CN103023066B - Optimal configuration method suitable for energy storage power of electrical power system with wind electricity - Google Patents

Abstract

The invention discloses an optimal configuration method suitable for the energy storage power of an electrical power system with wind electricity. The method comprises the following steps of: S1, obtaining the sample data of the wind power and the load of the electrical power system with wind electricity; S2, obtaining a positive rotation spare capacity and a negative rotation spare capacity according to the sample data and an energy storage power configuration model, wherein the energy storage power configuration model takes that the energy storage power used by the electrical power system in a dispatching cycle is the minimum as an object function, takes that the sum of the rated total force output upper limit of thermal power generating units in the electrical power system and the energy storage power upper limit is greater than the actually generated net load value as a positive rotation spare chance constraint, and takes that the sum of the rated total force output lower limit of the thermal power generating units in the electrical power system and the energy storage power lower limit is less than the actually generated net load value as a negative rotation spare chance constraint; and S3, obtaining the optimal configuration for the energy storage power needed by the electrical power system with wind electricity for coping a net load prediction error according to the positive rotation spare capacity and the negative rotation spare capacity. Via the method disclosed by the invention, the minimum configuration for the energy storage power can be obtained, safe operation can be ensured, and cost can be saved.

Description

A kind of Optimal Configuration Method being suitable for containing wind-powered electricity generation electric power system energy storage power

Technical field

The invention belongs to technical field of wind power generation, more specifically, relate to a kind of Optimal Configuration Method being suitable for containing wind-powered electricity generation electric power system energy storage power.

Background technology

The anti-peak regulation characteristic of wind-powered electricity generation, difficult predictability and strong stochastic volatility bring very big puzzlement to the power-balance of electric power system, only depend on conventional unit to be difficult to meet the demands, the day formulation of scheduling mode is also very difficult, and system need to possess stronger positive/negative power quick adjustment ability.Energy storage device is one of effective way solving the Power Systems balance that contains wind-powered electricity generation.A kind of energy storage system capacity configuration optimizing method that wind-powered electricity generation is received ability that improves is disclosed in Chinese invention patent specification CN102593853A, the method is the impact on energy-storage system configuration capacity by the income coordinating energy storage cost and receive wind-powered electricity generation more, reasonably optimizing energy storage system capacity, realizes total revenue and maximizes.A kind of stored energy capacitance choosing method for wind, light, storage micro-grid system is disclosed in Chinese invention patent specification CN102005771A, the method is poor by the electric weight of the generated output under the grid-connected pattern of simulation and prediction and island mode and forecast demand, calculate energy storage device capacity requirement under two kinds of patterns, and then calculate the stored energy capacitance of micro-grid system.A kind of method of determining wind energy turbine set access capacity of energy storing device based on spectrum analysis is disclosed in Chinese invention patent specification CN102255328A, the average wind electrical power to selected wind energy turbine set 1 year or the wind power sampled data sent for many years carry out spectrum analysis, will be after the wind power normalized of different cycles in wind-powered electricity generation frequency spectrum and successively cumulative, obtain the wind-powered electricity generation ability sum E of the different frequency range that sends in wind-powered electricity generation place _i(0 < E _i< 1) with the corresponding relation curve of t cycle time, on this curve, set the threshold values a (0 < a < 1) of wind-powered electricity generation energy, by t corresponding to the threshold values setting _adetermining corresponding capacity of energy storing device is

In three kinds of above-mentioned methods, capacity of energy storing device choose be according to historical wind power definite fluctuation characteristic calculate, fail to consider the stable statistical distribution characteristic of wind power and load prediction error, the stored energy capacitance configuring is difficult to the sight beyond reflecting history data.And it is based on solving the allocation problem of the power/capacity of single energy storage device.

Summary of the invention

Defect for prior art, the object of the present invention is to provide a kind of Optimal Configuration Method being suitable for containing wind-powered electricity generation electric power system reply wind power and the required energy storage power of load prediction deviation (being called net load prediction deviation), be intended to solve containing large-scale wind power system reply wind power and the required minimum energy storage power having of load prediction error and this problem of spinning reserve, being intended to solve choosing of capacity of energy storing device of the prior art is according to historical wind power data, fail to consider the stable statistical distribution characteristic of wind power and load prediction error, the stored energy capacitance configuring is difficult to the problem of the sight beyond reflecting history data.

The invention provides a kind of Optimal Configuration Method being suitable for containing wind-powered electricity generation electric power system energy storage power, comprise the steps:

S1: obtain containing the wind power of wind-powered electricity generation electric power system and the sample data of load;

S2: obtain positive and negative spinning reserve capacity according to described sample data and energy storage power configuration model; It is target function that described energy storage power configuration model be take the energy storage power minimum that electric power system is used in dispatching cycle, take the specified gross capability upper limit of fired power generating unit and the net load value that energy storage power upper limit sum is greater than actual generation in electric power system is the standby chance constraint of positive rotation, take the total lower limit of rated output of fired power generating unit in electric power system and net load value that energy storage power lower limit sum is less than actual generation to turn standby chance constraint as negative rotation;

S3: obtain containing the required optimum energy storage power configuration of wind-powered electricity generation electric power system reply net load predicated error according to positive and negative spinning reserve capacity.

Further, described target function is t=1 ..., T; represent that the t period is for guaranteeing the energy storage power of the required configuration of Power Systems balance, t represents optimization cycle.

Further, described positive rotation guest machine can be constrained to $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\}\&GreaterEqual;a^{\mathrm{rs}},$ Described negative rotation turns guest machine and can be constrained to $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&GreaterEqual;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\}\&GreaterEqual;a^{\mathrm{rs}};$ N _gthe number of units that represents fired power generating unit, represent t period power system load actual value, represent t period electric power system wind power actual value, the rated output upper limit that represents i platform fired power generating unit, the rated output lower limit that represents i platform fired power generating unit, a ^rsrepresent level of confidence, Pr represents probability.

Further, described step S2 is specially:

S21: according to the wind power data of historical load data, history and the net load prediction deviation of corresponding period and load prediction deviation acquisition net load predicated error;

S22: cumulative distribution function and its inverse function of adopting the method acquisition net load predicated error of non-parametric estmation;

S23: convert positive and negative spinning reserve chance constraint to positive and negative spinning reserve certainty constraint according to the inverse function of cumulative distribution function;

S24: obtain positive and negative spinning reserve capacity according to positive and negative spinning reserve certainty constraint.

Further, described net load predicated error is for the wind power prediction deviation of t period, for the load prediction deviation of t period, be the predicted value of the wind power of t period, it is the load prediction value of t period.

Further, described positive and negative spinning reserve certainty constraint is respectively $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\&GreaterEqual;(P_t^{L,F}-P_t^{W,F})(1+F_t^{-1}\left(a^{\mathrm{rs}}\right))$ With $\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\&le;(P_t^{L,F}-P_t^{W,F})(1-F_t^{-1}\left(a^{\mathrm{rs}}\right)),$ it is the inverse function of t period net load predicated error cumulative distribution function.

Further, described positive and negative spinning reserve capacity is respectively with $(P_t^{L,F}-P_t^{W,F})(1-F_t^{-1}\left(a^{\mathrm{rs}}\right)).$

Further, described step S3 specifically comprises:

S31: obtain the required positive energy storage power of electric power system according to the maximum output sum of all fired power generating unit in electric power system and positive rotation reserve capacity;

S32: obtain the required negative energy storage power of electric power system according to the minimum load sum of all fired power generating unit in electric power system and negative spinning reserve capacity;

S33: using the absolute value maximum of described positive energy storage power and described negative energy storage power as tackling the required optimum energy storage power configuration of net load predicated error containing wind-powered electricity generation electric power system.

The present invention is on the basis of historical wind-powered electricity generation and load data, excavate the random statistical rule of wind-powered electricity generation and load, obtain wind-powered electricity generation and the stable Statistical Distribution of load prediction error, set up based on this stored energy capacitance allocation models and propose method for solving, make the operating mode that determined stored energy capacitance can reflecting history, can adapt to the operating mode that may occur future again, thereby solve better the power-balance problem of wind-electricity integration electric power system.

Accompanying drawing explanation

Fig. 1 (a) is that the embodiment of the present invention being suitable for of providing is containing the realization flow figure of the Optimal Configuration Method of wind-powered electricity generation electric power system energy storage power;

Fig. 1 (b) is the sub-process figure of step S2;

Fig. 1 (c) is the sub-process figure of step S3;

Fig. 2 is that the embodiment of the present invention provides the electric power system wind power prediction curve on January 1st, 2012, load prediction curve, net load prediction curve, fired power generating unit maximum output curve and minimum load curve synoptic diagram;

Fig. 3 is net load predicated error cumulative distribution function, the empirical distribution function schematic diagram of electric power system period of providing of the embodiment of the present invention;

Fig. 4 is the electric power system day actual net load curve that provides of the embodiment of the present invention, prediction net load curve, set two kinds of sight schematic diagrames of exerting oneself;

Fig. 5 is the positive energy storage power that configures of electric power system day that the embodiment of the present invention provides, negative energy storage power, corresponding to the energy storage of scene 1 is actual, exerts oneself and corresponding to the actual schematic diagram of exerting oneself of energy storage of scene 2.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

The present invention is based on solving containing large-scale wind power system reply wind power and the required minimum energy storage power having of load prediction error and this problem of spinning reserve, overcome in existing stored energy capacitance configuring technical, only depend on historical wind power definite fluctuation characteristic and fail to consider and utilize wind power and load prediction error stable random statistical rule, be difficult to meet the deficiencies such as energy storage demand sight beyond historical data, a kind of energy storage power optimization collocation method that can simultaneously consider the statistical nature of wind power and load prediction deviation is provided; The present invention is on the basis of historical wind-powered electricity generation and load data, excavate the random statistical rule of wind-powered electricity generation and load prediction error, set up based on this energy storage power configuration model and propose method for solving, make the operating mode that determined stored energy capacitance can reflecting history, can adapt to the operating mode that may occur future again, thereby solve better the power-balance problem of wind-electricity integration electric power system.

In the present invention, as shown in Fig. 1 (a), Fig. 1 (b) and Fig. 1 (c), a kind of minimum energy storage power distribution method of wind-powered electricity generation electric power system that is suitable for specifically comprises:

(1) take system in whole dispatching cycle and use as few as possible energy storage power as target, set up minimum energy storage power optimization target function, method is as follows: in formula, be to be the energy storage device power of the required configuration of assurance system power balance the t period, f and unit be MW, T is optimization cycle;

(2) set up the positive and negative spinning reserve chance constraint of system, method is as follows:

Positive rotation Reserve Constraint: $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\}\&GreaterEqual;a^{\mathrm{rs}}......\left(2\right)$

Negative rotation turns Reserve Constraint: $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&GreaterEqual;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\}\&GreaterEqual;a^{\mathrm{rs}}......\left(3\right)$ In formula, N _gfor the number of units of fired power generating unit, be t period system loading actual value, be t period system wind power actual value, be the rated output upper limit of i platform fired power generating unit, be the rated output lower limit of i platform fired power generating unit, a ^rsfor level of confidence, Pr represents probability; .The physical meaning of its Chinese style (2): in system, the specified gross capability upper limit of fired power generating unit and energy storage power upper limit sum should be greater than the net load value of actual generation under confidence degree level (being conventionally not less than 95%), and title formula (2) is positive rotation Reserve Constraint; The physical meaning of formula (3): in system, the total lower limit of the rated output of fired power generating unit and energy storage power lower limit sum should be less than the net load value of actual generation under confidence degree level (being conventionally not less than 95%), title formula (3) turns Reserve Constraint for negative rotation; And hypothesis be in the present invention, to consider that load value is greater than the system of the installed capacity of wind power.

(3) chance constraint formula (2) and (3) are converted into certainty constraint, method is as follows:

(3.1) actual value of wind power and load is represented by its predicted value and prediction deviation respectively, is designated as: $P_t^{L,A}=P_t^{L,F}+{\mathrm{\&Delta;P}}_t^L......\left(4\right),P_t^{W,A}=P_t^{W,F}+{\mathrm{\&Delta;P}}_t^W......\left(5\right),$ In formula, be t period system loading predicted value, for stochastic variable, represent t period system loading prediction deviation, be t period system wind power prediction value, for stochastic variable, represent t period system wind power prediction deviation.

(3.2), by formula (4) and formula (5) substitution formula (2) and (3), obtain formula (6) and formula (7): $\mathrm{Pr}\{\mathrm{\&Delta;}{P}_t^L-{\mathrm{\&Delta;P}}_t^W\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,.F}+P_t^{W,F}\}\&GreaterEqual;a^{\mathrm{rs}}......\left(6\right),$ $\mathrm{Pr}\{{\mathrm{\&Delta;P}}_t^L-{\mathrm{\&Delta;P}}_t^W\&GreaterEqual;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}-P_t^{L,F}+P_t^{W,F}\}\&GreaterEqual;a^{\mathrm{rs}}......\left(7\right).$

(3.3) make net load predicated error formula (6) containing two stochastic variables and formula (7) can be converted to formula (8) and the formula (9) containing single stochastic variable: $\mathrm{Pr}({\left({\mathrm{\&Delta;P}}_t^{L-W}\right)}^{*}\&le;(C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,F}+P_t^{W,F})/(P_t^{L,E}-P_t^{W,F}))\&GreaterEqual;a^{\mathrm{rs}}......\left(8\right),$ $\mathrm{Pr}({\left({\mathrm{\&Delta;P}}_t^{L-W}\right)}^{*}\&le;(C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G+P_t^{L,F}-P_t^{W,F})/(P_t^{L,E}-P_t^{W,F}))\&GreaterEqual;a^{\mathrm{rs}}......\left(9\right)$

(3.4), according to wind power in recent years and the historical data (actual value, predicted value) of load, calculate corresponding period t wind power prediction deviation with load prediction deviation thereby calculate net load predicated error

(3.5) according to the predicated error of net load in recent years calculating numerical value, the method for employing non-parametric estmation, estimates net load predicated error cumulative distribution function value F _t=Pr (X≤a ^rs), and calculate its inverse function value

(3.6) by cumulative distribution function, define F _t(x)=Pr (X≤x), can be converted to formula (8): $F_t((C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,F}+P_t^{W,F})/(P_t^{L,F}-P_t^{W,F}))\&GreaterEqual;a^{\mathrm{rs}},$ Thereby be equivalent to: $(C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,F}+P_t^{W,F})/(P_t^{L,F}-P_t^{W,F})\&GreaterEqual;F_t^{-1}\left(a^{\mathrm{rs}}\right)$

(3.7) by cumulative distribution function, define F _t(x)=Pr (X≤x), can be converted to formula (9): $F_t((C_t^{\mathrm{ES}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G+P_t^{L,F}-P_t^{W,F})/(P_t^{L,F}-P_t^{W,F}))\&GreaterEqual;a^{\mathrm{rs}},$ Thereby be equivalent to: $(C_t^{\mathrm{ES}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_{i,t}^t{+P}_t^{L,F}-P_t^{W,F})/(P_t^{L,F}-P_t^{W,F})\&GreaterEqual;F_t^{-1}\left(a^{\mathrm{rs}}\right)$

(3.7) according to the inverse function value calculating formula (8) and formula (9) can be transformed to formula (10) and formula (11): $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_{i,t}^G\&GreaterEqual;(P_t^{L,F}-P_t^{W,F})(1+F_t^{-1}\left(a^{\mathrm{rs}}\right))......\left(10\right)$

$\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_{i,t}^G-C_t^{\mathrm{ES}}\&le;(P_t^{L,F}-P_t^{W,F})(1-F_t^{-1}\left(a^{\mathrm{rs}}\right))......\left(11\right);$ Thereby realize, probability constraints is converted to certainty constraint.

(4) the positive/negative spinning reserve coefficient of computing system t required configuration of period and the required positive/negative spinning reserve capacity of system, computational methods are:

Positive rotation reserve factor:

Negative rotation turns reserve factor:

Positive rotation reserve capacity:

Negative spinning reserve capacity:

(5) calculate containing the required minimum energy storage power configuration of wind-powered electricity generation electric power system, computational methods are as follows: (5.1) obtain formula (12) by formula (10) and formula (11):

$\left\{\begin{array}{c}{C}_t^{\mathrm{ES}}\&GreaterEqual;(P_t^{L,F}-P_t^{W,F})(1+F^{-1}\left(a^{\mathrm{rs}}\right))-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\\ C_t^{\mathrm{ES}}\&GreaterEqual;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-(P_t^{L,F}-P_t^{W,F})(1-F^{-1}\left(a^{\mathrm{rs}}\right))\end{array}\right.......\left(12\right)$

(5.2), according to formula (12), by formula (13), calculate and can obtain the t period containing the required minimum energy storage power of wind-powered electricity generation electric power system:

$C_t^{\mathrm{ES}}=\max \{(P_t^{L,F}-P_t^{W,F})(1+F^{-1}\left(a^{\mathrm{rs}}\right))-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G,\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-(P_t^{L,F}-P_t^{W,F})(1-F^{-1}\left(a^{\mathrm{rs}}\right))...\left(13\right)$

The present invention is based on taking into account the statistical property of wind power and two kinds of stochastic variables of load prediction error, determine and meet the required spinning reserve of system under predetermined level of confidence, obtain a kind of required minimum energy storage power configuration of wind-powered electricity generation electric power system reply wind-powered electricity generation-load (net load) predicated error that is suitable for, this method result of calculation comes from historical data sample, be not limited to again historical data sample, can better reflect the sight that is different from historical data sample, for the required stored energy capacitance configuration of fluctuating of balance wind-electricity integration provides theoretical foundation and reference, guarantee the safe and stable operation of electric power system, saved the cost investment excessively producing because of stored energy capacitance configuration simultaneously.

For further illustrate that the embodiment of the present invention provides containing wind-powered electricity generation electric power system energy storage power optimization collocation method, it is existing that in conjunction with instantiation, details are as follows:

Adopt the contained wind energy turbine set of certain system and the load predicted value of 1 year and actual value as the sample data of statistics net load predicated error, data sample be take 15min as sampling time interval.Positive/negative spinning reserve power and energy storage power by computing system day required configuration describe this paper method.Load data, wind power data and the fired power generating unit of plan day go out force data as shown in Figure 2.

Implementation step 1: obtain the cumulative distribution function of net load predicated error, be illustrated in figure 3 the net load predicated error cumulative distribution function of the 1st period.Suppose a ^rs=0.95, can be calculated under the level that to think in confidence level be 95%, the interval of net load predicated error is ± 10.6%, and the point shown in filled box is

Implementation step 2: the positive and negative spinning reserve of determining each period of the required configuration of system is respectively with t=1,2 ..., T, as shown in Table 1:

Period	1	2	3	4	5	6	7	8	9	10	11	12
													Standby	326	323	319	320	314	317	317	315	309	311	309	308
Period	13	14	15	16	17	18	19	20	21	22	23	24
													Standby	305	306	308	311	306	309	309	310	307	312	312	316
Period	25	26	27	28	29	30	31	32	33	34	35	36
													Standby	316	324	334	338	338	338	329	330	331	339	342	346
Period	37	38	39	40	41	42	43	44	45	46	47	48
													Standby	347	350	351	357	354	359	355	355	355	355	345	342
Period	49	50	51	52	53	54	55	56	57	58	59	60
													Standby	339	339	338	337	340	338	341	339	339	341	341	342
Period	61	62	63	64	65	66	67	68	69	70	71	72
													Standby	346	350	356	362	364	374	383	375	380	385	377	379
Period	73	74	75	76	77	78	79	80	81	82	83	84
													Standby	371	369	370	365	369	363	364	362	352	354	349	346
Period	85	86	87	88	89	90	91	92	93	94	95	96
													Standby	342	371	374	375	368	370	361	356	343	340	333	328

Table one system spinning reserve (± MW)

Implementation step 3: calculate the energy storage power of each required configuration of period, as shown in table 2;

Period	1	2	3	4	5	6	7	8	9	10	11	12
													Energy storage	0	0	0	0	9	0	0	5	53	36	51	61
Period	13	14	15	16	17	18	19	20	21	22	23	24
													Energy storage	86	80	66	38	80	57	50	45	70	26	29	0
Period	25	26	27	28	29	30	31	32	33	34	35	36
													Energy storage	0	0	0	0	0	0	0	0	0	0	0	0
Period	37	38	39	40	41	42	43	44	45	46	47	48
													Energy storage	0	0	0	0	0	0	0	0	0	0	0	0
Period	49	50	51	52	53	54	55	56	57	58	59	60
													Energy storage	0	0	0	0	0	0	0	0	0	0	0	0
Period	61	62	63	64	65	66	67	68	69	70	71	72
													Energy storage	0	0	0	0	0	102	198	115	165	217	138	151
Period	73	74	75	76	77	78	79	80	81	82	83	84
													Energy storage	71	47	56	5	46	0	0	0	0	0	0	0
Period	85	86	87	88	89	90	91	92	93	94	95	96
													Energy storage	0	75	101	112	42	65	0	0	0	0	0	0

The energy storage power (+MW) that table 2 system need configure

Implementation step 4: system operation test

In order to verify the validity of the set energy storage power of the present invention, the contingent two kinds of extreme scenes (scene 1, scene 2) that arrange below and net load actual value (scene 3) totally 3 kinds of sights are calculated, and can inspection institute's configuration energy storage tackle the prediction deviation of net load.

Scene 1: suppose that load prediction is in overgauge maximum (103%), wind power prediction is in minus deviation maximum (80%);

Scene 2: suppose that load prediction is in minus deviation maximum (97%), wind power prediction is in minus deviation maximum (120%);

Scene 3: actual wind power and the load occurring.

Corresponding scene setting as shown in Figure 4; As can be seen from Figure 4: (topmost straight line represents the higher limit of generating set to only depend on conventional power generation usage unit, bottom straight line represents the lower limit of generating set) can not balance scene 1 and the power of scene 2 systems, the actual net load curve occurring also had over the upper limit of fired power generating unit or the period of lower limit, now needed energy storage to carry out balance sysmte power.

Calculate and meet the required energy storage of set scene power-balance and exert oneself, as shown in Figure 5.Can find out, in scene 1, system only needs positive rotation standby, approaches and be less than configured stored energy capacitance; In scene 2, system only needs negative spinning reserve, and absolute value approaches and be less than configured stored energy capacitance; The actual net load occurring operates in the scope that fired power generating unit can regulate substantially, and a small amount of energy storage of needs carry out balance when peak.Visible, the inventive method configures the demand that energy storage power meets running and default extreme scenes simultaneously.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (6)

1. be suitable for the Optimal Configuration Method containing wind-powered electricity generation electric power system energy storage power, it is characterized in that, comprise the steps:

S1: obtain containing the wind power of wind-powered electricity generation electric power system and the sample data of load;

S2: obtain positive and negative spinning reserve capacity according to described sample data and energy storage power configuration model; It is target function that described energy storage power configuration model be take the energy storage power minimum that electric power system is used in dispatching cycle, take the specified gross capability upper limit of fired power generating unit and the net load value that energy storage power upper limit sum is greater than actual generation in electric power system is the standby chance constraint of positive rotation, take the total lower limit of rated output of fired power generating unit in electric power system and net load value that energy storage power lower limit sum is less than actual generation to turn standby chance constraint as negative rotation;

S3: obtain containing the required optimum energy storage power configuration of wind-powered electricity generation electric power system reply net load predicated error according to positive and negative spinning reserve capacity;

Described target function is represent that the t period is for guaranteeing the energy storage power of the required configuration of Power Systems balance, t represents optimization cycle;

Described positive rotation guest machine can be constrained to $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\}\&GreaterEqual;a^{\mathrm{rs}},$ Described negative rotation turns guest machine and can be constrained to $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&GreaterEqual;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\}\&GreaterEqual;a^{\mathrm{rs}};$ N _gthe number of units that represents fired power generating unit, represent t period power system load actual value, represent t period electric power system wind power actual value, the rated output upper limit that represents i platform fired power generating unit, the rated output lower limit that represents i platform fired power generating unit, a ^rsrepresent level of confidence, Pr represents probability.

2. Optimal Configuration Method as claimed in claim 1, is characterized in that, described step S2 is specially:

S21: according to the wind power data of historical load data, history and the wind power prediction deviation of corresponding period and load prediction deviation acquisition net load predicated error;

S22: cumulative distribution function and its inverse function of adopting the method acquisition net load predicated error of non-parametric estmation;

S23: convert positive and negative spinning reserve chance constraint to positive and negative spinning reserve certainty constraint according to inverse function;

S24: obtain positive and negative spinning reserve capacity according to positive and negative spinning reserve certainty constraint.

3. Optimal Configuration Method as claimed in claim 2, is characterized in that, described net load predicated error is be the wind power prediction deviation of t period, be the load prediction deviation of t period, represent t period Load Prediction In Power Systems value, represent t period electric power system wind power prediction value.

4. Optimal Configuration Method as claimed in claim 2, is characterized in that, described positive and negative spinning reserve certainty constraint is respectively $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\&GreaterEqual;(P_t^{L,F}-P_t^{W,F})(1+F_t^{-1}\left(a^{\mathrm{rs}}\right))$ With $\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\&le;(P_t^{L,F}-P_t^{W,F})(1-F_t^{-1}\left(a^{\mathrm{rs}}\right));$ it is the inverse function of t period net load predicated error cumulative distribution function.

5. Optimal Configuration Method as claimed in claim 2, is characterized in that, described positive and negative spinning reserve capacity is respectively with

6. Optimal Configuration Method as claimed in claim 2, is characterized in that, described step S3 specifically comprises:

S31: obtain the required positive energy storage power of electric power system according to the maximum output sum of all fired power generating unit in electric power system and positive rotation reserve capacity;

S32: obtain the required negative energy storage power of electric power system according to the minimum load sum of all fired power generating unit in electric power system and negative spinning reserve capacity;

S33: using the absolute value maximum of described positive energy storage power and described negative energy storage power as tackling the required optimum energy storage power configuration of net load predicated error containing wind-powered electricity generation electric power system.