CN113536650A - Method for solving multi-target multi-energy power supply planning model through particle swarm algorithm - Google Patents
Method for solving multi-target multi-energy power supply planning model through particle swarm algorithm Download PDFInfo
- Publication number
- CN113536650A CN113536650A CN202110644219.5A CN202110644219A CN113536650A CN 113536650 A CN113536650 A CN 113536650A CN 202110644219 A CN202110644219 A CN 202110644219A CN 113536650 A CN113536650 A CN 113536650A
- Authority
- CN
- China
- Prior art keywords
- cost
- power supply
- node
- distributed power
- nodes
- 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.)
- Pending
Links
- 239000002245 particle Substances 0.000 title claims abstract description 26
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000009826 distribution Methods 0.000 claims abstract description 44
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 230000007613 environmental effect Effects 0.000 claims abstract description 10
- 238000009434 installation Methods 0.000 claims description 39
- 238000004146 energy storage Methods 0.000 claims description 37
- 230000006870 function Effects 0.000 claims description 33
- 238000003912 environmental pollution Methods 0.000 claims description 24
- 239000007789 gas Substances 0.000 claims description 24
- 238000012423 maintenance Methods 0.000 claims description 20
- 238000010248 power generation Methods 0.000 claims description 18
- 239000003344 environmental pollutant Substances 0.000 claims description 12
- 231100000719 pollutant Toxicity 0.000 claims description 12
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 239000000356 contaminant Substances 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 238000011835 investigation Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 239000002699 waste material Substances 0.000 claims description 3
- 230000008685 targeting Effects 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 1
Classifications
-
- 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
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- 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
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Artificial Intelligence (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Primary Health Care (AREA)
- Marketing (AREA)
- Computer Hardware Design (AREA)
- Water Supply & Treatment (AREA)
- Geometry (AREA)
- Public Health (AREA)
- Life Sciences & Earth Sciences (AREA)
- Tourism & Hospitality (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of electric power, in particular to a method for solving a multi-target multi-energy power supply planning model by a particle swarm algorithm. Under the condition that the type, the capacity and the position of a distributed power supply accessed to each node of a power distribution network are uncertain, the operation economy, the reliability and the environmental protection performance in a whole life cycle are considered, the single targeting is carried out to set a total economy objective function, the Particle Swarm Optimization (PSO) is used to carry out minimum value optimization on the total objective function value, and the type, the position and the capacity of the distributed power supply access when the economic cost is minimum are obtained as a final result.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method for solving a multi-target multi-energy power supply planning model by a particle swarm algorithm.
Background
The prior art is limited on a single-objective function single-kind distributed power model, or a multi-objective single-kind distributed power, or a single-objective multi-kind distributed power, and the consideration is single, so that the requirement of multi-energy coordination planning of an active power distribution network cannot be completely met.
Disclosure of Invention
In order to effectively solve the problems in the background art, the invention provides a method for solving a multi-target multi-energy power supply planning model by a particle swarm algorithm, and the specific technical scheme is as follows;
a method for solving a multi-target multi-energy power supply planning model by a particle swarm algorithm is characterized by comprising the following steps: the method comprises the following steps:
step one, establishing an objective function
(1) Cost of distributed power supply construction and operation
The distributed power supply is installed once, maintained subsequently and has a residual value as an expression:
the distributed power supply type of the model is four, namely a photovoltaic power supply, a fan power generation element, a gas turbine power generation element and an energy storage element, wherein C is 4; the whole life cycle age is set to 25 years, and N is 25; the capital interest rate is 0.1, and r is 0.1;Cdgthe one-time installation cost of the distributed power supply is unit/kw; pgThe number of the distributed power supplies is equal to the number of the distributed power supplies; the loan proportion to the bank in the construction of the distributed power supply is 0.75, namely Rloan0.75; through investigation and research, the loan interest rate of the bank is 5.65 percent, Rint＝5.65％；comOperating and maintaining costs for various kinds of distributed power sources; crResidual values of the corresponding various distributed power supplies after use are obtained;
(2) cost of environmental pollution
The distributed generation environment costs are as follows:
three of these contaminant species are contemplated herein, including CO2, SO2, and nitrogen oxides, i.e., m-3; beta is adThe cost required for treating pollutants is unit; alpha is alphaodThe method is applicable to the discharge relationship of various distributed power supplies and various pollutants; pgAnd PsMeaning the same as an economic objective function;
(3) reliability economic cost expense
Wherein k is the number of load nodes of the power distribution network; t is the annual power failure time of the load point; l is the self load of the node; r is the power failure loss cost corresponding to the average power failure duration time of each kw load;
step two, establishing constraint conditions
(1) Distributed power source installation capacity constraints
The total active power of the distributed power supply connected to the power distribution network does not exceed 25 percent of the total load in the power distribution network system
P∑DG≤sP∑L (4-4)
Wherein, P∑DG is the total active power accessed into the distributed power supply; p∑LIs the total amount of load in the system; s is a coefficient, and 0.25 is taken;
(2) node installation capacity constraints
For a single node, in order to reduce the network loss of a power distribution network, the total capacity of a distributed power supply of an access node is smaller than the upper limit of the load capacity of the system;
Pidg＜Pimax (4-5)
wherein, PidgThe distributed power supply of the access node is always active; pimaxIs the upper limit of the system load of the node.
(3) Node voltage constraint
Uimin＜Ui＜Uimax (4-6)
Wherein, UiminIs the lower limit of the allowed voltage at node i; u shapeimaxIs the upper voltage limit allowed at node i;
Uiis the voltage at the node at a certain time.
Step three, selecting the model and the algorithm parameter
Taking an IEEE33 node model as an example, the method is adopted to consider the solution of the multi-energy power supply multi-target planning model under the whole life cycle. The IEEE33 node model has 33 nodes in total, a node 1 is assumed to be a balance node, a distributed power supply is not considered to be added, and the rest 32 nodes can be added with the distributed power supply, wherein the system voltage is 12.66kV, the reference power is 10MW, the active load of the system is 3715kW, and the reactive load is 2300 kVar;
planning to carry out site selection and volume fixing planning on a multi-energy distributed power supply in an IEEE33 node power distribution network, wherein the multi-energy power supply is divided into four types, namely photovoltaic power generation, fan power generation, gas turbine power generation and energy storage power generation. And the four power supplies are equivalent to PQ type, and each node in the power distribution network can only be accessed to one type of distributed power supply, and the maximum capacity of the access does not exceed the load carried by the node. Assume a capacity of 30KW per distributed power source. In the particle swarm optimization, the population number is set to be 50, the iteration times are set to be 10, and the population dimension is 64. The inertia weight is set to be 0.5, and learning factors C1 and C2 both take the value of 2.5;
TABLE 5-1 System node load Capacity
TABLE 5-2IEEE33 System line impedance
TABLE 5-3 parameters associated with accessing a distributed power supply in a power distribution network
TABLE 5-4 pollutant discharge and treatment costs for distributed power supplies
Tables 5-1 and 5-2 are load capacity and line impedance data, respectively, for the IEEE33 node power distribution network model. And tables 5-3 show the installation and maintenance costs and the scrapped residual value data of various distributed power supplies. Tables 5-4 show the pollutant emissions and treatment costs for various types of distributed power sources.
Step four, simulation result analysis
TABLE 5-5 location and Capacity of distributed Power Access categories under particle swarm optimization
TABLE 5-6 objective function values of particle swarm algorithm distributed power access modes
Tables 5-5 and 5-6 are the location type capacity of distributed access and each objective function value when considering different objective functions under the particle swarm algorithm planning.
As can be seen from tables 5 to 5: when the power failure cost, the environmental pollution cost and the installation and maintenance cost of the distributed power supply are all considered, 1 photovoltaic power supply and 13 photovoltaic power supplies are respectively connected to the 13 node, the 15 node and the 24 node when the total cost is optimal; 6 fans are connected to the 32 nodes; 2 nodes are connected into 2 gas turbines; 1, 1 and 1 energy storage device are respectively accessed to the 12 node, the 16 node and the 18 node. The total capacity is 780 kW. When the environmental pollution cost is not considered, and only the power failure cost and the power supply installation cost are considered, under the condition of optimal cost, 5 photovoltaic power supplies are required to be accessed to 8 nodes; 2 fans are connected to the 22 nodes; the 18 nodes are connected into 2 gas turbines; 2, 1, 2, 13, 1 and 4 energy storage devices are respectively connected into 2, 3, 5, 7, 9, 13, 20, 21, 23, 30 and 31, the total capacity is 1170kW, and when the power failure cost is not considered, only the environmental pollution cost and the power supply installation cost are considered, under the condition of optimal cost, 5 photovoltaic devices are required to be connected into 7 nodes; 1, 1 and 1 fan is respectively connected to the nodes 12, 22 and 26; 3 and 9 gas turbines are respectively connected to the nodes 10 and 24; 2, 5, 6, 17, 20, 21, 27, 28, 30 and 32 are respectively connected with 1, 2, 1, 5 and 1 energy storage device. The total capacity is 1050 kW; when only the installation cost of a power supply is considered, under the condition of optimal cost, 1 photovoltaic cell and 1 photovoltaic cell need to be respectively connected to the nodes 7 and 33; 1, 5, 1, 13 and 13 fans are respectively connected to 3, 8, 16, 24 and 25; 5 gas turbines are connected to the node 29; 1, 2 and 1 energy storage device is respectively connected to the nodes 13, 22 and 26. The total capacity was 1320 kW. Analyzing the tables 5-5, it can be known that, under the condition of considering different objective functions, the access types, the access positions and the capacities of the distributed power supplies are also different;
