CN111695793B - Method and system for evaluating energy utilization flexibility of comprehensive energy system - Google Patents
Method and system for evaluating energy utilization flexibility of comprehensive energy system Download PDFInfo
- Publication number
- CN111695793B CN111695793B CN202010474783.2A CN202010474783A CN111695793B CN 111695793 B CN111695793 B CN 111695793B CN 202010474783 A CN202010474783 A CN 202010474783A CN 111695793 B CN111695793 B CN 111695793B
- Authority
- CN
- China
- Prior art keywords
- energy
- supply
- demand
- unit
- flexibility
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012360 testing method Methods 0.000 claims abstract description 37
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000013507 mapping Methods 0.000 claims abstract description 14
- 230000000694 effects Effects 0.000 claims abstract description 6
- 238000011156 evaluation Methods 0.000 claims description 37
- 238000004146 energy storage Methods 0.000 claims description 34
- 238000004519 manufacturing process Methods 0.000 claims description 33
- 239000011159 matrix material Substances 0.000 claims description 16
- 230000005540 biological transmission Effects 0.000 claims description 15
- 230000008859 change Effects 0.000 claims description 15
- 150000001875 compounds Chemical class 0.000 claims description 15
- 238000009826 distribution Methods 0.000 claims description 15
- 230000005611 electricity Effects 0.000 claims description 14
- 238000005192 partition Methods 0.000 claims description 11
- 238000007599 discharging Methods 0.000 claims description 10
- 230000007613 environmental effect Effects 0.000 claims description 7
- 230000009194 climbing Effects 0.000 claims description 6
- 238000013210 evaluation model Methods 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 5
- 230000008901 benefit Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000000463 material Substances 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 claims description 3
- 238000004445 quantitative analysis Methods 0.000 claims description 3
- 238000013139 quantization Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 230000000630 rising effect Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 2
- 239000011248 coating agent Substances 0.000 claims 1
- 238000000576 coating method Methods 0.000 claims 1
- 238000002360 preparation method Methods 0.000 claims 1
- 230000003993 interaction Effects 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 3
- 230000002411 adverse Effects 0.000 abstract description 2
- 238000010438 heat treatment Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000001816 cooling Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000010220 Pearson correlation analysis Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a method and a system for evaluating the energy utilization flexibility of an integrated energy system. The method comprises the following steps: s1, establishing a mapping model of a comprehensive energy system based on physical characteristics of energy equipment and an energy network topological structure; s2, establishing uncertainty test sets of supply and demand ends according to historical operation data of the comprehensive energy system and historical load data in a supply area, and generating energy demand and energy supply pairs of demand ends under all the test sets; s3, respectively calculating the flexibility of different energy flows according to the generated energy demand and energy supply pairs; and S4, determining the overall energy utilization flexibility of the comprehensive energy system based on a fuzzy analytic hierarchy process. The invention can provide guidance for the cascade utilization of energy and the optimization of operation scheduling of the comprehensive energy system, enhance the interaction of the supply and demand ends and reduce the adverse effect caused by the uncertainty of the system.
Description
Technical Field
The invention belongs to the field of performance analysis and evaluation of an integrated energy system, and particularly relates to a method and a system for evaluating the energy utilization flexibility of the integrated energy system.
Background
The comprehensive energy system comprehensively optimizes various energy main bodies such as cold, heat, electricity, gas and the like by integrating advanced energy supply and transmission and distribution technologies, has the advantages of economy, environmental protection and the like compared with the traditional scattered single energy system, and is the trend of future energy development.
However, in the development process of the integrated energy system, as the permeability of new energy is improved and the coupling degree between multiple energy forms is increased, the system also needs to consider the coordination and complementation and the cascade utilization of multiple energy flows, and the complexity and uncertainty of an energy supply end become main development restriction factors. In addition, the market conditions of the demand end are changing continuously, and the user load is influenced by a plurality of factors to present volatility, such as weather conditions, user activity rules and the like. In order to improve the safety, flexibility and reliability of energy supply, the fluctuation of production and consumption of the comprehensive energy system requires that the system energy flows have the capability and means of interconnection, intercommunication and coordinated utilization. Therefore, how to establish effective indexes and a proper model to help the comprehensive energy system to carry out supply and demand interaction is the core of solving the problems. However, no evaluation and discussion of the flexibility of the integrated energy system is found in the existing documents and patents, and the robustness of system planning and scheduling is more focused, but the two methods are essentially different in nature and evaluation method.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the energy utilization flexibility of an integrated energy system, so as to solve the problem that the allocable resources in the integrated energy system cannot be accurately quantified.
The invention is realized by adopting the following technical scheme:
a method for evaluating the energy utilization flexibility of an integrated energy system comprises the following steps:
s1, establishing a comprehensive energy system mapping model based on physical characteristics of energy equipment and an energy network topological structure;
s2, establishing uncertainty test sets of supply and demand ends according to historical operation data of the comprehensive energy system and historical load data in a supply area, and generating energy demand and energy supply pairs of demand ends under all the test sets;
s3, respectively calculating the flexibility of different energy flows according to the generated energy demand and energy supply pairs;
and S4, determining the overall energy utilization flexibility of the comprehensive energy system based on a fuzzy analytic hierarchy process.
In the above technical solution, further, the step of establishing the mapping model of the integrated energy system based on the physical characteristics of the energy device and the energy network topology structure in the step S1 includes: s11, establishing an energy producer model and an energy consumer model, and providing a quantitative method of unit scheduling capability for the overall flexibility evaluation of the system; and S12, establishing a transmission and distribution network topology sub-model and connecting the supply and demand ends.
