CN106230026B - Power distribution network double-layer coordination planning method containing distributed power supply based on time sequence characteristic analysis - Google Patents
Power distribution network double-layer coordination planning method containing distributed power supply based on time sequence characteristic analysis Download PDFInfo
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Abstract
The invention discloses a DG-containing power distribution network double-layer coordination planning method based on time sequence characteristic analysis, and relates to the field of power distribution network planning. The method is characterized in that: which comprises the following steps: analyzing time sequence characteristics; determining planning content; establishing a double-layer coordination planning model; determining the annual investment and operation maintenance cost of DGs in the lower-layer DG configuration, annual loss reduction brought by grid connection, the maximum node voltage of annual energy generation and the expected value of the deviation rate of rated voltage; determining the annual investment and operation maintenance cost of the system, the annual active network loss cost of the system, the annual power purchase cost of the power distribution network, the annual fault power failure loss cost and the expected value of the deviation value of the system node voltage and the rated voltage; and planning a power distribution network containing DGs under the constraint. The method analyzes corresponding time sequence characteristics, considers the mutual influence of the DG and the power distribution network frame, and respectively constructs an upper and lower double-layer multi-target coordination planning model by taking the optimal overall comprehensive decision and the maximization of the DG grid-connected benefit as targets.
Description
Technical Field
The invention relates to the field of power distribution network planning, in particular to a double-layer coordination planning method for a power distribution network containing distributed power sources based on time sequence characteristic analysis.
Background
With the spread of energy crisis and the increasing demand for electric energy, the penetration rate of Distributed Generation (DG) including renewable energy generation in the power grid is gradually increasing. The distributed power supply is continuously paid attention to the characteristics of energy conservation, cleanness and environmental protection, the grid connection scale is continuously enlarged, however, the distribution network gradually becomes an active distribution network due to the large amount of penetration of the distributed power supply and brings new problems, the complexity of distribution network planning is increased, and new challenges are provided.
In the traditional power distribution network planning, line upgrading and reconstruction, load point amplification and network frame reconstruction are carried out based on load prediction and the existing power supply structure in a planning horizontal year, and finally, the optimization of economy and reliability is achieved. After the distributed power supply permeates, different configurations of grid-connected positions, installation capacity and installation types of the distributed power supply can greatly affect the power distribution network by changing the structure and the operation mode of network nodes. In addition, the DG output, represented by Photovoltaic (PV) and Wind Turbine (WT), is intermittent and random, adding to the uncertainty of the operation of the distribution grid. Therefore, it is necessary to finely configure the DG to maximize its benefits and reduce the impact on the distribution network. Meanwhile, the location and volume fixing of the DG and the planning of the power distribution network rack often affect each other, in order to achieve the optimal comprehensive planning of the two, the traditional distribution network planning is not adapted any more, and further research on the coordination planning of the distributed power supply and the power distribution network rack is necessary.
In recent years, research has gradually transitioned from individual planning of DGs to coordinated planning of DGs and grid frames, and various uncertainty factors have begun to be considered. In some technologies, DGs are planned based on indexes such as reliability, network loss and the like; the timing sequence characteristic of the DG is analyzed, the location and the volume are determined based on multiple targets, and the accuracy and the adaptability of the timing sequence characteristic are considered in the comparison of the example results; the technology plans the DG based on low carbon and environmental benefits on the basis of analyzing the time sequence characteristics, and the literature comprehensively analyzes the time sequence characteristics of DG output and load and performs multi-target planning on the multi-type DG; even if the DG and the power distribution network frame are coordinately planned through an improved genetic algorithm, the load point amplification is not involved in the model; comprehensively planning DGs (distributed generators) and net racks on the basis of considering line upgrading and load point amplification; when the DG and the power distribution network are coordinately planned, load and DG output uncertainty are considered, a system multi-state model is established according to a probability model, and then a multi-scene analysis method is adopted for processing; the power distribution network node classification idea is introduced into power distribution network planning containing distributed power supplies and converted into a Steiner tree problem to be solved.
From the existing results, when the DG and the distribution network frame are coordinated and planned and researched, the coordination processing of the network frame and the DG in the model can be continued deeply; meanwhile, the uncertainty simulation precision can be improved by considering the DG output and the time sequence characteristics of the load, and the planning accuracy is further improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a double-layer coordination planning method for a power distribution network containing a distributed power supply based on time sequence characteristic analysis, and aims to establish a double-layer coordination planning model based on mutual influence of a grid frame and DG configuration by analyzing the time sequence characteristics of DG output and load, and finally solve the double-layer model through an improved hybrid chaotic binary particle swarm optimization algorithm in a nested manner.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: the double-layer coordination planning method for the power distribution network with the distributed power supply based on the time sequence characteristic analysis is characterized by comprising the following steps of: which comprises the following steps:
(1) timing characteristic analysis
And determining a distributed power supply time sequence daily output model and a power grid daily load model according to the intermittent randomness of the output of the distributed power supply and the uncertainty of the power grid load.
