CN105552965B - Opportunity constraint planning-based distributed energy optimization configuration method - Google Patents
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Abstract
The invention provides a distributed energy optimization configuration method based on opportunity constraint planning, which comprises the steps of establishing a distributed energy comprehensive optimization configuration model; determining opportunity constraint conditions of a distributed energy source comprehensive optimization configuration model; determining an energy storage system configuration principle of the distributed energy comprehensive optimization configuration model; and solving the distributed energy comprehensive optimization configuration model. The method provided by the invention effectively improves the accepting capacity of the power distribution network to the distributed energy; the energy efficiency target and the low-carbon target of a planning scheme can be met, the economic requirement can be met, the problems of site selection and volume fixing of distributed energy sources in areas with different resource levels and economic development degrees can be solved, the large-scale application of the distributed energy sources is ensured, and the environmental pollution and the energy crisis caused by fossil energy are effectively relieved.
Description
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a distributed energy optimization configuration method based on opportunity constraint planning.
Background
With the large-scale access of distributed energy sources such as electric vehicles, energy storage and distributed power supplies, the power distribution network is changed from passive to active, the trend is changed from unidirectional to bidirectional, the increasingly complex characteristic of multi-source is presented, and the load characteristic, the power supply safety, the reliability, the asset utilization rate and the like of the power distribution network are deeply influenced. On the one hand, the distributed energy is connected into the power distribution network, and the operation and planning of the power system are influenced by the following factors: the uncertainty of the distribution of the distributed energy in time and space increases the difficulty of the operation control of the power grid; the nonlinear characteristics of electric vehicles, energy storage and the like can generate harmonic pollution to influence the power quality of a power grid; the traditional power distribution network planning criterion may not be suitable for the situation of large-scale access of distributed energy, and the large-scale access of the distributed energy puts new requirements on power distribution network planning. On the other hand, the large-scale access of the distributed energy brings new opportunities and challenges to the power grid company, and is beneficial to the power grid company to develop a power selling market, expand load regulation and control resources, improve the peak-valley characteristics of the power grid, and improve the utilization efficiency of the power grid.
Under the new situation, the research on the optimal configuration method of the distributed energy sources not only can provide a basis for planning the active power distribution network, but also can ensure the economical efficiency of the operation of the active power distribution system, improve the overall energy efficiency of the system, reduce the operation and maintenance cost, improve the return on investment rate and provide a foundation for upgrading and tamping the traditional power distribution network to the modern power distribution network. At present, no optimization method capable of effectively improving the accepting capacity of a power distribution network to distributed energy sources exists.
Disclosure of Invention
In view of the above, the distributed energy optimization configuration method based on opportunity constraint planning provided by the invention effectively improves the accepting capacity of a power distribution network to distributed energy; the energy efficiency target and the low-carbon target of a planning scheme can be met, the economic requirement can be met, the problems of site selection and volume fixing of distributed energy sources in areas with different resource levels and economic development degrees can be solved, the large-scale application of the distributed energy sources is ensured, and the environmental pollution and the energy crisis caused by fossil energy are effectively relieved.
The purpose of the invention is realized by the following technical scheme:
a distributed energy optimization configuration method based on opportunity constraint planning comprises the following steps:
step 1, establishing a distributed energy comprehensive optimization configuration model;
step 2, determining opportunity constraint conditions of the distributed energy comprehensive optimization configuration model;
step 3, determining an energy storage system configuration principle of the distributed energy comprehensive optimization configuration model;
and 4, solving the distributed energy comprehensive optimization configuration model.
