CN111754090A - Power consumer demand side regulation and control method based on big data - Google Patents
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Abstract
The invention provides a big data-based power consumer demand side regulation and control method, which comprises the following steps of S1, acquiring consumer power consumption data from consumer power consumption information, preprocessing the consumer power consumption data, and checking whether various data are missing or not; step S2, determining the load condition of each user according to the collected energy consumption data of each level and the daily electricity price information aiming at the operation data of different electric equipment; step S3, determining each index value and a weight value according to an index calculation model for user demand side regulation and control effect evaluation; step S4, obtaining an optimization model containing constraint conditions according to preference data set by a user and ambient temperature change data, and solving a control decision of the power consumer participating in power grid coordination; and step S5, forming an interactive benefit evaluation index of the user, and performing regulation, compensation and settlement on the demand side. The invention takes power consumers and equipment as objects, participates in demand side management and regulation and fine management of load, and improves the overall operation efficiency.
Description
Technical Field
The invention relates to the technical field of power system automation, in particular to a power consumer demand side regulation and control method based on big data.
Background
Along with the continuous development of economy and the continuous promotion of modernization construction in China, the energy consumption in China is rapidly increased, and the energy crisis is increasingly serious. With the proposals of measures such as 'coal power change', 'electric energy substitution' and the like, the electric energy is gradually paid attention as secondary side clean energy, the proportion of energy consumption is increased continuously, the rapid amplification of load power utilization brings challenges to the stable operation of a power grid, and on the other hand, the continuously increased peak-to-valley difference provides a huge test for the power supply quality of a user side. In order to meet the power supply requirement of peak load, not only more units need to be installed in the power generation link, but also additional capacity expansion and a series of supporting facilities need to be added in the power transmission link.
With the gradual release of the distribution and sales markets, power companies, load aggregators, and the like participate in the operation of the power system as a subject independent of the grid company and the users. Demand-side response is considered as an important mechanism for the above three interactions, and is classified into a price-type means and an incentive-type means. In the price type means, the electricity consumer changes its own electricity usage habit in response to the electricity price of the electricity selling company. In the incentive approach, the user actively changes own electricity usage habits by accepting economic compensation or gives the electricity-using equipment to a third-party agency for proxy control. Traditional research on electricity selling companies has focused on the application of price-type means. If a deep learning method is adopted, the optimal electricity price is repeatedly calculated in an iterative manner according to the response condition of the user; or a plurality of electricity price mechanisms are formulated according to different response degrees of users, and loss cost analysis of the power distribution network is carried out; consider the electricity price problem for multiple electricity vendors and a master-slave gaming problem between multiple energy providers and users. On the basis, users are divided into two types, and the effect of demand side regulation is improved by considering the game problem between a plurality of power selling companies and the two types of users or providing a self-adaptive electricity price mechanism according to the response degree of the users.
With the development of the electric power market in China, the frequent interaction trend of electricity selling companies, power grid companies and electricity utilization users is gradually formed, the response positive degree of the user side is also gradually diversified, and higher requirements are further provided for the improvement of the electricity utilization technology; grid companies start to be changed from traditional electric energy providers to electric energy service providers, traditional demand-side management and administration means for switching off and limiting electricity are difficult to meet the current demands, and the functions of economic means need to be further developed. The concept of 'ubiquitous power Internet of things' is provided on the technical level, the 5G communication technology is invented, intelligent hardware is continuously developed, and new energy is injected into the power bidirectional supply and demand interaction technology. How to realize the perfection of supply and demand interaction technology through an information architecture platform of a ubiquitous power internet of things is imperative.
Disclosure of Invention
The technical problem solved by the embodiment of the invention is that the problems that power users and equipment are taken as objects in the prior art, the participation in demand side management and regulation and the fine management of loads are insufficient, and the overall operation efficiency cannot be improved.
One aspect of the present invention provides a big data-based power consumer demand side regulation and control method, including:
step S1, obtaining user total load data, user each electricity consumption data, user environment data and user electricity consumption data from the electricity consumption information of the user, preprocessing, and checking whether various data are missing;
step S2, determining the load condition of each user according to the collected energy consumption data of each level and the daily electricity price information aiming at the operation data of different electric equipment;
step S3, determining each index value and a weight value according to an index calculation model for user demand side regulation and control effect evaluation;
step S4, obtaining an optimization model containing constraint conditions by combining load conditions, index values and weight values according to preference data and ambient temperature change data set by a user, and solving a control decision of power users participating in power grid coordination according to the optimization model;
and step S5, selecting the indexes concerned by the user from the interactive benefit evaluation index system according to the user type and the attention point of the user, forming the interactive benefit evaluation index of the user, and performing regulation, control, compensation and settlement on the demand side.
Further, in step S1, the preprocessing specifically includes:
monitoring the regional energy consumption, detecting the current and previous day load curve comparison of large users in the region, highlighting the maximum, minimum and average load comparison, and comparing the current and previous day power consumption curves of regional users according to the power supply regional range;
monitoring the energy consumption of the residential area, detecting the energy consumption condition under multiple scenes, optimizing the energy consumption proportion through real-time data acquisition of equipment, and carrying out centralized monitoring on comprehensive energy in a public area and energy management of residential users;
monitoring a power distribution facility, inquiring specific data of each line and a switch element of a simulation diagram of a residential substation, and monitoring and counting the number of main transformers of the substation, the electricity consumption in nearly one hour, the total capacity of the main transformers, incoming and outgoing line loads, real-time loads, equipment working conditions, acquisition success rate and monitoring real-time displacement data;
monitoring the electric automobile, inquiring the state information of each charging pile in a community, acquiring the information of single-phase, three-phase, direct-current charging power and the number of the charging piles, forming a real-time load curve of the charging piles in the community, and outputting the data information of the current load, the maximum load and the peak-valley difference;
monitoring the light storage, acquiring photovoltaic general information and energy storage general information, and forming a structural diagram of the light storage grid connection;
the user of the special transformer can monitor, obtain basic information of various large users, monitor the working condition of user equipment in real time, form measuring point distribution and energy consumption conditions of various equipment, monitor the real-time load and the peak-valley electric quantity of the user in the same month, and count the energy consumption data of the user on electricity, gas, water and heat.
Further, in step S2, the determining the load condition of each user specifically includes classifying the power loads according to the power consumption types, and dividing the total of all industrial power loads into industrial power loads; the household lighting and household electrical appliance load of urban and rural residents is divided into resident household electricity.
Further, in step S3, the index values Z are determined according to the following formula:
when the evaluation index is a positive index, the following formula is adopted for calculation:
when the evaluation index is a negative index, the following formula is adopted for calculation:
when the evaluation index is of an intermediate type, the following formula is adopted for calculation:
wherein Z is an evaluation index value, xminFor the minimum value possible for the evaluation index, xmaxTo evaluate the maximum value that the indicator may obtain, [ U [ ]1,U2]For evaluating the optimum interval of the index value, U1For evaluating the minimum value of the optimum interval of the index value, U2The maximum value of the optimal interval for evaluating the index value.
Further, in step S3, the determining the weight value of each index specifically includes establishing a hierarchical structure of the user according to different influence factors in the user data: a target layer, as the highest layer, containing only one element for analyzing a predetermined target or ideal result of the problem; the criterion layer is used as an intermediate layer and is used for setting intermediate links related to the realization of a target, and at least comprises a plurality of levels; and the scheme layer is used as the bottommost layer and used for realizing various measures and decision schemes which can be selected by the target.
Further, in step S3, the determining the weight value of each index specifically includes,
calculating the maximum principal feature root λ according to the following formulamax：
W＝(w1,w2…wn)T
Wherein W is the characteristic vector of A, n is the characteristic root of A, (A. W)iIs the ith element of A.W, W ═ W1,w2…wn)TIs the sorting weight vector of the n-order matrix, A is the consistency judgment matrix;
the consistency of the weight values is checked according to the following formula, where n > 2:
RC＝IC/IR
IC＝(λmax-m)/(m-1)
wherein R isCTo determine the random consistency ratio of the matrix, ICJudging the general consistency index of the matrix; i isRJudging the average random consistency index of the matrix;
when R isC<When 0.1, judging that the consistency of the judgment matrix is acceptable, and the weight coefficient distribution is reasonable; when the CI is more than or equal to 0.1, judging that the judgment matrix needs to be modified again, recalculating the weight of the corrected matrix and carrying out consistency check;
calculating the synthetic weight of each layer element to the user target, and carrying out normalization processing according to the following company feature vector w to obtain the relative weight of each evaluation index of a certain level with respect to the index of the upper level:
X＝AHP(x)＝Σw′ixi
wherein, vector w ═ w'1,w′2…w′n) The relative weight of each evaluation index of a certain level relative to the indexes of the upper level.
Further, in step S4, the constraint condition specifically includes:
the power constraint conditions of the power grid are as follows:
|Pgrid(t)|≤Pgrid,max(t)
in the formula, Pgrid,max(t) power exchange limits for each time period between the user and the grid;
the uninterruptible load constraints are:
tload,start≤tload≤tload,end-N,t∈N*
the air conditioner load constraint conditions are as follows:
0≤Pair(t)≤Pair,max
Tair,min≤Tin(t)≤Tair,max
wherein, Pair(t) is the power of the air conditioner in the t period of time when the air conditioner refrigerates, Pair,maxFor the rated power of the air conditioner, delta T is a time interval Tair,maxUpper limit of indoor temperature, Tair,minThe lower limit of the indoor temperature.
The electric heating load constraint conditions are as follows:
0≤Peh(t)≤Peh,max
Teh,min≤Teh(t)≤Teh,max
wherein, Teh(t) temperature of the hot water in the electric heater in the t-th period, Peh,maxFor heating electric heaters at rated power, Teh,maxIs the thermal upper limit temperature, T, of the electric heatereh,minThe lower thermal limit temperature of the electric heater.
Further, in step S4, the solving of the control decision that the power consumer participates in the power grid coordination according to the optimization model specifically includes:
when no risk factor is present, the economic objective is solved according to the following formula:
wherein, Pgrid(t) the amount of power purchased from the grid at the current time period, ρgrid(t) the price of electricity between the user and the grid during the period analyzed, CDGThe power generation cost and the loss depreciation cost of the distributed power supply are reduced;
when the risk coefficient is contained, extracting a random variable from the objective function, and solving the economic objective according to the following formula:
Pgrid(t)＝Pmust(t)+Pheat(t)+Pload(t)+Pbat(t)-Ppv(t)
wherein, Pgrid(t) is the power purchased during the t-th time period, Ppv(t) renewable energy generated Power, P, during the t-th time periodbat(t) is the electric vehicle charge and discharge power in the t-th time period, Pmust(t),Pheat(t) and Pload(t) is the load that cannot be adjusted.
Further, in step S5, the specific process of demand-side regulation compensation settlement includes:
acquiring daily load data of n power consumers, wherein the daily load data at least comprises the numbers of the power consumers and the power of corresponding time points;
normalizing the power load data according to the following formula:
selecting a peak-valley time period corresponding to the daily load data of the power users to form a data matrix, reserving the data of the peak-valley time period, and calculating the Euclidean distance between the users according to the following formula:
wherein the content of the first and second substances,
is the Euclidean distance between the ith and jth electrical loads, T is the set of time points of the peak-valley period;
summing the matrix according to rows, calculating the correlation coefficient of the other power loads and the row with the minimum distance sum, selecting users with the correlation coefficient r being more than or equal to 0.8, classifying the users in the m rows into one class, and recording the class as V ═ m1,m2,…]；
When the vector V is an empty set, classifying other users into one class, giving a single mark, and outputting a program result; and when the number n of the matrix rows is less than or equal to 2, outputting a program result, otherwise, removing the data of the row where the vector V element is located from the data matrix, and recalculating the Euclidean distance between the users.
Further, the calculating of the correlation coefficient between the remaining power loads and the distance and the row with the minimum sum is specifically to calculate the correlation coefficient according to the following formula:
where m is the distance sum minimum row, i is the number of bits of the power load, and r is the correlation coefficient.
In summary, the embodiment of the invention has the following beneficial effects:
the big data-based power consumer demand side regulation and control method provided by the invention can effectively regulate and control distributed equipment such as industrial, commercial and residential; compared with the prior mandatory direct load control means, the strategy provided by the invention fully considers the difference of the user response positive degrees, adopts a basic compensation mechanism and additional compensation to encourage the user to participate in direct load control, and establishes an optimal effect regulation strategy for gaining benefits from the total benefits of the power selling company.
The energy big data is fully applied, different targets and assessment indexes of demand side regulation and control are considered, in the aspects of carrying out weight processing and determining of different indexes, a hierarchical structure of users is established based on the relation among various factors in different users, and demand side regulation and control of different service demands including power balance regulation and control, peak load shifting regulation and control, power system frequency modulation service, emergency regulation and control under accident conditions and the like can be realized.
The method has good flexibility in the process of building and solving demand side regulation, can realize the functions of different targets and multi-service demands, loads the life style of the model on the basis of the customer demand mode, can minimize inconvenience brought to power users, forms the interactive benefit evaluation index of the users, and performs demand side regulation compensation settlement based on the benefit analysis of the evaluation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a main flow diagram of a big data-based power consumer demand side regulation and control method provided by the present invention.
Fig. 2 is a table of average random consistency index values of a decision matrix of a big data-based power consumer demand-side regulation method for the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an embodiment of a big data-based power consumer demand side regulation method according to the present invention. In this embodiment, the method comprises the steps of:
step S1, obtaining user total load data, user each electricity consumption data, user environment data and user electricity consumption data from the electricity consumption information of the user, preprocessing, and checking whether various data are missing;
in a specific embodiment, the preprocessing is energy consumption monitoring (including water, electricity and gas) for each level of equipment-user-aggregator, load conditions and overall energy consumption conditions of various cells, various special transformer users and resident users can be checked, data collection display is realized, and the preprocessing is also a data basis of other module functions; according to different monitoring main bodies, the monitoring method is divided into regional energy consumption monitoring, community energy consumption monitoring, power distribution facility monitoring, electric vehicle monitoring, optical storage monitoring, private substation user energy consumption monitoring, resident user energy consumption monitoring and the like.
Specifically, regional energy consumption monitoring is carried out, and mainly aiming at the range of a power supply region, the current load curve of a large user in the detection region is compared with the load curve of the previous day, the comparison among maximum load, minimum load and average load is highlighted, and the current power consumption curve of the regional user is compared with the power consumption curve of the previous day;
the method comprises the steps of monitoring energy consumption of a cell, wherein the cell is an important unit for demand side regulation and control, integrates a power distribution system, a charging pile, a photovoltaic system/energy storage system, a lighting system, a household energy management system and the like, detects energy consumption conditions under multiple scenes, optimizes energy consumption proportion through real-time data acquisition of equipment, optimizes energy consumption proportion, optimizes dispatching comprehensively, guides optimal energy consumption of the cell, reduces overall energy consumption of the cell, performs centralized monitoring on comprehensive energy in a public area and manages energy of residents, and saves operation cost;
monitoring a power distribution facility, inquiring specific data of each line and a switch element of a simulation diagram of a residential substation, and monitoring and counting the number of main transformers of the substation, the electricity consumption in nearly one hour, the total capacity of the main transformers, incoming and outgoing line loads, real-time loads, equipment working conditions, acquisition success rate and monitoring real-time displacement data;
monitoring the electric automobile, inquiring the state information of each charging pile in a community, acquiring the information of single-phase, three-phase, direct-current charging power and the number of the charging piles, forming a real-time load curve of the charging piles in the community, and outputting the data information of the current load, the maximum load and the peak-valley difference;
monitoring the photovoltaic profile information and the energy storage profile information by light storage, wherein the photovoltaic profile information and the energy storage profile information comprise accumulated energy generation, energy generation today, maximum power generation power, installed capacity, grid-connected voltage, battery charge state, accumulated charge and discharge times, energy storage capacity and maximum discharge power, and a structural diagram of the light storage grid-connected network is formed and comprises specific light storage voltage and charge and discharge information;
the private substation user can monitor and acquire basic information of various large users, including client numbers, client addresses, voltage levels, contract capacity, client states and the like; the method comprises the following steps of monitoring the working conditions of user equipment in real time to form measuring point distribution and energy utilization conditions of various equipment, wherein the measuring point distribution and energy utilization conditions comprise maximum load and occurrence time, power consumption of the current day and yesterday, overrun times of the current month and the previous month, operation and maintenance days of safe power utilization and abnormal power utilization; monitoring the real-time load and the peak-valley electric quantity of the user in the current month, and counting the energy consumption data of electricity, gas, water and heat of the user;
the household and civil energy monitoring system has the advantages that the household and civil energy monitoring system can be used for monitoring energy consumption of a user based on a non-household type household intelligent energy gateway, and the actual power consumption conditions of the user can be checked, wherein the actual power consumption conditions comprise daily power consumption conditions of various electric appliances of the user, power consumption proportion conditions and peak-valley power consumption conditions of the user; the actual condition of the electricity consumption of the resident users in the year/month/day can be monitored by switching the query conditions.
Step S2, determining the load condition of each user according to the collected energy consumption data of each level and the daily electricity price information aiming at the operation data of different electric equipment;
in a specific embodiment, the verified data is analyzed, and statistics include, but are not limited to, motor load equipment, air conditioner load power consumption data, lighting load power consumption data, elevator load power consumption data, distributed power supply operation data, electricity price data, air conditioner comfort level data, lighting comfort level data, elevator average waiting time data and the like according to the user-to-interactive benefit demand type data.
Specifically, the power loads are classified according to power utilization types, and the sum of all industrial power loads is divided into industrial power loads, including the sum of all power loads of a first industry, a second industry and a third industry; dividing domestic power loads of household lighting and household appliances of residents in cities and towns and villages into resident domestic power; the household load of residents is mainly the household load of residents, has the characteristics of increasing along with the increase of living standard and obvious seasonal fluctuation, and the characteristics of the household load of residents are closely related to the rules of daily life and work of residents. However, the seasonal variation of the residential load directly affects the seasonal variation of the peak load of the system in many cases, but the degree of the effect depends on the proportion of the residential electricity load in the total load of the system.
Step S3, according to the index calculation model of the user demand side regulation and control effect evaluation, based on different service requirements including power balance regulation and control, peak clipping and valley filling regulation and control, power system frequency modulation service, emergency regulation and control under accident conditions and the like, the index calculation model of the user demand side regulation and control effect evaluation is established, and indexes include demand response quantity, demand response rate, demand response implementation reliability and the like. The dimension of each index and the change interval of the index value are different, and the objective and reasonable evaluation result is ensured based on standardized processing; determining index values and weight values;
in a specific embodiment, the index values Z are determined according to the following formula, and the evaluation index values are normalized to dimensionless values between [0,1 ]:
when the evaluation index is a positive index, i.e., "larger is better", the following formula is used for calculation:
when the evaluation index is a negative index, i.e., "smaller is better", the following formula is used for calculation:
when the evaluation index is of the intermediate type, i.e., "moderate is appropriate", the following formula is used for calculation:
wherein Z is an evaluation index value, xminFor the minimum value possible for the evaluation index, xmaxTo evaluate the maximum value that the indicator may obtain, [ U [ ]1,U2]For evaluating the optimum interval of the index value, U1For evaluating the minimum value of the optimum interval of the index value, U2The maximum value of the optimal interval for evaluating the index value.
Specifically, when weight processing and determination of different indexes are performed, a hierarchical structure of a user is established according to different influence factors in user data: a target layer, as the highest layer, containing only one element for analyzing a predetermined target or ideal result of the problem; the criterion layer is used as an intermediate layer and is used for setting an intermediate link related to realizing a target, and at least comprises a plurality of levels including a criterion and a sub-criterion which need to be considered; the scheme layer is used as the bottommost layer and is used for realizing various measures and decision schemes which can be selected by a target; the importance of each element of the same level with respect to a criterion in the previous level is compared pairwise, and a pairwise comparison judgment matrix can be constructed.
Specifically, the next layer element governed by the marking layer element C is U1,U2,…UnThe decision maker needs to compare two elements U against criterion CiAnd UjWhich is more important and how important, the importance degree is assigned according to the scale, and a judgment matrix A is formed as (a)ij)n×nWherein a isijIs exactly the element UiAnd UjA scale of importance ratios with respect to criterion C; thus aijThe following properties are satisfied: a isij>0；aji＝1/aij；aii1, so the decision matrix can be expressed as:
calculating the relative weight of the compared elements to the criterion by the judgment matrix, and carrying out consistency check on the judgment matrix;
is provided withW＝(w1,w2…wn)TIs the sorting weight vector of the n-order matrix, if A is used to represent the consistency judgment matrix, then the following steps are provided:
by W ═ W1,w2…wn)TMultiplying the expression right to obtain AW ═ nW, which indicates that W is a feature vector of A and a feature root is n; therefore, the problem of solving the eigenvalue and the eigenvector of the judgment matrix can be solved by solving the weight.
Maximum eigenvalue λmaxThe corresponding feature vector is the weight vector to be solved, and the maximum principal feature root lambda is calculated according to the following formulamax：
Wherein W is the characteristic vector of A, n is the characteristic root of A, (A. W)iIs the ith element of A.W, W ═ W1,w2…wn)TIs the sorting weight vector of the n-order matrix, and A is the consistency judgment matrix.
The consistency of the weight values is checked according to the following formula, where n > 2:
RC＝IC/IR
IC＝(λmax-m)/(m-1)
wherein R isCTo determine the random consistency ratio of the matrix, ICJudging the general consistency index of the matrix; i isRIn order to judge the average random consistency index of the matrix, for the judgment matrix with the n being 1-9 orders, I isRThe values are shown in FIG. 2;
when R isC<When 0.1, judging that the consistency of the judgment matrix is acceptable, and the weight coefficient distribution is reasonable; when CI is greater than or equal to 0.1, judging that the judgment matrix needs to be modified again, and correctingThe corrected matrix is re-weighted and checked for consistency.
Specifically, the synthetic weight of each layer element to the user target is calculated, and after normalization processing is performed according to the following company feature vector w, the relative weight of each evaluation index of a certain level with respect to the index of the previous level is obtained:
X＝AHP(x)＝∑w′ixi
wherein, vector w ═ w'1,w′2…w′n) The relative weight of each evaluation index of a certain level relative to the indexes of the upper level.
Step S4, obtaining an optimization model containing constraint conditions by combining load conditions, index values and weight values according to preference data and ambient temperature change data set by a user, and solving a control decision of power users participating in power grid coordination according to the optimization model;
in a specific embodiment, the objective function: the economy and the user comfort level need to be comprehensively considered in the user demand side regulation and control of the built model, the purpose that the influence degree of the power consumption cost and the comfort level is minimum is respectively obtained, and the two objective functions are integrated to serve as an energy management optimization target under the condition that the user participates in the emergency dispatching of the power grid;
when no risk factor is present, the economic objective is solved according to the following formula:
wherein, Pgrid(t) the amount of power purchased from the grid at the current time period, ρgrid(t) the price of electricity between the user and the grid during the period analyzed, when Pgrid(t)>0，ρgrid(t) indicates power purchase, when Pgrid(t)<0，ρgrid(t) represents selling electricity, and the price of selling electricity is defined as half of the price of purchasing electricity at the moment; cDGGenerating power for distributed generationCost and loss depreciation costs, with distributed renewable energy sources the cost of electricity generation is 0, with micro gas turbines and the like the cost is gas costs; c1The total electricity consumption cost of the user;
when the risk coefficient is contained, extracting a random variable from the objective function, and solving the economic objective according to the following formula:
Pgrid(t)＝Pmust(t)+Pheat(t)+Pload(t)+Pbat(t)-Ppv(t)
wherein, Pgrid(t) is the power purchased during the t-th time period, Ppv(t) renewable energy generated Power, P, during the t-th time periodbat(t) is the electric vehicle charge and discharge power in the t-th time period, Pmust(t),Pheat(t) and Pload(t) is the load that cannot be adjusted.
More specifically, the demand side regulation of the power consumer mainly considers the following constraints:
the power constraint conditions of the power grid are as follows:
|Pgrid(t)|≤Pgrid,max(t)
in the formula, Pgrid,max(t) power exchange limits for each time period between the user and the grid;
the uninterruptible load constraints are:
tload,start≤tload≤tload,end-N,t∈N*
the air conditioner load constraint conditions are as follows:
0≤Pair(t)≤Pair,max
Tair,min≤Tin(t)≤Tair,max
wherein, Pair(t) is the power of the air conditioner in the t period of time when the air conditioner refrigerates, Pair,maxFor the rated power of the air conditioner, delta T is a time interval Tair,maxUpper limit of indoor temperature, Tair,minThe lower limit of the indoor temperature.
The electric heating load constraint conditions are as follows:
0≤Peh(t)≤Peh,max
Teh,min≤Teh(t)≤Teh,max
wherein, Teh(t) temperature of the hot water in the electric heater in the t-th period, Peh,maxFor heating electric heaters at rated power, Teh,maxIs the thermal upper limit temperature, T, of the electric heatereh,minThe lower thermal limit temperature of the electric heater.
And step S5, selecting the indexes concerned by the user from the interactive benefit evaluation index system according to the user type and the attention point of the user, forming the interactive benefit evaluation index of the user, and performing regulation, control, compensation and settlement on the demand side.
In a specific embodiment, the classification based on the load curve peak-valley difference is realized mainly by starting from the peak-valley difference of the user curve, and the subsequent power grid company can conveniently make a power price incentive strategy in a targeted manner.
Inputting: the resident user loads the power value every hour or every 15 minutes every day.
And (3) outputting: and outputting the user categories with different matching degrees with the regional load curves.
Specifically, daily load data of n power consumers are acquired, and the daily load data of the power consumers in common use is 24 or 96-point data, and at least comprises the numbers of the power consumers and the power of the corresponding time points;
normalizing the power load data according to the following formula:
selecting the peak-valley period corresponding to the daily load data of the power consumer, and selecting the peak-valley period to study according to the actual situation, for example, the selected peak-load period is t1～t2The valley period is t3～t4If the peak-valley period is multiple, performing multiple segmentation to form a data matrix, retaining the data of the peak-valley period according to the time sequence, removing the rest data, and calculating the Euclidean distance between users according to the following formula:
wherein the content of the first and second substances,
is the Euclidean distance between the ith and jth electrical loads, T is the set of time points of the peak-valley period;
summing the matrix according to rows, calculating the correlation coefficient of the other power loads and the row with the minimum distance sum, selecting users with the correlation coefficient r being more than or equal to 0.8, classifying the users in the m rows into one class, and recording the class as V ═ m1,m2,…](ii) a The correlation coefficient is calculated according to the following formula:
where m is the distance sum minimum row, i is the number of bits of the power load, and r is the correlation coefficient.
When the vector V is an empty set, classifying other users into one class, giving a single mark, and outputting a program result; when the number n of the matrix rows is less than or equal to 2, outputting the program result, otherwise, removing the data of the row where the vector V element is located from the data matrix, and recalculating the Euclidean distance between the users
In the embodiment of the invention, model solution is implemented by demand side regulation. And providing an improved coordinated evolution algorithm, solving the proposed Europeanization model, considering the cost benefits of each user, and making a power user participate in a power grid coordination control decision.
The design of smart grid demand side management algorithms requires the ability to handle a large number of several different types of controllable devices. Each type of controllable load may have different consumption characteristics over several hours, with several heuristic controllable load types. The designed algorithm should be able to handle these complex characteristics. Linear and dynamic programming methods are commonly used in the field. However, linear programming and dynamic programming algorithms do not adequately address these complexities. Evolutionary algorithms have shown potential approaches to solving these complex problems in other areas. These algorithms have several advantages over traditional mathematical algorithms, in addition to being able to provide near optimal results. Therefore, the invention provides an improved coordinated evolution algorithm, solves the Euro model provided in the step four, considers the cost benefit of each user, and makes the power users participate in the power grid coordination control decision.
The main advantage of the proposed algorithm is that it has no flexibility in building and developing algorithms that any other conventional method does not have. The evolutionary algorithm can flexibly realize the characteristic function, and the life style of the model is loaded on the basis of the customer demand mode, so that the inconvenience brought to the customer can be minimized. For example, consider two controllable loads: air conditioners and washing machines. They started consuming in the morning. Due to the life style of the client, the air conditioner can realize demand response by adjusting the temperature to be high when the temperature is high, but the air conditioner is not suitable for being moved to other moments to start up and operate. While washing machines do not have such a preference, it can be shifted to a later time of day. Some loads may have a higher priority than others, which may also be taken into account by the algorithm, which, depending on their importance, shifts these loads to the appropriate time step. Another major advantage of the algorithm is the ability to handle a large number of several different types of controllable devices. The size problem only affects the length of the chromosomes of the evolutionary algorithm.
The genetic algorithm employed comprises three basic operations: selection (selection), cross (crossover) and mutation (mutation). And improving selection operation by combining sorting selection and optimal worst selection, performing adaptive adjustment on crossover and mutation operators, and performing adaptive adjustment on a penalty function.
In the aspect of selection operation, a roulette wheel selection method can be adopted, and individuals with larger adaptability values are selected to be the processes of subsequent intersection and variation and then become the next generation of individuals, but the individuals with larger adaptability values have certain probability to be ignored. And the closer to convergence, the smaller the difference in fitness values between population individuals, and the greater the probability of this occurring. The proposed ranking method is improved: in each step of selection, 1/4 individuals with low individual net payout (or power fluctuation) are saved and used for replacing 1/4 individuals with high net payout (or power fluctuation) to select the middle individual roulette, so that a basis is provided for the next step of adaptive crossover and variation.
In terms of crossover and mutation operations, the present invention proposes classical genetic algorithms that adaptively adjust the mutation rate and crossover rate, although adaptive adjustment P is employed when individual fitness is higher than average fitnessc、PmValue, but a larger fixed P is used when the individual fitness is lower than the average fitnessc、PmThis can damage individuals of good quality who are carried by individuals of poor quality. Based on the above considerations, the adaptive crossover and mutation probability proposed by Yan spring et al is utilized, as shown in:
using arcsin function with faveThe transformation of (3) is to better discriminate the concentration and dispersion degree between population fitness, and pi/6 is adopted as a discrimination value because sin (pi/6) is 1/2. When in use
Time of flight
The fitness is distributed in a concentrated way, which shows that the mean value of the fitness is close to the maximum value of the fitness, namely the population fitness is distributed in a concentrated way close to the maximum fitness and then passes through the condition that pi/12 is not more than arcsin (f)ave/fmax)<Pi/3 to determine whether to perform crossover or mutation first. When the population is at the primary stage of evolution and the tail end of evolution, the population fitness is likely to be very dispersed or concentrated, and at the moment, the original formula is firstly subjected to mutation operation and then to cross operation, so that the convergence speed is accelerated.
Therefore, after the improvement according to the measures, the obtained population net expenditure (or power fluctuation) fitness value after population evolution is moderately dispersed, so that the obtained result can be better converged to a uniform value, and the convergence speed is increased. The obtained net expenditure (or power fluctuation) of the population individual can easily jump out of the local optimal solution and converge to the global optimal solution, so that the optimization result is more stable.
In summary, the embodiment of the invention has the following beneficial effects:
the big data-based power consumer demand side regulation and control method provided by the invention can effectively regulate and control distributed equipment such as industrial, commercial and residential; compared with the prior mandatory direct load control means, the strategy provided by the invention fully considers the difference of the user response positive degrees, adopts a basic compensation mechanism and additional compensation to encourage the user to participate in direct load control, and establishes an optimal effect regulation strategy for gaining benefits from the total benefits of the power selling company.
The energy big data is fully applied, different targets and assessment indexes of demand side regulation and control are considered, in the aspects of carrying out weight processing and determining of different indexes, a hierarchical structure of users is established based on the relation among various factors in different users, and demand side regulation and control of different service demands including power balance regulation and control, peak load shifting regulation and control, power system frequency modulation service, emergency regulation and control under accident conditions and the like can be realized.
The method has good flexibility in the process of building and solving demand side regulation, can realize the functions of different targets and multi-service demands, loads the life style of the model on the basis of the customer demand mode, can minimize inconvenience brought to power users, forms the interactive benefit evaluation index of the users, and performs demand side regulation compensation settlement based on the benefit analysis of the evaluation.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A big data-based power consumer demand side regulation and control method is characterized by comprising the following steps:
step S1, obtaining user total load data, user each electricity consumption data, user environment data and user electricity consumption data from the electricity consumption information of the user, preprocessing, and checking whether various data are missing;
step S2, determining the load condition of each user according to the collected energy consumption data of each level and the daily electricity price information aiming at the operation data of different electric equipment;
step S3, determining each index value and a weight value according to an index calculation model for user demand side regulation and control effect evaluation;
step S4, obtaining an optimization model containing constraint conditions by combining load conditions, index values and weight values according to preference data and ambient temperature change data set by a user, and solving a control decision of power users participating in power grid coordination according to the optimization model;
and step S5, selecting the indexes concerned by the user from the interactive benefit evaluation index system according to the user type and the attention point of the user, forming the interactive benefit evaluation index of the user, and performing regulation, control, compensation and settlement on the demand side.
2. The method according to claim 1, wherein in step S1, the preprocessing specifically includes:
monitoring the regional energy consumption, detecting the current and previous day load curve comparison of large users in the region, highlighting the maximum, minimum and average load comparison, and comparing the current and previous day power consumption curves of regional users according to the power supply regional range;
monitoring the energy consumption of the residential area, detecting the energy consumption condition under multiple scenes, optimizing the energy consumption proportion through real-time data acquisition of equipment, and carrying out centralized monitoring on comprehensive energy in a public area and energy management of residential users;
monitoring a power distribution facility, inquiring specific data of each line and a switch element of a simulation diagram of a residential substation, and monitoring and counting the number of main transformers of the substation, the electricity consumption in nearly one hour, the total capacity of the main transformers, incoming and outgoing line loads, real-time loads, equipment working conditions, acquisition success rate and monitoring real-time displacement data;
monitoring the electric automobile, inquiring the state information of each charging pile in a community, acquiring the information of single-phase, three-phase, direct-current charging power and the number of the charging piles, forming a real-time load curve of the charging piles in the community, and outputting the data information of the current load, the maximum load and the peak-valley difference;
monitoring the light storage, acquiring photovoltaic general information and energy storage general information, and forming a structural diagram of the light storage grid connection;
the method comprises the steps that a special transformer user can monitor, basic information of various large users is obtained, the working conditions of user equipment are monitored in real time, measuring point distribution and energy utilization conditions of various equipment are formed, the real-time load and the peak-valley electricity quantity of the user in the same month are monitored, and electricity, gas, water and heat energy utilization data of the user are counted;
the household power monitoring system is used for monitoring the actual electricity utilization condition of a user or monitoring the actual electricity utilization condition of the household user in year/month/day by switching the query conditions.
3. The method according to claim 2, wherein in step S2, the determining the load condition of each user is specifically to classify the power loads according to their power utilization types, and to divide the sum of all the power loads in industry into industry power loads; the household lighting and household electrical appliance load of urban and rural residents is divided into resident household electricity.
4. The method of claim 3, wherein in step S3, index values Z are determined according to the following formula:
when the evaluation index is a positive index, the following formula is adopted for calculation:
when the evaluation index is a negative index, the following formula is adopted for calculation:
when the evaluation index is of an intermediate type, the following formula is adopted for calculation:
wherein Z is an evaluation index value, xminFor the minimum value possible for the evaluation index, xmaxTo evaluate the maximum value that the indicator may obtain, [ U [ ]1,U2]For evaluating the optimum interval of the index value, U1For evaluating the minimum value of the optimum interval of the index value, U2The maximum value of the optimal interval for evaluating the index value.
5. The method as claimed in claim 4, wherein in step S3, the determining the weight values of the indexes includes establishing a hierarchical structure of the user according to different influence factors in the user data: a target layer, as the highest layer, containing only one element for analyzing a predetermined target or ideal result of the problem; the criterion layer is used as an intermediate layer and is used for setting intermediate links related to the realization of a target, and at least comprises a plurality of levels; and the scheme layer is used as the bottommost layer and used for realizing various measures and decision schemes which can be selected by the target.
6. The method as claimed in claim 5, wherein in step S3, the determining the weight values of the indexes comprises,
calculating the maximum principal feature root λ according to the following formulamax：
W＝(w1,w2…wn)T
Wherein W is the characteristic vector of A, n is the characteristic root of A, (A. W)iIs the ith element of A.W, W ═ W1,w2…wn)TIs the sorting weight vector of the n-order matrix, A is the consistency judgment matrix;
the consistency of the weight values is checked according to the following formula, where n > 2:
RC＝IC/IR
IC＝(λmax-m)/(m-1)
wherein R isCTo determine the random consistency ratio of the matrix, ICJudging the general consistency index of the matrix; i isRJudging the average random consistency index of the matrix;
when R isC<When 0.1, judging that the consistency of the judgment matrix is acceptable, and the weight coefficient distribution is reasonable; when the CI is more than or equal to 0.1, judging that the judgment matrix needs to be modified again, recalculating the weight of the corrected matrix and carrying out consistency check;
calculating the synthetic weight of each layer element to the user target, and carrying out normalization processing according to the following company feature vector w to obtain the relative weight of each evaluation index of a certain level with respect to the index of the upper level:
X＝AHP(x)＝∑w′ixi
wherein, vector w ═ w'1,w′2…w′n) The relative weight of each evaluation index of a certain level relative to the indexes of the upper level.
7. The method according to claim 6, wherein in step S4, the constraint condition specifically includes:
the power constraint conditions of the power grid are as follows:
|Pgrid(t)|≤Pgrid,max(t)
in the formula, Pgrid,max(t) power exchange limits for each time period between the user and the grid;
the uninterruptible load constraints are:
tload,start≤tload≤tload,end-N,t∈N*
the air conditioner load constraint conditions are as follows:
0≤Pair(t)≤Pair,max
Tair,min≤Tin(t)≤Tair,max
wherein, Pair(t) is the power of the air conditioner in the t period of time when the air conditioner refrigerates, Pair,maxFor the rated power of the air conditioner, delta T is a time interval Tair,maxUpper limit of indoor temperature, Tair,minThe lower limit of the indoor temperature.
The electric heating load constraint conditions are as follows:
0≤Peh(t)≤Peh,max
Teh,min≤Teh(t)≤Teh,max
wherein, Teh(t) temperature of the hot water in the electric heater in the t-th period, Peh,maxFor heating electric heaters at rated power, Teh,maxIs the thermal upper limit temperature, T, of the electric heatereh,minThe lower thermal limit temperature of the electric heater.
8. The method according to claim 7, wherein in step S4, the solving the control decision of the power consumer participating in the grid coordination according to the optimization model specifically includes:
when no risk factor is present, the economic objective is solved according to the following formula:
wherein, Pgrid(t) the amount of power purchased from the grid at the current time period, ρgrid(t) the price of electricity between the user and the grid during the period analyzed, CDGCost and loss depreciation for distributed power generation, C1The total electricity consumption cost of the user;
when the risk coefficient is contained, extracting a random variable from the objective function, and solving the economic objective according to the following formula:
Pgrid(t)＝Pmust(t)+Pheat(t)+Pload(t)+Pbat(t)-Ppv(t)
wherein, Pgrid(t) is the power purchased during the t-th time period, Ppv(t) renewable energy generated Power, P, during the t-th time periodbat(t) is the electric vehicle charge and discharge power in the t-th time period, Pmust(t),Pheat(t) and Pload(t) is the load that cannot be adjusted.
9. The method as claimed in claim 1, wherein in step S5, the demand-side regulation compensation settlement specific process includes:
acquiring daily load data of n power consumers, wherein the daily load data at least comprises the numbers of the power consumers and the power of corresponding time points;
normalizing the power load data according to the following formula:
selecting a peak-valley time period corresponding to the daily load data of the power users to form a data matrix, reserving the data of the peak-valley time period, and calculating the Euclidean distance between the users according to the following formula:
wherein the content of the first and second substances,
is the Euclidean distance between the ith and jth electrical loads, T is the set of time points of the peak-valley period;
summing the matrix according to rows, calculating the correlation coefficient of the other power loads and the row with the minimum distance sum, selecting users with the correlation coefficient r being more than or equal to 0.8, classifying the users in the m rows into one class, and recording the class as V ═ m1,m2,…]；
When the vector V is an empty set, classifying other users into one class, giving a single mark, and outputting a program result; and when the number n of the matrix rows is less than or equal to 2, outputting a program result, otherwise, removing the data of the row where the vector V element is located from the data matrix, and recalculating the Euclidean distance between the users.
10. The method according to claim 9, wherein said calculating the correlation coefficient of the remaining electrical loads with the distance and the row with the smallest sum is in particular calculating the correlation coefficient according to the following formula:
where m is the distance sum minimum row, i is the number of bits of the power load, and is the correlation coefficient of the phase r.
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| CN113746086B (en) * | 2021-08-03 | 2024-01-30 | 国网浙江省电力有限公司湖州供电公司 | Non-invasive demand response system and method |
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| CN115081946A (en) * | 2022-07-25 | 2022-09-20 | 国网信息通信产业集团有限公司 | Power consumption regulation and control method, system, device, electronic equipment and computer medium |
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| CN115630831A (en) * | 2022-12-06 | 2023-01-20 | 北京华联电力工程监理有限公司 | Auxiliary decision support system for power demand side management |
| CN116742645A (en) * | 2023-08-15 | 2023-09-12 | 北京中电普华信息技术有限公司 | Power load regulation and control task allocation method and device |
| CN116742645B (en) * | 2023-08-15 | 2024-02-27 | 北京中电普华信息技术有限公司 | Power load regulation and control task allocation method and device |
| CN117369813A (en) * | 2023-10-13 | 2024-01-09 | 湖北华中电力科技开发有限责任公司 | Visual display method of energy consumption monitoring index system based on data center |
| CN117726152A (en) * | 2024-02-18 | 2024-03-19 | 安徽泰然信息技术有限公司 | Energy storage scheme generation method and device based on enterprise information |
| CN117726152B (en) * | 2024-02-18 | 2024-05-17 | 安徽泰然信息技术有限公司 | Energy storage scheme generation method and device based on enterprise information |
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