Further, in the step S11, a quantitative method of unit schedulable capability is provided for the overall system flexibility evaluation, and the steps of establishing the energy production and consumer model are as follows:
step S111, according to the historical load data and the environmental factors influencing the current load change, load prediction is carried out on energy consumers in the system supply area, and an energy consumer model is established:
in the formula (I), the compound is shown in the specification,
load demand predicted value of the ith user in the time period t; k is an energy source type, such as cold, heat, electricity, gas, etc.; q is a characteristic quantity representing energy level, and taking the heat demand of a user as an example, q =60 ℃ for domestic hot water demand and q =50 ℃ for indoor heating demand;
historical load data for the same time period; xi t Factors influencing the current load change, such as the predicted value of the heat load, are influenced by factors such as the ambient temperature, the sunlight intensity, and the residence rate.
Step S112, modeling is carried out on different types of energy producers according to the physical characteristics of unit equipment, and specifically, the energy producers are divided into schedulable production units, new energy production units and energy storage units:
(1) Schedulable production unit
The construction of the comprehensive energy system needs to use a schedulable energy producer as an energy center to provide enough regulation space for the source side and ensure the energy balance and the system safety in the energy supply process. Common schedulable production units comprise a cogeneration unit, a combined cooling heating and power unit, a heat pump unit and the like, and the schedulable production units have the following flexibility quantization models:
in the formula, E represents a set of energy types including energy forms of cold, heat, electricity, gas and the like;
and
the energy supply amount of the unit p in the time period t and the upper limit and the lower limit thereof respectively represent the flexible supply capacity of the unit p for energy of a certain form, and can be expressed as functions of the unit capacity and the climbing rate:
in the formula (I), the compound is shown in the specification,
and
the upper limit and the lower limit of the capacity of the unit p are respectively set; RU (RU) p And RD p The climbing rates of the unit x during load rising and load falling are respectively.
(2) New energy production unit
New energy sources with obvious emission reduction benefits, such as wind energy, solar energy and other non-dispatchable resources are influenced by current weather conditions, and the access of the uncertain and fluctuating energy sources has great influence on the flexibility of the system, so that a new energy production unit needs to be modeled:
in the formula (I), the compound is shown in the specification,
outputting the quantity predicted value of the energy type k for the new energy unit n in the time period t; kappa type t In order to influence factors of current new energy production, for example, an output predicted value of the photovoltaic generator set is influenced by factors such as ambient temperature and sunlight intensity.
(3) Energy storage unit
The energy storage unit can finely manage the supply and demand balance of the system through sequential switching of two working modes of energy storage and energy release, realizes redistribution of intermittent energy output in time, achieves the effect of peak clipping and valley filling, and improves the flexibility of the system. Similar to schedulable production units, the flexibility of an energy storage unit is affected by the energy storage capacity and the charge and discharge rate and can therefore be represented by the following model:
wherein E represents a collection of energy species, including both heat and electricity storable forms of energy;
and
respectively the energy charging quantity and the energy discharging quantity of the energy storage unit s in the time period t;
and
respectively charging and discharging speed and rated capacity of the energy storage unit s;
the energy storage quantity of the energy storage units s in the time period t is obtained; eta s The charging and discharging efficiency of the energy storage unit s; at is a calculation time period.
Further, in the step S12, a transport and distribution network topology sub-model of various media types such as cold, heat, electricity, gas and the like is established through a node connection matrix, and an energy input interface and an energy output interface are added to the topology, so that the energy producer model and the energy consumer model established in the step S11 can be accessed into corresponding energy networks and connected with supply and demand ends to balance material flow and energy flow of the system. For the interface between the energy producer model and the energy consumer model, the following conditions should be met:
in the formula (I), the compound is shown in the specification,
and
respectively an energy input interface and an energy output interface; a and B are respectively the set of all energy input interfaces and energy output interfaces; delta epsilon (0, 1) is a loss coefficient representing the loss degree in the process of energy transmission and distribution;
and
respectively, an upper limit and a lower limit of the transmission capacity of the input interface.
Further, the step S2 of establishing a supply and demand end uncertainty test set according to historical operating data of the integrated energy system and historical load data in the supply area, and generating energy demand and energy supply pairs of the demand end under all the test sets includes: step S21, training and updating parameters of each layer of different models by combining the energy producer model, the energy consumer model and the transmission and distribution network topology sub-model established in the step S1 according to the productivity information generated by the energy producer model and the consumption information generated by the energy consumer model to form an uncertainty test set at both supply and demand ends; and S22, calculating correlation coefficients of all the energy input interfaces and all the energy output interfaces, dividing the correlation coefficients into regions, and generating pairs of energy demand and energy supply.
Further, the step of calculating correlation coefficients of all energy input interfaces and energy output interfaces and performing area division in step S22 to generate pairs of energy demand and energy supply is as follows:
step S221, calculating Pearson correlation coefficients of each energy output and input interface according to uncertainty test sets at both supply and demand ends to measure the degree of variation of output parameters of energy producers at different positions caused by local load variation:
in the formula (I), the compound is shown in the specification,
for energy input interface
And an energy output interface
Pearson correlation coefficient of (a);
covariance as the supply and demand end;
and
sample standard deviations for the supply side and demand side, respectively.
Step S222, screening out an energy supply and demand partition set based on the correlation coefficient threshold, then removing supply and demand ports with small similarity from the partition set, and generating energy demand and energy supply pairs:
in the formula (II) Se g The energy demand and energy supply pairs are integrated; rho 0 E (0, 1) is a similarity threshold.
Further, the step S3 of calculating different flexibility of energy flow according to the generated energy demand and energy supply pairs includes: step S31, determining the supply configuration condition in the energy demand and energy supply pairs; and step S32, establishing a single energy flow flexibility evaluation model.