Preferably, the step 1 comprises:
1-1, establishing a distributed energy comprehensive optimization configuration model:
in the formula (1), ω is a weight coefficient vector and the weight coefficient vector ω is determined by an analytic hierarchy process or an analytic hierarchy process; c is a cost vector; cDERFor construction costs; coperFor operating costs; cmainFor maintenance costs; clossThe cost for the network loss; ccarbA penalty for carbon emissions; zcostComprehensively configuring values for distributed energy;
1-2, decomposing the distributed energy comprehensive optimization configuration model:
in the formula (2), nDThe number of nodes to be selected of the DER can be accessed to a system containing a distributed power supply; f. ofDiIs a flag bit, and fDiA value of 0 or 1, wherein fDiWhen the number is 1, a DER is accessed to a node i for accessing a fan or a photovoltaic; cDiThe construction cost per unit volume DER; pDiRepresenting DER at node iInstalled capacity, and PDi>0；r0iAnd mDiRespectively representing discount rate and depreciation age limit; cOiThe operation cost of the DER unit capacity at the node i in unit time is calculated; t isDiIs the annual running time of DER at node i; s represents 4 quarters of a year 1, h represents a 24 period of a typical day for each quarter; cMiMaintenance cost per unit time for per unit capacity DER at node i; j is a branch; n isBThe number of system branches; celeThe price of the electric power is the price of the electric power; i isbshjThe current flowing through branch j for each time period on a typical day for each quarter; rbjIs the resistance of branch j; t isDmaxiThe maximum number of hours per year of DER at node i ξiThe discharge amount of the electric carbon is the discharge amount of direct carbon; psi is carbon emission penalty coefficient, and the unit is yuan/kg, psi according to regional economic development degree and low carbon requirement, the value is different.
Preferably, the step 2 comprises:
determining opportunity constraint conditions of the distributed energy source comprehensive optimization configuration model, wherein the opportunity constraint conditions are that under any working condition, the probability that the branch power is not out of limit is not less than α 0 and the probability that the node voltage is not out of limit is not less than β 0:
in the formula (3), Prob { } is the probability that the event { } holds, α0、β0Are confidence levels; p is a radical ofjIs the active power on branch j; p is a radical ofjmaxThe power limit allowed for branch j; u is a node voltage vector; u. ofmaxIs the upper limit of the node voltage; u. ofminIs the node voltage lower limit.
Preferably, the energy storage system configuration principle of the distributed energy source comprehensive optimization configuration model in step 3 includes:
a. performing energy storage configuration on each distributed power supply access point in the system containing the distributed power supplies in a distributed configuration mode, namely arranging an energy storage system at each distributed power supply access point;
b. not configuring the load nodes of the access micro gas turbine in the system with the distributed power supply with energy storage;
c. and configuring energy storage devices with capacity for the load nodes of the fan and the photovoltaic in the system with the distributed power supply.
Preferably, the capacity of the energy storage device is determined according to the node load characteristic and the output characteristic of the DG, that is, the capacity C of the energy storage deviceESSComprises the following steps:
in the formula (4), taA start time for short term load prediction; t is tbPredicted termination time for short term load; n is the number of samples during the prediction; y isα/2Is the corresponding upper α quantile when the confidence interval is 1- α, sigma2 DGPredicting error distribution variance for DG output; sigma2 loadThe error distribution variance is predicted for the load.
Preferably, the step 4 comprises:
4-1, initializing the decomposed distributed energy comprehensive optimization configuration model and algorithm parameters;
4-2, initializing the position and the capacity of the distributed energy;
4-3, checking the opportunity constraint condition;
and 4, solving the initialized distributed energy comprehensive optimization configuration model by using an intelligent optimization algorithm.
Preferably, said 4-1 comprises:
d. according to the load historical data, determining the initial load value of each load node and the normal distribution obeyed by the planning horizontal year;
e. determining wind speed and illumination parameters according to the wind speed and illumination historical data;
f. setting a confidence level parameter α for opportunistic constraint planning0And β0(ii) a And determining parameter values in the distributed energy comprehensive optimization configuration model.
Preferably, the 4-2 comprises:
determining the type of distributed energy preferentially accessed by the load node to be accessed according to the wind resource, the light resource and the construction condition of the region; and for the uncharacteristic load nodes to be accessed, randomly initializing the access positions and the capacities of the distributed energy sources, and finishing the initialization of the positions and the capacities of the distributed energy sources.
Preferably, said 4-3 comprises:
checking opportunity constraint conditions of the access scheme by using a Monte Carlo simulation method;
if the opportunity constraint condition is established, entering 4-4;
otherwise, returning to the step 2.
