CN115115251B - Power distribution network power supply planning method and system considering optimal acceptable domain of net load - Google Patents
Power distribution network power supply planning method and system considering optimal acceptable domain of net load Download PDFInfo
- Publication number
- CN115115251B CN115115251B CN202210821822.0A CN202210821822A CN115115251B CN 115115251 B CN115115251 B CN 115115251B CN 202210821822 A CN202210821822 A CN 202210821822A CN 115115251 B CN115115251 B CN 115115251B
- Authority
- CN
- China
- Prior art keywords
- unit
- cost
- constraint
- disturbance
- model
- 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
- 238000009826 distribution Methods 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000005457 optimization Methods 0.000 claims abstract description 86
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 claims abstract description 35
- 238000006243 chemical reaction Methods 0.000 claims abstract description 8
- 230000008030 elimination Effects 0.000 claims abstract description 7
- 238000003379 elimination reaction Methods 0.000 claims abstract description 7
- 230000014509 gene expression Effects 0.000 claims description 35
- 230000006870 function Effects 0.000 claims description 32
- 230000011218 segmentation Effects 0.000 claims description 17
- 238000010276 construction Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 5
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 2
- 230000007812 deficiency Effects 0.000 claims 2
- 238000004364 calculation method Methods 0.000 abstract description 41
- 230000008901 benefit Effects 0.000 description 8
- 239000000243 solution Substances 0.000 description 8
- 238000010248 power generation Methods 0.000 description 4
- 230000007423 decrease Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000009977 dual effect Effects 0.000 description 3
- 238000012886 linear function Methods 0.000 description 3
- 108010074506 Transfer Factor Proteins 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 230000003828 downregulation Effects 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000003827 upregulation Effects 0.000 description 2
- 241000238097 Callinectes sapidus Species 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000002699 waste material Substances 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
- G06Q10/06—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
- 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
- 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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power distribution network power planning method and system considering an optimal receivable domain of a net load, wherein the power distribution network power planning method comprises the following steps: acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of a generator set; the method comprises the steps of taking the sum of minimum annual unit investment cost, operation cost and reliability cost as a target, taking constraint after net load disturbance as constraint conditions, and establishing a power distribution network power supply planning target optimization model; and carrying out square term elimination and uncertainty conversion treatment on constraint conditions, converting a target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity. The invention adopts a new reliability cost efficient calculation method, does not need to introduce 0/1 variable, can efficiently calculate the reliability cost, and greatly improves the calculation efficiency of the proposed model.
Description
Technical Field
The invention relates to the technical field of power grid planning, in particular to a power distribution network power supply planning method and system considering an optimal receivable domain of a net load.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The construction of a clean and efficient novel power system is an important target for planning and constructing a power distribution network. Unlike conventional power systems, the novel power system has high permeability of renewable energy sources, and the high uncertainty brought by the high permeability is one of the core factors to be considered in power distribution network power supply planning. Thus, uncertainty of how to effectively cope with high proportions is a problem to be solved in power distribution network power planning.
The uncertainties considered in the planning problem include uncertainty in renewable energy output, uncertainty in load, uncertainty caused by equipment failure, and the like. These uncertainties seriously affect the safety and economy of system operation after the planning of the distribution network, so that they need to be properly considered in the planning stage. Modeling uncertainty based on the characteristics of the learned uncertainty information can be summarized as follows:
1) Only knowing the variation range of uncertainty, the uncertainty can be described by adopting intervals, and then an interval optimization or robust optimization model is established;
2) Knowing the probability information of uncertainty, the uncertainty can be described by probability distribution or scene with discrete probability distribution, and further establishing models such as random optimization, opportunity constraint planning, cost/benefit trade-off optimization and the like.
To cope with uncertainty caused by source-charge double-sided power perturbation, scholars have proposed the concept of node-admittable domains. The node admissible domain is a mapping of spare capacity in the system to the source of the spatial disturbance (i.e., load and renewable energy access nodes), i.e., immunity at the payload nodes. In an optimization model considering the node admissible domain, node power uncertainty is regarded as a flexible object which can be optimized instead of fixed parametric information, the range of an object to be optimized in the model is expanded, and an optimal admissible domain comprehensively considering economy and reliability of node power disturbance can be determined. In addition, compared with the traditional optimization decision of the standby resource, the optimization decision of the node admissible domain fully considers the transmission capability of the standby in the network, and has stronger space pertinence.
The prior art establishes a double-layer economic dispatch model, and takes security as a primary target, and an upper-layer target is the sum of node disturbance receivable domains in a maximized system; taking economy as a secondary target, taking the lower-layer target as a minimum system total running cost, taking the sum of the optimal node acceptable domains obtained by the upper-layer optimization as the constraint of the lower-layer optimization, so that the range of the total acceptable domains is still ensured. The prior art establishes a scheduling model that considers the maximum capacity of renewable energy (do-not-exceed limits), which can be converted into a robust optimization model that can be optimized in uncertain parameter intervals. In order to solve the problem, three modes of fixed participation factor affine strategies, optimal participation factor affine strategies and self-adaptive control strategies are respectively considered. The prior art establishes a scheduling model based on a cost/benefit compromise concept, and an objective function is to minimize the corresponding operation cost inside the admissible domain and the reliability cost outside the admissible domain, and simultaneously, to determine the operation base point and the optimal admissible domain of the payload of the unit.
However, the above studies have focused on the field of safe and economical operation of large power grids. In the distribution network planning problem, the node payload prediction error is more remarkable, so that the research on the node admissible domain is more necessary in the distribution network planning, and the research on the node admissible domain is not considered in the current distribution network planning.
The cost/benefit tradeoff optimization concept is particularly applicable to optimization decisions for node admissible domains. In a cost/benefit compromise optimization model of the optimal admissible domain of the decision node, the benefit is often expressed as a decrease in reliability cost corresponding to the disturbance outside the admissible domain, and the calculation of reliability cost is a difficulty in the calculation of the optimization model. Reliability costs are typically expressed as the product of penalty price and desired energy, such as desired air rejection (WIND SPILLAGE, EWS)/desired power shortage (Expected energy not supplied, EENS). With known probability distributions of power disturbances, the probability distributions are typically piecewise linearized and a 0/1 variable is introduced for each segment to indicate whether the segment is EENS and EWS present, resulting in piecewise linear expressions for EENS and EWS. However, when the probability distribution is piecewise linearized, if the number of the pieces is too small, the EENS and EWS calculation errors are too large; if the number of the segments is too large, in the power supply planning problem of the distribution network considering the node payload disturbance, because each linear segment of each payload node disturbance probability distribution at each moment needs to introduce a 0/1 variable, a large number of 0/1 variables exist in the model, and the calculation efficiency of the model is seriously affected.
