CN112183865B - Distributed scheduling method for power distribution network - Google Patents
Distributed scheduling method for power distribution network Download PDFInfo
- Publication number
- CN112183865B CN112183865B CN202011052362.7A CN202011052362A CN112183865B CN 112183865 B CN112183865 B CN 112183865B CN 202011052362 A CN202011052362 A CN 202011052362A CN 112183865 B CN112183865 B CN 112183865B
- Authority
- CN
- China
- Prior art keywords
- power
- distribution network
- power distribution
- scheduling
- micro
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 89
- 238000005265 energy consumption Methods 0.000 claims abstract description 67
- 230000008569 process Effects 0.000 claims abstract description 30
- 230000003993 interaction Effects 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 19
- 230000003044 adaptive effect Effects 0.000 claims abstract description 8
- 238000004146 energy storage Methods 0.000 claims description 51
- 238000002485 combustion reaction Methods 0.000 claims description 39
- 230000009977 dual effect Effects 0.000 claims description 28
- 238000005192 partition Methods 0.000 claims description 18
- 239000000446 fuel Substances 0.000 claims description 17
- 238000010248 power generation Methods 0.000 claims description 12
- 238000000638 solvent extraction Methods 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
- 230000029087 digestion Effects 0.000 claims description 5
- 238000012937 correction Methods 0.000 abstract description 3
- 238000000354 decomposition reaction Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 21
- 238000005457 optimization Methods 0.000 description 15
- 238000004364 calculation method Methods 0.000 description 11
- 230000002829 reductive effect Effects 0.000 description 6
- 230000008878 coupling Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 238000005859 coupling reaction Methods 0.000 description 5
- 238000002347 injection Methods 0.000 description 5
- 239000007924 injection Substances 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000007599 discharging Methods 0.000 description 4
- 230000010355 oscillation Effects 0.000 description 4
- 101100412394 Drosophila melanogaster Reg-2 gene Proteins 0.000 description 2
- 102100023882 Endoribonuclease ZC3H12A Human genes 0.000 description 2
- 101710112715 Endoribonuclease ZC3H12A Proteins 0.000 description 2
- 101001096074 Homo sapiens Regenerating islet-derived protein 4 Proteins 0.000 description 2
- 101000813777 Homo sapiens Splicing factor ESS-2 homolog Proteins 0.000 description 2
- 101000712674 Homo sapiens TGF-beta receptor type-1 Proteins 0.000 description 2
- 101001044053 Mus musculus Lithostathine-1 Proteins 0.000 description 2
- 102100037889 Regenerating islet-derived protein 4 Human genes 0.000 description 2
- 102100039575 Splicing factor ESS-2 homolog Human genes 0.000 description 2
- 102100033456 TGF-beta receptor type-1 Human genes 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- QGVYYLZOAMMKAH-UHFFFAOYSA-N pegnivacogin Chemical compound COCCOC(=O)NCCCCC(NC(=O)OCCOC)C(=O)NCCCCCCOP(=O)(O)O QGVYYLZOAMMKAH-UHFFFAOYSA-N 0.000 description 2
- 108700012361 REG2 Proteins 0.000 description 1
- 101150108637 REG2 gene Proteins 0.000 description 1
- 108091058543 REG3 Proteins 0.000 description 1
- 101100120298 Rattus norvegicus Flot1 gene Proteins 0.000 description 1
- 101100412403 Rattus norvegicus Reg3b gene Proteins 0.000 description 1
- 102100027336 Regenerating islet-derived protein 3-alpha Human genes 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000000418 atomic force spectrum Methods 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy 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
- H02J3/46—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
-
- 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]
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Power Engineering (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a distributed scheduling method of a power distribution network, which comprises the steps of firstly establishing a global scheduling model of the power distribution network, giving global robust cost according to maximum acceptable running risk, distributing robust cost coefficients for each REG power station in the global scheduling model of the power distribution network by minimizing standby requirements of a whole system, constructing the distributed scheduling model of the power distribution network through regional decomposition and model correction, finally solving the distributed scheduling model of the power distribution network by adopting an adaptive step length ADMM algorithm, obtaining scheduling results of the power distribution network, and scheduling the power distribution network. The invention reduces the system energy consumption brought by the scheduling strategy on the premise of ensuring the system operation reliability, and can realize the balance of the reliability and the economy of the power distribution network. Meanwhile, when the distributed scheduling model of the power distribution network is built, the interaction process among all the subareas is considered, and the distributed scheduling model of the power distribution network is solved by adopting an adaptive step length ADMM algorithm, so that the solving efficiency of the scheduling model of the system is greatly improved.
Description
Technical Field
The invention belongs to the field of intelligent power distribution network optimal scheduling, and particularly relates to a power distribution network distributed scheduling method.
Background
With the large-scale access of renewable energy power generation (Renewable Energy Generation, REG) and distributed power sources (Distributed Generation, DG) such as distributed energy storage, the system structure and the operation main body of the power distribution network are increasingly complex, and gradually evolve to an Active power distribution network (Active DistributionNetwork, ADN) with certain regulation and control capability. Under the background, the defects that the traditional centralized scheduling method has high communication requirement, cannot protect information privacy of an operation main body and the like are increasingly remarkable, and the safety and stable operation requirements of ADN cannot be met. Meanwhile, uncertainty of REG protrusion of photovoltaic, wind power and the like also brings serious challenges to reliable operation of the power distribution network.
The distributed optimization does not need to regulate and control central coordination, the bottleneck of centralized optimization in calculation and communication efficiency is overcome through local communication and parallel calculation, and the distributed optimization has strong adaptability to large-scale access of DG. Among the distributed optimization methods, the alternate direction multiplier method (Alternative Direction Method ofMultipliers, ADMM) is common and practical. By decomposing the power distribution network into a plurality of subsystems and reserving the tide information of the coupling branches, the ADMM can realize the distributed optimization of the whole system only by a small amount of information interaction of adjacent subsystems. The method combines the advantages of a dual decomposition method and an augmented Lagrangian multiplier method, has excellent robustness and distributed computing capacity, is widely applied to the aspect of optimizing operation of the intelligent power distribution network, and receives wide attention of academia and industry.
Reasonable selection of iteration step length is a key for influencing convergence performance of ADMM algorithm. The traditional ADMM algorithm usually gives an iteration step artificially, and is not adjusted in the calculation process, which may cause severe oscillation and overshoot of the objective function in the iteration process, seriously affects the convergence performance of the algorithm, and has lower calculation efficiency. Meanwhile, in order to cope with uncertainty of REG output when the power distribution network is optimally scheduled, means such as robust optimization are often adopted to improve reliability and robustness of a scheduling strategy. The robust optimization ensures that the scheduling strategy can ensure the safe operation of the system in a given REG uncertainty interval by considering the worst operation scene of the system, and is essentially a method for sacrificing the operation economy of the system in exchange for the reliability. However, the occurrence probability of the worst operation scene is extremely low, in the actual power grid operation process, a scheduler tends to bear a certain operation risk to improve the economy of a scheduling strategy, and the reliability and the economy of the power distribution network cannot be reasonably balanced.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a distributed scheduling method of a power distribution network, which aims to solve the technical problems that the computing efficiency is low and the reliability and the economy of the power distribution network cannot be balanced in the prior art.
