CN113283105B - Energy internet distributed optimal scheduling method considering voltage safety constraint - Google Patents

Energy internet distributed optimal scheduling method considering voltage safety constraint Download PDF

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CN113283105B
CN113283105B CN202110644084.2A CN202110644084A CN113283105B CN 113283105 B CN113283105 B CN 113283105B CN 202110644084 A CN202110644084 A CN 202110644084A CN 113283105 B CN113283105 B CN 113283105B
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power supply
energy
power
equipment
energy internet
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CN113283105A (en
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滕菲
张裕欣
单麒赫
杨添剀
李铁山
张琼月
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Dalian Maritime University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • 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
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides an energy internet distributed optimal scheduling method considering voltage safety constraint, which comprises the following steps: acquiring operating parameter information of the energy supply unit and the energy consumption unit according to intelligent acquisition equipment installed in the system; constructing an energy internet energy management model by taking the lowest equipment operation cost as a primary optimization target based on the acquired power supply equipment operation cost parameters; and acquiring an optimal scheme of the energy Internet optimal scheduling problem based on an energy management model of the energy Internet according to the actual load value required by each device in the energy Internet. The invention is based on the energy Internet simultaneously comprising distributed new energy equipment and traditional power generation equipment, takes economic benefits as a primary optimization target, realizes the optimized scheduling of power output of the power supply equipment, and fully responds to the actual load demand on the basis of ensuring safe operation in the actual operation period, thereby improving the safety performance of each node in the system and reducing the operation cost of the system.

Description

Energy internet distributed optimal scheduling method considering voltage safety constraint
Technical Field
The invention relates to the field of energy system optimization and scheduling and the field of energy internet optimization scheduling based on safety constraints, in particular to an energy internet distributed optimization scheduling method considering voltage safety constraints.
Background
The development of the human society and the progress of the society at present can not provide sufficient energy supply, but with the gradual exhaustion of the traditional fossil energy and the environmental pollution hazard brought by the traditional power supply mode, the problems of energy crisis and energy conservation and emission reduction are receiving more and more attention. Renewable energy power supply equipment such as a photovoltaic generator set and a wind generating set provides a new solution for the severe problem. An Energy Internet (EI) is taken as a new Energy system architecture for consuming renewable Energy and improving the use efficiency of distributed Energy, and the organic combination of an information communication technology and an Energy system is promoted. A large number of distributed new energy devices exist in an energy internet, certain uncertainty and volatility are brought to system operation, and the problems of low operation efficiency, low economic benefit and the like can be caused. Therefore, while the energy internet is economically optimized, the overall operation safety of the system in the scheduling process should be fully considered, and meanwhile, the quick response of the load demand is realized.
The economic optimization scheduling model based on the energy Internet is various in types, at present, supply and demand balance constraint is only concerned in the research of optimization problems, and because a large number of new energy devices such as photovoltaic generator sets and wind generating sets are arranged in the system and the conditions such as intermittence and volatility are bound to occur, the economic optimization model is accurately established, the system is guaranteed to operate stably and efficiently, and the establishment of the alternating current model fully considering the voltage safety characteristic is the key for effectively reducing the influence of the new energy devices on the stability of the system.
Because the limiting conditions such as the voltage safety constraint and the like considered at present are non-linear constraints which seriously affect the calculation efficiency of the optimization method, the operation scheme obtained by neglecting the actual factors is difficult to have practical significance to some extent. In view of the above problems, how to optimize a system model considering voltage safety constraints is still a problem to be solved urgently while ensuring the accuracy of a scheduling scheme and increasing the computation rate.
Disclosure of Invention
According to the technical problem that the calculation efficiency is low due to the nonlinearity of the optimization constraint condition, the distributed energy optimization scheduling method based on the alternative algorithm with the tracking characteristic is provided, which considers the voltage safety and is used for the energy Internet. According to the invention, the energy utilization efficiency is further improved by coordinating and optimizing the output power of the power supply equipment in the system, so that network optimization scheduling distribution is carried out, the characteristics of high calculation efficiency, accurate optimization scheme and the like are achieved, the problem of excessive calculation is reduced as far as possible, and the double improvement effects of economic benefit and system safety performance are realized.
The technical means adopted by the invention are as follows:
an energy internet distributed optimization scheduling method considering voltage safety constraint is realized based on an energy internet, and the energy internet comprises the following steps: the system comprises an energy supply unit, an energy consumption unit and an energy management center, wherein the energy supply unit comprises distributed new energy equipment and traditional power supply equipment; the energy consumption unit comprises a life load and a key load; the energy management center comprises a load data acquisition unit and a power output optimization scheduling center of the power supply equipment;
the method comprises the following steps:
s1: acquiring operation parameter information of the energy supply unit and the energy consumption unit according to intelligent acquisition equipment installed in a system, wherein the operation parameter information comprises the number of power supply equipment, the total load demand amount during operation and the operation cost parameter of the power supply equipment, and the number of the power supply equipment comprises the number of new energy power supply equipment and the number of traditional fuel power supply equipment;
s2: constructing an energy internet energy management model by taking the lowest equipment operation cost as a primary optimization target based on the acquired power supply equipment operation cost parameters;
s3: according to the actual load value required by each device in the energy Internet, based on an energy management model of the energy Internet, optimizing and iterating the power supply output power, the power supply error distance and the marginal cost of each power supply device in the energy supply unit by adopting a distributed alternative multiplier algorithm with a tracking characteristic, stopping iteration when an optimized iteration result meets all physical constraints of the energy Internet, and obtaining the power output scheme of the power supply device, namely the optimal scheme of the energy Internet optimization scheduling problem to be solved.
Furthermore, each energy node in the energy internet needs to satisfy voltage safety constraints, and a mathematical model of the energy node can be expressed as follows:
Figure GDA0003372038200000031
wherein, PjActive power, MW, injected/tapped at the kth node in the system;
Qj-the injected/tapped reactive power, Mvar, is tapped off at the kth node in the system;
Figure GDA0003372038200000032
-hyperplane coefficients relating to active power and reactive power, respectively, at a voltage lower limit;
Figure GDA0003372038200000033
-hyperplane coefficients relating to active power and reactive power at the upper voltage limit, respectively;
N+-total number of nodes in the energy internet.
Further, the distributed new energy device in the energy supply unit comprises a photovoltaic unit and a fan unit; the conventional power supply equipment comprises an internal combustion engine set, and the objective function of the optimization method also comprises the cost of routine maintenance of the power supply equipment.
Further, the optimization iteration equation in S3 is:
Figure GDA0003372038200000034
ΔPk+1＝WΔPk+AdPk+1-AdPk
μk+1＝Wμk+cΔPk+1
in the formula, Pk-the power supply device supplies power to the electrical output power state, MW, at the kth sub-optimal iteration,
Pk+1-the power supply device supplies power to the output power state, MW, at the k +1 th sub-optimal iteration,
omega-a feasible domain that satisfies supply and demand balance, voltage safety and output power constraints,
w-connection weight based on communication topology between power supply devices,
Ad-a communication topology between the power supply devices,
c-the iteration step size,
ΔPk-the power supply equipment power supply error, MW, at the kth sub-optimal iteration,
ΔPk+1-the power supply equipment power supply error, MW, at the k +1 th suboptimal iteration,
μk+1marginal cost status information at k + 1-th suboptimal iteration,
μkmarginal cost state information at kth suboptimal iteration.
Further, all physical constraints of the energy internet are subjected to linearization processing by a security domain method and then are analyzed and calculated.
Further, the connection weight between the power supply devices meets the requirement
Figure GDA0003372038200000035
And when i ≠ j,
Figure GDA0003372038200000041
when the value of i is equal to j,
Figure GDA0003372038200000042
wherein, | NiL-the number of power supply devices,
|Nijl-the number of neighbour devices of the power supply device i,
τ — minuscule positive number.
Compared with the prior art, the invention has the following advantages:
the method is researched aiming at the economic optimization scheduling problem of the energy Internet, and provides a distributed economic optimization scheduling method considering security constraints based on an alternative multiplier algorithm with tracking characteristics. On the basis of ensuring the maximum economic benefit of the operation of the whole system, the invention can output power for electricity through each power supply device in the energy-assisted internet, improve the consumption of renewable energy and carry out the optimal configuration on the energy level. According to simulation comparison, the method provided by the invention has high calculation efficiency, namely, the required iteration times are few, and the calculated optimized scheduling scheme can simultaneously meet the economical efficiency, safety and accuracy of system operation.
Based on the reason, the method can be widely popularized in the field of energy optimization scheduling.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a diagram of an energy Internet economic optimization management system architecture in accordance with the present invention, in which security constraints are taken into account;
FIG. 2 is a diagram of an IEEE33 power saving system and communication topology contemplated by the present invention;
FIG. 3 is a summary of characteristic information for the present invention and two sets of comparison algorithms;
FIG. 4 is a simulated (power supply error, marginal cost) output trajectory of a distributed economic optimization method of the present invention that takes into account safety constraints and conventional constraints (supply-demand balance, power supply output limit constraints);
FIG. 5 is a simulated (power supply error, marginal cost) output trajectory of a distributed economic optimization method considering only conventional constraints (supply-demand balance, power supply output limit constraints);
FIG. 6 is a simulated (power supply error, marginal cost) output trajectory of a centralized economic optimization method taking into account voltage constraints and conventional constraints (supply-demand balance, power supply output limit constraints);
FIG. 7 is a flow chart of a study protocol of the present invention;
FIG. 8 is a graph of the comparison of the power supply output of the power supply apparatus of the method of the present invention and two sets of comparison methods.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The energy internet distributed energy optimization scheduling method considering the safety constraint provided by the invention can ensure the safe reliability of the operation of the power supply equipment while optimally distributing the power supply output of the power supply equipment in the system, and fig. 1 is a system architecture diagram of the embodiment. The Energy internet System in the present invention can be roughly divided into a Load System (LS) and a power supply System (ES), wherein the Load System in the present embodiment mainly considers a business area, a residential area, a factory, and the like in the System; the power supply system mainly comprises renewable energy power supply equipment such as a photovoltaic unit (PV), a fan unit (WT) and the like and traditional power supply equipment such as an internal combustion engine unit (FG). And (3) optimizing and scheduling by considering a load demand target required to be achieved by each power supply device in a time period, and establishing a distributed optimization scheduling model taking the economic benefit of operation as an optimization target by fully considering constraint conditions of supply and demand balance, device output limit, voltage safety and the like of a system in the optimization process. Both the power supply system and the combined system can be considered as a set of energy units
Aiming at the optimization model, the invention provides a distributed alternative multiplier algorithm based on tracking characteristics, which can convert an energy optimization scheduling problem into an optimal cost strategy. And in the optimization process, optimization iteration is mainly carried out on three variables of a power supply output variable P, a power supply error variable delta P and a corresponding marginal cost mu of the power supply equipment.
Based on the embodiment, the invention is based on a standard IEEE33 node system as a research basis, and the corresponding communication topology mechanism is shown in fig. 2, wherein nodes 2 and 27 are connected to a wind turbine set, nodes 15 and 22 are connected to a photovoltaic set, nodes 20 and 31 are connected to an internal combustion engine set, and 27 nodes 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 23, 24, 25, 26, 28, 29, 30, 32, 33 and the like are pure load nodes.
The economic optimization scheduling scheme calculation flow is as follows:
step 1: the method comprises the following steps of establishing an objective function by taking the lowest operation cost of each power supply device of the energy internet as an optimization target, and analyzing the operation cost of the power supply device:
Figure GDA0003372038200000061
in the formula, PiThe power supply power of the ith power supply device is given;
Cfuelas a function of operating costs of conventional fuel-powered equipment;
Crenewablean operating cost function of the new energy power supply equipment;
and N is the number of power supply equipment in the system.
Figure GDA0003372038200000062
Figure GDA0003372038200000063
PhAnd PsRespectively for fuel-powered equipment andthe power of the new energy power supply equipment is given out;
m and n respectively represent the number of corresponding devices;
ah、bh、chparameters of a quadratic term, a primary term and a constant term in the operation cost function of the traditional power supply equipment are respectively set;
as、csand respectively providing parameters of a quadratic term and a constant term in the operation cost function of the new energy power supply equipment.
Reactive power Q for power supply devices in an objective functioniIn the invention, when the solving economic benefit is maximized, namely the energy consumption cost is minimized, each power supply device carries out reactive compensation, so that Q is ensurediThe method and the device are always kept stable and unchanged in the optimization process, so that the influence caused by reactive power can be ignored in the method and the device.
Step 2: on the basis of ensuring reasonable operation of the system, the safety and the stability in the process of optimizing and scheduling are improved, and constraint limiting conditions such as supply and demand balance constraint, power supply equipment power supply and output constraint, voltage safety constraint and the like are increased. The method specifically comprises the following steps:
step 2.1: supply and demand balance constraint:
considering the demands of residential areas, business areas, factories and the like on the load side in the energy internet on power loads, and combining the actual situation of power output by power supply equipment, the supply and demand balance constraint of active power and reactive power can be obtained, and the mathematical model can be expressed as follows:
Figure GDA0003372038200000071
Figure GDA0003372038200000072
note: compared with the sum of the outputs of all power supply equipment in the system, the loss value of the electric energy in the transmission process is small and negligible.
In the formula, Pi(MW)Representing the active power output by the power supply equipment at the ith node;
Qi(Mvar) represents the reactive power of the power supply equipment at the ith node;
Ldrepresents the electrical load requirement that the system as a whole needs to meet, and is a non-negative number, MW;
Lqrepresents the sum of the reactive power, Mvar, required to be achieved within the system;
and N represents the total number of nodes where the power generation equipment is located in the system.
Step 2.2: the power supply equipment is used for restraining the power output:
considering the working performance of the equipment, the output power of the equipment is limited and restricted:
Pi min≤Pi≤Pi max,
Figure GDA0003372038200000073
Figure GDA0003372038200000074
Pi minand Pi maxRespectively representing the minimum/large active power, MW output by the ith power supply equipment;
Figure GDA0003372038200000075
and
Figure GDA0003372038200000076
respectively representing the minimum/large reactive power, Mvar, output by the ith power supply equipment;
and N represents the total number of nodes where the power generation equipment is located in the system.
Step 2.3: voltage safety constraint:
based on the upper and lower voltage limits of the kth node in the system, the mathematical model can be expressed as:
Figure GDA0003372038200000077
Pjrepresents the active power injected/tapped at the kth node in the system (positive injected, negative tapped), MW;
Qjrepresents the reactive power injected/tapped at the kth node in the system (positive injected, negative tapped), Mvar;
when the power supply equipment exists in the node and is in a working state, PjAnd QjIs positive, when the node is a pure load node, PjAnd QjIs a negative value;
Figure GDA0003372038200000081
hyperplane coefficients representing active and reactive power at a lower voltage limit;
Figure GDA0003372038200000082
hyperplane coefficients representing active and reactive power at an upper voltage limit;
V0is the root node voltage, V in this embodiment0＝Vn(the voltage reference value),
Figure GDA0003372038200000083
and V isn＝12.66kV。
And step 3: data acquisition and energy supervision in the system:
in this embodiment, the power supply device and the load device in the energy internet system are equipped with instruments such as an intelligent electric meter for reasonable supervision and data information acquisition. The intelligent electric meter performs summary calculation on the collected load demand information, and transmits related data information to a data processing center for data storage through communication ways such as a computer network. In addition, a real-time supervision system is arranged in the system to collect the power supply equipment power output information, and when the load demand is not matched with the power supply equipment power output sum, namely the supply and demand balance constraint limit is not met, the supervision system can timely transmit the warning information to workers to provide reference for subsequent maintenance.
And 4, step 4: establishing an energy optimization management model based on the first three steps, and establishing a distributed economic optimization scheduling algorithm aiming at the model:
in this embodiment, optimization iteration is performed on three system variables, namely, the power supply output power of the power supply device, the device power supply error distance, and the marginal cost (lagrange multiplier), and a mathematical expression of the iteration process, that is, a distributed economic optimization scheduling algorithm, can be expressed as:
step 4.1: the power supply equipment outputs power to the power supply equipment in an iterative expression mode:
Figure GDA0003372038200000084
in the formula, Pk+1Representing the power supply equipment power supply output power state during the k +1 second optimization iteration;
Ω represents a feasible domain that satisfies all constraints (at this time, supply-demand balance, voltage safety, and output power limitation);
w is a connection weight based on a communication topological structure between power supply equipment;
Adrepresenting a communication topology between the power supply devices;
c is the step size, which in this embodiment is selected to be 0.001;
ΔPkindicating the power supply equipment power supply error at the kth suboptimal iteration.
Step 4.2: the power supply equipment power supply error iterative expression:
ΔPk+1＝WΔPk+AdPk+1-AdPk
in the formula,. DELTA.Pk+1Indicating the power supply equipment power supply error at the k +1 th sub-optimization iteration.
Step 4.3: marginal cost iteration expression:
μk+1＝Wμk+cΔPk+1
μk+1representing marginal cost state information when the (k + 1) th optimization iteration is carried out;
μkrepresenting marginal cost state information at the kth suboptimal iteration.
And 5: initialization setting (P) of system variables0、ΔP0、μ0And c), obtaining an optimized scheduling scheme by combining the load demand information according to an energy optimized management model and a distributed economic optimized scheduling algorithm:
step 5.1: performing first iteration on the power supply output power of the power supply equipment according to the energy management model and the economic optimization algorithm, and calculating to obtain P1；
Step 5.2: calculating power supply error state information of the power supply equipment, and calculating to obtain delta P1；
Step 5.3: calculating marginal cost state information, and calculating to obtain mu1；
Step 5.4: completing one iteration optimization, and acquiring system sequence track information;
step 5.5: checking whether the system sequence track information meets the convergence state, and if the system sequence track information meets the convergence state, ending the process to obtain an optimized scheduling scheme; and if the convergence is not satisfied, returning to continue the iterative computation.
As can be seen from fig. 4, the method provided in this embodiment reaches a convergence state around performing 10 iterations, that is, while considering the safety constraint, an optimal scheduling scheme when the economic benefit is maximized is found.
Fig. 3 is a summary of feature comparison information of the method and two sets of comparison methods provided by the present invention, fig. 5 and fig. 6 are optimized scheduling output trajectories obtained by the two sets of comparison methods in the prior art, wherein fig. 5 is an optimized scheduling output trajectory that does not consider safety constraints but only considers conventional constraints (supply and demand balance constraints, output power limit constraints), which cannot meet the actual requirements for safe operation of the power grid at present; fig. 6 is a centralized optimized scheduling output trajectory considering both the safety constraint and the conventional constraint, which cannot satisfy the actual situation of the strongly distributed characteristic of the current power grid system. Furthermore, comparing the method with this method at the level of computation rate (iteration number), the iteration number of the distributed economic optimization scheduling method proposed herein is equal to that of the centralized method.
FIG. 8 is a histogram of the power output of the method of the present invention and two sets of comparison methods, which can be objectively illustrated, and the distributed economic optimization scheduling method of the present invention has the characteristics of safety, reliability, high efficiency, etc.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, although the present invention is described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it is to be understood that modifications may be made to the technical solutions described in the foregoing embodiments, or some or all of the technical features may be equivalently replaced, and the modifications or the replacements may not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (3)

1. An energy internet distributed optimization scheduling method considering voltage safety constraint is realized based on an energy internet, and the energy internet comprises the following steps: the system comprises an energy supply unit, an energy consumption unit and an energy management center, wherein the energy supply unit comprises distributed new energy equipment and traditional power supply equipment; the energy consumption unit comprises a life load and a key load; the energy management center comprises a load data acquisition unit and a power output optimization scheduling center of the power supply equipment;
characterized in that the method comprises the following steps:
s1: acquiring operation parameter information of the energy supply unit and the energy consumption unit according to intelligent acquisition equipment installed in a system, wherein the operation parameter information comprises the number of power supply equipment, the total load demand amount during operation and the operation cost parameter of the power supply equipment, and the number of the power supply equipment comprises the number of new energy power supply equipment and the number of traditional fuel power supply equipment;
s2: constructing an energy internet energy management model based on the acquired power supply equipment operation cost parameters by taking the lowest equipment operation cost as an optimization target;
s3: according to the actual load value required by each device in the energy Internet, based on an energy management model of the energy Internet, optimizing and iterating the power supply output power, the power supply error distance and the marginal cost of each power supply device in an energy supply unit by adopting a distributed alternative multiplier algorithm with tracking characteristics, stopping iteration when an optimized iteration result meets all physical constraints of the energy Internet, wherein the power output scheme of the power supply device at the moment is the optimal scheme of the energy Internet optimization scheduling problem to be solved, and the optimization iteration equation is as follows:
Figure FDA0003372038190000011
ΔPk+1＝WΔPk+AdPk+1-AdPk
μk+1＝Wμk+cΔPk+1
in the formula, Pk-the power supply device supplies power to the electrical output power state, MW, at the kth sub-optimal iteration,
Pk+1-the power supply device supplies power to the output power state, MW, at the k +1 th sub-optimal iteration,
omega-a feasible domain that satisfies supply and demand balance, voltage safety and output power constraints,
w-connection weight based on communication topology between power supply devices,
Ad-a communication topology between the power supply devices,
c-the iteration step size,
ΔPk-the power supply equipment power supply error, MW, at the kth sub-optimal iteration,
ΔPk+1-the power supply equipment power supply error, MW, at the k +1 th suboptimal iteration,
μk+1marginal cost status information at k + 1-th suboptimal iteration,
μkmarginal cost state information at kth suboptimal iteration;
each energy node in the energy internet needs to meet voltage safety constraint, and a mathematical model of the energy internet can be expressed as follows:
Figure FDA0003372038190000021
wherein, PjActive power, MW, injected/tapped at the kth node in the system;
Qj-the injected/tapped reactive power, Mvar, is tapped off at the kth node in the system;
Figure FDA0003372038190000022
-hyperplane coefficients relating to active power and reactive power, respectively, at a voltage lower limit;
Figure FDA0003372038190000023
-hyperplane coefficients relating to active power and reactive power at the upper voltage limit, respectively;
N+-total number of nodes in the energy internet;
connection weight value satisfaction based on communication topological structure between power supply devices
Figure FDA0003372038190000024
And when i ≠ j,
Figure FDA0003372038190000025
when the value of i is equal to j,
Figure FDA0003372038190000026
wherein, | NiL-the number of power supply devices,
|Nijl-the number of neighbour devices of the power supply device i,
τ — minuscule positive number.
2. The energy internet distributed optimization scheduling method considering the voltage safety constraint is characterized in that the distributed new energy devices in the energy supply unit comprise a photovoltaic unit and a fan unit; the conventional power supply device comprises an internal combustion engine group, and the objective function of the optimization method also comprises the cost of routine maintenance of the power supply device.
3. The energy internet distributed optimization scheduling method considering the voltage safety constraint is characterized in that all physical constraints of the energy internet are subjected to linearization processing by a safety domain method and then are subjected to analysis and calculation.
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