CN103762589A - Method for optimizing new energy capacity ratio in layers in power grid - Google Patents
Method for optimizing new energy capacity ratio in layers in power grid Download PDFInfo
- Publication number
- CN103762589A CN103762589A CN201410007489.5A CN201410007489A CN103762589A CN 103762589 A CN103762589 A CN 103762589A CN 201410007489 A CN201410007489 A CN 201410007489A CN 103762589 A CN103762589 A CN 103762589A
- Authority
- CN
- China
- Prior art keywords
- msubsup
- mrow
- new energy
- math
- msub
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000005457 optimization Methods 0.000 claims abstract description 67
- 238000010248 power generation Methods 0.000 claims abstract description 27
- 238000004088 simulation Methods 0.000 claims abstract description 19
- 230000008878 coupling Effects 0.000 claims abstract description 6
- 238000010168 coupling process Methods 0.000 claims abstract description 6
- 238000005859 coupling reaction Methods 0.000 claims abstract description 6
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 38
- 238000004422 calculation algorithm Methods 0.000 claims description 28
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 19
- 239000001569 carbon dioxide Substances 0.000 claims description 19
- 239000003245 coal Substances 0.000 claims description 10
- 238000010438 heat treatment Methods 0.000 claims description 6
- 239000002245 particle Substances 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 230000001580 bacterial effect Effects 0.000 claims description 3
- 230000009194 climbing Effects 0.000 claims description 3
- 230000002431 foraging effect Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 17
- 230000008901 benefit Effects 0.000 abstract description 10
- 238000004519 manufacturing process Methods 0.000 abstract description 10
- 229910052799 carbon Inorganic materials 0.000 abstract description 6
- 230000009467 reduction Effects 0.000 abstract description 4
- 201000004569 Blindness Diseases 0.000 abstract description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 abstract description 2
- 230000005619 thermoelectricity Effects 0.000 abstract 1
- 238000011161 development Methods 0.000 description 7
- 238000009434 installation Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 238000004513 sizing Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for optimizing the new energy capacity ratio in layers in a power grid. The method comprises the steps that iteration calculation is conducted on the inner layer and the outer layer, a calculation model is that time series modeling is conducted on output force of the inner layer on the basis that the characteristics of new energy of a region are considered, the optimal energy-saving emission reduction benefit of the power grid serves as a goal, the factors such as the load characteristic, the unit peak shaving characteristic and the thermoelectricity coupling characteristics of heat supply units of different types are comprehensively considered, and therefore an annual time series production analog simulation model related to new energy power generation is established; the outer layer is provided with a capacity ratio optimizing model, the energy-saving emission reduction benefit of the model of the inner layer serves as a fitness function, so that the individual optimizing direction is updated, the new energy power generation ratio capacity is determined, the blindness of random generation of new energy installed capacity is reduced, and the optimizing efficiency and the accuracy are improved. The method for optimizing the new energy capacity ratio in layers in the power grid can be applied to new energy capacity optimization of the province-level power grid and has important guiding significance in grid source planning and practical power system dispatch of the province (region) power grid with the requirements for new energy installed capacity planning and low carbon electric power.
Description
Technical Field
The invention relates to a new energy capacity ratio optimization method in a power grid, in particular to a new energy capacity ratio layered optimization method in the power grid, and belongs to the technical field of energy conservation and emission reduction.
Background
The low carbon of the power industry is the key for coping with global warming and realizing the sustainable development of the socioeconomic performance of China. Against this background, china has introduced a series of energy development policies that encourage the large-scale development and utilization of renewable energy resources, particularly wind and solar energy resources. After the rapid development of the wind power industry, the solar energy industry is being developed to develop hot tide in recent years, a large number of new energy power generation provinces plan large-capacity wind energy and solar energy power generation at the same time, but at present, the wind power and photovoltaic development is only roughly planned according to the wind energy and solar energy resources in various places, and the optimal proportioning capacity of the wind power and photovoltaic power generation is not considered to be coordinated and optimized. Due to the fact that planning and construction periods of wind energy and solar energy power generation are short, the development process is disjointed with regional conventional power supply planning and power grid planning, and the phenomena of wind abandoning and light abandoning in actual operation are serious. In order to better improve the effect of a power grid in developing low-carbon economy, fully exert the natural complementary advantages of solar energy and wind energy in time and region, improve the accepting capability of high-capacity wind energy and solar energy power generation to the maximum extent, enable the planning result to be closer to the actual operation condition of a power system, uniformly coordinate and plan the installed capacity of wind power and photovoltaic power on the basis of the annual accepting capability of wind energy and solar energy power generation, and exert the maximum benefit.