In order to achieve the above object, in a first aspect, the present invention provides a distributed scheduling method for a power distribution network, including the following steps:
s1, taking the sum of minimized micro-fuel engine power generation energy consumption and external power grid auxiliary energy consumption as an objective function, and establishing a power distribution network global scheduling model by adopting an adjustable robust method;
s2, after the global robust cost is given according to the maximum acceptable running risk, distributing robust cost coefficients for each REG power station in the global scheduling model of the power distribution network by minimizing the standby requirement of the whole system;
s3, decoupling and partitioning the power distribution network by integrating renewable energy consumption capability indexes and community modularity indexes to obtain a plurality of subareas, correspondingly splitting a global scheduling model of the power distribution network according to the subareas to obtain scheduling models of the subareas, and correcting the scheduling models of the subareas by introducing interaction processes among the subareas to obtain a distributed scheduling model of the power distribution network;
s4, inputting the obtained REG power station output and electric load short-term prediction data into the distributed scheduling model of the power distribution network, solving by adopting an adaptive step length ADMM algorithm to obtain a scheduling result of the power distribution network, and scheduling the power distribution network according to the scheduling result.
Further preferably, the power distribution network includes: ac distribution grids, REG power stations accessed in photovoltaic or wind power form, battery energy storage devices, and ac loads.
Further preferably, the objective function of the global scheduling model of the power distribution network is:
wherein f is the total operation energy consumption of the system, C G The energy consumption for the micro-combustion engine operation; c (C) sup The energy consumption for auxiliary power supply of the upper power grid to the power distribution network is T, which is the total scheduling time length and N G P is the number of micro-fuel engines in the whole system G,s,t The output value of the s-th micro-combustion engine at the t moment, b s and cs Are all the energy consumption parameters of the s-th micro-combustion engine, u s,t The switching state of the s-th micro-combustion engine at the t moment; c sup,t and Psup,t And the energy consumption coefficient and the auxiliary power supply power of the auxiliary power supply of the upper power grid to the power distribution network at the t moment are respectively.
Further preferably, the constraint condition of the global scheduling model of the power distribution network includes: linearized distribution network DistFlow tide constraint, micro-combustion engine operation constraint, energy storage power station operation constraint, photovoltaic output uncertainty constraint based on robust cost, upper power grid auxiliary energy supply power constraint, system power balance and standby constraint.
Further preferably, the above-mentioned system-wide standby requirement D is:
wherein T is the total scheduling time length, N PV Delta, which is the total number of REG power stations in the whole system k,t The robust cost factor assigned to the kth REG power station for time t,for the power error limit value Γ of the kth REG power station at time t t For the global robust cost at time t, +.>Is the minimum of the robust cost coefficients that can be assigned to the kth REG power station.
Further preferably, the step S3 includes the steps of:
s31, comprehensive renewable energy source digestion capability indexAnd community modularity index rho to obtain comprehensive partition index wherein ,w1 and w2 The weight coefficients are respectively a renewable energy consumption capacity index and a community modularity index of the power distribution network;
s32, traversing all partition schemes by adopting a clustering method, decoupling and partitioning the power distribution network by taking the partition scheme with the minimum comprehensive partition index gamma as an optimal partition scheme to obtain a plurality of subareas, and correspondingly splitting a global scheduling model of the power distribution network according to the subareas to obtain a scheduling model of each subarea;
s33, correcting an objective function and power balance of the scheduling model of each subarea and reserve constraint after dual conversion by introducing an interaction process between each subarea, so as to obtain a distributed scheduling model of the power distribution network.
Further preferably, the objective function of the ith sub-area in the distributed scheduling model of the power distribution network is:
wherein ,fi For the equivalent energy consumption of the ith sub-area,energy consumption for operating the micro-combustion engine in the ith sub-region,/->Energy consumption for auxiliary power supply of the upper grid to the ith sub-area, < >>A consistency objective function for the ith sub-region; t is the total scheduling time length, < > and>for the number of micro-engines in the ith sub-area,/->For the output value of the s-th micro-gas engine in the i-th sub-zone at time t,/> and />Are all the energy consumption parameters of the s-th micro-combustion engine in the i-th subarea,/->The switching state of the s-th micro-fuel engine in the i-th subarea at the t moment; /> and />The energy consumption coefficient and the auxiliary power supply power of auxiliary power supply of the upper power grid to the ith sub-area of the power distribution network at the t moment respectively; { c k Is a set of subregions, X i,j For the ith sub-region c i And jth sub-region c j Inter-link decision variable set, x i,t Sub-region c at time t i Is a tie decision variable,/->The ith sub-area c at time t i Neighborhood c of (2) j C is i The given reference value of the decision variable of the mth generation interconnecting line, m is the iteration times of the alternative direction multiplier method; />For c in the mth iteration process i And c j Lagrangian multiplier, ρ m Is the iteration step length of the mth iteration process.
Further preferably, correcting the objective function and the power balance of the scheduling model of each subarea and the dual-converted standby constraint to obtain the power balance and the dual-standby constraint of each subarea of the distribution network distributed scheduling model; the power balance and dual standby constraint of the ith sub-area are as follows:
wherein ,for the number of energy storage stations in the ith sub-area, < +.>For the discharge state of the p-th energy-storage station in the i-th sub-zone at time t, +.>The discharge power of the p energy storage power station in the ith sub-area at the moment t,for the total number of REG power stations in the ith sub-region, < +.>For the actual power value, P, of the kth REG power station in the ith sub-zone at time t tra,i,j,t Time point of t region c j Direction c i Injected link power, +.>Is the total number of distribution network nodes in the ith sub-area, < +.>For the active load at the nth node in the ith sub-area at time t, +.>For the state of charge of the p-th energy-storage station in the i-th sub-zone at time t +.>For the maximum charging power of the p-th energy storage station in the i-th sub-zone, +.>Maximum value of auxiliary power for auxiliary power supply of the upper power grid to the distribution network in the ith sub-area, +.>Time point of t region c j Direction c i Maximum value of the injected link power, +.>For the upper limit value of the active power of the s-th micro-combustion engine in the i-th subarea, L% is the load reserve rate,> and />Are Lagrangian dual variables in the ith sub-region, +.>Robust cost factor assigned to kth REG plant for ith sub-region at time t，/>The power error limit value of the kth REG power station in the ith sub-area at the moment t.
Further preferably, the scheduling result of the power distribution network includes: the method comprises the steps of starting and stopping states of micro-combustion engines in all subareas, operating conditions of energy storage equipment, power exchange plans of all subareas, other subareas and an upper power grid, and dispatching output plans of the micro-combustion engines in all subareas and the energy storage equipment.
In a second aspect, the present invention further provides a computer readable storage medium, where the computer readable storage medium includes a stored computer program, where when the computer program is executed by a processor, the computer program controls a device where the storage medium is located to execute a distributed scheduling method for a power distribution network provided in the first aspect of the present invention.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
1. the invention provides a distributed scheduling method of a power distribution network, which comprises the steps of firstly establishing a global scheduling model of the power distribution network taking the sum of minimized micro-gas turbine power generation energy consumption and external power grid auxiliary energy consumption as an objective function, then establishing the distributed scheduling model of the power distribution network through regional decomposition and model correction, adopting an adaptive step length ADMM algorithm to realize the solution of the distributed scheduling model of the power distribution network, obtaining a scheduling result of the power distribution network, and scheduling the power distribution network. According to the invention, after the global robust cost is given according to the maximum acceptable operation risk after the global scheduling model of the power distribution network is obtained, the robust cost coefficient is allocated to each REG power station in the global scheduling model of the power distribution network by minimizing the standby requirement of the whole system, so that the standby burden of each power supply of the system can be effectively reduced, the adjustable interval of each power supply is increased, the operation flexibility of the system can be enhanced on the premise of ensuring the operation reliability of the system, the system energy consumption brought by a scheduling strategy is reduced, and the balance between the reliability and the economy of the power distribution network can be realized. Meanwhile, when the distributed scheduling model of the power distribution network is constructed, the interaction process among all the subareas is considered, the distributed scheduling model of the power distribution network is solved by adopting an adaptive step length ADMM algorithm, and the iteration step length of the algorithm is dynamically adjusted according to the result of each iteration calculation, so that the solving efficiency of the scheduling model of the system is greatly improved.