analyzing tables 5-6, after considering no environmental pollution cost, the system is always connected with 1170kW distributed power supply which is far more than 780kW when considering three objective function values, so that the economic cost is 135 ten thousand yuan which is about twice as high as 78 ten thousand yuan when considering the environmental cost, most of the connected energy storage devices are comprehensively considered, and due to the fact that the energy storage devices are low in manufacturing cost and low in maintenance cost, after considering no waste pollution cost of the energy storage devices, a large number of connected results are facilitated; and the environmental pollution index is added, so that the type of the accessed power supply can be controlled more appropriately, and the system can be more comprehensively considered to be added into the distributed power supply. When the power failure cost is not considered, the situation of three objective function indexes is considered in a contrast manner, the fact that the system is connected with more distributed power supplies is found, the environmental cost and the installation and maintenance cost are greatly improved, the electricity purchasing cost is not obviously reduced, and the total cost is 1010 ten thousand or even exceeds the cost value of 968 ten thousand when three objective functions are used; the more power supplies are accessed, the poorer the reliability of the system is, but due to the loss of power failure cost, no reliable measures are taken to influence the unlimited access of the distributed power supplies, so that the access capacity is increased; when only an economic objective function is considered, data show that a large amount of fan power supplies are connected to the system at the optimal cost, the fan power supplies account for about 75% of the total connected capacity, the fan is high in one-time installation cost, but low in operation and maintenance cost, the recovery residual value is high after operation for many years, and the fan is the optimal choice for connecting the system when power failure cost and environment cost are not considered.
Has the advantages that: under the comprehensive consideration of the environmental pollution cost, the power failure cost and the power supply installation and maintenance cost, most of the distributed power supplies are connected to the tail end of the power distribution network, so that the network loss of the power distribution network is reduced, the voltage of a node at the tail end is improved, and the reliability coefficient of the safety coefficient of the system is improved. The result shows that the photovoltaic cell, the fan and the energy storage device are connected more, and the gas turbine is connected less. The reason is that gas turbines are expensive to install and maintain and produce more pollutants. In the model, factors such as environmental pollution caused by an energy storage device, for example, an acid-base energy storage battery are considered, the environmental cost is similar to that of a gas turbine, but the installation cost of the energy storage device is low, so that the energy storage device is also applied to a power distribution network. Although the one-time installation cost of the photovoltaic and the fan is high, the photovoltaic and the fan are widely connected into a power distribution network by virtue of the characteristics of low maintenance cost and no pollution. Through calculation, when the distributed power supply is not added, the power purchasing cost of the IEEE33 node power distribution network to a superior power distribution network is 11145000 yuan each year, after various distributed power supplies are added, the power purchasing cost is reduced each year due to the fact that the distributed power supplies supply power to the power distribution network, and after the installation and operation and maintenance cost, the environmental pollution treatment cost caused by installation of the distributed power supplies and the power failure cost are calculated, the total cost of the power distribution network each year is still lower than the power purchasing cost when the power distribution network is not connected. The rationality of the model is verified.
