CN115630797A - Method for analyzing influence of electric power spot market on new energy utilization - Google Patents
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Abstract
A method for analyzing the influence of an electric power spot market on new energy utilization is based on the clearing principle and an optimization model of a typical provincial electric power spot market in China, adopts a qualitative analysis method of causal analysis, and provides relevant factors of the influence of the clearing of the electric power spot market on the consumption of new energy; the method for modeling the power grid topology information based on feature extraction is provided, the key information of the global topology is extracted, the detailed information of the local topology is retained, and the problems that the power grid topology conventional table is large in scale and sparse are solved on the premise that the key information of the topology is not lost; the method has the advantages that mathematical modeling is carried out on a plurality of market factors, coupling calculation and information refinement are carried out on a plurality of original factors, numerical representation is carried out on non-numerical factors, the applicability of statistical analysis is improved by causal analysis, and the coupling influence of a plurality of key factors on new energy consumption is reflected in a refining mode.
Description
Technical Field
The invention belongs to the technical field of new energy utilization influence analysis, and particularly relates to a method for analyzing the influence of an electric power spot market on new energy utilization.
Background
With the proposal of a 'double-carbon' target and the construction requirement of a novel power system, the construction of a power market facing the novel power system is a new trend and a new challenge in the evolution process of the power market in China. In recent years, with the adjustment of new energy policies, the advance of electric power market reformation and the like in China, the environment in which new energy participates in the electric power market is changed profoundly. The construction of the electric power spot market needs to be combined with a novel electric power system construction process, the requirements of energy structure transformation and source network load storage cooperative interaction level improvement are fully considered, and the energy value, the capacity value, the adjusting value and the green value of various main bodies are comprehensively reflected. The research on how the new energy consumption is influenced by the electric power spot market is a key link for accurately reflecting different influences of various market factors on new energy utilization for quantitative evaluation and further exploring an electric power market mechanism for promoting new energy consumption.
Under the electric power spot market environment, new energy resources participate in the market in two ways, one is in a 'report without quote' mode, the new energy resources are cleared according to predicted output, cannot be priced and accept market price; the other is that the 'reporting price' mode participates in the spot market, the power spot market optimizes the starting, stopping and output of the unit on the daily basis, and the settlement is carried out according to the node marginal electricity price, the bid amount of the new energy is possibly less than or equal to the predicted output, and the pricing can be carried out on the power spot market. It can be seen that, in the mode of participation of 'report quotation', the day-ahead and real-time output plans of the unit are the clearing results of the day-ahead spot market and the real-time spot market respectively, except for the influence of random factors such as weather and faults on the actual output of the new energy, and the influence of the electricity spot market on the consumption of the new energy is mainly that the controllability of the new energy unit in the aspect of following the output plan is poor compared with that of the conventional energy unit, but the bid winning result of the new energy in the spot market is an important factor influencing the consumption of the new energy.
The system dynamics model of the new energy consumption and the renewable energy quota system is built by the existing scholars, and the influence of different consumption weight formulation schemes on the consumption and the renewable energy installation growth under given conditions is analyzed. In addition, the learner constructs a model for calculating the contribution of the main influence factors of the new energy, evaluates the delivery capacity, the load and the medium and long-term transaction amount of the influence factors of the new energy consumption, and quantifies the influence degree of each factor. And the influence degree of new energy installation, electric quantity, load increase speed and the like on new energy consumption is quantitatively analyzed by a learner based on a sensitivity analysis method.
In consideration of factors, existing researches on new energy consumption influence factors are mainly focused on non-marketable factors such as delivery acceptance capacity, installed capacity and load increase speed, or influences of renewable energy quota markets and medium and long-term markets on new energy consumption are researched, and related influence analysis of clearing boundaries and clearing mechanisms of electric power spot markets on new energy consumption is not carried out. With the current electric power market and the deep reformation and the general popularization in China, most provinces in China are the current market mode of the total quantity of the current cargos, and how the current market is cleared to influence the utilization of new energy is an important factor which needs to be considered.
In terms of analysis methods, in the existing correlation analysis methods for new energy consumption influence factors, a system dynamics model method lacks quantitative analysis on the correlation factors; the sensitivity analysis method is suitable for quantitative analysis under the same scene and under the condition that other factors are not changed, and the quantitative analysis of related factors which have complicated and changeable boundary conditions, are mutually coupled and have statistical significance is lacked.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide an analysis method for the influence of the electric power spot market on the utilization of new energy, which is based on the clearing principle and the optimization model of the typical provincial electric power spot market in China and adopts a qualitative analysis method of causal analysis to provide relevant factors for the influence of the clearing of the electric power spot market on the consumption of the new energy; the mathematical modeling method of the power grid topology information based on feature extraction is provided, key information of global topology is extracted, detailed information of local topology is reserved, and the problems that a power grid topology conventional table is large in mathematical table scale and sparse are solved on the premise that the topology key information is not lost; the method has the advantages that mathematical modeling is carried out on a plurality of market factors, coupling calculation and information refinement are carried out on a plurality of original factors, numerical representation is carried out on non-numerical factors, the applicability of statistical analysis is improved by causal analysis, and the coupling influence of a plurality of key factors on new energy consumption is reflected in a refining mode.
The invention adopts the following technical scheme.
A method for analyzing the influence of an electric power spot market on new energy utilization comprises the following steps:
step 1: qualitative analysis of new energy consumption related factors based on the electric power spot market clearing principle;
and 2, step: carrying out mathematical modeling aiming at the relevant factors of new energy consumption;
and step 3: carrying out standardized processing on the modeling data of the new energy consumption related factors;
and 4, step 4: and carrying out quantitative analysis based on similarity analysis on the modeling data of the new energy consumption related factors after the normalization processing.
Preferably, the new energy consumption related factors include market demand and supply, load fluctuation, power grid topology and operation mode, market member declaration information, auxiliary service resource demand and supply, climbing capacity and output control capacity of the new energy unit.
Preferably, the market supply and demand include a market space determined by the sum of the declared amount of new energy load side marketized market members and the busbar load prediction sum of non-marketized load, the network loss of a new energy system, the sum of external power and the output of a new energy unit with fixed output in province, and a power generation side effective power generation capacity determined by the rated installed capacity of the new energy unit, the power generation capacity of the new energy unit, the declared maximum capacity of the new energy unit, the repaired capacity of the new energy unit and the plant power utilization rate of the new energy unit;
the load fluctuation comprises a load fluctuation rate determined by the new energy system load at a set moment and the new energy system average load at all time intervals, and a peak-valley difference determined by the new energy system load at a peak moment and the new energy system load at a valley moment;
the power grid topology and the operation mode are determined by the whole grid nodes of the new energy power grid and the section sensitivity matrix of the new energy power grid;
the market member declaration information is determined by the declaration price of the new energy unit;
the auxiliary service resource supply and demand comprise the demand condition of the auxiliary service and the supply condition of the auxiliary service, and the demand condition of the auxiliary service comprises the standby demand and the frequency modulation demand of the new energy system; the supply condition of the auxiliary service comprises the total quantity of the standby auxiliary service declaration of the new energy system, the total quantity of the actual available standby resource of the new energy system, the total quantity of the frequency modulation auxiliary service declaration of the new energy system and the total quantity of the available frequency modulation resource of the new energy system;
the climbing capacity comprises the integral climbing capacity and the landslide capacity of the new energy system and the climbing capacity and the landslide capacity of the concerned new energy unit;
the output control capability of the new energy unit is determined by the real-time market clearing result and the actual output of the new energy unit.