Preferably, said 4-4 comprises:
and solving the initialized distributed energy comprehensive optimization configuration model by using a genetic algorithm and a particle swarm algorithm in an intelligent optimization algorithm until a convergence condition is met or the upper limit of the iteration times is reached, and outputting a solving result.
According to the technical scheme, the invention provides a distributed energy optimization configuration method based on opportunity constraint planning, and a distributed energy comprehensive optimization configuration model is established; determining opportunity constraint conditions of a distributed energy source comprehensive optimization configuration model; determining an energy storage system configuration principle of the distributed energy comprehensive optimization configuration model; and solving the distributed energy comprehensive optimization configuration model. The method provided by the invention effectively improves the accepting capacity of the power distribution network to the distributed energy; the energy efficiency target and the low-carbon target of a planning scheme can be met, the economic requirement can be met, the problems of site selection and volume fixing of distributed energy sources in areas with different resource levels and economic development degrees can be solved, the large-scale application of the distributed energy sources is ensured, and the environmental pollution and the energy crisis caused by fossil energy are effectively relieved.
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
1. in the technical scheme provided by the invention, a distributed energy comprehensive optimization configuration model is established; determining opportunity constraint conditions of a distributed energy source comprehensive optimization configuration model; determining an energy storage system configuration principle of the distributed energy comprehensive optimization configuration model; and solving the distributed energy comprehensive optimization configuration model. The receiving capacity of the distribution network to the distributed energy is effectively improved.
2. The technical scheme provided by the invention can provide a basis for planning the active power distribution network, can ensure the economical efficiency of the operation of the active power distribution system, improves the overall energy efficiency of the system, reduces the operation and maintenance cost, improves the return on investment, and lays a foundation for upgrading the traditional power distribution network to the modern power distribution network.
3. The technical scheme provided by the invention can meet the energy efficiency target and the low-carbon target of a planning scheme, also can meet the economic requirement, can solve the problems of site selection and volume determination of distributed energy sources in areas with different resource levels and economic development degrees, ensures large-scale application of the distributed energy sources, and effectively relieves the environmental pollution and energy crisis caused by fossil energy.
4. The technical scheme provided by the invention has wide application and obvious social benefit and economic benefit.
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FIG. 1 is a flow chart of a distributed energy optimization configuration method based on opportunity constrained planning according to the present invention;
FIG. 2 is a schematic flow chart of step 1 in the distributed energy resource optimal configuration method of the present invention;
FIG. 3 is a schematic flow chart of step 4 of the distributed energy resource optimal configuration method of the present invention;
fig. 4 is a schematic diagram of an energy storage distribution configuration in a power distribution network energy storage system including DG in an embodiment of the present invention;
fig. 5 is a schematic diagram of an energy storage concentration configuration in an energy storage system of a power distribution network including a DG in a specific application example of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a distributed energy optimization configuration method based on opportunity constraint planning, which includes the following steps:
step 1, establishing a distributed energy comprehensive optimization configuration model;
step 2, determining opportunity constraint conditions of the distributed energy comprehensive optimization configuration model;
step 3, determining an energy storage system configuration principle of the distributed energy comprehensive optimization configuration model;
and 4, solving the distributed energy comprehensive optimization configuration model.
As shown in fig. 2, step 1 includes:
1-1, establishing a distributed energy comprehensive optimization configuration model:
in the formula (1), ω is a weight coefficient vector and the weight coefficient vector ω is determined by an analytic hierarchy process or an analytic hierarchy process; c is a cost vector; cDERFor construction costs; coperFor operating costs; cmainFor maintenance costs; clossThe cost for the network loss; ccarbA penalty for carbon emissions; zcostComprehensively configuring values for distributed energy;
1-2, decomposing a distributed energy comprehensive optimization configuration model:
in the formula (2), nDThe number of nodes to be selected of the DER can be accessed to a system containing a distributed power supply; f. ofDiIs a flag bit, and fDiA value of 0 or 1, wherein fDiWhen the number is 1, a DER is accessed to a node i for accessing a fan or a photovoltaic; cDiThe construction cost per unit volume DER; pDiRepresents the installed capacity of DER at node i, and PDi>0；r0iAnd mDiRespectively representing discount rate and depreciation age limit; cOiThe operation cost of the DER unit capacity at the node i in unit time is calculated; t isDiIs the annual running time of DER at node i; s represents 4 quarters of a year 1, h represents a 24 period of a typical day for each quarter; cMiMaintenance cost per unit time for per unit capacity DER at node i; j is a branch; n isBThe number of system branches; celeThe price of the electric power is the price of the electric power; i isbshjThe current flowing through branch j for each time period on a typical day for each quarter; rbjIs the resistance of branch j; t isDmaxiThe maximum number of hours per year of DER at node i ξiThe discharge amount of the direct carbon is the discharge amount of the direct carbon; psi is carbon emission penalty coefficient, and the unit is yuan/kg, psi according to regional economic development degree and low carbon requirement, the value is different.
Wherein, step 2 includes:
determining opportunity constraint conditions of the distributed energy source comprehensive optimization configuration model, wherein the opportunity constraint conditions are the constraint conditions that the probability that the branch power is not out of limit is not less than α 0 and the probability that the node voltage is not out of limit is not less than β 0 under any working condition:
in the formula (3), Prob { } is the probability that the event { } holds, α0、β0Are confidence levels; p is a radical ofjIs the active power on branch j; p is a radical ofjmaxThe power limit allowed for branch j; u is a node voltage vector; u. ofmaxIs the upper limit of the node voltage; u. ofminIs the node voltage lower limit.
The energy storage system configuration principle of the distributed energy source comprehensive optimization configuration model in the step 3 comprises the following steps:
a. performing energy storage configuration on each distributed power supply access point in a system containing distributed power supplies in a distributed configuration mode, namely arranging an energy storage system at each distributed power supply access point;
b. the energy storage configuration is not carried out on the load node of the access micro gas turbine in the system containing the distributed power supply;
c. and energy storage devices with capacity are configured for load nodes connected with a fan and a photovoltaic in a system containing the distributed power supply.
Wherein the capacity of the energy storage device is determined according to the node load characteristic and the output characteristic of the DG, namely the capacity C of the energy storage deviceESSComprises the following steps:
in the formula (4), taA start time for short term load prediction; t is tbPredicted termination time for short term load; n is the number of samples during the prediction; y isα/2Is the corresponding upper α quantile when the confidence interval is 1- α, sigma2 DGPredicting error distribution variance for DG output; sigma2 loadThe error distribution variance is predicted for the load.
As shown in fig. 3, step 4 includes:
4-1, initializing a decomposed distributed energy comprehensive optimization configuration model and algorithm parameters;
4-2, initializing the position and the capacity of the distributed energy;
4-3, checking opportunity constraint conditions;
and 4, solving the initialized distributed energy comprehensive optimization configuration model by using an intelligent optimization algorithm.
Wherein, 4-1 comprises:
d. according to the load historical data, determining the initial load value of each load node and the normal distribution obeyed by the planning horizontal year;
e. determining wind speed and illumination parameters according to the wind speed and illumination historical data;
f. setting a confidence level parameter α for opportunistic constraint planning0And β0(ii) a And determining parameter values in the distributed energy comprehensive optimization configuration model.
Wherein, 4-2 comprises:
determining the type of distributed energy preferentially accessed by the load node to be accessed according to the wind resource, the light resource and the construction condition of the region; and for the uncharacteristic load nodes to be accessed, randomly initializing the access positions and the capacities of the distributed energy sources, and finishing the initialization of the positions and the capacities of the distributed energy sources.
Wherein, 4-3 comprises:
checking opportunity constraint conditions of the access scheme by using a Monte Carlo simulation method;
if the opportunity constraint condition is established, entering 4-4;
otherwise, returning to the step 2.
Wherein, 4-4 comprises:
and solving the initialized distributed energy comprehensive optimization configuration model by using a genetic algorithm and a particle swarm algorithm in an intelligent optimization algorithm until a convergence condition is met or the upper limit of the iteration times is reached, and outputting a solving result.
The invention provides a specific application example of a distributed energy optimization configuration method based on opportunity constraint planning, which comprises the following steps:
(1) establishing a distributed energy comprehensive optimization configuration model
Energy efficiency, from a physical point of view, is the ratio of the amount of energy that is actually consumed to the amount of energy that plays a role in energy utilization. According to the definition, comprehensive optimization configuration of the distributed energy is realized by planning optimal energy efficiency of the power grid and combining a low-carbon target and annual average construction cost, operation cost and maintenance cost of the distributed energy, and a mathematical model is as follows.
In the formula:
ω -weight coefficient vector;
c-cost vector. Including construction costs, operating costs, maintenance costs, network loss costs, and carbon emission penalties.
Equation (1) is a comprehensive optimization objective function, which represents the minimization of comprehensive cost in the planning period, and can be decomposed into:
in the formula:
nDthe system can access the number of nodes to be selected of the DER;
fDia flag bit, the value of which is 0 or 1, fDi1 denotes an access DER at node i;
CDi-construction costs per unit volume DER;
COi-operating costs per unit time per unit capacity DER at node i;
CMimaintenance cost per unit of time per unit of capacity DER at node i.
If a fan or photovoltaic is connected to the node i, considering the node COi＝0；PDiRepresents the installed capacity (P) of DER at node iDi>0)；r0iAnd mDiRespectively representing discount rate and depreciation age limit; t isDiIs the annual running time of DER at node i; clossRepresenting the annual loss cost of the system after DER access; s represents 4 quarters of a year 1, h represents a 24 period of a typical day for each quarter; celeIs the price of electric power, nBIs the number of system branches, IbshjRepresenting the current, R, flowing through branch j during each time period of a typical day for each quarterbjIs the resistance of branch j; ccarbRepresents DER annual carbon emission penalty, TDmaxiNumber of DER maximum hours output per year at node i, ξiThe method is used for measuring the electric carbon emission (direct carbon emission, and indirect carbon emission is not included), psi is a carbon emission penalty value coefficient, the unit is Yuan/kg, and values are different according to the regional economic development degree and the low-carbon requirement.
The weight coefficient vector ω can be determined by a decision method such as an analytic hierarchy process, or the like.
(2) Explicit constraints
The specific application example adopts the following opportunity constraint conditions:
in the formula:
prob { } — probability of event { } being true;
α0、β0-a confidence level;
pj-active power on line j;
pjmax-the allowed power limit of line j;
u-node voltage vector;
umax-upper node voltage limit;
umin-node voltage lower limit.
Equation (3) shows that the probability that the branch power is not out of limit is not less than α under any working condition0The probability of the node voltage not exceeding the limit is not less than β0。
(3) Method for determining configuration of energy storage system
The energy storage is used in a system containing a distributed power supply, and has irreplaceable supporting and optimizing effects in the aspects of stabilizing power fluctuation, improving power grid stability and improving power quality, but due to the fact that the investment cost is high, and particularly for battery energy storage, the defect of limited cycle life cycle exists, and therefore in the aspects of economy of the investment cost and effectiveness of function realization, reasonable capacity configuration is very necessary during energy storage application. At present, the configuration modes of energy storage in a power distribution system with distributed power sources are mainly divided into two types, i.e. a decentralized configuration and a centralized configuration, as shown in fig. 4 and 5, respectively, and the principle of energy storage configuration considered here mainly has the following points:
a. a distributed configuration mode is adopted, namely the energy storage system is arranged near each distributed power supply access point;
b. for a load node connected to the micro gas turbine, no energy storage device is configured;
c. for load nodes connected to a fan and a photovoltaic, energy storage devices with certain capacity are configured, the capacity of the energy storage devices is determined by the load characteristics of the nodes and the output characteristics of DGs, and the method comprises the following steps:
respectively setting short-term load prediction errors and DG output prediction errors of a certain load nodeConform to Δ Pload～N(μload,σ2 load)，ΔPDG～N(μDG,σ2 DG) The sum distribution W is delta P and is characterized by the column dimension-Lindberg center limit theorem and the normal distributionload+ΔPDGConforms to W to N (mu)load+μDG,σ2 load+σ2 DG) Then the configurable energy storage capacity of the node is:
in the formula:
ta-start time of short term load prediction;
tb-the end time of the short term load forecast;
n-number of samples during prediction;
Yα/2-upper α quantiles corresponding to confidence intervals of 1- α;
σ2 DG-DG contribution prediction error distribution variance;
σ2 load-load prediction error distribution variance.
Confidence intervals 1- α are typically 95% and corresponding quantiles Yα/2It was 1.96. In practical application, typical days of 4 seasons in 1 year can be selected for short-term load prediction, and the capacity of the energy storage device configured for each node is measured as an average value of calculation results.
(4) Model solution
a. Model and algorithm parameters are initialized.
● determining the initial load value of each load node and the normal distribution N- (. mu.) obeyed by planning horizontal year according to the historical load dataL,σL 2)。
● according to the wind speed and the illumination historical data, the wind speed and the illumination relevant parameters are determined, namely the k and c parameters of Weibull distribution (wind speed) and the α and β parameters of Beta distribution (illumination) are determined.
● setting confidence level parameters for opportunistic constraint planningNumber α0、β0(ii) a And giving values of main parameters in the model.
b. Distributed energy location and capacity initialization.
According to the actual conditions of the region, considering wind and light resources and construction conditions, determining the type of distributed energy preferentially accessed by the load node to be accessed; and for the uncharacterized load node to be accessed, randomly initializing the access position and capacity of the distributed energy.
c. Constraint condition checking
And (4) verifying the constraint conditions of the access scheme through Monte Carlo simulation, if the conditions are met, executing the step 4, otherwise, returning to the step 2 for re-execution.
d. And solving the model through an intelligent optimization algorithm.
Solving the model by means of an intelligent optimization algorithm such as a genetic algorithm, a particle swarm algorithm and the like until a convergence condition is met or the maximum iteration number is reached, and outputting a result.
According to the distributed energy optimization configuration method based on opportunity constraint planning, provided by the specific application example, according to the focus of investment decision makers, the model can meet the energy efficiency target and the low-carbon target of a planning scheme, can also meet the economic requirement, can better solve the problems of site selection and volume fixing of distributed energy in areas with different resource levels and economic development degrees, ensures large-scale application of the distributed energy, and effectively relieves the environmental pollution and energy crisis caused by fossil energy.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (9)
1. A distributed energy optimization configuration method based on opportunity constraint planning is characterized by comprising the following steps:
step 1, establishing a distributed energy comprehensive optimization configuration model;
step 2, determining opportunity constraint conditions of the distributed energy comprehensive optimization configuration model;
step 3, determining an energy storage system configuration principle of the distributed energy comprehensive optimization configuration model;
step 4, solving the distributed energy comprehensive optimization configuration model;
the step 1 comprises the following steps:
1-1, establishing a distributed energy comprehensive optimization configuration model:
in the formula (1), ω is a weight coefficient vector and the weight coefficient vector ω is determined by an analytic hierarchy process or an analytic hierarchy process; c is a cost vector; cDERFor construction costs; coperFor operating costs; cmainFor maintenance costs; clossThe cost for the network loss; ccarbA penalty for carbon emissions; zcostComprehensively configuring values for distributed energy;
1-2, decomposing the distributed energy comprehensive optimization configuration model:
in the formula (2), nDThe number of nodes to be selected of the DER can be accessed to a system containing distributed energy; f. ofDiIs a flag bit, and fDiA value of 0 or 1, wherein fDiWhen the number is 1, a DER is accessed to a node i for accessing a fan or a photovoltaic; cDiThe construction cost per unit volume DER; pDiRepresents the installed capacity of DER at node i, and PDi>0；r0iAnd mDiRespectively representing discount rate and depreciation age limit; cOiThe operation cost of the DER unit capacity at the node i in unit time is calculated; t isDiIs the annual running time of DER at node i; s represents 4 quarters of a year, h represents each quarter24 periods of a typical day;
maintenance cost per unit time for per unit capacity DER at node i; j is a branch; n isBThe number of system branches; celeThe price of the electric power is the price of the electric power; i isbshjThe current flowing through branch j for each time period on a typical day for each quarter; rbjIs the resistance of branch j; t isDmaxiIs the maximum annual output hours of DER at node i; zetaiThe discharge amount of the electric carbon is the discharge amount of direct carbon; psi is carbon emission penalty coefficient, and the unit is yuan/kg, psi according to regional economic development degree and low carbon requirement, the value is different.
