CN108964124B - Distributed power supply grid-connected optimization configuration method for counting and electricity price response in incremental power distribution network - Google Patents
Distributed power supply grid-connected optimization configuration method for counting and electricity price response in incremental power distribution network Download PDFInfo
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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Abstract
The invention discloses a distributed power supply grid-connected optimization configuration method for counting and responding to electricity prices in an incremental power distribution network, relates to a distributed power supply grid-connected optimization configuration method for counting and responding to electricity prices, is a multi-objective nonlinear programming method, can be applied to the location determination and the peak-valley electricity price determination of a distributed power supply of the power distribution network, and belongs to the field of power system planning. The method comprises the steps of obtaining the wind speed and the sunlight intensity of the incremental distribution network area
The method comprises the steps of (1) data distribution on a full time sequence of each time period, and constructing a wind power and photovoltaic output model; building each load node in incremental distribution network
Distributing in full time sequence of each time interval, and constructing a load demand response model of the node; comprehensively considering the benefits of main parties of the incremental power distribution network market, and constructing objective functions of the main parties; the genetic algorithm is improved, so that the algorithm can solve the optimization problem related to the invention, the position and capacity distribution of the distributed power supply on each node of the power distribution network is obtained according to the result obtained by the genetic algorithm, and the value of the peak-valley time-of-use electricity price is obtained.
Description
Technical Field
The invention discloses a distributed power supply grid-connected optimization configuration method for counting and responding to electricity prices in an incremental power distribution network, relates to a distributed power supply grid-connected optimization configuration method for counting the electricity price responses, is a multi-objective nonlinear programming method, can be applied to site selection and volume determination of a distributed power supply of the power distribution network and establishment of peak-valley electricity prices, and belongs to the field of power system programming.
Background
As society develops, the demand for energy is increasing. In recent years, with the rapid development of scientific technology, various new energy sources are effectively developed and utilized, and the distributed power generation technology is gradually applied to some power distribution networks. The distributed power supply is used as a new energy source mainly developed by renewable energy sources, has the characteristics of randomness and uncontrollable performance, and can be scientifically connected to the distributed power supply, so that the situation that the current energy sources are increasingly tense can be relieved, and the electric energy quality of a power distribution network area can be effectively improved. Therefore, the first problem of accessing the distributed power supply to the power distribution network is where and when to access a large-capacity power supply, and the problems to be considered include the economic efficiency, reliability and the like of the power distribution network, and the problems should be reasonably considered and planned before the distributed power supply is connected to the power distribution network.
The electricity price response is a demand response behavior which mainly aims at stimulating users to participate in power grid peak shaving and guides the users to scientifically and reasonably use electricity. The electricity price response can realize that the power consumption is optimized, reduce the peak-valley difference, increase new forms of energy and consume, this not only needs the power company to formulate reasonable electricity price to guide the user to shift peak load, considers that distributed power source itself just has the chronology in the distribution network, the event still should combine reasonable planning overall arrangement of distributed power generation and configuration optimization to reduce the great electricity sales income of power supplier loss that the user that the demand elasticity is big loses the load and cause, just so can realize distributed power source and utilize and accomplish the overall planning of multiple target and compromise most rationally. For the problem of configuration optimization of a distributed power supply in a power distribution network, the following defects or shortcomings mainly exist in previous related researches:
1. distribution characteristics of the distributed power supply on a full time sequence are not considered, so that a certain error is generated in an optimization result of DG in power distribution network site selection and constant volume.
2. Some researches only consider the capacity configuration under the fixed-point condition or only consider the address selection problem with fixed capacity, and although the difficulty of the problem and the complexity of the algorithm are reduced to a certain extent, the application scene is greatly limited, and the obtained solution is not accurate enough.
3. Aiming at the problem of site selection optimization of DGs in the power distribution network, the selected intelligent optimization algorithm needs to be properly improved so as to accelerate convergence and not lose feasibility and reliability of solution.
4. Most studies only consider the analysis and research of a single optimization objective, and in consideration of actual needs, a plurality of optimization objectives are usually processed for decision-making and application.
5. Partial research carries out double-layer optimization on demand response and optimal configuration or scheduling of DGs, and the global optimal possibility of the solution in the complete set is reduced to a certain extent.
The operation mode of the power grid company in the incremental power distribution network is changed from 'purchasing electricity price difference' to 'admitting income' (general purchasing and general selling → logistics), the charging mode is changed from the state network company for replacing the power plant and charging the electricity selling company for uniformly charging the electricity to the electricity selling company by the user, and the electricity selling company respectively pays the electricity distribution fee to the power grid enterprise and pays the electricity to the electricity generating enterprise. Therefore, in the current distributed power optimization problem of the incremental power distribution network, a three-way game of a power grid enterprise, a user and a power generation enterprise needs to be considered, and multi-party target optimization needs to be constructed so as to provide multi-party decision for planning or prepare for deep multi-party cooperative planning.
Disclosure of Invention
In order to overcome various problems and difficulties caused by the fact that the distributed power supply is connected into a power distribution network to fix the volume and select the site, the invention provides a distributed power supply grid-connected optimization configuration method considering electricity price response in an incremental power distribution network.
The invention is realized by adopting the following technical scheme:
the invention discloses a distributed power supply grid-connected optimization configuration method for counting and responding to electricity prices in an incremental power distribution network, which comprises the following steps of:
step S1, acquiring the wind speed and the sunlight intensity of the incremental distribution network area
The method comprises the steps of (1) data distribution on a full time sequence of each time period, and constructing a wind power and photovoltaic output model;
step S2, constructing each load node in the incremental distribution network
Distribution in each time period and full time sequence is realized, differences of different types of industries in response to power price change are considered, and acquisition nodes are constructed according to similar historical data of demand response of other power distribution networks
The method comprises the steps of (1) establishing a load demand response model by using a time-interval full-time-sequence crossed elastic matrix;
step S3, the power supply main body aims at minimizing the load peak-valley difference of the power distribution network, minimizing the total investment cost of the power distribution network and minimizing the network loss of the power distribution network, and the power selling main body aims at maximizing the power selling income and constructs a multi-objective optimization function;
step S4, improving the genetic algorithm to solve, according to a plurality of objective functions in the power supply main body, establishing the weight based on the variance, calculating the comprehensive fitness of the individual, synthesizing the external objective function, namely synthesizing the single objective function of the power selling main body, obtaining the final total fitness, and adopting the replication operator, the self-adaptive crossover operator and the mutation operator of the elite strategy to accelerate the convergence;
s5, decoding according to the result of the genetic algorithm to obtain the distributed power distribution point position and the configuration capacity;
the above-mentioned
Typically 24 or 96.
Compared with other research methods of the same type, the method of the invention has the advantages that:
1. considering full time sequence distribution of distributed power supply output;
2. solving a combined planning problem of site selection, volume fixing and electricity price response of distributed power generation by adopting an improved genetic algorithm;
3. multi-target optimization is carried out by considering multi-party benefits of the incremental power distribution network;
the DG optimal configuration method considering the electricity price response, which is constructed by the invention, can provide better reference for the plan and decision of incremental power distribution service development.
Drawings
Fig. 1 is a flowchart of a distributed power grid-connected optimization configuration method for counting and responding to electricity prices in an incremental power distribution network.
FIG. 2 is a flow chart of an improved genetic algorithm in accordance with the present invention.
Detailed Description
The invention will be further described with reference to fig. 1 and 2.
The distributed power supply grid-connected optimization configuration method for counting and responding to electricity prices in the incremental power distribution network comprises the following steps of:
in the following examples, reference will be made to the description above
The time periods are set to 24 hours；
And S1, acquiring data distribution of wind speed and sunlight intensity of the incremental distribution network area in a full 24-hour time sequence, and constructing a wind power and photovoltaic output model.
S1.1, acquiring wind speed data of a 24-hour full time sequence in a typical day in the incremental power distribution network from a meteorological office, and establishing the following wind power generation output model for a single distributed wind power generator:
in the formula (9), the reaction mixture is,
is the wind speed of the distribution network area for a certain period of time,
cutting into the wind speed for the wind power generator,
is the rated wind speed, and is,
in order to cut out the wind speed,
is the rated power of a single distributed wind power generator.
S1.2, acquiring the 24-hour full-time illumination intensity distribution of the incremental distribution network in a typical day from a meteorological office, and constructing the following photovoltaic output model for a single distributed photovoltaic cell:
in the formula (10), the compound represented by the formula (10),
the active power output of a single photovoltaic cell is provided,
the size of the area of the battery is,
it is the intensity of the light that is irradiated,
the photoelectric conversion efficiency.
And S2, constructing the distribution of all load nodes in the incremental power distribution network in a full time sequence of 24 hours, and constructing a load demand response model of the nodes. In practice, the subscriber unit power factor is high and the power in calculating the bus bar nodes only takes into account the demand response of the active power. Acquiring the average electricity price of the power distribution network area
The following requirement elastic model is established according to the distribution data of each node in the full time sequence of 24 hours:
in the formula (11), the reaction mixture is,
the elastic coefficient of the electricity quantity in the m time period to the electricity price change in the n time period,
for a certain historical average electricity price,
in order to correspond to the electric quantity demand of the m-th time period under the electricity price,
、
electricity consumption in m periods respectivelyThe amount variation and the electricity price in the n period are compared with
The change that occurred.
When analyzing the influence factors of different user power demands, the invention mainly considers the following two aspects:
1. analyzing the bearing capacity of the user to the electricity price change, namely the magnitude of the self-elastic coefficient;
2. according to different industrial characteristics and electricity utilization habits, the capacity of transferring the electricity consumption in the peak time period to the electricity consumption in the valley time period, namely the size of the cross elasticity coefficient, of a user is analyzed when the peak-valley electricity price is analyzed.
The demand elasticity of different types of industries for 24 hours can be approximately calculated according to historical data of other power systems or other similar power distribution areas, so the demand elasticity coefficient of the current power distribution area can be calculated by the following formula:
in the formula (12), the reaction mixture is,
at the basic electricity price for the k-th industry
The weight of the load occupied by the lower period,
the corresponding spring constant.
In summary, the electric quantity variation of the full time sequence of the user at a certain node of the incremental distribution network can be obtained by the following formula:
the electricity consumption of the user t period after the electricity price response is carried out is as follows:
and step S3, constructing a multi-party objective function in consideration of different interest points of power generation and power supply parties.
From the grid company's perspective, assuming that the distributed power operations are all invested in full by the power supply company, the objective function may be considered:
in the formula (15), the first objective function is that the total loss of the network is minimum, the second objective function is that the peak-valley difference of the access public network node is minimum, the third objective function is that the total investment of the power grid company is minimum, wherein the first two items are the total investment of wind power and photovoltaic,
for the cost of the generation of the unit active power of the wind power,
in order to reduce the power generation cost per unit area of the photovoltaic cell,
and
the output of a single fan and the output of a photovoltaic cell are respectively, the third term is the distributed generation benefit,
in order to increase the price of the power transmission and distribution of the power distribution network,
and (4) distributing the electric quantity participating in market trading for the t period. From a power plant perspective, an objective function of the power selling income maximization of the whole network can be considered:
The following inequality constraint constraints are considered:
in the formula (17), the first term is constrained to be that the voltage is not out of limit, the second term is constrained to be that the distributed generation permeability is limited, the ratio of the total capacity of the accessed DGs to the total capacity of the power distribution network does not exceed the permeability, and the third term is limited to be that the electricity price does not exceed the maximum electricity price
And not lower than the lowest electricity price
。
And step S4, improving the genetic algorithm to solve, and acquiring the distributed power distribution point position and the configuration capacity of the distributed power. Assuming that the rated power and the battery area of a single distributed wind power and photovoltaic are fixed, so that the number of the wind power and photovoltaic configurations on each node of the distribution network area is obtained, the following method can be adopted for coding:
in the formula (18), the reaction mixture,
、
are respectively the first
The number of fans and photovoltaic devices installed on each node,
、
、
for the peak period, the usual period, and the valley period of the region, the electricity prices can be generally distributed as follows with respect to the period division: peaks (8: 00-11: 00, 16: 00-21: 00), plateaus (7: 00-8: 00, 11: 00-16: 00, 21: 00-23: 00), valleys (23: 00-7: 00).
The genetic algorithm solution steps are as follows:
y1, setting the population size N and setting the maximum variation rate
And minimum rate of variation
Setting the maximum crossing rate
And minimum crossing rate
Maximum number of iterations;
y2, forming an initial population, wherein the gene X adopts integer coding to represent the number of distributed power supplies, and the gene P adopts real number coding;
y3, calculating the value of each objective function. Firstly, considering an objective function of a power grid company level, because the objective functions are a plurality of objective functions in the company and no interest dispute exists among the objective functions, under the condition of seeking overall optimum, the following method is adopted to calculate the weight and the comprehensive fitness of each objective function:
and S4.1, carrying out load flow on the power distribution network represented by each individual, and calculating the value of each objective function of all the individuals. And (3) adopting a normalization method to scale each objective function value to an interval as the fitness of a single objective function, and assuming that
And
for the fitness of the maximum and minimum population of the current algebra, the normalization formula is:
s4.2, calculating the variance of the fitness values of all individuals on each objective function, wherein the larger the weight of the objective function with the large variance is, the larger the search area of the Pareto optimal solution is, so that the possibility that the subsequent solution is too concentrated, the larger the loss of the optimal solution is avoided:
in the formula (20), the reaction mixture is,
is as follows
The weight of the respective objective function is,
is as follows
The fitness of each objective function is calculated,
the number of objective functions is considered for the grid company. Therefore, the total fitness of the genetic algorithm individual multi-objective function for the power grid company is as follows:
in the formula (21), the compound represented by the formula,
is the fitness of the kth objective function. Similarly, the generating company fitness function is calculated in the same way.
And S4.3, considering the cooperation level of the comprehensive power generation company and the power supply company, and having agreed weight (for example, 1: 1) on each target, wherein the final fitness of each individual is as follows:
s4.4, copying operation. Adopting individual elite strategy to select the one with the maximum fitness
Directly enters the next generation operation as an individual elite. The rest of the
And selecting the wheel disc probability according to the fitness of the individual.
And S4.5, performing crossover operation. The current population is paired, and the fitness is maximum
Individuals did not participate in crossover, which was performed as follows:
in the formula (23), the number of distributed power supplies adopts integer intersection, and the electricity price adopts real number intersection. The improved crossover rate was as follows:
in the formula (24), T is the current population genetic algebra, T is the maximum genetic algebra, the cross rate is reduced along with the increase of the genetic algebra, and the convergence of the algorithm is accelerated. For each pair of genes, the roulette method was used, and if it was determined that the crossing was performed, the crossing was performed according to equation (23), and if the crossing was not performed, the alleles were directly introduced into the next generation.
And S4.6, mutation operation, wherein the individual elite does not participate in the mutation operation. Each gene is subjected to mutation operation by adopting a wheel disc method according to the following improved self-adaptive mutation rate:
in the formula (25), the reaction mixture,
for the purpose of the current average fitness measure,
in order to reach the current maximum fitness, the formula realizes that the variation rate is reduced along with the genetic algebra and the population average fitness is improved, so that the algorithm convergence is accelerated.
S4.7, if the maximum iteration number T is reached, ending the algorithm; if the number of iterations T is not the maximum number of iterations T, the process returns to step S4.1.
And step S5, selecting the optimal individual or individuals, acquiring the position and number information of the distributed power supply and the peak-valley bisection time interval electricity price, and calculating each objective function value to facilitate decision making.
The method is applied to the optimization configuration problem of the distributed power supply accessed to the power distribution network, meanwhile, the electricity price demand response of a user side is comprehensively considered, a demand response model of the user for electricity price change is established, so that a combined planning model of the distributed power supply and the electricity price decision is established, the multi-objective nonlinear optimization problem aiming at the minimum network loss, the minimum power grid load loss, the maximum comprehensive benefit, the minimum peak-valley difference and the like is considered, the solution is solved by improving a genetic algorithm, the optimization configuration scheme of the distributed power supply is obtained, and the peak-valley time-sharing electricity price is obtained at the same time.
The above-mentioned embodiments describe the present invention in further detail, and some parameters and functions are instantiated, so that equivalent substitutions can be made in practical applications, and suitable parameters can be selected according to specific situations.
Claims (5)
1. A distributed power supply grid-connected optimization configuration method for considering electricity price response in an incremental power distribution network is characterized by comprising the following steps:
step S1, acquiring the wind speed and the sunlight intensity of the incremental distribution network area
Establishing a wind power and photovoltaic output model by data distribution on a time interval full time sequence;
step S2, constructing each load node in the incremental distribution network
The method comprises the steps that the time intervals are distributed in a full time sequence, the difference of different types of industries in response to power price change is considered, and a cross elastic matrix for acquiring the full time sequence of each time interval of nodes is constructed according to similar historical data of demand response of other power distribution networks to establish a load demand response model;
step S3, the power supply main body aims at minimizing the load peak-valley difference of the power distribution network, minimizing the total investment cost of the power distribution network and minimizing the network loss of the power distribution network, and the power selling main body aims at maximizing the power selling income and constructs a multi-objective optimization function;
step S4, improving the genetic algorithm to solve, according to a plurality of objective functions in the power supply main body, establishing the weight based on the variance, calculating the comprehensive fitness of the individual, synthesizing the external objective function, namely synthesizing the single objective function of the power selling main body, obtaining the final total fitness, and adopting the replication operator, the self-adaptive crossover operator and the mutation operator of the elite strategy to accelerate the convergence;
s5, decoding according to the result of the genetic algorithm to obtain the distributed power distribution point position and the configuration capacity;
of the nodes constructed in step S2
The elements of the cross elastic matrix of the full time sequence of each period are obtained by elastically weighting and summing requirements of different industries, so that a calculation formula of the requirement elastic coefficient of the current power distribution network area is obtained:
in the formula (1), the reaction mixture is,
at the basic electricity price for the k-th industry
Lower part
The weight of the load taken up by the time period,
for the purpose of the corresponding elastic coefficient,
the demand elastic coefficient of the electricity quantity of a certain node at the m moment to the electricity price change at the n moment is obtained;
the electric quantity change of a user full time sequence under a certain node of the incremental power distribution network is obtained by the following formula:
the electricity consumption of the user t period after the electricity price response is carried out is as follows:
the multi-objective optimization function is constructed in step S3 as follows:
the formula (2) is a multi-objective function considered by the power supply main body, wherein the first objective function is the minimum total loss of the network, the second objective function is the minimum peak-to-valley difference of the nodes accessed to the public network,
the third objective function is the minimum total investment of the power distribution network for a full time sequence of electric quantity, wherein the first two items are the total investment sum of wind power and photovoltaic,
for the cost of the generation of the unit active power of the wind power,
in order to reduce the power generation cost per unit area of the photovoltaic cell,
and
the output of a single fan and the output of a photovoltaic cell are respectively, the third term is the distributed generation benefit,
for the time period t, the price of selling electricity,
in order to increase the price of the power transmission and distribution of the power distribution network,
the electric quantity participating in market trading is distributed generation at the t time period; the formula (3) is an objective function considered by the electricity selling main body and is an objective function for maximizing the income of electricity selling in the whole network;
step S4 is carried out on the grid-connected optimal configuration of the distributed power supply, meanwhile, the demand response of the user to the peak-valley electricity price is considered, a comprehensive planning model is established, and the genetic algorithm gene sequence is as follows:
step S4, optimizing the multi-target function in the power supply main body by applying a genetic algorithm, and determining the weight of each function by adopting the fitness variance contribution based on the target function in order to enlarge the search area of the Pareto optimal solution:
in the formula (5), the reaction mixture is,
is as follows
The weight of the respective objective function is,
is as follows
The fitness of each objective function is calculated,
the number of the functions of a certain target is shown in the formula (6)
Is as follows
The fitness of the individual objective functions is,
the comprehensive fitness of the multi-objective function in the power supply main body is obtained.
2. The distributed power grid-connected optimization configuration method for the counting and electricity price response in the incremental power distribution network according to claim 1, characterized in that:
the value is 24 or 96.
3. The distributed power grid-connected optimization configuration method for the neutralization electricity price response in the incremental power distribution network according to claim 1, wherein the optimization problem is solved according to the genetic algorithm applied in the step S4, a replication operator based on an elite strategy is adopted, and the maximum fitness is selected in each generation
Directly enters the next generation operation as the individual elite without mutation and cross operation.
4. The distributed power grid-connected optimization configuration method for counting and responding to electricity prices in incremental power distribution network according to claim 1, wherein the step S4 is executed according toSolving the optimization problem by applying a genetic algorithm, adopting a self-adaptive crossover operator, wherein T is the current population genetic algebra, T is the maximum genetic algebra,
in order to maximize the rate of cross-over,
minimum crossover rate:
5. the distributed power grid-connected optimization configuration method for the counting and electricity price response in the incremental power distribution network according to claim 1, wherein the optimization problem is solved according to the genetic algorithm applied in the step 4, an adaptive mutation operator is adopted,
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