Detailed Description
The following detailed description of the preferred embodiments will be made in conjunction with the accompanying drawings. A method for solving a multi-target multi-energy power supply planning model by a particle swarm algorithm comprises the following steps:
step one, establishing an objective function
(1) Cost of distributed power supply construction and operation
The distributed power supply is installed once, maintained subsequently and has a residual value as an expression:
the distributed power supply type of the model is four, namely a photovoltaic power supply, a fan power generation element, a gas turbine power generation element and an energy storage element, wherein C is 4; the whole life cycle age is set to 25 years, and N is 25; the capital interest rate is 0.1, and r is 0.1; cdgThe one-time installation cost of the distributed power supply is unit/kw; pgThe number of the distributed power supplies is equal to the number of the distributed power supplies; the loan proportion to the bank in the construction of the distributed power supply is 0.75, namely Rloan0.75; through investigation and research, the loan interest rate of the bank is 5.65 percent, Rint＝5.65％；comOperating and maintaining costs for various kinds of distributed power sources; crResidual values of the corresponding various distributed power supplies after use are obtained;
(2) cost of environmental pollution
The distributed generation environment costs are as follows:
three of these contaminant species are contemplated herein, including CO2, SO2, and nitrogen oxides, i.e., m-3; beta is adThe cost required for treating pollutants is unit; alpha is alphaodThe method is applicable to the discharge relationship of various distributed power supplies and various pollutants; pgAnd PsMeaning the same as an economic objective function;
(3) reliability economic cost expense
Wherein k is the number of load nodes of the power distribution network; t is the annual power failure time of the load point; l is the self load of the node; r is the power failure loss cost corresponding to the average power failure duration time of each kw load;
step two, establishing constraint conditions
(1) Distributed power source installation capacity constraints
The total active power of the distributed power supply connected to the power distribution network does not exceed 25 percent of the total load in the power distribution network system
PΣDG≤sPΣL (4-4)
Wherein, P∑DGThe total active power for accessing the distributed power supply; p∑LIs the total amount of load in the system; s is a coefficient, and 0.25 is taken;
(2) node installation capacity constraints
For a single node, in order to reduce the network loss of a power distribution network, the total capacity of a distributed power supply of an access node is smaller than the upper limit of the load capacity of the system;
Pidg＜Pimax (4-5)
wherein, PidgThe distributed power supply of the access node is always active; pimaxIs the upper limit of the system load of the node.
(3) Node voltage constraint
Uimin＜Ui＜Uimax (4-6)
Wherein, UiminIs the lower limit of the allowed voltage at node i; u shapeimaxIs the upper voltage limit allowed at node i; u shapeiIs the voltage at the node at a certain time.
Step three, selecting the model and the algorithm parameter
Taking an IEEE33 node model as an example, the method is adopted to consider the solution of the multi-energy power supply multi-target planning model under the whole life cycle. The IEEE33 node model has 33 nodes in total, a node 1 is assumed to be a balance node, a distributed power supply is not considered to be added, and the rest 32 nodes can be added with the distributed power supply, wherein the system voltage is 12.66kV, the reference power is 10MW, the active load of the system is 3715kW, and the reactive load is 2300 kVar;
planning to carry out site selection and volume fixing planning on a multi-energy distributed power supply in an IEEE33 node power distribution network, wherein the multi-energy power supply is divided into four types, namely photovoltaic power generation, fan power generation, gas turbine power generation and energy storage power generation. And the four power supplies are equivalent to PQ type, and each node in the power distribution network can only be accessed to one type of distributed power supply, and the maximum capacity of the access does not exceed the load carried by the node. Assume a capacity of 30KW per distributed power source. In the particle swarm optimization, the population number is set to be 50, the iteration times are set to be 10, and the population dimension is 64. The inertia weight is set to be 0.5, and learning factors C1 and C2 both take the value of 2.5;
TABLE 5-1 System node load Capacity
TABLE 5-2IEEE33 System line impedance
TABLE 5-3 parameters associated with accessing a distributed power supply in a power distribution network
TABLE 5-4 pollutant discharge and treatment costs for distributed power supplies
Tables 5-1 and 5-2 are load capacity and line impedance data, respectively, for the IEEE33 node power distribution network model. And tables 5-3 show the installation and maintenance costs and the scrapped residual value data of various distributed power supplies. Tables 5-4 show the pollutant emissions and treatment costs for various types of distributed power sources.
Step four, simulation result analysis
TABLE 5-5 location and Capacity of distributed Power Access categories under particle swarm optimization
TABLE 5-6 objective function values of particle swarm algorithm distributed power access modes
Tables 5-5 and 5-6 are the location type capacity of distributed access and each objective function value when considering different objective functions under the particle swarm algorithm planning.
As can be seen from tables 5 to 5: when the power failure cost, the environmental pollution cost and the installation and maintenance cost of the distributed power supply are all considered, 1 photovoltaic power supply and 13 photovoltaic power supplies are respectively connected to the 13 node, the 15 node and the 24 node when the total cost is optimal; 6 fans are connected to the 32 nodes; 2 nodes are connected into 2 gas turbines; 1, 1 and 1 energy storage device are respectively accessed to the 12 node, the 16 node and the 18 node. The total capacity is 780 kW. When the environmental pollution cost is not considered, and only the power failure cost and the power supply installation cost are considered, under the condition of optimal cost, 5 photovoltaic power supplies are required to be accessed to 8 nodes; 2 fans are connected to the 22 nodes; the 18 nodes are connected into 2 gas turbines; 2, 1, 2, 13, 1 and 4 energy storage devices are respectively connected into 2, 3, 5, 7, 9, 13, 20, 21, 23, 30 and 31, the total capacity is 1170kW, and when the power failure cost is not considered, only the environmental pollution cost and the power supply installation cost are considered, under the condition of optimal cost, 5 photovoltaic devices are required to be connected into 7 nodes; 1, 1 and 1 fan is respectively connected to the nodes 12, 22 and 26; 3 and 9 gas turbines are respectively connected to the nodes 10 and 24; 2, 5, 6, 17, 20, 21, 27, 28, 30 and 32 are respectively connected with 1, 2, 1, 5 and 1 energy storage device. The total capacity is 1050 kW; when only the installation cost of a power supply is considered, under the condition of optimal cost, 1 photovoltaic cell and 1 photovoltaic cell need to be respectively connected to the nodes 7 and 33; 1, 5, 1, 13 and 13 fans are respectively connected to 3, 8, 16, 24 and 25; 5 gas turbines are connected to the node 29; 1, 2 and 1 energy storage device is respectively connected to the nodes 13, 22 and 26. The total capacity was 1320 kW. Analyzing the tables 5-5, it can be known that, under the condition of considering different objective functions, the access types, the access positions and the capacities of the distributed power supplies are also different;
analyzing tables 5-6, after considering no environmental pollution cost, the system is always connected with 1170kW distributed power supply which is far more than 780kW when considering three objective function values, so that the economic cost is 135 ten thousand yuan which is about twice as high as 78 ten thousand yuan when considering the environmental cost, most of the connected energy storage devices are comprehensively considered, and due to the fact that the energy storage devices are low in manufacturing cost and low in maintenance cost, after considering no waste pollution cost of the energy storage devices, a large number of connected results are facilitated; and the environmental pollution index is added, so that the type of the accessed power supply can be controlled more appropriately, and the system can be more comprehensively considered to be added into the distributed power supply. When the power failure cost is not considered, the situation of three objective function indexes is considered in a contrast manner, the fact that the system is connected with more distributed power supplies is found, the environmental cost and the installation and maintenance cost are greatly improved, the electricity purchasing cost is not obviously reduced, and the total cost is 1010 ten thousand or even exceeds the cost value of 968 ten thousand when three objective functions are used; the more power supplies are accessed, the poorer the reliability of the system is, but due to the loss of power failure cost, no reliable measures are taken to influence the unlimited access of the distributed power supplies, so that the access capacity is increased; when only an economic objective function is considered, data show that a large amount of fan power supplies are connected to the system at the optimal cost, the fan power supplies account for about 75% of the total connected capacity, the fan is high in one-time installation cost, but low in operation and maintenance cost, the recovery residual value is high after operation for many years, and the fan is the optimal choice for connecting the system when power failure cost and environment cost are not considered.
Under the comprehensive consideration of the environmental pollution cost, the power failure cost and the power supply installation and maintenance cost, most of the distributed power supplies are connected to the tail end of the power distribution network, so that the network loss of the power distribution network is reduced, the voltage of a node at the tail end is improved, and the reliability coefficient of the safety coefficient of the system is improved. The result shows that the photovoltaic cell, the fan and the energy storage device are connected more, and the gas turbine is connected less. The reason is that gas turbines are expensive to install and maintain and produce more pollutants. In the model, factors such as environmental pollution caused by an energy storage device, for example, an acid-base energy storage battery are considered, the environmental cost is similar to that of a gas turbine, but the installation cost of the energy storage device is low, so that the energy storage device is also applied to a power distribution network. Although the one-time installation cost of the photovoltaic and the fan is high, the photovoltaic and the fan are widely connected into a power distribution network by virtue of the characteristics of low maintenance cost and no pollution. Through calculation, when the distributed power supply is not added, the power purchasing cost of the IEEE33 node power distribution network to a superior power distribution network is 11145000 yuan each year, after various distributed power supplies are added, the power purchasing cost is reduced each year due to the fact that the distributed power supplies supply power to the power distribution network, and after the installation and operation and maintenance cost, the environmental pollution treatment cost caused by installation of the distributed power supplies and the power failure cost are calculated, the total cost of the power distribution network each year is still lower than the power purchasing cost when the power distribution network is not connected. The rationality of the model is verified.
And a particle swarm algorithm is introduced to solve the multi-target multi-energy power supply planning model, so that the conditions that other algorithms are low in efficiency and easy to fall into local optimization are avoided. The particle swarm algorithm has the advantages of high population retention, small population particle number, small algorithm parameter setting and short iteration time, so that the algorithm can obtain good results when solving the multi-target multi-power-supply planning problem.
The IEEE33 node power distribution network is used as an example, relevant parameters in the algorithm are set, the particle swarm algorithm is used for solving, the result is analyzed in detail, and the reliability and the theoretical feasibility of the established model are verified.
