult to be locally consumed and reduced, and therefore the feeder line power interaction of the active distribution network and the transmission network is a necessary trend. Based on the method, the power transmission network is fully utilized as a carrier, and the power transmission network and each active distribution network are cooperatively scheduled under safety constraint, so that the space-time complementation of comprehensive energy in a wide area range is promoted, and the method has certain significance for comprehensive utilization of multiple energy sources and consumption of renewable energy sources.
In the past scheduling model, a power transmission network or a distribution network is often used as research objects respectively, the equivalence of the distribution network is used as a PQ load node during analysis of the power transmission network, the equivalence of the power transmission network is used as an infinite power supply during analysis of the distribution network, and the competition of the power transmission network and the distribution network and the internal multi-energy coupling of the active distribution network are not considered. In recent years, researchers pay attention to the integrated scheduling problem research of power transmission and distribution networks, and describe the problem as a decomposition coordination unit combination optimization model and solve the problem through an iterative optimization algorithm based on Benders decomposition, a system engineering theory and the like. Multiple energy sources such as electricity, gas and heat are coupled, converted and supplied through an energy hub, so that corresponding energy hub modeling and scheduling research is developed by students. Also, based on the phenomenon that a natural gas network and a power network are coupled and interconnected through an energy hub, a student researches an analysis method of a multi-energy hybrid energy flow and analyzes network energy interaction information characteristics.
In summary, a multi-energy coupling transmission and distribution network optimal energy flow modeling and solving method under the energy interconnection is researched to comprehensively coordinate the multi-target optimization of all transmission and distribution parties on the premise of meeting the static safety and stability of the system, such as production cost, pollutant gas emission, renewable energy consumption, line loss and the like.
Disclosure of Invention
The technical problem to be solved is as follows: aiming at the defects of the prior art, the invention discloses a multi-energy coupling transmission and distribution network optimal energy flow modeling and solving method, which provides a phenomenon of multiple uncertain injection of multi-energy sources on the source and load sides of a power system under energy interconnection, constructs a multi-target optimal energy flow model of a multi-energy coupling active distribution network and power transmission network integrated system based on an SoS idea, researches a solving step method of the multi-target optimal energy flow model, and aims to provide theoretical reference for a wide area energy source space-time complementary absorption scheduling method under the energy interconnection background.
The technical scheme is as follows: a modeling and solving method for optimal energy flow of a multi-energy coupling transmission and distribution network comprises the following steps:
step 1: basic data of the system in a scheduling period are obtained, and a random fuzzy time-space sequence model of large-scale wind power output, distributed power supply output and multi-energy load is obtained through historical data mining.
Step 2: and taking the power and voltage of the power transmission network and each active distribution network in the common node set as shared variables, and constructing a multi-objective system engineering theoretical dynamic optimal energy flow model which gives consideration to economy, low carbon and renewable energy consumption and loss reduction under the constraint of static safety.
And step 3: the method comprises the steps of obtaining a predicted value of a multi-energy source load space-time sequence through random fuzzy simulation, judging different operation modes of an active distribution network, and obtaining a Pareto optimal solution set, an optimal compromise solution and a corresponding energy flow result by adopting an improved system engineering theory layered optimization algorithm based on approximate dynamic programming and NSGA-II.
Has the advantages that: the invention is suitable for the development trend of multi-energy access, and effectively realizes the daily scheduling comprehensive coordination optimization of transmission and distribution parties on the premise of meeting the static safety and stability of the system.
Drawings
FIG. 1: the invention relates to an implementation process of an optimal energy flow modeling and solving method of a multi-energy coupling transmission and distribution network;
FIG. 2: schematic diagram of an example energy system of the invention;
FIG. 3: the invention relates to a flow chart of a solving algorithm of a multi-target random fuzzy dynamic optimal energy flow.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
The overall implementation flow of the optimal energy flow modeling and solving method for the multi-energy coupling transmission and distribution network provided by the invention is shown in fig. 1, and a certain energy system is taken as a specific embodiment to be explained in detail, and a schematic diagram of the energy flow modeling and solving method is shown in fig. 2. The examples are intended to illustrate but not to limit the invention.
Step 1: basic data of the system in a scheduling period are obtained, and a random fuzzy time-space sequence model of large-scale wind power output, distributed power output, multi-energy load and the like is obtained through historical data mining.
Acquiring basic information and data of basic topological structures, branch impedances and thermal power unit output limits of a power transmission network, each active distribution network and a natural gas network in a scheduling period; based on the mining and distribution parameter fitting of historical data of wind speed, illumination intensity and electricity/gas/heat load at different time/regions, a random fuzzy space-time sequence model and an opportunity measurement function thereof are obtained to describe a large-scale wind power plant, a distributed power supply, a multi-energy load and the like.
Step 2: and taking the power and voltage of the power transmission network and each active distribution network in the common node set as shared variables, and constructing a multi-objective system engineering theoretical dynamic optimal energy flow model which gives consideration to economy, low carbon and renewable energy consumption and loss reduction under the constraint of static safety.
The main designations, variables, subscripts, etc. appearing in the model are first described as follows:
t power transmission network;
d, actively distributing a network;
an H energy hub;
a period t;
i, numbering the nodes of the power transmission network;
j, actively distributing the number of the internal nodes of the network;
k, numbering natural gas network nodes;
ΦTa set of transmission grid nodes;
ΦT-Gthe nodes of the power transmission network and the thermal power generating unit are gathered,
ΦT-tDthe nodes connected with the traditional distribution network in the transmission network are gathered,
ΦT-Dthe nodes connected with the active distribution network in the power transmission network are gathered,
ΦDactively distributing an internal node set of the network;
ΦD-Hnodes connected with a natural gas network inside the active distribution network, namely an energy hub node set;
xtlocal decision variable vectors of the power transmission network, active power output of thermal power, voltage, wind power absorption power and the like;
ytactive distribution network local decision variable vectors, namely the absorbed power of a DG below the active distribution network local decision variable vectors, the natural gas inflow of an energy hub node, the electric power inflow, operation mode parameters and the like;
ztpower transmission network and active power distributionNet shared variable vector zt＝{(PLi,t,Ui,t)|i∈ΦT-DI.e. the active power, voltage of the connection node, is a subset of the decision variables.
The invention constructs an optimal energy flow model of a multi-energy coupling transmission and distribution network as follows:
(1) and (3) upper layer: power transmission network optimization model
1) An objective function:
a) economic objective
The economic objective of a power grid can be described as maximizing the benefit of the electricity purchasing/selling link, purchasing behavior including purchasing electricity from thermal power plants and active distribution networks that can reverse power, selling behavior including selling electricity to the active distribution networks and traditional distribution networks as loads, and cost of wind farms with additional consideration.
Wherein: a isi、bi、ciGenerating cost parameters of the generator set positioned at the node i; pGi,tThe active output of the generator set positioned at the node i, and the inflow node is positive; pLi,tFor the load power of the distribution network at the node i, the outflow node is positive, that is, the direction from the transmission network to the distribution network is positive, when the active distribution network is used as the power supply, PLi,t< 0, the second term in the above formula and the negative sign are positive after the second term and the negative sign indicate that the transmission network purchases electricity from the active distribution network side ξiAnd rhoiThe electricity selling (purchasing) fees of the active distribution network and the traditional distribution network are respectively represented and are positive numbers.
b) Low carbon goal
Described as thermal power generating unit CO2The emissions are minimized.
Wherein αi、βi、γiFor generating sets CO at node i2An emission parameter.
c) Renewable energy consumption target
Described as the maximum contribution of a scaled wind farm.
Wherein:
representing predicted output of the wind farm, PWG,tWhich is the actual force applied.
d) Loss reduction target
The network active loss described as the transmission grid is minimal.
Wherein: u shapei,tRepresents the voltage of node i; bi represents a node connected to the node i, and its set is ΓT，Ubi,tRepresents the voltage at node bi; gi,bi,tAnd Bi,bi,tRespectively representing the conductance and susceptance of a connecting branch of a node i and a node bi; deltai,bi,tRepresenting the phase angle difference.
2) Constraint conditions are as follows:
a) power balance constraint
For the joint node (namely the shared variable z) of the transmission network and the active distribution networktNodes involved):
for other nodes of the transmission network:
the difference between the formulas (5) and (6) is that P in the formula (5)Li,tAnd Ui,tBeing a decision variable of the transmission networkAnd (b) a portion.
b) Upper and lower limit constraints of node voltage
c) Branch transmission power constraint
d) Power constraint of feeder line for connecting power transmission network and active distribution network
e) Thermal power generating unit constraint
The method comprises the active output upper and lower limits, the reactive output upper and lower limits and the climbing constraint of the generator.
f) System spinning reserve
Per system maximum load
And a certain percentage upsilon of the system is considered for standby rotation and is borne by the thermal power generating unit.
g) Node static safety margin constraint
Wherein the content of the first and second substances,
is a reactive load of QLi,tVoltage breakdown point of time, Ai、Bi、CiRespectively, the P-Q-V quiescent voltage stability boundary expression coefficients of node i,
is the largest load margin and λ is the stability margin.