2. The method of claim 1, wherein the step 2 comprises:
determining opportunity constraint conditions of the distributed energy source comprehensive optimization configuration model, wherein the opportunity constraint conditions are that under any working condition, the probability that the branch power is not out of limit is not less than α 0 and the probability that the node voltage is not out of limit is not less than β 0:
in the formula (3), Prob { } is the probability that the event { } holds, α0、β0Are confidence levels; p is a radical ofjIs the active power on branch j; p is a radical ofjmaxThe power limit allowed for branch j; u is a node voltage vector; u. ofmaxIs the upper limit of the node voltage; u. ofminIs the node voltage lower limit.
3. The method according to claim 1, wherein the energy storage system configuration rule of the distributed energy source integrated optimization configuration model in the step 3 comprises:
a. performing energy storage configuration on each distributed energy access point in a system containing distributed energy in a distributed configuration mode, namely arranging an energy storage system at each distributed energy access point;
b. not performing energy storage configuration on a load node connected to a micro gas turbine in the system containing the distributed energy;
c. and configuring energy storage devices with capacity for the load nodes of the wind turbine and the photovoltaic in the system containing the distributed energy.
4. The method of claim 3, wherein the capacity of the energy storage device is determined based on node load characteristics and the force out characteristics of DG, i.e., energy storage device capacity C of the energy storage deviceESSComprises the following steps:
in the formula (4), taA start time for short term load prediction; t is tbPredicted termination time for short term load; n is the number of samples during the prediction; y isα/2Is the corresponding upper α quantile when the confidence interval is 1- α, sigma2 DGPredicting error distribution variance for DG output; sigma2 loadThe error distribution variance is predicted for the load.
5. The method of claim 1, wherein step 4 comprises:
4-1, initializing the decomposed distributed energy comprehensive optimization configuration model and algorithm parameters;
4-2, initializing the position and the capacity of the distributed energy;
4-3, checking the opportunity constraint condition;
and 4, solving the initialized distributed energy comprehensive optimization configuration model by using an intelligent optimization algorithm.
6. The method of claim 5, wherein the 4-1 comprises:
d. according to the load historical data, determining the initial load value of each load node and the normal distribution obeyed by the planning horizontal year;
e. determining wind speed and illumination parameters according to the wind speed and illumination historical data;
f. setting a confidence level parameter α for opportunistic constraint planning0And β0(ii) a And determining parameter values in the distributed energy comprehensive optimization configuration model.
7. The method of claim 5, wherein the 4-2 comprises:
determining the type of distributed energy preferentially accessed by the load node to be accessed according to the wind resource, the light resource and the construction condition of the region; and for the uncharacteristic load nodes to be accessed, randomly initializing the access positions and the capacities of the distributed energy sources, and finishing the initialization of the positions and the capacities of the distributed energy sources.
8. The method of claim 5, wherein the 4-3 comprises:
checking opportunity constraint conditions of the access scheme by using a Monte Carlo simulation method;
if the opportunity constraint condition is established, entering 4-4;
otherwise, returning to the step 2.
9. The method of claim 5, wherein the 4-4 comprises:
and solving the initialized distributed energy comprehensive optimization configuration model by using a genetic algorithm and a particle swarm algorithm in an intelligent optimization algorithm until a convergence condition is met or the upper limit of the iteration times is reached, and outputting a solving result.
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