CN111193263A - Method suitable for multi-region economic dispatching of smart power grid - Google Patents

Method suitable for multi-region economic dispatching of smart power grid Download PDF

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CN111193263A
CN111193263A CN202010076018.5A CN202010076018A CN111193263A CN 111193263 A CN111193263 A CN 111193263A CN 202010076018 A CN202010076018 A CN 202010076018A CN 111193263 A CN111193263 A CN 111193263A
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economic dispatching
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秦家虎
万艳妮
李曼
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University of Science and Technology of China USTC
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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
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers

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Abstract

The utility model provides a method suitable for multi-zone economic dispatch of a smart grid, which comprises the following steps: step 1: calculating the virtual single-region increment cost and the optimal output power of a plurality of regional power grid systems; step 2: calculating the power imbalance between the total power generation and the total demand in each area in the power grid system; and step 3: solving the economic dispatching problem of each area in the power grid system, namely solving the incremental cost of each area; and 4, step 4: comparing the incremental cost of the power grid system with the incremental cost of each area to determine the flow direction of the transfer power of the tie line; and step 5: and checking the capacity constraint of the tie line, determining a transfer power path, and completing the multi-region economic dispatching of the intelligent power grid. The method and the device can solve the technical problems of heavy calculation and communication burden, low expansibility, low algorithm efficiency and the like of a power grid centralized scheduling mode in the prior art.

Description

Method suitable for multi-region economic dispatching of smart power grid
Technical Field
The disclosure relates to the technical field of energy scheduling and management of smart power grids, in particular to a method suitable for multi-region economic scheduling of a smart power grid.
Background
With the rapid development of social economy, the traditional power grid can not meet various requirements of people in the aspect of electric power, and therefore, the smart power grid capable of realizing efficient utilization of electric energy becomes a development hotspot of current new technologies and new industries. The intelligent power grid dispatching is used as a key technology and link of power grid operation and is an important measure for ensuring the safe and stable operation of a power system.
The economic dispatching of the power grid can ensure the real-time balance of power generation and power utilization, and further ensure the safety, reliability and high efficiency of the power grid operation. Specifically, the start-stop states and the generating capacity of a plurality of generating units on the power supply side are controlled in real time according to the operation and load evaluation data of the power grid, and the safety constraints of the generating units and the system are considered, so that the real-time balance and the safe operation of the power grid system are ensured. In addition, an advanced detection technology is adopted in the intelligent power grid system, and the method is very beneficial to improving the economy and the stability of the power grid operation.
However, the existing scheduling algorithm is mainly centralized, and schedules the power generation amount of the power generation unit according to the power imbalance between the two ends of the power supply side and the power utilization side, and requires a centralized controller/aggregator in the power grid to collect or predict the information of the load and the power generation unit, and then centrally controls and schedules the states and the power generation amounts of all the power generation units. However, such a centralized scheduling method will greatly increase the burden of computation and communication, and requires a large storage space to store all the collected load and power generation unit information, so that the scalability of the system is not very friendly. In addition, the centralized scheduling algorithm cannot ensure real-time balance of power on the supply and demand sides of the power grid in the whole iteration process, and only realizes the supply and demand balance of the power when the iteration is terminated, so that efficient utilization of energy cannot be realized; on one hand, when the scale of the power grid system is large, the collection and processing of information can take longer time and cost; on the other hand, when some units fail or the grid system needs to add new loads or power generation units, the centralized controller/aggregator needs to re-run the scheduling algorithm to obtain a new scheduling scheme. The existing distributed scheduling algorithm mostly adopts dual decomposition and sub-gradient algorithm depending on first-order gradient information, and has the problems of easy falling into local optimum, low convergence speed, low algorithm efficiency and the like.
BRIEF SUMMARY OF THE PRESENT DISCLOSURE
Technical problem to be solved
Based on the above problems, the present disclosure provides a method suitable for multi-region economic scheduling of a smart power grid, so as to alleviate technical problems of heavy calculation and communication burden, low expansibility, low algorithm efficiency, and the like in a centralized scheduling manner of a power grid in the prior art.
(II) technical scheme
The utility model provides a method suitable for multi-zone economic dispatch of a smart grid, which comprises the following steps:
step 1: calculating the virtual single-region increment cost and the optimal output power of a plurality of regional power grid systems;
step 2: calculating the power imbalance between the total power generation and the total demand in each area in the power grid system;
and step 3: solving the economic dispatching problem of each area in the power grid system, namely solving the incremental cost of each area;
and 4, step 4: comparing the incremental cost of the power grid system with the incremental cost of each area to determine the flow direction of the transfer power of the tie line; and
and 5: and checking the capacity constraint of the tie line, determining a transfer power path, and completing the multi-region economic dispatching of the intelligent power grid.
