CN117977552A - Method for predicting long-term benefits of distributed energy power distribution network - Google Patents
Method for predicting long-term benefits of distributed energy power distribution network Download PDFInfo
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Abstract
The invention provides a method for predicting long-term benefits of a distributed energy power distribution network, and relates to the technical field of prediction calculation. The invention designs a long-term simulation model, and utilizes system dynamics to model the long-term trend of related factors, and the generated result can more effectively infer the long-term evolution trend of the power distribution network on a longer time scale; according to the invention, a double-time scale simulation model is adopted, long-term simulation is carried out through system dynamics, short-term simulation is carried out through optimal power flow constraint calculation and power system constraint related parameters, and the limitation of the system dynamics can be solved; in addition, the invention also constructs a comprehensive return on investment evaluation index system; the evaluation indexes are calculated according to the short-term simulation result, and the change of the influence of the indexes on the long-term evolution of the power distribution network is reflected through long-term simulation, so that the long-term benefits of the distributed energy power distribution network are effectively predicted.
Description
Technical Field
The invention relates to the technical field of prediction calculation, in particular to a method for predicting long-term benefits of a distributed energy power distribution network.
Background
Distributed Energy (DER) is widely recognized as one of the important renewable energy generation modes in power distribution networks. Distribution networks characterized by the acceptance of large-scale renewable energy sources have become a major trend and direction of future development of distribution networks. The distributed power supply changes the energy flow mode of the traditional power distribution network, and brings great challenges to the economy, reliability and electric energy quality of the power distribution network. Therefore, efficient and rational distribution network investment planning is a necessary condition to solve the above-mentioned problems. How to fully consider the influence of DG on the power distribution network and make reasonable investment decisions is one of the core tasks of power distribution network planning under the condition of limited investment.
In general, the availability of power generation resources is related to their operating characteristics. The same type of machine set has the same output characteristic, and for matching the load characteristic, a mode of jointly supplying power by a plurality of different types of power supplies is needed, and the optimal calculation aiming at the mode is the problem of capacity adequacy. The calculation takes into account demand uncertainty and system availability to ensure power supply capability availability during critical times. It can be measured by a variety of stability factors of the power system, including expected load loss probability, expected load loss, and out-of-service energy, among others.
On the other hand, one important indicator related to a genset is the effective load carrying capacity. It represents the additional load associated with the increased power generation capacity while maintaining the proper reservation system requirements. The ratio of effective load carrying capacity to generator nameplate capacity is referred to as capacity confidence. The exact calculation of the effective load carrying capacity is very complex, requiring a large amount of data, including not only the power demand, but also plant characteristics such as nameplate capacity, seasonal characteristics and forced outage probabilities.
Academia is working to address challenges presented by the incorporation of distributed renewable resources into a power distribution network through the interactive operation of generators, networks, and energy storage systems. But it is mainly focused on planning a distribution network with maximized economic benefit while taking into account uncertainty of renewable energy sources, grid constraints, and distributed energy integration. These methods can only invest in existing forms of distribution networks on a certain investment basis.
In fact, distribution network investment is interrelated and is subject to dynamic effects of many factors, such as load growth, DER growth, and GDP, over a long period of time. The conventional optimization method cannot represent the long-term influence of various dynamic external factors on the power distribution network demand. Moreover, it cannot analyze the long-term profile of the distribution network and consider the return on investment expectations of these factors.
Accordingly, there is a need to provide a method for predicting long-term benefits of a distributed energy distribution network to solve the above-mentioned technical problems.
Disclosure of Invention
In order to solve one of the technical problems, the method for predicting the long-term benefits of the distributed energy power distribution network is characterized by comprising the following steps:
step A: constructing an investment subsystem of the power distribution network, and setting a constraint of a charging state on available capacity;
and (B) step (B): constructing a revenue calculation subsystem and calculating the increase of the revenue of the distribution network;
Step C: constructing an end user subsystem, and calculating the user demand and DER growth of the power distribution network;
step D: and (3) constructing a linear tide calculation model, setting constraint and operation conditions and adding the influence of the solar cell panel, the wind turbine and the energy storage system on the distributed energy.
