CN103268570B - A kind of rational dispatching by power grids Pre-Evaluation system and method - Google Patents

Abstract

The present invention is a kind of rational dispatching by power grids Pre-Evaluation system and method。The rational dispatching by power grids Pre-Evaluation system of the present invention includes basic data acquisition subsystem, standardization benchmark modeling subsystem, deviation ratio computing subsystem, wherein standardization benchmark modeling subsystem sets up assessment models according to the basic data of basic data acquisition subsystem collection, and the assessment models that deviation ratio computing subsystem is set up according to standardization benchmark modeling subsystem calculates Pre-Evaluation deviation ratio。The rational dispatching by power grids Pre-Evaluation method of the present invention, comprises the steps: the 1st step: basic data acquisition subsystem gathers basic data；2nd step: standardization benchmark modeling subsystem sets up the normalized model based on Pre-Evaluation benchmark；3rd step: deviation ratio computing subsystem calculates Pre-Evaluation deviation ratio。The present invention is a kind of rational dispatching by power grids Pre-Evaluation system and method that efficiency is high and assessment accuracy rate is high of assessing, and assessment models specification。

Description

A kind of rational dispatching by power grids Pre-Evaluation system and method

Technical field

The invention belongs to power system rational dispatching by power grids field, particularly to a kind of rational dispatching by power grids Pre-Evaluation system and method, belong to the innovative technology of rational dispatching by power grids Pre-Evaluation system and method。

Background technology

In traditional scheduler field, under the thinking of " power system security is overriding " and " power system accident is it is possible to balance out economic load dispatching achievement decades " guides, the research of rational dispatching by power grids is relatively fewer。The research work of rational dispatching by power grids is progressively being carried out along with the propelling of electricity market, industrial quarters and academia。

So-called economic load dispatching Pre-Evaluation, it is exactly the information such as prediction case according to certain scheduling model and following 24 hours short terms, generate the desirable power generation dispatching plan of following 24 hours, and the power generation dispatching plan in 24 hours futures formulated with mode specialty compares, calculate the deviation ratio of purchases strategies, network loss, coal consumption and blowdown simultaneously, with the power generation dispatching plan a few days ago that assessment mode specialty is formulated, thus providing rational instruction。

In current rational dispatching by power grids Pre-Evaluation field, face problems with and deficiency:

1, the Pre-Evaluation means of automatization are lacked

The data source that Pre-Evaluation relates to is more, and data are dispersed in different control centre or multiple operation systems of same control centre, data acquisition difficulty。Existing Pre-Evaluation depends on to be collected by hand one by one, inefficient, and is easier to make mistakes。

2, normalized Pre-Evaluation model is lacked

The model that Pre-Evaluation relates to specification not enough, various constraintss are not also carried out by normalized mathematical modeling form completely。

Summary of the invention

It is an object of the invention to consider that the problems referred to above provide a kind of and assess the rational dispatching by power grids Pre-Evaluation system and method that efficiency is high and assessment accuracy rate is high。The assessment models specification of the present invention。

The technical scheme is that the rational dispatching by power grids Pre-Evaluation system of the present invention, include basic data acquisition subsystem, standardization benchmark modeling subsystem, deviation ratio computing subsystem, wherein standardization benchmark modeling subsystem sets up assessment models according to the basic data of basic data acquisition subsystem collection, and the assessment models that deviation ratio computing subsystem is set up according to standardization benchmark modeling subsystem calculates Pre-Evaluation deviation ratio。

The rational dispatching by power grids Pre-Evaluation method of the present invention, comprises the steps:

1st step: basic data acquisition subsystem gathers basic data；

2nd step: standardization benchmark modeling subsystem sets up the normalized model based on Pre-Evaluation benchmark；

3rd step: deviation ratio computing subsystem calculates Pre-Evaluation deviation ratio。

The present invention, according to sending between load prediction a few days ago, repair schedule, net by information such as electricity plan, network topology and unit parameters, formulates power generation dispatching optimal planning a few days ago based on energy-saving power generation dispatching model, electricity progress scheduling model and purchases strategies scheduling model。The plan of power generation dispatching a few days ago obtained from DMIS system is compared analysis from the optimal planning of power generation dispatching a few days ago obtained based on three different scheduling models, calculates the deviation ratio of purchases strategies, network loss, standard coal consumption and four indexs of blowdown。Deviation ratio information according to each evaluation index, generates Pre-Evaluation log file, and sends to economic load dispatching analysis personnel。The present invention is a kind of convenient and practical rational dispatching by power grids Pre-Evaluation system and method。

Accompanying drawing explanation

Fig. 1 is the schematic diagram of rational dispatching by power grids Pre-Evaluation system of the present invention。

Detailed description of the invention

Embodiment:

The schematic diagram of rational dispatching by power grids Pre-Evaluation system of the present invention is as shown in Figure 1, include basic data acquisition subsystem, standardization benchmark modeling subsystem, deviation ratio computing subsystem, wherein standardization benchmark modeling subsystem sets up assessment models according to the basic data of basic data acquisition subsystem collection, and the assessment models that deviation ratio computing subsystem is set up according to standardization benchmark modeling subsystem calculates Pre-Evaluation deviation ratio。

The rational dispatching by power grids Pre-Evaluation method of the present invention, comprises the steps:

1st step: basic data acquisition subsystem gathers basic data；

2nd step: standardization benchmark modeling subsystem sets up the normalized model based on Pre-Evaluation benchmark；

3rd step: deviation ratio computing subsystem calculates Pre-Evaluation deviation ratio。

Above-mentioned 1st step, basic data acquisition subsystem utilizes OSB bus collection to obtain basic data, and its detailed process comprises the steps:

1.1st step, gathers load prediction data a few days ago from always adjusting EMS (being called for short EMS system)。By total prediction load according to selected with reference to day each bus load condition be allocated, in order to be optimized analysis。

1.2nd step, from always adjusting scheduling information management system (being called for short DMIS system) to gather repair schedule, draws the maintenance stoppage in transit situation of each unit and circuit, it is ensured that the feasibility of the optimal planning of power generation dispatching a few days ago formulated。

1.3rd step, send by electricity plan from total tune, to consider the constraint sent between net by electricity plan between DMIS system acquisition net。

1.4th step, adjusts EMS system acquisition unit parameter and network topology data from total。

1.5th step, deposits coal information, water-storage storage capacity information from always tune DMIS system acquisition thermal power plant

1.6th step, gathers each reservoir level information from waterpower scheduling automation。

Above-mentioned 2nd step standardization benchmark modeling subsystem is set up the normalized model based on Pre-Evaluation benchmark and is included following process:

2.1st step, sets up the assessment benchmark obtained based on energy-saving power generation dispatching model。Total standard coal consumption that the object function that this assessment benchmark considers is each power plant is minimum。

$\min F_2=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}[M_i\left(p_{i,t}\right)+N_i(x_{i,t},u_{i,t})]u_{i,t}---\left(1\right)$

In formula: M_i(p_i,t) for unit i in the coal consumption of period t；N_i(x_i,t,u_i,t) set state change time, the start and stop coal consumption from period t-1 to period t；X_i,tFor the unit i continuous available machine time at period t；

2.2nd step, sets up the assessment benchmark obtained based on electricity progress scheduling model: the object function that this assessment benchmark considers is minimum with ideal generating schedule variance。

minf=F(p(i,t),p₀(i, t)) t=1,2,3 ..., T(2)

In formula: T is the time hop count during system call；(i, t) for unit i exerting oneself at period t for p；P₀(i t) exerts oneself in the ideal of period t for unit i；F (p (i, t), p₀(i, t)) is unit output and the desirable bias target function exerted oneself, and concrete modeling format can be taken as the linear summation of variation, deviation penalty function is linearly sued for peace, minimax deviation and minimax deviation penalty function etc.。

Optimized model for ensureing native system has solution, shown in bias target function takes as:

$\min f=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(p(i,t)-p_0(i,t))}^2,t=\mathrm{1,2,3},...,T---\left(3\right)$

In formula: N is intrasystem unit quantity。

2.3rd step, sets up the assessment benchmark obtained based on purchases strategies scheduling model: the object function that this assessment benchmark considers is that total purchases strategies is minimum。

$\min F=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}[C_i\·p_i\left(t\right)\·u_i\left(t\right)\·\mathrm{DT}+S_i\·(1-u_i(t-1))\·u_i\left(t\right)]---\left(4\right)$

In formula: T is the time hop count during system call；△ T is the hourage representated by the period；P_iT () is unit i exerting oneself in the t period；C_iRate for incorporation into the power network for unit i；U_iT () is the unit i state in the t period；U_iT ()=1 represents start；U_iT ()=0 represents shuts down；S_iCost of compensation is started for unit i。

2.4th step, sets up power-balance constraint；

$\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{p}_i\left(t\right)=p_d\left(t\right)+p_l\left(t\right)t=\mathrm{1,2},K,T---\left(5\right)$

In formula: p_dT () is the system loading of t period；P_lT () is the network loss of t period。

2.5th step, sets up Reserve Constraint；

$\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{r}_i\left(t\right)\≥p_r\left(t\right),t=\mathrm{1,2},K,T---\left(6\right)$

In formula: r_iT () is the unit i spinning reserve provided in the t period；P_rT spinning reserve demand that () is the system t period。

2.6th step, sets up line transmission power constraint；

The set (7) of i, j ∈ circuit two end node

In formula: p_ijActive power for circuit ij conveying；p _ij The lower limit of active power is carried for circuit ij；Circuit ij carries the upper limit of active power。

2.7th step, sets up the constraint of section transmission capacity；

G_jneg≤G_j≤G_jpos, j=1,2 ..., M(8)

In formula: G_jEffective power flow for section j；G_jneg, G_jposRepresent the forward of system section j and reverse transient stability limit respectively, obtain typically via calculated off line；M is system section sum。

A) unit output bound constraint；

p_min,t≤p_i,t≤p_max,tT=1,2 ..., T(9)

B) the minimum operation of unit and idle time constraint；

u_iIf (t)=1 $1\&le;x_i\left(t\right)<\stackrel{\&OverBar;}{t_i}---\left(10\right)$

u_iIf (t)=-1-t _i <x_i(t)≤-1(11)

In formula:For the unit i minimum operation time；t _i For unit i minimum idle time；Xi (t) is the unit i continuous startup-shutdown time when t；Xi (t) > 0 represents the continuous available machine time；Xi (t) < 0 represents continuous downtime。

2.8th step, sets up unit ramping rate constraints。

-Δ_i≤p(i,t)-p(i,t-1)≤Δ_i(12)

In formula: (i, t) for unit i exerting oneself at period t for p；Δ_iMaximum for the per period creep speed of unit i。

Above-mentioned 3rd step, deviation ratio computing subsystem calculates Pre-Evaluation deviation ratio, including the deviation ratio calculating purchases strategies, network loss, standard coal consumption and four indexs of blowdown, and generates Pre-Evaluation log file。

This technology support system, according to sending between load prediction a few days ago, repair schedule, net by information such as electricity plan, network topology and unit parameters, formulates power generation dispatching optimal planning a few days ago based on energy-saving power generation dispatching model, electricity progress scheduling model and purchases strategies scheduling model。The plan of power generation dispatching a few days ago obtained from DMIS system is compared analysis from the optimal planning of power generation dispatching a few days ago obtained based on three different scheduling models, calculates the deviation ratio of purchases strategies, network loss, standard coal consumption and four indexs of blowdown。Deviation ratio information according to each evaluation index, generates Pre-Evaluation log file, and sends to economic load dispatching analysis personnel。

Claims (5)

1. a rational dispatching by power grids Pre-Evaluation system, it is characterized in that including basic data acquisition subsystem, standardization benchmark modeling subsystem, deviation ratio computing subsystem, wherein basic data acquisition subsystem utilizes OSB bus, automatically gather send between load prediction data a few days ago, repair schedule data, net by electricity planning data, thermal power plant deposit coal information, water-storage storage capacity information, each reservoir level information, based on the electrical network primary equipment parameter of IEC61970 standard and network topology data；Standardization benchmark modeling subsystem sets up assessment models according to the basic data of basic data acquisition subsystem collection, and the assessment models that deviation ratio computing subsystem is set up according to standardization benchmark modeling subsystem calculates Pre-Evaluation deviation ratio；Standardization benchmark modeling subsystem is set up as follows based on the detailed process of the normalized model of Pre-Evaluation benchmark:

2.1st step, sets up the assessment benchmark obtained based on energy-saving power generation dispatching model；Total standard coal consumption that the object function that this assessment benchmark considers is each power plant is minimum；

In formula: M_i(p_i,t) for unit i in the coal consumption of period t；N_i(x_i,t,u_i,t) set state change time, the start and stop coal consumption from period t-1 to period t；X_i,tFor the unit i continuous available machine time at period t；