In the prior art, a unit combination model considering EENS and EWS indexes is established, and 0/1 variable is not introduced in EENS and EWS index calculation. In the prior art, after the EENS and the EWS are piecewise linearized, the piecewise positioning function of the EENS and the EWS is automatically realized through an optimized objective function, however, the total EENS and the EWS of the system are calculated without considering the admissible domain of the node and the topological structure of the network, but the EENS and the EWS on the node are calculated without considering the limitation of network constraint, and the EENS and the EWS numerical value can be obviously underestimated. In the prior art, after the wind power probability density function is discretized, EENS is further expressed as the product of the probability of the discrete scene and the relative cut load under the scene instead of the absolute cut load quantity, and the value of the EENS is seriously underestimated by the method.
Disclosure of Invention
In order to solve the problems, the invention provides a power supply planning method and a power supply planning system for a power distribution network, which consider an optimal acceptable domain of a net load, introduce the acceptable domain of the net load of a node into the power distribution network planning on the basis of the description of the uncertainty of the net load by probability distribution, and simultaneously provide a novel linear calculation method without introducing 0/1 variable, thereby remarkably improving the calculation efficiency of model solving.
In some embodiments, the following technical scheme is adopted:
a power distribution network power planning method that considers a payload optimal admissible domain, comprising:
acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of a generator set;
The method comprises the steps of taking the sum of minimum annual unit investment cost, operation cost and reliability cost as a target, taking constraint after net load disturbance as constraint conditions, and establishing a power distribution network power supply planning target optimization model;
And carrying out square term elimination and uncertainty conversion treatment on constraint conditions, converting a target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity.
In other embodiments, the following technical solutions are adopted:
a power distribution network power planning system that considers a payload optimal admissible domain, comprising:
The data acquisition module is used for acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of the generator set;
The model construction module is used for constructing a power distribution network power supply planning target optimization model by taking the sum of minimized annual unit investment cost, operation cost and reliability cost as a target and taking constraints after net load disturbance as constraint conditions;
and the model solving module is used for carrying out square term elimination and uncertainty conversion treatment on constraint conditions, converting the target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity.
In other embodiments, the following technical solutions are adopted:
A terminal device comprising a processor and a memory, the processor being configured to implement instructions; the memory is configured to store a plurality of instructions adapted to be loaded by the processor and to perform the power distribution network power planning method described above in view of the optimal acceptable domain of the payload.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the above-described power distribution network power planning method taking into account the payload optimal admissible domain.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention introduces the node payload admissible domain into the power supply planning problem of the distribution network, and simultaneously decides the optimal admissible domain of the node and the construction capacity of the distributed power supply; a power distribution network power supply planning model is provided, which considers a net load optimal admissible domain efficiently. In the model, disturbance in the optimal demarcation point is solved, and system call resources are fully handled; disturbance outside the optimal demarcation point is punished in the objective function by reliability cost.
(2) The invention adopts a new reliability cost efficient calculation method, the method combines the potential requirement of a physical model with mathematical optimization, compared with the prior reliability cost calculation method, the new proposed method does not need to linearize a known probability density function, uses the sum of relative abandoned wind cut load quantities to replace absolute abandoned wind cut load quantities, does not need to introduce the intercept of a 0/1 variable and a piecewise linear function, and the reliability cost can be directly expressed as a linear one-time expression, thereby remarkably simplifying the traditional reliability cost expression, efficiently calculating the reliability cost and greatly improving the calculation efficiency of the proposed model.
(3) According to the invention, the space disturbance source of the net load can be directly focused by the node optimal admissible domain, so that the planning economy and the running reliability of the system after planning are ensured, and a good compromise of the cost and the benefit is realized; the new reliability cost calculation method can calculate the power loss of each node efficiently, greatly improves the calculation efficiency of the model, and has important application value.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a graph of a probability density function of a payload disturbance in an embodiment of the present invention;
FIG. 2 is a schematic diagram of linearization of probability density functions in an embodiment of the invention;
FIG. 3 is a schematic diagram of a piecewise linear EENS function according to an embodiment of the invention;
FIG. 4 is a schematic view of a load shedding and admissible domain value direction in an embodiment of the present invention;
FIG. 5 is a diagram of an improved IEEE-33 node system in accordance with an embodiment of the present invention;
FIG. 6 is a graph of known active/reactive payload predictions in an embodiment of the present invention;
fig. 7 shows the calculation time of Case B and C in the embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1
In one or more embodiments, a power distribution network power supply planning method considering a net load optimal admissible domain is disclosed, a node net load admissible domain is introduced into power distribution network planning on the basis of a net load uncertainty described by probability distribution, and a new power distribution network power supply planning model considering the net load optimal admissible domain is provided. The disturbance in the domain can be accommodated, and the resource is called to be fully dealt with based on the robust thought; the disturbance outside the domain can be accepted, the corresponding reliability cost is calculated, and soft punishment is carried out in the objective function; the end point of the admissible domain is determined by investment costs, internal operating costs, external reliability cost tradeoff optimization.
When the external reliability cost is calculated, the wind power part with larger disturbance degree should be discarded preferentially when the wind is discarded, and the load part with larger disturbance degree should be cut preferentially when the load is cut. If the payload disturbance probability density function has a monotonically varying characteristic on both sides of its predicted value, the larger the disturbance amplitude of the payload, the smaller the probability of disturbance occurrence. According to this law, the EENS and EWS calculation formulas can be further simplified, thereby proposing a reliability cost calculation new expression without introducing 0/1 variable. Further, the model is further converted into a linear programming problem to be solved through the techniques of dual theory, alternate iteration heuristic algorithm and the like. The new reliability cost expression avoids the introduction of 0/1 variable, thereby remarkably improving the calculation efficiency of the model. Finally, based on an IEEE 33 node calculation example of the power distribution network, the effectiveness of the method and the model is proved.
The method of the embodiment specifically comprises the following steps:
(1) Acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of a generator set;
(2) The method comprises the steps of taking the sum of minimum annual unit investment cost, operation cost and reliability cost as a target, taking constraint after net load disturbance as constraint conditions, and establishing a power distribution network power supply planning target optimization model;
(3) And carrying out square term elimination and uncertainty conversion treatment on constraint conditions, converting a target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity.