Preferably, the step 2 specifically includes:
modeling market supply and demand, comprising:
modeling a market space according to equation (1):
I MarketSpace ＝L system +L oss -P transregion -P fixpower (1)
in the formula I MarketSpace Representing market space, L system Bus load forecast summation of declaration quantity of new energy load side marketized market member and non-marketized load, L oss Represents the network loss, P, of the new energy system transregion Representing external power, P fixpower Representing the sum of the output of the fixed output unit of the new energy in the province;
modeling the effective power generation capacity of the power generation side according to the formula (2):
in the formula I GenAbility Represents the effective generating capacity of the generating side of the transmission system, C i Refers to the rated installed capacity, A, of the transmission unit i i Representing the generating capacity of the transmission unit i, B i Representing declared maximum capacity, X, of transmission unit i i Represents the post-overhaul capacity u of the transmission unit i i The method is characterized by comprising the following steps of (1) indicating the station service power rate of a unit i, wherein i is a natural number;
modeling load fluctuations, comprising:
modeling the load fluctuation rate according to equation (3):
in the formula I LoadWave Represents the load fluctuation rate, L t New energy System load, L, representing time t ave,alltime Representing the average load of the new energy system in all time periods;
modeling the peak-to-valley difference according to equation (4):
I diff ＝L peak -L valley (4)
in the formula I diff Denotes the peak-to-valley difference, L peak New energy system load, L, at peak time valley Representing the load of the new energy system at the time of the valley;
modeling the topology and the operation mode of the power grid, which comprises the following steps:
solving the characteristic value and the characteristic vector of the sensitivity matrix of the whole network node pair section of the new energy power grid, and deleting the characteristic value of which the characteristic value is smaller than a certain threshold value and the corresponding characteristic vector to realize matrix dimensionality reduction;
modeling market member declaration information, comprising:
the declared prices of the generating side units of the new energy systems are subjected to equalization processing according to a quotation section according to a formula (5):
wherein price i Reporting price for unit capacity of new energy system generating side unit i, C i,l The capacity, pr, of the generating side unit i of the new energy system under the condition of the capacity section l i,l Reporting the price of the unit capacity of a new energy system power generation side unit i under the condition of a capacity section l, wherein l is a natural number;
modeling the new energy whole system power generation side declaration average price according to a formula (6):
in the formula I aveGenPrice Declare price sharing for new energy whole system power generation side i Reporting price for unit capacity of new energy system generating side unit i, C i The capacity of a generating side unit i of the new energy system is obtained;
modeling the new energy whole system load side declaration average price according to a formula (7):
in the formula I aveBlPrice Declare price per unit for new energy whole system load side bl Reporting price per unit capacity, C, of new energy system load side unit bl bl The capacity of a new energy system load side unit bl is represented, and bl is a natural number;
modeling the declared price of the new energy fleet of interest according to equation (8):
I aim ＝price aim (8)
in the formula I aim 、price aim Representing the declared price of a certain new energy unit to be subjected to correlation analysis research;
modeling auxiliary service resource supply and demand, comprising:
the richness of the auxiliary service is characterized by modeling according to equation (9):
in the formula I ratio Represents the ratio of the auxiliary service demand to the total predicted power sum of the new energy, D Reserve Indicating the reserve demand of the new energy system, D Reg Indicating the FM demand, P, of the new energy system NewEnergy Representing the total predicted electric power sum of the new energy;
modeling a climbing capability, comprising:
5) Modeling the overall climbing capacity of the new energy system according to formula (10):
in the formula I rampupAbility Represents the overall climbing capability, R, of the new energy system i,rampup Representing the climbing capacity of the unit i, and Prdmin representing the interval of the clear period;
6) Modeling the overall landslide capacity of the new energy system according to equation (11):
in the formula I rampdnAbility Represents the overall landslide capability, R, of the new energy system i,rampdn Expressing the landslide capability of the unit i, and Prdmin expressing the interval of the clear period;
modeling the capacity of the new energy cluster for output control, comprising:
real-time market clearing result P based on new energy clearing With actual force P real The deviation therebetween, as a mathematical model for characterizing the output control capability of the new energy source unit, is expressed by formula (12):
I ControlAbility ＝P clearing -P real (12)。
preferably, the step 3 specifically includes: carrying out data cleaning, data integration, data transformation and data reduction on the modeling data of the new energy consumption related factors;
the data cleaning comprises denoising modeling data of new energy consumption related factors;
the data integration comprises the step of combining the modeling data of the new energy consumption related factors into a whole;
the data transformation comprises the steps of coding the modeling data of the new energy consumption related factors to obtain coded data;
the data reduction comprises a unified dimension of modeling data of factors related to new energy consumption.
Preferably, the step 4 includes the following three methods:
the first method, that is, the pearson correlation coefficient is found according to the formula (13):
in the formula, ρ X,Y Pearson correlation coefficient representing continuous variable (X, Y), cov (X, Y) representing covariance of X, Y, rho X And rho Y Respectively representing respective standard deviations of X and Y, wherein X and Y are different modeling data of new energy consumption related factors after normalization processing;
the second method, that is, the spearman correlation coefficient is obtained according to the formula (14):
in the formula, values of two variables in pairs are respectively ranked from small to large or from large to small, R j Represents X j Order of (a), Q j Represents Y j N is the sample size, X j And Y j Respectively sampling j data in two groups of samples obtained from the modeling data of the new energy consumption related factors after the normalization processing;
the third method is to solve the kendel kendall correlation coefficient according to the formula (15):
and the Kendel correlation coefficient is the ratio of the difference between the same-sequence pair P and the different-sequence pair P in the modeling data of the new energy absorption correlation factors after the normalization processing to the total logarithm.
An apparatus for analyzing the influence of the electric power spot market on new energy utilization of the method comprises:
the qualitative module is used for carrying out qualitative analysis on the new energy consumption related factors based on the electric power spot market clearing principle;
the modeling module is used for carrying out mathematical modeling on the new energy consumption related factors;
the standardization processing module is used for carrying out standardization processing on the modeling data of the new energy consumption related factors;
and the quantitative analysis module is used for carrying out similarity analysis-based quantitative analysis on the modeling data of the new energy consumption related factors after the normalization processing.
Preferably, the new energy consumption related factors include market supply and demand, load fluctuation, power grid topology and operation mode, market member declaration information, auxiliary service resource supply and demand, climbing capacity and output control capacity of the new energy unit.
Preferably, the market demand and supply comprises a market space determined by the sum of declared quantity of market members at the new energy load side and bus load prediction of non-market load, the net loss of a new energy system, the sum of external power and new energy unit output of provincial fixed output, and an effective power generation capacity at the power generation side determined by the rated installed capacity of the new energy unit, the power generation capacity of the new energy unit, declared maximum capacity of the new energy unit, repaired capacity of the new energy unit and plant power consumption rate of the new energy unit;
the load fluctuation comprises a load fluctuation rate determined by the new energy system load at a set moment and the new energy system average load at all time intervals, and a peak-valley difference determined by the new energy system load at a peak moment and the new energy system load at a valley moment;
the power grid topology and the operation mode are determined by the whole grid nodes of the new energy power grid and the section sensitivity matrix of the new energy power grid;
the market member declaration information is determined by the declaration price of the new energy unit;
the auxiliary service resource supply and demand comprise the demand condition of the auxiliary service and the supply condition of the auxiliary service, and the demand condition of the auxiliary service comprises the standby demand and the frequency modulation demand of the new energy system; the supply condition of the auxiliary service comprises the total standby auxiliary service declaration amount of the new energy system, the actual available standby resource amount of the new energy system, the total frequency modulation auxiliary service declaration amount of the new energy system and the available frequency modulation resource amount of the new energy system;
the climbing capacity comprises the integral climbing capacity and the landslide capacity of the new energy system and the climbing capacity and the landslide capacity of the concerned new energy unit;
the output control capability of the new energy unit is determined by the real-time market clearing result and the actual output of the new energy unit.