2. According to the distributed scheduling method for the power distribution network, provided by the invention, the scheduling strategy is solved based on the self-adaptive step length ADMM algorithm, the convergence of the global scheduling strategy can be realized through iteration only through limited information interaction among scheduling main bodies (namely system sub-areas), the information safety and privacy of each sub-area of the system are protected, and the energy consumption for running the system is reduced.
Drawings
Fig. 1 is a flowchart of a distributed scheduling method for a power distribution network according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of sub-region decoupling and information interaction in embodiment 1 of the present invention;
fig. 3 is a topology diagram of a power distribution network and a schematic diagram of a partitioning result in embodiment 1 of the present invention;
FIG. 4 is a graph of predicted output baselines for the photovoltaic power generation and electrical load of the system of example 2 of this invention;
FIG. 5 is a graph of the power generated by the micro-gas turbine system of scenario 1 of example 2 of the present invention;
FIG. 6 is a graph of the system energy storage device output of scenario 1 of example 2 of the present invention;
FIG. 7 is a schematic diagram of switching power among sub-regions of the system of scenario 1 in embodiment 2 of the present invention;
FIG. 8 is a schematic diagram of an iterative convergence process of the running energy consumption of each scene system in embodiment 2 of the present invention;
fig. 9 is a schematic diagram of an iterative convergence process of the running energy consumption of the sub-region 1 of the scene 1 in the embodiment 2 of the present invention;
Fig. 10 is a schematic diagram of an iterative convergence process of the running energy consumption of the sub-region 2 of the scene 1 in the embodiment 2 of the present invention;
FIG. 11 is a schematic diagram of an iterative convergence process of the running energy consumption of the sub-region 3 of the scene 1 in the embodiment 2 of the present invention;
fig. 12 is a schematic diagram of an iterative convergence process of the running energy consumption of the sub-region 4 of the scene 1 in the embodiment 2 of the present invention;
FIG. 13 is a schematic diagram of the process of iterative convergence of the original residuals for scenario 1 and scenario 2 in embodiment 2 of the present invention;
FIG. 14 is a schematic diagram of the dual residual iterative convergence procedure of scenario 1 and scenario 2 in embodiment 2 of the present invention;
FIG. 15 is a plot of predicted output baselines for the generation of fan power employed in scenario 5 of example 3 of the present invention;
FIG. 16 is a graph showing the comparison of REG prediction error limits for scenario 4 in example 3 of the present invention;
FIG. 17 is a graph showing the comparison of REG prediction error limits for scenario 5 in example 3 of the present invention;
FIG. 18 is a graph showing the comparison of energy consumption for system operation under each of the robust cost allocation schemes of scenario 5 in example 3 of the present invention;
FIG. 19 is a graph showing the distribution of the robust cost coefficients for scenario 5 scheme 1 in example 3 of the present invention;
FIG. 20 is a graph showing the distribution of the robust cost coefficients under scenario 5 scheme 2 in embodiment 3 of the present invention;
fig. 21 is a robust cost coefficient allocation result under scenario 5 scheme 3 in embodiment 3 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Example 1,
As shown in fig. 1, the distributed scheduling method of the power distribution network comprises the following steps:
s1, taking the sum of minimized micro-fuel engine power generation energy consumption and external power grid auxiliary energy consumption as an objective function, and establishing a power distribution network global scheduling model by adopting an adjustable robust method; it should be noted that, the power distribution network in the present invention includes: ac distribution grids, REG power stations accessed in photovoltaic or wind power form, battery energy storage devices, and ac loads.
Specifically, the objective function of the global scheduling model of the power distribution network is as follows:
wherein f is the total operation energy consumption of the system, C G The energy consumption for the micro-combustion engine operation; c (C) sup The energy consumption for auxiliary power supply of the upper power grid to the power distribution network is T, which is the total scheduling time length and N G P is the number of micro-fuel engines in the whole system G,s,t The output value of the s-th micro-combustion engine at the t moment, b s and cs Are all the energy consumption parameters of the s-th micro-combustion engine, u s,t The switching state of the s-th micro-combustion engine at the t moment; c sup,t and Psup,t And the energy consumption coefficient and the auxiliary power supply power of the auxiliary power supply of the upper power grid to the power distribution network at the t moment are respectively. The constraint conditions of the global scheduling model of the power distribution network comprise: linearized distribution network DistFlow tide constraint, micro-combustion engine operation constraint, energy storage power station operation constraint, photovoltaic output uncertainty constraint based on robust cost, upper power grid auxiliary energy supply power constraint, system power balance and standby constraint; the method comprises the following steps:
(1) Linearized distribution network DistFlow flow constraint:
P vw,t ＝P uv,t -P v,t
Q vw,t ＝Q uv,t -Q v,t
Q v,t ＝Q L,v,t -Q PV,v,t -Q sup,v,t -Q G,v,t
V u,min ≤V u,t ≤V u,max
α ω P uv,t +β ω Q uv,t ≤δ ω S uv ,ω＝1,2…12
wherein ,ruv and xuv The resistance and reactance of the branch u-v formed by the u-th node and the v-th node are respectively; p (P) uv,t and Quv,t Active power flow and reactive power flow of branch u-v at time t respectively, P v,t and Qv,t Active power and reactive power respectively injected into the V-th node at t moment, V v,t For the voltage of the V-th node at time t, V ac,0 Rated voltage of alternating current distribution network, P L,v,t and QL,v,t Active load and reactive load at the v-th node at time t respectively, and />Respectively the power generation power and the power discharge power of the energy storage system on the node v at the moment t, P G,v,t and QG,v,t Respectively the active power and the reactive power of the micro-combustion engine on the v-th node, P PV,v,t and QPV,v,t Active power and reactive power of REG power station on the v-th node respectively, P sup,v,t and Qsup,v,t Active power and reactive power respectively injected into upper power grid on the V-th node, V u,max and Vu,min The upper and lower limit values of the voltage of the u-th node are respectively shown, omega is the number of the regular dodecagon constraint, alpha ω 、β ω 、δ ω Coefficients constrained by regular dodecagons, S uv Is the transmission capacity limit for the leg u-v.
(2) Micro-gas engine operating constraints:
|P G,t,s -P G,t-1,s |≤ramp G,s
wherein ,uG,i,t The start-stop state of the s-th micro-combustion engine at the t moment is specifically a 0-1 variable, wherein when the value is 1, the start-up state of the micro-combustion engine is represented, and when the value is 0, the shutdown state of the micro-combustion engine is represented; p (P) G,s,t and QG,s,t The active power and the reactive power output power of the s-th micro-combustion engine at the t moment are respectively, and />The upper limit value and the lower limit value of the active power of the s-th micro-combustion engine at the t moment are respectively; /> and />The upper limit value and the lower limit value of the reactive power of the s-th micro-gas turbine at the t moment are respectively; ramp (Ramp) G,s Limiting the climbing speed of the s-th micro-combustion engine; t (T) G,s,on,t and TG,s,off,t Respectively the continuous startup and shutdown time of the s-th micro-combustion engine at the t moment; /> and />The minimum continuous start-up time and the minimum continuous shut-down time of the s-th micro-gas turbine are respectively.