Specifically, the planning object in this embodiment is the position and capacity of the unit. The objective function is to minimize the sum of annual unit investment cost C inv, operating cost C oper (including operating cost C run and standby cost C res), and reliability cost C rel, which is specifically expressed as follows:
min(Cinv+d·Coper+d·Crel) (1)
Mg＝ug(1+ug)yg/[(1+ug)yg-1] (3)
in the formulae (2) to (4): d is the number of typical days of planning; g, t are indexes of the generator set and the optimization period respectively; n G is set up in the form of a block, N T is the set of the existing generator set, the generator set to be expanded and the optimization period of the system respectively. In the formula (2): m g is the capital recovery coefficient for unit g, which can be represented by formula (3); /(I)Initial investment cost for unit capacity of unit g; /(I)Operating and maintenance costs per unit capacity for a unit g, typically including labor costs and maintenance costs; /(I)The residual value of unit capacity of the unit g; cap g is the construction capacity of unit g. In the formula (3): u g is the discount rate; y g is the economic life of unit g. In the formula (4): /(I)Active and reactive running cost coefficients of the unit g are respectively; p g,t、Qg,t is the active and reactive output reference value of the unit g in the optimization period t respectively; /(I)The cost coefficients for up-regulation and down-regulation of the unit g are respectively; /(I)R dng,t is the up-regulation and down-regulation standby value of the unit g in the optimization period t respectively.
Constraints of the objective function include constraints based on the predicted value of the payload and constraints that take into account the disturbance of the payload. The constraint based on the payload predicted value is a constraint under a reference value, and no uncertainty exists; the constraint after the net load disturbance is considered ensures that physical constraints such as the unit output and the line power flow are not out of limit after the net load disturbance is considered, and uncertainty exists; both constraints need to be met.
Wherein the constraint based on the payload predictions is as follows:
1) Power balance constraint
The active and reactive output reference values of the generator set are required to be balanced with the active and reactive net load predicted values.
Wherein: i is a node index, N I is all node sets, and P fcsti,t,Qfcsti,t is the active and reactive net load predicted values of period t at node i respectively.
2) Line power flow and capacity constraints
Active and reactive power flows are typically considered in the distribution network, and can be simplified into the following linear expression:
Wherein: p ij,t,Qij,t is the active and reactive power flow reference value of the line ij in the period t; g ij,bij is the conductance value and susceptance value of the line ij; v i,t,Vj,t is the voltage reference value of node i, j in period t; θ i,t,θj,t is the phase angle reference value of the node i, j in the period t.
Based on the linear power flow expression (6), the line power flow can be expressed as a form of node injection power, and the specific expression is as follows:
Wherein: k is a node index, and the meaning of k is the same as that of i and j; An active and reactive power generation load transfer factor of the node k to the line ij; bus (g) =k means that the group g is installed at node k. The line capacity constraint may be expressed as follows:
Wherein: s maxij is the upper capacity limit of the line ij. The above equation indicates that the line flow based on the net load prediction value should not exceed its upper line capacity limit.
3) Node voltage, phase angle constraints
The matrix relationship of node voltage, phase angle and node injection power can be expressed as follows:
wherein: r is a real matrix. Thus, in the present embodiment model, the formula (9) can be expressed as (10):
wherein: H θi,k is the power generation load transfer factor of node i, j for voltage and phase angle. Further, equation (11) gives the limit constraint of node voltage, phase angle.
Wherein: v maxi,Vmini is the upper and lower limits of the voltage at node i; θ maxi is the upper limit of the angular deviation at node i.
4) Unit output constraint
The unit output constraint in the system is expressed as follows. The upper power supply is also regarded as an equivalent unit, and the output of the upper power supply is limited by the capacity of a transformer substation and other factors.
Wherein: lambda gPmaxg,λgPming is the product of the installed capacity coefficient and the upper and lower limits of the active output of the unit, and represents the upper and lower limits of the actual active output of the unit g. Lambda gSmaxg is the product of the installed coefficient and the apparent power upper limit of the unit, and represents the actual apparent power upper limit of the unit g.
5) Standby adjustment constraint
The power generation units in the system need to meet the following backup adjustment constraints:
Formula (14) is the reserve adjustment capacity constraint of the unit g; equation (15) is a back-up adjustment rate constraint for unit g, where The maximum ascending and descending speeds of the unit g in the unit time interval are respectively shown.
Constraints that consider the payload disturbance are as follows:
1) Power balance constraint
When the net load disturbance is considered, the active and reactive output values after the power adjustment of the unit are required to be balanced with the active and reactive net load values after the disturbance.
Wherein: The active and reactive output values of the unit g in the period t are considered during disturbance; /(I) To take into account the active and reactive payload values of the node i at time period t after the disturbance.
2) Line power flow and capacity constraints
The line flow constraint after considering the power disturbance can be expressed as follows:
wherein: To take into account the active, reactive current values of the line ij during the period t when the payload is disturbed.
3) Node voltage, phase angle constraints
Similar to (10) - (11), node voltages, phase angle constraints when considering the payload disturbance may be represented by (19).
4) Unit output constraint
The above constraints indicate that the unit power when considering the net load disturbance is still within its output constraints.
5) Spare adjustment capability constraints
The relation between the up and down adjustment amounts after the net load disturbance and the output change of the unit is given by the above formula.
In this embodiment, the reliability cost is calculated as follows:
① Payload admission capability
The present embodiment incorporates renewable energy sources and loads into a payload, taking into account its uncertainty. Taking a node in the system as an example, it is assumed that node i has a net load disturbance at period t as shown in fig. 1 (here, a normal distribution is taken as an example, and other distributions may be similarly represented).
In fig. 1, the abscissa x i,t is the actual value of the net load power at node i after the disturbance is considered, and the ordinate Pr (x i,t) is the probability density function thereof.Δar Li,t represents the size of the payload up and down perturbation acceptable domain for period t at node i, respectively, as a non-negative variable to be optimized. /(I)ΔDR Li,t represents the magnitude of the payload upper and lower perturbation domains, respectively, for period t at node i, as a given parameter that is not negative. It should be noted that, in the graph, the shadow on the right side of the admissible domain corresponds to a disturbance area on the node payload which cannot be admitted by the system, and the shadow on the left side of the admissible domain corresponds to a disturbance area under the node payload which cannot be admitted by the system.
The uncertainty of the payload of this embodiment is described in terms of a box uncertainty set expressed as follows:
wherein: The random disturbance quantity of active and reactive net load power which can be handled by the system in a period t at the node i can be positive or negative; η i,t is the proportionality coefficient between the active and reactive payloads within period t at node i. The following constraint is supplemented for reflecting the relation among the node disturbance domain, the node admissible domain and the node payload random disturbance quantity:
Equation (24) ensures that the node payload random disturbance magnitude is within the node admissible domain and the node admissible domain is within the node disturbance domain.
② Expression of traditional reliability costs
The reliability cost of a conventional node payload perturbation beyond the upper and lower limits of the system's admissible domain can be expressed as follows:
Wherein: pi NL,πWT is the penalty unit price of cut load and wind abandon cost respectively.
Where the payload perturbation of period t at node i exceeds the expected energy loss of the upper and lower acceptable domain limits of the node, EENS i,t and EWS i,t can be expressed as follows:
It can be seen that EENS i,t,EWSi,t are variables respectively Is a function of (2). Since EENS and EWS contain integral symbols, it is difficult to directly calculate, and it can be subjected to piecewise linearization processing. First, segment points are introduced into the node disturbance domain, then EENS/EWS values at the segment points are calculated through integration (26) - (27), and then the segment linear expression of EENS/EWS is obtained.
The specific linearization process is described as follows:
1) Segmentation of node perturbation domains
Taking as an example the disturbance on the payload of a node at a certain moment, the graph is shown in fig. 2. Firstly, cutting off a probability density function curve Pr (x up) at the upper limit of an upper disturbance domain, and then introducing segmentation pointsAnd equally dividing the cut upper disturbance domain to ensure that k up1 coincides with P fcst. In fig. 2, 7 segmentation points are schematically introduced to divide the disturbance domain into 6 segments, and the ordinate of the blue point is the probability density function value at the segmentation point.
2) Calculation of the segment Point EENS value and segment Linear EENS expression
Due to the upper limit of the admissible domainFor the variables to be optimized, the reliability cost cannot be directly calculated. Thus first let the admissible domain upper limit/>Coinciding with the segmentation points, the EENS value of the admissible domain upper bound at each discrete point, i.e. the ordinate value of the dot in fig. 3, is calculated as follows:
from the abscissa values at the dots in FIG. 3, the slope of the piecewise linear EENS function is determined Intercept/>The piecewise linear EENS expression is obtained as follows.
Wherein: To identify whether the upper limit of the actual admissible domain is located at the 0/1 variable of the s-th segment within the period t at node i. The second and third expressions in equation (29) ensure that the upper limit coordinates of the admissible domain can only be fetched for a certain segment of the EENS and not for multiple segments at the same time. Equation (30) gives the slope/> Intercept/>Is a calculation method of (a). Piecewise linearization of the EWS corresponding to the lower perturbation is similar and will not be described again.
③ New expression of reliability cost
In the traditional EENS and EWS piecewise linearization process, the greater the number of pieces, the more accurate the reliability index value calculation. However, when the number of segments s is increased and the load node i and the optimization period t are more, the EENS and the EWS linear expressions contain a large number of 0/1 variables, and the method needs to calculate the slope and intercept of the piecewise linear expressions at the same time, so that the reliability cost expression is more complicated, the calculation efficiency is obviously reduced, and the value of the method is weakened. This method is therefore not suitable for accurate calculation of reliability costs in large power systems.
Therefore, the embodiment provides a new reliability index linearization method. The method does not need to introduce 0/1 variable, simplifies the expression of the model, and greatly improves the calculation efficiency of the model. It should be noted that the new method proposed in this embodiment is applicable to the case where the probability density function of any uncertainty parameter has monotonicity (monotonically increasing on the left and monotonically decreasing on the right) on both sides of the expected value, and does not require that the probability density function be distributed axisymmetrically with the expected value.
1) EENS equivalent expression
Taking fig. 4 as an example (i and t are omitted from the subscripts of parameters and variables in the figure for simplicity, if the payload disturbance is excessive and the system needs to cut the load, the cut load in the figure must start from the disturbance upper limit point (the rightmost dot), take the value from right to left, take the previous segment and take the next segment, and similarly accept the upper limit coordinates of the domainIt is necessary to take values from left to right starting from the payload prediction point (leftmost dot). The left ordinate of FIG. 4 represents EENS i,t as/>The right ordinate represents EENS i,t as cut load/>, i.e., (29)The lower left/right arrows show/>, respectivelyAnd/>And the sum of the two is the disturbance quantity on the total net load of the node
Based on the above analysis, the length Δk upi,t of each segment after uniform segmentation can be introduced to separate EENS i,t fromIs rewritten as/>To convert expression (29) to (31):
wherein: The load quantity is cut for the segments. The segment S in the formula (31) needs to be taken from right to left, namely, the index S is taken to 1 from S max -1 in descending order; 0/1 variable/>, in the second and third expressions The existence of (3) ensures that the cut load must take a value from right to left, i.e., the cut load is single-valued in the right to left direction along the lower P NL of fig. 4. The fourth expression ensures that the total cut load is the sum of the cut loads of the segments. The formula still contains a large number of 0/1 variables, and the solution is complex.
Since reliability costs are minimized in the objective function, EENS tends to be minimized. When the number of segments s changes from right to left, i.e. the index s takes on a value in descending order from large to small, the following assumption is satisfied: slope of EENS expression in formula (31)Constant positive and monotonically increasing with descending value of s, then the corresponding EENS increment under each uniform segmentMonotonically increasing with decreasing value of s, i.e. inequalityThis is true. In this case, if the load is cut/>Presence of (2) results in the appearance of EENS, then/>The EENS corresponding to the right segment must be taken from right to left, i.e. the EENS corresponding to the left segment starts to be taken after the EENS corresponding to the right segment is fully taken. In short, if the above assumption is satisfied, the optimization model implies a phenomenon that the tangential load amount must be valued from right to left. This is in combination with the introduction of the 0/1 variable/>, in (31)The purpose of (3) is overlapped, so that 0/1 variable/>Can be deleted, whereby (31) can be further simplified expressed as follows:
as can be seen by comparing (29), (31) and (32), there is no 0/1 variable in EENS in (32), and expression is more concise.
For the processing procedure of the EWS consistent with EENS, the EWS i,t is rewritten from the function of the delta AR Li,t to the function of the value delta P WTs,i,t of the segmented waste air quantity, and the 0/1 variable is deleted.
2) Demonstration of piecewise linear EENS monotonicity
The above simplification from (31) to (32) is based on the assumption that: (31) Medium slopeConstant positive and monotonically increasing with the descending order of s. This assumption is always true, which is demonstrated below.
As can be seen from the view of figure 4,The value of s monotonically increases with the descending order of s. Combined expression (31) can be seenThe constant is positive. The adjacent two slope values are subjected to difference:
When (when) For a strictly convex function, equation (33) must be greater than 0, i.e., the slope monotonically increases. Due to the function/>Only/>And thus can pass/>Concerning/>Second derivative determination of/>Is convex-concave in nature:
wherein: is a known probability density function, and therefore (34) is constantly greater than 0, further yielding/> Is a strict convex function, so that (33) is constantly greater than 0, slope/>The conclusion that the value is constant and monotonically increasing is confirmed.
3) New expression of reliability cost
Since the reliability cost calculation mechanism is exactly the same at the time of the payload up/down disturbance, a simplified linear expression (35) of the total reliability cost of the system can be obtained according to EENS simplified calculation formula (32) in 2):
wherein: ΔP WTs,i,t is the continuous sectional load shedding amount and air discarding amount in the period t at the node i. Through the above conversion process, the reliability cost expression is converted from (25-27) to (35). Compared with the traditional reliability cost expression, the expression only introduces the slope of the piecewise EENS and EWS linear function and the piecewise wind-abandoning load variable, and can eliminate the integral number without introducing the piecewise EENS and the intercept of the EWS linear function and the piecewise positioning 0/1 variable, so that the model is remarkably simplified. In addition, compared with the traditional EENS and EWS simplified calculation expression, the expression can efficiently calculate EENS and EWS at each time period of each node after network topology constraint is considered, can directly pay attention to the source of space disturbance, and is suitable for a large-scale power system.
In this embodiment, the converting process for the constraint condition specifically includes:
① Constrained square term linearization
In the model constraints (8), (13), (18), (21), there are nonlinear square terms, and the present embodiment uses a power circle linearization method to eliminate the square terms. Taking (21) as an example, the formula (21) can be converted into the formula (36), and similar constraints (8), (13) and (18) can be removed by the same method, and the square terms are not repeated here for the sake of simplicity.
Wherein: m is the number of segments of the power circle inscription polyhedron; delta 2m,δ3m is the coefficient corresponding to the selected inscribed polyhedron.
② Uncertainty conversion processing
The box type uncertainty set (23) of the embodiment is introduced so that the model constraint condition contains random parametersAnd (3) withIt is difficult to solve directly. Therefore, the affine strategy is adopted in the embodiment, an affine relation between the unit power variation and the net load disturbance power is constructed, an affine adjustment robust optimization model is established, and the constraint is converted, and the method specifically comprises the following steps:
1) Constructing affine relations
Assuming an affine relationship exists between the net load disturbance and the unit output adjustment, as shown in (37):
wherein: The power adjustment quantity of the unit g in a period t after the net load disturbance is considered; ρ Pg,t,ρQg,t is the distribution coefficient of the unit g for the payload active/reactive disturbance during period t. The spare adjustment capability constraint (22) may be translated into (39):
After the processing, the original model is converted into an affine adjustment robust optimization model. The only system power balance (16) containing random parameters is automatically satisfied, and the random parameters in the model only appear in inequality, so that the further solution is facilitated.
2) Model processing
In order to convert the affine adjustment robust optimization model into a deterministic model, an auxiliary variable v i,t epsilon [0,1] is introduced to eliminate random parameters in the model. The power random disturbance can be expressed as follows:
inequality constraints where random parameters exist are handled. Taking (36) as an example, substituting (37), (40) into (36), the optimization problem can be expressed as follows:
Wherein: the max symbol is used to find the solution in the worst disturbance case, ω i,g,t,m is the dual variable. According to the dual theory, the optimization problem (41) can be converted into the following formula:
The transformation of formulas (18) - (19) may be performed in the same manner and will not be described again here for the sake of brevity.
After the above processing, the affine adjustment robust optimization model has been converted into a deterministic model without random parameters. Nonlinear terms still exist in the deterministic model, and are caused by multiplication of continuous distribution coefficient variables and admissible domain variables, and cannot be directly solved by a linear solver.
The method converts the original bilinear problem into iterative solution of two linear optimization problems, thereby realizing joint optimization of the distribution coefficient and the admissible domain, having the characteristic of high convergence speed and being capable of obtaining better optimization effect. At this time, the original affine adjustment robust optimization model is converted into a deterministic linear optimization model, and an existing linear business solver can be called for solving.
The distribution coefficient is a proportional coefficient of each unit for dealing with disturbance, namely the distribution coefficient of the unit, namely the proportional coefficient of the unit for dealing with the size of the disturbance of the net load, and the sum of the distribution coefficients of all units is equal to 1, and is a variable to be optimized.
An admissible domain is the ability at a node to cope with a disturbance in terms of power, with an effectively admissible domain within a given known disturbance domain. The admissible domain at the node is a mapping of the unit output at the source of the disturbance.
Calculation case analysis
The present embodiment is tested on a modified IEEE-33 node distribution network system, the topology of which is shown in fig. 5, which is composed of 33 nodes and 32 lines. And assuming that no distributed adjustable power supply exists in the system before planning, the initial power generation resource only comprises an upper power grid and a part of wind turbines. The example is based on a typical day for planning, with 24h payload predictions within the day as shown in fig. 6. Node 1 is selected as a reference node, and the upper and lower limits of node voltage are set to 1.05p.u./0.95p.u., and the phase angle variation range is [ -180 degrees, 180 degrees ]. The controllable DG to be built in the model can be built at the nodes 6,9, 13, 17, 21, 24, 27, 31 in fig. 5, and the upper limit of the installed capacity of each node is set to 1MW, and specific data are shown in table 1.
TABLE 1 specific data for generating units
Assuming that the payload perturbation per optimization period t per node i obeys a normal distributionWherein the mean μ i,t takes the payload prediction value P fcsti,t and the standard deviation σ i,t is set to 5% of the payload prediction value. Since the probability of disturbance of the parameters subject to normal distribution within three standard deviations is 99.74%, which has covered most of the disturbance, this embodiment truncates the normal distribution at + -3σ i,t, i.e./>Δdr Li,t is taken as 3σ i,t; the net load disturbance scaling factor η i,t is taken as the ratio of the reactive net load to the active net load forecast in fig. 6.
Initial investment cost of controllable DG unit capacityTaking 1500000$/MW, operation and maintenance cost/> Taking 3% of initial investment cost, equipment residual value/>Taking 5% of initial investment cost; the typical daily number d is 365 days, the discount rate u g is 6%, and the economic service life y g is 20 years; the up/down adjustment cost provided by the controllable DG is 10% of the highest marginal cost, the reactive running cost coefficient/>Set as the active running cost coefficient/>10% Of (2); the unit punishment price pi NL takes 5000$/MWh, pi WT takes 500$/MWh, and S max takes 7 when the reliability cost is piecewise linearized.
The model proposed in this embodiment calls the CPLEX solver solution based on the GAMS optimization platform programming. The computer used was configured as a Win10 system with a dominant frequency of 3.2GHz and a memory of 16GB, with a convergence accuracy of 0.01%.
Influence of node optimal admissible domain:
In order to analyze the influence of the introduction of the node optimal admissible domain on the planning result and the cost of each part, the embodiment provides the following example scenario:
CaseA: the node admissible domain is not considered, the safe and reliable target is adopted, namely, the disturbance in the node disturbance domain needs to call the absolute response of system resources, the wind abandoning and the load shedding are not allowed, and the reliability cost is 0. At this time, the whole optimization model is a linear programming (Linear Programming, LP) model, and a CPLEX solver is called for solving.
CaseB: the reliability cost calculation takes the traditional expression (29) with 0/1 variable considering the node optimal admissible domain. At this time, the optimization model is a Mixed-integer linear Programming (MILP) model, and a CPLEX solver is called for solving.
After the optimization is finished, planning results of cases A and B are shown in table 2, and cost results are shown in table 3. For simplicity, node information of the non-built units is not displayed.
Table 2case a, b planning results
Table 3case A, B cost results
As can be seen from table 2, there are fewer nodes to choose to build DG than Case B, caseA, but the total planned construction capacity is increased by 6.93% because the system does not invoke resource countermeasures but penalizes in the objective function considering the part of the cost/benefit tradeoff approach of the node's optimal admissible domain where the probability of occurrence of payload disturbance is small. Therefore, the investment cost can be reduced by considering that the demand of the model of the node optimal admissible domain on the system resource is relatively low.
As can be seen from table 3, the running cost of Case B was substantially unchanged from Case a, and the remaining costs were reduced to a different extent than the reliability costs. Because Case B makes an optimization decision on the acceptable domain of the payload disturbance, it is not necessary to guarantee complete acceptance of the payload disturbance, so the model in Case B has a relatively low demand for standby resources, and the standby cost is reduced by 24.5%. Although Case B increases the reliability cost, the partial cost is caused by the fact that the payload disturbance outside the optimal admissible domain is not handled, the probability weighting value is small, the occurrence probability is low, and the reliability influence on the planned system is small. In addition, the investment cost of Case B is reduced by 6.51% compared with Case A. In summary, the planning model considering the node optimal admissible domain can ensure the planning economy and the running reliability of the system after planning, and has higher application value.
New linearization method effectiveness analysis:
in order to analyze the effectiveness of the simplified linearization method proposed in this embodiment, this embodiment complements the following example scenario:
Case C: the new method for introducing the segmented admissible domain is adopted in the reliability cost linearization calculation by considering the node optimal admissible domain. At this time, no 0/1 variable exists in the whole optimization model, and the CPLEX solver is called for solving for the LP model.
This example compares cases B, C. As the number of segments of the payload up/down perturbation domain increases, the planning results of cases B, C are shown in table 4, the calculated time pair is shown in fig. 7, and the cost result pair is shown in table 5.
Table 4case b, c planning results
With the increase of the number of segments, the planning results of the two linearization methods are identical, so that the effectiveness of the new linearization method provided by the embodiment is verified. As can be seen from the planning results in table 4, as the number of segments increases, the planning capacity generally decreases, and when the number of segments is greater than 15, the planning total capacity tends to be unchanged, which means that the calculation of the reliability cost is more accurate when the number of segments is greater than 15, and the error of the number of segments is no longer a factor affecting the planning result.
The calculation time from fig. 7 can be seen as: with the increase of the number of segments, the calculation efficiency is obviously reduced because more 0/1 variables are introduced in the calculation of the reliability cost of the Case B; when the number of segments is greater than 15, it can be seen from table 4 that the power planning result is not changed, but the calculation efficiency decreases faster with the increase of the number of segments; the 0/1 variable is not introduced in the Case C cost calculation, and the calculation efficiency is not greatly changed. When the segmentation number is 15, the calculation efficiency of Case C is improved by 58.7% compared with Case B; when the number of segments is 18, the calculation efficiency of Case C is improved by 61.9% compared with Case B. Therefore, in the model of this embodiment, a new reliability cost linearization method is adopted, and the number of segments is set to 15, so that not only can better precision be obtained, but also higher solving efficiency can be obtained.
Table 5case B, C cost results
Comparing the cost results of Case B and C, it can be known that the cost results of the two linearization methods are substantially the same as the number of segments increases, wherein the reliability cost calculation is finer and gradually tends to a stable value, and the effectiveness of the linearization method provided by the embodiment is further verified. When the number of segments is greater than 15, the cost of each segment is already substantially unchanged. Therefore, in the model of this embodiment, a new reliability cost linearization method is adopted, and the number of segments is set to 15, so that not only can better precision be obtained, but also higher solving efficiency can be obtained.
Example two
In one or more embodiments, a power distribution network power planning system is disclosed that considers a payload optimal admissible domain, comprising:
The data acquisition module is used for acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of the generator set;
The model construction module is used for constructing a power distribution network power supply planning target optimization model by taking the sum of minimized annual unit investment cost, operation cost and reliability cost as a target and taking constraints after net load disturbance as constraint conditions;
and the model solving module is used for carrying out square term elimination and uncertainty conversion treatment on constraint conditions, converting the target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity.
It should be noted that, the specific implementation manner of each module has been described in detail in the first embodiment, and will not be described in detail herein.
Example III
In one or more embodiments, a terminal device is disclosed that includes a processor and a memory, the processor configured to implement instructions; the memory is configured to store a plurality of instructions adapted to be loaded by the processor and to perform the power distribution network power planning method described in embodiment one, taking into account the optimal acceptable domain of the payload.
Example IV
In one or more embodiments, a computer readable storage medium is disclosed, in which a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and to perform the power distribution network power planning method described in embodiment one, taking into account the optimal acceptable domain of the payload.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (5)
1. A power distribution network power planning method considering a payload optimal admissible domain, comprising:
acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of a generator set;
The method comprises the steps of taking the sum of minimized annual unit investment cost C inv, operation cost C oper and reliability cost C rel as targets, taking constraints after net load disturbance as constraint conditions, and establishing a power distribution network power supply planning target optimization model; the constraints after the net load disturbance are considered specifically include: taking power balance constraint, line power flow constraint, capacity constraint, node voltage constraint, phase angle constraint, unit output constraint and standby adjustment capability constraint after net load disturbance into consideration;
The power distribution network power supply planning target optimization model specifically comprises the following steps:
min(Cinv+d·Coper+d·Crel)；
Wherein d is the number of typical days of planning; g, t are indexes of the generator set and the optimization period respectively;
NG, N T is a set of existing generator sets, generator sets to be expanded and optimization time periods of the system respectively; m g is the capital recovery coefficient of unit g, u g is the discount rate, and y g is the economic service life of unit g; /(I) Initial investment cost for unit capacity of unit g; /(I)The operation and maintenance cost is the unit capacity of the unit g; pi g m is the residual value of unit capacity of the unit g; cap g is the construction capacity of unit g; /(I)Active and reactive running cost coefficients of the unit g are respectively; p g,t、Qg,t is the active and reactive output reference value of the unit g in the optimization period t respectively; /(I)The up-and-down adjustment standby cost coefficients of the unit g are respectively; the up-and-down adjustment standby values of the unit g in the optimization period t are respectively;
The reliability cost is calculated as follows:
introducing segmentation points into the node disturbance domain, calculating expected energy deficiency EENS/expected air rejection EWS values at the segmentation points, and further solving a piecewise linear expression of EENS/EWS;
Introducing the length of each segment after uniform segmentation, and taking EENS i,t from Is rewritten as a value of the segment cut load quantityFrom/>, EWS i,t The function of (2) is rewritten into the value/>, of the sectional air discarding quantityDeleting 0/1 variable to obtain the simplified total reliability cost of the system;
The simplified total reliability cost of the system is specifically as follows:
Wherein, C rel is the reliability cost, pi NL,πWT is the punishment unit price of the cut load and the abandoned wind cost, S max represents the number of the segmentation points, The values of the sectional load cutting quantity and the sectional air discarding quantity are respectively calculated by the method of/> The values of the total load and the air discarding quantity are respectively/>Slope of piecewise linear EENS and EWS functions in s-segment, respectively,/>The lengths of each section of the upper disturbance and the lower disturbance after uniform segmentation are given parameters;
The total reliability cost of the system satisfies the following constraint:
representing the size of the payload up and down perturbation admissible domains of period t at node i, The size of the upper and lower disturbance domains of the net load of the time period t at the node i is respectively represented; /(I)The random disturbance quantity of the active payload power which can be handled by the system in a period t at the node i is given;
Performing square term elimination and uncertainty conversion treatment on constraint conditions, converting a target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity;
the process of obtaining the deterministic model without random parameters is:
The square term in the constraint condition is eliminated by adopting a power circle linearization method; adopting an affine strategy to construct an affine relation between the unit power variation and the net load disturbance power, and establishing an affine adjustment robust optimization model; introducing an auxiliary variable v i,t E [0,1], and processing inequality constraint with random parameters to eliminate the random parameters in the affine adjustment robust optimization model; thereby converting the affine adjustment robust optimization model into a deterministic model without random parameters.
2. The power supply planning method for a power distribution network taking into account a payload optimal admissible domain according to claim 1, wherein the deterministic model is solved to obtain optimal unit positions and capacities, specifically:
And an alternate iterative heuristic algorithm is adopted to convert the original bilinear problem into iterative solution of two linear optimization problems, so that joint optimization of the distribution coefficient and the admissible domain is realized, and an optimal result of the unit position and capacity is obtained.
3. A power distribution network power planning system that considers a payload optimal admissible domain, comprising:
The data acquisition module is used for acquiring active and reactive output data, line tide and capacity data and node voltage and phase angle data of the generator set;
The model construction module is used for constructing a power distribution network power supply planning target optimization model by taking the sum of minimized annual unit investment cost, operation cost and reliability cost as a target and taking constraints after net load disturbance as constraint conditions;
The constraints after the net load disturbance are considered specifically include: taking power balance constraint, line power flow constraint, capacity constraint, node voltage constraint, phase angle constraint, unit output constraint and standby adjustment capability constraint after net load disturbance into consideration;
The power distribution network power supply planning target optimization model specifically comprises the following steps:
min(Cinv+d·Coper+d·Crel)；
Wherein d is the number of typical days of planning; g, t are indexes of the generator set and the optimization period respectively;
NG, N T is a set of existing generator sets, generator sets to be expanded and optimization time periods of the system respectively; m g is the capital recovery coefficient of unit g, u g is the discount rate, and y g is the economic service life of unit g; /(I) Initial investment cost for unit capacity of unit g; /(I)The operation and maintenance cost is the unit capacity of the unit g; /(I)The residual value of unit capacity of the unit g; cap g is the construction capacity of unit g; /(I)Active and reactive running cost coefficients of the unit g are respectively; p g,t、Qg,t is the active and reactive output reference value of the unit g in the optimization period t respectively; /(I)The up-and-down adjustment standby cost coefficients of the unit g are respectively; the up-and-down adjustment standby values of the unit g in the optimization period t are respectively;
The reliability cost is calculated as follows:
introducing segmentation points into the node disturbance domain, calculating expected energy deficiency EENS/expected air rejection EWS values at the segmentation points, and further solving a piecewise linear expression of EENS/EWS;
Introducing the length of each segment after uniform segmentation, and taking EENS i,t from Is rewritten as a value of the segment cut load quantityFrom/>, EWS i,t The function of (2) is rewritten into the value/>, of the sectional air discarding quantityDeleting 0/1 variable to obtain the simplified total reliability cost of the system;
The simplified total reliability cost of the system is specifically as follows:
Wherein, C rel is the reliability cost, pi NL,πWT is the punishment unit price of the cut load and the abandoned wind cost, S max represents the number of the segmentation points, The values of the sectional load cutting quantity and the sectional air discarding quantity are respectively calculated by the method of/> The values of the total load and the air discarding quantity are respectively/>Slope of piecewise linear EENS and EWS functions in s-segment, respectively,/>The lengths of each section of the upper disturbance and the lower disturbance after uniform segmentation are given parameters;
The total reliability cost of the system satisfies the following constraint:
representing the size of the payload up and down perturbation admissible domains of period t at node i, The size of the upper and lower disturbance domains of the net load of the time period t at the node i is respectively represented; /(I)The random disturbance quantity of the active payload power which can be handled by the system in a period t at the node i is given;
The model solving module is used for carrying out square term elimination and uncertainty transformation treatment on constraint conditions, converting a target optimization model into an affine adjustment robust optimization model, then converting the affine adjustment robust optimization model into a deterministic model without random parameters, and solving the deterministic model to obtain the optimal unit position and capacity;
The process of obtaining the deterministic model without random parameters is: the square term in the constraint condition is eliminated by adopting a power circle linearization method; adopting an affine strategy to construct an affine relation between the unit power variation and the net load disturbance power, and establishing an affine adjustment robust optimization model; introducing an auxiliary variable v i,t E [0,1], and processing inequality constraint with random parameters to eliminate the random parameters in the affine adjustment robust optimization model; thereby converting the affine adjustment robust optimization model into a deterministic model without random parameters.
4. A terminal device comprising a processor and a memory, the processor being configured to implement instructions; a memory for storing a plurality of instructions adapted to be loaded by a processor and to carry out the power distribution network power planning method according to any one of claims 1-2 taking into account the optimal acceptable domain of the payload.