Preferably, the modeling module is further configured to model market supply and demand, and includes:
modeling a market space according to equation (1):
I MarketSpace ＝L system +L oss -P transregion -P fixpower (1) (1)
in the formula I MarketSpace Representing market space, L system And (3) bus load prediction summation of declared quantity of new energy load side marketized market members and non-marketized load, L oss Represents the network loss, P, of the new energy system transregion Representing external power, P fixpower Representing the sum of the output of the fixed output unit of the new energy in the province;
modeling the effective power generation capacity of the power generation side according to the formula (2):
in the formula I GenAbility Representing the effective power generation capacity of the power generation side of the transmission system, C i Is the rated installed capacity, A, of the transmission unit i i Representing the generating capacity of the transmission unit i, B i Representing declared maximum capacity, X, of transmission unit i i Represents the post-overhaul capacity u of the transmission unit i i Means thatThe plant power rate of the unit i, i is a natural number;
modeling load fluctuations, comprising:
modeling the load fluctuation rate according to equation (3):
in the formula I LoadWave Represents the load fluctuation rate, L t New energy system load, L, representing time t ave,alltime Representing the average load of the new energy system in all time periods;
modeling the peak-to-valley difference according to equation (4):
I diff ＝L peak -L valley (4) (4)
in the formula I diff Denotes the peak-to-valley difference, L peak New energy system load, L, at peak time valley Representing the load of the new energy system at the time of the valley;
modeling the topology and the operation mode of the power grid, which comprises the following steps:
calculating the characteristic value and the characteristic vector of the sensitivity matrix of the whole network node to the section of the new energy power grid, and deleting the characteristic value of which the characteristic value is smaller than a certain threshold value and the corresponding characteristic vector to realize matrix dimension reduction;
modeling market member declaration information, comprising:
equalizing the declared prices of the generating side units of the new energy systems according to a quotation section according to a formula (5):
therein, price i Reporting price for unit capacity of new energy system generating side unit i, C i,l Is the capacity, pr of the generating side unit i of the new energy system under the condition of the capacity section l i,l Reporting the price of the unit capacity of a new energy system power generation side unit i under the condition of a capacity section l, wherein l is a natural number;
modeling the new energy whole system power generation side declaration average price according to a formula (6):
in the formula I aveGenPrice Price is declared for new energy whole system power generation side i Reporting price for unit capacity of new energy system generating side unit i, C i The capacity of a generating side unit i of the new energy system is obtained;
modeling the new energy whole system load side declaration average price according to a formula (7):
in the formula I aveBlPrice Declare price per unit for new energy whole system load side bl Reporting price per unit capacity, C, of new energy system load side unit bl bl The capacity of a new energy system load side unit bl is represented, and bl is a natural number;
modeling the declared price of the new energy fleet of interest according to equation (8):
I aim ＝price aim (8)
in the formula I aim 、price aim Expressing the declared price of a certain new energy unit to be subjected to correlation analysis research;
modeling supply and demand for auxiliary service resources, comprising:
the richness of the secondary service is characterized by modeling according to equation (9):
in the formula I ratio Represents the ratio of the auxiliary service demand to the total predicted power of the new energy, D Reserve Indicating the reserve demand of the new energy system, D Reg Indicating the FM demand, P, of the new energy system NewEnergy Representing the total predicted electric power sum of the new energy;
modeling climbing capacity, comprising:
7) Modeling the overall climbing capacity of the new energy system according to formula (10):
in the formula I rampupAbility Represents the overall climbing capability, R, of the new energy system i,rampup Representing the climbing capability of the unit i, and Prdmin represents the interval of the clearing time period;
8) Modeling the overall landslide capacity of the new energy system according to equation (11):
in the formula I rampdnAbility Represents the integral landslide capability, R, of the new energy system i,rampdn Expressing the landslide capability of the unit i, and Prdmin expressing the interval of the clear period;
modeling the capacity of the new energy cluster for output control, comprising:
real-time market clearing result P based on new energy clearing And the actual force P real The deviation between the two is used as a mathematical model for representing the output control capability of the new energy source unit, and the expression is shown in formula (12):
I ControlAbility ＝P clearing -P real 。
preferably, the normalization processing module is further configured to perform data cleaning, data integration, data transformation, and data reduction on the modeling data of the new energy consumption-related factors;
the data cleaning comprises denoising modeling data of new energy consumption related factors;
the data integration comprises the step of combining modeling data of new energy consumption related factors into a whole;
the data transformation comprises the steps of coding modeling data of new energy consumption related factors to obtain coded data;
the data reduction comprises a unified dimension of modeling data of factors related to new energy consumption.
Preferably, the quantitative analysis module is further configured to find a pearson correlation coefficient according to equation (13):
in the formula, ρ X,Y Pearson correlation coefficient representing continuous variable (X, Y), cov (X, Y) representing covariance of X, Y, rho X And rho Y Respectively representing respective standard deviations of X and Y, wherein X and Y are different modeling data of new energy consumption related factors after normalization processing; for solving the spearman correlation coefficient according to equation (14):
in the formula, values of two variables in pairs are respectively ranked from small to large or from large to small, R j Represents X j Order of (2), Q j Represents Y j N is the sample size, X j And Y j Respectively sampling jth data in two groups of samples obtained from the modeling data of the new energy consumption related factors after the normalization processing; for finding the Kendell correlation coefficient according to equation (15):
and the Kendel correlation coefficient is the ratio of the difference between the same-sequence pair P and the different-sequence pair P in the modeling data of the new energy absorption correlation factors after the normalization processing to the total logarithm.
The invention relates to a terminal, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of the method for analyzing the impact of the electricity spot market on new energy utilization.
A computer-readable storage medium according to the present invention, has stored thereon a computer program that, when being executed by a processor, implements the steps of the method for analyzing an impact of the electric power spot market on new energy utilization.
Compared with the prior art, the method has the beneficial effects that based on the clearing principle and the optimization model of the typical provincial power spot market in China, the method adopts a qualitative analysis method of cause and effect analysis, and provides relevant factors of the effect of the clearing of the power spot market on the consumption of new energy. The invention provides a mathematical modeling method of power grid topology information based on feature extraction, which extracts key information of global topology, retains detailed information of local topology, and solves the problems of large scale and sparseness of a power grid topology conventional table on the premise of not losing the key information of topology. The method carries out mathematical modeling on a plurality of market factors, carries out coupling calculation and information extraction on a plurality of original factors, carries out numerical representation on non-numerical factors, improves the applicability of statistical analysis by causal analysis, and refines and reflects the coupling influence of a plurality of key factors on new energy consumption.
Drawings
FIG. 1 is a flow chart of steps 1 to 4 described in the present invention;
fig. 2 is a block diagram of an apparatus for analyzing the influence of the electric power spot market on new energy utilization according to the present invention.
Detailed Description
With the deep advance of the reform of the electric power market and the large-scale development of new energy, the influence of the electric power spot market on the utilization of the new energy needs to be researched urgently, the influence of the clearing boundary and the clearing mechanism of the electric power spot market on the consumption of the new energy is analyzed, and then a foundation is laid for properly and comprehensively planning the construction of the electric power spot market and the reliable supply of electric power, the safety and stability of a power grid and the high-level utilization of the new energy. The method analyzes the influence of the electric power spot market on the utilization of the new energy from the angle of how the spot market clears the scalar in the new energy.
The related terms of the present invention are explained as follows:
electric power spot market: the electric power spot market generally refers to a market for electric energy trading in the future and in a shorter time. It is a concept relative to the medium and long term market for electricity, typically a lead period of time of day or less. The electric power spot market in China generally refers to electric energy trading of the day-ahead market and the real-time market, and auxiliary service trading such as standby service, frequency modulation and the like is developed in a matched manner. The electric power spot market is a key link for establishing a competition mechanism and realizing the optimal configuration of electric power resources, and is an important mechanism for changing the generation mode of the traditional dispatching power generation plan and taking a market trading result as a main basis for arranging the dispatching power generation plan;
optimizing and clearing in the electric power spot market: the spot market optimized clearing refers to the market state of realizing supply and demand balance of the electric power spot market commodity variety based on the quotation of market members, and the medium scalar quantity and the medium price of each market member are determined. In most provincial test points in China, a spot-inventory centralized clearing mode is adopted, namely, a unified clearing and centralized bidding mode is adopted, market members declare, a dispatching and trading mechanism performs unified optimized matching on the whole-network power generation resources and the power utilization requirements, the optimization process takes the lowest system power generation cost or the maximum social welfare and the like as optimization targets, and the clear power price of the unit are obtained through optimized calculation by considering system balance constraint, standby constraint, unit operation constraint, network safety constraint and the like;
and (3) correlation analysis: the correlation analysis refers to statistical analysis of two or more elements with certain correlation, so as to measure the degree of closeness of correlation between the variable factors, determine the influence of the variation of different variable factors on the result of the main factor, and judge the positive and negative correlation between the main factor and the variable factors.
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention relates to a method for analyzing the influence of an electric power spot market on new energy utilization, which comprises the following steps as shown in figure 1:
step 1: qualitative analysis of new energy consumption related factors based on the electric power spot market clearing principle;
specifically, the electric power spot market clearing principle includes day-ahead electric energy market clearing and real-time electric energy market clearing, wherein the day-ahead electric energy market clearing can be understood as determining the start, stop and output of the unit from low to high according to the generating cost based on the unit declared price, until the total output meets the system load demand, on the basis of the basic sequencing principle, the unit operation constraint, the section limit constraint, the system positive and negative standby constraint and the like need to be considered, and the unit operation constraint includes: the method comprises the following steps of (1) unit maximum/minimum output constraint, unit generating capacity constraint, unit minimum running time constraint, unit minimum downtime constraint, unit overhaul downtime constraint, gas quantity constraint and gas turbine and steam turbine matching constraint; the real-time electric energy market clearing is a unit start-stop result based on a day-ahead scheduling plan and day-in rolling correction, real-time unit output arrangement is carried out, the power generation cost in the objective function of the real-time electric energy market clearing model does not contain the starting cost any more, the constraint does not include the constraint related to the unit start-stop such as the minimum start-stop time of the unit any more, and other constraints are basically consistent with the day-ahead market clearing constraint principle. Optimizing complex constraints in the clearing process can cause that the unit with lower declared price cannot bid or cannot meet the full capacity and bid, and then calling the unit with higher declared price. The common influence of various constraints causes whether the bid winning of the new energy machine set is simply not ordered according to the quotation, so that the reason for winning the bid is more complicated. The factors influencing the bid winning result of the new energy unit comprise the following factors:
market supply and demand conditions, network limited conditions of new energy local areas, auxiliary service supply and demand conditions, climbing capacity of various units, output control capacity of new energy units, fuel price, market member quotation strategies and time interval coupling factors.
The market supply and demand conditions are determined by the reporting amount of market members on the load side, bus load prediction, trans-provincial power, the output plan of a fixed output unit, unit overhaul capacity, unit power generation capacity, unit station service power and other factors. In general, as for a certain new energy market member, the market supply and demand are in short supply, and the probability of winning a bid in a unit is higher; market supply and demand are loose, and winning probability in the unit is less.
Besides the influence of the overall supply and demand of the market, the network limitation condition of the new energy local area, that is, the supply and demand of the local area where the new energy unit is located, also influences the bid winning result of the new energy unit. The winning bid condition of the new energy unit is influenced by factors influencing the safety of the power grid, such as power grid blockage, local voltage support, minimum starting mode, N-1 safety check and the like. For example, the declaration price of a new energy is low, but because the output of the new energy causes the trend of a certain section to be out of limit, the bid amount of the new energy unit is correspondingly reduced.
The auxiliary service supply and demand conditions are that the new energy has large uncertainty and fluctuation specificity, and in the spot market mode, the market needs to provide enough regulation capacity to stabilize the fluctuation specificity of the new energy by reasonably arranging frequency modulation and standby resources, so that real-time power balance is ensured. The reserved sizes of system standby requirements, partition standby requirements and frequency modulation requirements, the abundant conditions of standby and frequency modulation resources, the price exciting performance conditions of standby and frequency modulation markets, the bid-winning conditions of standby and frequency modulation markets and the like can influence the bid-winning result and the actual operation consumption of the new energy unit.
The climbing capability of various units, namely the new energy has great fluctuation, and other units with sufficient fast climbing and landslide capability in the system are required to ensure real-time balance. The minimum startup and shutdown time, the climbing landslide rate, the plant power consumption rate, the minimum technical output, the maximum technical output and the power generation capacity of the unit, and the unit parameters of the fuel-gas ratio of the combined cycle unit can influence the bid winning condition of market members. For example, the minimum startup time of the gas turbine set a is shorter than that of the gas turbine set B, the minimum startup time of the gas turbine set a is the same as that of the gas turbine set B, the price reported by the gas turbine set a and the gas turbine set B is the same as that reported by the gas turbine set B, the system needs startup ejection force of the gas turbine set a or the gas turbine set B during individual peak hours, if the gas turbine set B is started, the time for calling the high-price gas turbine set is more, and the power generation cost of the system is increased, so the gas turbine set a wins the bid.
For the output control capability of the new energy machine set, generally speaking, the output control capability of the new energy machine set is poor, and the output control capability is excluded from AGC and is similar to negative load in the system; with the development and progress of the technology and the construction of matched energy storage, part of new energy units can actively and correspondingly schedule output plan instructions in a certain range as conventional units. Compared with a new energy unit with poor output control capability, the new energy unit with strong output control capability can avoid the benefit risk caused by output fluctuation to a greater extent.
For the fuel price, although the influence of the primary energy fuel price on the self power generation cost does not exist basically for the new energy unit, the primary energy fuel price can influence the power generation cost of other power generation side market members, further influences the quotation strategy of coal-fired and gas-fired units, and influences the bid amount of the new energy unit from two aspects: on one hand, if the price of primary energy rises, the economic advantage of the new energy unit is more obvious, and the overall bid amount of the new energy unit tends to increase; on the other hand, the change of the primary energy price affects the marginal electricity price of the system, and further affects the quotation decision of a new energy unit.
The market member quotation strategy is that for a certain new energy market member, the bid amount is slightly increased, the no-load quotation is increased, and the starting quotation is increased or decreased, so that the winning bid condition of the market member and other market members can be influenced. For a new energy source unit, the volatility characteristic is strong, and the signing power of medium and long-term contracts also has influence on the quotation strategy. The declared price of the market member is an important factor for determining the bid ordering of the market member, and in the absence of other constraints (for example, the market member is not affected by section blocking and is not limited by landslide constraints), the lower the bid price of the market member is, and the greater the probability of the bid is.
The time-interval coupling factors are that in the full-time coupling mode, the bidding condition of the new energy unit is influenced not only by the supply and demand, declaration and the like of a single time point, but also by the coupling relation between the time intervals, for example, the peak load demand is high, and if a certain unit is started at the peak time, the unit is influenced by the minimum starting time constraint, and the unit still needs to be in the starting state at the valley time.
In summary, in a preferred but non-limiting embodiment of the present invention, the new energy consumption related factors include market demand, load fluctuation, power grid topology and operation mode, market membership declaration information, auxiliary service resource demand, hill climbing capability, and output control capability of the new energy unit.
In a preferred but non-limiting embodiment of the present invention, the market demand and supply includes a market space determined by the sum of declared amount of new energy load side marketized market members and bus load forecast of non-marketized load, new energy system grid loss, sum of external power and new energy unit output of provincial fixed output, and a power generation side effective power generation capacity determined by the rated installed capacity of the new energy unit, the power generation capacity of the new energy unit, declared maximum capacity of the new energy unit, repaired capacity of the new energy unit, and plant power consumption rate of the new energy unit;
the load fluctuation comprises a load fluctuation rate determined by the new energy system load at a set moment and the new energy system average load at all time intervals, and a peak-valley difference determined by the new energy system load at a peak moment and the new energy system load at a valley moment;
the power grid topology and the operation mode are determined by the whole grid nodes of the new energy power grid and the section sensitivity matrix of the new energy power grid;
the market member declaration information is determined by the declaration price of the new energy unit;
the auxiliary service resource supply and demand comprise the demand condition of the auxiliary service and the supply condition of the auxiliary service, and the demand condition of the auxiliary service comprises the standby demand and the frequency modulation demand of the new energy system; the supply condition of the auxiliary service comprises the total quantity of the standby auxiliary service declaration of the new energy system, the total quantity of the actual available standby resource of the new energy system, the total quantity of the frequency modulation auxiliary service declaration of the new energy system and the total quantity of the available frequency modulation resource of the new energy system;
the climbing capacity comprises the integral climbing capacity and the landslide capacity of the new energy system and the climbing capacity and the landslide capacity of the concerned new energy unit;
the output control capability of the new energy unit is determined by the real-time market clearing result and the actual output of the new energy unit.
And 2, step: carrying out mathematical modeling aiming at the relevant factors of new energy consumption;
in order to further perform quantitative analysis on scalar quantity related factors in the new energy spot market, mathematical modeling needs to be performed on various qualitative new energy consumption related factors in the step 1, so that on one hand, mathematical quantitative expression on non-numerical, non-structural and super-large scale new energy consumption related factors is realized; on the other hand, quantitative correlation analysis of various factors and new energy consumption is conveniently further carried out, the influence of invalid and wrong data on modeling is reduced, and the accuracy of correlation analysis is improved.
In a preferred but non-limiting embodiment of the invention, said step 2 comprises in particular:
modeling market supply and demand, comprising:
the market demand condition is determined by the declaration amount of market members at the new energy load side, the prediction of the bus load of the non-marketized load, the power across provincial regions, the network loss and the like. The market supply condition is determined by factors such as the output plan of the new energy fixed output unit, the unit overhaul capacity, the unit power generation capacity, the unit service power and the like.
Market supply and demand can be characterized based on the following models:
modeling a market space according to equation (1):
I MarketSpace ＝L system +L oss -P transregion -P fixpower (1) (1)
in the formula I MarketSpace Representing market space, L system Bus load forecast summation of declaration quantity of new energy load side marketized market member and non-marketized load, L oss Represents the network loss, P, of the new energy system transregion Representing external power, P fixpower Solid for representing new energy in provinceDetermining the total sum of the forces of the force determining machine set;
modeling the effective power generation capacity of the power generation side according to the formula (2):
in the formula I GenAbility Represents the effective generating capacity of the generating side of the transmission system, C i Is the rated installed capacity, A, of the transmission unit i i Representing the generating capacity of the transmission unit i, B i Representing declared maximum capacity, X, of transmission unit i i Indicating the post-overhaul capacity, u, of the transmission unit i i The method is characterized by comprising the following steps of (1) indicating the station service power rate of a unit i, wherein i is a natural number;
modeling load fluctuations, comprising:
the load fluctuation situation is characterized based on the following model:
modeling the load fluctuation rate according to equation (3):
in the formula I LoadWave Represents the load fluctuation rate, L t New energy System load, L, representing time t ave,alltime Representing the average load of the new energy system in all time periods;
modeling the peak-to-valley difference according to equation (4):
I diff ＝L peak -L valley (4)
in the formula I diff Denotes the peak-to-valley difference, L peak New energy system load, L, at peak time valley Representing the load of the new energy system at the time of the valley;
modeling the topology and the operation mode of the power grid, which comprises the following steps:
the conventional characterization forms of the new energy power grid topology are a node-branch connection graph and a node connection matrix, or a node-to-section sensitivity matrix, and the matrix is large in scale and has certain sparsity. The large-scale matrix brings problems to correlation analysis and artificial intelligence-based electricity price prediction: 1) A large amount of calendar history data is run, which causes problems for data storage. 2) The data proportion of the power grid topology in various input data is too large, and the effects of other factors are seriously weakened. 3) Affecting the convergence of the algorithm.
The invention models the power grid topology and the operation mode, and comprises the following steps:
solving the characteristic value and the characteristic vector of the sensitivity matrix of the whole network node pair section of the new energy power grid, and deleting the characteristic value of which the characteristic value is smaller than a certain threshold value and the corresponding characteristic vector to realize matrix dimensionality reduction; only the local sensitivity matrix of the node corresponding to the marginal price of the node to be researched and the relevant section is considered. Correlation between the price of the excavator group and the maintenance equipment. And maintaining detailed local topology information while maintaining global topology key information.
Modeling market member declaration information, comprising:
the modeling of the market member declaration information has the following problems: the new energy clearing result is influenced by self-declared price and also influenced by the declared prices of other market members; each unit is quoted in a segmented mode, the quoted sections of the units are inconsistent, and how to put the units in a unified dimensional modeling mode is achieved. Therefore, the declared prices of the generating side units of each new energy system are equalized according to a quotation section according to a formula (5):
wherein price i Reporting price for unit capacity of new energy system generating side unit i, C i,l Is the capacity, pr of the generating side unit i of the new energy system under the condition of the capacity section l i,l Reporting the price of the unit capacity of a new energy system power generation side unit i under the condition of a capacity section l, wherein l is a natural number;
characterizing market member declared information scenarios based on the following model:
modeling the new energy whole system power generation side declaration average price according to a formula (6):
in the formula I aveGenPrice Price is declared for new energy whole system power generation side i Reporting price for unit capacity of new energy system generating side unit i, C i The capacity of a generating side unit i of the new energy system is obtained;
modeling the new energy whole system load side declaration average price according to a formula (7):
in the formula I aveBlPrice Price for reporting average price for new energy whole system load side bl Reporting price per unit capacity, C, of new energy system load side unit bl bl The capacity of a new energy system load side unit bl is represented, and bl is a natural number;
modeling the declared prices for the new energy unit of interest according to equation (8):
I aim ＝price aim (8)
in the formula I aim 、price aim Representing the declared price of a certain new energy unit to be subjected to correlation analysis research; in addition, there is a primary energy price as an important factor affecting the market member declaration strategy.
Modeling auxiliary service resource supply and demand, comprising:
the requirement condition of the auxiliary service comprises a standby requirement and a frequency modulation requirement. The supply condition of the auxiliary service comprises the total amount of the reported standby auxiliary service, the total amount of the actual available standby resource of the system, the total amount of the reported frequency modulation auxiliary service and the total amount of the available frequency modulation resource of the system.
In addition, in order to reflect the relationship of the auxiliary service to the new energy, the richness of the auxiliary service is characterized by modeling according to equation (9):
in the formula I ratio The ratio of the auxiliary service demand to the total predicted electric power of the new energy is represented, and the higher the ratio is, the more the new energy consumption is facilitated. D Reserve Indicating the reserve demand of the new energy system, D Reg Indicating the frequency modulation demand, P, of the new energy system NewEnergy Representing the total predicted electric power sum of the new energy;
modeling climbing capacity, comprising:
the climbing capacity comprises the climbing and landslide capacity of the whole system and the climbing and landslide capacity of the concerned new energy unit.
9) Modeling the overall climbing capacity of the new energy system according to formula (10):
in the formula I rampupAbility Represents the overall climbing capability, R, of the new energy system i,rampup Representing the climbing capacity of the unit i, and Prdmin representing the interval of the clear period;
10 Modeling the overall landslide capability of the new energy system according to equation (11):
in the formula I rampdnAbility Represents the integral landslide capability, R, of the new energy system i,rampdn Expressing the landslide capability of the unit i, and Prdmin expressing the clearing time interval;
modeling the capacity of the new energy cluster for output control, comprising:
real-time market clearing result P based on new energy clearing With actual force P real The deviation between the two is used as a mathematical model for representing the output control capability of the new energy source unit, and the expression is shown in formula (12):
I ControlAbility ＝P clearing -P real (12)。
and 3, step 3: carrying out standardized processing on the modeling data of the new energy consumption related factors;
since incomplete, inconsistent and noisy data are ubiquitous in the source of modeling data of correlation factors, correlation analysis cannot be directly performed generally, and normalization processing of correlation factor data is required, in a preferred but non-limiting embodiment of the present invention, the step 3 specifically includes: carrying out data cleaning, data integration, data transformation, data reduction and the like on the modeling data of the new energy consumption related factors;
the data cleaning comprises the step of carrying out denoising processing on the modeling data of the new energy consumption related factors;
the data integration comprises the step of combining modeling data of new energy consumption related factors into a whole;
the data transformation comprises the steps of coding the modeling data of the new energy consumption related factors to obtain coded data;
the data reduction comprises a unified dimension of modeling data of factors related to new energy consumption.
The high-quality modeling data serving as the historical data can improve the prediction precision, save computer resources and accelerate the convergence speed of the model, and the data normalization processing can effectively improve the accuracy of the correlation analysis, improve the data mining efficiency and reduce unnecessary resource loss.
And 4, step 4: and carrying out quantitative analysis based on similarity analysis on the modeling data of the new energy consumption related factors after the normalization processing.
Specifically, based on the following typical three correlation analysis methods, based on the mathematical models of the various factors established in step 2, after the data normalization processing in step 3, quantitative measurement calculation of the correlation between the various relevant factors and the new energy consumption power is performed.
In a preferred but non-limiting embodiment of the invention, said step 4 comprises the following three methods:
the first method, that is, the pearson correlation coefficient is found according to the formula (13):
in the formula, ρ X,Y Pearson correlation coefficient representing continuous variable (X, Y), cov (X, Y) representing covariance of X, Y, rho X And ρ Y Respectively representing respective standard deviations of X and Y, wherein X and Y are different modeling data of new energy consumption related factors after normalization processing; the method is suitable for solving the correlation of random variables which obey normal distribution.
The second method, that is, the spearman correlation coefficient is obtained according to the formula (14):
in the formula, values of two variables in pairs are respectively ranked from small to large or from large to small, R j Represents X j Order of (a), Q j Represents Y j N is the sample size, X j And Y j Respectively sampling j data in two groups of samples obtained from the modeling data of the new energy consumption related factors after the normalization processing; the method does not require the distribution of the original variables, and has wider application range. Correlations between variables, classes, or level variables that do not follow a normal distribution may employ spearman correlation coefficients.
The third method is to solve the kendel kendall correlation coefficient according to the formula (15):
the Kendel correlation coefficient is the ratio of the difference between the same-order pair P and the different-order pair (the total logarithm minus the same-order logarithm is the different-order pair) in the modeling data of the new energy absorption correlation factors after the normalization processing to the total logarithm. It is usually required that all of the correlation coefficients are ordered categorical variables.
Compared with the prior art, the method has the advantages that the method for analyzing the new energy consumption influence factors is suitable for the provincial power spot market of China), different influences of various factors of the spot market on new energy utilization are quantitatively evaluated, and a foundation is laid for further exploring a power market mechanism for promoting new energy consumption. The whole set of correlation analysis method and flow provided by the invention have the characteristics of combination of qualitative analysis and quantitative analysis and combination of model drive and data drive. The causal relationship and the statistical relationship between relevant factors of the electric power spot market and the new energy consumption are fused, a big data analysis method is adopted, the correlation under the mutual coupling effect of various complex factors is reflected, and the scientificity and the accuracy of the new energy utilization correlation analysis are improved.
As shown in fig. 2, the device for analyzing the influence of the electric power spot market on the utilization of new energy according to the method of the present invention includes:
the qualitative module is used for carrying out qualitative analysis on the new energy consumption related factors based on the electric power spot market clearing principle;
the modeling module is used for carrying out mathematical modeling on the new energy consumption related factors;
the standardization processing module is used for carrying out standardization processing on the modeling data of the new energy consumption related factors;
and the quantitative analysis module is used for carrying out quantitative analysis based on similarity analysis on the modeling data of the new energy consumption related factors after the normalization processing.
In a preferred but non-limiting embodiment of the present invention, the new energy consumption related factors include market demand and supply, load fluctuation, power grid topology and operation mode, market member declaration information, auxiliary service resource demand and supply, climbing capability and output control capability of the new energy unit.
In a preferred but non-limiting embodiment of the present invention, the market demand and supply includes a market space determined by the sum of the declared amount of the new energy load side marketized market member and the bus load prediction of the non-marketized load, the net loss of the new energy system, the sum of the external power and the output of the new energy unit of the provincial fixed output, and an effective power generation capacity of the power generation side determined by the rated installed capacity of the new energy unit, the power generation capacity of the new energy unit, the declared maximum capacity of the new energy unit, the repaired capacity of the new energy unit, and the plant power consumption rate of the new energy unit;
the load fluctuation comprises a load fluctuation rate determined by the new energy system load at a set moment and the new energy system average load at all time intervals, and a peak-valley difference determined by the new energy system load at a peak moment and the new energy system load at a valley moment;
the power grid topology and the operation mode are determined by the whole grid nodes of the new energy power grid and the section sensitivity matrix of the new energy power grid;
the market member declaration information is determined by the declaration price of the new energy unit;
the auxiliary service resource supply and demand comprise the demand condition of the auxiliary service and the supply condition of the auxiliary service, and the demand condition of the auxiliary service comprises the standby demand and the frequency modulation demand of the new energy system; the supply condition of the auxiliary service comprises the total quantity of the standby auxiliary service declaration of the new energy system, the total quantity of the actual available standby resource of the new energy system, the total quantity of the frequency modulation auxiliary service declaration of the new energy system and the total quantity of the available frequency modulation resource of the new energy system;
the climbing capacity comprises the integral climbing capacity and the landslide capacity of the new energy system and the climbing capacity and the landslide capacity of the concerned new energy unit;
the output control capability of the new energy unit is determined by the real-time market clearing result and the actual output of the new energy unit.
In a preferred but non-limiting embodiment of the invention, the modeling module is further configured to model market supply and demand, comprising:
modeling a market space according to equation (1):
I MarketSpace ＝L system +L oss -P transregion -P fixpower (1) (1)
in the formula I MarketSpace Representing market space, L system Bus load forecast summation of declaration quantity of new energy load side marketized market member and non-marketized load, L oss Represents the network loss, P, of the new energy system transregion Representing external power, P fixpower Representing the sum of the output of the fixed output unit of the new energy in the province;
modeling the effective power generation capacity of the power generation side according to the formula (2):
in the formula I GenAbility Representing the effective power generation capacity of the power generation side of the transmission system, C i Is the rated installed capacity, A, of the transmission unit i i Representing the generating capacity of the transmission unit i, B i Representing declared maximum capacity, X, of transmission unit i i Represents the post-overhaul capacity u of the transmission unit i i The method is characterized by comprising the following steps of (1) indicating the station service power rate of a unit i, wherein i is a natural number;
modeling load fluctuations, comprising:
modeling the load fluctuation rate according to equation (3):
in the formula I LoadWave Represents the load fluctuation rate, L t New energy system load, L, representing time t ave,alltime Representing the average load of the new energy system in all time periods;
modeling the peak-to-valley difference according to equation (4):
I diff ＝L peak -L valley (4) (4)
in the formula I diff Denotes the peak-to-valley difference, L peak New energy system load, L, at peak hours valley Representing the load of the new energy system at the time of the valley;
modeling the topology and the operation mode of the power grid, which comprises the following steps:
solving the characteristic value and the characteristic vector of the sensitivity matrix of the whole network node pair section of the new energy power grid, and deleting the characteristic value of which the characteristic value is smaller than a certain threshold value and the corresponding characteristic vector to realize matrix dimensionality reduction;
modeling market member declaration information, comprising:
equalizing the declared prices of the generating side units of the new energy systems according to a quotation section according to a formula (5):
therein, price i Reporting price for unit capacity of new energy system generating side unit i, C i,l The capacity, pr, of the generating side unit i of the new energy system under the condition of the capacity section l i,l Reporting the price of the unit capacity of a new energy system power generation side unit i under the condition of a capacity section l, wherein l is a natural number;
modeling the new energy whole system power generation side declaration mean price according to a formula (6):
in the formula I aveGenPrice Declare price sharing for new energy whole system power generation side i Reporting price for unit capacity of new energy system generating side unit i, C i The capacity of a generating side unit i of the new energy system is obtained;
modeling the new energy whole system load side declaration average price according to a formula (7):
in the formula I aveBlPrice Declare price per unit for new energy whole system load side bl Reporting price per unit capacity, C, of new energy system load side unit bl bl For the capacity of the new energy system load side unit bl,bl is a natural number;
modeling the declared price of the new energy fleet of interest according to equation (8):
I aim ＝price aim (8)
in the formula I aim 、price aim Expressing the declared price of a certain new energy unit to be subjected to correlation analysis research;
modeling supply and demand for auxiliary service resources, comprising:
the richness of the secondary service is characterized by modeling according to equation (9):
in the formula I ratio And the ratio of the auxiliary service demand to the total predicted electric power sum of the new energy is represented, and the higher the ratio is, the more favorable the new energy consumption is. D Reserve Indicating the reserve demand of the new energy system, D Reg Indicating the frequency modulation demand, P, of the new energy system NewEnergy Representing the total predicted electric power sum of the new energy;
modeling climbing capacity, comprising:
11 Modeling the overall climbing capability of the new energy system according to equation (10):
in the formula I rampupAbility Represents the overall climbing capability, R, of the new energy system i,rampup Representing the climbing capability of the unit i, and Prdmin represents the interval of the clearing time period;
12 Modeling the overall landslide capability of the new energy system according to equation (11):
in the formula I rampdnAbility Represents the integral landslide capability, R, of the new energy system i,rampdn Expressing the landslide capability of the unit i, and Prdmin expressing the interval of the clear period;
modeling the capacity of the new energy cluster for output control, comprising:
real-time market clearing result P based on new energy clearing With actual force P real The deviation therebetween, as a mathematical model for characterizing the output control capability of the new energy source unit, is expressed by formula (12):
I ControlAbility ＝P clearing -P real 。
in a preferred but non-limiting embodiment of the present invention, the normalization processing module is further configured to perform data cleaning, data integration, data transformation, and data reduction on the modeling data of the new energy consumption-related factor;
the data cleaning comprises denoising modeling data of new energy consumption related factors;
the data integration comprises the step of combining the modeling data of the new energy consumption related factors into a whole;
the data transformation comprises the steps of coding the modeling data of the new energy consumption related factors to obtain coded data;
the data reduction comprises a unified dimension of modeling data of factors related to new energy consumption.
In a preferred but non-limiting embodiment of the invention, the quantitative analysis module is also used to find the pearson correlation coefficient according to equation (13):
in the formula, ρ X,Y Pearson correlation coefficient representing continuous variable (X, Y), cov (X, Y) representing covariance of X, Y, rho X And rho Y Respectively representing respective standard deviations of X and Y, wherein X and Y are different modeling data of new energy consumption related factors after normalization processing; for solving the spearman correlation coefficient according to equation (14):
in the formula, values of two variables in pairs are respectively ranked from small to large or from large to small, R j Represents X j Order of (a), Q j Represents Y j N is the sample size, X j And Y j Respectively sampling j data in two groups of samples obtained from the modeling data of the new energy consumption related factors after the normalization processing; for solving the kendel kendall correlation coefficient according to equation (15):
and the Kendel correlation coefficient is the ratio of the difference between the same-sequence pair P and the different-sequence pair P in the modeling data of the new energy absorption correlation factors after the normalization processing to the total logarithm.
The invention relates to a terminal, which comprises a processor and a storage medium;
the storage medium is to store instructions;
the processor is configured to operate according to the instructions to perform the steps of the method for analyzing the impact of the electricity spot market on new energy utilization.
A computer-readable storage medium according to the invention has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for analyzing the impact of the electricity spot market on new energy utilization.
Compared with the prior art, the method has the beneficial effects that based on the clearing principle and the optimization model of the typical provincial power spot market in China, the method adopts the qualitative analysis method of causal analysis and provides relevant factors of the influence of the clearing of the power spot market on the consumption of new energy. The invention provides a mathematical modeling method of power grid topology information based on feature extraction, which extracts key information of global topology, retains detailed information of local topology, and solves the problems of large scale and sparsity of a conventional power grid topology table on the premise of not losing the key information of topology. The method carries out mathematical modeling on a plurality of market factors, carries out coupling calculation and information extraction on a plurality of original factors, carries out numerical representation on non-numerical factors, improves the applicability of statistical analysis by causal analysis, and refines and reflects the coupling influence of a plurality of key factors on new energy consumption.
While the best mode for carrying out the invention has been described in detail and illustrated in the accompanying drawings, it is to be understood that the foregoing is illustrative of the preferred embodiments of the invention and is not to be construed as limiting the scope of the invention, but rather is intended to cover all modifications and variations within the spirit of the invention.
Claims (14)
1. A method for analyzing the influence of an electric power spot market on new energy utilization is characterized by comprising the following steps:
step 1: qualitative analysis of new energy consumption related factors based on the electric power spot market clearing principle;
and 2, step: carrying out mathematical modeling aiming at the relevant factors of new energy consumption;
and 3, step 3: carrying out standardized processing on the modeling data of the new energy consumption related factors;
and 4, step 4: and carrying out quantitative analysis based on similarity analysis on the modeling data of the new energy consumption related factors after the normalization processing.
2. The method for analyzing the influence of the power spot market on the utilization of new energy resources according to claim 1, wherein the factors related to the consumption of new energy resources comprise market demand and supply, load fluctuation, power grid topology and operation mode, market member declaration information, auxiliary service resource demand and supply, climbing capacity and output control capacity of a new energy resource unit.
3. The method according to claim 2, wherein the market demand and supply comprises a market space determined by the sum of the declared amount of new energy load side marketized market members and the busbar load forecast of non-marketized load, the net loss of new energy system, the sum of external power and the new energy unit output of provincial fixed output, and the effective power generation capacity of power generation side determined by the rated installed capacity of new energy unit, the power generation capacity of new energy unit, the declared maximum capacity of new energy unit, the repaired capacity of new energy unit and the plant power consumption rate of new energy unit;
the load fluctuation comprises a load fluctuation rate determined by the new energy system load at a set moment and the new energy system average load at all time intervals, and a peak-valley difference determined by the new energy system load at a peak moment and the new energy system load at a valley moment;
the power grid topology and the operation mode are determined by the whole grid nodes of the new energy power grid and the section sensitivity matrix of the new energy power grid;
the market member declaration information is determined by the declaration price of the new energy unit;
the auxiliary service resource supply and demand comprise the demand condition of the auxiliary service and the supply condition of the auxiliary service, and the demand condition of the auxiliary service comprises the standby demand and the frequency modulation demand of the new energy system; the supply condition of the auxiliary service comprises the total quantity of the standby auxiliary service declaration of the new energy system, the total quantity of the actual available standby resource of the new energy system, the total quantity of the frequency modulation auxiliary service declaration of the new energy system and the total quantity of the available frequency modulation resource of the new energy system;
the climbing capacity comprises the integral climbing capacity and the landslide capacity of the new energy system and the climbing capacity and the landslide capacity of the concerned new energy unit;
and the output control capability of the new energy set is determined by the real-time market clearing result and the actual output of the new energy set.
4. The method for analyzing the influence of the electric power spot market on the utilization of new energy according to claim 1, wherein the step 2 specifically comprises:
modeling market supply and demand, comprising:
modeling a market space according to equation (1):
I MarketSpace ＝L system +L oss -P transregion -P fixpower (1)
in the formula I MarketSpace Representing market space, L system And (3) bus load prediction summation of declared quantity of new energy load side marketized market members and non-marketized load, L oss Represents the network loss, P, of the new energy system transregion Indicating external power, P fixpower Representing the sum of the output of the fixed output unit of the new energy in the province;
modeling the effective power generation capacity of the power generation side according to the formula (2):
in the formula I GenAbility Represents the effective generating capacity of the generating side of the transmission system, C i Is the rated installed capacity, A, of the transmission unit i i Representing the generating capacity of the transmission unit i, B i Representing the declared maximum capacity, X, of the transmission unit i i Indicating the post-overhaul capacity, u, of the transmission unit i i The method is characterized by comprising the following steps of (1) indicating the station service power rate of a unit i, wherein i is a natural number;
modeling load fluctuations, comprising:
modeling the load fluctuation rate according to equation (3):
in the formula I LoadWave Represents the load fluctuation rate, L t New energy system load, L, representing time t ave,alltime Representing the average load of the new energy system in all time periods;
modeling the peak-to-valley difference according to equation (4):
I diff ＝L peak -L valley (4)
in the formula I diff Denotes the peak-to-valley difference, L peak New energy system load, L, at peak time valley Representing the load of the new energy system at the time of the valley;
modeling the topology and the operation mode of the power grid, which comprises the following steps:
calculating the characteristic value and the characteristic vector of the sensitivity matrix of the whole network node to the section of the new energy power grid, and deleting the characteristic value of which the characteristic value is smaller than a certain threshold value and the corresponding characteristic vector to realize matrix dimension reduction;
modeling market member declaration information, comprising:
equalizing the declared prices of the generating side units of the new energy systems according to a quotation section according to a formula (5):
wherein price i Reporting price for unit capacity of new energy system generating side unit i, C i,l Is the capacity, pr of the generating side unit i of the new energy system under the condition of the capacity section l i,l Reporting the price of the unit capacity of a new energy system power generation side unit i under the condition of a capacity section l, wherein l is a natural number;
modeling the new energy whole system power generation side declaration mean price according to a formula (6):
in the formula I aveGenPrice Price is declared for new energy whole system power generation side i Reporting price for unit capacity of new energy system generating side unit i, C i The capacity of a generating side unit i of the new energy system is obtained;
modeling the new energy whole system load side declaration average price according to a formula (7):
in the formula I aveBlPrice Declare price per unit for new energy whole system load side bl Reporting price per unit capacity, C, of new energy system load side unit bl bl The capacity of a new energy system load side unit bl is represented, and bl is a natural number;
modeling the declared price of the new energy fleet of interest according to equation (8):
I aim ＝price aim (8)
in the formula I aim 、price aim Representing the declared price of a certain new energy unit to be subjected to correlation analysis research;
modeling supply and demand for auxiliary service resources, comprising:
the richness of the auxiliary service is characterized by modeling according to equation (9):
in the formula I ratio Represents the ratio of the auxiliary service demand to the total predicted power of the new energy, D Reserve Indicating the reserve demand of the new energy system, D Reg Indicating the frequency modulation demand, P, of the new energy system NewEnergy Representing the total predicted electric power sum of the new energy;
modeling climbing capacity, comprising:
1) Modeling the overall climbing capability of the new energy system according to the formula (10):
in the formula I rampupAbility Represents the overall climbing capability, R, of the new energy system i,rampup Representing the climbing capacity of the unit i, and Prdmin representing the interval of the clear period;
2) Modeling the overall landslide capacity of the new energy system according to equation (11):
in the formula I rampdnAbility Represents the integral landslide capability, R, of the new energy system i,rampdn Expressing the landslide capability of the unit i, and Prdmin expressing the clearing time interval;
modeling the output control capability of the new energy cluster, comprising:
real-time market clearing result P based on new energy clearing With actual force P real The deviation therebetween, as a mathematical model for characterizing the output control capability of the new energy source unit, is expressed by formula (12):
I ControlAbility ＝P clearing -P real (12)。
5. the method for analyzing the influence of the electric power spot market on the utilization of new energy according to claim 1, wherein the step 3 specifically comprises: carrying out data cleaning, data integration, data transformation and data reduction on the modeling data of the new energy consumption related factors;
the data cleaning comprises the step of carrying out denoising processing on the modeling data of the new energy consumption related factors;
the data integration comprises the step of combining the modeling data of the new energy consumption related factors into a whole;
the data transformation comprises the steps of coding the modeling data of the new energy consumption related factors to obtain coded data;
the data reduction comprises a unified dimension of modeling data of factors related to new energy consumption.
6. The method for analyzing the influence of the electric power spot market on the utilization of new energy resources according to claim 1, wherein the step 4 comprises the following three methods:
the first method, that is, the correlation coefficient of pearson is found according to equation (13):
in the formula, ρ X,Y Pearson correlation coefficient representing continuous variable (X, Y), cov (X, Y) representing covariance of X, Y, rho X And rho Y Respectively representing respective standard deviations of X and Y, wherein X and Y are different modeling data of new energy consumption related factors after normalization processing;
the second method, that is, the spearman correlation coefficient is obtained according to the formula (14):
in the formula, values of two variables in pairs are respectively ranked from small to large or from large to small, R j Represents X j Order of (a), Q j Represents Y j N is the sample size, X j And Y j Respectively sampling j data in two groups of samples obtained from the modeling data of the new energy consumption related factors after the normalization processing;
the third method is to find the Kendell correlation coefficient according to the formula (15):
and the Kendel correlation coefficient is the ratio of the difference between the same-sequence pair P and the different-sequence pair P in the modeling data of the new energy absorption correlation factors after the normalization processing to the total logarithm.