As a further solution, the step a specifically comprises the following sub-steps:
Step A1: setting investment objects, and giving corresponding weights to the investment objects of each power distribution network investment subsystem; the investment object comprises electrochemical energy storage, a static reactive compensator, CRC, a transformer and a power transmission line;
Step A2: setting an investment decision scheme; wherein the investment decision scheme is based on an economic indicator of investment profitability.
As a further solution, the step B specifically comprises the following sub-steps:
step B1: calculating the annual peak load of the power distribution network through trend change and random fluctuation;
Step B2: carrying out time and space average on the power quality indexes of the load access node and the grid-connected node of the power distribution network; the power quality index comprises voltage deviation degree and three-phase voltage unbalance degree;
Step B3: calculating the self-consumption power quantity and the total load power supply quantity of the power distribution network in the statistical time to represent the energy source smoothness, and calculating the self-supply capacity of the power distribution network;
step B4: the project benefits are reflected by using line load coefficients, clean energy power rejection rate and equipment utilization rate;
Step B5: setting a safety quality parameter, an environment influence parameter and an economic parameter description total benefit, and giving corresponding weight indexes.
As a further solution, the step C specifically comprises the following sub-steps:
step C1: describing the increase in DER by a plurality of metrics; wherein the plurality of metrics includes energy source feed rate and renewable energy grid-tie rate;
Step C2: the average power supply shortage percentage of the users is counted, and the method is used for quantifying the improvement benefit of various access modes on the reliability index of the power distribution network
Step C3: and comparing the benefit cost ratio of the various access modes in sequence, and obtaining the best benefit measure.
As a still further solution, the step D specifically comprises the following sub-steps:
Step D1: constructing an objective function of the optimal power flow problem of the distributed energy power distribution network, wherein the objective function comprises the following elements: minimum loss of the distribution network, minimum node voltage deviation and minimum running cost of distributed energy sources;
Step D2, setting network constraint; the network constraint comprises node active power input, distributed energy storage and reactive power compensation input, DER output constraint, current constraint, node voltage constraint, energy storage constraint, reactive power compensation and transformer capacity constraint;
Step D3: different scenes are set, and the standard is divided into a high or low RES growth rate and a high or low load growth rate, so that investment trends under different practical conditions are obtained.
As a still further solution, said step A2 describes an investment decision by a benefit factor, expressed as:
where IV is the current capacity ratio and dis is the discount factor.
As a still further solution, in said step B5, the safety quality parameters include self-sufficient capacity and electricity reliability; the environmental impact parameters include self-sufficient capacity, new energy disposal rate, line utilization rate and utility utilization rate; economic parameters include power consumption reliability and three-phase voltage imbalance; the total benefit is expressed as:
RFSUM＝α1RFQ+α2RFECO+α3RFEN；
Wherein RF SUM represents a total benefit parameter, RF Q represents a safety quality parameter, F ECO represents an environmental impact parameter, and RF EN represents an economic parameter; alpha 1、α2、α3 is the weight occupied by each factor, and can be adjusted according to the actual development plan.
As a still further solution, in said step B1, the peak load is calculated by the following formula:
Where a is the average peak load growth rate over a long period of time and x is the peak load growth rate in the current year.
As a further solution, in the step D1, a short time scale objective equation is constructed, considering that the power distribution network loss is minimum, the node voltage deviation is minimum, and the distributed energy operation cost is minimum, which is expressed as follows:
Wherein, C loss、CU、Cres、Cess represents the power distribution network loss, node voltage deviation, the penalty cost of RES reduction and the charge and discharge cost of the energy storage device respectively.
As a still further solution, the steps a to C use system dynamics for long-time scale investment deduction, and the step D uses short-term market settlement simulation to achieve double-time scale investment deduction.
Compared with the related art, the method for predicting the long-term benefits of the distributed energy power distribution network has the following beneficial effects:
1. The traditional optimization model can not capture the dynamic influence of various factors on the long-term investment return of the power distribution network; the invention designs a long-term simulation model, and utilizes system dynamics to model the long-term trend of related factors, and the generated result can more effectively infer the long-term evolution trend of the power distribution network on a longer time scale.
2. Multiple constraints exist in the operation of the power distribution network, and the conventional system dynamics cannot perform constraint calculation; the invention designs a double-time scale simulation model. The system dynamics is adopted for long-term simulation, and the short-term simulation is carried out through optimal power flow constraint calculation and power system constraint related parameters, so that the limitation of the system dynamics can be solved.
3. The invention constructs a comprehensive return on investment evaluation index system; the evaluation indexes are calculated according to the short-term simulation result, and the change of the influence of the indexes on the long-term evolution of the power distribution network is reflected through long-term simulation.
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Fig. 1 is a schematic diagram of a method for predicting long-term benefits of a distributed energy power distribution network.
Detailed Description
The invention will be further described with reference to the drawings and embodiments.
As shown in fig. 1, a method for predicting long-term benefits of a distributed energy power distribution network is characterized in that the long-term benefits of the power distribution network are predicted by the following steps:
step A: constructing an investment subsystem of the power distribution network, and setting a constraint of a charging state on available capacity;
and (B) step (B): constructing a revenue calculation subsystem and calculating the increase of the revenue of the distribution network;
Step C: constructing an end user subsystem, and calculating the user demand and DER growth of the power distribution network;
step D: and (3) constructing a linear tide calculation model, setting constraint and operation conditions and adding the influence of the solar cell panel, the wind turbine and the energy storage system on the distributed energy.
As a further solution, the step a specifically comprises the following sub-steps:
Step A1: setting investment objects, and giving corresponding weights to the investment objects of each power distribution network investment subsystem; the investment object comprises electrochemical energy storage, a static reactive compensator, CRC, a transformer and a power transmission line;
Step A2: setting an investment decision scheme; wherein the investment decision scheme is based on an economic indicator of investment profitability.
It should be noted that: the step A mainly comprises the steps of constructing the internal relation of an investment subsystem of the power distribution network, calculating the improvement of the power grid capacity caused by different investment combinations, and considering five investment objects, namely electrochemical energy storage, SVC, CRC, a transformer and a power transmission line, providing a concept of storing decision variables, describing the charge/discharge capacity, the charge state and the charge time, realizing the constraint of the charge state on the available capacity, and measuring the influence of energy integration and renewable energy permeability on DER growth. The effect of DER growth on system benefit is also described.
As a further solution, the step B specifically comprises the following sub-steps:
step B1: calculating the annual peak load of the power distribution network through trend change and random fluctuation;
Step B2: carrying out time and space average on the power quality indexes of the load access node and the grid-connected node of the power distribution network; the power quality index comprises voltage deviation degree and three-phase voltage unbalance degree;
Step B3: calculating the self-consumption power quantity and the total load power supply quantity of the power distribution network in the statistical time to represent the energy source smoothness, and calculating the self-supply capacity of the power distribution network;
step B4: the project benefits are reflected by using line load coefficients, clean energy power rejection rate and equipment utilization rate;
Step B5: setting a safety quality parameter, an environment influence parameter and an economic parameter description total benefit, and giving corresponding weight indexes.
It should be noted that: step B describes comprehensive benefits of investment on the power grid from multiple aspects of energy quality, self-supporting capability, electric energy reliability, line utilization rate and the like, and the influences of the parameters are quantized by comprehensively searching different investment benefit parameters so as to comprehensively reflect the overall benefits brought by an investment scheme; in the aspect of economy, the electricity reliability and the imbalance degree of three-phase voltage are considered; in the aspect of electric energy safety, self-supporting capacity and electricity reliability are considered; in the aspect of environmental protection, self-supporting capability, new energy disposal rate, line utilization rate and public facility utilization rate are considered. Three factors are proposed to represent economic, safety and environmental benefits, respectively, each represented by investment benefit parameters in the above-mentioned considerations.
As a further solution, the step C specifically comprises the following sub-steps:
step C1: describing the increase in DER by a plurality of metrics; wherein the plurality of metrics includes energy source feed rate and renewable energy grid-tie rate;
Step C2: the average power supply shortage percentage of the users is counted, and the method is used for quantifying the improvement benefit of various access modes on the reliability index of the power distribution network
Step C3: and comparing the benefit cost ratio of the various access modes in sequence, and obtaining the best benefit measure.
It should be noted that: step C provides effective references for the grid investment in steps 1 and 2 by comprehensively describing the user behavior requirements based on external variables such as power requirements, peak loads, DER capacity, and the like for the end user behavior prediction.
As a still further solution, the step D specifically comprises the following sub-steps:
Step D1: constructing an objective function of the optimal power flow problem of the distributed energy power distribution network, wherein the objective function comprises the following elements: minimum loss of the distribution network, minimum node voltage deviation and minimum running cost of distributed energy sources;
Step D2, setting network constraint; the network constraint comprises node active power input, distributed energy storage and reactive power compensation input, DER output constraint, current constraint, node voltage constraint, energy storage constraint, reactive power compensation and transformer capacity constraint;
Step D3: different scenes are set, and the standard is divided into a high or low RES growth rate and a high or low load growth rate, so that investment trends under different practical conditions are obtained.
It should be noted that: and (3) through the four steps from the step A to the step D, the reliability evaluation of the renewable energy capacity can be completed.
As a still further solution, said step A2 describes an investment decision by a benefit factor, expressed as:
where IV is the current capacity ratio and dis is the discount factor.
As a still further solution, in said step B5, the safety quality parameters include self-sufficient capacity and electricity reliability; the environmental impact parameters include self-sufficient capacity, new energy disposal rate, line utilization rate and utility utilization rate; economic parameters include power consumption reliability and three-phase voltage imbalance; the total benefit is expressed as:
RFSUM＝α1RFQ+α2RFECO+α3RFEN；
Wherein RF SUM represents a total benefit parameter, RF Q represents a safety quality parameter, F ECO represents an environmental impact parameter, and RF EN represents an economic parameter; alpha 1、α2、α3 is the weight occupied by each factor, and can be adjusted according to the actual development plan.
As a still further solution, in said step B1, the peak load is calculated by the following formula:
Where a is the average peak load growth rate over a long period of time and x is the peak load growth rate in the current year.
It should be noted that: calculating peak load is one of the most important parameters for this step. Considering that peak loads exhibit a relatively steady trend increase, they may be expressed as steady increases. But at the same time, in order to describe the uncertainty of the growth, a normal distribution random fluctuation is added on the basis of the stable acceleration.
As a further solution, in the step D1, a short time scale objective equation is constructed, considering that the power distribution network loss is minimum, the node voltage deviation is minimum, and the distributed energy operation cost is minimum, which is expressed as follows:
Wherein, C loss、CU、Cres、Cess represents the power distribution network loss, node voltage deviation, the penalty cost of RES reduction and the charge and discharge cost of the energy storage device respectively.
As a still further solution, the steps a to C use system dynamics for long-time scale investment deduction, and the step D uses short-term market settlement simulation to achieve double-time scale investment deduction.
It should be noted that: steps a to C are long-scale investment deductions using system dynamics. And D, carrying out short-period market settlement simulation to realize double-time-scale investment deduction, mainly calculating aiming at tide calculation, voltage control, a new energy unit and energy storage equipment in a distributed energy network, adding constraint conditions for an integral model and returning key data to carry out long-time-scale planning.
The foregoing is only illustrative of the present invention and is not to be construed as limiting the scope of the invention, and all equivalent structures or equivalent flow modifications which may be made by the teachings of the present invention and the accompanying drawings or which may be directly or indirectly employed in other related art are within the scope of the invention.
Claims (10)
1. The method for predicting the long-term benefits of the distributed energy power distribution network is characterized by comprising the following steps of:
step A: constructing an investment subsystem of the power distribution network, and setting a constraint of a charging state on available capacity;
and (B) step (B): constructing a revenue calculation subsystem and calculating the increase of the revenue of the distribution network;
Step C: constructing an end user subsystem, and calculating the user demand and DER growth of the power distribution network;
step D: and (3) constructing a linear tide calculation model, setting constraint and operation conditions and adding the influence of the solar cell panel, the wind turbine and the energy storage system on the distributed energy.
2. A method for predicting long term benefits of a distributed energy power distribution network as recited in claim 1, wherein said step a specifically includes the sub-steps of:
Step A1: setting investment objects, and giving corresponding weights to the investment objects of each power distribution network investment subsystem; the investment object comprises electrochemical energy storage, a static reactive compensator, CRC, a transformer and a power transmission line;
Step A2: setting an investment decision scheme; wherein the investment decision scheme is based on an economic indicator of investment profitability.