5. A computer readable storage medium, in which a plurality of instructions are stored, characterized in that the instructions are adapted to be loaded by a processor of a terminal device and to carry out the power distribution network power planning method taking into account the optimal admissible domain of net load according to any of claims 1-2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210821822.0A CN115115251B (en) | 2022-07-13 | 2022-07-13 | Power distribution network power supply planning method and system considering optimal acceptable domain of net load |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210821822.0A CN115115251B (en) | 2022-07-13 | 2022-07-13 | Power distribution network power supply planning method and system considering optimal acceptable domain of net load |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115115251A CN115115251A (en) | 2022-09-27 |
| CN115115251B true CN115115251B (en) | 2024-05-03 |
Family
ID=83333109
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210821822.0A Active CN115115251B (en) | 2022-07-13 | 2022-07-13 | Power distribution network power supply planning method and system considering optimal acceptable domain of net load |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115115251B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106058918A (en) * | 2016-06-04 | 2016-10-26 | 东北电力大学 | Photovoltaic acceptance feasible region-based energy storage control policy and economy evaluation method |
| CN106940869A (en) * | 2017-03-10 | 2017-07-11 | 国网山东省电力公司经济技术研究院 | Consider the reference power network robust optimizing evaluation method of negative rules |
| CN109713716A (en) * | 2018-12-26 | 2019-05-03 | 中国南方电网有限责任公司 | A kind of chance constraint economic load dispatching method of the wind-electricity integration system based on security domain |
| WO2019165701A1 (en) * | 2018-02-28 | 2019-09-06 | 东南大学 | Random robust coupling optimization scheduling method for alternating-current and direct-current hybrid micro-grids |
| CN110739687A (en) * | 2019-10-24 | 2020-01-31 | 福州大学 | electric power system distribution robust scheduling method considering wind power high-order uncertainty |
| CN112597633A (en) * | 2020-12-04 | 2021-04-02 | 山东大学 | Power system standby optimization method and system considering multiple fault scenes |
| CN113191549A (en) * | 2021-04-29 | 2021-07-30 | 国网山东省电力公司德州供电公司 | Power distribution network source and network joint planning optimization method considering network loss and simplifying alternating current power flow |
| CN113420991A (en) * | 2021-06-25 | 2021-09-21 | 国网山东省电力公司经济技术研究院 | Power supply energy storage joint planning method and system considering expected power shortage linearization |
-
2022
- 2022-07-13 CN CN202210821822.0A patent/CN115115251B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106058918A (en) * | 2016-06-04 | 2016-10-26 | 东北电力大学 | Photovoltaic acceptance feasible region-based energy storage control policy and economy evaluation method |
| CN106940869A (en) * | 2017-03-10 | 2017-07-11 | 国网山东省电力公司经济技术研究院 | Consider the reference power network robust optimizing evaluation method of negative rules |
| WO2019165701A1 (en) * | 2018-02-28 | 2019-09-06 | 东南大学 | Random robust coupling optimization scheduling method for alternating-current and direct-current hybrid micro-grids |
| CN109713716A (en) * | 2018-12-26 | 2019-05-03 | 中国南方电网有限责任公司 | A kind of chance constraint economic load dispatching method of the wind-electricity integration system based on security domain |
| CN110739687A (en) * | 2019-10-24 | 2020-01-31 | 福州大学 | electric power system distribution robust scheduling method considering wind power high-order uncertainty |
| CN112597633A (en) * | 2020-12-04 | 2021-04-02 | 山东大学 | Power system standby optimization method and system considering multiple fault scenes |
| CN113191549A (en) * | 2021-04-29 | 2021-07-30 | 国网山东省电力公司德州供电公司 | Power distribution network source and network joint planning optimization method considering network loss and simplifying alternating current power flow |
| CN113420991A (en) * | 2021-06-25 | 2021-09-21 | 国网山东省电力公司经济技术研究院 | Power supply energy storage joint planning method and system considering expected power shortage linearization |
Non-Patent Citations (1)
| Title |
|---|
| 计及需求响应柔性调节的分布鲁棒DG优化配置;贺帅佳;高红均;刘俊勇;刘友波;王家怡;向月;;中国电机工程学报;20190420(第08期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115115251A (en) | 2022-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Risk-averse coordinated operation of a multi-energy microgrid considering voltage/var control and thermal flow: An adaptive stochastic approach | |
| Xie et al. | Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks | |
| Li et al. | Distributed distributionally robust dispatch for integrated transmission-distribution systems | |
| Rodriguez et al. | Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms | |
| Zhao et al. | Distributed risk-limiting load restoration for wind power penetrated bulk system | |
| Li et al. | Investment optimization of grid-scale energy storage for supporting different wind power utilization levels | |
| CN106684915A (en) | Wind-hydrogen coupling power generation system optimization method and device thereof | |
| Xie et al. | Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach | |
| Ma et al. | Decentralized and coordinated scheduling model of interconnected multi-microgrid based on virtual energy storage | |
| Huanna et al. | Flexible‐regulation resources planning for distribution networks with a high penetration of renewable energy | |
| CN113193600A (en) | Electric power system scheduling method and device considering wind power probability distribution uncertainty | |
| Mokhtari et al. | Optimal coordination of thyristor controlled series compensation and transmission expansion planning: Distributionally robust optimization approach | |
| Lin et al. | Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state‐space approximate dynamic programming | |
| Panda et al. | Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles | |
| Alobaidi et al. | Stochastic expansion planning of battery energy storage for the interconnected distribution and data networks | |
| Liu et al. | Energy-storage configuration for EV fast charging stations considering characteristics of charging load and wind-power fluctuation | |
| CN105528668A (en) | Dynamic environment and economy scheduling method of grid-connected wind power system | |
| Sadek et al. | Adaptive robust energy management for isolated microgrids considering reactive power capabilities of distributed energy resources and reactive power costs | |
| He et al. | Hierarchical optimal energy management strategy of hybrid energy storage considering uncertainty for a 100% clean energy town | |
| Yang et al. | Network-constrained transactive control for multi-microgrids-based distribution networks with soft open points | |
| Dong et al. | Intelligent decision-making of distribution network planning scheme with distributed wind power generations | |
| Zhang et al. | Decentralized optimization of multiarea interconnected traffic-power systems with wind power uncertainty | |
| Su et al. | Research on robust stochastic dynamic economic dispatch model considering the uncertainty of wind power | |
| Jiang et al. | Coordinative frequency-constrained unit commitment model for HVDC interconnected AC systems | |
| Miah et al. | Energy storage controllers and optimization schemes integration to microgrid: an analytical assessment towards future perspectives |
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 |