In an embodiment of the present disclosure, the step 1 includes:
step 1.1: initializing a power generation cost coefficient and giving a set of initial values;
step 1.2: calculating the dual variable ωk；
Step 1.3: calculating the Newton direction Deltaxk；
Step 1.4: selecting a suitable step length d by using a backtracking search methodkNamely, internal circulation is carried out;
step 1.5: updating step length dk←βdkWherein β is a constant taken from the value (0, 1), and
step 1.6: by using the generated energy x at the time of kkThe generated energy x at the moment of k +1 is obtained through updatingk+1。
In an embodiment of the present disclosure, step 1.4 comprises:
step 1.4.1: initializing an arbitrary step size dk；
Step 1.4.2: calculating an initial vector:
step 1.4.3: executing an average consistency algorithm; and
step 1.4.4: the termination condition of the internal loop is judged.
In the embodiment of the present disclosure, the termination condition of the internal loop (i.e., the loop with index τ) is determined as follows:
Figure BDA0002378140370000031
Figure BDA0002378140370000032
and
Figure BDA0002378140370000033
respectively represent vectors v2Sum vector v3The average consistency algorithm is executed and then the two norms of the convergence values are obtained.
In this disclosure, step 1 further includes:
step 1.7: judging the termination condition of the outer loop (i.e. the loop with index k)
Figure BDA0002378140370000034
Wherein σ > 0 and is a constant,
Figure BDA0002378140370000035
and
Figure BDA0002378140370000036
respectively represent vectors v1Sum vector v2Executing the two norms of the convergence value after the average consistency algorithm is executed, and if the condition is met, executing the step 1.8:
step 1.8: checking upper and lower limit constraints of generated energy, i.e. judging Pi *＞Pi maxAnd Pi *＜Pi minI ∈ {1, 2., n }, where Pi maxAnd Pi minRespectively representing the maximum and minimum allowable power generation amounts, P, of the power generating unit ii *Represents the optimal amount of power generation of the ith power generation unit; if two stripsIf the conditions are not satisfied simultaneously, x output in step 1.6k+1I.e. the optimum output power P*。
In the disclosed embodiment, in step 1.8, if Pi *＞Pi maxSatisfy, let Pi *＝Pi max(ii) a If Pi *<Pi minSatisfy, let Pi *＝Pi min，i∈ΓPWherein r isPRepresenting the set of all the power generation units exceeding the power generation upper and lower limit constraints, resetting the parameters and variables, and then returning to the step 1.1.
In the embodiment of the present disclosure, in step 2, the formula is used
Figure BDA0002378140370000037
Calculating a power imbalance between total power production and total demand in each zone, wherein Ps IMIndicating a power imbalance in the region s,
Figure BDA0002378140370000038
the optimal amount of power generation, P, of the m-th power generation cell representing the area sDsThe total load requirement of the region s is represented, s is larger than 0, and m is larger than 0, and index indexes of the power generation units in the region and the region are represented respectively.
In the embodiment of the present disclosure, in step 3, a plurality of single-region economic scheduling problems are solved through a distributed newton algorithm, that is, each region is regarded as a single-region system, and the incremental cost of each region is obtained.
In the embodiment of the present disclosure, in step 4, if λ isarea，s＞λsysThen transfer power Ps IMFlowing into the region s; if λarea，s<λsysThen transfer power Ps IMFlowing from region s; wherein λ issysFor incremental cost of the system, λarea，sThe incremental cost of region s.
In the disclosed embodiment, in step 5, the tie line capacity constraint is checked
Figure BDA0002378140370000041
Wherein T isss′Representing the transfer of power between region s and region s';
Figure BDA0002378140370000042
represents the maximum margin for transferring power between region s and region s'; if the inequality constraint is satisfied, directly outputting the optimal power generation amount and the optimal tie line transfer power; otherwise, the optimal transfer power of the region s will be set to its maximum transfer power, and the excess transfer power will be transferred through other intermediate regions with less transfer power that do not reach the upper limit, and finally transferred to the original destination region s'.
(III) advantageous effects
According to the technical scheme, the method for multi-region economic dispatching of the smart grid has at least one or part of the following beneficial effects:
(1) communication pressure and calculation burden are reduced;
(2) the integrated controller is not required to be communicated with all the power generation units to obtain the global information required by iteration, and only the information of local and adjacent power generation units is relied on, so that the iteration process of the whole algorithm can be realized in a distributed mode, and the reliability and the expandability of the system are further improved.
Drawings
Fig. 1 is a schematic flow chart of a method for multi-region economic scheduling of a smart grid according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a multi-region economic dispatch of a smart grid according to an embodiment of the disclosure.
Fig. 3 is a schematic flowchart architecture diagram of a method for multi-region economic scheduling of a smart grid according to an embodiment of the present disclosure.
Fig. 4 is a schematic flowchart of the process architecture of step 1 in the method for multi-region economic scheduling of the smart grid according to the embodiment of the present disclosure.
Detailed Description
The utility model provides a method suitable for multi-region economic dispatch of smart power grids, utilize the Newton method to update the generated energy of power generation unit to decide the optimal tie line and shift power, thereby under the prerequisite of guaranteeing all power generation units and system safety constraint, minimize the power generation cost of system. The updating process utilizes second-order gradient information, namely a Hessian matrix (Hessian matrix), and the convergence rate of the algorithm is obviously improved. In addition, in the iteration process, each power generation unit only needs to perform information interaction with adjacent power generation units, and then a consistency algorithm is executed to obtain required common necessary information, so that the communication pressure and the calculation load are reduced to a great extent. In the process, the centralized controller is not required to be communicated with all the power generation units to obtain the global information required by iteration, and only the information of the local power generation units and the information of the adjacent power generation units are relied on, so that the iteration process of the whole algorithm can be realized in a distributed mode, and the reliability and the expandability of the system are further improved.
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
In an embodiment of the present disclosure, a method for multi-region economic dispatching of a smart grid is provided, which is shown in fig. 1 to 4, and includes:
step 1: calculating the virtual single-region increment cost and the optimal output power of a plurality of regional power grid systems;
solving a virtual single-area economic dispatch problem (SAEDP) by using a Distributed Newton Method (DNM) to calculate the incremental cost lambda of the power grid systemsysAnd optimal output power P under single region*；
The step 1 comprises the following steps:
step 1.1: initializing a power generation cost coefficient and giving a set of initial values;
step 1.2: by passing
Figure BDA0002378140370000051
Calculating the dual variable ωk(corresponding to. lambda.)sys) Wherein A is an element of all 1n-dimensional row vectors, wherein n is more than or equal to 1 and represents the number of all power generation units in the whole virtual single-region system; hkA hessian matrix representing a cost function f at time k;
Figure BDA0002378140370000052
representing cost function f with respect to power generation vector xkFirst order gradient information of (1);
step 1.3: by passing
Figure BDA0002378140370000053
Calculating the Newton direction Deltaxk；
Step 1.4: selecting a suitable step length d by using a backtracking search methodk；
Step 1.4.1: initializing an arbitrary step size dk；
Step 1.4.2: calculating an initial vector:
Figure BDA0002378140370000061
wherein
Figure BDA0002378140370000062
i ∈ {1, 2.. multidata, n } indicates that the ith power generation unit at time k provides power
Figure BDA0002378140370000063
Cost per unit of electricity consumed;
Figure BDA0002378140370000064
wherein
Figure BDA0002378140370000065
i ∈ {1, 2.. multidata, n } indicates that the ith power generation unit at time k provides power
Figure BDA0002378140370000066
Cost per unit of electricity consumed;
Figure BDA0002378140370000067
i∈{1，2，.., n represents the Newton direction of the ith power generation unit at time k;
Figure BDA0002378140370000068
wherein α is a constant taken from the value of (0, 1/2),
Figure BDA0002378140370000069
i belongs to {1, 2.. multidata, n } and represents first-order gradient information of the cost function of the ith power generation unit at the moment k;
step 1.4.3: performing an average-consensus algorithm (average-consensus algorithm) vj(τ+1)＝vj(τ)-εLvj(tau), j belongs to {1, 2, 3}, wherein j represents a vector index, tau represents an iteration index of an inner loop, L represents a Laplace matrix corresponding to a communication topological graph of the power generation unit, and epsilon is a value of (0, 1/max)ilii) Constant of (c 1)iiIs the diagonal element of the matrix L;
step 1.4.4: determining termination conditions of internal circulation
Figure BDA00023781403700000610
Figure BDA00023781403700000611
And
Figure BDA00023781403700000612
respectively represent vectors v2Sum vector v3The average consistency algorithm is executed and then the two norms of the convergence values are obtained. If the condition is met, continuing to execute the next step (step 1.5); otherwise, returning to the step 1.2 to perform the next iteration of the inner loop;
step 1.5: updating step length dk←βdkWherein β is a constant taken from the value of (0, 1);
step 1.6: using xk+1＝xk+dkΔxkTo the generated energy x at the moment kkThe generated energy x at the moment of k +1 is obtained through updatingk+1；
Step 1.7: determining termination conditions of external circulation
Figure BDA00023781403700000613
Wherein σ > 0 and is a constant,
Figure BDA00023781403700000614
and
Figure BDA00023781403700000615
represent the two norms of the convergence values after the vector v1 and the vector v2 respectively execute the average consistency algorithm; if the condition is met, the next step (step 1.8) is executed; otherwise, returning to the step 1.2 to perform the next iteration of the outer loop;
step 1.8: checking upper and lower limit constraints of generated energy, i.e. judging Pi *＞Pi maxAnd Pi *<Pi minI ∈ {1, 2., n }, where Pi maxAnd Pi minRespectively representing the maximum and minimum allowable power generation amounts, P, of the power generating unit ii *Indicates the optimum amount of power generation by the ith power generation unit. If both conditions are not satisfied, x output in step 1.6k+1I.e. the optimum output power P*(ii) a Otherwise (when either condition is satisfied): if P is greater than Pi maxSatisfy, let Pi *＝Pi maxIf P isi *<Pi minSatisfy, let Pi *＝Pi min，i∈ΓPWherein r isPRepresenting the set of all the power generation units exceeding the power generation upper and lower limit constraints, resetting the parameters and variables such as x, n, stop and the like, and then returning to the step 1.1.
Step 2: calculating the power imbalance between the total power generation and the total demand in each area in the power grid system;
calculating the power imbalance (power imbalance) between the total power generation and the total demand in each region
Figure BDA0002378140370000071
Wherein P iss IMIndicating a power imbalance in the region s,
Figure BDA0002378140370000072
the optimal amount of power generation, P, of the m-th power generation cell representing the area sDsRepresenting the total load demand of a region s, wherein s is more than 0, and m is more than 0, and the index indexes of the power generation units in the region and the region are respectively represented;
and step 3: solving the economic dispatching problem of each area in the power grid system, namely solving the incremental cost of each area;
a plurality of single-region economic scheduling problems are solved by using a distributed newton's algorithm (DNM) shown in fig. 4, that is, each region is regarded as a single-region system, and an incremental cost of each region is obtained, taking region s as an example, and the incremental cost of the region is λarea，s；
And 4, step 4: and comparing the incremental cost of the power grid system with the incremental cost of each area to determine the flow direction of the tie line transfer power.
Comparing system incremental cost λsysAnd the area incremental cost λarea，sTo determine the direction of flow of tie-line transfer power. In particular, if λarea，s＞λsysThen transfer power Ps IMFlowing into the region s; if λarea，s<λsysThen transfer power Ps IMFlowing from region s;
and 5: checking the capacity constraint of a tie line, determining a transfer power path, and completing multi-region economic dispatching of the intelligent power grid;
checking tie line capacity constraints
Figure BDA0002378140370000081
Wherein T isss"indicates that power is transferred between the region s and the region s';
Figure BDA0002378140370000082
representing the maximum margin for transferring power between region s and region s'. If the inequality constraint is satisfied, directly outputting the optimal power generation amount and the optimal tie line transfer power; otherwise, the optimum transfer power of the region s will be set to its maximum transfer power, and the excess transfer power will have less chance to reach the upper limit by othersThe intermediate zone transfer of power is transferred and finally transferred to the original destination zone s'.
In the embodiment of the present disclosure, as shown in fig. 2, a schematic diagram of a multi-zone economic dispatch of a power grid system is shown, where the system includes 4 zones, and each zone has 4 distributed power generation units, where P issmS belongs to {1, 2, 3, 4}, and m belongs to {1, 2, 3, 4}, and represents the power generation amount of the mth distributed power generation unit of the region s; t isss', s ∈ {1, 2, 3}, and s ' ∈ {2, 3, 4}, which indicate the transfer power of the region s to the region s ' through the tie line. The objective of multi-zone economic scheduling is therefore to determine the optimal scheduling of all generators in each zone, subject to a set of constraints
Figure BDA0002378140370000083
And optimal tie line transfer power between regions
Figure BDA0002378140370000084
Cost of power generation of the system CtolAnd minimum.
In addition to the field of smart grid economic dispatch listed in the present disclosure, the present disclosure can also be applied to the problems of smart grid demand response and energy management, logistics field logistics vehicle intelligent dispatch, and large-scale factory/workshop production dispatch and resource allocation.
So far, the embodiments of the present disclosure have been described in detail with reference to the accompanying drawings. It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. Furthermore, the above definitions of the various elements and methods are not limited to the particular structures, shapes or arrangements of parts mentioned in the examples, which may be easily modified or substituted by one of ordinary skill in the art, for example:
iteration step length d of Newton's method in this disclosurekThe acquisition (i.e. step 1.4) method of (a) is not unique, and for example, the quadratic interpolation line search method can also obtain a suitable iteration step size at a small cost.
From the above description, those skilled in the art should clearly recognize that the method of the present disclosure is applicable to multi-region economic dispatch of a smart grid.
In summary, the present disclosure provides a method suitable for multi-region economic scheduling of a smart grid, that is, a Newton method-based distributed scheduling algorithm, which avoids the defects caused by a centralized scheduling algorithm and improves the reliability, flexibility and expandability of the algorithm. In addition, the invention aims to further improve the convergence rate and the operation efficiency of the algorithm by adopting second-order gradient information, namely a Hessian matrix, update the generated energy of the power generation unit by using a Newton method, and determine the optimal tie line transfer power, thereby minimizing the power generation cost of the system on the premise of ensuring the safety constraint of all the power generation units and the system. The updating process utilizes second-order gradient information, namely a Hessian matrix (Hessian matrix), and the convergence rate of the algorithm is obviously improved. In addition, in the iteration process, each power generation unit only needs to perform information interaction with adjacent power generation units, and then a consistency algorithm is executed to obtain required common necessary information, so that the communication pressure and the calculation load are reduced to a great extent. In the process, the centralized controller is not required to be communicated with all the power generation units to obtain the global information required by iteration, and only the information of the local power generation units and the information of the adjacent power generation units are relied on, so that the iteration process of the whole algorithm can be realized in a distributed mode, and the reliability and the expandability of the system are further improved.
It should also be noted that directional terms, such as "upper", "lower", "front", "rear", "left", "right", and the like, used in the embodiments are only directions referring to the drawings, and are not intended to limit the scope of the present disclosure. Throughout the drawings, like elements are represented by like or similar reference numerals. Conventional structures or constructions will be omitted when they may obscure the understanding of the present disclosure.
And the shapes and sizes of the respective components in the drawings do not reflect actual sizes and proportions, but merely illustrate the contents of the embodiments of the present disclosure. Furthermore, in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.
Unless otherwise indicated, the numerical parameters set forth in the specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by the present disclosure. In particular, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". Generally, the expression is meant to encompass variations of ± 10% in some embodiments, 5% in some embodiments, 1% in some embodiments, 0.5% in some embodiments by the specified amount.
Furthermore, the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
The use of ordinal numbers such as "first," "second," "third," etc., in the specification and claims to modify a corresponding element does not by itself connote any ordinal number of the element or any ordering of one element from another or the order of manufacture, and the use of the ordinal numbers is only used to distinguish one element having a certain name from another element having a same name.
In addition, unless steps are specifically described or must occur in sequence, the order of the steps is not limited to that listed above and may be changed or rearranged as desired by the desired design. The embodiments described above may be mixed and matched with each other or with other embodiments based on design and reliability considerations, i.e., technical features in different embodiments may be freely combined to form further embodiments.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Also in the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various disclosed aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, disclosed aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this disclosure.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims (10)

1. A method suitable for multi-region economic dispatching of a smart grid comprises the following steps:
step 1: calculating the virtual single-region increment cost and the optimal output power of a plurality of regional power grid systems;
step 2: calculating the power imbalance between the total power generation and the total demand in each area in the power grid system;
and step 3: solving the economic dispatching problem of each area in the power grid system, namely solving the incremental cost of each area;
and 4, step 4: comparing the incremental cost of the power grid system with the incremental cost of each area to determine the flow direction of the transfer power of the tie line; and
and 5: and checking the capacity constraint of the tie line, determining a transfer power path, and completing the multi-region economic dispatching of the intelligent power grid.
2. The method for multi-region economic dispatching of the smart grid according to claim 1, wherein the step 1 comprises:
step 1.1: initializing a power generation cost coefficient and giving a set of initial values;
step 1.2: calculating the dual variable ωk；
Step 1.3: calculating the Newton direction Deltaxk；
Step 1.4: selecting a suitable step length d by using a backtracking search methodkNamely, internal circulation is carried out;
step 1.5: updating step length dk←βdkWherein β is a constant taken from the value (0, 1), and
step 1.6: by using the generated energy x at the time of kkThe generated energy x at the moment of k +1 is obtained through updatingk+1。
3. The method applicable to multi-region economic dispatching of the smart grid according to claim 2, wherein the step 1.4 comprises the following steps:
step 1.4.1: initializing an arbitrary step size dk；
Step 1.4.2: calculating an initial vector:
step 1.4.3: executing an average consistency algorithm; and
step 1.4.4: the termination condition of the internal loop is judged.
4. According to claimThe method applicable to multi-region economic scheduling of the smart grid according to claim 3, wherein the termination condition of the internal loop (i.e. the loop with the index τ) is determined as follows:
Figure FDA0002378140360000021
Figure FDA0002378140360000022
and
Figure FDA0002378140360000023
respectively represent vectors v2Sum vector v3The average consistency algorithm is executed and then the two norms of the convergence values are obtained.
5. The method for multi-region economic dispatching of the smart grid according to claim 1, wherein the step 1 further comprises:
step 1.7: judging the termination condition of the outer loop (i.e. the loop with index k)
Figure FDA0002378140360000024
Wherein σ > 0 and is a constant,
Figure FDA0002378140360000025
and
Figure FDA0002378140360000026
respectively represent vectors v1Sum vector v2Executing a two-norm of the convergence value after the average consistency algorithm is executed, and executing a step 1.8 if the condition is met;
step 1.8: checking upper and lower limit constraints of generated energy, i.e. judging Pi *＞Pi maxAnd Pi *<Pi minI ∈ {1, 2., n }, where Pi maxAnd Pi minRespectively representing the maximum and minimum allowable power generation amounts, P, of the power generating unit ii *Represents the optimal amount of power generation of the ith power generation unit; if both conditions are simultaneously absentIf yes, x output in step 1.6k+1I.e. the optimum output power P*。
6. The method for multi-region economic dispatching of smart grid according to claim 5, wherein in step 1.8, if P isi *＞Pi maxSatisfy, let Pi *＝Pi max(ii) a If Pi *<Pi minSatisfy, let Pi *＝Pi min，i∈ΓPWherein r isPRepresenting the set of all the power generation units exceeding the power generation upper and lower limit constraints, resetting the parameters and variables, and then returning to the step 1.1.
7. The method for multi-region economic dispatching of the smart grid according to claim 1, wherein in step 2, the economic dispatching is performed according to a formula
Figure FDA0002378140360000027
Calculating a power imbalance between the total power production and the total demand in each zone, wherein
Figure FDA0002378140360000028
Indicating a power imbalance in the region s,
Figure FDA0002378140360000029
the optimal amount of power generation, P, of the m-th power generation cell representing the area sDsThe total load requirement of the region s is represented, s is larger than 0, and m is larger than 0, and index indexes of the power generation units in the region and the region are represented respectively.
8. The method as claimed in claim 1, wherein in step 3, the multiple single-region economic dispatching problem is solved through a distributed newton's algorithm, that is, each region is regarded as a single-region system, and the incremental cost of each region is obtained.
9. According to claim 1In the method suitable for multi-region economic dispatching of the smart grid, in the step 4, if lambda is determinedarea，s＞λsysThen transfer power
Figure FDA0002378140360000031
Flowing into the region s; if λarea，s<λsysThen transfer power
Figure FDA0002378140360000032
Flowing from region s; wherein λ issysFor incremental cost of the system, λarea，sThe incremental cost of region s.
10. The method for multi-region economic dispatching of the smart grid according to claim 1, wherein in step 5, the tie line capacity constraint is checked
Figure FDA0002378140360000033
Wherein T isss＇Representing the transfer of power between region s and region s';
Figure FDA0002378140360000034
represents the maximum margin for transferring power between region s and region s'; if the inequality constraint is satisfied, directly outputting the optimal power generation amount and the optimal tie line transfer power; otherwise, the optimal transfer power of the region s will be set to its maximum transfer power, and the excess transfer power will be transferred through other intermediate regions with less transfer power that do not reach the upper limit, and finally transferred to the original destination region s'.
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