(2) Determining planning content
And selecting the grid-connected position, type and capacity of the distributed power supply, the line needing upgrading and transformation in the grid structure and the newly-built line accessed by the newly-added load point as decision variables for the coordinated planning of the distributed power supply configuration and the power distribution grid structure.
(3) Establishing a double-layer coordination planning model
And (3) establishing an upper-layer power distribution network frame random code according to the step (2), then configuring a lower-layer distributed power supply on the basis of the upper-layer power distribution network frame to obtain corresponding optimal configuration of the distributed power supply, further feeding a result back to the upper-layer power distribution network frame, and finally performing overall planning on the basis of the upper-layer power distribution network frame.
(4) And (3) determining the annual investment and operation maintenance cost of the distributed power supply in the lower distributed power supply configuration in the step (3), the annual loss reduction brought by grid connection, the maximum node voltage of annual energy generation and the expected value of the rated voltage deviation rate according to the distributed power supply time sequence daily output model in the step (1) and the power grid daily load model.
(5) And (4) determining the system annual investment and operation maintenance cost, the system annual active network loss cost, the power distribution network annual electricity purchase cost, the annual fault power failure loss cost and the expected value of the deviation value of the system node voltage and the rated voltage of the upper-layer power distribution network frame according to the steps (3) and (4).
(6) Under the constraints of node power balance, node voltage upper and lower limits, branch power limit and distributed power supply installation capacity, planning a power distribution network containing the distributed power supply according to the steps (4) and (5).
The further technical scheme is that the distributed power supply comprises photovoltaic and wind power.
The method further comprises the step of enabling the distributed power supply time-series solar output model to be a fan typical solar output time-series characteristic model and a photovoltaic typical solar output time-series characteristic model.
The further technical scheme is that the annual investment and operation and maintenance cost of the distributed power supply are the minimum:
in the formula, NDGTotal number of nodes to be installed, alpha, for distributed poweriFixed annual average cost coefficient for installing distributed power for node i, NtTypical total number of distributed power contribution and load, djIs the number of days in a typical case j of a year, T is the number of hours in a day, Δ T is the duration of a unit period, Ci,WTGAnd Ci,PVGRespectively the unit active capacity investment cost, S, corresponding to the wind power and the photovoltaic of the node i to be selectedi,WTGAnd Si,PVGRated capacities, lambda, of wind and photovoltaic at node i to be selected, respectivelyWTGAnd λPVGRespectively the power factors of wind power and photovoltaic power,
and
respectively the operation and maintenance costs of the wind power and the photovoltaic unit generating capacity installed at the node i to be selected,
and
the active power output of the wind power and the photovoltaic power installed at the node i to be selected in the typical situation j in the time period t is respectively.
The further technical scheme is that the maximum annual loss reduction caused by grid connection of the distributed power supply is
In the formula, NbrFor the total number of branch circuits, R, of the distribution network lineiIs the resistance of branch I, IijtAnd l'ijtThe current in branch i during a time period t in a typical situation j after the distributed power supply is not installed and installed, respectively.
The further technical scheme is that the annual generating capacity of the distributed power supply is maximum as follows:
the further technical scheme is that the deviation rate expectation value of the node voltage and the rated voltage is minimum.
In the formula, NnodeIs the total number of load nodes, UijstIs the voltage of node i during a period t in a typical case j, UNIs a rated voltage.
The further technical scheme is that the system has the minimum annual investment and operation and maintenance cost as follows:
in the formula, NELAnd NnewRespectively adding the number of network branches before load point amplification and the total number of lines to be newly built; x is the number ofiAnd xjThe variable is 0-1, and respectively indicates whether the line i to be upgraded and modified and the line j to be newly built are selected; alpha is alphaeliUpgrading and transforming the investment year average cost coefficient for the ith established line; beta is aliAnd betaljRespectively operating and maintaining the expense rates of the line i and the line j; cELiAnd CnewjRespectively drawing up the construction cost of unit length for the ith established line and the jth proposed line; liAnd ljThe lengths of the ith established line and the jth proposed line are respectively set.