Under the condition that the type, the capacity and the position of a distributed power supply accessed to each node of a power distribution network are uncertain, the operation economy, the reliability and the environmental protection performance in a whole life cycle are considered, a single target is carried out on the distributed power supply to establish a total economy objective function, a Particle Swarm Optimization (PSO) is used for carrying out minimum value optimization on the total objective function value, and the type, the position and the capacity of the distributed power supply access when the economic cost is minimum are obtained as a final result.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (1)
1. A method for solving a multi-target multi-energy power supply planning model by a particle swarm algorithm is characterized by comprising the following steps: the method comprises the following steps:
step one, establishing an objective function
(1) Cost of distributed power supply construction and operation
The distributed power supply is installed once, maintained subsequently and has a residual value as an expression:
the distributed power supply type of the model is four, namely a photovoltaic power supply, a fan power generation element, a gas turbine power generation element and an energy storage element, wherein C is 4; the whole life cycle age is set to 25 years, and N is 25; the capital interest rate is 0.1, and r is 0.1; cdgThe one-time installation cost of the distributed power supply is unit/kw; pgThe number of the distributed power supplies is equal to the number of the distributed power supplies; the loan proportion to the bank in the construction of the distributed power supply is 0.75, namely Rloan0.75; through investigation and research, the loan interest rate of the bank is 5.65 percent, Rint＝5.65％；comOperating and maintaining costs for various kinds of distributed power sources; crResidual values of the corresponding various distributed power supplies after use are obtained;
(2) cost of environmental pollution
The distributed generation environment costs are as follows:
three of these contaminant species are contemplated herein, including CO2, SO2, and nitrogen oxides, i.e., m-3; beta is adThe cost required for treating pollutants is unit; alpha is alphaodThe method is applicable to the discharge relationship of various distributed power supplies and various pollutants; pgAnd PsMeaning the same as an economic objective function;
(3) reliability economic cost expense
Wherein k is the number of load nodes of the power distribution network; t is the annual power failure time of the load point; l is the self load of the node; r is the power failure loss cost corresponding to the average power failure duration time of each kw load;
step two, establishing constraint conditions
(1) Distributed power source installation capacity constraints
The total active power of the distributed power supply connected to the power distribution network does not exceed 25 percent of the total load in the power distribution network system
P∑DG≤sP∑L (4-4)
Wherein, P∑DGThe total active power for accessing the distributed power supply; p∑LIs the total amount of load in the system; s is a coefficient, and 0.25 is taken;
(2) node installation capacity constraints
For a single node, in order to reduce the network loss of a power distribution network, the total capacity of a distributed power supply of an access node is smaller than the upper limit of the load capacity of the system;
Pidg＜Pimax (4-5)
wherein, PidgThe distributed power supply of the access node is always active; pimaxIs the upper limit of the system load of the node.
(3) Node voltage constraint
Uimin＜Ui＜Uimax (4-6)
Wherein, UiminIs the lower limit of the allowed voltage at node i; u shapeimaxIs the upper voltage limit allowed at node i; u shapeiIs the voltage at the node at a certain time.
Step three, selecting the model and the algorithm parameter
Taking an IEEE33 node model as an example, solving of a multi-energy power supply multi-target planning model under a full life cycle is considered by adopting the method, 33 nodes are totally arranged in the IEEE33 node model, a node 1 is assumed to be a balance node, a distributed power supply is not considered to be added, and the rest 32 nodes can be added with the distributed power supply, wherein the system voltage is 12.66kV, the reference power is 10MW, the active load of the system is 3715kW, and the reactive load is 2300 kVar;
planning to carry out site selection and volume fixing planning on a multi-energy distributed power supply in an IEEE33 node power distribution network, wherein the multi-energy power supply is divided into four types, namely photovoltaic power generation, fan power generation, gas turbine power generation and energy storage power generation, the four types of power supplies are equivalent to PQ type, each node in the power distribution network can only be accessed to one type of distributed power supply, the maximum accessed capacity does not exceed the load carried by the node, and the capacity of each distributed power supply is 30 KW. In the particle swarm optimization, the population number is set to be 50, the iteration times are set to be 10, the population dimension is 64, the inertia weight is set to be 0.5, and the learning factors C1 and C2 both take on the value of 2.5;
step four, simulation result analysis
TABLE 5-5 location and Capacity of distributed Power Access categories under particle swarm optimization
TABLE 5-6 objective function values of particle swarm algorithm distributed power access modes
Tables 5-5 and 5-6 are respectively the location type capacity of distributed access and each objective function value when different objective functions are considered under the particle swarm algorithm planning;
as can be seen from tables 5 to 5: when the power failure cost, the environmental pollution cost and the installation and maintenance cost of the distributed power supply are all considered, 1 photovoltaic power supply and 13 photovoltaic power supplies are respectively connected to the 13 node, the 15 node and the 24 node when the total cost is optimal; 6 fans are connected to the 32 nodes; 2 nodes are connected into 2 gas turbines; 1, 1 and 1 energy storage device are respectively connected to 12 nodes, 16 nodes and 18 nodes, the total capacity is 780kW, and when the environmental pollution cost is not considered and only the power failure cost and the power supply installation cost are considered, under the condition of optimal cost, 5 photovoltaic power supplies are required to be connected to 8 nodes; 2 fans are connected to the 22 nodes; the 18 nodes are connected into 2 gas turbines; 2, 1, 2, 13, 1 and 4 energy storage devices are respectively connected into 2, 3, 5, 7, 9, 13, 20, 21, 23, 30 and 31, the total capacity is 1170kW, and when the power failure cost is not considered, only the environmental pollution cost and the power supply installation cost are considered, under the condition of optimal cost, 5 photovoltaic devices are required to be connected into 7 nodes; 1, 1 and 1 fan is respectively connected to the nodes 12, 22 and 26; 3 and 9 gas turbines are respectively connected to the nodes 10 and 24; 2, 5, 6, 17, 20, 21, 27, 28, 30 and 32 are respectively connected with 1, 2, 1, 5 and 1 energy storage devices, and the total capacity is 1050 kW; when only the installation cost of a power supply is considered, under the condition of optimal cost, 1 photovoltaic cell and 1 photovoltaic cell need to be respectively connected to the nodes 7 and 33; 1, 5, 1, 13 and 13 fans are respectively connected to 3, 8, 16, 24 and 25; 5 gas turbines are connected to the node 29; 1, 2 and 1 energy storage device are respectively connected to the nodes 13, 22 and 26, and the total capacity is 1320 kW. Analyzing the tables 5-5, it can be known that, under the condition of considering different objective functions, the access types, the access positions and the capacities of the distributed power supplies are also different;
analyzing tables 5-6, after considering no environmental pollution cost, the system is always connected with 1170kW distributed power supply which is far more than 780kW when considering three objective function values, so that the economic cost is 135 ten thousand yuan which is about twice as high as 78 ten thousand yuan when considering the environmental cost, most of the connected energy storage devices are comprehensively considered, and due to the fact that the energy storage devices are low in manufacturing cost and low in maintenance cost, after considering no waste pollution cost of the energy storage devices, a large number of connected results are facilitated; the environmental pollution indexes are added, the types of the accessed power supplies can be controlled more appropriately, the system can be more comprehensively considered to add the distributed power supplies, after the power failure cost is not considered, the situation that three target function indexes are considered is contrastingly considered, the system is accessed with more distributed power supplies, the environmental cost and the installation and maintenance cost are greatly improved, the electricity purchasing cost is not obviously reduced, and the total cost is 1010 ten thousand or even exceeds the cost value of 968 ten thousand when three target functions are used; the more power supplies are accessed, the poorer the reliability of the system is, but due to the loss of power failure cost, no reliable measures are taken to influence the unlimited access of the distributed power supplies, so that the access capacity is increased; when only an economic objective function is considered, data show that a large amount of fan power supplies are connected to the system at the optimal cost, the fan power supplies account for about 75% of the total connected capacity, the fan is high in one-time installation cost, but low in operation and maintenance cost, the recovery residual value is high after operation for many years, and the fan is the optimal choice for connecting the system when power failure cost and environment cost are not considered.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110644219.5A CN113536650A (en) | 2021-06-09 | 2021-06-09 | Method for solving multi-target multi-energy power supply planning model through particle swarm algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110644219.5A CN113536650A (en) | 2021-06-09 | 2021-06-09 | Method for solving multi-target multi-energy power supply planning model through particle swarm algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113536650A true CN113536650A (en) | 2021-10-22 |
Family
ID=78095762
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110644219.5A Pending CN113536650A (en) | 2021-06-09 | 2021-06-09 | Method for solving multi-target multi-energy power supply planning model through particle swarm algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113536650A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114035434A (en) * | 2021-11-22 | 2022-02-11 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107590744A (en) * | 2016-07-08 | 2018-01-16 | 华北电力大学（保定） | Consider the active distribution network distributed power source planing method of energy storage and reactive-load compensation |
| CN107688879A (en) * | 2017-10-20 | 2018-02-13 | 云南电网有限责任公司 | A kind of active distribution network distributed power source planing method of consideration source lotus matching degree |
| CN108681823A (en) * | 2018-05-23 | 2018-10-19 | 云南电网有限责任公司 | A kind of power distribution network distributed generation resource planing method containing micro-capacitance sensor |
| CN109508499A (en) * | 2018-11-15 | 2019-03-22 | 国网江苏省电力有限公司经济技术研究院 | Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method |
| CN110909939A (en) * | 2019-11-22 | 2020-03-24 | 国网四川省电力公司经济技术研究院 | Multi-stage planning method for power distribution network with distributed power supplies |
| CN112380694A (en) * | 2020-11-13 | 2021-02-19 | 华北电力大学（保定） | Power distribution network optimization planning method based on differential reliability requirements |
-
2021
- 2021-06-09 CN CN202110644219.5A patent/CN113536650A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107590744A (en) * | 2016-07-08 | 2018-01-16 | 华北电力大学（保定） | Consider the active distribution network distributed power source planing method of energy storage and reactive-load compensation |
| CN107688879A (en) * | 2017-10-20 | 2018-02-13 | 云南电网有限责任公司 | A kind of active distribution network distributed power source planing method of consideration source lotus matching degree |
| CN108681823A (en) * | 2018-05-23 | 2018-10-19 | 云南电网有限责任公司 | A kind of power distribution network distributed generation resource planing method containing micro-capacitance sensor |
| CN109508499A (en) * | 2018-11-15 | 2019-03-22 | 国网江苏省电力有限公司经济技术研究院 | Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method |
| CN110909939A (en) * | 2019-11-22 | 2020-03-24 | 国网四川省电力公司经济技术研究院 | Multi-stage planning method for power distribution network with distributed power supplies |
| CN112380694A (en) * | 2020-11-13 | 2021-02-19 | 华北电力大学（保定） | Power distribution network optimization planning method based on differential reliability requirements |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114035434A (en) * | 2021-11-22 | 2022-02-11 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
| CN114035434B (en) * | 2021-11-22 | 2023-09-01 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xin-gang et al. | Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization | |
| Ziogou et al. | Optimal production of renewable hydrogen based on an efficient energy management strategy | |
| Elsied et al. | An advanced energy management of microgrid system based on genetic algorithm | |
| CN106786603A (en) | A kind of regional complex energy resource system multiobjective optimization mixed current algorithm | |
| CN102510108B (en) | Method for calculating maximum wind power installed capacity of district power network | |
| CN111291963A (en) | Park comprehensive energy system planning method for coordinating economy and reliability | |
| CN112633702B (en) | Power system reliability rapid evaluation method considering renewable energy | |
| CN115017854A (en) | Method for calculating maximum allowable capacity of DG (distributed generation) of power distribution network based on multidimensional evaluation index system | |
| Lin et al. | Scenario generation and reduction methods for power flow examination of transmission expansion planning | |
| CN115660343A (en) | Urban comprehensive energy development planning method for carbon neutralization | |
| CN111884203A (en) | Micro-grid coordination optimization configuration method based on double-layer non-dominated sorting genetic algorithm | |
| CN113536650A (en) | Method for solving multi-target multi-energy power supply planning model through particle swarm algorithm | |
| CN113158547B (en) | Regional comprehensive energy system optimal configuration method considering economy and reliability | |
| Lu et al. | Clean generation mix transition: Large-scale displacement of fossil fuel-fired units to cut emissions | |
| CN111625770A (en) | Energy efficiency evaluation method and system for power distribution network with distributed power supply | |
| Shen et al. | Multi-stage low-carbon power system planning considering generation retirement and R retrofit | |
| CN111030191B (en) | Cell power grid planning method based on multi-target cooperation and self-optimization operation | |
| Onen et al. | Optimal Expansion Planning of Integrated natural gas and electricity network with high penetration of wind and solar power under uncertainty | |
| Cheng et al. | Real Options based Optimal Planning for Integrated Energy Systems under Long-term Uncertainties | |
| Yang et al. | Coordinated Optimal Configuration Method of Hybrid Energy Storage Systems in Energy Internet System | |
| CN112069676A (en) | Micro-grid energy management method containing clean energy | |
| CN111416351A (en) | Regional power grid disaster recovery method considering scheduling coefficient and rationality | |
| Ancona et al. | Optimal Design of Renewable Hydrogen Production for Gas Turbine Test Facilities | |
| Ashoornezhad et al. | Optimal Siting and Sizing of Distributed Generation Under Uncertainties Using Point Estimate Method | |
| Chen et al. | The optimal planning and dynamic operation of distributed generation method based on modified multiobjective optimization in power distribution system |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211022 |