Further, the specific steps of determining the supply configuration condition within the energy demand and energy supply pair in step S31 are: determining each energy demand and energy supply pair Se g The types and numbers of energy producers and energy consumers in the system to evaluate Se according to the calculated energy topological parameters g Energy transport capacity and user consumption capacity of (c):
in the formula, ET g And CT g Are respectively Se g The transport capacity and the user consumption capacity of a certain energy form are internally provided; delta g E (0, 1) is Se g The energy network transportation loss coefficient of (1);
and
is Se g An input interface and an output interface for some form of energy source.
Further, the specific steps of establishing the single fluence flexibility evaluation model in step S32 are: defining the satisfaction degree of energy supply, and representing whether the supply of a certain energy form k meets the requirement of a user:
in the formula, λ k,g ∈(0,1]Is Se g The smaller the value of the satisfaction of the supply of energy in the form of energy k, the lower the satisfaction. Further, the overall satisfaction of the integrated energy system on certain energy supply can be calculated:
in the formula, w g Are weighting factors representing the importance of energy demand and energy supply.
Further, in the step S4, based on the fuzzy analytic hierarchy process, the step of determining the overall energy consumption flexibility of the integrated energy system includes: s41, constructing a multi-energy flow fuzzy judgment matrix; s42, obtaining satisfaction weights of different energy flows through defuzzification processing; and S43, calculating the overall energy use flexibility of the comprehensive energy system.
Further, the specific steps of constructing the multi-energy flow fuzzy judgment matrix in step S41 are: according to the importance degree of energy forms such as cold, heat, electricity, gas and the like to the production and life of users, establishing a fuzzy evaluation matrix:
wherein, a ij The importance of energy form i relative to energy form j. On the basis of the fuzzy evaluation matrix, the comprehensive fuzzy value of the energy form k can be calculated:
further, the specific steps of obtaining the satisfaction weights of different energy flows through the defuzzification processing in step S42 are as follows: calculating the probability that the integrated fuzzy value of one energy form is greater than that of other energy forms:
d(Fu k )＝min P(Fu k ≥Fu i ),i＝1,2,...,k-1
in the formula, P (Fu) k ≥Fu i ) The integrated fuzzy value for energy form k is greater than the probability for energy form i. Standardizing all the possible degrees obtained by solving, and obtaining the satisfaction weight of various energy flows:
in the formula, K is the total number of energy forms in the system, and if four energy forms of cold, heat, electricity and gas exist in the system, K is correspondingly 4.
Further, the calculating of the overall energy use flexibility of the integrated energy system in step S43 specifically includes:
in the formula, lambda is the flexibility of the overall energy utilization of the comprehensive energy system.
In another aspect, the present invention further provides an energy utilization flexibility assessment system for an integrated energy system, the system comprising: the device comprises a sensing unit, a model unit, a test unit and an evaluation unit;
a sensing unit: the system is responsible for monitoring energy flow data of each interface in the comprehensive energy system in real time and providing real-time data input for the model unit, the test unit and the evaluation unit;
a model unit: the system is in charge of storing the mapping model of the comprehensive energy system and parameters thereof, when the topological structure and the parameters of the comprehensive energy system change, the model can be updated and corrected in real time, and meanwhile, the system is in charge of storing historical operation data of the comprehensive energy system and historical load data in a supply area and providing data input for a test unit and an evaluation unit;
a test unit: the comprehensive energy system mapping model interface and the model training interface are connected in charge, uncertainty test sets at the two ends of supply and demand are stored and updated, energy demand and energy supply pairs are generated according to the supply and demand change correlation, and data input is provided for the evaluation unit;
an evaluation unit: the evaluation unit receives data input from the sensing unit, the model unit and the test unit, thereby evaluating the flexibility of the system in real time and previewing the flexibility change of the system caused by different scheduling schemes.
The beneficial effects of the invention are:
according to the method and the system for evaluating the energy utilization flexibility of the comprehensive energy system, on one hand, the supply and demand states of the system can be quantitatively analyzed, and the time-space coupling and complementary substitution of different links such as source-network-load-storage are combed, so that the cascade utilization of energy of different grades is promoted; on the other hand, guidance can be provided for operation optimization of the system, the problems of low energy flow density, obvious fluctuation and the like of new energy are solved, energy waste is reduced, accurate energy supply according to needs is achieved, and the overall energy utilization efficiency of the system is improved.
According to the invention, the real-time energy flow data of each interface of the comprehensive energy system is monitored in real time, and the comprehensive energy system model and the energy flow flexibility evaluation model are combined to quantitatively analyze the supply and demand flexibility of the system, so that guidance is provided for the energy cascade utilization and the operation scheduling optimization of the comprehensive energy system, the interaction of the supply and demand ends is enhanced, and the adverse effect caused by the uncertainty of the system is reduced.
Drawings
FIG. 1 is a flow chart of an embodiment of the method for evaluating the flexibility of energy utilization of an integrated energy system according to the present invention;
FIG. 2 is a schematic diagram of the power flow synthesis fuzzy number of the present invention;
fig. 3 is a schematic block diagram of the energy usage flexibility evaluation system of the integrated energy system of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams each illustrating the basic structure of the present invention only in a schematic manner, and thus show only the constitution related to the present invention.
Example 1
As shown in fig. 1, the invention provides a method for evaluating the energy utilization flexibility of an integrated energy system, which comprises the following steps:
s1, establishing a mapping model of a comprehensive energy system based on physical characteristics of energy equipment and an energy network topological structure; s2, establishing uncertainty test sets of supply and demand ends according to historical operation data of the comprehensive energy system and historical load data in a supply area, and generating energy demand and energy supply pairs of demand ends under all the test sets; s3, respectively calculating the flexibility of different energy flows according to the generated energy demand and energy supply pairs; and S4, determining the overall energy utilization flexibility of the comprehensive energy system based on a fuzzy analytic hierarchy process.
The method is explained by combining the implementation flow of fig. 1, specifically, step S1 is to establish a mapping model layer of the integrated energy system in fig. 1, divide an energy producer in the system into a schedulable production unit, a new energy production unit and an energy storage unit according to energy production characteristics and respectively model the schedulable production unit, the new energy production unit and the energy storage unit to form a sub-model of the energy producer, then summarize historical load characteristics and environmental impact factors, establish an energy consumer model, further establish a distribution network topology sub-model connected with a supply and demand model by a material energy flow balance equation and a distribution loss rule, and form the mapping model of the integrated energy system by three sub-models; step S2, corresponding to the effective energy supply and demand generation layer in the graph 1, establishing uncertainty test sets at the two ends of supply and demand by receiving capacity information and consumption information of the model layer, and generating energy demand and energy supply pairs by adopting statistical means such as Pearson correlation analysis, coefficient threshold screening and similarity partition rejection on the test sets to realize separation and decoupling of the complex energy system; step S3, as shown in an energy flow flexibility evaluation layer in FIG. 1, according to decoupling results of effective energy supply and demand generation layers, the transport and consumption capacities of specific energy flows of different partitions are evaluated, the supply satisfaction degrees of all energy forms of each supply and demand pair are calculated, different weights are given according to the actual conditions of the system, and different energy flow supply satisfaction degrees of the system level are analyzed; and S4, establishing a system flexibility evaluation layer in the figure 1, establishing an energy form importance fuzzy evaluation matrix through the industrial characteristics of a system functional area, generating comprehensive fuzzy values of different energy flows after matrix transformation, and performing defuzzification and normalization operations according to the contrast possibility of the comprehensive fuzzy values to obtain an overall energy utilization flexibility quantitative evaluation index of the comprehensive energy system.
In the step S1, the step of establishing the mapping model of the integrated energy system based on the physical characteristics of the energy device and the energy network topology includes: s11, establishing an energy producer model and an energy consumer model; and S12, establishing a transmission and distribution network topology sub-model and connecting the supply and demand ends.
In the step S11, a method for quantifying the schedulable capability of the units is provided for the overall flexibility evaluation of the system, and the steps of establishing the energy producer model and the energy consumer model are as follows:
step S111, according to the historical load data and the environmental factors influencing the current load change, load prediction is carried out on energy consumers in the system supply area, and an energy consumer model is established:
in the formula (I), the compound is shown in the specification,
load demand predicted value of the ith user in the time period t; k is an energy source type, such as cold, heat, electricity, gas, etc.; q is a characteristic quantity representing energy level, taking the heat demand of a user as an example, q =60 ℃ for the domestic hot water demand and q =50 ℃ for the indoor heating demand;
historical load data for the same time period; xi t In order to influence the environmental factors of the current load change, for example, the predicted value of the heat load is influenced by factors such as the ambient temperature, the sunlight intensity, and the residence rate.
Step S112, modeling different types of energy producers according to the physical characteristics of the unit equipment:
(1) Dispatchable production unit
The construction of the comprehensive energy system needs to use a schedulable energy producer as an energy center to provide enough regulation space for the source side and ensure the energy balance and the system safety in the energy supply process. Common schedulable production units comprise a cogeneration unit, a combined cooling heating and power unit, a heat pump unit and the like, and the flexibility quantization models of the production units are as follows:
in the formula, E represents a set of energy types including energy forms of cold, heat, electricity, gas and the like;
and
the energy supply amount of the unit p in the time period t and the upper limit and the lower limit thereof respectively represent the flexible supply capacity of the unit p for energy of a certain form, and can be expressed as a function of the unit capacity and the climbing rate:
in the formula (I), the compound is shown in the specification,
and
the upper limit and the lower limit of the capacity of the unit p are respectively set; RU p And RD p The climbing rates of the unit x during load rising and load falling are respectively.
(2) New energy production unit
New energy sources with obvious emission reduction benefits, such as wind energy, solar energy and other non-dispatchable resources are influenced by current weather conditions, and the access of the uncertain and fluctuating energy sources has great influence on the flexibility of the system, so that a new energy production unit needs to be modeled:
in the formula (I), the compound is shown in the specification,
outputting the quantity predicted value of the energy type k for the new energy unit n in the time period t; kappa t In order to influence the current new energy production, for example, the output predicted value of the photovoltaic generator set is influenced by factors such as ambient temperature and sunlight intensity.
(3) Energy storage unit
The energy storage unit can finely manage the supply and demand balance of the system by switching the energy storage working mode and the energy release working mode in succession, realize the redistribution of the intermittent energy output in time, achieve the effect of peak clipping and valley filling, and improve the flexibility of the system. Similar to the schedulable production unit, the flexibility of the energy storage unit is affected by the energy storage capacity and the charge-discharge rate, and can therefore be represented by the following model:
wherein E represents a collection of energy species, including both heat and electricity storable forms of energy;
and
respectively the energy charging quantity and the energy discharging quantity of the energy storage unit s in the time period t;
and
respectively charging and discharging speed and rated capacity of the energy storage unit s;
the energy storage quantity of the energy storage units s in the time period t is obtained; eta s The charging and discharging efficiency of the energy storage unit s; at is a calculation time period.
And S12, establishing various types of transmission and distribution network topology submodels through the node connection matrix, and adding an energy input interface and an energy output interface on the topology, so that the energy producer model and the energy consumer model established in the step S11 can be accessed into corresponding energy networks and connected with supply and demand ends to balance the material flow and the energy flow of the system. For the interface between the energy producer model and the energy consumer model, the following conditions should be met:
in the formula (I), the compound is shown in the specification,
and
respectively an energy input interface and an energy output interface; a and B are respectively the set of all energy input interfaces and energy output interfaces; delta epsilon (0, 1) is a loss coefficient representing the loss degree in the energy transmission and distribution process;
and
respectively, an upper limit and a lower limit of the transmission capacity of the input interface.
In the step S2, according to the historical operating data of the integrated energy system and the historical load data in the supply area, a supply and demand end uncertainty test set is established, and the step of generating energy demand and energy supply pairs of the demand end under all the test sets includes: step S21, training and updating parameters of each layer of different models by combining the energy producer model, the energy consumer model and the transmission and distribution network topology submodel established in the step S1 according to capacity information generated by the energy producer model and consumption information generated by the energy consumer model to form uncertainty test sets at the two supply and demand ends; and S22, calculating correlation coefficients of all the energy input interfaces and the energy output interfaces, dividing the correlation coefficients into regions, and generating energy demand and energy supply pairs.
In step S22, calculating correlation coefficients of all energy interfaces and performing region division to generate pairs of energy demand and energy supply, the steps are as follows:
step S221, calculating Pearson correlation coefficients of each energy output and input interface according to uncertainty test sets at both supply and demand ends to measure the degree of variation of output parameters of energy producers at different positions caused by local load variation:
in the formula (I), the compound is shown in the specification,
for energy input interface
And energy output interface
Pearson correlation coefficient of (a);
covariance as the supply and demand end;
and
sample standard deviations for supply and demand sides, respectively.
Step S222, screening out an energy supply and demand partition set based on the correlation coefficient threshold, then eliminating supply and demand ports with small similarity from the partition set, and generating energy demand and energy supply pairs:
in the formula, se g Integrating energy demand and energy supply pairs; ρ is a unit of a gradient 0 And epsilon (0, 1) is a similarity threshold.
For example, b is known 1 And b 2 Respectively belonging to supply pair sets Se 1 And Se 2 Energy producer a 1 And energy consumers b 1 Has a correlation coefficient of 0.6 with energy consumer b 2 Has a correlation coefficient of 0.8, if in terms of ρ 0 If the threshold value of =0.5 is selected, a can be selected 1 Dividing the data into the two sets, namely screening out an energy supply and demand partition set based on the correlation coefficient threshold in the process; then, according to the similarity contrast of the conflict ports among the partition sets,
thus a will be 1 Rejecting supply pair set Se 1 In this process, the supply and demand ports with smaller similarity are removed from the partition set. The above two processes continue to end when the following two conditions are met:
·
wherein G is the total number of energy demand and energy supply pair sets;
·Se 1 ∪Se 2 ∪…∪Se G ＝A∪B。
the step S3 of calculating respectively different energy flow flexibilities according to the generated energy demand and energy supply pair includes: step S31, determining the supply configuration condition in the energy demand and energy supply pairs; and step S32, establishing a single energy flow flexibility evaluation model.
The specific steps of determining the supply configuration condition in the energy demand and energy supply pair in step S31 are as follows: it doesDetermining each energy demand and energy supply pair Se g The types and numbers of energy producers and energy consumers in the system to evaluate Se according to the calculated energy topological parameters g Energy transport capacity and user consumption capacity of (c):
in the formula, ET g And CT g Are respectively Se g The transport capacity and the user consumption capacity of a certain energy form are internally controlled; delta g E (0, 1) is Se g Energy network transport loss factor of (2);
and
is Se g An input interface and an output interface for some form of energy source.
The specific steps of establishing the energy flow flexibility evaluation model in the step S32 are as follows: defining the satisfaction degree of energy supply, and representing whether the supply of a certain energy form k meets the requirement of a user:
in the formula, λ k,g ∈(0,1]Is Se g The smaller the value of the satisfaction of the supply of energy in the form of energy k, the lower the satisfaction. Further, the overall satisfaction of the integrated energy system on certain energy supply can be calculated:
in the formula, w g Are weighting factors representing the importance of energy demand and energy supply.
The step S4 of determining the overall energy utilization flexibility of the comprehensive energy system based on the fuzzy analytic hierarchy process comprises the following steps: s41, constructing a multi-energy flow fuzzy judgment matrix; s42, obtaining satisfaction degree weights of different energy flows through defuzzification processing; and S43, calculating the overall energy use flexibility of the comprehensive energy system.
The specific steps of constructing the multi-energy flow fuzzy judgment matrix in the step S41 are as follows: according to the importance degree of energy forms such as cold, heat, electricity, gas and the like to the production and life of users, establishing a fuzzy evaluation matrix:
wherein, a ij The importance of energy form i relative to energy form j. On the basis of the fuzzy evaluation matrix, the comprehensive fuzzy value of the energy form k can be calculated:
the specific steps of obtaining the satisfaction degree weights of different energy flows through defuzzification in the step S42 are as follows: calculating the probability that the integrated fuzzy value of one energy form is greater than that of other energy forms:
d(Fu k )＝minP(Fu k ≥Fu i ),i＝1,2,...,k-1
in the formula, P (Fu) k ≥Fu i ) The integrated fuzzy value for energy form k is greater than the probability for energy form i. As shown in fig. 2, the deblurring probability of the triangular blur number is adopted, and the calculation method is as follows:
in the formula I k ,u k ,m k And l i ,u i ,m i The values are respectively the values when the lower bound, the upper bound and the membership degree of the comprehensive fuzzy number of the energy form k and the energy form i are 1.
Standardizing all the possible degrees obtained by solving, and obtaining the satisfaction degree weight of various energy flows:
in the formula, K is the total number of energy forms in the system, and if four energy forms of cold, heat, electricity and gas exist in the system, K is correspondingly 4.
The step S43 of calculating the flexibility of the energy consumption of the integrated energy system includes:
in the formula, lambda is the flexibility of the overall energy utilization of the comprehensive energy system.
Example 2
On the basis of the embodiment 1, as shown in fig. 3, the invention further provides an energy utilization flexibility evaluation system for an integrated energy system, which comprises the following parts:
a sensing unit: the energy flow data of each interface in the comprehensive energy system is monitored in real time, and real-time data input is provided for the model unit, the test unit and the evaluation unit;
a model unit: the system is in charge of storing the mapping model and parameters of the comprehensive energy system, updating and correcting the model in real time when the topological structure and the parameters of the comprehensive energy system change, and storing historical operation data of the comprehensive energy system and historical load data in a supply area to provide data input for a test unit and an evaluation unit;
a test unit: the comprehensive energy system mapping model interface and the model training interface are connected in charge, uncertainty test sets at the two ends of supply and demand are stored and updated, energy demand and energy supply pairs are generated according to the supply and demand change correlation, and data input is provided for the evaluation unit;
an evaluation unit: the evaluation unit receives data input from the sensing unit, the model unit and the test unit, thereby evaluating the flexibility of the system in real time and previewing the flexibility change of the system caused by different scheduling schemes.
Claims (8)
1. The method for evaluating the energy utilization flexibility of the comprehensive energy system is characterized by comprising the following steps of:
s1, establishing a mapping model of a comprehensive energy system based on physical characteristics of energy equipment and an energy network topological structure;
s2, establishing uncertainty test sets of supply and demand ends according to historical operation data of the comprehensive energy system and historical load data in a supply area, and generating energy demand and energy supply pairs of demand ends under all the test sets;
s3, respectively calculating the flexibility of different energy flows according to the generated energy demand and energy supply pairs;
s4, determining the overall energy utilization flexibility of the comprehensive energy system based on a fuzzy analytic hierarchy process;
the step S1 includes the steps of:
s11, establishing an energy producer model and an energy consumer model, and providing a quantitative method of unit scheduling capability for the overall flexibility evaluation of the system;
step S12, establishing a transmission and distribution network topology sub-model, and connecting a supply end and a demand end;
the step S11 includes the steps of:
step S111, according to the historical load data and the environmental factors influencing the current load change, load prediction is carried out on energy consumers in the system supply area, and an energy consumer model is established:
in the formula (I), the compound is shown in the specification,
load demand predicted value of the ith user in the time period t; k is the energy type; q is the characteristic quantity of the energy level;
historical load data for the same time period; xi shape t Environmental factors affecting the current load change;
step S112, modeling is carried out on energy producers of different types according to the physical characteristics of unit equipment, wherein the energy producers are divided into a schedulable production unit, a new energy production unit and an energy storage unit:
(1) Dispatchable production unit
The construction of the comprehensive energy system must use a schedulable energy producer as an energy center to provide enough regulation space for the source side and ensure the energy balance and the system safety in the energy supply process, and the flexibility quantization model of the schedulable production unit is as follows:
wherein E represents a set of energy species;
and
the energy supply amount of the unit p in the time period t and the upper limit and the lower limit thereof respectively represent the flexible supply capacity of the unit p for energy of a certain form, and can be expressed as a function of the unit capacity and the climbing rate:
in the formula (I), the compound is shown in the specification,
and
the upper limit and the lower limit of the capacity of the unit p are respectively set; RU p And RD p The climbing rates of the unit p during load rising and load falling are respectively set;
(2) New energy production unit
Non-dispatchable resources in new energy with obvious emission reduction benefits are influenced by current weather conditions, and the access of uncertainty and fluctuating energy has great influence on the flexibility of the system, so that a new energy production unit needs to be modeled:
in the formula (I), the compound is shown in the specification,
outputting the quantity predicted value of the energy type k for the new energy unit n in the time period t; kappa type t Factors influencing the current new energy production;
(3) Energy storage unit
The energy storage unit can finely manage the supply and demand balance of the system through the sequential switching of two working modes of energy storage and energy release, realize the redistribution of intermittent energy output in time, reach the effect of peak clipping and valley filling, improve the flexibility of the system, and the flexibility of the energy storage unit is influenced by energy storage capacity and charging and discharging rate and can be expressed by the following models:
wherein E represents a collection of energy species, including both heat and electricity storable forms of energy;
and
respectively the energy charging quantity and the energy discharging quantity of the energy storage unit s in the time period t;
and
respectively charging and discharging speed and rated capacity of the energy storage unit s;
the energy storage quantity of the energy storage units s in the time period t is obtained; eta s The charging and discharging efficiency of the energy storage unit s; Δ t is a calculation time period;
the step S12 is to establish a transmission and distribution network topology sub-model of several medium types of cold, heat, electricity and gas through a node connection matrix, and add an energy input interface and an energy output interface on the topology, so that the energy producer model and the energy consumer model established in the step S11 are connected into the corresponding energy network and connected with a supply end and a demand end to balance the material flow and the energy flow of the system; for the interface between the energy producer model and the energy consumer model, the following conditions should be met:
in the formula (I), the compound is shown in the specification,
and
respectively an energy input interface and an energy output interface; a and B are respectively the set of all energy input interfaces and energy output interfaces; delta epsilon (0, 1) is a loss coefficient representing the loss degree in the process of energy transmission and distribution;
and
respectively, an upper limit and a lower limit of the transmission capacity of the input interface.
2. The method for evaluating the flexibility of energy utilization of an integrated energy system according to claim 1, wherein the step S2 comprises the steps of:
step S21, training and updating parameters of each layer of different models by combining the energy producer model, the energy consumer model and the transmission and distribution network topology sub-model established in the step S1 according to the productivity information generated by the energy producer model and the consumption information generated by the energy consumer model to form an uncertainty test set at both supply and demand ends;
and S22, calculating correlation coefficients of all the energy input interfaces and all the energy output interfaces, dividing the correlation coefficients into regions, and generating pairs of energy demand and energy supply.
3. The method for evaluating the flexibility of energy utilization of an integrated energy system according to claim 2, wherein the step S22 comprises the steps of:
step S221, calculating Pearson correlation coefficients of each energy output and input interface according to uncertainty test sets at both supply and demand ends to measure the degree of variation of output parameters of energy producers at different positions caused by local load variation:
in the formula (I), the compound is shown in the specification,
for energy input interface
And an energy output interface
Pearson correlation coefficient of (a);
covariance as the supply and demand end;
and
sample standard deviations for the supply side and demand side, respectively;
step S222, screening out an energy supply and demand partition set based on a Pearson correlation coefficient threshold, then eliminating supply and demand ports with small similarity from the partition set, and generating energy demand and energy supply pairs:
in the formula (II) Se g Integrating energy demand and energy supply pairs; rho 0 E (0, 1) is a similarity threshold.
4. The method for evaluating the flexibility of energy utilization of an integrated energy system according to claim 3, wherein the step S3 comprises the steps of:
step S31, determining the supply configuration condition of the energy demand and the energy supply pair;
and step S32, establishing a single energy flow flexibility evaluation model.
5. The method for evaluating the flexibility of energy utilization of an integrated energy system according to claim 4, wherein the step S31 specifically comprises: determining each energy demand and energy supply pair Se g The types and numbers of energy producers and energy consumers in the system to evaluate Se according to the calculated energy topological parameters g Energy transport capacity and user consumption capacity of (c):
in the formula, ET g And CT g Are respectively Se g The transport capacity and the user consumption capacity of a certain energy form are internally controlled; delta. For the preparation of a coating g E (0, 1) is Se g Energy network transport loss factor of (2);
and
is Se g An input interface and an output interface for receiving a form of energy;
the step S32 is specifically: defining the satisfaction degree of energy supply, and representing whether the supply of a certain energy form k meets the requirement of a user:
in the formula of lambda k,g ∈(0,1]Is Se g The smaller the numerical value of the satisfaction degree of energy supply to the energy form k is, the lower the satisfaction degree is; further, the overall satisfaction degree of the comprehensive energy system on certain energy supply can be calculated:
in the formula, w g Is a weight coefficient representing the importance of energy demand and energy supply.
6. The method for evaluating the flexibility of energy utilization of an integrated energy system according to claim 5, wherein the step S4 comprises the steps of:
s41, constructing a multi-energy flow fuzzy judgment matrix;
s42, obtaining satisfaction weights of different energy flows through defuzzification processing;
and S43, calculating the overall energy use flexibility of the comprehensive energy system.
7. The method for evaluating the flexibility of energy utilization of an integrated energy system according to claim 6, wherein the step S41 specifically comprises: establishing a fuzzy evaluation matrix according to the importance degree of each energy form to the production and life of a user:
wherein, a ij The importance of energy form i relative to energy form j; on the basis of the fuzzy evaluation matrix, the comprehensive fuzzy value of the energy form k can be calculated:
the step S42 specifically includes: calculating the probability that the integrated fuzzy value of one energy form is greater than that of other energy forms:
d(Fu k )＝minP(Fu k ≥Fu i ),i＝1,2,...,k-1
in the formula, P (Fu) k ≥Fu i ) The probability that the comprehensive fuzzy value of the energy form k is greater than that of the energy form i; standardizing all the possible degrees obtained by solving, and obtaining the satisfaction degree weight of various energy flows:
in the formula, K is the total number of energy forms in the system;
the step S43 of calculating the flexibility of the energy consumption of the integrated energy system includes:
in the formula, lambda is the flexibility of the overall energy utilization of the comprehensive energy system.
8. An integrated energy system energy use flexibility evaluation system for evaluating the integrated energy system energy use flexibility by adopting the method according to any one of claims 1 to 7, which is characterized by comprising a sensing unit, a model unit, a testing unit and an evaluation unit;
a sensing unit: the system is responsible for monitoring energy flow data of each interface in the comprehensive energy system in real time and providing real-time data input for the model unit, the test unit and the evaluation unit;
a model unit: the system is in charge of storing the mapping model and parameters of the comprehensive energy system, updating and correcting the model in real time when the topological structure and the parameters of the comprehensive energy system change, and storing historical operation data of the comprehensive energy system and historical load data in a supply area to provide data input for a test unit and an evaluation unit;
a test unit: the comprehensive energy system mapping model interface is connected with the model training interface, uncertainty test sets at the two ends of supply and demand are stored and updated, energy demand and energy supply pairs are generated according to supply and demand change correlation, and data input is provided for the evaluation unit;
an evaluation unit: the evaluation unit receives data input from the sensing unit, the model unit and the test unit, thereby evaluating the flexibility of the system in real time and previewing the flexibility change of the system caused by different scheduling schemes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010474783.2A CN111695793B (en) | 2020-05-29 | 2020-05-29 | Method and system for evaluating energy utilization flexibility of comprehensive energy system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010474783.2A CN111695793B (en) | 2020-05-29 | 2020-05-29 | Method and system for evaluating energy utilization flexibility of comprehensive energy system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111695793A CN111695793A (en) | 2020-09-22 |
| CN111695793B true CN111695793B (en) | 2022-11-29 |
Family
ID=72478870
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010474783.2A Active CN111695793B (en) | 2020-05-29 | 2020-05-29 | Method and system for evaluating energy utilization flexibility of comprehensive energy system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111695793B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112180797A (en) * | 2020-10-08 | 2021-01-05 | 国网山东省电力公司电力科学研究院 | Measurement and control device and measurement and control method of comprehensive energy system |
| CN113094909B (en) * | 2021-04-16 | 2022-04-19 | 浙江大学 | Method and system for evaluating operation monitoring priority of integrated energy system equipment |
| CN113408851A (en) * | 2021-05-17 | 2021-09-17 | 上海电力大学 | Comprehensive energy system flexibility evaluation method |
| CN113364050A (en) * | 2021-06-22 | 2021-09-07 | 山东交通职业学院 | New energy reliability evaluation method in energy internet power distribution system |
| CN113489063B (en) * | 2021-07-01 | 2022-07-15 | 上海发电设备成套设计研究院有限责任公司 | Combined dispatching system and method for regional comprehensive energy |
| CN113850497A (en) * | 2021-09-22 | 2021-12-28 | 广东电网有限责任公司 | Electric power energy scheduling method, device, storage medium and platform |
| CN116090798B (en) * | 2023-04-07 | 2023-07-11 | 山东历控能源有限公司 | Comprehensive energy scheduling method and system |
| CN116739306B (en) * | 2023-07-27 | 2023-12-26 | 华北电力大学 | Heat pump load flexibility quantification method, system and equipment |
| CN116736780B (en) * | 2023-08-15 | 2023-11-24 | 贵州汇通华城股份有限公司 | Startup and shutdown control optimization method and system for regional energy station |
| CN117498399B (en) * | 2023-12-29 | 2024-03-08 | 国网浙江省电力有限公司 | Multi-energy collaborative configuration method and system considering elastic adjustable energy entity access |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109327042A (en) * | 2018-09-27 | 2019-02-12 | 南京邮电大学 | A kind of micro-grid multi-energy joint optimal operation method |
| CN110009152A (en) * | 2019-04-03 | 2019-07-12 | 东南大学 | A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods |
| CN110751377A (en) * | 2019-09-27 | 2020-02-04 | 国网天津市电力公司 | DC power distribution network topology structure design evaluation method and device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9569804B2 (en) * | 2012-08-27 | 2017-02-14 | Gridium, Inc. | Systems and methods for energy consumption and energy demand management |
| CN110782363A (en) * | 2019-08-15 | 2020-02-11 | 东南大学 | AC/DC power distribution network scheduling method considering wind power uncertainty |
-
2020
- 2020-05-29 CN CN202010474783.2A patent/CN111695793B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109327042A (en) * | 2018-09-27 | 2019-02-12 | 南京邮电大学 | A kind of micro-grid multi-energy joint optimal operation method |
| CN110009152A (en) * | 2019-04-03 | 2019-07-12 | 东南大学 | A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods |
| CN110751377A (en) * | 2019-09-27 | 2020-02-04 | 国网天津市电力公司 | DC power distribution network topology structure design evaluation method and device |
Non-Patent Citations (1)
| Title |
|---|
| 多能流系统合作协同的不确定多目标决策;翟延伟等;《控制理论与应用》;20191023;第1-9页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111695793A (en) | 2020-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111695793B (en) | Method and system for evaluating energy utilization flexibility of comprehensive energy system | |
| CN109492815B (en) | Energy storage power station site selection and volume fixing optimization method for power grid under market mechanism | |
| CN112116153B (en) | Park multivariate load joint prediction method coupling Copula and stacked LSTM network | |
| CN112084705B (en) | Grid-connected coordination planning method and system for comprehensive energy system | |
| CN111525587B (en) | Reactive load situation-based power grid reactive voltage control method and system | |
| CN112131733A (en) | Distributed power supply planning method considering influence of charging load of electric automobile | |
| Kong et al. | Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants | |
| Xu et al. | Adaptive DE algorithm for novel energy control framework based on edge computing in IIoT applications | |
| Wang et al. | Optimal scheduling for integrated energy system considering scheduling elasticity of electric and thermal loads | |
| CN114219212A (en) | Flexible scheduling method for demand side resources considering ubiquitous power internet of things and edge calculation | |
| CN113592133A (en) | Energy hub optimal configuration method and system | |
| CN117595391A (en) | Coordination control method of power grid system | |
| Rouzbahani et al. | Optimizing scheduling policy in smart grids using probabilistic Delayed Double Deep Q-Learning (P3DQL) algorithm | |
| Tao et al. | Reserve evaluation and energy management of micro-grids in joint electricity markets based on non-intrusive load monitoring | |
| CN109978236B (en) | Small hydropower station short-term power refined prediction method based on feature combination | |
| CN113937781A (en) | Virtual power plant rolling scheduling technology considering temperature control load polymer | |
| Wang et al. | Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units | |
| CN113364043A (en) | Micro-grid group optimization method based on condition risk value | |
| Wang et al. | Self-organizing maps for scenario reduction in long-term hydropower scheduling | |
| CN114358474A (en) | Typical multi-energy user model building method | |
| CN113988549A (en) | Energy storage real-time optimization method based on wind power anti-peak-shaving characteristics | |
| Gamst et al. | Time aggregation techniques applied to a capacity expansion model for real-life sector coupled energy systems | |
| Rouzbahani | Energy scheduling in IoE-enabled smart grids using probabilistic delayed double deep Q-learning (P3DQL) algorithm | |
| Daniel | Smart Grid Stability Prediction with Machine Learning | |
| Liu et al. | Sustainability indicator of renewable energy system based on fuzzy multi-criteria decision making methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |