CN111754091A - Power consumer demand side regulation and control system - Google Patents
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Abstract
The invention provides a power consumer demand side regulation and control system, which comprises a regulation and control decision module, a regulation and control decision module and a regulation and control decision module, wherein the regulation and control decision module is used for verifying and establishing a corresponding interaction strategy for power consumption data of a demand side consumer and optimizing the strategy; the data acquisition module is used for acquiring or acquiring power utilization data of a user; the mechanism module is used for acquiring an incentive mechanism and bilateral protocol contents of a power company or an electricity selling company to form an evaluation index value; the evaluation module is used for evaluating an incentive mechanism adapted to the user at the demand side and determining user behavior and user potential; and the simulation verification module is used for performing simulation verification on the target user by utilizing the interaction strategy determined by the regulation and control decision module and judging whether the demand response strategy is reasonable or not. The invention realizes the information interaction, event interaction and transaction interaction process between the power grid side and the user side in the supply and demand interaction process, and maintains the normal implementation of the supply and demand interaction project function.
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 system.
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. For example, a deep learning method is adopted to repeatedly and iteratively solve the optimal electricity price according to the response condition of the user or a plurality of electricity price mechanisms are formulated according to different response degrees of the user, and the electricity price problem of the master-slave game problem among a plurality of electricity selling companies and a plurality of energy suppliers and the user is considered aiming at the loss cost analysis of the power distribution network. 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 promotion 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 demand side management and regulation and the fine management of the load are insufficient, and the whole operation efficiency cannot be improved.
In one aspect of the present invention, a power consumer demand side regulation and control system is provided, including:
the system comprises a regulation decision module, a data acquisition module, a mechanism module, an evaluation module and a simulation verification module, wherein the data acquisition module, the mechanism module, the evaluation module and the simulation verification module are respectively connected with the input end of the regulation decision module;
the regulation and control decision module is used for verifying the electricity consumption data of the user at the demand side to establish a corresponding interaction strategy and optimizing the strategy;
the data acquisition module is used for acquiring or acquiring power utilization data of a user;
the mechanism module is used for acquiring an incentive mechanism and bilateral protocol contents of a power company or an electricity selling company to form an evaluation index value;
the evaluation module is used for evaluating an incentive mechanism adapted to the user at the demand side and determining user behavior and user potential;
and the simulation verification module is used for performing simulation verification on the target user by utilizing the interaction strategy determined by the regulation and control decision module and judging whether the demand response strategy is reasonable or not.
Further, the system also comprises an effect evaluation module connected with the output end of the regulation and control decision module and a transaction settlement module connected with the output end of the effect evaluation module;
the effect evaluation module is used for carrying out related evaluation on the response result processed by the control decision module and sorting and feeding back evaluation content;
and the transaction settlement module is used for sorting and storing the processing result of the regulation and control decision module.
Further, the data acquisition module acquires user total load data, user each electricity consumption data, user environment data and user electricity consumption cost data from the electricity consumption information of the user, the data acquisition module classifies the power loads according to electricity consumption types, and the sum of all industrial electricity consumption loads is divided into industrial electricity consumption loads; the household lighting and household electrical appliance load of urban and rural residents is divided into resident household electricity.
Further, the mechanism module normalizes the evaluation index value to a dimensionless value between [0,1] 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, the evaluation module establishes 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, the evaluation module determines the user behavior and the user potential according to the weight number of the index, specifically:
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 ordering weight of the n-order matrixA weight vector, wherein A is a 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.
Furthermore, the simulation verification module adopts a roulette wheel selection method to calculate the probability, the probability is selected to be subjected to subsequent crossing or mutation to become a next generation of individuals, and the difference of fitness values between the individuals close to convergence is judged to be small.
Further, the verifying and establishing the corresponding interaction strategy by the regulation and control decision module specifically comprises establishing a constraint condition according to the following formula:
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, the step of verifying and establishing the corresponding interaction strategy by the regulation and control decision module specifically comprises the step of establishing the interaction strategy according to the following formula:
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.
Further, the optimizing the strategy by the regulation and control decision module specifically includes the following steps:
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:
wherein, the first and second connecting parts are connected with each other;
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.
In summary, the embodiment of the invention has the following beneficial effects:
the power consumer demand side regulation and control system provided by the invention realizes real-time acquisition and transmission of power consumption data of a power grid side and a consumer side, and realizes functions of data classification, bad data elimination, data preprocessing and the like in an energy consumption detection module of a platform. The preprocessed data can be input into a plurality of platform function modules, corresponding functions are realized based on a decision model embedded in the platform, and the preprocessed data can be used as input data of a supply and demand interaction strategy to provide theory and data support for the function realization of the supply and demand interaction module.
Configuring corresponding supply and demand interaction targets according to different supply and demand scenes, and firstly performing a simulation process under the interaction targets based on a simulation verification module; if the simulation verification effect is better, the supply and demand interaction result information is sent to the user side equipment and the like; if the effect of the simulation verification does not meet the requirement, the interaction strategy needs to be optimized, and then the interaction result information is issued. After the supply and demand interaction event is finished, the actual interaction effect needs to be uploaded to the effect evaluation function module, and the event result and the event benefit are analyzed. And finally, carrying out transaction settlement according to a certain transaction rule based on the actual response effect of the transaction settlement module on each power user or load aggregator participating in the supply and demand interaction event, and storing the transaction outline and the result into the platform.
Through the operation of the interactive platform, a series of automatic management of monitoring, tracking, recording, feedback, integration, analysis, summarization and the like of the power grid information can be ensured, so that the information interaction, event interaction and transaction interaction processes between the power grid side and the user side in the supply and demand interaction process are realized, and the normal implementation of the supply and demand interaction project function is maintained.
Drawings
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 schematic structural diagram of a power consumer demand side regulation system provided by 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 power consumer demand side regulation system according to the present invention. In this embodiment, the system includes: the system comprises a regulation decision module, a data acquisition module, a mechanism module, an evaluation module and a simulation verification module, wherein the data acquisition module, the mechanism module, the evaluation module and the simulation verification module are respectively connected with the input end of the regulation decision module; the effect evaluation module is connected with the output end of the regulation and control decision module, and the transaction settlement module is connected with the output end of the effect evaluation module;
the regulation and control decision module is used for verifying the electricity consumption data of the user at the demand side to establish a corresponding interaction strategy and optimizing the strategy;
specifically, the following constraints are specifically established:
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,minIs the thermal lower limit temperature of the electric heater;
specifically, when no risk factor is included, 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.
Specifically, the optimization of the policy includes the following steps:
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, and the daily load data of the common power consumers is 24 or 96 points of data;
normalizing the power load data according to the following formula:
wherein, the first and second connecting parts are connected with each other;
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 time intervals are multiple peak-valley time intervals, multiple segmentation is carried out; forming a data matrix, retaining the data of the peak-valley period according to the time sequence, 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,…]The correlation coefficient calculation formula is as follows:
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.
The data acquisition module is used for acquiring or acquiring power utilization data of a user; the method comprises the steps that total load data of users, all electricity consumption data of the users, environment data of the users and electricity consumption cost data of the users are obtained from electricity consumption information of the users, the data acquisition module classifies power loads according to electricity consumption types, and the sum of all industrial power loads is divided into industrial power loads; the household lighting and household electrical appliance load of urban and rural residents is divided into resident household electricity.
The mechanism module is used for acquiring an incentive mechanism and bilateral protocol contents of a power company or an electricity selling company to form an evaluation index value; based on different service requirements including power balance regulation, peak clipping and valley filling regulation, power system frequency modulation service, emergency regulation under accident conditions and the like, an index calculation model for user demand side regulation 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.
Specifically, let [ xmin,xmax]For the change interval of the k-th evaluation index value, i.e. xminIs the smallest value, x, possible for the indexmaxFor the maximum value that can be obtained for this index, the evaluation index value is normalized to [0,1] using the following formula]Dimensionless values in between.
When the evaluation index is a positive index, i.e., "larger is better", the following formula is employed.
When the evaluation index is a negative index, i.e., "smaller is better", the following formula is adopted.
When the evaluation index is of the intermediate type, i.e. "moderate is appropriate" [ U ] is set1,U2]The following equation is adopted as the optimum interval of the index value.
The evaluation module is used for evaluating an incentive mechanism adapted to the user at the demand side and determining user behavior and user potential;
specifically, the method is used for carrying out weight processing and determination of different indexes. The specific implementation content is as follows: analyzing the relationship among various factors in different users and establishing a hierarchical structure of the users; these hierarchies can be divided into three categories: a) highest layer (target layer): only one element, typically a predetermined target or ideal result of an analytical problem; b) intermediate layer (criterion layer): the method comprises an intermediate link related to the realization of a target, and the intermediate link can be composed of a plurality of layers, including a criterion and a sub-criterion which need to be considered; c) bottom layer (scheme layer): various measures, decision schemes, etc. are included that may be selected to achieve the objectives. Comparing every two of the elements of the same level with respect to the importance of a criterion in the previous level, and constructing a comparison judgment matrix;
the element of the next layer 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 UjThe importance scale relative to criterion C. Thus aijThe following properties are satisfied: 1) a isij>0；2)aji＝1/aij；3)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;
let W be (W)1,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 above equation to the right yields AW ═ nW, indicating that W is the feature vector of a and the feature root is n. The weight calculation can be summarized as the problem of calculating the eigenvalue and the eigenvector of the judgment matrix. Maximum eigenvalue λmaxThe corresponding feature vector is the weight vector.
Computing the maximum principal feature root λmax：
Wherein, (A. W)iRepresents the ith element of A.W.
The consistency test (when n > 2) is carried out according to the following formula:
RC＝IC/IR
IC＝(λmax-m)/(m-1)
in the formula, the random consistency ratio of the matrix is judged; i isCJudging 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 0, 0, 0.58, 0.90, 1.12, 1.24, 1.32, 1.41, 1.45 in that order.
In general when R isC<When the weight coefficient is normalized, the weight coefficient can be directly used after normalization, and when the weight coefficient is 0.1, the consistency of the judgment matrix is considered to be acceptable, so that the weight coefficient distribution is reasonable; when CI is greater than or equal to 0.1, the judgment should be conducted againAnd (4) properly modifying the broken matrix, recalculating the weight of the corrected matrix and carrying out consistency check.
And calculating the synthetic weight of each layer element to the user target to obtain the target layer index. After normalization processing is carried out on the feature vector w, the relative weight of each evaluation index of a certain level with respect to the index of the upper level can be obtained.
Vector w ═ w1',w'2…w'n) The relative weight of each evaluation index of a certain level relative to the indexes of the upper level.
X＝AHP(x)＝∑w′ixi。
The simulation verification module is used for performing simulation verification on the target user by utilizing the interaction strategy determined by the regulation decision module and judging whether the demand response strategy is reasonable or not; and calculating the probability by adopting a roulette wheel selection method, selecting the individuals to be subjected to subsequent crossing or mutation to become next generation individuals, and judging that the difference of fitness values between the individuals close to convergence is small.
In particular, although adaptive adjustment P is used when the individual fitness is higher than the 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, adaptive crossover and mutation probabilities are utilized, as shown in the following equation:
using arcsin function with faveThe transformation of (3) to better discriminate the concentration and dispersion degree between population fitness, adopts pi/6 as the judgment value because of sin (pi/6)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.
The effect evaluation module is used for carrying out related evaluation on the response result processed by the control decision module, sorting and feeding back evaluation content, and analyzing an event result and event benefits;
and the transaction settlement module is used for sorting and storing the processing results of the regulation and control decision module, settling the transaction according to a certain transaction rule aiming at the actual response effect of each power consumer or load aggregator participating in the supply and demand interaction event, and storing the transaction outline and the results into the platform.
In summary, the embodiment of the invention has the following beneficial effects:
the power consumer demand side regulation and control system provided by the invention realizes real-time acquisition and transmission of power consumption data of a power grid side and a consumer side, and realizes functions of data classification, bad data elimination, data preprocessing and the like in an energy consumption detection module of a platform. The preprocessed data can be input into a plurality of platform function modules, corresponding functions are realized based on a decision model embedded in the platform, and the preprocessed data can be used as input data of a supply and demand interaction strategy to provide theory and data support for the function realization of the supply and demand interaction module.
Configuring corresponding supply and demand interaction targets according to different supply and demand scenes, and firstly performing a simulation process under the interaction targets based on a simulation verification module; if the simulation verification effect is better, the supply and demand interaction result information is sent to the user side equipment and the like; if the effect of the simulation verification does not meet the requirement, the interaction strategy needs to be optimized, and then the interaction result information is issued. After the supply and demand interaction event is finished, the actual interaction effect needs to be uploaded to the effect evaluation function module, and the event result and the event benefit are analyzed. And finally, carrying out transaction settlement according to a certain transaction rule based on the actual response effect of the transaction settlement module on each power user or load aggregator participating in the supply and demand interaction event, and storing the transaction outline and the result into the platform.
Through the operation of the interactive platform, a series of automatic management of monitoring, tracking, recording, feedback, integration, analysis, summarization and the like of the power grid information can be ensured, so that the information interaction, event interaction and transaction interaction processes between the power grid side and the user side in the supply and demand interaction process are realized, and the normal implementation of the supply and demand interaction project function is maintained.
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 power consumer demand side regulation system, comprising: the system comprises a regulation decision module, a data acquisition module, a mechanism module, an evaluation module and a simulation verification module, wherein the data acquisition module, the mechanism module, the evaluation module and the simulation verification module are respectively connected with the input end of the regulation decision module;
the regulation and control decision module is used for verifying the electricity consumption data of the user at the demand side to establish a corresponding interaction strategy and optimizing the strategy;
the data acquisition module is used for acquiring or acquiring power utilization data of a user;
the mechanism module is used for acquiring an incentive mechanism and bilateral protocol contents of a power company or an electricity selling company to form an evaluation index value;
the evaluation module is used for evaluating an incentive mechanism adapted to the user at the demand side and determining user behavior and user potential;
the simulation verification module is used for performing simulation verification on the target user by using the interaction strategy determined by the regulation decision module and judging whether the demand response strategy is reasonable or not.
2. The system of claim 1, further comprising an effect evaluation module coupled to an output of the regulatory decision module, a transaction settlement module coupled to an output of the effect evaluation module;
the effect evaluation module is used for carrying out related evaluation on the response result processed by the control decision module and sorting and feeding back evaluation content;
and the transaction settlement module is used for sorting and storing the processing result of the regulation and control decision module.
3. The system of claim 2, wherein the data acquisition module acquires user total load data, user electricity consumption data, user environment data and user electricity consumption cost data from the electricity consumption information of the user, the data acquisition module classifies the power loads according to electricity consumption types, and the total of all industrial power loads is divided into industrial power loads; the household lighting and household electrical appliance load of urban and rural residents is divided into resident household electricity.
4. The system of claim 3, wherein the mechanism module normalizes the evaluation index value to a dimensionless value between [0,1], in particular:
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 system of claim 4, wherein the evaluation module builds a hierarchy of users based on different influencing 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 system of claim 5, wherein the evaluation module determines the user behavior and the user potential according to the weight number of the indicator, specifically:
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, PCTo 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 system of claim 6, wherein the simulation verification module calculates probabilities using roulette wheel selection, selects for subsequent crossover or mutation to a next generation of individuals, and determines that the fitness value differs by a small amount between individuals who are close to convergence.
8. The system of claim 7, wherein the regulatory decision module verifies that the corresponding interaction policy is established, specifically establishing 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.
9. The system of claim 8, wherein the regulatory decision module verifies that the corresponding interaction policy is established by specifically establishing the following interaction policies:
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.
10. The system of claim 9, wherein the tuning decision module optimizing the strategy specifically comprises:
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:
wherein, the first and second connecting parts are connected with each other;
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.
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