CN116365506A - Energy-saving and loss-reducing optimization method and system for active power distribution network containing distributed photovoltaic - Google Patents
Energy-saving and loss-reducing optimization method and system for active power distribution network containing distributed photovoltaic Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
- H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
Abstract
The invention discloses an energy-saving and loss-reducing optimization method and system for a distributed photovoltaic active power distribution network, and belongs to the technical field of energy saving and loss reduction of the distributed photovoltaic active power distribution network. Acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement meteorological data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve; acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; and acquiring the parameter data of the active power distribution network, and obtaining the optimal power or voltage of each load node and load of the active power distribution network according to the corrected photovoltaic output curve, the comprehensive load model, the parameter data of the power distribution network and the preset energy-saving and loss-reducing optimization model. The active and reactive resources in the distribution network are jointly optimized, so that energy conservation and loss reduction are realized to the maximum extent; the method solves the problem that the loss reduction optimization of the power distribution network is affected without simultaneously considering active optimization and reactive optimization in the prior art.
Description
Technical Field
The invention relates to the technical field of energy conservation and loss reduction of a distributed photovoltaic active power distribution network, in particular to an energy conservation and loss reduction optimization method and system of the distributed photovoltaic active power distribution network.
Background
The statements in this section merely relate to the background of the present disclosure and may not necessarily constitute prior art.
The uncertainty of the operation of the power system is aggravated by the large-scale distributed photovoltaic resource grid connection, so that the operation of the power system receives great challenges such as increased grid loss, economic deterioration and the like. In this context, conventional distribution networks are transitioning to active distribution networks to actively and flexibly control internal resources of the distribution networks.
However, firstly, the response speed of the distributed photovoltaic resource is high, the uncertainty is high, and difficulty is brought to the establishment of the optimization strategy of the active power distribution network. Secondly, because the power distribution network has a relatively high R/X ratio, the traditional active scheduling or reactive voltage optimization effect is limited. In addition, as multiple loads are accessed, the complexity of the load electrical characteristics is increased. All of these problems can bring challenges to the formulation of energy-saving and loss-reducing optimization strategies of the active power distribution network.
In order to solve the above-mentioned challenges, researchers have conducted a great deal of research. In the aspect of the random output characteristics of the distributed power supply, the random output characteristics of the wind turbine generator set are considered based on a probability tide theory; based on the mathematical statistics theory, the uncertainty of the output and the load of the distributed power supply is considered. In research considering the uncertainty of the output of a new energy unit, most of active economic optimization is considered, less researchers consider reactive power optimization at the same time, the active economic optimization only optimizes the economic cost of the system operation, reactive power voltage is ignored, and the network loss is closely related to voltage distribution in the system operation.
With the development of intelligent power distribution technology, the intelligent power distribution technology has important significance for realizing energy conservation and emission reduction, and CVR is a main component of the intelligent power distribution technology. In the prior art, various reactive compensation devices are considered to realize CVR under the condition of new energy access, but the economic cost of the power distribution network is ignored in the actual operation process, so that the economy is weaker.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides the energy-saving and loss-reducing optimization method and system for the active distribution network containing the distributed photovoltaic, which take the voltage reduction regulation into consideration, take the random characteristics of the distributed photovoltaic resources into consideration, establish the connection between load power and voltage, use a CVR technology to regulate the voltage by matching with various reactive compensation devices, realize the reduction of social energy, ensure the reliability of power supply while ensuring the voltage reduction range within the allowable range of voltage deviation, and jointly optimize the active and reactive resources in the distribution network to maximally realize energy saving and loss reduction.
In a first aspect, the invention provides an energy-saving and loss-reducing optimization method for a distributed photovoltaic active power distribution network;
the energy-saving and loss-reducing optimization method for the active power distribution network with the distributed photovoltaic comprises the following steps:
acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve;
acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; the comprehensive load model represents load state information according to the sensitivity index of load power to voltage change;
and acquiring the parameter data of the active power distribution network, and obtaining the optimal power or voltage of each load node and load of the active power distribution network according to the corrected photovoltaic output curve, the comprehensive load model, the parameter data of the power distribution network and the preset energy-saving and loss-reducing optimization model.
Further, the energy-saving and loss-reducing optimization model is as follows:
and a multi-objective optimization model constructed by taking the minimization of active power distribution network load energy consumption, electricity purchasing cost and active loss as targets, and taking the expectation of each target as a target function.
Further, constraint conditions of the energy-saving and loss-reducing optimization model comprise power flow constraint, power distribution network operation safety constraint, transmission network tie line constraint, distributed energy storage constraint, distributed power source active and reactive power coupling out-force constraint and reactive voltage regulation resource constraint.
Further, the obtaining the historical actual measurement meteorological data, obtaining the photovoltaic output predicted value and correcting the photovoltaic predicted error according to the historical actual measurement meteorological data, and obtaining the photovoltaic output curve after error correction includes:
acquiring historical actual measurement meteorological data, and acquiring a predicted value of the historical photovoltaic output according to the historical actual measurement meteorological data; acquiring a photovoltaic output prediction error according to the historical photovoltaic output predicted value and the historical photovoltaic output data;
fitting probability distribution of photovoltaic output prediction errors in different power sections by adopting a Gaussian mixture model according to historical photovoltaic output data, and sampling the photovoltaic output prediction errors from the obtained distribution by adopting a Monte Carlo simulation method;
according to historical actual measurement meteorological data, obtaining a photovoltaic output predicted value, and according to the photovoltaic output predicted values at different moments and the sampled photovoltaic output predicted errors, generating a photovoltaic output curve.
Further, the integrated load model is expressed as
Wherein P is l,i At operating voltage v for load node i Active power at the bottom, P n,i Rated consumption of active power for load nodes, a p ，b p ，c p The active power index polynomial model coefficient;
wherein Q is l,i At operating voltage v for load node i Reactive power at P n,i Rated reactive power consumption for load nodes, a q ，b q ，c q Is a coefficient of a reactive power index polynomial model.
Further, the method further comprises the following steps:
controlling the power or voltage of each load node and load of the active power distribution network according to the optimal energy consumption of each load node and load of the active power distribution network;
and evaluating the voltage reducing effect of the active power distribution network through the voltage adjustment factor.
Further, the voltage adjustment factor is
Wherein f CVR As the voltage adjustment factor, Δe% is the percentage change in energy consumption, and Δv% is the percentage change in voltage.
In a second aspect, the invention provides an energy-saving and loss-reducing optimization system for a distributed photovoltaic active power distribution network;
contain energy-conserving loss-reducing optimizing system of distributed photovoltaic initiative distribution network, include:
a photovoltaic output stochastic property simulation module configured to: acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve;
the comprehensive load model acquisition module is configured to: acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; the comprehensive load model represents load state information according to the sensitivity index of load power to voltage change;
the energy-saving and loss-reducing optimizing module is configured to: and acquiring the parameter data of the active power distribution network, and obtaining the optimal power or voltage of each load node and load of the active power distribution network according to the corrected photovoltaic output curve, the comprehensive load model, the parameter data of the power distribution network and the preset energy-saving and loss-reducing optimization model.
In a third aspect, the present invention provides an electronic device;
an electronic device comprises a memory, a processor and computer instructions stored on the memory and running on the processor, wherein the computer instructions, when run by the processor, complete the steps of the energy-saving and loss-reducing optimization method for the active power distribution network containing the distributed photovoltaic.
In a fourth aspect, the present invention provides a computer-readable storage medium;
a computer readable storage medium for storing computer instructions that, when executed by a processor, perform the steps of the energy conservation and loss reduction optimization method for a distributed photovoltaic-containing active power distribution network.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the technical scheme, in order to accurately track fluctuation of the distributed photovoltaic output value, a method for fitting probability density distribution of prediction errors in a sectionalized mode according to the photovoltaic power prediction values is provided, differences of the probability distribution of the prediction errors when the photovoltaic power prediction values are located in different area ranges are analyzed through historical data, and a Gaussian mixture model is adopted to fit the probability density distribution of the photovoltaic power prediction errors; and establishing a distributed photovoltaic prediction error model, accurately describing the distributed photovoltaic prediction error, tracking the fluctuation of the distributed photovoltaic output, and obtaining a distributed photovoltaic power output scene fitting the actual running state, thereby being beneficial to describing accurate voltage and loss distribution and making an energy-saving loss-reducing strategy.
2. According to the technical scheme provided by the invention, in the active power distribution network accessed by the high-proportion distributed photovoltaic, an energy-saving loss-reducing optimization model considering CVR is provided, the reactive compensation capability of distributed resources is fully excavated by the model, the dynamic reactive reserve of a power distribution system is improved, and the operation safety of the active power distribution network is improved by adjusting reactive control elements such as taps, capacitors and the like of the on-load voltage regulating transformer.
3. According to the technical scheme provided by the invention, based on the CVR technology, the load type of the power distribution system is considered, the comprehensive load model of energy-saving voltage reduction control is established, the consumption of the load is expressed as a function of voltage through the comprehensive load model of energy-saving voltage reduction control, the connection between load power and voltage is established, the voltage of a distribution feeder line can be strategically reduced on the premise of ensuring the energy supply quality so as to reduce the overall energy consumption of the system, and the energy saving and the loss reduction are realized by utilizing voltage regulation.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a schematic flow chart provided in an embodiment of the present invention;
fig. 2 is a block diagram of a power distribution system according to an embodiment of the present invention;
FIG. 3 is a network topology diagram of an improved IEEE-33 node test system provided in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a curve fit of a predictive error of a distributed photovoltaic output provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of comparing system network loss levels of three scenarios provided in an embodiment of the present invention;
fig. 6 is a schematic diagram of comparing load energy consumption of a power distribution system before and after a CVR policy is introduced according to an embodiment of the present invention;
FIG. 7 is a schematic diagram showing voltage level comparison of nodes at peak load time in three scenarios according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of loss distribution for different load types according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of analysis of voltage results of different load factor levels of the valley load in two scenarios according to an embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating analysis of voltage results of different load factor levels for peak load provided by an embodiment of the present invention under two scenarios;
fig. 11 is a schematic diagram of a change of line loss and transformer core loss copper loss of a distribution network with different load factor levels according to an embodiment of the present invention;
fig. 12 is a schematic voltage diagram of each node at different load factor levels when the installed photovoltaic capacity is 300kW according to the embodiment of the present invention;
FIG. 13 is a schematic voltage diagram of each node at different load factor levels for a photovoltaic installed capacity of 600kW according to an embodiment of the present invention;
fig. 14 is a schematic diagram of different types of losses at different photovoltaic permeability levels provided by an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
In the prior art, as a large number of distributed photovoltaics and multiple loads are connected into a power distribution network in a large number, random power injection causes rapid and wide fluctuation of the operation conditions of the power distribution network, so that the power distribution network loss energy consumption optimization and adjustment face a serious challenge. Therefore, the invention provides an energy-saving and loss-reducing optimization method for the active power distribution network containing the distributed photovoltaic.
With reference to fig. 1-14, the energy-saving and loss-reducing optimization method for the active power distribution network with the distributed photovoltaic comprises the following steps:
s1, acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve; the method comprises the following specific steps:
s101, acquiring historical actual measurement meteorological data, and acquiring a predicted value of the historical photovoltaic output according to the historical actual measurement meteorological data; and acquiring a photovoltaic output prediction error according to the historical photovoltaic output predicted value and the corresponding historical photovoltaic output data.
Obtaining a photovoltaic historical actual output power value according to the historical actual measurement meteorological data; predicting by adopting a support vector machine method to obtain a photovoltaic historical output predicted value; and obtaining a historical output prediction error according to the difference value between the photovoltaic historical actual output power value and the photovoltaic historical output prediction value.
Illustratively, obtaining the actual photovoltaic output power value of the previous dispatching cycle according to the actually measured meteorological data of the previous dispatching cycle; according to the historical actual measurement meteorological data before the previous scheduling period, a support vector machine method is adopted for prediction, and a photovoltaic output predicted value of the previous scheduling period is obtained; and obtaining a photovoltaic output prediction error according to the difference value of the photovoltaic actual output power value and the photovoltaic output prediction value.
S102, dividing a photovoltaic historical output predicted value into 10 power sections at equal intervals according to historical photovoltaic output data; and fitting probability distribution of the photovoltaic output prediction errors in different power sections by adopting a Gaussian mixture model, and sampling the prediction errors from the obtained distribution by adopting a Monte Carlo simulation method.
Specifically, a model of the photovoltaic power prediction error is built by adopting the Gaussian mixture modeling method of the power division section according to the historical output prediction error, and a probability density function representing the photovoltaic power prediction error distribution is obtained. And sampling prediction errors from the obtained distribution by adopting a Monte Carlo simulation method, and generating a photovoltaic output curve for model solving.
The output of the distributed photovoltaic unit has stronger uncertainty, and the prediction errors of different power sections show different distribution characteristics due to the influence of factors such as weather forecast errors, terrain differences and the like. The single probability distribution model cannot well describe the actual characteristics of power prediction error probability density distribution peak, thick tail, asymmetry, multimodal and the like. The Gaussian mixture distribution is used as a linear combination of a plurality of normal distributions, and has the advantages of flexible shape and high applicability for describing power prediction error distribution with different characteristics.
The energy-saving and loss-reducing strategy is formulated according to the voltage distribution characteristic and the loss distribution characteristic of the power distribution network in the existing operation scene. Due to uncertainty of the output force of the distributed photovoltaic power, random fluctuation exists in an operation scene, and voltage distribution and loss distribution which are fit with an actual operation scene are difficult to determine.
Therefore, in this embodiment, taking into consideration the difference of the error probability distribution in the range of different sections of the predicted power, the probability distribution of the prediction error in the different power sections is fitted by using a gaussian mixture model, and a specific formula is as follows:
wherein f i (P err ) Probability density function, ω, of the i-th power segment prediction error k Is a weight coefficient of normal distribution,
probability density function as normal distribution, mu k And->
The expectation and variance, respectively.
According to the actual measurement historical photovoltaic output data, equally dividing the predicted power into 10 power sections at equal intervals, combining partial adjacent sections with similar distribution to avoid too few historical samples in certain sections, and finally using [0,0.2P ] respectively rate ]、[0.2P rate ,0.4P rate ]、[0.4P rate ,P rate ]The 3 sections describe the probability distribution of the distributed photovoltaic output power prediction error.
S103, predicting by using a support vector machine method according to the historical actual measurement meteorological data, obtaining a photovoltaic output predicted value of the next scheduling period, and generating an error-corrected photovoltaic output curve according to the photovoltaic output predicted value and the photovoltaic output predicted error at different moments.
In this embodiment, the output value of the distributed photovoltaic in the scheduling period is regarded as the sum of a predicted value and an error value, where the predicted value is a deterministic variable and the error value is a random variable, and the formula is as follows:
wherein P is pv,t For the distributed photovoltaic output at time t,
for the predicted value of the distributed photovoltaic output at time t,/->
And the prediction error of the distributed photovoltaic output at the time t is obtained. S104, performing scene generation and scene reduction by using a sampling and clustering method to form a typical scene for describing the random characteristic of the distributed photovoltaic output.
S2, acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; the comprehensive load model represents load state information according to the sensitivity index of load power to voltage change; load types are broadly divided into three categories: industrial loads, residential loads, and commercial loads.
In medium-low voltage distribution networks, with the increasing access of power electronics and the influence of sensitive loads, most of the loads exhibit different degrees of voltage dependence, that is to say their load demands are highly correlated with the magnitude of the feeder voltage, so that it is possible to achieve energy conservation purposes by CVR (Conservation voltage reduction buck energy saving technology).
In the embodiment, the purpose of energy saving is achieved through the CVR, and the reliability of power supply is ensured while the voltage reduction range is enabled to be within the allowable range of voltage deviation.
The effect of the implementation of the CVR is indistinguishable from the type of load borrowed in the distribution system and the modeling of the load. In power distribution systems, the bus voltage is also very sensitive to active power due to its special radial structure and large impedance ratio. Thus, to accurately model the CVR effect, a detailed load model must first be built. Since the power flow calculation in the active power distribution network does not usually consider frequency variation, in this embodiment, an exponential polynomial (ZIP) model is used to replace a constant-power load model to perform power flow calculation, so that the consumption of the load is expressed as a function of voltage, and load state information is accurately expressed through the sensitivity index of load power to voltage variation.
The integrated load model is expressed as
Wherein P is l,i At operating voltage v for load node i Active power at the bottom, P n,i Rated consumption of active power for load nodes, a p ，b p ，c p Is the coefficient of an active power index polynomial model.
Wherein Q is l,i At operating voltage v for load node i Reactive power at P n,i Rated reactive power consumption for load nodes, a q ，b q ，c q Is a coefficient of a reactive power index polynomial model. The CVR principle is to strategically reduce the voltage of the distribution feeder line to reduce the overall energy consumption of the system on the premise of ensuring the energy supply quality. CVR can be implemented by a voltage adjustment factor f CVR The representation is as follows:
wherein Δe% and Δv% represent the percentage change in energy consumption and the percentage change in voltage, respectively.
S3, acquiring parameter data of the active power distribution network, and obtaining optimal power or voltage of each load node and load of the active power distribution network according to the corrected photovoltaic output curve, the comprehensive load model, the parameter data of the power distribution network and a preset energy-saving and loss-reducing optimization model.
In this embodiment, the day-ahead energy-saving and loss-reducing optimization operation of the active power distribution network is considered, the energy-saving and loss-reducing optimization model is a multi-objective optimization model constructed by taking the minimum total energy consumption and electricity purchase cost of the power distribution network in one day as targets, and the minimum expected total energy consumption and electricity purchase cost of the power distribution network in one day as targets, wherein the total energy consumption comprises the active energy consumption and the load energy consumption of the power distribution network. The objective function is expressed as
Where ζ represents the different uncertainty scenarios, T and Δt represent the total time range and time interval,
representative electricity purchase price->
Representing active power purchased from a transmission network, L representing a set of lines I in the distribution network ij Representing the current on line ij, r ij For the branch resistance, B represents the set of load nodes, P l,i Representing the active power of the load node.
Constraint conditions of the energy-saving loss-reducing optimization model comprise power flow constraint, power distribution network operation safety constraint, transmission network tie line constraint, distributed energy storage constraint, active and reactive coupling out force constraint of a distributed power supply and reactive voltage regulation resource constraint; the specific formula is as follows:
(1) Tidal current constraint
Wherein P is ij,t And Q ij,t Active power and reactive power, P, respectively, on branch ij j,t And Q j,t Active power and reactive power, P, respectively injected for node j l,j,t And Q l,j,t The active and reactive of the load respectively.
And->
Representing the charge and discharge power of the stored energy. />
And the reactive power output of the energy storage is realized. />
And->
Active and reactive outputs of RDGs, respectively, < >>
Representing the reactive output of the CB. />
Representing the reactive output of SVC. U (U) j,t For the voltage amplitude of node j, I ij,t Is the current on branch ij. r is (r) ij And x ij For line resistance and reactance.
(2) Power distribution network operation safety constraint
For safety reasons, the node voltage amplitude should meet the voltage quality requirement, and the branch current should meet the line setting value requirement, so that the power distribution network should limit the bus voltage and the branch current to a certain range during operation. The expression is as follows:
in the method, in the process of the invention,
and->
Representing the upper and lower limits of the i node voltage, +.>
Representing the upper and lower limits of the current of branch ij.
(3) Grid tie constraint
It is assumed that the distribution network can purchase power from an upper transmission grid. Considering the ramp rate limit of the grid-side generator, the grid tie constraint can be expressed as:
in the method, in the process of the invention,
and->
Representing active and reactive power transmitted from the transmission network to the distribution network, ΔP Grid,min 、ΔP Grid,max 、ΔQ Grid,min And DeltaQ Grid,max Representing the upper and lower hill climbing limits of active and reactive power, respectively.
(4) Distributed energy storage constraint
The distributed energy storage considered in the embodiment consists of storage battery energy storage and an inverter, and four-quadrant real-time adjustment of the output power of the distributed energy storage is realized by controlling the on-off of a power electronic device. The distributed energy storage constraints are as follows:
in the method, in the process of the invention,
and->
Is the maximum charge-discharge power of ESS, +.>
And->
Is a 0-1 variable representing the charge and discharge state of ESS,/->
And->
Representing the charge-discharge efficiency of the ESS.
SOC i,0 ＝SOC i,T
In SOC i,t Representing the state of charge of the ESS.
(5) Active and reactive coupling out-force constraint of distributed power supply
The distributed power source considered in the embodiment comprises a distributed photovoltaic and a miniature gas turbine, the distributed photovoltaic and the miniature gas turbine are connected into a power distribution network through an inverter, and reactive power is generated through the multiplexing technology of the inverter. Taking a distributed photovoltaic power supply as an example, the active and reactive coupling output constraints are as follows:
wherein P is pv,t As the available active power of the photovoltaic power plant at node i,
is the rated capacity of the photovoltaic inverter at node i.
(6) Reactive voltage regulation resource constraints
The energy-saving loss-reducing optimization method for the active power distribution network with the distributed photovoltaic comprises the following steps of: an on-load tap changer, a parallel capacitor bank, and an SVC device.
The on-load tap changer constraints are expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is a discrete variable representing the OLTC ratio, +.>
And->
Represents the maximum/minimum transformation ratio of the transformer, < > for>
Is an integer variable, represents the current gear sequence of the OLTC transformer, N OLTC Representing the total number of gear steps of OLTC.
The capacitor bank constraints are expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the total reactive output of the capacitor bank, +.>
Integer variable representing the switching gear and the total gear of the capacitor bank, < >>
Representing the reactive compensation quantity of each capacitor bank,/->
Is a 0-1 variable representing the operating state of the capacitor bank,/->
Representing the maximum operating time of the capacitor bank.
The SVC device output constraints are expressed as follows:
v i,t P i,t ,Q i,t
wherein, the liquid crystal display device comprises a liquid crystal display device,
and->
Representing SVC maximum and minimum outputs.
Obtaining optimal power or voltage of each load node and load of the active power distribution network according to a typical scene of the random characteristic of the distributed photovoltaic output, a comprehensive load model, power distribution network parameter data and an energy-saving and loss-reducing optimization model; wherein, the distribution network parameter data comprises a distribution network topological structure and a line resistance r ij And reactance x ij Access locations and system parameters for each distributed resource (photovoltaic power station, micro gas turbine, group switched capacitor bank, SVC, energy storage).
The energy-saving and loss-reducing optimization model is subjected to linearization relaxation reduction by utilizing a second-order cone relaxation method, the solved problem is equivalent to a mixed integer second-order cone planning problem, a mature Cplex solver is called for solving, and the voltage v of each node at each moment corresponding to the optimal objective function value is output i,t Active power and reactive power P consumed by each node at each moment i,t ,Q i,t The whole system network is lost, electricity purchasing cost is realized, and the available active power of the photovoltaic power station is available at each moment.
In order to verify the effectiveness of the method, an improved IEEE-33 node test system is adopted to verify the proposed active power distribution network energy-saving and loss-reducing optimization method considering voltage reduction and energy saving, and a network topology diagram of the IEEE-33 node test system is shown in fig. 3.
For the prediction error of the distributed photovoltaic output, the method provided by the embodiment is used for determining specific distribution parameters and fitting probability density functions in a power division section, and a specific fitting curve is shown in fig. 4. And (3) performing scene generation and scene reduction by using a sampling and clustering method to form a typical scene for describing the random characteristic of the distributed photovoltaic output.
In addition, three scenes were set to verify the superiority of the method proposed by the present embodiment, as follows:
case1, only reactive power is optimized, and voltage reduction and energy saving are not considered
case2 active and reactive combined optimization, and voltage reduction and energy saving are not considered
case3 active and reactive combined optimization, considering voltage reduction and energy saving
In order to evaluate the effect of the proposed method on energy saving and loss reduction, the optimization results in three cases are given in table 1, including electricity purchase cost, load energy consumption and network loss.
TABLE 1
As can be seen from the table, since Case1 only optimizes the reactive power, the least network loss is achieved, but the purchase cost is higher. And the Case2 performs combined optimization on the active power and the reactive power of the system, so that the electricity purchasing cost of the power distribution system is greatly reduced while less network loss is obtained. The Case3 introduces the CVR technology while carrying out active and reactive combined optimization on the system, realizes the step-down operation through the adjustment of the OLTC, and ensures that the power supply of the system meets the power quality requirement by matching with the reactive adjustment of the capacitor bank, the SVC and the RDGs, thereby playing a role in saving energy on the premise of not increasing extra investment, obtaining the least load energy consumption and electricity purchasing cost, and simultaneously ensuring that the network loss is within an acceptable range. Therefore, the method provided by the embodiment realizes the energy-saving and loss-reducing operation of the power distribution system through reasonable reactive power management, and reduces the social energy cost.
Fig. 5 shows the system loss levels of three cases, and the method provided by the embodiment can reduce the voltage and save the energy while the network loss levels are slightly increased, but the whole network loss is maintained within an acceptable range through the mutual matching of various reactive compensation devices. The load energy consumption of the power distribution system before and after the CVR strategy is introduced is shown in fig. 6, and as can be seen from the figure, the load energy consumption is reduced especially at the moment of load peak by CVR control, so that the overall energy saving of the power distribution system is facilitated. Fig. 7 shows the voltage levels at the various nodes of the three cases at peak load times. It can be seen that, although the method provided by the embodiment realizes energy saving through the step-down means, the voltage per unit value of each node is above 0.95, and the power supply voltage standard is satisfied.
The superiority of the energy-saving and loss-reducing optimization method of the active power distribution network taking voltage reduction and energy saving into consideration is verified through the comparative analysis of three scenes, and the influence on voltage sensitivity and loss distribution is further analyzed from three aspects of load factor, load rate and photovoltaic load rate.
(1) Load factor
Considering that load types can influence the voltage of the load terminal, different parameters are selected to build different load models, and the influence of the different load types on the voltage of the load terminal is analyzed. Building a power flow model of a distribution transformer by using C# and enumerating and traversing parameters related to load types at intervals of 0.1 to substitute the parameters into the model to calculate power flow, so as to obtain voltage results of 4356 load types, wherein the voltage results show that the voltage is the lowest when the load type is a constant power model; the voltage is highest when the load type is a constant impedance model.
Considering that the load type can influence the loss distribution, a constant power load model, a constant impedance load model and a constant impedance load model are respectively taken, and comparison analysis is carried out when the constant power model of the constant current load model of the constant impedance load model accounts for 1:1:3 and 2:3:5, and the result is shown in figure 8.
(2) Load factor
Considering that different load factor levels can influence the load terminal voltage, selecting two typical moments in the day as research objects, and analyzing the influence of the different load factor levels on the load terminal voltage.
And C# is used for building a main transformer and eleven power flow models of the distribution transformer. And selecting two time period scenes of the valley load and the peak load by taking actual measurement data in one day of the Weifang as a reference, increasing the load factor level at intervals of 10%, and obtaining and analyzing voltage results of different load factor levels in the two scenes.
The results at the time of the valley load and the peak load are shown in fig. 9 and 10, respectively.
As the load factor increases, the load-side voltage level decreases gradually. And the load rate level difference of different nodes at the peak load moment is larger, so that the voltage reduction phenomenon at the peak load moment is more obvious at the valley load moment. Therefore, as the load factor level increases gradually, the peak load moment should pay more attention to the guarantee of the voltage qualification rate.
Considering that different load factor levels can affect loss distribution, by changing load factor changes, the line loss of the power distribution network and the copper loss change of the iron loss of the transformer are compared, and different types of loss conditions under different load factor levels are shown in fig. 11.
It can be found that the line loss, copper loss and iron loss increase with increasing load factor, and the iron loss increases slowly.
(3) Photovoltaic load bearing rate
By changing the photovoltaic access proportion, when the photovoltaic permeability is continuously increased, a load end voltage result is obtained under the condition of power reversal, and analysis is performed. And selecting the Weifang actual measurement load data at twelve midday moments as a research object, and analyzing the voltage of each node under different load factor levels when the photovoltaic installed capacity is 300kW and 600kW, wherein the results are shown in figures 12 and 13 respectively.
From the graph, the photovoltaic reverse feeding phenomenon can increase the voltage of the load terminal, and the voltage fluctuation of each node is more severe along with the increase of the photovoltaic permeability.
Considering that different photovoltaic bearing rates can influence loss distribution, the change of the line loss of the distribution network and the copper loss of the iron loss of the transformer is compared by changing the photovoltaic access proportion. The different types of losses at different photovoltaic permeability levels are shown in figure 14.
It can be found that as the distributed photovoltaic permeability is improved, the line loss copper loss is reduced and then increased, and the change of the iron loss is less obvious.
In the embodiment, the Gaussian mixture function power division section is adopted to fit the distributed photovoltaic prediction error, so that the random characteristic of the output characteristic of the distributed photovoltaic is effectively simulated; by adopting a CVR method, energy consumption is reduced by using a load model of energy-saving voltage reduction control and voltage regulation, so that energy-saving operation of a power distribution system is realized, and the cost of social energy consumption is effectively reduced; through the cooperation of a plurality of reactive compensation devices, the voltage level is maintained while the voltage is reduced and the energy is saved, the power supply voltage standard is met, the reactive capacity of distributed resources is effectively utilized, and sufficient dynamic reactive reserve is reserved for the system.
Example two
The embodiment discloses contain energy-conserving loss reduction optimizing system of distributed photovoltaic initiative distribution network, include:
a photovoltaic output stochastic property simulation module configured to: acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement meteorological data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve;
the comprehensive load model acquisition module is configured to: acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; the comprehensive load model represents load state information according to the sensitivity index of load power to voltage change;
the energy-saving and loss-reducing optimizing module is configured to: and acquiring the parameter data of the active power distribution network, and obtaining the optimal power or voltage of each load node and load of the active power distribution network according to the corrected photovoltaic output curve, the comprehensive load model, the parameter data of the power distribution network and the preset energy-saving and loss-reducing optimization model.
It should be noted that, the above-mentioned photovoltaic output random characteristic simulation module, the comprehensive load model obtaining module, and the energy-saving and loss-reducing optimization module correspond to the steps in the first embodiment, and the above-mentioned modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The third embodiment of the invention provides an electronic device, which comprises a memory, a processor and a computer instruction stored on the memory and running on the processor, wherein the computer instruction is executed by the processor to complete the steps of the energy-saving and loss-reducing optimization method for the distributed photovoltaic active power distribution network.
Example IV
The fourth embodiment of the invention provides a computer readable storage medium for storing computer instructions, which when executed by a processor, complete the steps of the energy-saving and loss-reducing optimization method for the active power distribution network containing the distributed photovoltaic.
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.
The foregoing embodiments are directed to various embodiments, and details of one embodiment may be found in the related description of another embodiment.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The energy-saving and loss-reducing optimization method for the active power distribution network with the distributed photovoltaic is characterized by comprising the following steps of:
acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement meteorological data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve;
acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; the comprehensive load model represents load state information according to the sensitivity index of load power to voltage change;
and acquiring the parameter data of the active power distribution network, and obtaining the optimal power or voltage of each load node and load of the active power distribution network according to the photovoltaic output curve after error correction, the comprehensive load model, the parameter data of the power distribution network and a preset energy-saving and loss-reducing optimization model.
2. The energy-saving and loss-reducing optimization method for the active power distribution network with the distributed photovoltaic system according to claim 1, wherein the energy-saving and loss-reducing optimization model is as follows:
and a multi-objective optimization model constructed by taking the minimization of active power distribution network load energy consumption, electricity purchasing cost and active loss as targets, and taking the expectation of each target as a target function.
3. The energy-saving and loss-reducing optimization method for the active power distribution network with the distributed photovoltaic system according to claim 1, wherein constraint conditions of the energy-saving and loss-reducing optimization model comprise power flow constraint, power distribution network operation safety constraint, transmission network tie line constraint, distributed energy storage constraint, active and reactive power coupling out-force constraint of the distributed power supply and reactive voltage regulation resource constraint.
4. The method for optimizing energy conservation and loss reduction of the active power distribution network with distributed photovoltaic according to claim 1, wherein the steps of obtaining historical actual measurement meteorological data, obtaining a photovoltaic output predicted value according to the historical actual measurement meteorological data, correcting a photovoltaic prediction error, and obtaining a photovoltaic output curve after error correction comprise the following steps:
acquiring historical actual measurement meteorological data, and acquiring a predicted value of the historical photovoltaic output according to the historical actual measurement meteorological data; acquiring a photovoltaic output prediction error according to the historical photovoltaic output predicted value and the historical photovoltaic output data;
fitting probability distribution of photovoltaic output prediction errors in different power sections by adopting a Gaussian mixture model according to historical photovoltaic output data, and sampling the photovoltaic output prediction errors from the obtained distribution by adopting a Monte Carlo simulation method;
according to historical actual measurement meteorological data, obtaining a photovoltaic output predicted value, and according to the photovoltaic output predicted values at different moments and the sampled photovoltaic output predicted errors, generating a photovoltaic output curve.
5. The energy-saving and loss-reducing optimization method for active power distribution network with distributed photovoltaic according to claim 1, wherein the comprehensive load model is expressed as
Wherein P is l,i At operating voltage v for load node i Active power at the bottom, P n,i Rated consumption of active power for load nodes, a p ，b p ，c p The active power index polynomial model coefficient;
wherein Q is l,i At operating voltage v for load node i Reactive power at P n,i Rated reactive power consumption for load nodes, a q ，b q ，c q Is a coefficient of a reactive power index polynomial model.
6. The energy-saving and loss-reducing optimization method for the active power distribution network with the distributed photovoltaic system according to claim 1, further comprising:
controlling the power or voltage of each load node and load of the active power distribution network according to the optimal energy consumption of each load node and load of the active power distribution network;
and evaluating the voltage reducing effect of the active power distribution network through the voltage adjustment factor.
7. The energy-saving and loss-reducing optimization method for active power distribution network with distributed photovoltaic according to claim 6, wherein the voltage adjustment factor is
Wherein f CVR As the voltage adjustment factor, Δe% is the percentage change in energy consumption, and Δv% is the percentage change in voltage.
8. Contain energy-conserving loss reduction optimizing system of distributed photovoltaic initiative distribution network, characterized by includes:
a photovoltaic output stochastic property simulation module configured to: acquiring historical actual measurement meteorological data, acquiring a photovoltaic output predicted value according to the historical actual measurement meteorological data, correcting a photovoltaic predicted error, and acquiring an error corrected photovoltaic output curve;
the comprehensive load model acquisition module is configured to: acquiring a comprehensive load model according to the load type and the static voltage characteristic of the active power distribution network; the comprehensive load model represents load state information according to the sensitivity index of load power to voltage change;
the energy-saving and loss-reducing optimizing module is configured to: and acquiring the parameter data of the active power distribution network, and obtaining the optimal power or voltage of each load node and load of the active power distribution network according to the corrected photovoltaic output curve, the comprehensive load model, the parameter data of the power distribution network and the preset energy-saving and loss-reducing optimization model.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of any of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of any of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116956594A (en) * | 2023-07-25 | 2023-10-27 | 广州锐兴科技有限公司 | Rural power grid optimization method, device and equipment based on topological structure |
| CN117196180A (en) * | 2023-10-17 | 2023-12-08 | 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 |
| CN117526443A (en) * | 2023-11-07 | 2024-02-06 | 北京清电科技有限公司 | Novel power system-based power distribution network optimization regulation and control method and system |
| CN117526443B (en) * | 2023-11-07 | 2024-04-26 | 北京清电科技有限公司 | Power system-based power distribution network optimization regulation and control method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116956594A (en) * | 2023-07-25 | 2023-10-27 | 广州锐兴科技有限公司 | Rural power grid optimization method, device and equipment based on topological structure |
| CN116956594B (en) * | 2023-07-25 | 2024-02-09 | 广州锐兴科技有限公司 | Rural power grid optimization method, device and equipment based on topological structure |
| CN117196180A (en) * | 2023-10-17 | 2023-12-08 | 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 |
| 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 |
| CN117526443A (en) * | 2023-11-07 | 2024-02-06 | 北京清电科技有限公司 | Novel power system-based power distribution network optimization regulation and control method and system |
| CN117526443B (en) * | 2023-11-07 | 2024-04-26 | 北京清电科技有限公司 | Power system-based power distribution network optimization regulation and control method and system |
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