(3) Energy storage power station operation constraint:
wherein , and />The charging power and the discharging power of the p-th energy storage power station at the t moment are respectively; /> and />The state quantity of charge and discharge of the p-th energy storage power station at the t moment is specifically 0-1 variable; /> and />The charging power limit value and the discharging power limit value of the p-th energy storage power station are respectively set; /> and />The charging and discharging efficiencies of the p-th energy storage power station are respectively; e (E) ESS,p,t The residual charge capacity of the p-th energy storage power station at the t moment; /> and />The upper limit value and the lower limit value of the residual capacity of the p-th energy storage power station are respectively set; ΔE ESS,p And the maximum change value of the residual capacity of the p-th energy storage power station before and after the scheduling is set.
(4) Photovoltaic output uncertainty constraint based on robust cost:
|Q PV,k,t |≤tan(acos(0.95))·P PV,k,t
wherein ,PPV,k,t 、ΔP PV,k,t 、δ k,t The power error value, the power error limit value, the actual power value and the robust cost coefficient are respectively the predicted power generation power, the power error value and the power error limit value of the kth REG power station at the t moment; Γ -shaped structure t For the global robust cost, the conservation degree measurement of the model to the total uncertainty of the photovoltaic power of the whole system is represented; q (Q) PV,k,t The reactive power of the kth REG power station at the moment t is determined according to the predicted active power.
(5) Auxiliary energy supply power constraint of upper power grid:
wherein ,Psup,t Auxiliary power for auxiliary power supply of the upper power grid to the power distribution network at the t moment, And (5) the maximum value of auxiliary power supply power for auxiliary power supply of the upper power grid to the power distribution network.
(6) System power balancing and standby constraints:
wherein ,NG Is the number of micro-fuel engines in the whole system, N ESS N is the number of energy storage power stations in the whole system L Is the total number of nodes of the power distribution network, P L,n,t The active load at the nth node at the time t is L% the load reserve rate, and the value in the embodiment is 10% -30%.
Further, the power balance and the standby constraint are subjected to dual conversion to obtain dual standby constraint, a violation probability constraint of the dual standby constraint and an auxiliary function, wherein the expressions are respectively as follows:
wherein ,λ1,t 、λ 2,t And μ is a Lagrangian dual variable; ζ is the probability that the above first equation is violated, +.; c (a, b) is an auxiliary function of the model calculation, and a, b are two input variables of C (a, b), respectively.
S2, after the global robust cost is given according to the maximum acceptable running risk, distributing robust cost coefficients for each REG power station in the global scheduling model of the power distribution network by minimizing the standby requirement of the whole system;
specifically, the global robust cost Γ is known by combining the photovoltaic output uncertainty constraint condition based on the robust cost, the dual standby constraint and the violation probability constraint of the dual standby constraint in step S1 t The conservation degree measurement of the model to the total uncertainty of the photovoltaic output of the whole system is characterized, and when Γ t and NPV After the given, determining the probability of being violated by the dual standby constraint; Γ -shaped structure t From the robust cost coefficient delta of each photovoltaic power station k,t (robust cost coefficient allocated to kth REG power station at time t) is added; thus, maintain Γ t Unchanged, delta of each REG power station is regulated k,t The distribution scheme can adjust the economy of the scheduling strategy on the premise of ensuring that the conservation degree of the model is unchanged. From the photovoltaic output uncertainty constraint based on the robust cost, delta can be seen k,t The tolerable range of the characterization model for the corresponding REG plant output uncertainty. In using the same delta kt At the time, the output error limit valueLarger REG power station, which does not determine the output section +.>Also, the larger, the primary power balance and dual backup constraints, the greater the reserve capacity that needs to be reserved for the plant when considering power balance and system backup. Thus, at Γ t Under the premise of a certain degree, is +.>Lower REG power stations assign larger delta k,t At the same time as far as possible +.>Higher REG power stations assign smaller delta k,t The standby requirement of the scheduling model can be effectively reduced, and the economy of the scheduling model is optimized on the basis of unchanged conservation of the scheduling strategy.
Therefore, after the global robust cost is given according to the maximum acceptable running risk, the robust cost coefficient is distributed to each REG power station in the global scheduling model of the power distribution network by minimizing the standby requirement of the whole system; the standby requirement D of the whole system is as follows:
wherein T is the total scheduling time length, N PV Delta, which is the total number of REG power stations in the whole system k,t The robust cost factor assigned to the kth REG power station for time t,for the power error limit value Γ of the kth REG power station at time t t For the global robust cost at time t, +.>Is the minimum of the robust cost coefficients that can be assigned to the kth REG power station.
S3, decoupling and partitioning the power distribution network by integrating renewable energy consumption capability indexes and community modularity indexes to obtain a plurality of subareas, correspondingly splitting a global scheduling model of the power distribution network according to the subareas to obtain scheduling models of the subareas, and correcting the scheduling models of the subareas by introducing interaction processes among the subareas to obtain a distributed scheduling model of the power distribution network;
specifically, step S3 includes the following steps:
s31, comprehensive renewable energy source digestion capability indexAnd community modularity index rho to obtain comprehensive partition index wherein ,w1 and w2 The weight coefficients of the renewable energy consumption capacity index and the community modularity index of the power distribution network are respectively 1.
Specifically, the renewable energy source digestion capability index is:
wherein ,the method is characterized in that the method is used for obtaining renewable energy source digestion capability indexes of the whole power distribution network after partitioning, C is a partitioned set of the power distribution network after partitioning, and C k Represents the kth subinterval, +.>For time subinterval c of t k Maximum value of total power generated by internal REG power station, +.>For time subinterval c of t k The capacity of the internal REG station, in particular by subinterval c k All loads and all adjustable power of the adjustable units at the time t are added.
The community modularity index is:
wherein ρ is a modular strength index of the partitioned system, e ij An edge weight of a branch u-v formed by a u-th node and a v-th node, and e when the u-th node is connected with the v-th node in the undirected unauthorized network ij =1, otherwise e ij ＝0；The sum of the edge weights of all lines of the network; />Representing the sum of the weights of all edges connected to the u-th node; delta (u, v) =1 when the u-th node and the v-th node are in the same partition, whereas delta (u, v) =0.
S32, traversing all partition schemes by adopting a clustering method, decoupling and partitioning the power distribution network by taking the partition scheme with the minimum comprehensive partition index gamma as an optimal partition scheme to obtain a plurality of subareas, and correspondingly splitting a global scheduling model of the power distribution network according to the subareas to obtain a scheduling model of each subarea;
Specifically, fig. 2 is a schematic diagram of sub-region decoupling and information interaction according to the present invention. After the power distribution network partition is completed, decoupling of each power distribution network partition is achieved through the following method. For adjacent sub-areas A, B of the line connection through the u-th and v-th end nodes, nodes v ', u' are incorporated as virtual power injection nodes into sub-area A, B, respectively. The sub-region A, B only needs to perform the tie variable X when the subsequent scheduling model is solved A,B ＝[P u ,P v ,Q u ,Q v ,V u ,V v ,P u,v ]Thereby improving the network communication efficiency and ensuring the information privacy of each power distribution network subarea. Wherein P is u and Pv Active power injection values of the ith node and the nth node respectively; q (Q) u and Qv The reactive power injection values are the nth node and the nth node respectively; v (V) u and Vv The voltage amplitude values of the ith node and the ith node are respectively; p (P) u,v Active power flow from the node to the node v is the u. The schematic diagram of the partitioning result after the power distribution network is partitioned is shown in fig. 3. After the power distribution network is decoupled and partitioned by the method, the types of scheduling resources contained in each subsystem are the same and are consistent with the types of scheduling resources contained in the original power distribution network, and the global scheduling model of the power distribution network can be correspondingly split according to the regions to obtain the scheduling model of each sub-region.
