CN116738627A - Distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method and system - Google Patents
Distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method and system Download PDFInfo
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Abstract
The invention discloses a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method and system, wherein the method comprises the following steps: establishing a first mathematical model for determining the access position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and a boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model; establishing a comprehensive objective function of active network loss of the power distribution network and active reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
Description
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method and system.
Background
With the gradual exhaustion of resources such as petroleum, coal and the like, clean energy plays an increasingly important role, and renewable energy is fully utilized to generate electricity, so that the energy safety of China is improved, the energy shortage is solved, and the necessary choice of social environment is improved. Photovoltaic large-scale access has become a trend, but the photovoltaic digestion capability of the power distribution network is limited, and if exceeding the digestion capability, the photovoltaic digestion capability of the power distribution network may threaten the safe and economic operation of the power distribution network, so that the distributed photovoltaic digestion capability of the power distribution network needs to be improved. Through reasonable location and volume fixing of the multi-element source load, the photovoltaic digestion capacity of the power distribution network can be improved. The distributed power supply has the advantages that the distributed power supply has influence on aspects of short circuit current, node voltage, line power flow, network reliability, power grid operation safety and the like. As a clean traffic mode, electric vehicles have been widely popularized in recent years, but the charging and discharging of electric vehicles have randomness in time and space, and certain impact may be brought to the safety and stability of a power grid in the process of accessing the power distribution network. A large amount of electric automobile charging loads can be intensively connected in a peak load period, and increased load demands can burden an electric power system and can cause overload of a local power grid; the construction of infrastructure such as charging piles changes the network topology structure of the power distribution network, increases network nodes, increases line transformation difficulty, power grid loss and the like, and brings a series of negative effects and difficulties to power grid planning operation. Through reasonably planning the position and the capacity of the multi-element source load in the power distribution network, the grid-connected influence of the multi-element source load can be effectively reduced, and the photovoltaic digestion capacity of the power distribution network is improved.
At present, scholars at home and abroad research on a multi-source load locating and sizing method, partial scholars put forward the definition of the admittance capacity of the distributed power supply admitted by the distributed power supply at any position and any capacity, and research on a mathematical model of the maximum admittance capacity of the distributed power supply considering load uncertainty. But do not take into account constraints of power flow, voltage and power, and are dependent on taking into account the distribution of loads. For electric vehicles accessing to a power distribution network, the electric vehicles are mainly realized by electric vehicle charging stations, but only the charging requirements and the service range are partially considered, the constraint conditions such as power flow, voltage and power of the power grid are not considered, and when a large number of electric vehicles are accessed, voltage fluctuation can be caused.
Therefore, development of a multi-source load locating and sizing method and system for a power distribution network, which are oriented to improvement of the capacity of the distributed photovoltaic and the electric vehicle, is needed to be developed by fully considering the operation constraint of the distributed photovoltaic and the electric vehicle and the safe operation constraint of the power distribution network.
Disclosure of Invention
The technical scheme of the invention provides a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method and system, which aim to solve the problem of multi-source load locating and sizing for improving the photovoltaic absorption capacity of the power distribution network.
In order to solve the problems, the invention provides a distributed photovoltaic-oriented power distribution network multi-source load location and volume-fixing method, which comprises the following steps:
establishing a first mathematical model for determining the access position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and a boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model;
establishing a comprehensive objective function of active network loss of the power distribution network and active reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
Preferably, the method further comprises:
and establishing an optimal objective function with maximum admittance capacity of the power distribution network multi-source load and technical indexes of the power distribution network, solving the optimal objective function based on the power grid operation safety constraint, the power flow constraint, a mathematical model of distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint, and determining a power distribution network multi-source load locating and sizing method.
Preferably, the first mathematical model comprises:
max P DGk
η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max
S Lm ≤S Lm,max
wherein ,Ps,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i, G ij 、B ij Respectively representing the real part and the imaginary part of the network admittance matrix, U i 、U j Representing the voltage amplitudes of nodes i and j, respectively, θ ij Representing the voltage phase difference of nodes i and j, P DGk 、Q DGk Respectively representing active power and reactive power emitted by photovoltaic at node k, S DGk Representing the maximum photovoltaic accessible capacity at node k, P DGk,max 、P Load,max Respectively represents the maximum active power of the photovoltaic and the maximum load which can be born by the current power distribution network, eta DG,t Representing the ratio of the active power generated at time t to the maximum power thereof, eta Load,t The ratio of the load in the network at the moment t to the maximum load born by the power distribution network is represented, gamma represents the power flow inverting coefficient, U N Represents the rated voltage of the network, deltaU% min 、ΔU％ max Respectively represent the upper and lower limits of the voltage deviation, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m, wherein N is the total number of nodes.
Preferably, the second mathematical model comprises:
max P Lk
S Lm ≤S Lm,max
wherein ,PLk Representing the active power that the load characteristic device is accessing at node k.
Preferably, the integrated objective function includes:
wherein ,cLoss 、c PV Respectively representing system network loss and distributed photovoltaic active power reduction loss coefficient, r ij Representing the impedance between node I and node j, I ij,t Representing the current between node i and node j at time t,representing the maximum active power of the distributed photovoltaic injection node i at time t, +.>Active power of the distributed photovoltaic injection node i at the time T is represented, T represents the time T, and T represents the total access time.
Preferably, the mathematical model of distributed photovoltaic power generation comprises:
in the formula ：the active power, reactive power and self capacity provided by the photovoltaic connected to the node i at the time t are respectively represented as N PV For a PVs set of mesh points, i represents a node.
Preferably, the electric vehicle aggregate power constraint includes:
wherein ,Pev,i (t) represents the active power of the aggregation node i of the electric automobile at the moment t,and the maximum active power of the electric automobile aggregation node i at the time t is shown.
Preferably, the electric vehicle SOC constraint includes:
wherein ,representing the minimum state of charge, SOC, of the battery of the electric vehicle at the node i at the time t ev,i (t) represents the state of charge of the battery of the electric vehicle at node i, +.>And (5) representing the maximum state of charge of the battery of the electric automobile on the node i at the time t.
Preferably, the optimal objective function includes:
wherein ,distributed light Fu Rong representing node i access at time tQuantity (S)>Representing the energy storage capacity of node i at time t access,/-, and>indicating energy storage capacity of electric automobile accessed at time t at node i,/->Representing the loss of each line>The voltage deviation of each node is represented, T represents the time T, and T represents the total access time.
Based on another aspect of the invention, the invention provides a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing system, which comprises:
an initial unit for establishing a first mathematical model for determining the admission position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and a boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model;
the execution unit is used for establishing a comprehensive objective function of active power loss of the power distribution network and active power reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
The technical scheme of the invention provides a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method and system, wherein the method comprises the following steps: establishing a first mathematical model for determining the access position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model; establishing a comprehensive objective function of active network loss of the power distribution network and active reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint. The invention provides a multi-source load locating and sizing method, which comprises the steps of firstly, modeling and analyzing the admittance position and the boundary capacity of multi-source load, and providing an alternative scheme of multi-source load access for a power grid company; and secondly, based on the research on the operation regulation mode of the multi-source load, the multi-source load site selection and volume fixation method is provided, the distributed photovoltaic absorption capacity of the power distribution network is effectively improved, and the photovoltaic absorption potential of the power distribution network is exerted while the safe and stable operation of the power distribution network is ensured.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
fig. 1 is a flow chart of a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method according to a preferred embodiment of the invention;
FIG. 2 is a flow chart of a multi-source load addressing and sizing method according to a preferred embodiment of the invention;
FIG. 3 is a schematic diagram of a 33 node system architecture in accordance with a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of the spatial distribution of Pareto fronts according to a preferred embodiment of the present invention; and
fig. 5 is a block diagram of a distributed photovoltaic-oriented power distribution network multi-source load location and volume determination system according to a preferred embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Fig. 1 is a flow chart of a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method according to a preferred embodiment of the invention. The distributed photovoltaic permeability in the power distribution network continuously rises, and the photovoltaic digestion capacity of the power distribution network needs to be improved to ensure safe and stable operation of the power distribution network. The invention provides a method for locating and sizing multiple source loads of a power distribution network for improving the distributed photovoltaic digestion capability. Firstly, modeling analysis is carried out on the admittance position and the boundary capacity of the multi-element source load, and secondly, a multi-element source load locating and sizing method is provided based on the research on the operation regulation and control mode of the multi-element source load.
As shown in fig. 1, the invention provides a distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method, which comprises the following steps:
step 101: establishing a first mathematical model for determining the access position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model;
preferably, the first mathematical model comprises:
max P DGk
η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max
S Lm ≤S Lm,max
wherein ,Ps,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i, G ij 、B ij Respectively representing the real part and the imaginary part of the network admittance matrix, U i 、U j Representing the voltage amplitudes of nodes i and j, respectively, θ ij Representing the voltage phase difference of nodes i and j, P DGk 、Q DGk Respectively representing active power and reactive power emitted by photovoltaic at node k, S DGk Representing the maximum photovoltaic accessible capacity at node k, P DGk,max 、P Load,max Respectively represents the maximum active power of the photovoltaic and the maximum load which can be born by the current power distribution network, eta DG,t Representing the ratio of the active power of photovoltaic power generation at time t to the maximum power thereof, eta Load,t The ratio of the load in the network at the moment t to the maximum load born by the power distribution network is represented, gamma represents the power flow inverting coefficient, U N Represents the rated voltage of the network, deltaU% min 、ΔU％ max Respectively represent the upper and lower limits of the voltage deviation, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m, wherein N is the total number of nodes.
Preferably, the second mathematical model comprises:
max P Lk
S Lm ≤S Lm,max
wherein ,PLk Representing the active power that the load characteristic device is accessing at node k.
The method determines the admittance position and boundary capacity of the multi-source load of the power distribution network.
1) Mathematical model of distributed power supply access position and boundary capacity
When a high-proportion distributed power supply is connected into a power distribution network, the phenomena of node voltage rise, line overload and the like are caused, and sometimes the phenomenon of heavy power flow back transmission is caused. If the current automation adjustment level of the network is insufficient to cope with the inverted tide in the network, the phenomena of network loss increase, malfunction of the relay protection device and the like may be caused, and the normal operation of the existing equipment in the network may be threatened at some time. Taking a distributed power supply as an example, when calculating the boundary condition of grid connection of power supply characteristic equipment, the constraint of flow pouring needs to be considered, and the maximum admittance capacity of PVs is required by a guide rule aiming at the flow pouring phenomenon: the total capacity of the PVs grid connection should be lower than 25% of the maximum load in the upper level transformer power supply range. The provision in the guide rule provides a certain reference for the selection of PVs access capacity, but a specific network is also specifically analyzed according to actual conditions, and when the network automation level to be calculated, relay protection configuration and the like cannot meet the requirements, the phenomenon of power flow backflow is prevented; when the network to be calculated has certain automatic adjustment and protection configuration capability, a certain degree of power flow pouring can be allowed according to actual conditions.
In summary, the mathematical model of the boundary condition, which is established by the invention and considers the grid connection of PVs, is shown in formulas (1) - (7), and mainly considers the maximum power that PVs can access at the node k under the constraint conditions of load and PVs power change, power flow dumping, node voltage deviation, PVs power factor, line capacity and the like.
max P DGk (1)
η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max (5)
S Lm ≤S Lm,max (7)
Formulae (2) - (3) are the flow balance equation of node i, P s,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i; g ij 、B ij Respectively representing a real part and an imaginary part of a network admittance matrix; u (U) i 、U j Respectively representing the voltage amplitudes of the nodes i and j; θ ij Indicating the voltage phase difference of nodes i and j.
Formula (4) is reactive power constraint of PVs, S DGk Representing the maximum capacity accessible by the photovoltaic at node k; p (P) DGk 、Q DGk Respectively representing active power and reactive power emitted by photovoltaic at a node k;
formula (5) is tidal current inverted delivery constraint, P DGk,max 、P Load,max Respectively representing the maximum active power of the photovoltaic and the maximum load which can be born by the current power distribution network; η (eta) DG,t Representing the ratio of the active power of photovoltaic power generation at time t to the maximum power thereof, eta Load,t The ratio of the load in the network to the maximum load born by the power distribution network at the moment t is represented; gamma represents the tidal current inverting coefficient and can be passed throughCalculating the ratio of the maximum power flow back-off amount allowed by the network to the maximum load amount bearable by the power distribution network, wherein in general, gamma is more than or equal to 0, and when gamma=0, the current network does not allow power flow back-off;
formula (6) is node voltage deviation constraint, U N Representing the nominal voltage of the network; deltaU% min 、ΔU％ max Representing upper and lower limits of the voltage deviation;
formula (7) is a line capacity constraint, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m.
2) Mathematical model of access position and boundary capacity of electric automobile
The boundary conditions of the electric automobile grid connection mainly consider the flow constraint, the voltage constraint and the capacity constraint of the line of the network, and the mathematical model of the boundary conditions can be represented by formulas (8) to (12).
max P Lk (8)
S Lm ≤S Lm,max (12)
in the formula ：PLk Representing the active power that the load characteristic device is accessing at node k.
Step 102: establishing a comprehensive objective function of active network loss of the power distribution network and active reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
Preferably, the synthesis objective function comprises:
wherein ,cLoss 、c PV Respectively representing system network loss and distributed photovoltaic active power reduction loss coefficient, r ij Representing the impedance between node I and node j, I ij,t Representing the current between node i and node j at time t,representing the maximum active power of the distributed photovoltaic injection node i at time t, +.>Active power of the distributed photovoltaic injection node i at the time T is represented, T represents the time T, and T represents the total access time.
Preferably, the mathematical model of distributed photovoltaic power generation comprises:
in the formula ：the active power, reactive power and self capacity provided by the photovoltaic connected to the node i at the time t are respectively represented as N PV For a PVs set of mesh points, i represents a node.
Preferably, the electric vehicle aggregate power constraint comprises:
wherein ,Pev,i (t) TableActive power of the electric vehicle aggregation node i is shown at time t,and the maximum active power of the electric automobile aggregation node i at the time t is shown.
Preferably, the electric vehicle SOC constraint includes:
wherein ,representing the minimum state of charge, SOC, of the battery of the electric vehicle at the node i at the time t ev,i (t) represents the state of charge of the battery of the electric vehicle at node i, +.>And (5) representing the maximum state of charge of the battery of the electric automobile on the node i at the time t.
(2) Operation regulation and control method for multi-element source load
The operation regulation and control capability of the multi-element source load under the economic operation scene is fully considered, an operation scheduling model of the multi-element source load after being connected into the power distribution network is established by taking the minimum system active network loss and the minimum distributed photovoltaic active reduction as targets, and the genetic algorithm of elite retention strategy is used for solving, and the method specifically comprises the following steps:
1) Objective function
in the formula ：cLoss ，c PV The loss coefficients of the system network loss and the distributed photovoltaic active power reduction are respectively calculated. Equation (13) represents the comprehensive objective function of the active network loss of the power distribution network and the active reduction of the distributed photovoltaic.
2) Constraint conditions
Power grid operation safety constraint
The power distribution network should always meet the voltage and current safety constraints, which are as follows:
S Lm ≤S Lm,max (15)
formula (14) is node voltage deviation constraint, U N Representing the nominal voltage of the network; deltaU% min 、ΔU％ max Representing upper and lower limits of the voltage deviation;
equation (15) is a line capacity constraint, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m.
Tidal current constraint
Formulas (16) - (17) are the flow balance equation of node i, P s,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i; g ij 、B ij Respectively representing a real part and an imaginary part of a network admittance matrix; u (U) i 、U j Respectively representing the voltage amplitudes of the nodes i and j; θ ij Indicating the voltage phase difference of nodes i and j.