At present, the literature researches the wind-solar ratio of the provincial (district) power grid. A capacity optimization model of a wind-solar hybrid system is established in Size optimization for a hybrid photovoltaic-Energy system (Electrical Power and Energy Systems, volume 42, page 448), and capacity configuration calculation is performed on different constraint conditions based on the model. However, the method cannot reflect daily wind power and photovoltaic output characteristics and a whole network operation mode under the condition of maximum load peak-valley difference, and if the method is used for guiding regional wind power and photovoltaic installed capacity, the calculation result is necessarily over conservative, so that the energy-saving and emission-reduction benefits of a power grid are not favorably improved. In the second document, "Multicriteria optimal sizing of photo-Wind Turbine Grid Connected Systems" (IEEE Trans on energy conversion, volume 28, phase 2, page 370), an improved particle swarm algorithm based on time sequence simulation is adopted to solve the Wind and light optimal capacity configuration of a certain area, and sensitivity analysis is performed on the Wind speed and the light intensity, so that the Wind and light installed capacity configuration under different natural conditions is obtained. Because the model adopts random simulation to obtain the wind speed and the illumination intensity, the wind and light output time sequence characteristics and the power time sequence balance of the area cannot be accurately reflected, and therefore, effective technical support cannot be provided for the actual power grid planning and the wind and light construction of the area. The document three (A New method for Optimizing the Size of hybrid PV/wind System) (IEEE International Conference on Sustainable energy technologies, page 922) adopts a differential evolution algorithm to solve the optimal wind-light ratio of a regional power grid, and proves that the wind-light combined power generation System has better economy and reliability than a single wind power generation System. However, in the process of establishing the mathematical model, the start-stop characteristic of the conventional unit and the thermoelectric coupling characteristic of the heat supply unit are not considered, so that the deviation between the operation result of the conventional unit and the actual power system is large, and the credibility of the wind-solar ratio calculation result is influenced.
In summary, the existing methods mostly adopt a power and electric quantity balancing method based on typical days, the new energy power generation balancing situation under the most serious situation is provided, the characteristics of daily wind power and photovoltaic output and how to optimize the start, stop and maintenance arrangement of a unit in the whole network cannot be reflected, and the consideration is not comprehensive enough when modeling is carried out on the power grid production simulation. If the method is used for guiding the planning of the installed capacity of wind power and photovoltaic, the calculation result is necessarily over-conservative, and the energy-saving and emission-reducing benefits of the power grid are not improved.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art, provide a new energy capacity proportioning hierarchical optimization method in a power grid, consider the actual working condition change of a power system, further improve the reliability of an optimization result, and have the advantages of strong adaptability and high reliability.
The invention adopts the following technical scheme to solve the technical problems:
a method for optimizing new energy capacity ratio in a power grid in a layered mode includes the following steps:
step 1, initializing installed capacities of various new energy sources in a power grid as initial values of an outer layer optimization model;
st.
wherein,the starting state of the jth thermal power generating unit at the moment t is represented by a binary variable, 1 represents that the unit is starting, and 0 represents that the unit is not in the starting state;the system is also a binary variable and represents the shutdown state of the jth thermal power generating unit at the moment t, wherein 1 represents that the unit is shutdown, and 0 represents that the unit is not in the shutdown state;
the output of the jth thermal power generating unit at the moment t; 
and
are all independent variables; n is a radical ofjRepresenting the total number of the thermal power generating units participating in optimization; t represents the time length of the primary simulation of the inner layer; alpha is alphajStarting coal consumption for the jth thermal power generating unit; beta is ajStopping for jth thermal power generating unitCoal consumption of the engine; a isjThe slope of coal consumption of a single thermal power generating unit along with the change of power; bjThe coal consumption constant of a single thermal power generating unit is obtained; gamma is a carbon dioxide emission coefficient;
the climbing rate and the descending rate of the jth fire radio set unit are respectively;
respectively a minimum output value and a maximum output value of the jth thermal power generating unit;
the binary variable represents the t-time running state of the jth thermal power generating unit, 1 represents that the unit is running, and 0 represents that the unit is not running; k is a preset minimum startup or shutdown time step;
the output of a back pressure type heat supply unit and the output of an air extraction type heat supply unit in the thermal power unit at the moment t are respectively output in the heat supply period;
the thermal load at time t;
the thermoelectric coupling coefficient of the heating unit is set;
the total power load of the power grid at the moment t;electric power generated by various new energy sources admitted by power grid at time tSumming; sp、SNRespectively rotating the power grid positively/negatively for standby;
generating a credible capacity of the new energy in each time period;electric power generated by the i-th type new energy accepted by the power grid at the moment t; n is a radical ofiThe installed capacity of the ith type new energy in the power grid input for the outer optimization model;
the normalized value of the long-time scale output time sequence of the ith type new energy in the power grid is obtained; CL is the category total number of new energy in the power grid;
step 3, outputting the obtained minimum carbon dioxide emission to an outer layer optimization model;
step 4, judging whether a preset iteration termination condition is met by the outer layer optimization model, if so, determining the installed capacity N of each new energy source with the minimum carbon dioxide emissioniAnd starting state of thermal power generating unit
Shutdown state
Output of thermal power generating unit
Outputting as a final optimization result, and finishing the optimization; and if not, updating the installed capacity of various new energy sources by taking the minimum carbon dioxide emission output by the inner-layer optimization model as a fitness function value, and then turning to the step 2.
Preferably, the inner-layer optimization model uses a Branch and Bound (BAB) method to solve the model.
Preferably, the outer layer optimization model uses Particle Swarm Optimization (PSO) or modified bacterial foraging algorithm based on Particle Swarm optimization (BFAPSO).
Preferably, the variable solution space constraint of the outer optimization model is as follows:
where theta denotes a variable to be optimized, m denotes the m-th individual of the variable theta,
and respectively representing the maximum planned installed capacity value and the existing installed capacity value of the i-th new energy.
Compared with the prior art, the technical scheme and the optimized technical scheme thereof have the following beneficial effects:
1. the invention utilizes the historical data to generate the long-time scale time sequence which is used as the constraint condition of the optimization algorithm, greatly improves the reliability of the optimization result, and can more accurately reflect various new energy and load output time sequence characteristics of the area.
2. The calculation amount can be reduced to the maximum extent by the hierarchical optimization algorithm, the problem of new energy installation planning of the power grid is effectively solved, and the calculation requirements of planners are met.
3. The outer layer algorithm adopts a BFAPSO algorithm, and can effectively improve the calculation precision and the calculation efficiency.
4. The inner layer adopts a BAB algorithm to solve the production simulation problem based on a time sequence simulation method, the annual characteristics of various new energy sources can be fully considered, the actual grid-connected power and electric quantity of the various new energy sources is improved to the maximum extent, the requirements of operation and low-carbon electric power of an actual power system are better met, and the method has important guiding significance for planning and scheduling the actual power system and making relevant government policies.
5. Because the inner layer adopts the production simulation based on the time sequence, the model is more consistent with an actual power system, and the reasonability and the credibility of the new energy capacity proportioning result can be increased by planning the new energy installed capacity on the basis.
6. Because the inner layer of the method adopts the production simulation based on the time sequence, the new energy power generation operation condition under the planning scene can be evaluated and analyzed in the planning process, the new energy power generation and the conventional thermal power generation can be coordinated and optimized, and the units in the network can be scheduled according to different seasons; the method can provide reference for new energy annual operation mode and industry development planning. The economic efficiency of system operation and the energy-saving and emission-reducing benefits are increased on the principle that more new energy is used for generating electricity; the power limiting factor of the power grid operation is considered, and the scientificity and the rationality of a new energy power generation plan can be guaranteed.
Drawings
FIG. 1 is a basic flow diagram of a method embodiment of the present invention;
FIG. 2 is a wind-power sequence normalized by the horizontal year of a certain province in northeast of China;
FIG. 3 is a horizontal annual normalized photovoltaic sequence of a certain province in northeast of China;
FIG. 4 is a horizontal year load output sequence of a certain province in northeast China;
FIG. 5 is a distribution diagram of wind-solar overall average power limit per week in certain provinces in northeast of China.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings:
the invention provides a new energy capacity ratio hierarchical optimization method in a power grid, which is divided into an inner layer and an outer layer for iterative computation, wherein the computation models are respectively as follows: the inner layer carries out time series modeling on the output of various new energy resources in the area on the basis of considering the characteristics of the new energy resources, the best energy saving and emission reduction benefits of a power grid are taken as a target, factors such as load characteristics, unit peak regulation characteristics, different types of heat supply unit thermoelectric coupling characteristics and the like are comprehensively considered, an annual time series production simulation model considering new energy power generation is established, the model is more consistent with an actual power system, the installed capacity of the new energy resources is planned on the basis, and the reasonability and credibility of new energy matching results can be improved. The outer layer is a new energy capacity matching optimization model, the individual optimizing direction is updated by taking the energy-saving and emission-reducing benefits of the inner layer model as a fitness function, the power generation matching capacity of various new energies is determined, the blindness of randomly generating the installed capacity of the new energy is reduced, and the optimization efficiency and precision are improved.
The inner-layer optimization model is a typical mixed integer programming problem, and the method preferably adopts an efficient branch-and-bound method for solving. For a large complex power system, the model is complex and more variables are involved. In order to further improve the optimization efficiency, the improved Bacterial Foraging Algorithm (BFAPSO) based on the particle swarm optimization is preferably adopted, the search space is enlarged, the premature is avoided, and the local search capability is enhanced, so that the purposes of improving the optimization capability and greatly improving the algorithm efficiency are achieved. The algorithms are stable and mature operation technologies, and the algorithms have high engineering practicability through tests, so that the requirements of the technical scheme of the invention are met.
For the public understanding and to make the present invention more realistic, a preferred embodiment of the present invention will be described below by taking a provincial power grid considering only two new energy sources, wind power and photovoltaic power generation, as an example.
The basic algorithm idea of the preferred embodiment is as follows: initializing wind and light installed capacity by adopting a BFAPSO algorithm at the outer layer; after the installed capacity of the wind power and the photovoltaic of the outer layer is transmitted, the BAB algorithm is adopted for time sequence production simulation, the starting and stopping plan and the output of the unit are optimized, and the wind power and the photovoltaic output are received as much as possible on the basis of ensuring the minimum carbon dioxide emission of the system. And returning the carbon dioxide emission value to the outer layer model (as a fitness function value), and optimizing the installed capacities of the wind power and the photovoltaic by adopting a BFAPSO algorithm until the terminal condition is met.
The basic flow of the preferred embodiment is shown in fig. 1, and specifically includes the following steps:
(1) modeling the time sequence:
modeling the long-time scale wind power output time sequence, and carrying out normalization treatment:
in the formula:in order to normalize the wind power output time series,
for historical wind power output time series, CwThe total installed capacity of wind power in the region of the year.
Modeling the long-time scale photovoltaic output time sequence, and carrying out normalization treatment:
in the formula:
in order to normalize the photovoltaic output time series,for historical photovoltaic output time series, CvThe total installed capacity of the photovoltaic in the region of the year.
