CN110969284B - Double-layer optimized scheduling method for power distribution network - Google Patents

Double-layer optimized scheduling method for power distribution network Download PDF

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CN110969284B
CN110969284B CN201911034888.XA CN201911034888A CN110969284B CN 110969284 B CN110969284 B CN 110969284B CN 201911034888 A CN201911034888 A CN 201911034888A CN 110969284 B CN110969284 B CN 110969284B
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余晓鹏
尹硕
白宏坤
李虎军
杨萌
杨钦臣
宋大为
马任远
金曼
邓方钊
赵文杰
柴喆
蒋传文
王旭
王玲玲
罗舒瀚
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
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Abstract

The invention discloses a double-layer optimized dispatching method and a double-layer optimized dispatching device for a power distribution network, wherein the method comprises the following steps of: acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in a load aggregation provider LA, and acquiring basic information of a topological structure, a power supply and the loads of a power distribution network; inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result; the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model; according to the invention, the LA obtains economic benefits and plays a positive role in safe and economic operation of the power distribution network at the same time by establishing the power distribution network double-layer optimization scheduling model considering the LA participation.

Description

Double-layer optimized scheduling method for power distribution network
Technical Field
The invention belongs to the field of optimized scheduling of power systems, and particularly relates to a double-layer optimized scheduling method and device for a power distribution network.
Background
With the shortage of fossil energy and the continuous deterioration of natural environment, renewable energy sources typified by wind power and photovoltaic have been rapidly developed, and the rate in power distribution networks has been gradually increased in recent years. How to coordinate various distributed energy sources, energy storage devices and active loads in an active power distribution network and effectively participate in the current power market, so that the voltage level of the power distribution network is improved, the operation cost of the power distribution network is reduced, and the problem to be solved urgently is formed. As a professional load side resource management subject, a load aggregator is one of the important forms for integrating and managing various distributed power sources, energy storage devices and active loads on the load side in the future electric power market. The flexible demand response service can effectively solve the scheduling obstacle of the main network to the scattered load side resources and realize source-load interaction.
However, current research on load aggregators mainly focuses on economic scheduling and risk assessment, and neglects the influence of the load aggregators to optimize scheduling strategies on the load flow and voltage of the whole network. In the market environment, with the massive access of various renewable energy sources, the two-way interaction between users and the power grid increases the complexity of the optimization problem of the power distribution network. Therefore, it is very important to research a new double-layer optimized scheduling method for the power distribution network.
The invention patent with the publication number of CN106712114B discloses an energy layering optimization scheduling method for an active power distribution network considering environmental factors. The method comprises the following steps: 1) dividing distributed units directly participating in optimization scheduling of the active power distribution network into distributed power supply units, distributed energy storage units, loads and micro-grid groups consisting of multiple micro-grids, and performing optimization scheduling by 3 layers; 2) the active power distribution network dispatching center collects and summarizes relevant output information reported by all distributed power supplies, the energy storage devices and the micro-grid group dispatching center and interaction electricity prices of the power distribution network and a superior power grid, and first-layer optimized dispatching is carried out by combining loads accessed into the power distribution network; 3) optimizing the middle layer on the basis of the optimization of the upper layer, and optimizing the micro-grid group dispatching center; 4) and the bottom layer optimization is based on the middle layer optimization and is used for optimizing and scheduling each micro-grid in the micro-grid group. The invention provides an optimized scheduling method for each microgrid in a microgrid group, but the influence of aggregators serving as a professional load side resource management main body on the complexity of the optimization problem of the power distribution network is not considered.
The invention application with the authorization publication number of CN104361416B provides a power grid double-layer optimization scheduling method considering large-scale electric vehicle access, which researches the charge-discharge strategy of an electric vehicle from two levels of a power transmission network and a power distribution network, obtains the optimal charging time of the electric vehicle from the power transmission network, and further guides the optimal charging position of the electric vehicle in the power distribution network, and the load of the power distribution network is concentrated on a certain node on the power transmission network as shown in figure 1. The invention also comprehensively considers the coordination of wind power, base load, a thermal generator set and the charge and discharge of the electric automobile, and provides effective suggestions for the charge and discharge time and position of the electric automobile by using the model. However, the method is not suitable for the double-layer optimized scheduling method of the power distribution network.
Disclosure of Invention
In view of the above, the present invention aims to provide a method and an apparatus for double-layer optimized scheduling of a power distribution network, which aim to achieve the purpose of minimizing the active network loss of the power distribution network on the upper layer and maximizing the LA profit on the lower layer by establishing a double-layer optimized scheduling model of the power distribution network in consideration of the participation of the load aggregators LA, so that LA can obtain economic benefits and play a positive role in the safe and economic operation of the power distribution network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a double-layer optimized dispatching method for a power distribution network comprises the following steps:
acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in a load aggregation provider LA, and acquiring basic information of a power distribution network topological structure, a power supply and the loads;
inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result;
the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the maximum LA profit as an optimization target, the power distribution network optimization model takes the minimum power distribution network loss as an optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model.
Wherein, the objective function of the LA economic dispatch model is as follows:
the LA benefit function is:
Figure BDA0002251208090000031
in the formula, T is a scheduling period;
Figure BDA0002251208090000032
and
Figure BDA0002251208090000033
respectively showing the power sale and the power purchase of the LA to and from the main network at the time t,
Figure BDA0002251208090000034
is the corresponding price of the electricity sold,
Figure BDA0002251208090000035
the corresponding electricity purchase price;
the constraint conditions of the LA economic dispatching model are as follows:
2) supply and demand balance constraints
Figure BDA0002251208090000036
In the formula, the content of the active carbon is shown in the specification,
Figure BDA0002251208090000037
distributing the output of the formula power i, n, over a period of t LA DG The number of power supplies is a distribution formula;
Figure BDA0002251208090000038
storing energy charging power and discharging power at the moment t respectively;
Figure BDA0002251208090000039
respectively representing the electricity purchasing quantity from the main network and the electricity selling quantity to the main network in the LA in the t period;
2) restraint of stored energy
E s,t ＝E s,t-1 +η ch P ch,t -P dis,t /η dis (3)
Figure BDA0002251208090000041
Figure BDA0002251208090000042
λ min E s ≤E s,t ≤λ max E s (6)
b ch,t +b dis,t ≤1 (7)
E s,0 ＝E s,T (8)
In the formula, b ch,t 、b dis,t The variable is 0-1 and represents the charging state and the discharging state of energy storage at the moment t;
Figure BDA0002251208090000043
a maximum charging power and a maximum discharging power representing the stored energy; eta ch 、η dis Indicating charge of stored energyElectrical efficiency and discharge efficiency; e s,t The capacity of the energy storage device at the moment t; e s Is the rated capacity of the energy storage device; lambda max 、λ min Respectively representing the maximum state of charge and the minimum state of charge of the stored energy; formula (7) limits the energy storage device to be in a charging or discharging state at the same time; equation (8) shows that the final stored energy is equal to the initial state in one period;
3) interacting power constraints with distribution networks
Figure BDA0002251208090000044
In the formula, P max And the LA is the upper limit value of the power of interaction with the distribution network.
The objective function of the power distribution network optimization model is as follows:
selecting the minimum network loss of the power distribution network as an optimization target, wherein a mathematical expression formula is as follows:
Figure BDA0002251208090000045
in the formula, # l Is a set of branch circuits of the power distribution network,
Figure BDA0002251208090000046
the active network loss value of the branch circuit l in the time period t is obtained;
Figure BDA0002251208090000047
the value of the current flowing through branch l for the time period t;
the constraint conditions of the power distribution network optimization model are as follows:
1) network flow constraints
Because the distribution network has a radial structure, the equation constraint of the Distflow power flow equation can be written as the following equation:
Figure BDA0002251208090000051
in the formula, i ∈ u (j) represents all branch sets with j as end nodes; k ∈ v (j) represents all the branch sets with j as the head-end node; p ij 、Q ij Respectively representing the active power and the reactive power of a node i flowing into a node j; p j 、Q j Respectively representing the active power and the reactive power of the node j; r is ij 、x ij The resistance and reactance values of the branches (i, j), respectively; u shape i Is the voltage amplitude of system node i; i is ij Represents the value of the current flowing through the branch (i, j);
Figure BDA0002251208090000052
to purchase power from the distribution grid for LA,
Figure BDA0002251208090000053
selling power to the distribution network for LA;
2) node voltage constraint
U min ≤U i ≤U max ,i∈ψ b (12)
In the formula, U max 、U min Respectively, an upper system node voltage limit and a lower system node voltage limit, psi b Is a system node set;
3) branch power constraint
Figure BDA0002251208090000054
In the formula, I max Represents the upper limit of the transmission power allowed by branch l;
4) capacitor bank operation constraints
The switching of the parallel capacitor bank SCs is the most common reactive power optimization measure of the power distribution network, and the switching is a discrete decision variable in the operation scheduling process and has the following constraint
Figure BDA0002251208090000061
In the formula, the first step is that,
Figure BDA0002251208090000062
installing reactive power of a node for the kth SCs at the moment t; n is a radical of k,t 、
Figure BDA0002251208090000063
The SCs are put into operation respectively for group number and single group reactive power; n is a radical of hydrogen k,max The maximum number of SCs in-transit groups;
from the economic point of view, the switching times of SCs are limited in the operation process; according to actual operation experience, the number of switching times of the parallel capacitor banks SCs is allowed to be 5 times per day, namely
Figure BDA0002251208090000064
In the formula, sign is a sign function;
5) SVG operation constraints
Compared with a parallel capacitor bank SCs, the reactive power of the SVG can be continuously adjusted, the voltage sudden change condition caused by frequent power fluctuation in a power grid can be more flexibly coped with, and the operation constraint is
Figure BDA0002251208090000065
In the formula, the first step is that,
Figure BDA0002251208090000066
for the kth installation node SVG reactive power,
Figure BDA0002251208090000067
respectively an upper limit of the SVG compensation capacity and a lower limit of the SVG compensation capacity.
Further comprising: performing second-order cone relaxation on a power flow equation (11) of the power distribution network optimization model, converting the power distribution network optimization model into a mixed integer second-order cone planning MISOCP model, and reducing the problem solving difficulty by the following steps:
order to
Figure BDA0002251208090000068
Then the formula (11) becomes
Figure BDA0002251208090000071
The second order cone relaxation is performed on the last term equality constraint in equation (11):
Figure BDA0002251208090000072
further obtain the
(2P ij ) 2 +(2Q ij ) 2 +(l ij -V i ) 2 ≤(l ij +V i ) 2 (19)
Converting equation (19) into the following form
Figure BDA0002251208090000073
Replacing the last term in the formula (17) with the formula (20), and obtaining a power flow equation of the power distribution network optimization model through second-order cone relaxation, wherein the power flow equation is as follows:
Figure BDA0002251208090000074
accordingly, the formulas (12), (13) become
Figure BDA0002251208090000075
Figure BDA0002251208090000076
Inputting basic information of loads, wind power, photovoltaic and energy storage equipment inside a load aggregation provider LA and basic information of a topological structure, a power supply and the loads of a power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result, wherein the optimization scheduling result comprises the following steps:
obtaining the electricity buying quantity and the electricity selling quantity of LA at each moment according to the load in the LA, the wind power, the photovoltaic and the basic information of the energy storage equipment and the LA economic dispatching model;
and obtaining the electric quantity sold to the LA by the power distribution network according to the basic information of the topology structure, the power supply and the load of the power distribution network, the electric quantity bought and sold at each moment of the LA and the power distribution network optimization model.
The method for obtaining the electric quantity sold to the LA by the power distribution network according to the basic information of the topology structure, the power supply and the load of the power distribution network, the electric quantity bought and sold at each time of the LA and the optimization model of the power distribution network comprises the following steps:
calculating the electric quantity sold to LA by the power distribution network when the power distribution network loss is minimum according to the topological structure of the power distribution network, basic information of a power supply and loads, the electric quantity purchased and sold at each moment of LA and a power distribution network optimization model;
in the process of calculating the electric quantity sold to LA by the distribution network when the network loss of the distribution network is minimum, if the voltage and the line power of the distribution network are out of limit, increasing or reducing the electric quantity bought and sold at each moment of LA, and finally obtaining the electric quantity sold to LA by the distribution network on the premise of meeting the safety and stability constraints of the distribution network.
And solving the obtained double-layer optimized scheduling model of the power distribution network by utilizing Matlab software, Cplex software, MOSEK software, SDPT3 software or SEDUMI software to obtain an optimized scheduling result.
In addition, the invention also provides a double-layer optimized dispatching device for the power distribution network, which comprises the following components:
the system comprises a basic information acquisition module, a load aggregation provider LA and a power distribution network topology structure, wherein the basic information acquisition module is used for acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and acquiring basic information of the power distribution network topology structure, the power supply and the loads;
the optimization scheduling module is used for inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result;
the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the maximum LA profit as an optimization target, the power distribution network optimization model takes the minimum power distribution network loss as an optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model.
Further comprising:
the model conversion module is used for performing second-order cone relaxation on the power flow equation of the power distribution network optimization model, converting the power distribution network optimization model into a mixed integer second-order cone planning MISOCP model and reducing the solving difficulty of the problem;
and the solving module is used for solving the power distribution network double-layer optimized scheduling model converted by the model conversion module to obtain an optimized scheduling result.
Further, the optimized scheduling module includes:
the first processing unit is used for obtaining electricity buying quantity and electricity selling quantity of LA at each moment according to loads inside LA, basic information of wind power, photovoltaic and energy storage equipment and an LA economic dispatching model;
the second processing unit is used for calculating the electric quantity sold to the LA by the power distribution network according to the topology structure of the power distribution network, basic information of a power supply and loads, the electric quantity purchased and sold at each moment of the LA and the power distribution network optimization model; in the process of calculating the electric quantity sold to LA by the distribution network when the distribution network loss is minimum, if the voltage and the line power of the distribution network are out of limit, the electric quantity bought and sold at each moment of LA is increased or reduced, and finally the electric quantity sold to LA by the distribution network is obtained on the premise of meeting the safety and stability constraints of the distribution network.
The beneficial effects of the invention are:
according to the method, the influence of a load aggregator LA (resource aggregation) economic scheduling strategy on the load flow and the voltage of the whole power distribution network is considered, a power distribution network double-layer optimization scheduling model considering the load aggregator LA, the active network loss minimization of the power distribution network is taken as an optimization target at the upper layer, and LA profit maximization is considered at the lower layer.
In addition, the invention also has the following advantages:
(1) by performing second-order cone relaxation on the power flow equation, the original mixed integer nonlinear programming problem is converted into a mixed integer second-order cone programming (MISOCP) model, and the problem solving difficulty is reduced.
(2) The power distribution network double-layer optimization strategy considering LA participation can enable LA to obtain economic benefits and play a positive role in safe and economic operation of a power distribution network.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
FIG. 1 is a flow chart of a power distribution network double-layer optimization scheduling method of the present invention;
FIG. 2 is a diagram of a power distribution network double-layer optimization model according to the present invention;
fig. 3 is a comparison graph of daily voltage fluctuation curves of a certain load node in a certain place under a power distribution network double-layer optimization strategy considering only LA economic dispatch and LA.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
The inventor notices in the process of invention that:
in a market environment, with the massive access of various renewable energy sources, the bidirectional interaction between users and a power grid increases the complexity of the optimization problem of the power distribution network. At present, the electric power marketization level of China is still in a starting stage, the problem that a large amount of load resources on a demand side are idle and cannot be utilized still exists, numerous scholars at home and abroad research application implementation means of demand response, and a model and an algorithm [ J ] of user response behaviors under time-of-use electricity prices are researched by adopting a support vector machine regression method in a model and an algorithm [ J ] of power grid technology, 2013,37(10):2973 plus 2978 ], so that the relation between electricity price change and user electricity consumption change is disclosed; the document [ Rahimi F, Ipakci A. demand response as a market resource unit [ J ]. IEEE Transactions on Smart Grid,2010,1(1):82-88.] discusses in detail the key points of demand response application in the Smart Grid environment; the optimized dispatching of the wind power-containing power system considering the demand response [ J ] power system automation 2014,38(13): 115-. On the other hand, the deep understanding of the user load characteristics is a precondition for developing source-load interaction demand response, and documents [ Xun Han Jun, Zhang laugh, Wang Li Jun ], user classification and power utilization behavior analysis based on cluster analysis [ J ]. Shanxi electric power, 2016,36(4):23-27 ] analyze the power utilization behavior of the user from the clustering angle, so as to provide a reference for providing differentiated services for power selling companies; the literature [ Lu Tingting, Gao Ciwei, Sun Lining, et al.A. computing of Power users' demand response Power under the angle of big data [ C ]// 20155 th International Conference on Electric Utility Definition and Reconstruction and Power Technologies (DRPT) [ Changsha: Changsha University of Science and Technology,2015: 127-.
Most of the research is evaluated from application and benefit analysis of demand response, the problems of how to integrate and manage load side resources and the like are less considered, and the major network is ignored to schedule the load side resources, so that obstacles exist. As a professional load side resource management subject, the load aggregator is one of the important forms for integrating and managing various distributed power sources, energy storage devices and active loads on the load side in the future power market. The flexible demand response service can effectively solve the scheduling obstacle of the main network to the scattered load side resources and realize source-load interaction. However, current research on load aggregators mainly focuses on economic scheduling and risk assessment, and neglects the influence of the load aggregators to optimize scheduling strategies on the load flow and voltage of the whole network. According to the method, the influence of the LA economic dispatching strategy of the load aggregator on the power flow and the voltage of the whole power distribution network is fully considered, a power distribution network double-layer optimization dispatching model taking the LA participation of the load aggregator into consideration is established, the upper layer takes the minimization of the active network loss of the power distribution network as an optimization target, and the lower layer takes the maximization of LA profit into consideration, so that the LA obtains economic benefits and plays a positive role in safe and economic operation of the power distribution network.
The method mainly focuses on the aspects of economic dispatching and risk assessment aiming at the current research on the load aggregators, neglects the influence of the load aggregators optimization dispatching strategy on the trend and the voltage of the whole network, and increases the complexity of the optimization problem of the power distribution network due to the bidirectional interaction of users and the power grid along with the massive access of various renewable energy sources in the market environment. The double-layer optimized dispatching method for the power distribution network comprises the following steps: acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in a load aggregation provider LA, and acquiring basic information of a power distribution network topological structure, a power supply and the loads; inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result; the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the LA profit to be the maximum optimization target, the power distribution network optimization model takes the power distribution network loss to be the minimum optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model.
The method comprises the following steps of inputting basic information of loads, wind power, photovoltaic and energy storage equipment in a load aggregation provider LA and basic information of a topological structure, a power supply and the loads of a power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result, wherein the basic information comprises the following steps: obtaining the electricity buying quantity and the electricity selling quantity of LA at each moment according to the load in the LA, the wind power, the photovoltaic and the basic information of the energy storage equipment and the LA economic dispatching model; and obtaining the electric quantity sold to the LA by the power distribution network according to the topological structure of the power distribution network, the basic information of the power supply and the load, the electric quantity bought and sold at each moment of the LA and the optimization model of the power distribution network.
In addition, the obtaining of the electric quantity sold to the LA by the power distribution network according to the basic information of the topology structure, the power supply and the load of the power distribution network, the electric quantity bought and sold at each time of the LA and the optimization model of the power distribution network comprises the following steps: calculating the electric quantity sold to LA by the power distribution network when the power distribution network has the minimum loss according to the topology structure of the power distribution network, the basic information of a power supply and a load, the electric quantity bought and sold at each time of LA and a power distribution network optimization model; in the process of calculating the electric quantity sold to the LA by the power distribution network when the network loss of the power distribution network is minimum, if the voltage and the line power of the power distribution network are out of limit, the electric quantity purchased and sold at each moment of the LA is increased or reduced, and finally the electric quantity sold to the LA by the power distribution network is obtained on the premise of meeting the safety and stability constraint of the power distribution network.
And finally, solving the obtained double-layer optimized scheduling model of the power distribution network by utilizing Matlab software, Cplex software, MOSEK software, SDPT3 software or SEDUMI software to obtain an optimized scheduling result.
Example one
Referring to fig. 1-3, wherein fig. 1 is a flowchart of a power distribution network double-layer optimal scheduling method of the present invention, fig. 2 is a power distribution network double-layer optimal model of the present invention, fig. 3 is a comparison graph of daily voltage fluctuation curves of a certain load node in a certain area under a power distribution network double-layer optimization strategy considering only LA economic scheduling and considering LA, and a certain area is taken as an example to explain in detail a power distribution network double-layer optimal scheduling method of the present invention, which includes the following steps:
acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in a load aggregation provider LA, and acquiring basic information of a power distribution network topological structure, a power supply and the loads;
inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result;
the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the maximum LA profit as an optimization target, the power distribution network optimization model takes the minimum power distribution network loss as an optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model.
The objective function of the LA economic dispatch model is as follows:
the LA benefit function is:
Figure BDA0002251208090000141
in the formula, T is a scheduling period;
Figure BDA0002251208090000142
and
Figure BDA0002251208090000143
respectively showing the power sale and the power purchase of the LA to and from the main network at the time t,
Figure BDA0002251208090000144
in order to correspond to the price of electricity sold,
Figure BDA0002251208090000145
the corresponding electricity purchase price;
the constraint conditions of the LA economic dispatching model are as follows:
3) supply and demand balance constraints
Figure BDA0002251208090000146
In the formula, the first step is that,
Figure BDA0002251208090000147
the output of a distributed formula power supply i, n, in a period of t LA DG The number of power supplies is a distribution formula;
Figure BDA0002251208090000148
storing energy charging power and discharging power at the moment t respectively;
Figure BDA0002251208090000149
respectively representing the electricity purchasing quantity from the main network and the electricity selling quantity to the main network in the LA in the t period;
2) restraint of stored energy
E s,t ＝E s,t-1 +η ch P ch,t -P dis,t /η dis (3)
Figure BDA00022512080900001410
Figure BDA00022512080900001411
λ min E s ≤E s,t ≤λ max E s (6)
b ch,t +b dis,t ≤1 (7)
E s,0 ＝E s,T (8)
In the formula, b ch,t 、b dis,t Is a variable of 0 to 1, tableShowing the charging state and the discharging state of energy storage at the moment t;
Figure BDA0002251208090000151
a maximum charging power and a maximum discharging power representing the stored energy; eta ch 、η dis The charging efficiency and the discharging efficiency of the stored energy are represented; e s,t The capacity of the energy storage device at the moment t; e s Is the rated capacity of the energy storage device; lambda [ alpha ] max 、λ min Respectively representing the maximum state of charge and the minimum state of charge of the stored energy; formula (7) limits the energy storage device to be in a charging or discharging state at the same time; formula (8) shows that in one period, the stored energy is equal to the initial state finally;
3) interacting power constraints with distribution networks
Figure BDA0002251208090000152
In the formula, P max And the LA is the upper limit value of the power of interaction with the distribution network.
The objective function of the power distribution network optimization model is as follows:
the minimum network loss of the power distribution network is selected as an optimization target, and the mathematical expression formula is as follows:
Figure BDA0002251208090000153
in the formula, # l Is a set of branch circuits of the power distribution network,
Figure BDA0002251208090000154
the active network loss value of the branch circuit l in the time period t is obtained;
Figure BDA0002251208090000155
the value of the current flowing through branch l for the time period t;
the constraint conditions of the power distribution network optimization model are as follows:
1) network flow constraints
Because the distribution network has a radial structure, the equation constraint of the Distflow power flow equation can be written as the following formula:
Figure BDA0002251208090000161
in the formula, i ∈ u (j) represents all branch sets with j as end nodes; k ∈ v (j) represents all the branch sets with j as the head-end node; p ij 、Q ij Respectively representing the active power and the reactive power of a node i flowing into a node j; p j 、Q j Respectively representing the active power and the reactive power of the node j; r is a radical of hydrogen ij 、x ij The resistance and reactance values of the branches (i, j), respectively; u shape i Is the voltage amplitude of the system node i; i is ij Represents the value of the current flowing through the branch (i, j);
Figure BDA0002251208090000162
to purchase power from the distribution grid for LA,
Figure BDA0002251208090000163
selling the power to the distribution network for LA;
2) node voltage constraint
U min ≤U i ≤U max ,i∈ψ b (12)
In the formula, U max 、U min Upper limit of system node voltage and lower limit of system node voltage psi b Is a system node set;
3) branch power constraint
Figure BDA0002251208090000164
In the formula, I max Represents the upper limit of the transmission power allowed by branch l;
4) capacitor bank operation constraints
The switching of the parallel capacitor banks SCs is the most common reactive power optimization measure of the power distribution network, and the switching is a discrete decision variable in the operation scheduling process and is constrained as follows
Figure BDA0002251208090000165
In the formula, the first step is that,
Figure BDA0002251208090000171
installing reactive power of a node for the kth SCs at the moment t; n is a radical of k,t
、
Figure BDA0002251208090000172
The SCs are put into operation respectively for group number and single group reactive power; n is a radical of k,max The maximum number of SCs in-transit groups;
from the economical point of view, the switching times of SCs are limited in the operation process; according to actual operation experience, the number of switching times of the parallel capacitor banks SCs is allowed to be 5 times per day, namely
Figure BDA0002251208090000173
In the formula, sign is a sign function;
5) static var generator operation constraints
Compared with a parallel capacitor bank SCs, the reactive power of the SVG can be continuously adjusted, the voltage sudden change condition caused by frequent power fluctuation in a power grid can be more flexibly coped with, and the operation constraint is
Figure BDA0002251208090000174
In the formula, the content of the active carbon is shown in the specification,
Figure BDA0002251208090000175
for the kth installation node SVG reactive power,
Figure BDA0002251208090000176
are respectively asAn upper limit of SVG compensation capacity and a lower limit of SVG compensation capacity.
Further comprising: performing second-order cone relaxation on a power flow equation (11) of the power distribution network optimization model, converting the power distribution network optimization model into a mixed integer second-order cone programming (MISOCP) model, and reducing the solving difficulty of the problem as follows:
order to
Figure BDA0002251208090000177
Then the formula (11) becomes
Figure BDA0002251208090000181
The second order cone relaxation is performed on the last term equation constraint in equation (11):
Figure BDA0002251208090000182
further obtain the
(2P ij ) 2 +(2Q ij ) 2 +(l ij -V i ) 2 ≤(l ij +V i ) 2 (19)
Converting equation (19) into the following form
Figure BDA0002251208090000183
Replacing the last term in the formula (17) with the formula (20), and obtaining a power flow equation of the power distribution network optimization model through second-order cone relaxation, wherein the power flow equation is as follows:
Figure BDA0002251208090000184
accordingly, the formulas (12), (13) become
Figure BDA0002251208090000185
Figure BDA0002251208090000186
The method comprises the following steps of inputting basic information of loads, wind power, photovoltaic and energy storage equipment inside a load aggregation provider LA and basic information of a power distribution network topological structure, a power supply and the loads into a pre-established power distribution network double-layer optimization scheduling model to obtain an optimization scheduling result, wherein the optimization scheduling result comprises the following steps:
obtaining the electricity buying quantity and the electricity selling quantity of LA at each moment according to the load in the LA, the wind power, the photovoltaic and the basic information of the energy storage equipment and an LA economic dispatching model;
and obtaining the electric quantity sold to the LA by the power distribution network according to the topological structure of the power distribution network, the basic information of the power supply and the load, the electric quantity bought and sold at each moment of the LA and the optimization model of the power distribution network.
Wherein, the electric quantity that the distribution network sold for LA is obtained according to the basic information of distribution network topology, power and load, the electric quantity of buying and selling of LA each moment and distribution network optimization model, includes:
calculating the electric quantity sold to LA by the power distribution network when the power distribution network has the minimum loss according to the topology structure of the power distribution network, the basic information of a power supply and a load, the electric quantity bought and sold at each time of LA and a power distribution network optimization model;
in the process of calculating the electric quantity sold to the LA by the power distribution network when the network loss of the power distribution network is minimum, if the voltage and the line power of the power distribution network are out of limit, the electric quantity purchased and sold at each moment of the LA is increased or reduced, and finally the electric quantity sold to the LA by the power distribution network is obtained on the premise of meeting the safety and stability constraint of the power distribution network.
And solving the obtained double-layer optimized scheduling model of the power distribution network by utilizing Matlab software, Cplex software, MOSEK software, SDPT3 software or SEDUMI software to obtain an optimized scheduling result.
Example two
Based on the same inventive concept, the invention also provides a double-layer optimized dispatching device for the power distribution network, and a method for solving the technical problem is similar to the double-layer optimized dispatching method for the power distribution network, repeated parts are not repeated, and the description is given below.
A double-layer optimized dispatching device for a power distribution network comprises:
the system comprises a basic information acquisition module, a load aggregation provider LA and a power distribution network topology structure, wherein the basic information acquisition module is used for acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and acquiring basic information of the power distribution network topology structure, the power supply and the loads;
the optimization scheduling module is used for inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result;
the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the LA profit to be the maximum optimization target, the power distribution network optimization model takes the power distribution network loss to be the minimum optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model.
Further comprising:
the model conversion module is used for performing second-order cone relaxation on the power flow equation of the power distribution network optimization model, converting the power distribution network optimization model into a mixed integer second-order cone planning MISOCP model and reducing the solving difficulty of the problem;
and the solving module is used for solving the power distribution network double-layer optimized scheduling model converted by the model conversion module to obtain an optimized scheduling result.
Further, the optimized scheduling module includes:
the first processing unit is used for obtaining electricity buying quantity and electricity selling quantity of LA at each moment according to loads inside LA, basic information of wind power, photovoltaic and energy storage equipment and an LA economic dispatching model;
the second processing unit is used for calculating the electric quantity sold to the LA by the power distribution network according to the topology structure of the power distribution network, basic information of a power supply and loads, the electric quantity purchased and sold at each moment of the LA and the power distribution network optimization model; in the process of calculating the electric quantity sold to the LA by the power distribution network when the network loss of the power distribution network is minimum, if the voltage and the line power of the power distribution network are out of limit, the electric quantity purchased and sold at each moment of the LA is increased or reduced, and finally the electric quantity sold to the LA by the power distribution network is obtained on the premise of meeting the safety and stability constraint of the power distribution network.
As shown in fig. 3, for a comparison graph of daily voltage fluctuation curves of a certain load node in a certain place under a power distribution network double-layer optimization strategy considering only LA economic dispatch and LA, the power distribution network voltage safety constraint range is 0.95-1.05pu, and it can be seen from the graph that only LA economic dispatch is considered, the power distribution network voltage fluctuation is large, and overvoltage occurs at 18 hours, which is not beneficial to the safe operation of the power distribution network. The double-layer optimized scheduling provided by the invention ensures that the voltage in each time interval is in a reasonable range, the intensity of voltage fluctuation is reduced, and the voltage level is obviously improved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and other modifications or equivalent substitutions made by the technical solutions of the present invention by those of ordinary skill in the art should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.

Claims (9)

1. A double-layer optimized dispatching method for a power distribution network is characterized by comprising the following steps:
acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in a load aggregation provider LA, and acquiring basic information of a power distribution network topological structure, a power supply and the loads;
inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result;
the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the maximum LA profit as an optimization target, the power distribution network optimization model takes the minimum power distribution network loss as an optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model;
inputting basic information of loads, wind power, photovoltaic and energy storage equipment inside the LA, and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result, wherein the optimization scheduling result comprises the following steps:
obtaining the electricity buying quantity and the electricity selling quantity of LA at each moment according to the load in the LA, the wind power, the photovoltaic and the basic information of the energy storage equipment and the LA economic dispatching model;
obtaining the electric quantity sold to LA by the power distribution network according to the topology structure of the power distribution network, basic information of a power supply and a load, the electric quantity bought and sold at each moment of LA and an optimization model of the power distribution network;
the objective function of the LA economic dispatch model is as follows:
Figure FDA0003759959510000011
in the formula, T is a scheduling period; p t sell And P t buy Respectively indicating the power sale from LA to main network and the power purchase from main network at t time, lambda t sell For corresponding electricity selling prices, lambda t buy The corresponding electricity purchase price;
the objective function of the power distribution network optimization model is as follows:
selecting the minimum network loss of the power distribution network as an optimization target, wherein a mathematical expression formula is as follows:
Figure FDA0003759959510000021
in the formula, psi l is a power distribution network branch set, loss l t The active network loss value of the branch circuit l in the time period t; i is t ij The value of the current flowing through branch l for the time period t;
the constraint conditions of the power distribution network optimization model comprise:
network flow constraints
Because the distribution network has a radial structure, the equation constraint of the Distflow power flow equation can be written as the following equation:
Figure FDA0003759959510000022
in the formula, i ∈ u (j) represents all branch sets with j as end nodes; k ∈ v (j) represents all the branch sets with j as the head-end node; p ij 、Q ij Active power and reactive power flowing into node j for node i; p j 、Q j Is the active power and reactive power of node j; r is ij 、x ij The resistance and reactance values of the branch (i, j); u shape i Is the voltage amplitude of system node i; i is ij Represents the value of the current flowing through the branch (i, j).
2. The power distribution network double-layer optimization scheduling method according to claim 1, characterized in that:
the constraint conditions of the LA economic dispatching model are as follows:
1) supply and demand balance constraints
Figure FDA0003759959510000031
In the formula, P i t The output of the distribution formula power supply i in the period LA of t, nDG is the quantity of the distribution formula power supplies;
Figure FDA0003759959510000032
respectively stores energy and charges power for t momentDischarge power; p is t buy 、P t sell Respectively representing the electricity purchasing amount from the main network and the electricity selling amount to the main network of the LA in the period of t;
2) restraint of stored energy
E s,t ＝E s,t-1 +η ch P ch,t -P dis,t /η dis (3)
Figure FDA0003759959510000033
Figure FDA0003759959510000034
λ min E s ≤E s,t ≤λ max E s (6)
b ch,t +b dis,t ≤1 (7)
E s,0 ＝E s,T (8)
In the formula, b ch,t 、b dis,t The variable is 0-1, and represents the charging state and the discharging state of the stored energy at the moment t;
Figure FDA0003759959510000035
a maximum charging power and a maximum discharging power representing the stored energy; eta ch 、η dis The charging efficiency and the discharging efficiency of the stored energy are represented; e s,t The capacity of the energy storage device at time t; e s Is the rated capacity of the energy storage device; lambda [ alpha ] max 、λ min Respectively representing the state of charge and the minimum state of charge of the stored energy; formula (7) limits the energy storage device to be in a charging or discharging state at the same time; equation (8) shows that the final stored energy is equal to the initial state in one period;
3) interacting power constraints with distribution networks
Figure FDA0003759959510000041
In the formula, P max And interacting the upper limit value of the power for LA and the distribution network.
3. The power distribution network double-layer optimization scheduling method according to claim 1, wherein: the constraint conditions of the power distribution network optimization model further comprise:
1) node voltage constraint
U min ≤U i ≤U max ,i∈ψ b (12)
In the formula, U max 、U min Respectively, an upper system node voltage limit and a lower system node voltage limit, psi b Is a system node set;
2) branch power constraint
Figure FDA0003759959510000042
In the formula, I max Represents the upper limit of the allowed transmission power of branch l;
3) capacitor bank operation constraints
The switching of the parallel capacitor bank SCs is the most common reactive power optimization measure of the power distribution network, and the switching is a discrete decision variable in the operation scheduling process and has the following constraint
Figure FDA0003759959510000043
In the formula, the first step is that,
Figure FDA0003759959510000044
installing reactive power of a node for the kth SCs at the moment t; n is a radical of k,t 、
Figure FDA0003759959510000045
Respectively operating group number and single group reactive power for SCs；N k,max The maximum number of SCs in-transit groups;
from the economical point of view, the switching times of SCs are limited in the operation process; according to actual operation experience, the number of switching times of the parallel capacitor banks SCs is allowed to be 5 times per day, namely
Figure FDA0003759959510000046
In the formula, sign is a sign function;
4) static var generator operation constraints
Compared with a parallel capacitor bank SCs, the reactive power of the SVG can be continuously adjusted, the voltage sudden change condition caused by frequent power fluctuation in a power grid can be more flexibly coped with, and the operation constraint is
Figure FDA0003759959510000051
In the formula, the first step is that,
Figure FDA0003759959510000052
for the kth installation node SVG reactive power,
Figure FDA0003759959510000053
respectively is an upper limit of the SVG compensation capacity and a lower limit of the SVG compensation capacity.
4. The power distribution network double-layer optimized scheduling method of claim 3, further comprising: performing second-order cone relaxation on a power flow equation (11) of the power distribution network optimization model, converting the power distribution network optimization model into a mixed integer second-order cone planning MISOCP model, and reducing the problem solving difficulty by the following steps:
order to
Figure FDA0003759959510000054
Then the formula (11) becomes
Figure FDA0003759959510000055
The second order cone relaxation is performed on the last term equality constraint in equation (11):
Figure FDA0003759959510000056
further obtain the
(2P ij ) 2 +(2Q ij ) 2 +(l ij -V i ) 2 ≤(l ij +V i ) 2 (19)
Converting equation (19) into the following form
Figure FDA0003759959510000061
Replacing the last term in the formula (17) with the formula (20), and obtaining a power flow equation of the power distribution network optimization model through second-order cone relaxation, wherein the power flow equation is as follows:
Figure FDA0003759959510000062
accordingly, the formulas (12), (13) become
Figure FDA0003759959510000063
Figure FDA0003759959510000064
5. The power distribution network double-layer optimization scheduling method according to claim 1, wherein: the electric quantity that the distribution network sells LA is obtained according to the basic information of distribution network topology, power and load, the electric quantity of buying and selling of LA each moment and distribution network optimization model, includes:
calculating the electric quantity sold to LA by the power distribution network when the power distribution network loss is minimum according to the topological structure of the power distribution network, basic information of a power supply and loads, the electric quantity purchased and sold at each moment of LA and a power distribution network optimization model;
in the process of calculating the electric quantity sold to the LA by the power distribution network when the network loss of the power distribution network is minimum, if the voltage and the line power of the power distribution network are out of limit, the electric quantity purchased and sold at each moment of the LA is increased or reduced, and finally the electric quantity sold to the LA by the power distribution network is obtained on the premise of meeting the safety and stability constraint of the power distribution network.
6. The power distribution network double-layer optimization scheduling method according to claim 1, wherein: and solving the obtained double-layer optimized scheduling model of the power distribution network by utilizing Matlab software, Cplex software, MOSEK software, SDPT3 software or SEDUMI software to obtain an optimized scheduling result.
7. A power distribution network double-layer optimized dispatching device for implementing the power distribution network double-layer optimized dispatching method as claimed in claim 1, wherein the dispatching device comprises:
the system comprises a basic information acquisition module, a load aggregation provider LA and a power distribution network topology structure, wherein the basic information acquisition module is used for acquiring basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and acquiring basic information of the power distribution network topology structure, the power supply and the loads;
the optimization scheduling module is used for inputting basic information of loads, wind power, photovoltaic and energy storage equipment in the load aggregation provider LA and basic information of a topological structure, a power supply and the loads of the power distribution network into a pre-established double-layer optimization scheduling model of the power distribution network to obtain an optimization scheduling result;
the power distribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model, the LA economic scheduling model takes the maximum LA profit as an optimization target, the power distribution network optimization model takes the minimum power distribution network loss as an optimization target, the optimization result of the LA economic scheduling model is substituted into the power distribution network optimization model, and the power distribution network optimization model outputs the optimization scheduling result according to the optimization result of the LA economic scheduling model.
8. The double-layer optimized dispatching device for the power distribution network of claim 7, further comprising:
the model conversion module is used for performing second-order cone relaxation on the power flow equation of the power distribution network optimization model, converting the power distribution network optimization model into a mixed integer second-order cone planning MISOCP model and reducing the solving difficulty of the problem;
and the solving module is used for solving the power distribution network double-layer optimized scheduling model converted by the model conversion module to obtain an optimized scheduling result.
9. The double-layer optimized dispatching device for the power distribution network according to claim 7, wherein the optimized dispatching module comprises:
the first processing unit is used for obtaining electricity buying quantity and electricity selling quantity of LA at each moment according to loads inside the LA, basic information of wind power, photovoltaic and energy storage equipment and an LA economic dispatching model;
the second processing unit is used for calculating the electric quantity sold to the LA by the power distribution network according to the topology structure of the power distribution network, basic information of a power supply and loads, the electric quantity purchased and sold at each moment of the LA and the power distribution network optimization model;
in the process of calculating the electric quantity sold to the LA by the power distribution network when the network loss of the power distribution network is minimum, if the voltage and the line power of the power distribution network are out of limit, the electric quantity purchased and sold at each moment of the LA is increased or reduced, and finally the electric quantity sold to the LA by the power distribution network is obtained on the premise of meeting the safety and stability constraint of the power distribution network.
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