The annual active network loss cost of the system is as follows:
in the formula, ClossIn terms of the cost per unit of loss of the network,
for the active network loss of branch i in time period t in typical case j, the number of branches of the distribution network can be expressed as formula (13):
the minimum annual electricity purchasing cost of the power distribution network is as follows:
in the formula, CppThe unit price of electricity purchased from the transmission grid for the distribution grid,
is a nodej active load during the time period t in the typical situation i,
and
and respectively representing the wind power active output and the photovoltaic active output of the candidate node m in the time period t under the typical situation i.
The annual fault power failure loss cost is minimum
In the formula, gammajMean annual fault rate, P, for a unit length of line jist,jkA line j blackout during a period t under a typical situation i results in an under-supplied power at load point k.
Pit,jkCan take values according to three conditions[16]: after the fault of the line j, if the node k is connected with the line of the distribution network without power failure, P it,jk0; if the node k is not connected with the line of the power distribution network without power failure and does not contain the distributed power supply, Pit,jk＝Pit,kLIn which P isit,kLThe load of the node k in the time period t under the typical situation i; if the node k is not connected with the power distribution network uninterrupted part, but the node contains a distributed power supply, the expression is as follows:
Pit,jk＝Pit,kL-Pit,k,∑DG (10)
in the formula, Pit,k,∑DGAnd the distributed power supply accessed by the node k has the total active output value in the time period t under the typical situation i.
The further technical scheme is that the minimum deviation value between the system node voltage and the rated voltage is as follows:
min g5＝f4 (11)
the further technical proposal is that the constraint condition is
In the formula, PiAnd QiActive and reactive injected power, G, respectively, for node iijAnd BijThe real and imaginary parts, theta, of the nodal admittance matrix, respectivelyijIs the voltage phase angle difference between node i and node j;
and
the upper and lower voltage limits of the node i are respectively; pijAnd
the active power flowing through the branch ij and the upper limit thereof are respectively;
and Pi,maxThe WT, PV maximum active capacity and both total capacity maximum allowed access for node i, respectively.
The further technical scheme is that the method adopts an improved hybrid chaotic binary particle swarm algorithm to perform nested solution.
The further technical scheme is that the upper-layer power distribution network frame and the distributed power supply are both configured by binary codes.
The further technical proposal is that the particle velocity v of the binary particle swarm optimization algorithmidAnd position xidIs given in v id0 and v are less than or equal toid>When 0, respectively:
the further technical scheme is that the ergodicity of chaotic search is utilized to carry out chaotic mapping on the inertia weight so as to enhance the global search capability, and the following formula is shown as follows:
wn+1＝μwn(1-wn) 0≤w0≤1 (18)
adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the invention provides a multi-target double-layer planning model of a power distribution network containing distributed power supplies based on time sequence characteristic analysis. The method comprises the steps of analyzing corresponding time sequence characteristics aiming at the conditions of wind power and photovoltaic distributed power supply output intermittency and load uncertainty in a power distribution network, dividing the daily output and daily load of the distributed power supply into a plurality of typical situations, and selecting a prediction numerical value of a typical day as a representative. Considering the mutual influence of the distributed power supply and the power distribution network frame, an upper and lower double-layer multi-target coordination planning model is respectively constructed by taking the optimization of the overall comprehensive decision and the maximization of the grid-connected benefit of the distributed power supply as targets. And finally, nesting and solving the double-layer model through an improved hybrid chaotic binary particle swarm optimization algorithm, and verifying the correctness and the effectiveness of the model and the algorithm by taking an IEEE33 node power distribution system as an example.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a typical sunrise time sequence characteristic of a wind turbine;
FIG. 2 is a photovoltaic typical solar output timing characteristic;
FIG. 3 is a spring typical daily load timing characteristic;
FIG. 4 is a lower level computational flow diagram;
FIG. 5 is an overall calculation flow diagram;
FIG. 6 is a network topology diagram of an IEEE33 node power distribution system;
FIG. 7 is a graph of node voltage at a time during a typical weekday in spring;
fig. 8 is a graph of branch line loss at a certain time in a typical working day in spring.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
The invention discloses a multi-target double-layer coordination planning research scheme of a power distribution network containing distributed power sources based on time sequence characteristic analysis, which comprises the following steps:
1. timing characteristic analysis
According to the invention, two distributed power supplies of wind power and photovoltaic power are selected, and the uncertainty of the power distribution network is increased by the intermittent randomness of the output and the uncertainty of the load.
Wind power output is mainly influenced by wind speed, photovoltaic output mainly depends on illumination intensity, and wind speed and illumination intensity are greatly influenced by natural environment factors such as weather and meteorology, so that wind power output and photovoltaic output have certain intermittency. Wind speed and light illumination tend to exhibit different overall variations with seasonal variations, so WT and PV solar output can be described roughly divided into four typical cases, spring, summer, fall and winter.
The load part is analyzed by the total load of the power distribution system,the load not only being influenced by seasons[7]And different change rules are presented under different date types of working days, saturday and holidays, so that the load can be divided into three typical date case descriptions under each season, and the total number of the typical cases is 12.
Based on the above, 12 typical cases of DG daily output and daily load can be obtained in a one-year time series. In each case, the daily output and daily load of each DG are basically changed according to the same rule, and each typical case can be approximately represented by the predicted values of the daily DG output and load. The predicted values of the typical daily output of the fan and the photovoltaic in each typical case and the typical daily load in spring are shown in fig. 1, fig. 2 and fig. 3, respectively.
2 double-layer coordination planning model
2.1 planning content
In order to realize the coordinated planning of the location and capacity determination (namely, the distributed power supply configuration) of the DG and the power distribution grid structure, the invention selects the DG grid-connected position, type and capacity, a line needing to be upgraded and modified in the grid structure and a newly-built line accessed by a newly-added load point as decision variables. Since the configuration of the DG and the change of the distribution grid structure affect each other, a concept of double-layer planning is proposed here, in which a lower-layer DG part (i.e., distributed power supply configuration planning) and an upper-layer distribution grid structure are further planned on the basis of a lower-layer result to obtain an overall optimal configuration including the grid structure and the DG.
2.2 objective function
When the single-layer planning is solved by using intelligent algorithm coding, the distribution network frame and the DG part are jointly randomly coded, the double-layer planning model randomly codes the upper-layer network frame firstly, then the lower layer obtains the corresponding DG optimal configuration on the basis of the upper-layer network frame, the result is returned to the upper layer, and finally the upper layer performs the overall planning on the basis. Therefore, the upper layer optimally establishes multiple targets through overall comprehensive decision, and the lower layer establishes multiple targets by comprehensively considering the influence of DG infiltration on the power distribution network and the brought benefits.
2.2.1 lower layer objective function
1) The DG annual investment and operation and maintenance costs are minimal.
In the formula, NDGTotal number of nodes to be installed, α, for DGiInstallation of a fixed annual average cost coefficient, N, of DGs for node itTotal typical of DG forces and loads, djIs the number of days in a typical case j of a year, T is the number of hours in a day, Δ T is the duration of a unit period, Ci,WTGAnd Ci,PVGRespectively the unit active capacity investment cost, S, corresponding to the wind power and the photovoltaic of the node i to be selectedi,WTGAnd Si,PVGRated capacities, lambda, of wind and photovoltaic at node i to be selected, respectivelyWTGAnd λPVGRespectively the power factors of wind power and photovoltaic power,
and
respectively the operation and maintenance costs of the wind power and the photovoltaic unit generating capacity installed at the node i to be selected,
and
the active power output of the wind power and the photovoltaic power installed at the node i to be selected in the typical situation j in the time period t is respectively.
2) The annual loss reduction brought by the DG grid connection is the largest.
In the formula, NbrFor the total number of branch circuits, R, of the distribution network lineiIs the resistance of branch I, IijtAnd l'ijtThe current in branch i during a period t in a typical situation j after no DG was installed and installed, respectively.
3) The DG annual energy production is the largest.
After the wind power generation DG and the photovoltaic DG are connected to the power grid, the output power of the wind power generation DG and the photovoltaic DG can reduce the power generation of part of coal consumption units, so that the environmental benefit is brought, and meanwhile, the power purchased from a power transmission network is reduced, so that the maximum annual power generation amount is selected as a target.
4) The deviation rate of the node voltage from the rated voltage is minimum.
In the formula, NnodeIs the total number of load nodes, UijstIs the voltage of node i during a period t in a typical case j, UNIs a rated voltage.
2.2.2 Upper layer objective function
1) The annual investment and the operation and maintenance cost of the system are minimum.
In the formula, NELAnd NnewRespectively adding the number of network branches before load point amplification and the total number of lines to be newly built; x is the number ofiAnd xjThe variable is 0-1, and respectively indicates whether the line i to be upgraded and modified and the line j to be newly built are selected; alpha is alphaeliUpgrading and transforming the investment year average cost coefficient for the ith established line; beta is aliAnd betaljRespectively operating and maintaining the expense rates of the line i and the line j; cELiAnd CnewjRespectively drawing up the construction cost of unit length for the ith established line and the jth proposed line; liAnd ljThe lengths of the ith established line and the jth proposed line are respectively set.
2) The annual active network loss cost of the system is minimum.
In the formula, ClossIn terms of the cost per unit of loss of the network,
for the active network loss of branch i in time period t in typical case j, the number of branches of the distribution network can be expressed as formula (13):
3) the annual electricity purchasing cost of the power distribution network is minimum.
In the formula, CppThe unit price of electricity purchased from the transmission grid for the distribution grid,
for node j the active load during time period t in a typical situation i,
and
and respectively representing the wind power active output and the photovoltaic active output of the candidate node m in the time period t under the typical situation i.
4) Annual fault power failure loss cost is minimum.
In the formula, gammajMean annual fault rate, P, for a unit length of line jist,jkA line j blackout during a period t under a typical situation i results in an under-supplied power at load point k.
Pit,jkValues can be taken in three situations: after the fault of the line j, if the node k is connected with the line of the distribution network without power failure, P it,jk0; if node k and node c are matchedIf the lines of the power grid without power failure are not connected and do not contain DGs, P isit,jk＝Pit,kLIn which P isit,kLThe load of the node k in the time period t under the typical situation i; if the node k is not connected with the part of the power distribution network without power failure, but the node contains DG, the expression is as follows:
Pit,jk＝Pit,kL-Pit,k,∑DG (10)
in the formula, Pit,k,∑DGAnd (4) the DG accessed by the node k has the total active output value within the time t under the typical situation i.
5) The deviation of the system node voltage from the rated voltage is minimized.
min g5＝f4 (11)
2.3 constraint Condition
And (3) taking a node power balance equation, node voltage upper and lower limits, branch power limit and DG installation capacity constraint as constraint conditions, and respectively showing the formulas (12) to (15).
In the above formula, PiAnd QiActive and reactive injected power, G, respectively, for node iijAnd BijThe real and imaginary parts, theta, of the nodal admittance matrix, respectivelyijIs the voltage phase angle difference between node i and node j;
and
the upper and lower voltage limits of the node i are respectively; pijAnd
the active power flowing through the branch ij and the upper limit thereof are respectively;
and Pi,maxThe WT, PV maximum active capacity and both total capacity maximum allowed access for node i, respectively.
3 model solution
For the multi-target solution of the double-layer model, the improved hybrid chaotic binary particle swarm algorithm is adopted for nested solution.
3.1 application of the Algorithm
The grid frame and the DG are both configured by binary codes, so that the binary particle swarm algorithm is partially improved. Velocity v of the particlesidUpdating formula invariant, position xidThen, the bit rate is updated according to different probabilities with the speed change, and when v is used for improving the relation between the bit variation probability and the speed change id0 and v are less than or equal toid>At 0, update is performed according to equations (16) and (17), respectively:
the location update is similar to mutation operations in genetic algorithms, so further crossover operations are introduced, enabling two-step mixed updating of locations to enhance diversity.
Chaotic mapping is carried out on the inertial weight by utilizing the ergodicity of chaotic search to enhance the global search capability, as shown in the following formula:
wn+1＝μwn(1-wn) 0≤w0≤1 (18)
get mu to 4 and make it fall into a completely chaotic state, and
in the particle updating process, the group optimal particle is found from the pareto solution set based on the niche sharing technology. Let the pareto solution concentrate particles as N dimensions and number as NsDefine particle xiAnd xjThe crowding distance between is:
the method comprises the following steps: 1) calculating the radius r of the nichesh：
In the formula (d)i＝min{dij}(i≠j,j＝1,2,…Ns)。
2) Establishing a shared function S (d)ij)：
3) Calculating the sharing degree S of each particlei：
The larger the sharing degree is, the larger the similarity of the individual in the group is, the S is selectediThe smallest particles are used as the optimal particles of the group, so that the probability of selecting similar individuals is reduced, and the local convergence is reduced.
After the iteration is completed, the optimal solution is selected from the pareto solution set compromise based on fuzzy membership and variance weighting, and the steps are as follows:
1) calculating the jth target membership of the ith pareto solutionεijIn the upper and lower layers, as shown in formulas (23) and (24), respectively:
in the formula, gijAnd fijRespectively the jth target of the ith pareto solution in the upper and lower layers,
and
respectively the maximum and minimum values of the target j in the upper layer,
and
the maximum and minimum values of target j in the lower layer, respectively.
2) Variance weighting of the target:
in the formula, wjThe weight of the target j, M is the target number, and N is the number of solutions in the pareto solution set.
3) Calculating the preference degree of the ith pareto solution:
and taking the particle with the highest preference as the optimal solution for compromise.
3.2 calculation procedure
The upper and lower layer particles encode the following formula:
in the formula, CuAnd CdCoding structures of an upper-layer grid frame and a lower-layer DG respectively; m is the number of lines to be transformed, l is the number of lines to be newly built, and n is the number of candidate nodes for DG installation; u shapeiIndicates whether line i is modified, NiShowing the specific line number of the line i to be newly built; swiAnd SpiWT and PV grid-connected capacity cardinality of the node i to be selected are respectively.
When the fitness of each particle is calculated in the lower layer, checking whether each constraint condition is met or not, if so, normally calculating, and otherwise, enabling f for solving the minimum value1And f4The fitness value of (a) is positive infinity, and f of the maximum value is calculated2And f3The fitness value of (a) is negative infinity to ensure that particles which do not meet the constraint are eliminated when constructing the pareto non-dominating set.
The lower layer calculation flowchart and the overall calculation flowchart are respectively shown in fig. 4 and fig. 5:
4 example analysis
4.1 brief introduction to the examples
The invention adopts an improved IEEE33 node power distribution system as an example, and a network topological graph is shown in figure 6.
The wind power installation cost is 6000 yuan/kW, and the operation and maintenance cost is 0.2 yuan/(kW.h); the photovoltaic installation cost is 8000 yuan/kW, and the operation and maintenance cost is 0.15 yuan/(kWh & h); the DG annual average investment cost coefficient is 0.1, and the power factor is 0.85; the grid-connected candidate positions of the DGs are 8 nodes including the nodes 1, 3, 7, 13, 23, 24, 29 and 31, the minimum grid-connected unit capacity of the nodes is 40kVA, and if the grid-connected base number of a certain type of DGs is m (m is 0, 1, 2, … and 7), the installation capacity of the nodes is m multiplied by 40 kVA.
4.2 planning results of Each case
The invention selects four planning schemes for comparison:
scheme 1: coordinately planning the DG and the net rack according to the double-layer model;
scheme 2: planning in two stages, namely planning DG on an original net rack without a new load point and an upgraded line according to a lower model, and planning the net rack according to an upper model based on the DG configuration;
scheme 3: coordinately planning the DG only containing wind power and the net rack;
scheme 4: and C, coordinately planning the DG containing only the photovoltaic grid and the grid frame.
The planning results of the schemes are shown in table 1, and the target values are shown in tables 2 and 3.
TABLE 1DG and net rack integral planning result
Table 2 upper layer objective function values for each scheme
Table 3 lower objective function values of each scheme
According to the planning result, the grid-connected capacity of the nodes with large loads such as 7, 13, 24 and 29 is large, the capacity of the nodes with small loads such as 1 and 3 is small, and the power supply pressure is balanced to a certain extent. Compared with the scheme 1 and the scheme 2, the DG grid-connected capacity coordinated and planned increases 560kVA compared with the stage planning, and the independent planning of the DG on the unplanned grid structure can limit the penetration rate of the DG because the mutual influence of the grid structure and the DG is neglected in the stage planning; the newly-built lines in the two schemes are different, and the upgraded lines in the scheme 1 are relatively fewer, which shows that the coordinated planning can delay the transformation of the lines to a certain extent.
From the upper-level target, the coordinated planning has higher annual total investment and operation maintenance cost due to the high infiltration rate of the DG, but the other 3 items of cost are obviously lower than that of the staged planning, which shows that the DG is greatly connected with the grid for operation, so that the grid loss is reduced, the output of the DG also supplies a large amount of loads and relieves the electricity purchasing pressure of the power distribution network from a superior power grid, meanwhile, the cost of the fault power failure loss is also reduced, the reliability is enhanced, the voltage deviation rate is relatively lower, and the voltage quality is further improved; from the lower-layer target, the voltage offset rate is raised after the second-stage planning is implemented in the scheme 2, and due to the fact that the capacity of the DG is limited, the loss reduction amount and the power generation amount are low, and the grid-connected benefit of the DG cannot be maximized. Therefore, the coordinated planning is more comprehensive and reasonable than the phased planning, and the benefits of DGs are fully exerted.
The scheme 3 and the scheme 4 only contain one DG, the total investment is very low compared with the scheme 1, but other 4 indexes are inferior to the scheme 1, the economic and reliable operation of the system is not facilitated in the long run, and the multi-type DG grid connection relative benefit is better. And different DG infiltrates and contrasts, can find that two schemes have certain difference in line upgrading and newly-built aspect, upgrade the line to be less than the photovoltaic by 1 in the planning of the wind-powered electricity generation infiltration, and its grid-connected capacity is great, and the network loss, the electricity purchasing, the loss of power failure and voltage excursion expect to be superior to the photovoltaic, the benefit brought for joining in marriage the net is more apparent, therefore both can be selected wind-powered electricity generation by priority when joining in the net.
According to the planning results of the four schemes, at a certain moment in spring on a typical working day, the voltages of the nodes and the network loss of the branches are shown in FIGS. 7-8:
in the four schemes, the voltage deviation rate is expected to be within the allowable range of +/-5%, the voltage of each node is basically about 12.66kV, and the voltage deviation of the scheme containing only one DG is larger than that of the scheme containing a plurality of DGs; compared with the other three schemes, the coordinated planning has the advantages that the voltage deviation is minimum, the loss of each branch circuit is small, the reliable and economic operation of the power distribution network is facilitated, and the advantages of the scheme are demonstrated.
5 conclusion
Aiming at the uncertainty of DG output and load, the invention considers the annual time sequence characteristics and divides the annual time sequence characteristics into a plurality of typical situations, and selects a typical day to plan horizontal annual prediction to obtain corresponding time sequence characteristics. Considering the mutual influence of DG and the net rack, establishing a double-layer calculation model, and the example result shows that: compared with the sectional planning, the coordinated planning greatly improves the DG penetration rate, maximizes the DG grid-connected benefit and reduces the upgrading lines; compared with a single-type DG, a plurality of types of DGs (the grid connection is easier to give play to benefits; wind power has higher grid connection priority than photovoltaic power, and along with the expansion of a grid in a power distribution network, the line upgrading and the large-scale grid connection of more types of DGs, reasonable comprehensive coordination planning is necessary, and the optimization of the overall benefits is realized.
Claims (3)
1. The double-layer coordination planning method for the power distribution network with the distributed power supply based on the time sequence characteristic analysis is characterized by comprising the following steps of: which comprises the following steps:
(1) timing characteristic analysis
Determining a distributed power supply time sequence daily output model and a power grid daily load model according to the intermittent randomness of the output of the distributed power supply and the uncertainty of the power grid load;
(2) determining planning content
Selecting a grid-connected position, type and capacity of the distributed power supply, a line needing upgrading and transformation in a grid frame and a newly-built line accessed by a newly-added load point as decision variables for the coordinated planning of the distributed power supply configuration and the power distribution grid frame;
(3) establishing a double-layer coordination planning model
Establishing an upper-layer power distribution network frame random code according to the step (2), then configuring a lower-layer distributed power supply on the basis of the upper-layer power distribution network frame to obtain a corresponding optimal configuration of the distributed power supply, further feeding a result back to the upper-layer power distribution network frame, and finally performing overall planning on the basis of the upper-layer power distribution network frame;
(4) determining the annual investment and operation maintenance cost of the distributed power supply in the lower distributed power supply configuration in the step (3), the annual loss reduction brought by grid connection, the maximum annual power generation amount, and the expected value of the deviation rate of the node voltage and the rated voltage according to the distributed power supply time sequence daily output model and the power grid daily load model in the step (1);
(5) determining the system annual investment and operation maintenance cost, the system annual active network loss cost, the power distribution network annual electricity purchase cost, the annual fault power failure loss cost and the expected value of the deviation value of the system node voltage and the rated voltage of the upper-layer power distribution network frame according to the steps (3) and (4);
(6) under the constraints of node power balance, node voltage upper and lower limits, branch power limits and distributed power supply installation capacity, planning a power distribution network containing the distributed power supply according to the steps (4) and (5);
the annual investment and operation and maintenance cost of the distributed power supply are the minimum:
in the formula, NDGTotal number of nodes to be installed, alpha, for distributed poweriFixed annual average cost coefficient for installing distributed power for node i, NtTypical total number of distributed power contribution and load, djIs the number of days in a typical case j of a year, T is the number of hours in a day, Δ T is the duration of a unit period, Ci，WTGAnd Ci，PVGInvestment costs of unit active capacity corresponding to wind power and photovoltaic at node i to be selected respectively，Si，WTGAnd Si，PVGRated capacities, lambda, of wind and photovoltaic at node i to be selected, respectivelyWTGAnd λPVGRespectively the power factors of wind power and photovoltaic power,
and
respectively the operation and maintenance costs of the wind power and the photovoltaic unit generating capacity installed at the node i to be selected,
and
respectively the active power output of the wind power and the photovoltaic power installed at a node i to be selected in a typical situation j within a time period t;
the maximum annual loss reduction caused by grid connection of the distributed power supply
In the formula, NbrFor the total number of branch circuits, R, of the distribution network lineiIs the resistance of branch I, IijtAnd l'ijtCurrent in the branch i during a period t in a typical situation j after the distributed power supply is uninstalled and installed, NtTypical total number of distributed power contribution and load, djIs the number of days in a typical case j of a year, T is the number of time segments in a day, Δ T is the unit time duration;
the maximum annual generated energy of the distributed power supply is as follows:
in the formula, NDGFor distributed power supplyTotal number of installed nodes, djIs the number of days in a typical case j of the year, T is the number of hours in the day, at is the unit period duration,
and
respectively the active power output of the wind power and the photovoltaic power installed at a node i to be selected in a typical situation j within a time period t;
the deviation rate of the node voltage from the rated voltage is minimum:
in the formula, NnodeIs the total number of load nodes, UijtIs the voltage of node i during a period t in a typical case j, UNAt rated voltage, NtT is the total number of typical conditions of distributed power supply output and load, and T is the number of time periods in one day;
the annual investment and operation maintenance cost of the system are the minimum:
in the formula (f)1Annual investment and operational maintenance costs for distributed power supplies, NELAnd NnewRespectively adding the number of network branches before load point amplification and the total number of lines to be newly built; x is the number ofiAnd xjThe variable is 0-1, and respectively indicates whether the line i to be upgraded and modified and the line j to be newly built are selected; alpha is alphaeliUpgrading and transforming the investment year average cost coefficient for the ith established line; beta is aliAnd betaljRespectively operating and maintaining the expense rates of the line i and the line j; cELiAnd CnewjRespectively drawing up the construction cost of unit length for the ith established line and the jth proposed line; liAnd ljAre respectively established for the ith stripThe length of the line and the jth proposed line;
the annual active network loss cost of the system is as follows:
in the formula, ClossIn terms of the cost per unit of loss of the network,
for the active network loss of branch i in time period t in typical case j, NtTypical total number of distributed power contribution and load, djThe number of days in a typical case j in a year, T is the number of time periods in a day, Δ T is the duration of a unit time period, and the total number of branch lines of the distribution network can be expressed as formula (7):
the minimum annual electricity purchasing cost of the power distribution network is as follows:
in the formula, CppThe unit price of electricity purchased from the transmission grid for the distribution grid,
for node j the active load during time period t in a typical situation i,
and
wind power and photovoltaic active power output N of the candidate node m in the time period t under the typical situation inodeAs a load node assemblyNumber, diIs the number of days in a typical situation i of the year, NDGThe total number of nodes to be installed is DG;
the annual fault power failure loss cost is minimum
In the formula, gammajMean annual fault rate, P, for a unit length of line jit，jkUnder the typical situation i, the power supply shortage of the load point k is caused by the power failure of the line j in the time period t;
Pit，jkvalues can be taken in three situations: after the fault of the line j, if the node k is connected with the line of the distribution network without power failure, Pit，jk0; if the node k is not connected with the line of the power distribution network without power failure and does not contain the distributed power supply, Pit，jk＝Pit，kLIn which P isit，kLThe load of the node k in the time period t under the typical situation i; if the node k is not connected with the power distribution network uninterrupted part, but the node contains a distributed power supply, the expression is as follows:
Pit，jk＝Pit，kL-Pit，k，∑DG (10)
in the formula, Pit，k，∑DGThe total active power output value of a distributed power supply accessed by a node k in a time period t under a typical situation i;
the minimum deviation value between the system node voltage and the rated voltage is as follows:
min g5＝f4 (11)
the constraint condition is
In the formula (f)4Is a desired value of the deviation ratio of the node voltage from the rated voltage, PiAnd QiActive and reactive injected power, G, respectively, for node iijAnd BijThe real and imaginary parts, theta, of the nodal admittance matrix, respectivelyijIs the voltage phase angle difference between node i and node j;
and
the upper and lower voltage limits of the node i are respectively; pijAnd
the active power flowing through the branch ij and the upper limit thereof are respectively; lambda [ alpha ]WTGAnd λPVGPower factors of wind power and photovoltaic power respectively; si，WTGAnd Si，PVGRespectively the wind power rated capacity and the photovoltaic rated capacity of the node i to be selected; u shapei、UjVoltages of nodes i and j, respectively; n is a radical ofnodeThe total number of the load nodes is;
and Pi，maxThe WT, PV maximum active capacity and both total capacity maximum allowed access for node i, respectively.
2. The time sequence characteristic analysis-based double-layer coordination planning method for power distribution networks with distributed power supplies according to claim 1, wherein the distributed power supplies comprise photovoltaic power and wind power.
3. The time sequence characteristic analysis-based double-layer coordination planning method for the power distribution network comprising the distributed power supply according to claim 1, wherein the distributed power supply time sequence solar output model is a fan typical solar output time sequence characteristic model and a photovoltaic typical solar output time sequence characteristic model.
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