(2) The lower layer: active distribution network optimization model (with active distribution network at node i of power transmission network as research object)
1) An objective function:
a) economic objective
The economic objective of the active distribution network is described as minimizing the electricity purchase costs, including those from the main network and from its internal distributed energy sources.
Wherein, ξDG,jRepresenting the electricity price, P, of the electricity purchased from the distributed energy source in the active distribution networkDG,j,tRepresenting distributed energy contribution.
Also have
The active distribution network operation mode and its determination will be discussed in detail in the algorithm description of step 3.
b) Low carbon goal
CO for a gas turbine of a cogeneration unit described as an active distribution network lower energy hub node2The discharge amount is the smallest.
Wherein eEHCO of gas turbine of cogeneration unit representing energy hub node2Coefficient of emission, CgeRepresenting the conversion coefficient, P, of natural gas energy into electric powergj,tIndicating natural gas inflow
c) Renewable energy consumption target
Wind/light distributed renewable energy described as an active distribution network gives the greatest absorption.
Wherein, PDGj,tAnd
the actual internet power and the predicted power of the distributed power supply are respectively.
d) Loss reduction target
The network described as an active distribution network has minimal active loss.
2) Constraint conditions are as follows:
a) active distribution network node power balance constraint
For the node (i.e. shared variable z) connected with the transmission network in the active distribution networktNodes involved, i.e. active distribution network bus):
wherein P isESSj,tThe power of the energy storage device is positive with its charged state as a load. Then formula (17) means: adding the sum of all electric power loads in the active power distribution network to all energy storage device powers and all network losses, and deducting all distributed energy output to obtain bus exchange power PLi,tIts positive and negative blocksAnd determining the operation mode of the active distribution network.
For energy hub node, PLj,tSubstantially representing the electric power flowing from the grid into the energy hub, where the natural gas energy flows into Pgj,tOther energy sources such as distributed energy sources are introduced as
The electrical load under the energy hub is
Natural gas load of
A heat load of
Wherein the superscript "" indicates that the variable is a random ambiguity uncertainty. The multi-energy power balance constraint of the energy hub node is expressed as
For other nodes of the active distribution network:
b) active distribution network and transmission network connection feeder power constraint
c) Active distribution network node voltage constraints
d) Natural gas network node flow balance constraint
Wherein
e) natural gas network node pressure constraints
f) Natural gas network pipeline flow restriction
g) Energy storage device restraint
The energy storage device at the node j should satisfy the charge and discharge power constraint, the electric quantity state constraint and the like during operation scheduling.
And step 3: the method comprises the steps of obtaining a predicted value of a multi-energy source load space-time sequence through random fuzzy simulation, judging different operation modes of an active distribution network, and obtaining a Pareto solution set, an optimal compromise solution and a corresponding energy flow result by adopting an improved system engineering theory layered optimization algorithm based on approximate dynamic programming and NSGA-II.
The model created in step 3 is first transformed. In the optimization model of the upper-layer power transmission network,will share variable ztLocal variables η expressed as upper layer optimizationstIn the lower-layer active distribution network optimization model, z is settExpressed as a local variable mutThus ensuring ηt＝μtThe optimization results of the transmission network and the active distribution network can be consistent. Defining a relaxation variable ct＝ηt-μtAnd its penalty function fπ(ct)＝σctWhere σ is a dynamic penalty coefficient that increases gradually as the number of iterations increases, fπ(ct) Attached to each objective function. Thus, the double-layer optimization models are decoupled, optimized and mutually constrained, and iteration is carried out until ctIf the value is less than epsilon, a decomposition coordination optimization solution is obtained.
The algorithm flow is shown in fig. 3 and is set forth below.
1) Generating a time-space sequence of output of a large-scale wind power plant, distributed wind/light and multi-energy load of each node by adopting a random fuzzy simulation method;
2) respectively setting the iteration times of the inner layer and the outer layer to zero, namely setting l to 0 and w to 0, and initializing and setting a penalty coefficient sigma;
3) in order to enable the active distribution network to fully exert self-sufficient coordination capability and realize the consumption of internal distributed renewable energy sources, firstly, the problem of multi-target dynamic optimal energy flow of the lower active distribution network is solved, and the corresponding mu at the moment is obtained according to the optimization resultt(0)＝{(PLi,t,Ui,t)|i∈ΦT-D}，PLi,tThe positive/negative/zero of (1) respectively corresponds to three operation modes of load/power supply/island of the active distribution network, if the operation mode is the load/power supply mode, the operation is switched to 5), and if the operation mode is the island mode, the operation is switched to 4);
4) correspondingly disconnecting the contact feeder of the power transmission network and the active distribution network in the island mode, completely decoupling the power transmission network and the active distribution network for operation, and performing independent optimization solution on the power transmission network and the active distribution network to 9);
5) w is w +1, in min { f }n[xt,ηt(w)]+fπ[ηt(w)-μt(w-1)]1,2,3 and 4, solving the multi-target dynamic optimal power flow of the upper layer power transmission network to obtain ηt(w)；
6) In min { fn[yt,μt(w)]+fπ[ηt(w)-μt(w)]1,2,3 and 4, solving the multi-target dynamic optimal energy flow of the lower active distribution network, and acquiring mut(w)；
7) Determining η whether an inner iteration convergence criterion is satisfiedt(w)-ηt(w-1) < ε and μt(w)-μt(w-1) < epsilon, if not, turning to 4), and if yes, turning to 8);
8) determining η whether an outer iteration convergence criterion is satisfiedt(w)-μt(w) < epsilon, if not, l is l +1, updating the penalty coefficient sigma, and turning to the step 4), and if yes, turning to the step 9);
9) and outputting the optimized solution and the energy flow result, and finishing the algorithm.
In the above process:
(1) in step 3), in order to make the active distribution network fully exert self-sufficient coordination capability and realize the consumption of internal distributed renewable energy, three operation modes in the optimization of the active distribution network and the determination thereof are explained as follows.
After a multi-energy load space-time prediction sequence generated by random fuzzy simulation, when an active distribution network is optimized, firstly calculating a formula (19) in the current state, if P isLi,tIf the current value is less than 0, indicating that surplus is generated in distributed energy resources in the active distribution network, firstly considering to call an energy storage device for charging and storing, and if surplus still exists after the energy storage device is fully called on the premise of meeting safety constraint, transmitting power to the power transmission network on the premise of meeting safety and bus power constraint conditions, wherein the active distribution network is in a power supply state; if it is initially PLi,tIf the self-supply of the active distribution network cannot be realized, the discharge supply of the energy storage device is considered to be called, if the self-supply of the energy storage device cannot be realized after the self-supply is called fully, the electric power is absorbed from the power transmission network, and the active distribution network operates in a load state; if P is enabled in the initial state or by calling up the energy storage deviceLi,tAnd (5) considering the active distribution network as an isolated island and optimizing the decoupling operation with the power transmission network when the active distribution network is 0. In the above process, if complete consumption of renewable energy cannot be achieved under the constraint conditions, it is considered to reduce a partial output by means of active management.
(2) The adopted multi-target dynamic optimal energy flow solving method is a multi-target optimal energy flow algorithm based on approximate dynamic programming and NSGA-II.
The method comprises the steps of obtaining a Pareto optimal solution set by adopting NSGA-II, calculating the satisfaction degree of each target value of each non-dominated solution in the Pareto optimal solution set by adopting a partial small fuzzy satisfaction calculation formula, calculating the comprehensive satisfaction degree of each non-dominated solution, and selecting a path with the maximum accumulated satisfaction degree through strategy iterative approximate dynamic programming to form the solution of the multi-target dynamic optimal energy flow.
The above embodiments are merely illustrative, and not restrictive, and those skilled in the relevant art can make various changes and modifications without departing from the spirit and scope of the invention, and therefore all equivalent technical solutions also fall within the scope of the invention.
Claims (6)
1. A method for modeling and solving optimal energy flow of a multi-energy coupling transmission and distribution network is characterized by comprising the following steps:
step 1: acquiring basic data of the system in a scheduling period, and acquiring a random fuzzy time-space sequence model of large-scale wind power output, distributed power output and multi-energy load through historical data mining;
step 2: taking the power and voltage of the power transmission network and each active distribution network in the common node set as shared variables, and constructing a system engineering theoretical dynamic optimal energy flow model which gives consideration to economic, low-carbon and renewable energy consumption and loss reduction multiple targets under the constraint of static safety;
and step 3: the method comprises the steps of obtaining a predicted value of a multi-energy source load space-time sequence through random fuzzy simulation, judging different operation modes of an active distribution network, and obtaining a Pareto optimal solution set, an optimal compromise solution and a corresponding energy flow result by adopting an improved system engineering theory layered optimization algorithm based on approximate dynamic programming and NSGA-II.
2. The optimal energy flow modeling and solving method for the multi-energy coupling transmission and distribution network according to claim 1, wherein basic information and data of basic topological structures, branch impedances and thermal power generating unit output limits of the transmission network, each active distribution network and the natural gas network in a dispatching cycle are obtained in step 1; and acquiring a random fuzzy space-time sequence model and an opportunity measurement function thereof to describe large-scale wind power, distributed power and multi-energy loads based on mining and distribution parameter fitting of historical data of wind speed, illumination intensity, electricity/gas/heat load at different time/regions.
3. The method for modeling and solving the optimal energy flow of the multi-energy coupling transmission and distribution network according to claim 1, wherein step 2 considers static safety stability margins in static safety constraints, and considers the safety constraints of the stochastic fuzzy multi-energy supply and demand balance and the natural gas network in the active distribution network model, wherein the safety constraints are coupled with the natural gas network at energy hub nodes.
4. The method for modeling and solving the optimal energy flow of the multi-energy coupling transmission and distribution network according to claim 1, wherein in the step 3, a double-layer optimization model is converted to solve the coupling coordination, and a shared variable z is used in an upper-layer transmission network optimization modeltExpressed as its local variable ηtZ is set in the optimization model of the lower active distribution networktExpressed as a local variable mutDefine the relaxation variable ct＝ηt-μtAnd its penalty function fπ(ct)＝σctWhere σ is a dynamic penalty coefficient that increases gradually as the number of iterations increases, fπ(ct) Adding the parameters to each objective function to enable the double-layer optimization model to be decoupled and optimized and mutually constrained, and iterating until ctIf the value is less than epsilon, a decomposition coordination optimization solution is obtained.
5. The method for modeling and solving the optimal energy flow of the multi-energy coupling transmission and distribution network according to claim 1, wherein the system engineering theory in the step 3 is optimized hierarchically to enable the active distribution network to fully exert self-sufficient coordination capability and realize the consumption of internal distributed renewable energy sources, and methods for determining three operation modes of the active distribution network are provided, namely, firstly, the method for calculating the initial state is usedActive distribution network and transmission network connecting feeder power PLi,tIf P isLi,tIf the distributed energy resources in the active distribution network have surplus in power generation, the energy storage device is considered to be called for charging and storing, on the premise that safety constraint is met, if surplus still exists after the energy storage device is called fully, power is transmitted to the power transmission network on the premise that safety and bus power constraint conditions are met, and at the moment, the active distribution network is in a power supply state; if it is initially PLi,tIf the self-supply of the active distribution network cannot be realized, the discharging supply of the energy storage device is called, if the self-supply of the energy storage device cannot be realized after the self-supply is fully called, the electric power is absorbed from the power transmission network, and the active distribution network operates in a load state; if P is enabled in the initial state or by calling up the energy storage deviceLi,tAnd (3) considering that the active distribution network is an isolated island, decoupling operation optimization is carried out on the active distribution network and the power transmission network, and in the process, if complete consumption of renewable energy sources cannot be realized under the constraint condition, partial output is reduced by adopting an active management means.
6. The optimal energy flow modeling and solving method for the multi-energy coupling transmission network according to claim 1, characterized in that a multi-objective optimal energy flow algorithm based on approximate dynamic programming and NSGA-II is adopted in step 3, a Pareto optimal solution set is solved by adopting NSGA-II, for each non-dominated solution in the Pareto optimal solution set, a partial small fuzzy satisfaction calculation formula is adopted to calculate the satisfaction degree of each target value, then the comprehensive satisfaction degree of each non-dominated solution is calculated, and a path with the maximum accumulated satisfaction degree is selected through strategy iterative approximate dynamic programming to form a solution of the multi-objective dynamic optimal energy flow.
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