3) PV model
PV is a new type of power generation system that converts solar energy into electrical energy, with the intensity of illumination generally conforming to the beta distribution. Active and reactive support is provided for the power distribution network through an inverter, and a mathematical model is as follows:
in the formula ：active power, reactive power and self capacity provided at time t for the photovoltaic accessed by the node i respectively; n (N) PV Is a set of PVs mesh points.
4) Electric automobile model
Electric automobile aggregate power constraint
The charging power of the electric automobile should not exceed the maximum charging power at every moment:
SOC constraint of electric automobile
In order to meet the demands of users in travel time periods, and meanwhile, the service life of the battery of the electric automobile is prolonged, the SOC of the storage battery is required to meet a certain range:
preferably, the method further comprises:
and establishing an optimal objective function with maximum admittance capacity of the multiple source charges of the power distribution network and technical indexes of the power distribution network, solving the optimal objective function based on the operation safety constraint, the power flow constraint, the mathematical model of distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint, and determining a multiple source charge locating and sizing method of the power distribution network.
Preferably, the optimal objective function comprises:
wherein ,representing the distributed photovoltaic capacity of node i at time t access,/->Representing the energy storage capacity of node i at time t access,/-, and>indicating energy storage capacity of electric automobile accessed at time t at node i,/->Representing the network loss of each line, deltaU i The voltage deviation of each node is represented, T represents time T, and T represents total access time.
(3) Multi-source load locating and sizing method for power distribution network for improving distributed photovoltaic digestion capacity
The method aims at maximizing the multi-source charge access capacity and optimizing the operation index of the power distribution network, and fully considers the operation constraint of distributed photovoltaic, electric vehicles and the like and the safe operation constraint of the power distribution network. And generating multiple source load access positions and capacities, and solving the multiple objective functions by adopting a genetic algorithm and a pareto algorithm of an elite retention strategy.
1) Objective function:
the method uses the optimal target of the technical index of the power distribution network with the maximum admittance capacity of the multiple source charges, and the specific function is as follows:
in the formula (21), the amino acid sequence of the amino acid,distributed photovoltaic representing access of node i at time tCapacity; />Representing the energy storage capacity of the node i at the time t; />And the energy storage capacity of the electric automobile accessed at the moment t at the node i is represented.
In the formula (22), the amino acid sequence of the compound,representing the network loss of each line; />Indicating the voltage deviation of each node.
Constraint conditions:
power grid operation safety constraint
The power distribution network should always meet the voltage and current safety constraints, which are as follows:
S Lm ≤S Lm,max (24)
equation (23) is node voltage bias constraint, U N Representing the nominal voltage of the network; deltaU% min 、ΔU％ max Representing upper and lower limits of the voltage deviation;
formula (24) is a line capacity constraint, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m.
Tidal current constraint
Formulae (25) - (26) are the flow balance equations of node i, P s,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i; g ij 、B ij Respectively representing a real part and an imaginary part of a network admittance matrix; u (U) i 、U j Respectively representing the voltage amplitudes of the nodes i and j; θ ij Indicating the voltage phase difference of nodes i and j.
3) PV model
PV is a novel power generation system for converting solar energy into electrical energy, providing active and reactive support to a power distribution network through an inverter, and the mathematical model is:
in the formula ：active power, reactive power and self capacity provided at time t for the photovoltaic accessed by the node i respectively; n (N) PV Is a set of PV grid-connected points.
4) Electric automobile model
Electric automobile aggregate power constraint
The charging power of the electric automobile should not exceed the maximum charging power at every moment:
SOC constraint of electric automobile
In order to meet the demands of users in travel time periods, and meanwhile, the service life of the battery of the electric automobile is prolonged, the SOC of the storage battery is required to meet a certain range:
(1) The invention fully considers the absorption capacity of the power distribution network to the multi-element source load, establishes a mathematical model of the admission boundary condition and the admission capacity of the multi-element source load equipment, and provides a certain reference for a power grid company;
(2) Based on the analysis result of the operation regulation and control capability of the distribution network to the multi-source load, the multi-source load site selection and volume determination method of the distribution network for improving the distributed photovoltaic digestion capability is provided. And taking maximum multi-source load access capacity and optimal operation index of the power distribution network as targets, and taking operation constraints of distributed photovoltaics, electric vehicles and the like and safe operation constraints of the power distribution network into consideration to finally obtain multi-source load access positions and capacities and maximally improve photovoltaic digestion capacity of the power distribution network. As shown in fig. 2.
The example employed by the present invention is the IEEE33 node example system as shown. As shown in fig. 3.
The example is assumed as follows:
1) The electric automobile demand power is 800kW.
2) The photovoltaic capacity of the specified node installation is 200kW, and the charging station capacity is 200kW.
3) The equipment investment repayment period is t=20 years, r 0 The annual maximum load utilization hours T =0.1 max =4500 h, the photovoltaic power proportion is not more than 30% of the maximum load, the confidence level of the node voltage and the branch power is 0.9, the unit electricity price is 0.5 yuan/kWh, the photovoltaic power generation node and the charging station are regarded as PQ nodes, and the power factor is set to be 0.9. The illumination intensity accords with beta distribution, the parameter alpha is 0.45, and the parameter beta is 9.41; the load distribution conforms to a normal distribution. The photovoltaic power and electric vehicle charging station investment operating cost parameters are shown in table 1.
TABLE 1 investment costs and operation and maintenance costs
4) In the solving process by using NSGA-II algorithm, the objective function is 3, the population is 60, the iteration number is 100, the coding form is [ ABCD ], A represents whether PV is installed or not, B represents the capacity of PV, C represents whether a node is installed or not at a charging station, and D represents the capacity of the charging station.
The result analysis of the example of the invention is as follows:
the result of the example optimization can obtain a Pareto front, which is the distribution of the non-dominant solution set in the solution space, as shown in fig. 4.
As can be seen from fig. 4, as the investment cost increases, both the network loss and the environmental cost are significantly reduced, i.e. as the photovoltaic power generation amount and the charging station capacity of the electric vehicle increase, the power grid loss decreases, and the environmental cost is also significantly reduced; conversely, both grid loss costs and environmental costs increase. In the solution set distributed in fig. 4, all solutions are optimal solutions, so the final planning scheme is determined according to the specific emphasis of the investor.
The 4 schemes are selected for analysis, wherein the expected value of the membership degree of one objective function is 0.9, the expected value of the membership degree of the other two objective functions is 0.9, and the other two objective functions are 0.65, so that 3 schemes are compared at a time.
Table 2 planning configuration results table
/>
The planning configuration results are shown in table 2: the investment cost is expected to be high in the scheme I, so the investment cost is the lowest in the 4 schemes; the scheme II has higher expected value on network loss, so the network loss is the lowest in the 4 schemes; in the third scheme, the cost of the environment is lowest, and the configured photovoltaic power supply capacity is the largest, so that the problem of the environment is fundamentally solved by the access of the photovoltaic power supply. In scheme 4, however, there are higher expectations for all 3 objective functions, so the resulting total investment cost is minimal.
(1) And modeling the admission position and boundary capacity of the multi-element source load.
(2) A multi-source load locating and sizing method of a power distribution network for improving the distributed photovoltaic digestion capability.
Fig. 5 is a block diagram of a distributed photovoltaic-oriented power distribution network multi-source load location and volume determination system according to a preferred embodiment of the present invention.
As shown in fig. 5, the invention provides a distributed photovoltaic-oriented power distribution network multi-source load location and volume-fixing system, which comprises:
an initial unit 501, configured to establish a first mathematical model for determining a distributed power supply access location and a boundary capacity; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model;
preferably, the first mathematical model comprises:
max P DGk
η DGt P DGkmax -η Loadt P Loadmax ≤γP Loadmax
S Lm ≤S Lm,max
wherein ,Ps,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i, G ij 、B ij Respectively representing the real part and the imaginary part of the network admittance matrix, U i 、U j Representing the voltage amplitudes of nodes i and j, respectively, θ ij Representing the voltage phase difference of nodes i and j, P DGk 、Q DGk Respectively representing active power and reactive power emitted by photovoltaic at node k, S DGk Representing the maximum photovoltaic accessible capacity at node kAmount, P DGk,max 、P Load,max Respectively represents the maximum active power of the photovoltaic and the maximum load which can be born by the current power distribution network, eta DG,t Representing the ratio of the active power of photovoltaic power generation at time t to the maximum power thereof, eta Load,t The ratio of the load in the network at the moment t to the maximum load born by the power distribution network is represented, gamma represents the power flow inverting coefficient, U N Represents the rated voltage of the network, deltaU% min 、ΔU％ max Respectively represent the upper and lower limits of the voltage deviation, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m, wherein N is the total number of nodes.
Preferably, the second mathematical model comprises:
max P Lk
S Lm ≤S Lm,max
wherein ,PLk Representing the active power that the load characteristic device is accessing at node k.
The execution unit 502 is used for establishing a comprehensive objective function of active power loss of the power distribution network and active power reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
Preferably, the synthesis objective function comprises:
wherein ,cLoss 、c PV Respectively representing system network loss and distributed photovoltaic active power reduction loss coefficient, r ij Representing the impedance between node I and node j, I ij,t Representing the current between node i and node j at time t,representing the maximum active power of the distributed photovoltaic injection node i at time t, +.>Active power of the distributed photovoltaic injection node i at the time T is represented, T represents the time T, and T represents the total access time.
Preferably, the mathematical model of distributed photovoltaic power generation comprises:
in the formula ：the active power, reactive power and self capacity provided by the photovoltaic connected to the node i at the time t are respectively represented as N PV For a PVs set of mesh points, i represents a node.
Preferably, the electric vehicle aggregate power constraint comprises:
wherein ,Pev,i (t) represents the active power of the aggregation node i of the electric automobile at the moment t,and the maximum active power of the electric automobile aggregation node i at the time t is shown.
Preferably, the electric vehicle SOC constraint includes:
wherein ,representing the minimum state of charge, SOC, of the battery of the electric vehicle at the node i at the time t ev,i (t) represents the state of charge of the battery of the electric vehicle at node i, +.>And (5) representing the maximum state of charge of the battery of the electric automobile on the node i at the time t.
Preferably, the execution unit 502 is further configured to:
and establishing an optimal objective function with maximum admittance capacity of the multiple source charges of the power distribution network and technical indexes of the power distribution network, solving the optimal objective function based on the operation safety constraint, the power flow constraint, the mathematical model of distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint, and determining a multiple source charge locating and sizing method of the power distribution network.
Preferably, the optimal objective function comprises:
wherein ,representing the distributed photovoltaic capacity of node i at time t access,/->Representing the energy storage capacity of node i at time t access,/-, and>indicating energy storage capacity of electric automobile accessed at time t at node i,/->Representing the loss of each line>The voltage deviation of each node is represented, T represents the time T, and T represents the total access time.
The distributed photovoltaic-oriented power distribution network multi-source load locating and volume determining system of the preferred embodiment corresponds to the distributed photovoltaic-oriented power distribution network multi-source load locating and volume determining method of the preferred embodiment, and details are omitted herein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The invention has been described with reference to a few embodiments. However, as is well known to those skilled in the art, other embodiments than the above disclosed invention are equally possible within the scope of the invention, as defined by the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a// the [ means, component, etc ]" are to be interpreted openly as referring to at least one instance of means, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
Claims (10)
1. A distributed photovoltaic-oriented power distribution network multi-source load locating and sizing method, the method comprising:
establishing a first mathematical model for determining the access position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and a boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model;
establishing a comprehensive objective function of active network loss of the power distribution network and active reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
2. The method of claim 1, further comprising:
and establishing an optimal objective function with maximum admittance capacity of the multi-element source load of the power distribution network and technical indexes of the power distribution network, solving the optimal objective function based on the power grid operation safety constraint, the power flow constraint, the PV mathematical model, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint, and determining a multi-element source load locating and sizing method of the power distribution network.
3. The method of claim 1, the first mathematical model comprising:
max P DGk
η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max
S Lm ≤S Lm,max
wherein ,Ps,i 、Q s,i Respectively representing the active power and the reactive power injected by the node i, G ij 、B ij Respectively representing the real part and the imaginary part of the network admittance matrix, U i 、U j Representing the voltage amplitudes of nodes i and j, respectively, θ ij Representing the voltage phase difference of nodes i and j, P DGk 、Q DGk Respectively representing active power and reactive power emitted by photovoltaic at node k, S DGk Representing the maximum photovoltaic-accessible capacity at node k, P DGk,max 、P Load,max Respectively represents the maximum active power of the photovoltaic and the maximum load which can be born by the current power distribution network, eta DG,t Representing the ratio of the active power of photovoltaic power generation at time t to the maximum power thereof, eta Load,t Representing the ratio of the load in the network at time t to the maximum load that the power distribution network can bearGamma represents the tidal current reverse coefficient, U N Represents the rated voltage of the network, deltaU% min 、ΔU％ max Respectively represent the upper and lower limits of the voltage deviation, S Lm 、S Lm,max Respectively representing the current apparent power and the maximum allowable capacity of the line m, wherein N is the total number of nodes.
4. A method according to claim 3, the second mathematical model comprising:
max P Lk
S Lm ≤S Lm,max
wherein ,PLk Representing the active power that the load characteristic device is accessing at node k.
5. The method of claim 1, the integrated objective function comprising:
wherein ,cLoss 、c PV Respectively representing system network loss and distributed photovoltaic active power reduction loss coefficient, r ij Representing the impedance between node I and node j, I ij,t Representing the current between node i and node j at time t,representing the maximum active power of the distributed photovoltaic injection node i at time t, +.>Active power of the distributed photovoltaic injection node i at the time T is represented, T represents the time T, and T represents the total duration of a typical day.
6. The method of claim 1, the mathematical model of distributed photovoltaic power generation comprising:
in the formula ：the active power, reactive power and self capacity provided by the photovoltaic connected to the node i at the time t are respectively represented as N PV For a PVs set of mesh points, i represents a node.
7. The method of claim 1, the electric vehicle aggregate power constraint comprising:
wherein ,Pev,i (t) represents the active power of the aggregation node i of the electric automobile at the moment t,and the maximum active power of the electric automobile aggregation node i at the time t is shown.
8. The method of claim 1, the electric vehicle SOC constraint comprising:
wherein ,representing the minimum state of charge, SOC, of the battery of the electric vehicle at the node i at the time t ev,i (t) represents the state of charge of the battery of the electric vehicle at node i, +.>And (5) representing the maximum state of charge of the battery of the electric automobile on the node i at the time t.
9. The method of claim 2, the optimal objective function comprising:
wherein ,representing the distributed photovoltaic capacity of node i at time t access,/->Representing the energy storage capacity of node i at time t access,/-, and>indicating energy storage capacity of electric automobile accessed at time t at node i,/->Representing the network loss of each line, deltaU i The voltage deviation of each node is represented, T represents the time T, and T represents the total time of a typical day.
10. A distributed photovoltaic-oriented power distribution network multi-source load locating and sizing system, the system comprising:
an initial unit for establishing a first mathematical model for determining the admission position and the boundary capacity of the distributed power supply; establishing a second mathematical model for determining the access position and the boundary capacity of the electric automobile; determining a multi-source load admittance position and a boundary capacity of the power distribution network based on the first mathematical model and the second mathematical model;
the execution unit is used for establishing a comprehensive objective function of active power loss of the power distribution network and active power reduction of the distributed photovoltaic; determining the operation safety constraint and the tide constraint of the power grid; establishing a mathematical model of distributed photovoltaic power generation; determining electric vehicle aggregate power constraint and electric vehicle SOC constraint; and regulating and controlling the operation of the multiple source loads of the power distribution network based on the comprehensive objective function, the power grid operation safety constraint, the tide constraint, the mathematical model of the distributed photovoltaic power generation, the electric vehicle aggregate power constraint and the electric vehicle SOC constraint.
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| CN117196180B (en) * | 2023-10-17 | 2024-04-26 | 无锡市广盈电力设计有限公司 | Distribution line photovoltaic collection point site selection method containing high-proportion distributed photovoltaic |
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