S33, in order to achieve coordination optimization among the subareas, interaction among the subareas is fully considered, and the objective function and the power balance of the scheduling model of each subarea and the standby constraint after dual conversion are corrected by introducing the interaction process among the subareas, so that the distributed scheduling model of the power distribution network is obtained.
Specifically, after correcting the objective function of the scheduling model of the ith sub-area, the objective function of the ith sub-area in the distributed scheduling model of the power distribution network is as follows:
wherein ,fi For the equivalent energy consumption of the ith sub-area,energy consumption for operating the micro-combustion engine in the ith sub-region,/->Energy consumption for auxiliary power supply of the upper grid to the ith sub-area, < >>A consistency objective function for the ith sub-region; t is the total scheduling time length, < > and>for the number of micro-engines in the ith sub-area,/->For the output value of the s-th micro-gas engine in the i-th sub-zone at time t,/> and />Are all the energy consumption parameters of the s-th micro-combustion engine in the i-th subarea,/->The switching state of the s-th micro-fuel engine in the i-th subarea at the t moment; /> and />The energy consumption coefficient and the auxiliary power supply power of auxiliary power supply of the upper power grid to the ith sub-area of the power distribution network at the t moment respectively; { c k Is a set of subregions, X i,j For the ith sub-region c i And jth sub-region c j Inter-link decision variable set (specifically including voltage amplitude, active and reactive power injection amounts and link active power flow of nodes at two ends of link), x i,t Sub-region c at time t i Is a tie decision variable,/->The ith sub-area c at time t i Neighborhood c of (2) j C is i The given reference value of the decision variable of the mth generation interconnecting line, m is the iteration times of the alternative direction multiplier method; />For c in the mth iteration process i And c j Lagrangian multiplier, ε m Is the iteration step length of the mth iteration process.
In addition, the objective function and the power balance of the scheduling model of each subarea and the standby constraint after dual conversion are corrected to obtain the power balance and the dual standby constraint of each subarea of the distribution network distributed scheduling model. Unlike the original distribution grid, the power interaction of each sub-region with the outside world comprises two parts: power interaction with the upper grid and power interaction with adjacent sub-areas. The application regards the interactive power of each subarea and the adjacent subareas as virtual power supply to be brought into the system, and after the power balance of the subareas and the standby constraint after the dual conversion are corrected, the power balance of the ith subarea and the dual standby constraint in the distribution network distributed scheduling model are as follows:
wherein ,for the number of energy storage stations in the ith sub-area, < +.>For the discharge state of the p-th energy-storage station in the i-th sub-zone at time t, +.>The discharge power of the p energy storage power station in the ith sub-area at the moment t,for the total number of REG power stations in the ith sub-region, < +.>For the actual power value, P, of the kth REG power station in the ith sub-zone at time t tra,i,j,t Time point of t region c j Direction c i Injected link power (which is injected through the virtual node),>is the total number of distribution network nodes in the ith sub-area, < +.>For the active load at the nth node in the ith sub-area at time t,for the state of charge of the p-th energy-storage station in the i-th sub-zone at time t +.>For the maximum charging power of the p-th energy storage station in the i-th sub-zone, +.>Maximum value of auxiliary power for auxiliary power supply of the upper power grid to the distribution network in the ith sub-area, +.>Time point of t region c j Direction c i Maximum value of the injected link power, +.>For the upper limit value of the active power of the s-th micro-combustion engine in the i-th subarea, L% is the load reserve rate,> and />Are Lagrangian dual variables in the ith sub-region, +.>A robust cost coefficient assigned to the kth REG power station for the ith sub-region at time t, The power error limit value of the kth REG power station in the ith sub-area at the moment t. />
S4, inputting the obtained REG power station output and electric load short-term prediction data into the distributed scheduling model of the power distribution network, solving by adopting an adaptive step length ADMM algorithm to obtain a scheduling result of the power distribution network, and scheduling the power distribution network according to the scheduling result.
Specifically, the method comprises the following steps:
s41, inputting grid frame parameters of the power distribution network in each region after partitioning, REG power station output and electric load short-term prediction data of each region into a scheduling model, and initializing iteration operators and initial values of tie variables to enable iteration times to be m=1;
s42, extracting a tie line coupling variable reference value from each subarea, obtaining a tie line coupling variable result of the mth iteration, and calculating an original residual error of the mth iterationDual residual +.>
S43, checking whether the calculation result of each region meets a convergence criterion, if so, ending the iteration, and turning to S44; otherwise, the mth iteration calculation result is used as an mth+1th iteration reference value, an iteration operator is updated, the iteration times m=m+1 are set, and the S42 is returned. Specifically, the method for updating the iterative operator comprises the following steps:
in the above-mentioned method, the step of,c is j C is i The reference vector of the decision variable of the mth generation comprises a subarea c j And c i Voltage at two end nodes of interconnecting lineAmplitude, active and reactive power injection and tie active power flow; /> and />The original residual and the dual residual of the mth iteration are respectively.
S44, unit scheduling of all the subareas is carried out according to scheduling results of the power distribution network. Wherein, each sub-region scheduling result comprises: the method comprises the steps of starting and stopping states of micro-combustion engines in all subareas, operating conditions of energy storage equipment, power exchange plans of all subareas, other subareas and an upper power grid, and dispatching output plans of the micro-combustion engines in all subareas and the energy storage equipment.
In order to further illustrate the beneficial effects of the distributed scheduling method for a power distribution network provided by the present invention, the following details are described in connection with embodiment 2 and embodiment 3:
EXAMPLE 2,
The topology diagram of the distribution network adopted by the distribution network in this embodiment is still shown in fig. 3, where the distribution network is a modified IEEE-33 node-based system. The system is provided with micro-engines, denoted G1-G5, at nodes 9, 17, 24, 28 and 32, respectively. The nodes 16 and 30 are respectively provided with an energy storage power station which is respectively marked as ESS1 and ESS2, and the initial charge quantity of the energy storage system is half of the maximum value; the nodes 14, 29, 23, and 20 were respectively connected to photovoltaic cells having capacities of 1000kW, 800kW, 500kW, and 400kW, which are denoted as REG1 to REG4. The detailed scheduling parameters of the micro-fuel machine and the energy storage device are shown in tables 1 and 2, respectively. Taking the energy consumption coefficient of the upper power grid for auxiliary power supply to the power distribution network as a fixed value, and calculating to obtain the energy consumption coefficient of 974 (m 3 ·MW -1 )。
TABLE 1
TABLE 2
| Model parameters | ESS1 (node 16) | ESS2 (node 30) |
| Maximum charge/discharge/MW | 0.4 | 0.8 |
| Maximum charge/MW.h | 2 | 5 |
| Minimum charge/MW.h | 0.2 | 0.5 |
| Charging efficiency | 0.97 | 0.97 |
| Discharge efficiency | 0.95 | 0.95 |
Fig. 4 shows a graph of predicted output baselines of photovoltaic power generation capacity and electric load of the system in this embodiment, wherein dotted lines are predicted output baselines of renewable energy sources in the power distribution network, and in this embodiment, the renewable energy sources in the power distribution network are all photovoltaic (REG 1 to REG 4). The power distribution network line and load data adopt classical IEEE-33 node system parameters. Meanwhile, the load reserve rate L% is 10%, and the prediction error limit value of each REG power station is 30% of the predicted output.
Based on the power distribution network topology, the power distribution network and the equipment parameters, a power distribution network distributed scheduling model is established in combination with the power distribution network distributed scheduling method provided in the embodiment 1. The correlation model has been given in the description of steps S1 to S3 in embodiment 1, and only the correlation data need be substituted into the foregoing model, so that the established model will not be explained in detail.
And then, inputting the obtained prediction data (shown in fig. 4) of the photovoltaic power generation amount and the electric load into a distributed scheduling model of the power distribution network to obtain a scheduling result of the power distribution network, and then scheduling the power distribution network according to the scheduling result. The scheduling is formulated to be executed every 24 hours, the unit scheduling time length is 1 hour, the scheduling time window is 24 hours, and the distributed optimization scheduling result comprises the start-stop states and the operation working conditions of all the micro-fuel engines and the energy storage devices and the scheduling plans of all the micro-fuel engines and the energy storage devices in all the areas.
In order to verify the effectiveness of the model solving method of the present invention, the following 3 operation scenarios are set. The scene 1 adopts the adaptive step length ADMM algorithm (DSS-ADMM) adopted by the invention to carry out model solving; scenario 2 model solving using a conventional ADMM algorithm (conventional ADMM, C-ADMM); and carrying out model solving on the scene 3 by adopting a traditional centralized optimization method. The system micro-gas turbine power generation power curve chart, the system energy storage device output curve chart and the system sub-region exchange power schematic diagrams of the scene 1 are respectively shown in fig. 5, 6 and 7. From the graph, the photovoltaic output is smaller and the load demand is lower in the period of 1:00-6:00, and the micro-fuel machine and the energy storage system jointly meet the load demand. The energy storage system maintains higher-level power generation, reduces the residual electric quantity of the system while fully reducing the running cost of the system, and provides for absorbing the photovoltaic electric energy in the photovoltaic output peak period. Photovoltaic is the primary source of electrical energy for the system during periods of high power of photovoltaic, i.e., 7:00 to 15:00 periods. Because the distance between each subarea photovoltaic power station and the micro-fuel machine is relatively close in system distribution, and the micro-fuel machine is mostly distributed at the tail end of a subarea power supply line and limited by the constraint of the tidal current, the micro-fuel machine maintains relatively small output force or stops, and only G5 generates power to meet the load demands of nearby nodes. Meanwhile, the energy storage system is charged in a high power mode, so that the residual energy of the energy storage reaches a high level, and the energy storage system is prepared for discharging in a high-load period. Within 16:00-24:00, the system load level is maintained at a higher level while the photovoltaic output gradually decreases to 19:00 down to 0. In this stage, the system micro-combustion engine and the energy storage device both maintain a high output in order to meet the system load demand. The distribution of regional tie power is examined, and in the photovoltaic output peak period (9:00-13:00), the power of the subarea II is briefly inverted to the subarea I, and the subarea I carries out higher-level power transmission to the subarea III. On the one hand, the power interaction at this time is helpful for transferring the photovoltaic which cannot be fully consumed in the subarea I and the subarea II; on the other hand, the load demands of the nodes 4-8 in the subarea III are also assisted to be met, and the problem of flow congestion caused by the fact that DG in the subarea III supplies energy to the partial nodes is solved. In general, the power interaction of the subarea I, the subarea II and the subarea IV with other areas is mainly represented as normal tide interaction, and the photovoltaic absorption capacity of the subarea III is strongest, so that the subarea I, the subarea II and the subarea IV can assist the full utilization of the photovoltaic to a certain extent.
Fig. 8 is a schematic diagram of an iterative convergence process of the running energy consumption of each scene system in embodiment 1 of the present invention. As can be seen from FIG. 8, after a limited number of iterations, the DSS-ADMM and the C-ADMM can gradually and gradually agree with the centralized optimization result, and the problem of solving the distributed scheduling model of the power distribution network can be effectively solved. In terms of convergence performance, the distributed scheduling method provided by the invention completes convergence through 11 iterations, and the average time consumption for each subarea to complete solving is 10.07s; C-ADMM converges through 24 iterations, spends 22.73s of solving time in a parallel computing environment; the centralized optimization calculation time is 12.45s. It can be seen that the DSS-ADMM algorithm has significant advantages in terms of solution efficiency. Meanwhile, as can be seen from fig. 8, the DSS-ADMM algorithm significantly improves the oscillation characteristic of the C-ADMM algorithm in the process of converging the global objective function, so that the convergence direction of the global objective function always approaches toward the global optimal solution.
Fig. 9 to 12 are schematic diagrams of operation energy consumption iteration convergence processes of sub-regions 1, 2, 3 and 4 of the scene 1 respectively. The convergence trend of the running energy consumption of each subarea is consistent with the overall running energy consumption, and the running energy consumption gradually rises along with the increase of the iteration times. In the early stage of calculation, each subarea mainly aims at optimizing the local economy to set a scheduling strategy, and the coupling constraint between each subarea and the adjacent subarea is ignored to a certain extent. With the increase of the iteration times, the adjacent subintervals continuously perform information interaction, and the consistency objective function is gradually reduced by sacrificing part of local operation economy and increasing local operation energy consumption, so that the convergence of the tie coupling variable is promoted, and the global power balance is gradually realized.
Fig. 13 and 14 are schematic diagrams of an original residual iterative convergence process and a dual residual iterative convergence process of scene 1 and scene 2, respectively. From the trend of residual convergence, the residual convergence direction of the DSS-ADMM algorithm has better stability and reaches the convergence limit 10 faster -4 . Meanwhile, as can be seen from fig. 8, the residual convergence direction of the C-ADMM algorithm is adjusted according to the change condition of the objective function in the previous iteration process, so that the convergence process of the objective function value and the system residual exhibits an oscillation characteristic. Therefore, after the self-adaptive step correction method is introduced, the invention can effectively avoid the oscillation of the objective function and the system residual error in the iterative process, and remarkably improve the convergence rate of the distributed optimization model.
EXAMPLE 3,
In order to verify the advantages of the invention in terms of the conservation degree of the balanced scheduling strategy and the energy consumption of the system operation, an embodiment 3 is developed. The topology of the power distribution network, parameters of equipment of the power distribution network, renewable energy sources and predicted data of electric loads adopted in the embodiment 3 are the same as those of the embodiment 2, and the effectiveness and advantages of the robust cost coefficient optimization distribution method in the step S2 of the embodiment 1 are verified by comparing the influence of different robust cost coefficient distribution schemes on the energy consumption of the system operation. Assuming that the system-wide dual standby constraint violation probability is less than 75%, as known from the dual standby constraint violation probability calculation in step S2, when the REG number is 4, the global robust cost Γ t = 3.3648. For the distribution network topology and the zoning results shown in fig. 3, the ratio of the uncertain output intervals of the 4 REG power stations is reg1:reg2:reg3:reg4=10:8:5:4.
The present embodiment sets the following two scenarios: in the scene 4, the system is only connected with a single REG, and REG 1-REG 4 are all photovoltaic power stations; the system in the scene 5 is simultaneously connected with photovoltaic and wind power, wherein REG1 and REG4 are photovoltaic power stations, and REG2 and REG3 are fans; the fan predicted output curve is shown in fig. 15.
For the two scenarios, the following three robust cost coefficient allocation schemes are adopted to perform robust cost allocation of each REG: scheme 1 adopts the method proposed in the invention (i.e. step S2), considers the time-varying characteristic of the REG power station prediction error limit value, and performs the optimized allocation of each REG robust cost coefficient; scheme 2 adopts the magnitude of the prediction error limit value according to the corresponding moment of the REG prediction output peak value to carry out static allocation of the robust cost coefficient; scheme 3 employs an average distribution method.
The REG prediction error limit comparison diagrams and REG prediction error limit comparison diagrams of scene 4 are shown in fig. 16 and 17, respectively. As can be seen from fig. 16, since only the photovoltaic power plant is connected in the scenario 4, the trend of the predicted output curve of each REG is identical, and the trend of the predicted error limit of each REG is also identical in each scheduling period. In this scenario, the ratio of the REG prediction error limits is the same throughout the scheduling period At this time, the robust cost distribution is performed considering the whole scheduling period, and the same distribution effect is obtained as that obtained by the distribution (scheme 2) according to the predicted output peak value only. In fact, in scenario 4, the allocation results of the robust cost coefficients of scheme 1 and scheme 2 are REG 1:reg2:reg3:reg4=0.6:0.7648:1:1, and the scheduling costs are 50852.11m 3 9.87% lower than scheme 3 (average distribution), indicating that the invention can reduce the energy consumption of system operation. However, as shown in FIG. 17, whenWhen the system is simultaneously accessed with REGs of different types, each REG power station in different time periods presents different proportions, and the scheme 1 is adopted to obtain the different robust cost distribution effects from the scheme 2.
As shown in fig. 18, a schematic diagram of the energy consumption of the system under each robust cost allocation scheme of scenario 5 is shown. The results of the robust cost coefficient assignment in scenario 5, scenario 1, scenario 2, and scenario 3 are shown in fig. 19-21, respectively. As can be seen from fig. 18 and 19, when the system simultaneously accesses multiple REGs, the scheduling energy consumption obtained according to scheme 1 is lower than that of schemes 2 and 3, and the robust cost coefficient allocation result of scheme 1 exhibits time-varying characteristics. Analysis shows that neither scheme 2 nor scheme 3 takes into account the time-varying limits of REG prediction errors and the differences in the force profiles of different REGs, and considers the reserve demand due to uncertainty in REG force as a constant value during calculation. However, in practice, the standby requirement of each scheduling period of the system is affected by the actual uncertain output interval of each REG power station in the corresponding period, and the time-varying characteristic of the REG prediction curve is considered to perform robust cost parameter distribution, so that the standby requirement of each REG power station in each scheduling period can be accurately analyzed and effectively met. Therefore, when the system is simultaneously connected into multiple REGs or the predicted output curves of each REG power station in the system are inconsistent, the robust cost optimization distribution method provided by the invention is adopted to distribute the robust cost coefficient as low as possible to the REG power station with larger prediction error limit value, so that the minimum of the standby requirement of the system can be ensured, and the energy consumption for the system operation can be effectively reduced.
In summary, the optimized scheduling method provided by the invention has effectiveness and rationality.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The distributed scheduling method for the power distribution network is characterized by comprising the following steps of:
s1, taking the sum of minimized micro-fuel engine power generation energy consumption and external power grid auxiliary energy consumption as an objective function, and establishing a power distribution network global scheduling model by adopting an adjustable robust method;
s2, after global robust cost is given according to the maximum acceptable running risk, robust cost coefficients are distributed to each REG power station in the power distribution network global scheduling model by minimizing the standby requirement of the whole system; the standby requirement D of the whole system is as follows:
wherein T is the total scheduling time length, N PV Delta, which is the total number of REG power stations in the whole system k,t The robust cost factor assigned to the kth REG power station for time t,for the power error limit value Γ of the kth REG power station at time t t For the global robust cost at time t, +. >Is the minimum value of the robust cost coefficient that can be assigned to the kth REG power station;
s3, decoupling and partitioning the power distribution network by integrating renewable energy consumption capability indexes and community modularity indexes to obtain a plurality of subareas, correspondingly splitting the global scheduling model of the power distribution network according to the subareas to obtain scheduling models of the subareas, and correcting the scheduling models of the subareas by introducing interaction processes among the subareas to obtain a distributed scheduling model of the power distribution network; the method specifically comprises the following steps: correcting the objective function and the power balance of the scheduling model of each subarea and the standby constraint after dual conversion to obtain the objective function and the power balance and the dual standby constraint of each subarea of the distribution network distributed scheduling model;
the objective function of the ith sub-area in the distribution network distributed scheduling model is as follows:
wherein ,fi For the equivalent energy consumption of the ith sub-area,energy consumption for operating the micro-combustion engine in the ith sub-region,/->Energy consumption for auxiliary power supply of the upper grid to the ith sub-area, < >>A consistency objective function for the ith sub-region; t is the total scheduling time length, < > and>for the number of micro-engines in the ith sub-area,/->For the output value of the s-th micro-gas engine in the i-th sub-zone at time t,/ > and />Are all the energy consumption parameters of the s-th micro-combustion engine in the i-th subarea,/->The switching state of the s-th micro-fuel engine in the i-th subarea at the t moment; /> and />The energy consumption coefficient and the auxiliary power supply power of auxiliary power supply of the upper power grid to the ith sub-area of the power distribution network at the t moment respectively; { c k Is a set of subregions, X i,j For the ith sub-region c i And jth sub-region c j Inter-link decision variable set, x i,t Sub-region c at time t i Is a tie decision variable,/->The ith sub-area c at time t i Neighborhood c of (2) j C is i The given reference value of the decision variable of the mth generation interconnecting line, m is the iteration times of the alternative direction multiplier method; />For c in the mth iteration process i And c j Lagrangian multiplier, ε m The iteration step length of the mth iteration process;
the power balance and dual standby constraint of the ith sub-area are:
wherein ,for the number of energy storage stations in the ith sub-area, < +.>For the discharge state of the p-th energy-storage station in the i-th sub-zone at time t, +.>For the discharge power of the p-th energy-storage power station in the i-th sub-zone at time t, +.>For the total number of REG power stations in the ith sub-region, < +.>For the actual power value, P, of the kth REG power station in the ith sub-zone at time t tra,i,j,t Time point of t region c j Direction c i Injected link power, +.>Is the total number of distribution network nodes in the ith sub-area, < +.>For the active load at the nth node in the ith sub-area at time t, +.>For the state of charge of the p-th energy-storage station in the i-th sub-zone at time t +.>For the maximum charging power of the p-th energy storage station in the i-th sub-zone, +.>Maximum value of auxiliary power for auxiliary power supply of the upper power grid to the distribution network in the ith sub-area, +.>Time point of t region c j Direction c i Maximum value of the injected link power, +.>For the upper limit value of the active power of the s-th micro-combustion engine in the i-th subarea, L% is the load reserve rate,> and />Are Lagrangian dual variables in the ith sub-region, +.>For the robust cost factor assigned to the kth REG plant in the ith sub-region at time t,/-, for>The power error limit value of the kth REG power station in the ith sub-area at the t moment;
s4, inputting the obtained REG power station output and electric load short-term prediction data into the distribution network distributed scheduling model, solving by adopting an adaptive step length ADMM algorithm to obtain a scheduling result of the distribution network, and scheduling the distribution network according to the scheduling result.
2. The power distribution network distributed scheduling method according to claim 1, wherein the power distribution network comprises: ac distribution grids, REG power stations accessed in photovoltaic or wind power form, battery energy storage devices, and ac loads.
3. The distributed scheduling method of a power distribution network according to claim 1, wherein the objective function of the global scheduling model of the power distribution network is:
wherein f is the total operation energy consumption of the system, C G The energy consumption for the micro-combustion engine operation; c (C) sup The energy consumption for auxiliary power supply of the upper power grid to the power distribution network is T, which is the total scheduling time length and N G P is the number of micro-fuel engines in the whole system G,s,t The output value of the s-th micro-combustion engine at the t moment, b s and cs Are all the energy consumption parameters of the s-th micro-combustion engine, u s,t The switching state of the s-th micro-combustion engine at the t moment; c sup,t and Psup,t And the energy consumption coefficient and the auxiliary power supply power of the auxiliary power supply of the upper power grid to the power distribution network at the t moment are respectively.
4. A method of distributed scheduling of a power distribution network according to any one of claims 1-3, wherein the constraints of the global scheduling model of the power distribution network include: linearized distribution network DistFlow tide constraint, micro-combustion engine operation constraint, energy storage power station operation constraint, photovoltaic output uncertainty constraint based on robust cost, upper power grid auxiliary energy supply power constraint, system power balance and standby constraint.
5. The distributed scheduling method of a power distribution network according to claim 1, wherein the step S3 comprises the steps of:
S31, comprehensive renewable energy source digestion capability indexAnd community modularity index rho to obtain comprehensive partition index wherein ,w1 and w2 The weight coefficients are respectively a renewable energy consumption capacity index and a community modularity index of the power distribution network;
s32, traversing all partition schemes by adopting a clustering method, decoupling and partitioning the power distribution network by taking the partition scheme with the minimum comprehensive partition index gamma as an optimal partition scheme to obtain a plurality of subareas, and correspondingly splitting the global scheduling model of the power distribution network according to the subareas to obtain the scheduling model of each subarea;
s33, correcting an objective function and power balance of the scheduling model of each subarea and reserve constraint after dual conversion by introducing an interaction process between each subarea, so as to obtain a distributed scheduling model of the power distribution network.
6. The distributed scheduling method of a power distribution network according to claim 1, wherein the scheduling result of the power distribution network comprises: the method comprises the steps of starting and stopping states of micro-combustion engines in all subareas, operating conditions of energy storage equipment, power exchange plans of all subareas, other subareas and an upper power grid, and dispatching output plans of the micro-combustion engines in all subareas and the energy storage equipment.
7. A computer readable storage medium comprising a stored computer program, wherein the computer program, when run by a processor, controls a device in which the storage medium resides to perform the distribution network distributed scheduling method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011052362.7A CN112183865B (en) | 2020-09-29 | 2020-09-29 | Distributed scheduling method for power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011052362.7A CN112183865B (en) | 2020-09-29 | 2020-09-29 | Distributed scheduling method for power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112183865A CN112183865A (en) | 2021-01-05 |
| CN112183865B true CN112183865B (en) | 2023-10-31 |
Family
ID=73946527
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011052362.7A Active CN112183865B (en) | 2020-09-29 | 2020-09-29 | Distributed scheduling method for power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112183865B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112801379B (en) * | 2021-01-30 | 2022-04-19 | 河南城建学院 | Smart power grid distributed energy management system based on cloud computing and big data |
| CN113393085B (en) * | 2021-05-18 | 2022-05-31 | 国网湖北省电力有限公司电力科学研究院 | Cluster dividing method considering flexibility supply and demand balance and response speed |
| CN113224761B (en) * | 2021-05-20 | 2022-06-28 | 东北电力大学 | Energy community energy management-oriented distribution network producer and consumer distributed optimal scheduling method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897942A (en) * | 2017-01-24 | 2017-06-27 | 中国电力科学研究院 | A kind of power distribution network distributed parallel method for estimating state and device |
| CN110322372A (en) * | 2019-07-10 | 2019-10-11 | 国网能源研究院有限公司 | The Unit Combination dispatching method that multistage power grid links in regional power system |
| CN110460036A (en) * | 2019-05-10 | 2019-11-15 | 四川大学 | A kind of probabilistic alternating current-direct current power distribution network distributed optimization method of consideration wind-powered electricity generation |
-
2020
- 2020-09-29 CN CN202011052362.7A patent/CN112183865B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897942A (en) * | 2017-01-24 | 2017-06-27 | 中国电力科学研究院 | A kind of power distribution network distributed parallel method for estimating state and device |
| CN110460036A (en) * | 2019-05-10 | 2019-11-15 | 四川大学 | A kind of probabilistic alternating current-direct current power distribution network distributed optimization method of consideration wind-powered electricity generation |
| CN110322372A (en) * | 2019-07-10 | 2019-10-11 | 国网能源研究院有限公司 | The Unit Combination dispatching method that multistage power grid links in regional power system |
Non-Patent Citations (3)
| Title |
|---|
| 主动配电网分布式优化运行控制研究;梁俊文;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;全文 * |
| 主动配电网分布式有功无功优化调控方法;寇凌峰;吴鸣;李洋;屈小云;谢辉;高博;陈凡;胡存刚;;中国电机工程学报(06);全文 * |
| 考虑风电不确定性的交直流混合配电网分布式优化运行;王家怡 等;《中国电机工程学报》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112183865A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112183865B (en) | Distributed scheduling method for power distribution network | |
| CN109980685B (en) | Uncertainty-considered active power distribution network distributed optimization operation method | |
| CN110322372B (en) | Multi-stage power grid linkage unit combination scheduling method in regional power system | |
| CN111049173B (en) | Self-organizing droop control method for multi-terminal direct-current distribution network | |
| CN112803505B (en) | Distributed voltage source converter cooperative control method and alternating current-direct current parallel-serial micro-grid | |
| CN108923461B (en) | Distributed inverter power supply network access optimization method considering relay protection constraint | |
| CN110323979B (en) | Generator excitation difference adjustment coefficient optimization setting method considering voltage stability | |
| CN114928057B (en) | Multi-target power flow optimization method suitable for new energy multi-terminal flexible direct system | |
| CN115954882A (en) | Scheduling optimization method and device for electric-carbon fusion power distribution system | |
| CN112909930A (en) | Energy storage system optimal configuration method for stabilizing fluctuation of hybrid power distribution network | |
| CN117039882A (en) | Resource aggregation regulation and control method and system based on binary consistency algorithm | |
| CN111884203A (en) | Micro-grid coordination optimization configuration method based on double-layer non-dominated sorting genetic algorithm | |
| Li et al. | A dynamics-constrained method for distributed frequency regulation in low-inertia power systems | |
| CN113258590B (en) | Method and system for controlling voltage of AC/DC system in stages under high wind power permeability | |
| CN117134325A (en) | Extension planning method for distribution network containing distributed photovoltaic power taking line margin into consideration | |
| CN116073453A (en) | Alternating current-direct current hybrid power distribution network optimal scheduling method based on graph calculation | |
| CN114398777B (en) | Power system flexible resource allocation method based on Yu Bashen game theory | |
| CN113078633B (en) | Method for improving restoring force of power transmission and distribution coupling system containing renewable energy | |
| CN115425697A (en) | Distributed trans-regional and trans-provincial scheduling method and system based on alternative direction multiplier method | |
| CN115912496A (en) | Power distribution network source-storage-load multi-mode power scheduling method | |
| CN110707762B (en) | Multi-energy complementary alternating current-direct current hybrid micro-grid load flow feasible region calculation method | |
| CN113471995A (en) | Energy storage configuration method for improving frequency stability of new energy high-occupancy-ratio region based on improved average value method | |
| Li et al. | Cooperative optimal scheduling of interconnected transmission-distribution-micro power system | |
| Zhu et al. | Research on Site Selection and Capacity of Flexible Control Equipment Considering Wind Power | |
| Ferreira et al. | Multiobjective approach for power flow and unbalance control in low-voltage networks considering distributed energy resources |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |