CN109245175B - Large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation - Google Patents
Large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation Download PDFInfo
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Abstract
The invention discloses a large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation, and relates to the field of wind power plant energy storage capacity optimization methods; it includes S1: after the alleviation degree of the auxiliary service cost of the system is quantized, the system is used as auxiliary service compensation, and the wind-storage combined operation income is calculated according to the economic benefits of the system and the wind farm configuration energy storage; s2: after the stored energy provides a standby for the uncertainty of the wind power, updating BESS constraint adaptive to a scheduling plan; s3: building a constraint condition set by combining BESS constraint with other constraints, obtaining an objective function through maximizing profit, and building a wind power plant energy storage capacity optimization model according to the objective function and the maximum profit; s4: inputting the collected parameters of the power system into a model to solve and obtain the optimal capacity; the method solves the problems of low wind power storage combined operation yield and poor positivity of wind power plant configuration energy storage caused by the fact that auxiliary service compensation is not considered in the existing wind power plant energy storage capacity optimization, and achieves the effects of effectively exciting the wind power plant configuration energy storage and realizing wind power friendly grid-connected consumption.
Description
Technical Field
The invention relates to the field of wind power plant energy storage capacity optimization methods, in particular to a large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation.
Background
The Energy storage system serving as a flexible and schedulable power supply provides a new idea for dealing with the wind power grid-connection problem, the wind power plant configuration Energy storage system becomes one of the future modes for large-scale wind power development, and compared with other Energy storage technologies, the Battery Energy storage (BESS) has the advantages of low requirement on geographic conditions, high Energy efficiency and the widest application prospect in the power system.
At present, the most direct benefit of configuring the BESS for the wind power plant is that additional wind power grid-connected quantity can be obtained, and the loss of abandoned wind is reduced; the configuration of the BESS of the wind power plant needs to comprehensively consider energy storage planning and operation, is limited by the investment cost of the BESS, and needs to mainly solve the balance problem between the energy storage configuration cost and the translation effect;
when the existing economy of wind power plant configuration energy storage is analyzed, the aim of minimum wind abandon or maximum profit of the wind power plant is usually taken, or the aim of minimum energy storage investment and operation cost is taken from the energy storage perspective; the operation economy of the wind storage combined operation is not analyzed from the angle of the wind storage combined operation, so that the value evaluation accuracy of the wind storage combined operation is low, and the corresponding economic benefit is difficult to obtain. Meanwhile, in the prior art, capacity optimization is performed by neglecting auxiliary service compensation, so that on one hand, the cost is high, the wind power storage combined operation income is low, the economic advantage in the popularization process is insufficient, and the positivity of the wind power plant for configuring energy storage is not high; on the other hand, the optimization capacity calculated without considering the auxiliary service compensation factor is not accurate, the application value is not clear, and the mechanism participating in the market is not perfect. Therefore, a large-scale wind power plant energy storage capacity optimization method is needed, which considers the auxiliary service cost to solve the problems, improves the wind power plant combined operation yield, and improves the positivity of wind power plant configuration energy storage.
Disclosure of Invention
The invention aims to: the invention provides a large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation, and solves the problems that wind power plant energy storage capacity optimization does not consider auxiliary service compensation, so that wind power plant combined operation income is low, and the positivity of wind power plant configuration energy storage is poor.
The technical scheme adopted by the invention is as follows:
a large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation comprises the following steps:
step 1: the alleviation degree of the system auxiliary service cost before and after the addition of the quantified BESS is used as auxiliary service compensation, and the wind power storage combined operation income is calculated according to the auxiliary service compensation and the obtained direct economic benefit of the wind power plant configuration energy storage;
step 2: after the stored energy provides a standby for the uncertainty of the wind power, updating BESS constraint adaptive to a scheduling plan;
and step 3: the updated BESS constraint is combined with other conventional constraints to form a constraint condition set, an objective function is obtained through maximizing wind power storage combined operation income, and a wind power plant energy storage capacity optimization model is constructed according to the constraint condition set and the objective function;
and 4, step 4: and inputting the collected power system parameters into a wind power plant capacity optimization model to solve to obtain the optimal capacity of the wind power plant configuration battery energy storage system.
Preferably, the step 1 comprises the steps of:
step 1.1: quantifying the remission degree of the BESS on the auxiliary service cost of the system before and after the addition;
step 1.2: the direct economic benefits of the energy storage configured in the wind power plant comprise BESS cost and wind abandoning income, the BESS cost is calculated, and the calculation is shown as formula 1:
Ccap＝αs·Pcap+βs·Scap
wherein, CcapRepresents the BESS cost; alpha is alphasRepresenting the unit price of the energy storage system reduced to annual capacity; beta is asRepresenting the unit price of power of the energy storage system reduced to each year; r represents the proportion of kW.h/kW cost; cERepresents the investment cost of an energy storage unit, $/kW.h; t islifeRepresents the equivalent operating life of the BESS; cOMRepresents the operating maintenance cost, $/year;
step 1.3: and (3) taking the relief degree of the system peak shaving and the rotary standby auxiliary service before and after the BESS is added as auxiliary service compensation, and calculating the wind curtailment income and the auxiliary service compensation, as shown in a formula 2:
wherein S issaveIndicating combined wind and storage operationCompared with the wind abandoning benefit brought by the independent wind power grid connection; c'serveThe method comprises the steps of representing the alleviation degree of the wind storage combined operation on the system auxiliary service after BESS is added, namely obtaining auxiliary service compensation; pWloss,tAnd P'Wloss,tRespectively representing the wind abandon conditions of the wind power plant at each moment when the wind power plant contains or does not contain an energy storage system; rhowindRepresenting the price of the wind power on-line electricity; l represents quarterly, blRepresents the number of days per quarter; cserveRepresenting the auxiliary service cost caused by independent wind power grid connection;
representing the auxiliary service cost caused by the combined operation of the wind storage and the wind storage;
step 1.4: and calculating the wind-storage combined operation income according to the BESS cost, the wind curtailment income and the auxiliary service compensation, as shown in formula 3:
f＝Ssave+C'serve-Ccap (3)
wherein f represents the wind-storage combined operation income; ssaveThe wind energy storage combined operation is compared with the wind energy single grid connection to bring the wind curtailment reduction yield; c'serveIndicating auxiliary service compensation; ccapRepresenting the BESS cost.
Preferably, said step 1.1 comprises the steps of:
step 1.1.1: the cost difference value of the auxiliary service provided by the system before and after the independent wind power grid connection quantifies the auxiliary service cost caused by the wind power, as shown in formula 4:
Cserve＝Cfixed+Cfluctuant
wherein, CserveRepresenting the auxiliary service costs incurred during the independent integration of wind power, CfixedRepresenting fixed auxiliary service costs when wind power is solely integrated, CfluctuantRepresenting the cost of the shift auxiliary service when the wind power is independently connected to the grid; cAIIndicating participation in auxiliary servicesThe unit investment cost of the regular unit is reduced to the unit investment cost of each day; M/MwindRespectively representing the number of the units participating in the auxiliary service before/after the independent wind power grid connection;
rated installed capacity for participating in the auxiliary service unit;
the unit coal consumption cost of the front/rear unit of the wind power independent grid connection is respectively; pGS,i(t) the output of the auxiliary service unit i at the moment t; t is 24 hours a day;
step 1.1.2: the auxiliary service cost caused by the wind storage combined operation is quantified through the cost difference value of the auxiliary service provided by the system before and after the wind storage combined operation, as shown in formula 5:
wherein the content of the first and second substances,
representing the total auxiliary service cost caused by the wind storage combined operation;
represents the fixed auxiliary service cost when the wind storage combined operation is carried out,
representing the cost of the variable auxiliary service during the combined operation of the wind storage;
the number of the units participating in the auxiliary service after the wind storage combined operation is represented;
representing the unit coal consumption cost of the unit after the wind storage combined operation;
step 1.1.3: the difference in cost of providing ancillary services through BESS joining pre-and post-systems
And CserveAnd (3) quantifying the degree of mitigation of the BESS to the system ancillary service cost.
Preferably, the step 2 comprises the steps of:
step 2.1: on the premise of allowing the wind curtailment, the charge-discharge power and the charge state of the BESS are defined as shown in formula 6:
wherein S istRepresents the charge and discharge power of the storage battery at the time t; pwind,tRepresenting the predicted value of wind power at the time t; pcombined,tRepresenting grid-connected power during wind storage combined operation at the moment t; pwloss,tRepresenting a wind curtailment value at the time t; ssoc,t-1Represents the charge capacity of BESS at time t; etasThe charge and discharge efficiency is shown; Δ t represents a scheduling time interval, 1 h;
step 2.2: the BESS constraint at time t is expressed as shown in equation 7, based on the BESS rated charge-discharge power and energy storage capacity limitations:
wherein, PcapRepresents the rated power of the energy storage system; scapRepresenting the capacity of the energy storage system;
step 2.3: after the BESS provides a rotation reserve for the wind power output prediction error, updating BESS constraint at the time t, as shown in a formula 8:
wherein epsilonsAnd representing BESS as the reserve degree of the wind power output uncertainty.
Preferably, the step 3 comprises the steps of:
step 3.1: the updated BESS constraint at time t in combination with other conventional constraints forms a constraint set, as shown in equation 9:
wherein, Pnet,tRepresents the net load at time t; pload,tRepresenting the system load demand at the moment t; pwind,tRepresenting the predicted value of wind power at the time t;
representing the maximum climbing speed upper limit/lower limit allowed by the online unit at the moment t;Ngrepresenting the number of conventional units;
representing the maximum/minimum output power allowed by the conventional unit i; u. ofi,tRepresenting the starting and stopping state of the unit i in a time period t, and a variable of 0-1;
representing an upper/lower deviation range of the wind power predicted value; rload,tRepresenting the standby demand of the load, the standard deviation of the prediction error of the daily load curve is generally proportional to the load size, R is takenload,t＝0.05Pload,t；
Representing the minimum startup/shutdown time of the conventional unit i;
representing the accumulated starting-up/shutdown time of the conventional unit i in the time period t;
step 3.2: maximizing the wind storage combined operation income as an objective function, as shown in equation 10:
maxf＝Ssave+C'serve-Ccap (10)
step 3.3: and finishing the construction of the wind power plant energy storage capacity optimization model according to the objective function and the constraint condition set.
Preferably, the solution in step 4 is implemented by constraint optimization problem solving software or a constraint optimization problem solving algorithm.
Preferably, the step 2 further comprises BESS equivalent life loss calculation for increasing the wind-storage combined operation income.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the method, the alleviation degree of BESS on auxiliary service cost caused by wind power integration is quantified, the auxiliary service cost (caused by wind power) of a system which is relieved after the wind power plant is configured with energy storage is used as compensation for an energy storage system, the maximum goal of wind-storage combined operation income is taken, the problems that the wind-storage combined operation income is low and the positivity of wind power plant configuration energy storage is poor due to the fact that the auxiliary service cost is not considered in the existing wind power plant energy storage capacity optimization are solved, the wind power plant configuration energy storage is effectively stimulated, and the wind power friendly grid-integration absorption effect is realized;
2. the BESS provides reserve for the uncertainty of wind power, and the BESS constraint applicable to the dispatching plan is obtained, so that the energy storage capacity planning and the system dispatching plan are combined, namely the energy storage capacity planning and the operation are combined, the energy storage configuration capacity is more reasonable, the actual dispatching operation is more consistent, and the pressure of a conventional unit for providing rotary reserve auxiliary service is relieved;
3. the BESS is used for wind power standby shallow charging and shallow discharging, the cycle life of the BESS which can be increased is obtained according to equivalent life loss calculation, and the increase of the cycle life of the BESS is beneficial to reducing the unit investment cost of energy storage, namely improving the yield of combined operation;
4. under the condition of considering a scheduling plan, an energy storage capacity optimization model is established based on cost benefit analysis, and energy storage configuration cost and translation effect are effectively balanced;
5. the invention takes the peak regulation and the rotary standby auxiliary service compensation into consideration, effectively excites the wind power configuration energy storage system, and realizes the win-win of the dispatching operation of the wind power plant and the system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a graph of typical daily load/wind power fluctuation for four seasons in an actual system of the present invention;
FIG. 3 is a schematic diagram of a typical winter solar wind power prediction interval according to the present invention;
FIG. 4 is a schematic diagram illustrating a charging/discharging process under different conditions according to the present invention;
FIG. 5 is a graph of BESS investment cost versus optimized capacity for the present invention;
FIG. 6 is a graph of BESS cycle life versus optimized capacity for the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The technical problem is as follows: the problems that wind power plant energy storage capacity optimization does not consider auxiliary service compensation, so that wind power plant combined operation yield is low and the positivity of wind power plant configuration energy storage is poor are solved;
the technical means is as follows: a large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation comprises the following steps:
step 1: the alleviation degree of the system auxiliary service cost before and after the addition of the quantified BESS is used as auxiliary service compensation, and the wind power storage combined operation income is calculated according to the auxiliary service compensation and the obtained direct economic benefit of the wind power plant configuration energy storage;
step 2: after the stored energy provides a standby for the uncertainty of the wind power, updating BESS constraint adaptive to a scheduling plan;
and step 3: the updated BESS constraint is combined with other conventional constraints to form a constraint condition set, an objective function is obtained through maximizing wind power storage combined operation income, and a wind power plant energy storage capacity optimization model is constructed according to the constraint condition set and the objective function;
and 4, step 4: and inputting the collected power system parameters into a wind power plant capacity optimization model to solve to obtain the optimal capacity of the wind power plant configuration battery energy storage system.
The step 1 comprises the following steps:
step 1.1: quantifying the remission degree of the BESS on the auxiliary service cost of the system before and after the addition;
step 1.2: the direct economic benefits of the energy storage configured in the wind power plant comprise BESS cost and wind abandoning income, the BESS cost is calculated, and the calculation is shown as formula 1:
Ccap＝αs·Pcap+βs·Scap
wherein, CcapRepresents the BESS cost; alpha is alphasRepresenting the unit price of the energy storage system reduced to annual capacity; beta is asRepresenting the unit price of power of the energy storage system reduced to each year; r represents the proportion of kW.h/kW cost; cERepresents the investment cost of an energy storage unit, $/kW.h; t islifeRepresents the equivalent operating life of the BESS; cOMRepresents the operating maintenance cost, $/year;
step 1.3: and (3) taking the relief degree of the system peak shaving and the rotary standby auxiliary service before and after the BESS is added as auxiliary service compensation, and calculating the wind curtailment income and the auxiliary service compensation, as shown in a formula 2:
wherein S issaveThe wind energy storage combined operation is compared with the wind energy abandon income brought by the wind power independent grid connection; c'serveThe method comprises the steps of representing the alleviation degree of the wind storage combined operation on the system auxiliary service after BESS is added, namely obtaining auxiliary service compensation; pWloss,tAnd P'Wloss,tRespectively representing the wind abandon conditions of the wind power plant at each moment when the wind power plant contains or does not contain an energy storage system; rhowindRepresenting the price of the wind power on-line electricity; l represents quarterly, blRepresents the number of days per quarter; cserveRepresenting the auxiliary service cost caused by independent wind power grid connection;
representing the auxiliary service cost caused by the combined operation of the wind storage and the wind storage;
step 1.4: and calculating the wind-storage combined operation income according to the BESS cost, the wind curtailment income and the auxiliary service compensation, as shown in formula 3:
f＝Ssave+C'serve-Ccap (3)
wherein f represents the wind-storage combined operation income; ssaveThe wind energy storage combined operation is compared with the wind energy single grid connection to bring the wind curtailment reduction yield; c'serveIndicating auxiliary service compensation; ccapRepresenting the BESS cost.
Step 1.1 comprises the following steps:
step 1.1.1: the cost difference value of the auxiliary service provided by the system before and after the independent wind power grid connection quantifies the auxiliary service cost caused by the wind power, as shown in formula 4:
Cserve＝Cfixed+Cfluctuant
wherein, CserveRepresenting the auxiliary service costs incurred during the independent integration of wind power, CfixedRepresenting fixed auxiliary service costs when wind power is solely integrated, CfluctuantRepresenting the cost of the shift auxiliary service when the wind power is independently connected to the grid; cAIThe unit investment cost of the conventional units participating in the auxiliary service is reduced to each day; M/MwindRespectively representing the number of the units participating in the auxiliary service before/after the independent wind power grid connection;
rated installed capacity for participating in the auxiliary service unit;
the unit coal consumption cost of the front/rear unit of the wind power independent grid connection is respectively; pGS,i(t) the output of the auxiliary service unit i at the moment t; t is 24 hours a day;
step 1.1.2: the auxiliary service cost caused by the wind storage combined operation is quantified through the cost difference value of the auxiliary service provided by the system before and after the wind storage combined operation, as shown in formula 5:
wherein the content of the first and second substances,
representing the total auxiliary service cost caused by the wind storage combined operation;
represents the fixed auxiliary service cost when the wind storage combined operation is carried out,
representing the cost of the variable auxiliary service during the combined operation of the wind storage;
the number of the units participating in the auxiliary service after the wind storage combined operation is represented;
representing the unit coal consumption cost of the unit after the wind storage combined operation;
step 1.1.3: the difference in cost of providing ancillary services through BESS joining pre-and post-systems
And CserveThe difference value of (a) to (b),and quantifying the degree of mitigation of the BESS to the system-assisted service cost.
The step 2 comprises the following steps:
step 2.1: on the premise of allowing the wind curtailment, the charge-discharge power and the charge state of the BESS are defined as shown in formula 6:
wherein S istRepresents the charge and discharge power of the storage battery at the time t; pwind,tRepresenting the predicted value of wind power at the time t; pcombined,tRepresenting grid-connected power during wind storage combined operation at the moment t; pwloss,tRepresenting a wind curtailment value at the time t; ssoc,t-1Represents the charge capacity of BESS at time t; etasThe charge and discharge efficiency is shown; Δ t represents a scheduling time interval, 1 h;
step 2.2: the BESS constraint at time t is expressed as shown in equation 7, based on the BESS rated charge-discharge power and energy storage capacity limitations:
wherein, PcapRepresents the rated power of the energy storage system; scapRepresenting the capacity of the energy storage system;
step 2.3: after the BESS provides a rotation reserve for the wind power output prediction error, updating BESS constraint at the time t, as shown in a formula 8:
wherein epsilonsAnd representing BESS as the reserve degree of the wind power output uncertainty.
The step 3 comprises the following steps:
step 3.1: the updated BESS constraint at time t in combination with other conventional constraints forms a constraint set, as shown in equation 9:
wherein, Pnet,tRepresents the net load at time t; pload,tRepresenting the system load demand at the moment t; pwind,tRepresenting the predicted value of wind power at the time t;
representing the maximum climbing speed upper limit/lower limit allowed by the online unit at the moment t;Ngrepresenting the number of conventional units;
representing the maximum/minimum output power allowed by the conventional unit i; u. ofi,tRepresenting the starting and stopping state of the unit i in a time period t, and a variable of 0-1;
representing an upper/lower deviation range of the wind power predicted value; rload,tRepresenting the standby demand of the load, the standard deviation of the prediction error of the daily load curve is generally proportional to the load size, R is takenload,t＝0.05Pload,t；
Representing the minimum startup/shutdown time of the conventional unit i;
representing the accumulated starting-up/shutdown time of the conventional unit i in the time period t;
step 3.2: maximizing the wind storage combined operation income as an objective function, as shown in equation 10:
maxf＝Ssave+C'serve-Ccap (10)
step 3.3: and finishing the construction of the wind power plant energy storage capacity optimization model according to the objective function and the constraint condition set.
And 4, solving by using constraint optimization problem solving software or a constraint optimization problem solving algorithm.
The technical effects are as follows: according to the method, the alleviation degree of BESS on auxiliary service cost caused by wind power integration is quantified, the auxiliary service cost (caused by wind power) of a system which is relieved after the wind power plant is configured with energy storage is used as compensation for an energy storage system, the maximum goal of wind-storage combined operation income is taken, the problems that the wind-storage combined operation income is low and the positivity of wind power plant configuration energy storage is poor due to the fact that the auxiliary service cost is not considered in the existing wind power plant energy storage capacity optimization are solved, the wind power plant configuration energy storage is effectively stimulated, and the wind power friendly grid-integration absorption effect is realized; the BESS provides reserve for the uncertainty of the wind power, and the BESS constraint suitable for the dispatching plan is obtained, so that the energy storage capacity planning and the system dispatching plan are combined, namely the energy storage capacity planning and the operation are combined, the energy storage configuration capacity is more reasonable, the actual dispatching operation is more consistent, and the pressure of a conventional unit for providing rotary reserve auxiliary service is relieved; the BESS is the wind power standby shallow charging and shallow discharging, the cycle life of the BESS which can be increased is obtained according to equivalent life loss calculation, and the increase of the cycle life of the BESS is beneficial to reducing the unit investment cost of energy storage, namely improving the yield of combined operation.
The features and properties of the present invention are described in further detail below with reference to examples.
Example 1
A large-scale wind power plant energy storage capacity optimization method considering auxiliary service compensation comprises the following steps:
step 1: the alleviation degree of the system auxiliary service cost before and after the addition of the quantified BESS is used as auxiliary service compensation, and the wind power storage combined operation income is calculated according to the auxiliary service compensation and the obtained direct economic benefit of the wind power plant configuration energy storage;
step 2: after the stored energy provides a standby for the uncertainty of the wind power, updating BESS constraint adaptive to a scheduling plan;
and step 3: the updated BESS constraint is combined with other conventional constraints to form a constraint condition set, an objective function is obtained through maximizing wind power storage combined operation income, and a wind power plant energy storage capacity optimization model is constructed according to the constraint condition set and the objective function;
and 4, step 4: and inputting the collected power system parameters into a wind power plant capacity optimization model to solve to obtain the optimal capacity of the wind power plant configuration battery energy storage system.
The step 1 comprises the following steps:
step 1.1: quantifying the remission degree of the BESS on the auxiliary service cost of the system before and after the addition;
step 1.2: the direct economic benefits of the energy storage configured in the wind power plant comprise BESS cost and wind abandoning income, the BESS cost is calculated, and the calculation is shown as formula 1:
Ccap＝αs·Pcap+βs·Scap
wherein, CcapRepresents the BESS cost; alpha is alphasRepresenting the unit price of the energy storage system reduced to annual capacity; beta is asRepresenting the unit price of power of the energy storage system reduced to each year; r represents the proportion of kW.h/kW cost; cERepresents the investment cost of an energy storage unit, $/kW.h; t islifeRepresents the equivalent operating life of the BESS; cOMRepresents the operating maintenance cost, $/year;
step 1.3: and (3) taking the relief degree of the system peak shaving and the rotary standby auxiliary service before and after the BESS is added as auxiliary service compensation, and calculating the wind curtailment income and the auxiliary service compensation, as shown in a formula 2:
wherein S issaveThe wind energy storage combined operation is compared with the wind energy abandon income brought by the wind power independent grid connection; c'serveThe method comprises the steps of representing the alleviation degree of the wind storage combined operation on the system auxiliary service after BESS is added, namely obtaining auxiliary service compensation; pWloss,tAnd P'Wloss,tRespectively representing the wind abandon conditions of the wind power plant at each moment when the wind power plant contains or does not contain an energy storage system; rhowindRepresenting the price of the wind power on-line electricity; l represents quarterly, blRepresents the number of days per quarter; cserveRepresenting the auxiliary service cost caused by independent wind power grid connection;
representing the auxiliary service cost caused by the combined operation of the wind storage and the wind storage;
step 1.4: and calculating the wind-storage combined operation income according to the BESS cost, the wind curtailment income and the auxiliary service compensation, as shown in formula 3:
f＝Ssave+C'serve-Ccap (3)
wherein f represents the wind-storage combined operation income; ssaveThe wind energy storage combined operation is compared with the wind energy single grid connection to bring the wind curtailment reduction yield; c'serveIndicating auxiliary service compensation; ccapRepresenting the BESS cost.
Step 1.1 comprises the following steps:
step 1.1.1: the cost difference value of the auxiliary service provided by the system before and after the independent wind power grid connection quantifies the auxiliary service cost caused by the wind power, as shown in formula 4:
Cserve＝Cfixed+Cfluctuant
wherein, CserveRepresenting the auxiliary service costs incurred during the independent integration of wind power, CfixedRepresenting fixed auxiliary service costs when wind power is solely integrated, CfluctuantRepresenting the cost of the shift auxiliary service when the wind power is independently connected to the grid; cAIThe unit investment cost of the conventional units participating in the auxiliary service is reduced to each day; M/MwindRespectively representing the number of the units participating in the auxiliary service before/after the independent wind power grid connection;
rated installed capacity for participating in the auxiliary service unit;
the unit coal consumption cost of the front/rear unit of the wind power independent grid connection is respectively; pGS,i(t) the output of the auxiliary service unit i at the moment t; t is 24 hours a day;
step 1.1.2: the auxiliary service cost caused by the wind storage combined operation is quantified through the cost difference value of the auxiliary service provided by the system before and after the wind storage combined operation, as shown in formula 5:
wherein the content of the first and second substances,
representing the total auxiliary service cost caused by the wind storage combined operation;
represents the fixed auxiliary service cost when the wind storage combined operation is carried out,
representing the cost of the variable auxiliary service during the combined operation of the wind storage;
the number of the units participating in the auxiliary service after the wind storage combined operation is represented;
representing the unit coal consumption cost of the unit after the wind storage combined operation;
step 1.1.3: providing assistance via BESS join pre-and post-systemDifference in cost of service assistance
And CserveAnd (3) quantifying the degree of mitigation of the BESS to the system ancillary service cost.
The system comprises a wind power grid, a wind power grid and a wind power grid, wherein the wind power grid is not connected with the grid and corresponds to a system auxiliary service cost C1, the wind power grid is connected with the system auxiliary service cost C2, the wind power grid is connected with the grid and enables the system to need more auxiliary services (wind power causes extra auxiliary services), namely the wind power grid is represented in a state that C2 is larger than C1, and the auxiliary service cost caused by independent wind power grid connection is C2-C1; after the wind power plant is configured with BESS, the auxiliary service caused by wind storage combined operation is greatly reduced compared with the independent wind power grid connection, at the moment, the auxiliary service cost of the system is C3, which is reflected in that C1 is greater than C3 and less than C2, and the auxiliary service cost caused by wind storage combined operation is C3-C1; the (C2-C1) - (C3-C1), namely C2-C3, is called the relief degree of the auxiliary service cost of the system before and after the BESS is added.
And 4, solving by using constrained optimization problem solving software or a constrained optimization problem solving algorithm, wherein the solving software comprises CPLEX, and the solving algorithm comprises a particle swarm algorithm or a genetic algorithm.
Multi-scene analysis: taking a typical winter day with a particularly remarkable wind power anti-peak regulation characteristic as an example, a foundation is provided for the follow-up calculation of the wind curtailment reduction amount and the BESS mitigation auxiliary service cost in the wind storage combined operation. BESS is based on unit cost 250$/kW · h and cycle life 4000 times:
case 1: the system does not contain an energy storage system and does not have wind power; (the objective function is the unit operating cost CGenMinimum)
Case 2: the wind power generation system does not contain an energy storage system, and has wind power, so that the wind power generation system allows wind to be abandoned;
case 3: the BESS is used for standby wind power with auxiliary service compensation; (this method)
Case 4: without auxiliary service compensation, BESS stands by wind power;
case 5: the method comprises the following steps of (1) auxiliary service compensation is included, and BESS wind power is not used for standby;
case 6: and auxiliary service compensation is not contained, and BESS wind power standby is not available.
Case1 and Case2 are used as comparison bases, Case3 is the BESS capacity optimization taking the auxiliary service compensation into account proposed by the method, comparison analysis is carried out between Case4-Case6 and Case3, and the optimization result in one scheduling period is shown in Table 1:
TABLE 1 optimization results under different scenarios
Note: c. CgThe coal consumption of the conventional unit is used for measuring the running cost of the conventional unit; q is a wind abandon ratio; f is the wind-storage combined operation income; pcapA rated power of a BESS configured for the wind farm; scapCapacity of a BESS configured for a wind farm; n is a radical ofcycIs the BESS equivalent cycle number in one scheduling period.
cases 3-6 BeSS-containing cgCompared with case2 without BESS (21.534$/MW · h), the method is obviously reduced, mainly because BESS relieves the auxiliary service cost of a conventional unit caused by the wind power inverse peak shaving characteristic through the time-space transfer of wind power, and the charging and discharging process of energy storage in FIG. 4 is more intuitively shown; it can be seen from the comparison result between the case5 and the case6 that, when the case6 does not take the auxiliary service compensation into account, the benefit of the wind farm energy storage system for pursuit is maximized, and only the wind power transfer in the wind curtailment period is realized with smaller energy storage capacity, the point can be clearly illustrated in the charging and discharging process of the case6 in fig. 4, and the fluctuation improvement of the BESS on the system net load is not great, so the operation cost c of the case6 system is lowgNo significant improvement was obtained; c comparing case3 with case4gThe value shows that when BESS participates in uncertainty of the output of the standby wind power, the operation cost of the conventional unit is further relieved; as can be seen from case4 profit f, without accounting for ancillary services compensation, even if the unit investment cost of BESS is assumed herein to fall to 250$/kWh, no positive profit can be achieved, which would seriously hinder the aggressiveness of wind farm configuration BESS.
In conclusion, the economical efficiency of operation after the wind power plant is configured with BESS is analyzed through the wind power storage combined operation angle, the auxiliary service cost (caused by the wind power plant) of the system which is buffered after the wind power plant is configured with energy storage is used as compensation for the energy storage system, the maximum investment income of the wind power storage combined operation is the target, and the income of the wind power storage combined operation can be effectively improved.
Example 2
Based on example 1, step 2 comprises the following steps:
step 2.1: on the premise of allowing the wind curtailment, the charge-discharge power and the charge state of the BESS are defined as shown in formula 6:
wherein S istRepresents the charge and discharge power of the storage battery at the time t; pwind,tRepresenting the predicted value of wind power at the time t; pcombined,tRepresenting grid-connected power during wind storage combined operation at the moment t; pwloss,tRepresenting a wind curtailment value at the time t; ssoc,t-1Represents the charge capacity of BESS at time t; etasThe charge and discharge efficiency is shown; Δ t represents a scheduling time interval, 1 h;
step 2.2: the BESS constraint at time t is expressed as shown in equation 7, based on the BESS rated charge-discharge power and energy storage capacity limitations:
wherein, PcapRepresents the rated power of the energy storage system; scapRepresenting the capacity of the energy storage system;
step 2.3: after the BESS provides a rotation reserve for the wind power output prediction error, updating BESS constraint at the time t, as shown in a formula 8:
wherein epsilonsAnd representing BESS as the reserve degree of the wind power output uncertainty.
According to multi-scene analysis, the following results are obtained: comparing case3 and case5, case3 has further alleviated the auxiliary service cost that wind-powered electricity causes through the uncertainty of reserve wind-powered electricity, cg is 21.236$/MW · h and has been very close to the operation result of system when case1 does not contain wind-powered electricity yet, shows that this wind-powered electricity generation field configuration energy storage that the characteristic is stronger to the peak-back regulation will realize "friendly being incorporated into the power networks" of wind-powered electricity generation. And when the case5 does not take the energy storage standby wind power into account, the wind and energy storage combined income and the operation benefit of the system are lower than those of the case3 due to the compensation income and the equivalent cycle times (the case5 is increased compared with the case 3). The embodiment analyzes the BESS standby wind power under the condition that the standby degree is 100%, the optimum profit is actually considered, the standby degree can be correspondingly changed, but the energy can be stored according to data analysis to provide standby for the uncertainty of the wind power, and compared with the condition that the standby is not provided, the method is more favorable for combining the energy storage capacity planning with the system scheduling operation, and the profit of the wind power and storage combined operation is further improved.
Example 3
Based on example 1, step 3 comprises the following steps:
the step 3 comprises the following steps:
step 3.1: the updated BESS constraint at time t in combination with other conventional constraints forms a constraint set, as shown in equation 9:
wherein, Pnet,tRepresents the net load at time t; pload,tRepresenting the system load demand at the moment t; pwind,tRepresenting the predicted value of wind power at the time t;
representing the maximum climbing speed upper limit/lower limit allowed by the online unit at the moment t;Ngrepresenting the number of conventional units;
representing the maximum/minimum output power allowed by the conventional unit i; u. ofi,tRepresenting the on-off state of the unit i in the time period t, 0-1 variable;
representing an upper/lower deviation range of the wind power predicted value; rload,tRepresenting the standby demand of the load, the standard deviation of the prediction error of the daily load curve is generally proportional to the load size, R is takenload,t＝0.05Pload,t；
Representing the minimum startup/shutdown time of the conventional unit i;
representing the accumulated starting-up/shutdown time of the conventional unit i in the time period t;
step 3.2: maximizing the wind storage combined operation income as an objective function, as shown in equation 10:
maxf＝Ssave+C'serve-Ccap (10)
step 3.3: and finishing the construction of the wind power plant energy storage capacity optimization model according to the objective function and the constraint condition set.
Sensitivity analysis was performed for the solved optimal capacity:
effect of degree of readiness of BESS:
the backup degree of the BESS in the cases 3 and 5 can be described as 100% backup and 0% backup with uncertain wind power output; for the energy storage analysis of the influence of different reserve degrees on the optimization result, on the basis of 100% reserve in the case3, sequentially reducing the reserve space of the energy storage to be not reserved in an equal proportion (0% reserve case5), and analyzing the variation relationship between the different reserve degrees and the configuration capacity, wherein the results are shown in table 2:
TABLE 2 optimization results at different degrees of redundancy
As can be seen from table 2, as the uncertainty of the BESS backup wind power decreases, the profit of the BESS system tends to increase first and then decrease, and it can be seen that the benefit of the wind power storage combined operation is the maximum when the degree of the BESS backup wind power is 60%, and the operational benefit of the system is also ensured; this is mainly because the investment cost of energy storage is large, if the BESS provides full reserve for the uncertainty of wind power, more additional energy storage capacity is needed; however, as the level of redundancy decreases, the ancillary service compensation available to the BESS decreases, while the wind storage revenue decreases due to the limitations of the equivalent cycle times. Through the analysis of the reserve degree, the maximum wind-storage combined operation income is obtained under the condition of considering the investment cost of energy storage and BESS reserve wind power, and the outstanding effect of the method is verified.
Example 4
Based on embodiment 2, the equivalent service life breakage rate calculation method is adopted to obtain the equivalent operating life of the battery energy storage system, as shown in formula 11:
wherein D isiThe value range of the charge-discharge depth is 0-100%; l iscyc,DiRepresenting the number of the cycle life of the energy storage system under the ith charging and discharging depth of the BESS,Nrepresents the number of charge and discharge cycles in one year.
Sensitivity analysis was performed based on optimal capacity:
BESS investment cost and cycle life:
in order to find a balance point of additional benefits and additional costs when the wind power plant is configured with the BESS in consideration of auxiliary service compensation, the variation relation of BESS configuration capacity along with investment cost and cycle life is respectively calculated by taking case3 as a calculation condition, as shown in FIGS. 5 and 6:
from fig. 5, it can be seen that if a certain compensation can be provided for the participation of the BESS system in the auxiliary service, Lcyc,NUnder the condition of 4000 times, the balance of balance is reached when the cost of energy storage is reduced to 360 $/kW.h, namely CE<The positive benefit of wind-storage combined operation can be guaranteed within 360$/kW · h, and the method is used for energy storage configuration of a large-scale wind power plant;
as can be seen from FIG. 6, CEWhen the cycle life of the stored energy is reduced to 2800 times under the condition of 250$/kW · h, the BESS configuration capacity is 0, i.e. Lcyc,N>And the system can be used for energy storage configuration of a large-scale wind power plant after 2800 times.
In conclusion, as the stored energy provides reserve for the wind power, the construction capacity is relatively increased, and the investment cost is increased; however, the reserved spare space enables the energy storage system to be shallow charged and shallow discharged, and different spare levels can affect the depth of energy storage charging and discharging, so that the cycle life of energy storage is affected. The method introduces an equivalent life loss model of the BESS to measure the influence of the charging and discharging depth on the energy storage cycle life, reflects the influence of different backup levels of the BESS on the energy storage cycle life, and further determines the optimal BESS backup level. Due to one-time investment of energy storage construction, the BESS actually prolongs the service life of the BESS for wind power standby, and can equivalently reduce the investment cost of the BESS unit, so that the combined operation income is increased.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A configuration method of large-scale wind power plant energy storage capacity considering auxiliary service compensation is characterized by comprising the following steps: the method comprises the following steps:
step 1: the alleviation degree of the auxiliary service cost of the system before and after the addition of the quantized battery energy storage BESS is used as auxiliary service compensation, and the wind-storage combined operation income is calculated according to the auxiliary service compensation and the obtained direct economic benefit of the wind power plant configuration energy storage;
step 2: after the stored energy provides a standby for the uncertainty of the wind power, updating BESS constraint adaptive to a scheduling plan;
and step 3: establishing a constraint condition set by combining the updated BESS constraint with the conventional constraint, acquiring an objective function through maximizing the wind power storage combined operation income, and establishing a wind power plant energy storage capacity optimization model according to the constraint condition set and the objective function;
and 4, step 4: inputting the collected power system parameters into a wind power plant capacity optimization model to solve to obtain the optimal capacity of a wind power plant configuration battery energy storage system;
the step 1 comprises the following steps:
step 1.1: quantifying the remission degree of the BESS on the auxiliary service cost of the system before and after the addition;
step 1.2: the direct economic benefits of the energy storage configured in the wind power plant comprise BESS cost and wind abandoning income, the BESS cost is calculated, and the calculation is shown as formula 1:
Ccap＝αs·Pcap+βs·Scap
wherein, CcapRepresents the BESS cost; alpha is alphasRepresenting the unit price of the energy storage system reduced to annual capacity; beta is asRepresenting the unit price of power of the energy storage system reduced to each year; r represents the proportion of kW.h/kW cost; cERepresents the investment cost of an energy storage unit, $/kW.h; t islifeRepresents the equivalent operating life of the BESS; cOMRepresents the operating maintenance cost, $/year; pcapRepresents the rated power of the energy storage system; scapRepresenting the capacity of the energy storage system;
step 1.3: and (3) taking the relief degree of the system peak shaving and the rotary standby auxiliary service before and after the BESS is added as auxiliary service compensation, and calculating the wind curtailment income and the auxiliary service compensation, as shown in a formula 2:
wherein S issaveThe wind energy storage combined operation is compared with the wind energy abandon income brought by the wind power independent grid connection; c'serveThe method comprises the steps of representing the alleviation degree of the wind storage combined operation on the system auxiliary service after BESS is added, namely obtaining auxiliary service compensation; pWloss,tAnd P'Wloss,tRespectively represent containing and not containingThe wind abandon condition of the wind power plant at each moment when the energy storage system is used; rhowindRepresenting the price of the wind power on-line electricity; l represents quarterly, blRepresents the number of days per quarter; cserveRepresenting the auxiliary service cost caused by independent wind power grid connection;
representing the auxiliary service cost caused by the combined operation of the wind storage and the wind storage;
step 1.4: and calculating the wind-storage combined operation income according to the BESS cost, the wind curtailment income and the auxiliary service compensation, as shown in formula 3:
f＝Ssave+C'serve-Ccap (3)
wherein f represents the wind-storage combined operation income; ssaveThe wind energy storage combined operation is compared with the wind energy single grid connection to bring the wind curtailment reduction yield; c'serveIndicating auxiliary service compensation; ccapRepresents the BESS cost;
the step 1.1 comprises the following steps:
step 1.1.1: the cost difference value of the auxiliary service provided by the system before and after the independent wind power grid connection quantifies the auxiliary service cost caused by the wind power, as shown in formula 4:
Cserve＝Cfixed+Cfluctuant
wherein, CserveRepresenting the auxiliary service costs incurred during the independent integration of wind power, CfixedRepresenting fixed auxiliary service costs when wind power is solely integrated, CfluctuantRepresenting the cost of the shift auxiliary service when the wind power is independently connected to the grid; cAIThe unit investment cost of the conventional units participating in the auxiliary service is reduced to each day; M/MwindRespectively representing the number of the units participating in the auxiliary service before/after the independent wind power grid connection;
rated installed capacity for participating in the auxiliary service unit; c. Cg/
The unit coal consumption cost of the front/rear unit of the wind power independent grid connection is respectively; pGS，i(t) the output of the auxiliary service unit i at the moment t; t is 24 hours a day;
step 1.1.2: the auxiliary service cost caused by the wind storage combined operation is quantified through the cost difference value of the auxiliary service provided by the system before and after the wind storage combined operation, as shown in formula 5:
wherein the content of the first and second substances,
representing the total auxiliary service cost caused by the wind storage combined operation;
represents the fixed auxiliary service cost when the wind storage combined operation is carried out,
representing the cost of the variable auxiliary service during the combined operation of the wind storage;
the number of the units participating in the auxiliary service after the wind storage combined operation is represented;
unit unit for expressing wind storage combined operationCoal consumption cost;
step 1.1.3: the difference in cost of providing ancillary services through BESS joining pre-and post-systems
And CserveAnd (3) quantifying the degree of mitigation of the BESS to the system ancillary service cost.
2. The method for configuring large-scale wind farm energy storage capacity with consideration of auxiliary service compensation according to claim 1, characterized by comprising the following steps: the step 2 comprises the following steps:
step 2.1: on the premise of allowing the wind curtailment, the charge-discharge power and the charge state of the BESS are defined as shown in formula 6:
wherein S istRepresents the charge and discharge power of the storage battery at the time t; pwind,tRepresenting the predicted value of wind power at the time t; pcombined,tRepresenting grid-connected power during wind storage combined operation at the moment t; pwloss,tRepresenting a wind curtailment value at the time t; ssoc，tRepresents the charge capacity of BESS at time t; etasThe charge and discharge efficiency is shown; Δ t represents a scheduling interval, 1 h;
step 2.2: the BESS constraint at time t is expressed as shown in equation 7, based on the BESS rated charge-discharge power and energy storage capacity limitations:
wherein, PcapRepresents the rated power of the energy storage system; scapRepresenting the capacity of the energy storage system;
step 2.3: after the BESS provides a rotation reserve for the wind power output prediction error, updating BESS constraint at the time t, as shown in a formula 8:
wherein epsilonsAnd representing BESS as the reserve degree of the wind power output uncertainty.
3. The method for configuring large-scale wind farm energy storage capacity with consideration of auxiliary service compensation according to claim 1 or 2, characterized by comprising the following steps: the step 3 comprises the following steps:
step 3.1: the updated BESS constraint at time t combines with the conventional constraint to form a constraint set, as shown in equation 9:
wherein, Pnet,tRepresents the net load at time t; pload,tRepresenting the system load demand at the moment t; pwind,tRepresenting the predicted value of wind power at the time t; r ist upRepresents the maximum climbing speed upper limit, r, allowed by the online unit at the moment tt dnRepresenting the maximum climbing speed lower limit allowed by the online unit at the time t; n is a radical ofgRepresenting the number of conventional units;
representing the maximum/minimum output power allowed by the conventional unit i; u. ofi,tRepresenting the starting and stopping state of the unit i in a time period t, and a variable of 0-1;
representing an upper/lower deviation range of the wind power predicted value; rload,tRepresenting the standby demand of the load, the standard deviation of the prediction error of the daily load curve is proportional to the load size due to the high repeatability of the daily load curve, R is takenload,t＝0.05pload,t；
Representing the minimum startup/shutdown time of the conventional unit i;
representing the accumulated starting-up/shutdown time of the conventional unit i in the time period t; ssoc，tRepresents the charge capacity of BESS at time t;
step 3.2: maximizing the wind storage combined operation income as an objective function, as shown in equation 10:
max f＝Ssave+C'serve-Ccap (10)
step 3.3: and finishing the construction of the wind power plant energy storage capacity optimization model according to the objective function and the constraint condition set.
4. The method for configuring large-scale wind farm energy storage capacity with consideration of auxiliary service compensation according to claim 3, characterized by comprising the following steps: and in the step 4, solving software or algorithm for solving the constrained optimization problem is adopted.
5. The method for configuring large-scale wind farm energy storage capacity with consideration of auxiliary service compensation according to claim 2, characterized by comprising the following steps: the step 2 also comprises BESS equivalent life loss calculation for increasing the wind-storage combined operation income.
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