2.2nd step, sets up the assessment benchmark obtained based on electricity progress scheduling model: the object function that this assessment benchmark considers is minimum with ideal generating schedule variance；

Minf=F (p (i, t), p₀(i, t)) t=1,2,3 ..., T (2)

In formula: T is the time hop count during system call；(i, t) for unit i exerting oneself at period t for p；P₀(i t) exerts oneself in the ideal of period t for unit i；F (p (i, t), p₀(i, t)) is unit output and the desirable bias target function exerted oneself, and concrete modeling format is taken as the linear summation of variation, deviation penalty function is linearly sued for peace, minimax deviation and minimax deviation penalty function；

Optimized model for ensureing native system has solution, shown in bias target function takes as:

In formula: N is intrasystem unit quantity；

2.3rd step, sets up the assessment benchmark obtained based on purchases strategies scheduling model: the object function that this assessment benchmark considers is that total purchases strategies is minimum；

In formula: T is the time hop count during system call；△ T is the hourage representated by the period；P_iT () is unit i exerting oneself in the t period；C_iRate for incorporation into the power network for unit i；U_iT () is the unit i state in the t period；U_iT ()=1 represents start；U_iT ()=0 represents shuts down；S_iCost of compensation is started for unit i；

2.4th step, sets up power-balance constraint；

In formula: p_dT () is the system loading of t period；P_lT () is the network loss of t period；

2.5th step, sets up Reserve Constraint；

In formula: r_iT () is the unit i spinning reserve provided in the t period；P_rT spinning reserve demand that () is the system t period；

2.6th step, sets up line transmission power constraint；

The set (7) of i, j ∈ circuit two end node

In formula: p_ijActive power for circuit ij conveying；p _ij The lower limit of active power is carried for circuit ij；Circuit ij carries the upper limit of active power；

2.7th step, sets up the constraint of section transmission capacity；

G_jneg≤G_j≤G_jpos, j=1,2 ..., M (8)

In formula: G_jEffective power flow for section j；G_jneg, G_jposRepresent the forward of system section j and reverse transient stability limit respectively, obtain typically via calculated off line；M is system section sum；

A) unit output bound constraint；

p_min,t≤p_i,t≤p_max,tT=1,2 ..., T (9)

B) the minimum operation of unit and idle time constraint；

u_iIf (t)=1

u_iIf (t)=-1-t _i < x_i(t)≤-1(11)

In formula:For the unit i minimum operation time；t _i For unit i minimum idle time；Xi (t) is the unit i continuous startup-shutdown time when t；Xi (t) > 0 represents the continuous available machine time；Xi (t) < 0 represents continuous downtime；

2.8th step, sets up unit ramping rate constraints,

-Δ_i≤p(i,t)-p(i,t-1)≤Δ_i(12)

In formula: (i, t) for unit i exerting oneself at period t for p；Δ_iMaximum for the per period creep speed of unit i。

2. a rational dispatching by power grids Pre-Evaluation method, it is characterised in that comprise the steps:

1st step: basic data acquisition subsystem gathers basic data；

2nd step: standardization benchmark modeling subsystem sets up the normalized model based on Pre-Evaluation benchmark；

3rd step: deviation ratio computing subsystem calculates Pre-Evaluation deviation ratio；

Above-mentioned 1st step, basic data acquisition subsystem utilizes OSB bus, automatically gather send between load prediction data a few days ago, repair schedule data, net by electricity planning data, thermal power plant deposit coal information, water-storage storage capacity information, each reservoir level information, based on the electrical network primary equipment parameter of IEC61970 standard and network topology data；

The detailed process of above-mentioned 2nd step is as follows:

2.1st step, sets up the assessment benchmark obtained based on energy-saving power generation dispatching model；Total standard coal consumption that the object function that this assessment benchmark considers is each power plant is minimum；

In formula: M_i(p_i,t) for unit i in the coal consumption of period t；N_i(x_i,t,u_i,t) set state change time, the start and stop coal consumption from period t-1 to period t；X_i,tFor the unit i continuous available machine time at period t；

2.2nd step, sets up the assessment benchmark obtained based on electricity progress scheduling model: the object function that this assessment benchmark considers is minimum with ideal generating schedule variance；

Minf=F (p (i, t), p₀(i, t)) t=1,2,3 ..., T (2)

In formula: T is the time hop count during system call；(i, t) for unit i exerting oneself at period t for p；P₀(i t) exerts oneself in the ideal of period t for unit i；F (p (i, t), p₀(i, t)) is unit output and the desirable bias target function exerted oneself, and concrete modeling format is taken as the linear summation of variation, deviation penalty function is linearly sued for peace, minimax deviation and minimax deviation penalty function；

Optimized model for ensureing native system has solution, shown in bias target function takes as:

In formula: N is intrasystem unit quantity；

2.3rd step, sets up the assessment benchmark obtained based on purchases strategies scheduling model: the object function that this assessment benchmark considers is that total purchases strategies is minimum；

In formula: T is the time hop count during system call；△ T is the hourage representated by the period；P_iT () is unit i exerting oneself in the t period；C_iRate for incorporation into the power network for unit i；U_iT () is the unit i state in the t period；U_iT ()=1 represents start；U_iT ()=0 represents shuts down；S_iCost of compensation is started for unit i；

2.4th step, sets up power-balance constraint；

In formula: p_dT () is the system loading of t period；P_lT () is the network loss of t period；

2.5th step, sets up Reserve Constraint；

In formula: r_iT () is the unit i spinning reserve provided in the t period；P_rT spinning reserve demand that () is the system t period；

2.6th step, sets up line transmission power constraint；

The set (7) of i, j ∈ circuit two end node

In formula: p_ijActive power for circuit ij conveying；p _ij The lower limit of active power is carried for circuit ij；Circuit ij carries the upper limit of active power；

2.7th step, sets up the constraint of section transmission capacity；

G_jneg≤G_j≤G_jpos, j=1,2 ..., M (8)

In formula: G_jEffective power flow for section j；G_jneg, G_jposRepresent the forward of system section j and reverse transient stability limit respectively, obtain typically via calculated off line；M is system section sum；

A) unit output bound constraint；

p_min,t≤p_i,t≤p_max,tT=1,2 ..., T (9)

B) the minimum operation of unit and idle time constraint；

u_iIf (t)=1

u_iIf (t)=-1-t _i < x_i(t)≤-1(11)

In formula:For the unit i minimum operation time；t _i For unit i minimum idle time；Xi (t) is the unit i continuous startup-shutdown time when t；Xi (t) > 0 represents the continuous available machine time；Xi (t) < 0 represents continuous downtime；

2.8th step, sets up unit ramping rate constraints,

-Δ_i≤p(i,t)-p(i,t-1)≤Δ_i(12)

In formula: (i, t) for unit i exerting oneself at period t for p；Δ_iMaximum for the per period creep speed of unit i。

3. rational dispatching by power grids Pre-Evaluation method according to claim 2, it is characterised in that above-mentioned 2nd step standardization benchmark models subsystem and sets up the normalized model of the assessment benchmark assessed benchmark, obtain based on purchases strategies scheduling model assessed benchmark, obtain based on electricity progress scheduling model obtained based on energy-saving power generation dispatching model。

4. rational dispatching by power grids Pre-Evaluation method according to claim 2, it is characterized in that above-mentioned 3rd step deviation ratio computing subsystem calculates four kinds of Pre-Evaluation deviation ratios, and generating Pre-Evaluation log file, four kinds of Pre-Evaluation deviation ratios include the deviation ratio calculating purchases strategies, network loss, standard coal consumption and four indexs of blowdown。

5. rational dispatching by power grids Pre-Evaluation method according to claim 2, it is characterised in that the detailed process of above-mentioned 1st step is as follows:

1.1st step, adjusts EMS to gather load prediction data a few days ago from total, by total prediction load according to selected with reference to day each bus load condition be allocated, in order to be optimized analysis；

1.2nd step, adjusts scheduling information management system to gather repair schedule from total, draws the maintenance stoppage in transit situation of each unit and circuit, it is ensured that the feasibility of the optimal planning of power generation dispatching a few days ago formulated；

1.3rd step, send by electricity plan from total tune, to consider the constraint sent between net by electricity plan between scheduling information management system collection net；

1.4th step, adjusts EMS to gather unit parameter and network topology data from total；

1.5th step, gathers thermal power plant from total tune scheduling information management system and deposits coal information, water-storage storage capacity information；

1.6th step, gathers each reservoir level information from waterpower scheduling automation。