3. A method for predicting long term benefits of a distributed energy power distribution network as recited in claim 1, wherein said step B specifically includes the sub-steps of:
step B1: calculating the annual peak load of the power distribution network through trend change and random fluctuation;
Step B2: carrying out time and space average on the power quality indexes of the load access node and the grid-connected node of the power distribution network; the power quality index comprises voltage deviation degree and three-phase voltage unbalance degree;
Step B3: calculating the self-consumption power quantity and the total load power supply quantity of the power distribution network in the statistical time to represent the energy source smoothness, and calculating the self-supply capacity of the power distribution network;
step B4: the project benefits are reflected by using line load coefficients, clean energy power rejection rate and equipment utilization rate;
Step B5: setting a safety quality parameter, an environment influence parameter and an economic parameter description total benefit, and giving corresponding weight indexes.
4. A method for predicting long term profits of a distributed energy power distribution network according to claim 1, wherein said step C comprises the following concrete sub-steps:
step C1: describing the increase in DER by a plurality of metrics; wherein the plurality of metrics includes energy source feed rate and renewable energy grid-tie rate;
Step C2: the average power supply shortage percentage of the users is counted, and the method is used for quantifying the improvement benefit of various access modes on the reliability index of the power distribution network
Step C3: and comparing the benefit cost ratio of the various access modes in sequence, and obtaining the best benefit measure.
5. A method for predicting long term profits of a distributed energy power distribution network according to claim 1, wherein said step D comprises the following concrete sub-steps:
Step D1: constructing an objective function of the optimal power flow problem of the distributed energy power distribution network, wherein the objective function comprises the following elements: minimum loss of the distribution network, minimum node voltage deviation and minimum running cost of distributed energy sources;
Step D2, setting network constraint; the network constraint comprises node active power input, distributed energy storage and reactive power compensation input, DER output constraint, current constraint, node voltage constraint, energy storage constraint, reactive power compensation and transformer capacity constraint;
Step D3: different scenes are set, and the standard is divided into a high or low RES growth rate and a high or low load growth rate, so that investment trends under different practical conditions are obtained.
6. A method of predicting long term revenue for a distributed energy power network in accordance with claim 2, wherein said step A2 describes investment decisions by a revenue factor expressed as:
Wherein IV is the current capacity ratio, dis is the discount factor, R i is the main body profit, C i is the main body cost, n is the simulation duration, and F i is the total capacity.
7. A method of predicting long term profits in a distributed energy power grid according to claim 3, wherein in said step B5, the safety quality parameters include self-sufficient capacity and electricity reliability; the environmental impact parameters include self-sufficient capacity, new energy disposal rate, line utilization rate and utility utilization rate; economic parameters include power consumption reliability and three-phase voltage imbalance; the total benefit is expressed as:
RFSUM＝α1RFQ+α2RFECO+α3RFEN；
Wherein RF SUM represents a total benefit parameter, RF Q represents a safety quality parameter, F ECO represents an environmental impact parameter, and RF EN represents an economic parameter; alpha 1、α2、α3 is the weight occupied by each factor, and can be adjusted according to the actual development plan.
8. A method of predicting long term revenue for a distributed energy power grid in accordance with claim 3, wherein in step B1, peak load is calculated by the formula:
Where a is the average peak load growth rate over a long period, x is the peak load growth rate in the current year, and σ is the standard deviation of distributed power generation.
9. The method for predicting long term benefits of a distributed energy power distribution network of claim 5, wherein in step D1, a short time scale objective equation is constructed, taking into account minimum power distribution network loss, minimum node voltage deviation, and minimum distributed energy running cost, as follows:
Wherein, C i(Pi,t,Qi,t,Ui,t) is the running cost of the node i, and C loss、CU、Cres、Cess represents the power distribution network loss, the node voltage deviation, the penalty cost for RES reduction, and the charge and discharge cost of the energy storage device, respectively.
10. The method of claim 1, wherein steps a to C use system dynamics for long-time scale investment deductions, and step D uses short-time market settlement simulation to implement double-time scale investment deductions.
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