CN110429642B - Micro-grid distributed scheduling method considering renewable energy - Google Patents

Micro-grid distributed scheduling method considering renewable energy Download PDF

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CN110429642B
CN110429642B CN201910652786.8A CN201910652786A CN110429642B CN 110429642 B CN110429642 B CN 110429642B CN 201910652786 A CN201910652786 A CN 201910652786A CN 110429642 B CN110429642 B CN 110429642B
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王旭辉
刘志坚
罗灵琳
晏永飞
徐慧
周于尧
刘瑞光
韩江北
王一妃
李晓磊
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Kunming University of Science and Technology
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Abstract

The invention relates to a micro-grid distributed scheduling method considering renewable energy sources, and belongs to the field of economic scheduling of power systems. Aiming at the problem of economic dispatching of a smart power grid comprising a renewable energy power generation unit, a conventional power generation unit and an energy storage unit, the invention provides an optimal economic dispatching model of the smart power grid considering transmission loss, and the model realizes the metering of the transmission loss and the running cost of the renewable energy power generation unit; secondly, deducing a coordination equation of the optimization problem by adopting a Lagrange multiplier method, and analyzing the output coordination relation of the power generation unit in an analytic mode; then, the invention provides a fully distributed algorithm to effectively solve the economic dispatching problem, and the algorithm not only can effectively reduce the calculation and communication burden, but also can protect the privacy of the power generation unit to a great extent; finally, the invention verifies the correctness and validity of the proposed distributed algorithm based on 3 examples of the IEEE-30 bus system.

Description

Micro-grid distributed scheduling method considering renewable energy
Technical Field
The invention relates to a micro-grid distributed scheduling method considering renewable energy sources, and belongs to the field of economic scheduling of power systems.
Background
Economic dispatch is used as an important problem of technical and economic optimization in the operation of a power system, and aims to minimize the operation cost of the system by optimizing load distribution requirements and reasonably arranging a power generation plan on the premise of meeting system-level and unit-level operation constraints. For the economic dispatch problem, researchers at home and abroad propose a plurality of mature solutions, which can be generally divided into two categories: analytical algorithms (e.g., iterative, newton, linear programming, etc.) and heuristic algorithms (e.g., genetic algorithms, particle swarm optimization, swarm search, etc.).
In the existing economic dispatching solving method, most of the economic dispatching solving methods are realized in a centralized mode, which requires that a control center and each dispatching unit carry out information interaction, global information is collected to calculate the optimal solution of the system, and a power generation unit of the command dispatching system is issued to arrange a power output plan so as to realize the goal of economic dispatching. However, with the future larger and larger scale of the power grid, the centralized algorithm faces a series of problems of heavy calculation burden, large communication pressure, sensitivity to single-point faults and the like; furthermore, centralized approaches are difficult to handle network topology changes and meet the "plug and play" functionality of the power generation unit. On the contrary, the distributed method has better expansibility and robustness, can better adapt to plug and play characteristics and process network topology change, and can fully utilize sparse communication network and limited local information to realize information interaction among power generation units, thereby effectively protecting enterprise and user privacy.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a microgrid distributed scheduling method considering renewable energy sources, and the algorithm not only can effectively reduce calculation and communication burden, but also can protect the privacy of a power generation unit to a great extent; in addition, the provided algorithm can guarantee real-time balance of supply and demand of the system, and the convergence and the optimality of the algorithm are proved.
The technical scheme adopted by the invention is as follows: a microgrid distributed scheduling method considering renewable energy sources comprises the following steps:
step 1: defining the minimum total operating cost of the system as an objective function, and defining system parameters contained in the objective function, wherein the system parameters comprise: three cost functions of a conventional power generation unit, a renewable energy power generation unit and an energy storage unit;
step 2: defining power balance constraint and power generation unit output limit constraint in an algorithm;
step 3: and (5) solving by a complete consistency algorithm to find an optimal solution.
The three steps specifically comprise the following steps:
step 1: the micro-grid economic dispatching aims at minimizing the total running cost of the system, and an algorithm objective function is specifically described as follows:
Figure BDA0002135823390000021
in the formula: f represents the total running cost of the system, $; frRepresents the total operating cost, $, of the renewable energy power generation unit; fcRepresents the total operating cost, $, of the conventional power generation unit; fs represents the total operating cost, $, of the energy storage unit; f. ofri(Pri) And PriRespectively representing the running cost and output, and the $ and MW, of the ith renewable energy power generation unit; f. ofci(Pci) And PciRespectively representing the operation cost and output, $ and MW of the ith conventional power generation unit; f. ofsi(Psi) And PsiRespectively representing the operation cost and output power, $ and MW of the ith energy storage unit; n isr、nc、nsRespectively representing the total number of the various types of power generating units.
Step 2: a renewable energy power generation unit cost function is defined. The operation cost of the renewable energy power generation unit not only comprises direct operation cost, but also comprises punishment cost of limited unit output, and the operation cost is specifically described as follows:
Figure BDA0002135823390000022
in the formula: priRepresenting the output of the ith renewable energy power generation unit, ai、
Figure BDA0002135823390000023
Respectively representing the second term, the first term and the constant term of the ith renewable energy power generation unitThis parameter, ρiDenotes the non-compromise factor, P, of the ith renewable energy power generation unitri MAnd Pri mRespectively representing the upper limit and the lower limit of the output of the ith renewable energy power generation unit.
To simplify the expression, the above formula can be described as follows:
Figure BDA0002135823390000024
in the formula: bi,ciAnd representing the coefficient and the constant term of the first-order term of the cost function of the renewable energy power generation unit after simplified expression.
And step 3: a cost function of a conventional power generation unit is defined. The operating costs of conventional power generation units are generally described in quadratic form as follows:
Figure BDA0002135823390000031
in the formula: a'i、b′i、c′iRepresenting a cost parameter for the ith conventional power generation unit.
And 4, step 4: an energy storage unit cost function is defined. The operating cost of the energy storage unit can be described in the form of a quadratic function as follows:
Figure BDA0002135823390000032
in the formula: a ″)i、
Figure BDA0002135823390000033
Representing a cost parameter for the ith energy storage unit.
To simplify the expression, the above formula can be described as follows:
Figure BDA0002135823390000034
in the formula: b ″)i c″iAnd the coefficient of the first-order term and the constant term represent the cost function of the energy storage unit after simplified expression.
The steps 2-4 are not in sequence.
And 5: the system needs to satisfy the power balance equality constraint during operation, which is described in detail as follows:
Figure BDA0002135823390000035
in the formula: Δ P represents the system power deviation, MW; pDRepresents the system load demand, MW; pLRepresents the system transmission loss, MW; wherein, PLThe representation can be described as follows:
Figure BDA0002135823390000036
in the formula: beta is ariRepresenting the loss factor of the ith renewable energy power generation unit; beta is aciRepresents the loss factor of the ith conventional power generation unit; beta is asiAnd the loss coefficient of the ith energy storage and power generation unit is shown.
Step 6: when the power generation unit operates, the inequality constraint of output limit also needs to be satisfied, which is specifically described as follows:
Figure BDA0002135823390000037
Figure BDA0002135823390000038
in the formula: pci MAnd Pci mRespectively represent the upper and lower limits of output of the ith conventional power generation unit.
Figure BDA0002135823390000039
In the formula:Psi Mand Psi mRespectively representing the upper limit and the lower limit of the output of the ith energy storage unit.
And 7: calculating the optimal solution lambda of the incremental cost of each power generation unit by adopting a distributed algorithmi＝λ*The iterative formula of the distributed algorithm is shown as follows:
Figure BDA0002135823390000041
in the formula: lambdaiDenotes incremental cost, λ'iDenotes λiDerivative of (a), λj(t) represents the incremental cost of the adjacent node j of the ith power generation unit at time t, and λ i (t) represents the incremental cost of the ith power generation unit at time t.
And 8: let Ω ∈ { i ∈ V | Pi<Pi m∪Pi>Pi MRepresents the set of power generation units with output exceeding the limit, then:
Figure BDA0002135823390000042
in the formula: pi MRepresenting the upper output limit, P, of all the power generating unitsi mRepresents the lower limit of output, P, of all power generating unitsi *Represents the optimal output, lambda, of the ith power generation uniti *Represents the optimal incremental cost, σ, of the ith power generation unitiRepresenting a loss penalty factor for the ith power generation unit.
If there are all the power generation units
Figure BDA0002135823390000044
And if so, ending the algorithm and outputting the optimal solution.
And step 9: and if any power generation unit i belongs to omega, correcting the initial value of the output of the power generation unit as follows:
Figure BDA0002135823390000043
in the formula: pi(0) Represents the calculation result of the output of the ith power generation unit at the time point 0, betaiRepresenting the loss factor of the ith power generation unit.
Step 10: when generating unit
Figure BDA0002135823390000045
And then, recalculating a new convergence solution according to the steps, returning to the step 8 for judgment again until the result converges to obtain an optimal solution, and ending the algorithm.
The invention has the beneficial effects that:
1. firstly, an economic dispatching model of the smart grid considering transmission loss is provided, the model simultaneously models a renewable energy power generation unit, a conventional power generation unit and an energy storage unit, and the constraint conditions comprehensively consider the supply and demand balance constraint of the smart grid and the output limit constraint of the power generation unit;
2. secondly, deriving a KKT condition of the optimization problem by adopting a Lagrange multiplier method to determine a coordination equation, analyzing the output coordination relation of the power generation unit, and solving a centralized optimization scheduling result considering transmission loss;
3. then, a fully distributed algorithm is provided to effectively solve the optimization problem, the algorithm not only can effectively reduce calculation and communication burden, but also can protect the privacy of the power generation unit to a great extent; in addition, the provided algorithm can guarantee real-time balance of supply and demand of the system, and the convergence and optimality of the algorithm are proved;
drawings
FIG. 1 is an overall flow chart of the present algorithm;
FIG. 2 is a block diagram of an IEEE-30 bus test system;
FIG. 3 is a power generation unit communication topology;
FIG. 4 is a system power generation unit incremental cost iterative change result;
FIG. 5 is a result of an iterative change in power generation unit output;
fig. 6 is a power balance iteration change result.
Detailed Description
In order to make the objects, technical solutions and features of the present invention more apparent, the present invention will be described in further detail below with reference to embodiments and the accompanying drawings.
Example 1: in order to verify the effectiveness of the proposed fully distributed algorithm for solving the economic dispatch optimization problem of the microgrid, an IEEE-30 bus test system is adopted for simulation analysis, the system structure diagram and the communication topology diagram are shown in fig. 2 and 3, and G1 to G6 represent six power generation units. When t is 100s, G5 in normal operation is quitted from operation, and G is detected by the rest power generation units5Exiting and providing more power under new conditions to make up for the power deficit; subsequently, when t is 150s, it is reconnected, and all power generation units automatically adapt to the new topology change and recalculate the output.
The implementation process of the invention can be divided into three major steps:
step 1: defining the minimum total operating cost of the system as an objective function, and defining system parameters contained in the objective function, wherein the system parameters comprise: three cost functions of a conventional power generation unit, a renewable energy power generation unit and an energy storage unit;
step 2: defining power balance constraint and power generation unit output limit constraint in an algorithm;
and step 3: and (4) performing complete consistency algorithm calculation, and searching an optimal solution of the output of the target power grid power generation unit.
Further, the specific steps of step1 are as follows:
step 1.1: the micro-grid economic dispatching aims at minimizing the total running cost of the system, and an algorithm objective function is specifically described as follows:
Figure BDA0002135823390000051
in the formula: f represents the total running cost of the system, $; frRepresents the total operating cost, $, of the renewable energy power generation unit; fcRepresents the total operating cost, $, of the conventional power generation unit; fs representsThe total running cost, $, of the energy storage unit; f. ofri(Pri) And PriRespectively representing the operation cost and output, $ and MW of the ith renewable energy power generation unit; f. ofci(Pci) And PciRespectively representing the operation cost and output, $ MW of the ith conventional power generation unit; f. ofsi(Psi) And PsiRespectively representing the running cost and output of the ith energy storage unit, $ MW; n isr、nc、nsRespectively representing the total number of the various types of power generating units.
Step 1.2: a renewable energy power generation unit cost function is defined. The operation cost of the renewable energy power generation unit not only includes direct operation cost, but also includes punishment cost of limited unit output, and the specific description is as follows:
Figure BDA0002135823390000061
in the formula: priRepresenting the output of the ith renewable energy power generation unit, ai、
Figure BDA0002135823390000062
Respectively represents the cost parameters of the quadratic term, the primary term and the constant term of the ith renewable energy power generation unit, rhoiDenotes the non-compromise factor, P, of the ith renewable energy power generation unitri MAnd Pri mRespectively representing the upper limit and the lower limit of the output of the ith renewable energy power generation unit.
For simplicity of expression, the above formula can be described as follows:
Figure BDA0002135823390000063
in the formula: bi,ciAnd representing the coefficient and the constant term of the first-order term of the cost function of the renewable energy power generation unit after simplified expression.
Specific parameter settings are shown in table 1.
TABLE 1 Power Generation Unit parameters
Figure BDA0002135823390000064
Wherein, assume G1-G4Being a conventional power generating unit, G5Generating power for renewable energy sources, G6For energy storage units and setting system load demand PD＝380MW。
Step 1.3: a cost function of a conventional power generation unit is defined. The operating costs of conventional power generation units are generally described in quadratic form as follows:
Figure BDA0002135823390000071
in the formula: a'i、b′i、c′iRepresenting a cost parameter for the ith conventional power generation unit.
Step 1.4: an energy storage unit cost function is defined. The operating cost of the energy storage unit can be described in the form of a quadratic function as follows:
Figure BDA0002135823390000072
in the formula: a ″)i、
Figure BDA0002135823390000073
Representing a cost parameter for the ith energy storage unit.
To simplify the expression, the above formula can be described as follows:
Figure BDA0002135823390000074
in the formula: b ″)i c″iAnd the coefficient of the first-order term and the constant term represent the cost function of the energy storage unit after simplified expression.
Specific parameter settings are shown in table 1.
The steps 1.2 to 1.4 are not in sequence.
Further, the specific steps of step2 are as follows:
step 2.1: the system needs to satisfy the power balance equality constraint during operation, which is described in detail as follows:
Figure BDA0002135823390000075
in the formula: Δ P represents the system power deviation, MW; pDRepresents the system load demand, MW; pLRepresents the system transmission loss, MW; wherein, PLThe representation can be described as follows:
Figure BDA0002135823390000076
in the formula: beta is ariRepresenting the loss factor of the ith renewable energy power generation unit; beta is aciRepresents the loss factor of the ith conventional power generation unit; beta is asiAnd the loss coefficient of the ith energy storage and power generation unit is shown.
Step 2.2: when the power generation unit operates, the constraint of an output limit inequality is also required to be met, and the specific description is as follows:
Figure BDA0002135823390000077
Figure BDA0002135823390000078
in the formula: pci MAnd Pci mRespectively represent the upper and lower limits of output of the ith conventional power generation unit.
Figure BDA0002135823390000079
In the formula: psi MAnd Psi mRespectively representAnd the upper limit and the lower limit of the output of the ith energy storage unit.
Further, the specific steps of step3 are as follows:
step 3.1: calculating the optimal solution lambda of the incremental cost of each power generation unit by adopting a distributed algorithmi＝λ*The iterative formula of the distributed algorithm is shown as follows:
Figure BDA0002135823390000081
in the formula: lambda [ alpha ]iDenotes incremental cost, λ'iDenotes λiDerivative of (a), λj(t) represents the incremental cost of the adjacent node j of the ith power generation unit at time t, and λ i (t) represents the incremental cost of the ith power generation unit at time t.
Step 3.2: let Ω ∈ { i ∈ V | Pi<Pi m∪Pi>Pi MDenotes the set of power generation units whose output exceeds the limit, then there are:
Figure BDA0002135823390000082
in the formula: pi MRepresenting the upper output limit, P, of all the power generating unitsi mRepresents the lower limit of output of all power generating units, Pi *Represents the optimal output, lambda, of the ith power generation uniti *Represents the optimal incremental cost, σ, of the ith power generation unitiRepresenting a loss penalty factor for the ith power generation unit.
If there are all the power generation units
Figure BDA0002135823390000085
And if so, ending the algorithm and outputting the optimal solution.
Step 3.3: and if any power generation unit i belongs to omega, correcting the initial value of the output of the power generation unit as follows:
Figure BDA0002135823390000083
in the formula: pi(0) Represents the calculation result of the output of the ith power generation unit at the time point 0, betaiRepresenting the loss factor of the ith power generation unit.
Step 3.4: when generating unit
Figure BDA0002135823390000086
And then, recalculating a new convergence solution according to the steps, returning to the step3.2 for judgment again until the result converges to obtain an optimal solution, and ending the algorithm. Through repeated iterative solution, the incremental cost of the power generation unit converges to the optimal solution lambda*9.3626, supply and demand real-time balance is realized under the premise of considering transmission loss. The optimal output of the system power generation unit is shown in table 2, the economic dispatching target result of the system is shown in table 3, the incremental cost iterative change result of the system power generation unit is shown in fig. 4, the output iterative change result of the power generation unit is shown in fig. 5, and the power balance iterative change result is shown in fig. 6.
TABLE 2 optimal output of the power generating unit
Figure BDA0002135823390000084
Figure BDA0002135823390000091
TABLE 3 System economic dispatch target
Figure BDA0002135823390000092
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes and modifications can be made without departing from the spirit and scope of the present invention.

Claims (3)

1. A micro-grid distributed scheduling method considering renewable energy is characterized by comprising the following steps: the method comprises the following steps:
step 1: defining the minimum total operating cost of the system as an objective function, and defining system parameters contained in the objective function, wherein the system parameters comprise: three cost functions of a conventional power generation unit, a renewable energy power generation unit and an energy storage unit;
step 2: defining power balance constraint and power generation unit output limit constraint;
step 3: performing complete consistency algorithm calculation, and searching an optimal solution of a target power grid power generation unit;
the specific steps of Step1 are as follows:
step1.1: the micro-grid economic dispatching aims at minimizing the total running cost of the system, and an algorithm objective function is specifically described as follows:
Figure FDA0003637648970000011
in the formula: f represents the total running cost of the system, $; frRepresents the total running cost, $, of the renewable energy power generation unit; fcRepresents the total operating cost, $, of the conventional power generation unit; fs represents the total operating cost, $, of the energy storage unit; f. ofri(Pri) And PriRespectively representing the operation cost and output, $ and MW of the ith renewable energy power generation unit; f. ofci(Pci) And PciRespectively representing the operation cost and output, $ and MW of the ith conventional power generation unit; f. ofsi(Psi) And PsiRespectively representing the operation cost and output power, $ and MW of the ith energy storage unit; n isr、nc、nsRespectively representing the total number of various power generation units;
step1.2: defining a renewable energy power generation unit cost function: the operation cost of the renewable energy power generation unit not only includes direct operation cost, but also includes punishment cost of limited unit output, and the specific description is as follows:
Figure FDA0003637648970000012
in the formula: priRepresenting the output of the ith renewable energy power generation unit, ai、
Figure FDA0003637648970000013
Respectively represents the cost parameters of the quadratic term, the primary term and the constant term of the ith renewable energy power generation unit, rhoiDenotes the non-compromise factor, P, of the ith renewable energy power generation unitri MAnd Pri mRespectively representing the upper limit and the lower limit of the output of the ith renewable energy power generation unit;
to simplify the expression, the above formula can be described as follows:
Figure FDA0003637648970000014
in the formula: bi,ciRepresenting the coefficient and the constant term of the first-order term of the cost function of the renewable energy power generation unit after simplified expression;
step1.3: defining a cost function for a conventional power generation unit: the operating costs of a conventional power generation unit are described in the form of a quadratic function as follows:
Figure FDA0003637648970000021
in the formula: a'i、b′i、c′iRepresenting a cost parameter for the ith conventional power generation unit;
step1.4: defining an energy storage unit cost function: the operating cost of the energy storage unit can be described in the form of a quadratic function as follows:
Figure FDA0003637648970000022
in the formula: a ″)i、
Figure FDA0003637648970000023
Representing a cost parameter of the ith energy storage unit;
to simplify the expression, the above formula can be described as follows:
Figure FDA0003637648970000024
in the formula: b ″)ic″iRepresenting a first-order term coefficient and a constant term of the cost function of the energy storage unit after simplified expression;
the steps Step1.2-1.4 are not in sequence.
2. The distributed scheduling method for the microgrid considering renewable energy according to claim 1, characterized in that: the specific steps of Step2 are as follows:
step2.1: the system needs to satisfy the power balance equality constraint during operation, which is described in detail as follows:
Figure FDA0003637648970000025
in the formula: Δ P represents the system power deviation, MW; pDRepresents the system load demand, MW; pLRepresents the system transmission loss, MW; wherein, PLThe representation can be described as follows:
Figure FDA0003637648970000026
in the formula: beta is ariRepresenting the loss factor of the ith renewable energy power generation unit; beta is aciRepresents the loss factor of the ith conventional power generation unit; beta is asiRepresenting the loss coefficient of the ith energy storage and power generation unit;
step2.2: when the power generation unit operates, the constraint of an output limit inequality is also required to be met, and the specific description is as follows:
Figure FDA0003637648970000027
Figure FDA0003637648970000031
in the formula: pci MAnd Pci mRespectively representing the upper limit and the lower limit of the output of the ith conventional power generation unit;
Figure FDA0003637648970000032
in the formula: psi MAnd Psi mRespectively representing the upper limit and the lower limit of the output of the ith energy storage unit.
3. The distributed scheduling method for the microgrid considering renewable energy according to claim 2, characterized in that: the specific steps of Step3 are as follows:
step3.1: calculating the optimal solution lambda of the incremental cost of each power generation unit by adopting a distributed algorithmi＝λ*The iterative formula of the distributed algorithm is shown as follows:
Figure FDA0003637648970000033
in the formula: lambdaiDenotes the incremental cost, λ, of the ith power generation uniti' means lambdaiDerivative of (a), λj(t) represents the incremental cost of the adjacent node j of the ith power generation unit at the moment t, and λ i (t) represents the incremental cost of the ith power generation unit at the moment t;
step3.2: let Ω ∈ { i ∈ V | Pi<Pi m∪Pi>Pi MExpressesThe set of power generating units whose force exceeds the limit then has:
Figure FDA0003637648970000034
in the formula: pi MRepresenting the upper output limit, P, of all the power generating unitsi mRepresents the lower limit of output, P, of all power generating unitsi *Represents the optimal output, lambda, of the ith power generation uniti *Represents the optimal incremental cost, σ, of the ith power generation unitiA loss penalty factor representing the ith power generation unit;
if there are all the power generation units
Figure FDA0003637648970000035
If so, ending the algorithm and outputting an optimal solution;
step3.3: and if any power generation unit i belongs to omega, correcting the initial value of the output of the power generation unit as follows:
Figure FDA0003637648970000036
in the formula: pi(0) Represents the calculation result of the output of the ith power generation unit at the time point 0, betaiRepresents the loss factor of the ith power generation unit;
step3.4: when generating unit
Figure FDA0003637648970000037
And then, recalculating a new convergence solution according to the steps, returning to the step3.2 for judgment again until the result converges to obtain an optimal solution, and ending the algorithm.
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