Obtaining a load output time sequence of
(2) Initializing installed capacity of wind power and photovoltaic:
SW,min<SW,0<SW,max
SS,min<SS,0<SS,max
in the formula: sW,0For initial wind installed capacity, SS,0To initial photovoltaic installed capacity, SW,minFor the existing installed capacity of wind power, SS,minFor the existing photovoltaic installed capacity, SW,maxTo allow maximum installed capacity of wind power, SS,maxTo allow for maximum photovoltaic installed capacity.
(3) Inner BAB algorithm optimization calculation:
the objective function is established as follows:
in the formula, F is carbon dioxide emission;
the starting state of the jth thermal power generating unit at the moment t is represented by a binary variable, 1 represents that the unit is starting, and 0 represents that the unit is not in the starting state;the system is also a binary variable and represents the shutdown state of the jth thermal power generating unit at the moment t, wherein 1 represents that the unit is shutdown, and 0 represents that the unit is not in the shutdown state;
the output of the jth thermal power generating unit at the moment t;
and
are all independent variables; n is a radical ofjRepresenting the total number of the thermal power generating units participating in optimization; t represents the time length of the primary simulation of the inner layer; alpha is alphajStarting coal consumption for the jth thermal power generating unit; beta is ajStopping the thermal power generating unit for the jth station; a isjThe slope of coal consumption of a single thermal power generating unit along with the change of power; bjThe coal consumption constant of a single thermal power generating unit is obtained; gamma is a carbon dioxide emission coefficient.
Setting constraint conditions of the thermal power generating unit:
1) ramp rate constraint of thermal power generating unit
In the formula:
respectively the climbing rate and the descending rate of the jth unit.
2) Thermal power unit output constraint
In the formula:
respectively a minimum force output value and a maximum force output value of the unit;
the binary variable represents the operation state of the jth unit at the moment t, 1 represents that the unit is in operation, and 0 represents that the unit is not in operation.
3) Thermal power generating unit starting and stopping state constraint
In the formula: k is determined by a unit minimum startup or minimum shutdown time parameter, which reflects the time step of the minimum startup or shutdown. The consideration of the constraint is mainly due to the restriction of the physical characteristics of the unit and the coal consumption cost for starting and stopping the unit, and the unit cannot be frequently started and stopped.
4) Output characteristic constraint of heat supply unit in heat supply period
The invention considers 2 types of heat supply units: back pressure type heat supply unit and air exhaust type heat supply unit.
And (3) output constraint of the backpressure unit:
the output of the air extractor is restrained:
in the formula:
the thermal load at time t;
the thermoelectric coupling coefficient of the heating unit is shown.
Setting system constraint conditions:
1) regional load balancing constraints
In the formula:
is the total power load of the system (power grid);
wind power admitted at time t;
photovoltaic power admitted at time t.
2) System positive/negative rotation reserve capacity constraints
In the formula: sp、SNRespectively rotating the system positively/negatively for standby;
for the credible capacity of the new energy power generation in each time period, a certain proportion of the sum of the new energy power generation output in each time period can be taken according to the actual condition, and for the wind and light power generation in the embodiment, the value is 60% -80% of the sum of the wind and light output in each time period; the wind and light output credible capacity in each time period is brought into the starting capacity calculation category of the conventional unit, the starting capacity is reduced, and wind energy and solar energy power generation can be better accommodated.
3) System wind power and photovoltaic output constraint
In the formula: n is a radical ofw、NvRespectively representing installed capacities of wind power and photovoltaic power, and transmitting values of the installed capacities after the installed capacities are optimized from the outer layer;
and (3) respectively obtaining normalized values of wind power output and photovoltaic output from the step (1).
And performing optimization calculation by utilizing a mature and stable BAB algorithm according to the constraint conditions and the objective function to obtain the minimum carbon dioxide emission of the system under the limitation of the current peak regulation capacity. After the nth sub-optimization iterative computation is recorded, the minimum emission of carbon dioxide of the system is Fn。
(4) Outer layer BFAPSO algorithm:
setting a variable solution space constraint:
in the formula: theta represents a variable needing to be optimized, m represents the mth individual of the variable theta, s represents an individual dimension, wind power installed capacity is represented when s =1, and photovoltaic installed capacity is represented when s = 2;representing the maximum value of the planned wind and light installed capacity,
representing the value of the installed capacity of the existing wind and light.
Setting a fitness function:
in the formula: j is the fitness function of the outer layer model, the value of the fitness function is the inner layer target function value, namely the carbon dioxide emission of the system, and the outer layer algorithm updates the individual optimizing direction according to the value of the fitness function.
For the nth sub-optimization iteration, the minimum emission F of carbon dioxide is setn-1Substituting the optimized wind power and photovoltaic installed capacity into a mature and stable BFAPSO algorithm, and solving the optimized wind power and photovoltaic installed capacity respectively to be S by utilizing the constraint conditions and the fitness functionW,nAnd SS,n
(5) Judging whether the termination condition is met:
setting the upper limit of iteration times to nmaxIf the number of optimization iterations n = nmaxAnd (4) ending the optimization iteration and turning to the step (6). If n is<nmaxAnd (4) returning to the step (3) to perform optimization iterative computation.
(6) Outputting an optimal result:
output the result of the optimal iteration, i.e.
The optimal wind power and photovoltaic installed capacities of the system are S respectivelyW,pAnd SS,pAnd at the installed capacity, the starting state and the stopping state of the thermal power generating unit
Output of thermal power generating unit
In order to test the effectiveness of the method, simulation verification is carried out on the wind-solar ratio planning of a certain province in northeast China by applying the method in the specific embodiment.
According to the requirement of the provincial overall planning scheme, the planning level year wind power and photovoltaic total installation is not more than 8000 MW. At present, the province has a fan 2646.4MW, a photovoltaic installation 530.83MW, and the sum of wind power and photovoltaic accounts for 14.01% of the total installation of the system. The normalized horizontal annual wind power sequence, annual photovoltaic sequence and load output sequence are shown in fig. 2-4, and the simulation time step is 1 hour. And performing wind-solar ratio optimization calculation on the data to obtain the following results:
the results of the wind-solar ratio optimization using different algorithms are shown in table 1. The calculation result of the comparative analysis can be known as follows: the inner layer model uses the operation mode proposed based on typical days, and the acceptance condition of the wind and the light under the most serious condition is considered. Compared with an inner layer model, the time sequence production simulation is adopted, the system discharges 0.054 million tons of carbon dioxide in multiple rows, the calculation of new energy acceptance capacity is conservative, and the reliability of a planning result is low. Under the constraint of low-carbon requirements of a power grid, the wind power installation machine is increased from 2646.4MW to 3668MW, and the increase is 38.60%; the photovoltaic installation is increased from 530.83MW to 3185MW by 500%. In the region, the existing wind-light ratio is 4.985:1, the planned wind-light ratio is 1.152:1, and the solar energy industry needs to be supported by relevant government policies urgently.
TABLE 1 optimization results of different algorithms
The optimization time of the hierarchical optimization algorithm and the exhaustive method is shown in table 2. The time for optimizing 1 time in the inner-layer time sequence production simulation is 21 minutes, so that 186291 minutes are reduced by adopting a layering optimization method compared with an exhaustion method, and the requirement of provincial (regional) power grid planning calculation time is effectively met.
TABLE 2 comparison of solution times for different algorithms
Because the inner layer adopts the production simulation based on the time sequence, the new energy power generation operation condition under the planning scene can be evaluated and analyzed in the planning process, and the wind and light optimal proportioning case is further researched and analyzed. Fig. 5 is a distribution diagram of the wind-solar total average power limit rate per week, and it can be found that the wind-solar photovoltaic admission capacity of the system is stronger than that of the system in the non-heating period, and the power limit rate is smaller. This is due to the enhancement of the peak shaving capacity of the heating unit during the non-heating period. Therefore, the time sequence simulation method of the model inner layer can schedule and operate the in-network units according to different seasons, and the wind energy and solar energy receiving capacity is improved.
The method is particularly suitable for optimizing and calculating the installed capacity of the new energy power generation of the provincial power grid, and has important guiding significance for planning the installed capacity of the new energy of the provincial (regional) power grid in China, planning the grid source under the low-carbon power requirement and scheduling the actual power system.
Claims (8)
1. A method for optimizing new energy capacity ratio in a power grid in a layered mode is characterized in that an electric energy source in the power grid comprises thermal power and at least one new energy, and the method comprises the following steps:
step 1, initializing installed capacities of various new energy sources in a power grid as initial values of an outer layer optimization model;
step 2, inputting the installed capacity of various current new energy sources obtained by the outer layer optimization model into the inner layer optimization model; the inner-layer optimization model obtains the annual operation state of the thermal power generating unit which enables the carbon dioxide emission F to be minimum under the installed capacity of various current new energy sources by solving the following models:
st.
wherein,
the starting state of the jth thermal power generating unit at the moment t is represented by a binary variable, 1 represents that the unit is starting, and 0 represents that the unit is not in the starting state;
the system is also a binary variable and represents the shutdown state of the jth thermal power generating unit at the moment t, wherein 1 represents that the unit is shutdown, and 0 represents that the unit is not in the shutdown state;
the output of the jth thermal power generating unit at the moment t;
and
are all independent variables; n is a radical ofjRepresenting the total number of the thermal power generating units participating in optimization; t represents the time length of the primary simulation of the inner layer; alpha is alphajStarting coal consumption for the jth thermal power generating unit; beta is ajStopping the thermal power generating unit for the jth station; a isjThe slope of coal consumption of a single thermal power generating unit along with the change of power; bjThe coal consumption constant of a single thermal power generating unit is obtained; gamma is a carbon dioxide emission coefficient;
the climbing rate and the descending rate of the jth fire radio set unit are respectively;
respectively a minimum output value and a maximum output value of the jth thermal power generating unit;
the binary variable represents the t-time running state of the jth thermal power generating unit, 1 represents that the unit is running, and 0 represents that the unit is not running; k is a preset minimum startup or shutdown time step;
the output of a back pressure type heat supply unit and the output of an air extraction type heat supply unit in the thermal power unit at the moment t are respectively output in the heat supply period;
the thermal load at time t;the thermoelectric coupling coefficient of the heating unit is set;
the total power load of the power grid at the moment t;
the sum of the electric power generated by various new energy sources admitted by the power grid at the moment t; sp、SNRespectively rotating the power grid positively/negatively for standby;
generating a credible capacity of the new energy in each time period;
electric power generated by the i-th type new energy accepted by the power grid at the moment t; n is a radical ofiThe installed capacity of the ith type new energy in the power grid input for the outer optimization model;the normalized value of the long-time scale output time sequence of the ith type new energy in the power grid is obtained; CL is the category total number of new energy in the power grid;
step 3, outputting the obtained minimum carbon dioxide emission to an outer layer optimization model;
step 4, judging whether a preset iteration termination condition is met by the outer layer optimization model, if so, judging the installed capacity Ni of each new energy source with the minimum carbon dioxide emission and the starting state of the thermal power generating unit
Shutdown stateOutput of thermal power generating unit
Outputting as a final optimization result, and finishing the optimization; and if not, updating the installed capacity of various new energy sources by taking the minimum carbon dioxide emission output by the inner-layer optimization model as a fitness function value, and then turning to the step 2.
2. The new energy capacity allocation hierarchical optimization method in the power grid according to claim 1, wherein the inner layer optimization model solves the model by using a branch-and-bound method.
3. The new energy capacity allocation hierarchical optimization method in the power grid according to claim 1, wherein the outer layer optimization model uses a particle swarm algorithm or an improved bacterial foraging algorithm based on the particle swarm algorithm.
4. The new energy capacity allocation hierarchical optimization method in the power grid according to claim 1, wherein the variable solution space constraint of the outer layer optimization model is as follows:
where theta denotes a variable to be optimized, m denotes the m-th individual of the variable theta,
and respectively representing the maximum planned installed capacity value and the existing installed capacity value of the i-th new energy.
5. The new energy capacity allocation hierarchical optimization method in the power grid according to claim 1, wherein the preset iteration termination condition is a preset maximum iteration number of the outer optimization model.
6. The method for proportioning and optimizing the capacity of the new energy resources in a power grid in a layered mode according to claim 1, wherein the new energy resources are wind power generation and photovoltaic power generation.
7. The method for proportioning and hierarchically optimizing the new energy capacity in the power grid as claimed in claim 6, wherein the new energy power generation has a credible capacity in each time period
The value of (A) is 60-80% of the sum of the new energy output in each time period.
8. The method for optimizing the new energy capacity allocation hierarchy in the power grid according to claim 1, wherein the power grid is a provincial power grid.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410007489.5A CN103762589B (en) | 2014-01-08 | 2014-01-08 | A kind of new forms of energy capacity ratio hierarchy optimization method in electrical network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410007489.5A CN103762589B (en) | 2014-01-08 | 2014-01-08 | A kind of new forms of energy capacity ratio hierarchy optimization method in electrical network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103762589A true CN103762589A (en) | 2014-04-30 |
| CN103762589B CN103762589B (en) | 2015-10-21 |
Family
ID=50529779
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410007489.5A Expired - Fee Related CN103762589B (en) | 2014-01-08 | 2014-01-08 | A kind of new forms of energy capacity ratio hierarchy optimization method in electrical network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103762589B (en) |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104573875A (en) * | 2015-01-27 | 2015-04-29 | 国家电网公司 | Low-carbon power source and power grid optimization planning method |
| CN105470957A (en) * | 2015-12-29 | 2016-04-06 | 中国电力科学研究院 | Power network load modeling method for production analogue simulation |
| CN106651473A (en) * | 2017-01-23 | 2017-05-10 | 国网福建省电力有限公司 | Method for promoting wind power acceptance level by considering recent electricity price per hour and multiple demand responses |
| CN106779207A (en) * | 2016-12-08 | 2017-05-31 | 中国电力科学研究院 | A kind of new energy for considering to generate electricity and open machine expense is dissolved Economic Analysis Method |
| CN106877316A (en) * | 2017-03-21 | 2017-06-20 | 大连理工大学 | A kind of construction method of the active distribution network net capability model of meter and controllable electric power economic load dispatching |
| CN108539781A (en) * | 2018-03-29 | 2018-09-14 | 国网江苏省电力有限公司电力科学研究院 | Improve the extension black-start scheme bi-level programming optimization method of recovery process safety |
| CN108539787A (en) * | 2018-05-02 | 2018-09-14 | 天津大学前沿技术研究院有限公司 | A kind of wind-light complementary system capacity configuration planing method considering power generation role |
| CN109038655A (en) * | 2018-07-18 | 2018-12-18 | 天津大学 | It rations the power supply and requires the mating stored energy capacitance calculation method in lower large-sized photovoltaic power station |
| CN109386429A (en) * | 2017-08-04 | 2019-02-26 | 中国电力科学研究院 | A kind of wind-powered electricity generation and photo-thermal power generation complementary system coordinated operation control method and device |
| CN109950933A (en) * | 2018-05-24 | 2019-06-28 | 国网河南省电力公司安阳供电公司 | A kind of wind-solar-storage joint peak regulation optimization method based on improvement particle swarm algorithm |
| CN109961224A (en) * | 2019-03-22 | 2019-07-02 | 大连理工大学 | It is a kind of meter and various energy resources monthly power trade plan time stimulatiom method |
| CN110829410A (en) * | 2019-10-10 | 2020-02-21 | 国网吉林省电力有限公司经济技术研究院 | New energy consumption time sequence simulation method based on regional decomposition iteration |
| CN111030091A (en) * | 2019-11-28 | 2020-04-17 | 新奥数能科技有限公司 | Method and system for determining installed electric capacity of distributed renewable energy |
| CN111753437A (en) * | 2020-07-06 | 2020-10-09 | 国网山西省电力公司电力科学研究院 | Credible capacity evaluation method and device for wind storage power generation system |
| CN112800619A (en) * | 2021-02-09 | 2021-05-14 | 上海电力大学 | Modeling and planning method for multi-source heterogeneous fully-renewable energy source thermoelectric storage coupling system |
| CN108964128B (en) * | 2018-08-07 | 2021-07-20 | 燕山大学 | Low-carbon economic dispatching solving method based on coordinated heat supply of electric boiler and heat storage device |
| CN113690916A (en) * | 2021-09-24 | 2021-11-23 | 广东电网有限责任公司 | Direct-current power grid energy storage configuration method, device and medium based on time sequence production simulation |
| CN114204553A (en) * | 2021-12-09 | 2022-03-18 | 软通动力信息技术(集团)股份有限公司 | Power generation proportioning method, device and equipment |
| CN114294630A (en) * | 2021-11-30 | 2022-04-08 | 淮阴工学院 | ORC-based carbon emission regulation system, method and medium for novel system |
| CN114336730A (en) * | 2021-11-21 | 2022-04-12 | 国网江苏省电力有限公司 | Low-carbon optimal scheduling method considering auxiliary service optimization for power system |
| CN116822803A (en) * | 2023-08-29 | 2023-09-29 | 北京国网信通埃森哲信息技术有限公司 | Carbon emission data graph construction method, device and equipment based on intelligent algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008067481A (en) * | 2006-09-06 | 2008-03-21 | Univ Of Ryukyus | Operation assisting method and device of power supply facility |
| CN103414213A (en) * | 2013-08-13 | 2013-11-27 | 中国能源建设集团广东省电力设计研究院 | Power supply optimal configuration method for sea island type micro-grid |
-
2014
- 2014-01-08 CN CN201410007489.5A patent/CN103762589B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008067481A (en) * | 2006-09-06 | 2008-03-21 | Univ Of Ryukyus | Operation assisting method and device of power supply facility |
| CN103414213A (en) * | 2013-08-13 | 2013-11-27 | 中国能源建设集团广东省电力设计研究院 | Power supply optimal configuration method for sea island type micro-grid |
Non-Patent Citations (1)
| Title |
|---|
| 邓晶等: "计及低碳效益的分布式发电优化配置", 《电力系统及其自动化学报》 * |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104573875B (en) * | 2015-01-27 | 2017-09-26 | 国家电网公司 | A kind of method of the power generating facilities and power grids optimization planning of low-carbon |
| CN104573875A (en) * | 2015-01-27 | 2015-04-29 | 国家电网公司 | Low-carbon power source and power grid optimization planning method |
| CN105470957A (en) * | 2015-12-29 | 2016-04-06 | 中国电力科学研究院 | Power network load modeling method for production analogue simulation |
| CN105470957B (en) * | 2015-12-29 | 2021-01-15 | 中国电力科学研究院 | Power grid load modeling method for production simulation |
| CN106779207B (en) * | 2016-12-08 | 2023-08-18 | 中国电力科学研究院 | New energy consumption economical evaluation method considering power generation and start-up costs |
| CN106779207A (en) * | 2016-12-08 | 2017-05-31 | 中国电力科学研究院 | A kind of new energy for considering to generate electricity and open machine expense is dissolved Economic Analysis Method |
| CN106651473B (en) * | 2017-01-23 | 2021-01-29 | 国网福建省电力有限公司 | Method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses |
| CN106651473A (en) * | 2017-01-23 | 2017-05-10 | 国网福建省电力有限公司 | Method for promoting wind power acceptance level by considering recent electricity price per hour and multiple demand responses |
| CN106877316A (en) * | 2017-03-21 | 2017-06-20 | 大连理工大学 | A kind of construction method of the active distribution network net capability model of meter and controllable electric power economic load dispatching |
| CN106877316B (en) * | 2017-03-21 | 2019-05-10 | 大连理工大学 | It is a kind of meter and controllable electric power economic load dispatching active distribution network net capability model construction method |
| CN109386429A (en) * | 2017-08-04 | 2019-02-26 | 中国电力科学研究院 | A kind of wind-powered electricity generation and photo-thermal power generation complementary system coordinated operation control method and device |
| CN109386429B (en) * | 2017-08-04 | 2021-01-15 | 中国电力科学研究院 | Coordinated operation control method and device for wind power and photo-thermal power generation complementary system |
| CN108539781A (en) * | 2018-03-29 | 2018-09-14 | 国网江苏省电力有限公司电力科学研究院 | Improve the extension black-start scheme bi-level programming optimization method of recovery process safety |
| CN108539787A (en) * | 2018-05-02 | 2018-09-14 | 天津大学前沿技术研究院有限公司 | A kind of wind-light complementary system capacity configuration planing method considering power generation role |
| CN109950933A (en) * | 2018-05-24 | 2019-06-28 | 国网河南省电力公司安阳供电公司 | A kind of wind-solar-storage joint peak regulation optimization method based on improvement particle swarm algorithm |
| CN109950933B (en) * | 2018-05-24 | 2022-09-13 | 国网河南省电力公司安阳供电公司 | Wind-solar-storage combined peak regulation optimization method based on improved particle swarm optimization |
| CN109038655A (en) * | 2018-07-18 | 2018-12-18 | 天津大学 | It rations the power supply and requires the mating stored energy capacitance calculation method in lower large-sized photovoltaic power station |
| CN108964128B (en) * | 2018-08-07 | 2021-07-20 | 燕山大学 | Low-carbon economic dispatching solving method based on coordinated heat supply of electric boiler and heat storage device |
| CN109961224A (en) * | 2019-03-22 | 2019-07-02 | 大连理工大学 | It is a kind of meter and various energy resources monthly power trade plan time stimulatiom method |
| CN110829410A (en) * | 2019-10-10 | 2020-02-21 | 国网吉林省电力有限公司经济技术研究院 | New energy consumption time sequence simulation method based on regional decomposition iteration |
| CN111030091A (en) * | 2019-11-28 | 2020-04-17 | 新奥数能科技有限公司 | Method and system for determining installed electric capacity of distributed renewable energy |
| CN111030091B (en) * | 2019-11-28 | 2021-11-30 | 新奥数能科技有限公司 | Method and system for determining installed electric capacity of distributed renewable energy |
| CN111753437A (en) * | 2020-07-06 | 2020-10-09 | 国网山西省电力公司电力科学研究院 | Credible capacity evaluation method and device for wind storage power generation system |
| CN112800619B (en) * | 2021-02-09 | 2022-08-05 | 上海电力大学 | Modeling and planning method for multi-source heterogeneous fully-renewable energy source thermoelectric storage coupling system |
| CN112800619A (en) * | 2021-02-09 | 2021-05-14 | 上海电力大学 | Modeling and planning method for multi-source heterogeneous fully-renewable energy source thermoelectric storage coupling system |
| CN113690916A (en) * | 2021-09-24 | 2021-11-23 | 广东电网有限责任公司 | Direct-current power grid energy storage configuration method, device and medium based on time sequence production simulation |
| CN114336730A (en) * | 2021-11-21 | 2022-04-12 | 国网江苏省电力有限公司 | Low-carbon optimal scheduling method considering auxiliary service optimization for power system |
| CN114336730B (en) * | 2021-11-21 | 2023-08-15 | 国网江苏省电力有限公司 | Low-carbon optimal scheduling method for electric power system considering auxiliary service optimization |
| CN114294630A (en) * | 2021-11-30 | 2022-04-08 | 淮阴工学院 | ORC-based carbon emission regulation system, method and medium for novel system |
| CN114204553A (en) * | 2021-12-09 | 2022-03-18 | 软通动力信息技术(集团)股份有限公司 | Power generation proportioning method, device and equipment |
| CN116822803A (en) * | 2023-08-29 | 2023-09-29 | 北京国网信通埃森哲信息技术有限公司 | Carbon emission data graph construction method, device and equipment based on intelligent algorithm |
| CN116822803B (en) * | 2023-08-29 | 2023-11-24 | 北京国网信通埃森哲信息技术有限公司 | Carbon emission data graph construction method, device and equipment based on intelligent algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103762589B (en) | 2015-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103762589B (en) | A kind of new forms of energy capacity ratio hierarchy optimization method in electrical network | |
| Zhang et al. | A multi-agent deep reinforcement learning approach enabled distributed energy management schedule for the coordinate control of multi-energy hub with gas, electricity, and freshwater | |
| CN109767105B (en) | Wind, light, water, fire and storage combined system-based multi-energy complementary coordinated power generation scheduling method | |
| Luo et al. | Design and analysis of a combined desalination and standalone CCHP (combined cooling heating and power) system integrating solar energy based on a bi-level optimization model | |
| CN102184475B (en) | Optimizing and dispatching method for microgrid economical operation on basis of multiple time scale coordination | |
| CN103151803B (en) | Method for optimizing wind power system-contained unit and backup configuration | |
| CN113095791B (en) | Comprehensive energy system operation method and system | |
| CN102496968A (en) | Generation plan optimizing method in intermittent energy and conventional energy coordinated dispatching mode | |
| CN103414206A (en) | Water, fire and light combined optimization power generation scheduling optimization method considering security constraints | |
| CN112583017B (en) | Hybrid micro-grid energy distribution method and system considering energy storage operation constraint | |
| Kong et al. | Optimization of the hybrid solar power plants comprising photovoltaic and concentrating solar power using the butterfly algorithm | |
| CN110957717A (en) | Multi-target day-ahead optimal scheduling method for multi-power-supply power system | |
| CN110391655B (en) | Multi-energy-coupling micro-energy-network economic optimization scheduling method and device | |
| CN104377693A (en) | Production simulation model for power generation | |
| CN106786509A (en) | Large-scale wind power and the combined heat and power dispatching method based on many scenario simulations off the net | |
| CN103473393A (en) | Method for modeling power transmission margin control model considering random probability | |
| CN112053024A (en) | Optimized scheduling method based on town energy Internet double-layer collaborative architecture | |
| CN105244870A (en) | Method for rapidly calculating wind curtailment rate of power grid wind power plant and generating capacity of unit | |
| CN111049179A (en) | New energy power generation system multi-objective optimization scheduling method considering uncertainty | |
| Zhu et al. | Design optimization and uncertainty analysis of multi-energy complementary system for residential building in isolated area | |
| CN103577893A (en) | Energy-saving optimization method for new energy and thermal power bidirectionally supplying power for high energy carrying loads | |
| CN113496300A (en) | Planning and designing method and system for park comprehensive energy station | |
| Guo et al. | The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps | |
| Petrichenko et al. | Reserve power estimation according to the Baltic power system 2050 development plan | |
| Liu et al. | Research on the optimal dispatch of wind power consumption based on combined heat and power with thermal energy storage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20151021 Termination date: 20190108 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |