CN109687443B - Micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling - Google Patents

Micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling Download PDF

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CN109687443B
CN109687443B CN201811611315.4A CN201811611315A CN109687443B CN 109687443 B CN109687443 B CN 109687443B CN 201811611315 A CN201811611315 A CN 201811611315A CN 109687443 B CN109687443 B CN 109687443B
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蔡泽祥
李立浧
苏忠阳
刘平
谢鹏
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Guangzhou Smart Energy Technology Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract

The invention discloses a micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling, which is characterized by adopting an envelope model aiming at the random uncertainty characteristics of source and load presentation in the long-time scale energy storage capacity investment planning problem, and derives an energy balance capacity index for quantitatively depicting the relation between an energy storage system capacity investment decision and a micro-grid system energy balance capacity, thereby being beneficial to reducing the blindness of energy storage capacity investment planning; aiming at the characteristics of uncertainty of prediction errors presented by sources and loads in the short-time-scale microgrid system optimization operation problem, a box type interval model is adopted to represent the microgrid system optimization operation problem, and a robustness coordination cost index is constructed and used for objectively reflecting the economic cost of improvement of the microgrid system optimization operation robustness.

Description

Micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling
Technical Field
The invention relates to the field of electric power, in particular to a micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling.
Background
The problems of wind abandoning, light abandoning and load losing caused by uncertainty of renewable power supply output and load demand are main obstacles for a micro-grid system to present a main grid friendly characteristic, and the energy transfer and power support capability provided by an energy storage system is an effective means for solving the problems. In consideration of the problems of expensive investment and maintenance cost of the energy storage system at the present stage, power exchange (possibly causing interference to the scheduling operation of the main network and bringing extra market transaction cost) and the like with the main network in the grid-connected operation of the micro-grid system, the long-time scale investment decision problem and the short-time scale optimization operation problem of the energy storage system are deeply coupled, and therefore the complexity of the capacity optimization problem is increased;
renewable power output and load requirements in a microgrid system are uncertain, and the characteristics presented by the uncertainties in different time scales are different: in the investment decision of the energy storage capacity of a long time scale, the inherent random uncertainty of the source and the load needs sufficient energy capacity to provide capacity support of energy balance; in the optimization operation of the energy storage system in a short time scale, the uncertainty of the prediction error of the source and charge power requires that the energy storage system has enough power capacity to maintain the power balance of the microgrid system. Therefore, the multi-time scale uncertainty coupling (including the source uncertainty and the load uncertainty at each time scale, and the differentiation characteristics of the uncertainty at different time scales) has a decisive influence on the energy storage capacity optimization result.
The consideration of the multi-time scale uncertainty coupling influence in the energy storage capacity optimization problem of the microgrid system by the existing processing mode is not comprehensive enough, for example: some scholars study the capacity optimization problem of the micro-grid energy storage system containing high proportion of wind power infiltration, but do not fully consider the uncertainty of the load side; scholars studied the capacity optimization problem of a wind-light-storage hybrid system and neglected the load demand uncertainty effect. The scholars account for the influence of uncertainty factors in the problem of optimizing the energy storage capacity of the microgrid system, but do not consider uncertainty on both the source side and the charge side at the same time. In addition, a scholars constructs a random optimization model of the energy storage capacity of the microgrid system, and although the random uncertainty of the renewable energy output in the long-time-scale investment decision problem is considered, the mismatch of the renewable power supply predicted output and the actual output in the short-time-scale optimization operation problem is not considered.
Disclosure of Invention
In view of the above, an object of the present invention is to overcome the defects in the prior art, and provide a microgrid energy storage capacity optimization configuration method considering multi-time scale uncertainty coupling, wherein the influence of multi-time scale uncertainty coupling is comprehensively considered in the microgrid system energy storage capacity optimization problem: the method simultaneously relates to renewable power supply output (including wind power and photovoltaic power generation) and load demand bilateral uncertainty; and in the long-time scale investment decision problem and the short-time scale optimization operation problem of the energy storage system, the influences of source and load random uncertainty and prediction error uncertainty are respectively involved.
The invention relates to a micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling, which comprises the following steps of:
s1: carrying out outer layer optimization based on a long time scale and inner layer optimization based on a short time scale on the energy storage capacity of the micro-grid:
s2: solving the outer layer optimization problem to obtain an initial energy storage system rated capacity optimization result and transmitting the initial energy storage system rated capacity optimization result to the inner layer optimization model;
s3: based on the rated capacity of the energy storage system given by the outer layer optimization as an input variable of the inner layer optimization model, solving the minimum optimization operation cost of the micro-grid, and returning the minimum optimization operation cost to the outer layer optimization model;
s4: and circularly repeating the step S2 and the step S3 until an iteration convergence criterion is met, and obtaining a micro-grid energy storage capacity optimization configuration scheme which is beneficial to realizing the core objective of considering the economy and the main grid friendly characteristic of the micro-grid system.
Further, in step S1, the outer layer is optimized to be a random uncertainty characteristic presented by the source and the charge in the long-time scale energy storage capacity investment planning problem, and an envelope model is used to characterize the outer layer and derive an energy balance capability index, which is used to quantitatively characterize the energy storage system capacity investment decision and the energy balance capability relation of the microgrid system;
the inner layer optimization is characterized by adopting a box type interval model aiming at the uncertainty characteristic of prediction errors presented by sources and loads in the optimization operation problem of the micro-grid system in a short time scale, and a robustness coordination cost index is constructed and used for objectively reflecting the economic cost of the robustness improvement of the optimization operation of the micro-grid system.
Further, an energy balance capability index is constructed using the following formula:
Figure BDA0001924815920000031
Prw(t) probability of energy excess state, Prl(t) probability of energy deficit state and PrO(t) is the probability of the energy balance state, the upper bound of the probability of the energy excess state and the upper bound of the probability of the energy deficit stateAre respectively represented as
Figure BDA0001924815920000032
The sum of the occurrence probability of each state at any time in the micro-grid system source-storage-charge energy balance process is 1, so that the time period [0, T ] can be obtained]Internal energy balance state probability PrOLower boundary of (T)
Figure BDA0001924815920000033
The energy balance capability index is then expressed as:
Figure BDA0001924815920000034
when B is presentess,nAt θ, DO,θThe larger the numerical value is, the larger the influence of uncertainty factors on the occurrence possibility of the energy balance state of the microgrid system is, namely the weaker the energy balance capability of the microgrid system is, the more unreasonable the energy storage capacity configuration scheme is; otherwise, DO,θThe smaller the numerical value of the energy balance state is, the smaller the influence of uncertainty factors on the occurrence probability of the energy balance state is, namely, the stronger the energy balance capability of the microgrid system is, which indicates that the energy storage capacity configuration is more reasonable.
The invention has the beneficial effects that: the invention discloses a micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling, which is characterized by adopting an envelope model aiming at the random uncertainty characteristics of source and load presentation in the long-time scale energy storage capacity investment planning problem, and derives an energy balance capacity index for quantitatively depicting the relation between an energy storage system capacity investment decision and a micro-grid system energy balance capacity, thereby being beneficial to reducing the blindness of energy storage capacity investment planning; aiming at the characteristics of uncertainty of prediction errors presented by sources and loads in the short-time-scale microgrid system optimization operation problem, a box type interval model is adopted to represent the microgrid system optimization operation problem, and a robustness coordination cost index is constructed and used for objectively reflecting the economic cost of improvement of the microgrid system optimization operation robustness.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic diagram of a microgrid energy storage capacity optimization method in the invention;
fig. 2 is a schematic diagram of the energy balance capability of the microgrid system according to the present invention.
Detailed Description
As shown in the figure, in this embodiment, a method for optimally configuring energy storage capacity of a micro-grid considering multi-time scale uncertainty coupling includes the following steps
1. Long time scale energy storage capacity investment planning (outer layer optimization):
the layer optimization takes the rated capacity of the energy storage system as a decision variable, takes the lowest investment cost of the energy storage capacity (which can be composed of daily average investment cost, daily average fixed maintenance cost and minimum optimized operation cost of the microgrid) and the lowest index of the energy balance capacity as an objective function, and takes the capacity investment limit constraint of the energy storage system and the maximum charge-discharge multiplying power constraint of the energy storage system and the like into consideration under constraint conditions.
2. Short-time-scale microgrid optimization operation (inner layer optimization):
the rated capacity of the energy storage system given by the outer-layer optimization is used as a decision variable, the lowest optimized operation cost of the microgrid system is used as a target function (including robustness coordination penalty cost), and the constraint conditions can be composed of power balance constraint, reserve capacity constraint, energy storage system charge and discharge power constraint, energy storage system state of charge limit constraint, energy storage system daily energy balance constraint and the like.
3. Iteration of loop
Firstly, solving an outer layer optimization problem to obtain an initial energy storage system rated capacity optimization result and transmitting the initial energy storage system rated capacity optimization result to an inner layer optimization model; based on the rated capacity of the energy storage system given by the outer layer as an inner layer input variable, solving the minimum optimized operation cost of the micro-grid, and returning the minimum optimized operation cost to the outer layer optimization model; and (4) circularly and iteratively solving until an iterative convergence criterion is met, and finally obtaining a micro-grid energy storage capacity optimization configuration scheme which is beneficial to realizing the core objective of considering both the economy and the main grid friendly characteristic of the micro-grid system.
Wherein:
1) source and load random uncertainty characterization
In order to accurately depict the random uncertainty characteristics of the source and the load, an envelope model shown as follows is constructed to characterize the output process and the load demand process of the renewable power supply in a time period [ s, t ]:
Figure BDA0001924815920000051
in the formula: ek(s, t) represents the uncertain source k (wind, photovoltaic, or load) over a time period [ s, t ]]Energy output/demand process in, and Ek(s,t)＝Ek(t)-Ek(s)；
Figure BDA0001924815920000052
And
Figure BDA0001924815920000053
respectively an upper limit function and a lower limit function of the energy output/demand process;
Figure BDA0001924815920000054
is a probability boundary function; sup is the supremum operator.
2) Energy balance capability index construction
Because the output process and the load demand process of the renewable power supply have random uncertainty, if the output energy of the renewable power supply is greater than the load demand energy in a certain period of time, the excess energy can be stored in the energy storage system until the stored energy exceeds the rated capacity to generate excess energy; if the output energy of the renewable power supply is smaller than the energy required by the load in a certain period, the energy storage system gradually releases the stored energy until the stored energy is completely released and the energy requirement is still difficult to meet, and energy shortage is generated. the excess energy w (t) and the deficit energy l (t) at time t can be expressed as:
Figure BDA0001924815920000055
in the formula: b (t-1) represents the energy storage at the t-1 momentStored energy in the system; b isESS,nIs rated capacity; ePV、EWTAnd ELDRespectively representing solar energy, wind energy and load demand energy; etacAnd ηdRespectively representing charge and discharge efficiency coefficients;
Figure BDA0001924815920000056
and
Figure BDA0001924815920000057
respectively representing the upper limit value and the lower limit value of the state of charge of the energy storage system; [ x ] of]+＝max{x,0}。
Three possible states can occur in the micro-grid system source-storage-charge energy balance process: an energy excess state, an energy deficit state, and an energy balance state. Based on the source and the charge envelope representation model and the energy model of the energy storage system, the energy excess state probability Pr can be derivedw(t) probability of energy deficit state Prl(t) and energy balance state probability PrO(t) expression:
Figure BDA0001924815920000061
let the energy excess state probability upper bound and the energy deficit state probability upper bound be expressed as
Figure BDA0001924815920000062
Figure BDA0001924815920000063
The sum of the occurrence probability of each state at any time in the micro-grid system source-storage-charge energy balance process is 1, so that the time period [0, T ] can be obtained]Internal energy balance state probability PrOLower boundary of (T)
Figure BDA0001924815920000064
The energy balance ability index mentioned in the invention can be expressed as Bess,nVariable energy balance state probability of micro-grid system at thetaExtent length, e.g. D in FIG. 2O,θ
Figure BDA0001924815920000065
As shown. DO,θThe larger the numerical value is, the larger the influence of uncertainty factors on the occurrence possibility of the energy balance state of the microgrid system is, namely the weaker the energy balance capability of the microgrid system is, the more unreasonable the energy storage capacity configuration scheme is; otherwise, DO,θThe smaller the numerical value of the energy balance state is, the smaller the influence of uncertainty factors on the occurrence probability of the energy balance state is, namely, the stronger the energy balance capability of the microgrid system is, which indicates that the energy storage capacity configuration is more reasonable.
3) Prediction error uncertainty characterization
In order to accurately depict the prediction error uncertainty characteristics of the source and the load, a box type interval model shown as the following is constructed to characterize the output and the load requirements of the renewable power supply at the time t:
Figure BDA0001924815920000066
in the formula: pk,t、
Figure BDA0001924815920000067
Respectively representing the actual output/demand and the predicted output/demand of the uncertain source;
Figure BDA0001924815920000068
the deviation of the actual value and the predicted value is obtained;
Figure BDA0001924815920000071
upper and lower bounds of deviation range, and upper order of value rk,tIs a coordination factor.
In order to avoid the over conservation of the energy storage capacity optimization scheme of the micro-grid system, the invention adopts a box type interval model with coordination factors to describe the uncertainty of source and load prediction errors. The smaller the value of the coordination factor is, the smaller the tolerance range of the box type interval model to the uncertainty of the source and load prediction errors is. When r is 0, the wind power, the photovoltaic output and the load demand are considered as the predicted expected values.
4) Robust coordination cost index construction
With the continuous increase of the set value of the coordination factor, the spare capacity of the micro-grid system for source and load prediction error uncertainty is improved, and therefore the economy of the optimization result is necessarily reduced. In order to objectively embody the above rule, the following robustness coordination cost index is constructed:
Figure BDA0001924815920000072
in the formula: penalty cost coefficient KpenReflecting the economic penalty for increased system spare capacity due to the extended tolerance range of the kth source of uncertainty.
5) Micro-grid energy storage capacity optimization method considering multi-time scale uncertainty coupling
In order to give consideration to the economy of the energy storage capacity optimization configuration scheme and the major network friendly characteristic of micro-grid system operation, the invention provides a method for optimizing the energy storage capacity of the micro-grid by adopting a layered optimization idea and fully considering the multi-time scale uncertainty coupling influence in the process of optimizing the energy storage capacity of the micro-grid. Specifically, source and load random uncertainty is considered on the long-time scale (year) energy storage capacity investment planning level, and an investment cost index and an energy balance capability index of the energy storage system capacity are used as an objective function of outer layer optimization; and considering source and load prediction error uncertainty in the optimization operation level of the micro-grid system in a short time scale (hour), and taking an operation cost index (including robustness coordination cost) of the micro-grid system as an objective function of inner-layer optimization. Based on the above full consideration of the multi-time scale uncertainty coupling influence in the micro-grid system energy storage capacity optimization problem, the method is beneficial to ensuring that the micro-grid system has enough energy balance capability while not sacrificing the economy of the energy storage capacity optimization result, and can realize the energy storage capacity optimization taking the economy and the main network friendly characteristic of the micro-grid system as the core target. The overall idea of the double-layer optimization method for the energy storage capacity of the microgrid is shown in figure 1.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (2)

1. A micro-grid energy storage capacity optimal configuration method considering multi-time scale uncertainty coupling is characterized by comprising the following steps: comprises the following steps:
s1: carrying out outer layer optimization based on a long time scale and inner layer optimization based on a short time scale on the energy storage capacity of the micro-grid;
s2: solving the outer layer optimization problem to obtain an initial energy storage system rated capacity optimization result and transmitting the initial energy storage system rated capacity optimization result to the inner layer optimization model;
s3: based on the rated capacity of the energy storage system given by the outer layer optimization as an input variable of the inner layer optimization model, solving the minimum optimization operation cost of the micro-grid, and returning the minimum optimization operation cost to the outer layer optimization model;
s4: circularly repeating the step S2 and the step S3 until an iteration convergence criterion is met, and obtaining a micro-grid energy storage capacity optimization configuration scheme which is beneficial to achieving the core objective of considering both the economy and the main grid friendly characteristic of the micro-grid system;
in step S1, the outer layer is optimized to be a random uncertainty characteristic presented by the source and the charge in the long-time scale energy storage capacity investment planning problem, and an envelope model is used to characterize the outer layer and derive an energy balance capability index, which is used to quantitatively characterize the energy storage system capacity investment decision and the energy balance capability relation of the microgrid system;
the inner layer optimization is characterized by adopting a box type interval model aiming at the uncertainty characteristic of prediction errors presented by sources and loads in the optimization operation problem of the micro-grid system in a short time scale, and a robustness coordination cost index is constructed and used for objectively reflecting the economic cost of the robustness improvement of the optimization operation of the micro-grid system.
2. The method according to claim 1, wherein the optimal configuration method for the energy storage capacity of the microgrid with consideration of multi-time scale uncertainty coupling is characterized in that: an energy balance capability index is constructed using the following formula:
Figure FDA0003467875840000021
Prw(t) probability of energy excess state, Prl(t) probability of energy deficit state and PrO(t) is the probability of the energy balance state, and the upper boundary of the probability of the energy excess state and the upper boundary of the probability of the energy deficit state are respectively expressed as
Figure FDA0003467875840000022
The sum of the occurrence probability of each state at any time in the micro-grid system source-storage-charge energy balance process is 1, so that the time period [0, T ] can be obtained]Internal energy balance state probability PrOLower boundary of (T)
Figure FDA0003467875840000023
The energy balance capability index is then expressed as:
Figure FDA0003467875840000024
when B is presentess,nAt θ, DO,θThe larger the numerical value is, the larger the influence of uncertainty factors on the occurrence possibility of the energy balance state of the microgrid system is, namely the weaker the energy balance capability of the microgrid system is, the more unreasonable the energy storage capacity configuration scheme is; otherwise, DO,θThe smaller the numerical value of the energy balance state is, the smaller the influence of uncertainty factors on the occurrence probability of the energy balance state is, namely, the stronger the energy balance capability of the microgrid system is, which indicates that the energy storage capacity configuration is more reasonable.
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