7. An apparatus for analyzing the impact of the electric power spot market on new energy utilization by using the method of any one of claims 1 to 6, comprising:
the qualitative module is used for carrying out qualitative analysis on the new energy consumption related factors based on the electric power spot market clearing principle;
the modeling module is used for carrying out mathematical modeling on the new energy consumption related factors;
the standardization processing module is used for carrying out standardization processing on the modeling data of the new energy consumption related factors;
and the quantitative analysis module is used for carrying out quantitative analysis based on similarity analysis on the modeling data of the new energy consumption related factors after the normalization processing.
8. The apparatus for analyzing influence of the electric power spot market on new energy utilization according to claim 7, wherein the new energy consumption-related factors include market demand and supply, load fluctuation, power grid topology and operation mode, market member declaration information, auxiliary service resource demand and supply, climbing capability, and output control capability of the new energy unit.
9. The apparatus of claim 8, wherein the market demand and supply comprises a market space determined by a sum of a bus load forecast of a new energy load side marketized market member and a non-marketized load, a new energy system grid loss, a sum of an external power and a new energy unit output of an intra-provincial fixed output, and a power generation side effective power generation capacity determined by a rated installed capacity of the new energy unit, a power generation capacity of the new energy unit, a declared maximum capacity of the new energy unit, a repaired capacity of the new energy unit, and a plant power consumption rate of the new energy unit;
the load fluctuation comprises a load fluctuation rate determined by the new energy system load at a set moment and the new energy system average load at all time intervals, and a peak-valley difference determined by the new energy system load at a peak moment and the new energy system load at a valley moment;
the power grid topology and the operation mode are determined by the whole grid nodes of the new energy power grid and the section sensitivity matrix of the new energy power grid;
the market member declaration information is determined by the declaration price of the new energy unit;
the auxiliary service resource supply and demand comprise the demand condition of the auxiliary service and the supply condition of the auxiliary service, and the demand condition of the auxiliary service comprises the standby demand and the frequency modulation demand of the new energy system; the supply condition of the auxiliary service comprises the total standby auxiliary service declaration amount of the new energy system, the actual available standby resource amount of the new energy system, the total frequency modulation auxiliary service declaration amount of the new energy system and the available frequency modulation resource amount of the new energy system;
the climbing capacity comprises the integral climbing capacity and the landslide capacity of the new energy system and the climbing capacity and the landslide capacity of the concerned new energy unit;
the output control capability of the new energy unit is determined by the real-time market clearing result and the actual output of the new energy unit.
10. The apparatus of claim 7, wherein the modeling module is further configured to model market demand and supply, and comprises:
modeling a market space according to equation (1):
I MarketSpace ＝L system +L oss -P transregion -P fixpower (1) (1)
in the formula I MarketSpace Representing market space, L system And (3) bus load prediction summation of declared quantity of new energy load side marketized market members and non-marketized load, L oss Represents the network loss, P, of the new energy system transregion Representing external power, P fixpower Representing the sum of the output of the fixed output unit of the new energy in the province;
modeling the effective power generation capacity of the power generation side according to the formula (2):
in the formula I GenAbility Representing the effective power generation capacity of the power generation side of the transmission system, C i Is the rated installed capacity, A, of the transmission unit i i Representing the generating capacity of the transmission unit i, B i Representing declared maximum capacity, X, of transmission unit i i Indicating the post-overhaul capacity, u, of the transmission unit i i The method is characterized by comprising the following steps of (1) indicating the station service power rate of a unit i, wherein i is a natural number;
modeling load fluctuations, comprising:
modeling the load fluctuation rate according to the formula (3):
in the formula I LoadWave Represents the load fluctuation rate, L t New energy system load, L, representing time t ave,alltime Representing the average load of the new energy system in all time periods;
modeling the peak-to-valley difference according to equation (4):
I diff ＝L peak -L valley (4) (4)
in the formula I diff Denotes the peak-to-valley difference, L peak New energy system load, L, at peak time valley Representing the load of the new energy system at the time of the valley;
modeling the topology and the operation mode of the power grid, which comprises the following steps:
calculating the characteristic value and the characteristic vector of the sensitivity matrix of the whole network node to the section of the new energy power grid, and deleting the characteristic value of which the characteristic value is smaller than a certain threshold value and the corresponding characteristic vector to realize matrix dimension reduction;
modeling market member declaration information, comprising:
equalizing the declared prices of the generating side units of the new energy systems according to a quotation section according to a formula (5):
wherein price i Reporting price for unit capacity of new energy system generating side unit i, C i,l The capacity, pr, of the generating side unit i of the new energy system under the condition of the capacity section l i,l Reporting the price of the unit capacity of a new energy system power generation side unit i under the condition of a capacity section l, wherein l is a natural number;
modeling the new energy whole system power generation side declaration average price according to a formula (6):
in the formula I aveGenPrice Declare price sharing for new energy whole system power generation side i Reporting price for unit capacity of new energy system generating side unit i, C i The capacity of a generating side unit i of the new energy system is obtained;
modeling the new energy whole system load side declaration mean price according to a formula (7):
in the formula I aveBlPrice Declare price per unit for new energy whole system load side bl Reporting price per unit capacity, C, of new energy system load side unit bl bl The capacity of a new energy system load side unit bl is represented, and bl is a natural number;
modeling the declared price of the new energy fleet of interest according to equation (8):
I aim ＝price aim (8)
in the formula I aim 、price aim Expressing the declared price of a certain new energy unit to be subjected to correlation analysis research;
modeling auxiliary service resource supply and demand, comprising:
the richness of the auxiliary service is characterized by modeling according to equation (9):
in the formula I ratio Represents the ratio of the auxiliary service demand to the total predicted power of the new energy, D Reserve Indicating the reserve demand of the new energy system, D Reg Indicating the FM demand, P, of the new energy system NewEnergy Representing the total predicted electric power sum of the new energy;
modeling a climbing capability, comprising:
3) Modeling the overall climbing capacity of the new energy system according to formula (10):
in the formula I rampupAbility Represents the overall climbing capability, R, of the new energy system i,rampup Representing the climbing capacity of the unit i, and Prdmin representing the interval of the clear period;
4) Modeling the overall landslide capacity of the new energy system according to equation (11):
in the formula I rampdnAbility Represents the overall landslide capability, R, of the new energy system i,rampdn Expressing the landslide capability of the unit i, and Prdmin expressing the clearing time interval;
modeling the capacity of the new energy cluster for output control, comprising:
real-time market clearing result P based on new energy clearing And the actual force P real The deviation therebetween, as a mathematical model for characterizing the output control capability of the new energy source unit, is expressed by formula (12):
I ControlAbility ＝P clearing -P real 。
11. the apparatus for analyzing the influence of the electric power spot market on the utilization of new energy according to claim 7, wherein the normalization processing module is further configured to perform data cleaning, data integration, data transformation and data reduction on the modeling data of the new energy consumption-related factors;
the data cleaning comprises the step of carrying out denoising processing on the modeling data of the new energy consumption related factors;
the data integration comprises the step of combining modeling data of new energy consumption related factors into a whole;
the data transformation comprises the steps of coding the modeling data of the new energy consumption related factors to obtain coded data;
the data reduction comprises a unified dimension of modeling data of factors related to new energy consumption.
12. The apparatus for analyzing new energy utilization according to claim 7, wherein the quantitative analysis module is further configured to obtain Pearson correlation coefficients according to equation (13):
in the formula, ρ X,Y Pearson correlation coefficient representing continuous variable (X, Y), cov (X, Y) representing covariance of X, Y, rho X And rho Y Respectively representing respective standard deviations of X and Y, wherein X and Y are different modeling data of new energy consumption related factors after normalization processing; for solving the spearman correlation coefficient according to equation (14):
in the formula, the values of the two variables in pairs are respectively ordered from small to large or from large to small, R j Represents X j Rank ofSecond, Q j Represents Y j N is the sample size, X j And Y j Respectively sampling j data in two groups of samples obtained from the modeling data of the new energy consumption related factors after the normalization processing; for solving the kendel kendall correlation coefficient according to equation (15):
and the Kendell correlation coefficient is the ratio of the difference between the same-sequence pair P and the different-sequence pair P in the modeling data of the new energy absorption related factors after the normalization processing to the total logarithm.
13. A terminal comprising a processor and a storage medium;
the storage medium is used for storing instructions;
characterized in that the processor is configured to operate according to the instructions to perform the steps of the method for analyzing the impact of the power spot market on new energy utilization according to any one of claims 1-6.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for analyzing the impact of a power spot market on new energy utilization according to any one of claims 1-6.
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