CN117973617A - Thermal power plant value-added optimization scheduling method based on value chain and electric power market transaction - Google Patents
Thermal power plant value-added optimization scheduling method based on value chain and electric power market transaction Download PDFInfo
- Publication number
- CN117973617A CN117973617A CN202410163745.3A CN202410163745A CN117973617A CN 117973617 A CN117973617 A CN 117973617A CN 202410163745 A CN202410163745 A CN 202410163745A CN 117973617 A CN117973617 A CN 117973617A
- Authority
- CN
- China
- Prior art keywords
- value
- power plant
- thermal power
- enterprise
- added
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000005457 optimization Methods 0.000 title claims description 35
- 230000005611 electricity Effects 0.000 claims abstract description 78
- 238000011156 evaluation Methods 0.000 claims abstract description 76
- 238000004458 analytical method Methods 0.000 claims abstract description 27
- 230000008569 process Effects 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 30
- 239000000446 fuel Substances 0.000 claims description 28
- 238000002485 combustion reaction Methods 0.000 claims description 26
- 238000004519 manufacturing process Methods 0.000 claims description 22
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 21
- 230000007246 mechanism Effects 0.000 claims description 16
- 238000010248 power generation Methods 0.000 claims description 11
- 230000008901 benefit Effects 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 claims description 8
- 238000010977 unit operation Methods 0.000 claims description 8
- 239000013598 vector Substances 0.000 claims description 8
- 230000000295 complement effect Effects 0.000 claims description 7
- 238000000354 decomposition reaction Methods 0.000 claims description 7
- 238000011161 development Methods 0.000 claims description 7
- 238000010438 heat treatment Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 230000002708 enhancing effect Effects 0.000 claims description 6
- 230000006872 improvement Effects 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 6
- 238000005096 rolling process Methods 0.000 claims description 6
- 239000002893 slag Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 4
- 239000000463 material Substances 0.000 claims description 4
- 238000002156 mixing Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 3
- 229910052799 carbon Inorganic materials 0.000 claims description 3
- 238000005094 computer simulation Methods 0.000 claims description 3
- 230000018044 dehydration Effects 0.000 claims description 3
- 238000006297 dehydration reaction Methods 0.000 claims description 3
- 238000006477 desulfuration reaction Methods 0.000 claims description 3
- 230000023556 desulfurization Effects 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 238000004146 energy storage Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000011068 loading method Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012946 outsourcing Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 239000002994 raw material Substances 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000011144 upstream manufacturing Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 6
- 230000007774 longterm Effects 0.000 description 4
- 230000005484 gravity Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 239000000498 cooling water Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a value-added optimal scheduling method for a thermal power plant based on a value chain and electric market transaction, which comprises the following steps: dividing a thermal power plant enterprise value chain model based on a value chain theory; forecasting the demands of users of the multi-energy products, introducing an electric power market trading strategy to bid and clear the market, and forming dynamic trading electric quantity and trading electricity price; according to the enterprise value chain of the thermal power plant and the electric power market trading strategy, constructing a comprehensive evaluation index system influencing the enterprise value of the thermal power plant; determining the comprehensive weight value of each index affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process-entropy weight process, and comprehensively evaluating the value of each index by adopting a fuzzy comprehensive evaluation method; and performing value-added measure analysis on non-value-added and low-value-added operations in the value chain of the thermal power plant enterprise to form value-added scheduling schemes of the operations, and solving and outputting the optimal value-added scheduling schemes on each value chain of the thermal power plant enterprise with the aim of maximizing the value added of the thermal power plant enterprise.
Description
Technical Field
The invention belongs to the technical field of thermal power plant dispatching, and particularly relates to a value-added optimization dispatching method for a thermal power plant based on a value chain and electric power market transaction.
Background
The thermal power plant enterprises generally have the problems of high power supply coal consumption, low energy efficiency of units and large environmental protection investment, and the rapid development of informatization brings pressure to the thermal power plant enterprises, brings power and business opportunities, analyzes the current situation of the cost control of the thermal power plant enterprises, finds that the existing cost control system of the thermal power plant enterprises is more traditional, single and has limitations, cannot accurately control the cost of each link and operation area of production, effectively challenges the external environment, plays own advantages, improves the self value of the thermal power plant enterprises, and is a common problem for the thermal power plant enterprises in the future.
The value chain does not simply reduce the internal cost of the enterprise, but performs various analyses from the aspects of purchasing, production, sales, after-sales service and the like, analyzes activities with competitive advantages, strengthens valuable profit parts, reduces non-profit parts, thereby reducing the relevant operation cost and optimizing the value chain structure of the enterprise. In addition, the opening of the electricity selling side under the reform of the electric power market promotes the multidirectional selection of the main body of the electric power market, and according to the actual situation of the thermal power plant, the related policy of electric power market trade is introduced to conduct contract trade, so that the method is also an important way for saving the cost of enterprises of the thermal power plant. Therefore, how to introduce the value chain theory and the electric power market transaction into the thermal power plant enterprise, consider cost control from the perspective of the value chain, help the thermal power plant enterprise reduce the cost and increase the efficiency, and realize the maximization of the thermal power plant enterprise value is the problem which needs to be solved at present.
Based on the technical problems, a new value-added optimization scheduling method of the thermal power plant based on the value chain and the electric power market transaction needs to be designed.
Disclosure of Invention
The invention aims to solve the technical problems of overcoming the defects of the prior art, providing a value-added optimizing dispatching method of a thermal power plant based on a value chain and electric power market transaction, which can effectively divide each link and an operation area value chain of a thermal power plant enterprise by introducing a value chain theory, an electric power market transaction system and a fuzzy comprehensive evaluation system, and introduce an electric power market transaction mechanism to perfect an electric power transaction product and strengthen the optimization of an electric quantity transaction mechanism at an electricity selling side, so that market price fluctuation can be effectively prevented, the electric power market transaction is more stable and efficient, the influence of each index in each value chain on the value of the thermal power plant enterprise is comprehensively evaluated, the links and operation indexes which can be added are accurately known, the effectiveness, objectivity and scientificity of evaluation are improved, the reference and the basis are provided for reasonably guiding the value-added optimizing dispatching, the overall value of the thermal power plant enterprise is further improved, and the cost reduction and synergy are realized.
In order to solve the technical problems, the technical scheme of the invention is as follows:
The invention provides a value-added optimal scheduling method for a thermal power plant based on a value chain and electric market transaction, which comprises the following steps:
s1, taking a thermal power plant enterprise taking fuel as input and electric, thermal and gas multifunctional products as output as a profit main body, and dividing a thermal power plant enterprise value chain model into a thermal power plant enterprise transverse value chain, a thermal power plant enterprise internal value chain and a thermal power plant enterprise longitudinal value chain based on a value chain theory;
The thermal power plant enterprise transverse value chain is a competitor value chain; the thermal power plant enterprise internal value chain comprises a thermal power plant enterprise production operation value chain and a thermal power plant enterprise auxiliary operation value chain; the longitudinal value chain of the thermal power plant enterprise comprises an upstream supplier value chain and a downstream multi-functional product vendor value chain of the thermal power plant enterprise;
s2, forecasting the demands of users of electric, heat and gas multifunctional products, introducing an electric market trading strategy for electric energy sales in a downstream multifunctional product seller value chain, bidding by thermal power plant enterprises and competitor manufacturers and electric power users, and carrying out market clearing by taking quotations of all manufacturers and electricity purchasing demands of the users as judgment, and forming dynamic trading electric quantity and trading electric price under a full-period multi-trading market mechanism of the thermal power plant enterprises by combining operation constraint;
s3, constructing a comprehensive evaluation index system influencing the enterprise value of the thermal power plant according to the thermal power plant enterprise transverse value chain, the thermal power plant enterprise internal value chain and the thermal power plant enterprise longitudinal value chain and combining with an electric power market transaction strategy;
s4, determining comprehensive weight values of all indexes influencing the enterprise value of the thermal power plant in a comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process-entropy weight method, and comprehensively evaluating the values of all indexes by adopting a fuzzy comprehensive evaluation method, wherein the comprehensive weight values are divided into non-added value, low-added value, medium-added value and high-added value operation;
and S5, performing value-added measure analysis on non-value-added and low-value-added operations in the transverse value chain of the thermal power plant enterprise, the internal value chain of the thermal power plant enterprise and the longitudinal value chain of the thermal power plant enterprise to form value-added scheduling schemes of each operation, and solving and outputting the optimal value-added scheduling schemes on each value chain of the thermal power plant enterprise with the aim of maximizing the value added of the thermal power plant enterprise.
Further, in the step S1, the thermal power plant enterprise production operation value chain includes a combustion supply operation value chain, a combustion system operation value chain, a steam-water system operation value chain, an electric system operation value chain, a power supply system operation value chain, a heating system operation value chain, a gas supply system operation value chain, and an enterprise indirect production operation value chain;
the combustion supply operation value chain comprises a fuel loading and unloading operation, a fuel conveying operation, a fuel storage operation and a fuel blending operation; the combustion system operation value chain comprises boiler combustion operation, slag dehydration operation, slag unloading and ash removal operation and desulfurization and denitrification operation; the steam-water system operation value chain comprises water taking operation, chemical water treatment operation, steam turbine operation, temperature and pressure reduction operation and steam-water conveying operation; the electric system operation value chain comprises electric operation, self-power-consumption operation and external power-supply operation; the operation value chain of the power supply system comprises the operation of converting steam heat energy of a steam turbine into mechanical energy and converting mechanical energy of a generator into electric energy; the heating system operation chain comprises heat energy operation of converting fuel combustion chemical energy into steam; the operation value chain of the air supply system comprises the operation of converting electric energy into air pressure energy by a compressor; the enterprise indirect production operation value chain comprises environmental protection equipment operation, overhaul maintenance and inspection and checking;
the thermal power plant enterprise auxiliary operation value chain comprises manpower resource management, information system management, material management, equipment transformation management and marketing.
Further, the S2 specifically includes:
Predicting the electricity consumption, heat consumption and gas consumption of users in a downstream multi-functional product vendor value chain, and obtaining predicted values of the annual, monthly, daily and ultra-short-term electricity consumption demands, the predicted values of the heat consumption demands and the predicted values of the gas consumption demands of the users;
Introducing an electric power market transaction strategy to electric energy sales in a downstream multi-energy product sales value chain, publishing an annual electricity consumption prediction range to manufacturers including thermal power plant enterprises and competitors by a transaction center, carrying out annual data declaration and bidding of electric quantity of contract transaction by manufacturers participating in annual contract market transaction bidding, carrying out market clearing according to annual data declaration information and an annual electricity consumption prediction value, checking by combining unit operation constraint and safety constraint conditions of each manufacturer, formulating an annual transaction contract, and forming annual transaction electric quantity and transaction electric price of the thermal power plant enterprise;
the method comprises the steps of performing month decomposition of annual electricity consumption and day decomposition of the monthly electricity consumption, sequentially performing month data reporting, day data reporting, real-time data reporting and bidding on contract transaction electricity quantity by a manufacturer participating in the month, day and real-time transaction bidding according to month, day and ultra-short term electricity consumption prediction range by a transaction center, and performing market clearing and checking to obtain month, day and real-time contract transaction electricity quantity and transaction electricity price;
Wherein, in the electric power market trading strategy, further comprising: annual contract market transaction rolling optimization and monthly contract market transaction rolling optimization: measuring external factors of a power grid maintenance plan and starting of a unit in a thermal power plant, adjusting a power generation plan and a power transaction plan of the future month of the year, ensuring the electric quantity in the annual contract transaction to be completed when the external factors change by adjusting the formed annual contract transaction month by month, adjusting the power generation plan and the power transaction plan of each day of the future of the month, and ensuring the electric quantity in the current month contract transaction to be completed when the external factors change by adjusting the formed monthly contract transaction day by day;
And after the dynamic transaction electric quantity and the transaction electric price under the full-cycle multi-transaction market mechanism of the thermal power plant enterprise are formed, further comprising: based on the heat consumption and gas consumption demand in the downstream multi-functional product vendor value chain, the unit operation constraint of the thermal power plant enterprise and the safety operation constraint of the heat supply network and the gas network are combined to form transaction heat quantity, transaction gas quantity, transaction heat price and transaction gas price.
Further, in the step S3, a comprehensive evaluation index system affecting the enterprise value of the thermal power plant is constructed, which specifically includes:
setting first-level indexes influencing the enterprise value of the thermal power plant, wherein the first-level indexes comprise a transverse value index, an internal value index and a longitudinal value index;
the secondary indexes corresponding to the transverse value indexes comprise competitor value indexes; the three-level indexes corresponding to the competitor value indexes comprise the price quantity attribute of the competitor enterprise products, the technical development of the competitor enterprise, the purchasing and selling mode of the competitor enterprise and the competitor product service;
The secondary indexes corresponding to the internal value indexes comprise a combustion supply operation value index, a combustion system operation value index, a steam-water system operation value index, an electric system operation value index, a power supply system operation value index, a heat supply system operation value index, a gas supply system operation value index, an enterprise indirect production operation value index and an enterprise auxiliary operation value index; setting three-level indexes corresponding to each two-level index in the internal value indexes according to the content corresponding to each operation value chain;
The secondary indexes corresponding to the longitudinal value indexes comprise provider value indexes and multi-functional product vendor value indexes; the three-level indexes corresponding to the provider value indexes comprise a raw material provider cost index, an outsourcing electricity provider cost index, a standby capacity cost index and a carbon emission trading index; the multi-energy product seller value index comprises electric power transaction amount, electric power transaction electricity price, transaction heat, transaction air quantity, transaction heat price, transaction air price, multi-energy product transaction period, electric quantity demand forecast values, heat demand forecast values and air quantity demand forecast values of different periods of users.
Further, in the step S4, a fuzzy analytic hierarchy process-entropy weighting method is adopted to determine a comprehensive weight value of each index affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system, and the method specifically includes:
a fuzzy analytic hierarchy process is adopted to obtain subjective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system;
An entropy weight method is adopted to obtain objective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system;
and fusing the subjective weight and the objective weight of each index to obtain the comprehensive weight value of each index.
Further, the method for obtaining subjective weights of all indexes affecting the enterprise value of the thermal power plant in the comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process specifically comprises the following steps:
Constructing a fuzzy complementary judgment matrix by comparing the importance degrees of the indexes, wherein the fuzzy complementary judgment matrix is expressed as follows:
n is the number of indexes; b ij is the importance degree of the index a i compared with the index a j, and the larger the value is, the more important the index a i is;
A fuzzy consistency decision matrix B f＝(fij)n×n is established, B i is the sum of the elements of the i-th row in matrix B; bj is the sum of the j-th row elements in matrix B;
the subjective weight of each index is calculated and expressed as:
w zi is the subjective weight of index a i; alpha is a parameter, and meets alpha not less than (n-1)/2;
the method for solving the objective weight of each index affecting the enterprise value of the thermal power plant in the comprehensive evaluation index system by adopting the entropy weight method comprises the following steps:
Establishing a comprehensive evaluation matrix X= (X ij)n×m;xij is an index value, i=1, 2, …, n, j=1, 2, …, m, n is an index number, and m is a sample number;
after normalization processing is performed on the comprehensive evaluation matrix X, the method is expressed as follows:
Calculating entropy of each index
The objective weights of the various indicators are calculated and expressed as:
w ki is the objective weight of index a i;
the method for obtaining the comprehensive weight value of each index comprises the following steps of: the subjective weight and the objective weight of each index are fused by adopting a linear weighting method, and the comprehensive weight of each index is obtained and expressed as follows:
beta is a balance factor between subjective weights; μ is a balance factor between objective weights.
Further, in the step S4, the value of each index is comprehensively evaluated by adopting a fuzzy comprehensive evaluation method, and is divided into non-added value, low-added value, medium-added value and high-added value operations, which specifically include:
Establishing an index set and a corresponding weight set which influence the enterprise value of the thermal power plant;
Establishing a comment set according to the actual value-added situation and expert opinion of the thermal power plant enterprise;
Calculating the membership degree of each index by adopting a triangular membership degree function;
Establishing fuzzy evaluation vectors of the indexes according to membership degrees of the indexes in the comment set, and combining the fuzzy evaluation vectors of the indexes to establish a fuzzy evaluation matrix;
performing fuzzy calculation on the fuzzy evaluation matrix and the comprehensive weight of each index to obtain a comprehensive evaluation result of each index value;
And dividing the operation under each index into non-increment, low-increment, medium-increment and high-increment operation according to the comprehensive evaluation result of the value of each index and a preset operation increment interval.
Further, in S5, value-added measure analysis is performed on non-value-added and low-value-added operations in the thermal power plant enterprise lateral value chain, the thermal power plant enterprise internal value chain and the thermal power plant enterprise longitudinal value chain, so as to form an operation value-added scheduling scheme, which specifically includes:
And carrying out value-added measure analysis aiming at non-value-added and low-value-added operation in a transverse value chain of a thermal power plant enterprise, wherein the value-added measure analysis at least comprises the following steps: analyzing the value advantages and disadvantages of the thermal power plant enterprises and competitors, and carrying out the development and upgrading of enterprise equipment and technology, the improvement of purchasing and selling links and the collaborative energy supply among other enterprises;
And carrying out value-added measure analysis aiming at non-value-added and low-value-added operation in an enterprise internal value chain of the thermal power plant, wherein the value-added measure analysis at least comprises the following steps: performing dynamic simulation, parameter optimization adjustment and intelligent diagnosis and inspection by adopting a machine learning algorithm on operations in a production operation value chain of a thermal power plant enterprise; staff training and system management improvement are carried out on auxiliary operation of the thermal power plant enterprise;
And carrying out value-added measure analysis on non-value-added and low-value-added operations in a longitudinal value chain of the thermal power plant enterprise, wherein the value-added measure analysis at least comprises the following steps: enhancing cooperation with fuel suppliers, enhancing electricity consumption during valley time by using peak-valley flat electricity price charge of a power grid, reducing external purchase electricity quantity by energy storage equipment, and introducing photovoltaic power generation and optimizing an electricity market transaction model.
Further, the optimizing of the power market transaction model includes: and (3) performing game bidding by thermal power plant enterprises and other competitor manufacturers, based on enterprise electric quantity data reporting information and user electricity consumption requirements, taking the maximum gain of the thermal power plant enterprises as a target and the minimum electricity consumption cost of the users as a target, establishing an electric power market transaction optimization model by combining constraint conditions of electric power market transaction and constraint conditions of electricity purchasing of the users, and outputting optimal transaction electric quantity and transaction electric price.
Further, in the step S5, with the goal of maximizing the added value of the thermal power plant enterprise, the optimal added value scheduling scheme on each value chain of the output thermal power plant enterprise is solved, and the method specifically includes:
And calculating the value added result of the operation of the thermal power plant enterprise under each scheduling scheme aiming at each operation value added scheduling scheme, establishing a thermal power plant enterprise value added optimization scheduling model aiming at the maximum value added of the thermal power plant enterprise and combining constraint conditions of each operation, and determining the optimal value added scheduling scheme on each value chain of the thermal power plant enterprise by utilizing an intelligent optimization algorithm based on a simulation platform.
The beneficial effects of the invention are as follows:
According to the invention, a thermal power plant enterprise taking fuel as input and electric, thermal and gas multi-functional products as output is taken as a profit main body, and a thermal power plant enterprise value chain model is divided into a thermal power plant enterprise transverse value chain, a thermal power plant enterprise internal value chain and a thermal power plant enterprise longitudinal value chain based on a value chain theory; the method comprises the steps of predicting the demands of users of electric, heat and gas multifunctional products, introducing an electric market trading strategy for electric energy selling in a downstream multifunctional product seller value chain, bidding by thermal power plant enterprises and competitor manufacturers and electric power users, carrying out market clearing by taking quotations of all manufacturers and electricity purchasing demands of the users as judgment, and combining operation constraint to form dynamic trading electric quantity and trading electric price of the thermal power plant enterprises under a full-period multi-trading market mechanism; according to a thermal power plant enterprise transverse value chain, a thermal power plant enterprise internal value chain and a thermal power plant enterprise longitudinal value chain, and combining an electric power market trading strategy, constructing a comprehensive evaluation index system influencing the value of the thermal power plant enterprise; determining the comprehensive weight value of each index affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process-entropy weight method, and comprehensively evaluating the value of each index by adopting a fuzzy comprehensive evaluation method, wherein the comprehensive weight value is divided into non-added value, low-added value, medium-added value and high-added value operation; performing value-added measure analysis on non-value-added and low-value-added operations in a transverse value chain of a thermal power plant enterprise, an internal value chain of the thermal power plant enterprise and a longitudinal value chain of the thermal power plant enterprise to form value-added scheduling schemes of each operation, and solving and outputting optimal value-added scheduling schemes on each value chain of the thermal power plant enterprise with the aim of maximizing the value added of the thermal power plant enterprise; the method can effectively divide each link and the operation area value chain of the thermal power plant enterprise by introducing a value chain theory, electric power market trading and fuzzy comprehensive evaluation system, and introduces an electric power market trading mechanism, so that electric power trading products are perfected, electric quantity trading mechanism optimization at the electricity selling side is enhanced, market price fluctuation can be effectively prevented, electric power market trading is more stable and efficient, meanwhile, comprehensive evaluation is performed on the influence of each index in each value chain on the value of the thermal power plant enterprise, links and operation indexes which can be added are accurately known, the evaluation effectiveness, objectivity and scientificity are improved, reference and basis are provided for reasonably guiding value-added optimization scheduling, the overall value of the thermal power plant enterprise is further improved, and cost reduction and synergy are realized.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a value-added optimizing dispatching method of a thermal power plant based on a value chain and electric power market transaction;
FIG. 2 is a schematic diagram of the enterprise value chain structure of the thermal power plant.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
FIG. 1 is a flow chart of a value-added optimizing dispatching method of a thermal power plant based on a value chain and electric market transaction.
FIG. 2 is a schematic diagram of an enterprise value chain structure of a thermal power plant according to the present invention.
As shown in fig. 1 and 2, embodiment 1 provides a value-added optimization scheduling method for a thermal power plant based on a value chain and an electric power market transaction, which includes:
s1, taking a thermal power plant enterprise taking fuel as input and electric, thermal and gas multifunctional products as output as a profit main body, and dividing a thermal power plant enterprise value chain model into a thermal power plant enterprise transverse value chain, a thermal power plant enterprise internal value chain and a thermal power plant enterprise longitudinal value chain based on a value chain theory;
The thermal power plant enterprise transverse value chain is a competitor value chain; the thermal power plant enterprise internal value chain comprises a thermal power plant enterprise production operation value chain and a thermal power plant enterprise auxiliary operation value chain; the longitudinal value chain of the thermal power plant enterprise comprises an upstream supplier value chain and a downstream multi-functional product vendor value chain of the thermal power plant enterprise;
s2, forecasting the demands of users of electric, heat and gas multifunctional products, introducing an electric market trading strategy for electric energy sales in a downstream multifunctional product seller value chain, bidding by thermal power plant enterprises and competitor manufacturers and electric power users, and carrying out market clearing by taking quotations of all manufacturers and electricity purchasing demands of the users as judgment, and forming dynamic trading electric quantity and trading electric price under a full-period multi-trading market mechanism of the thermal power plant enterprises by combining operation constraint;
s3, constructing a comprehensive evaluation index system influencing the enterprise value of the thermal power plant according to the thermal power plant enterprise transverse value chain, the thermal power plant enterprise internal value chain and the thermal power plant enterprise longitudinal value chain and combining with an electric power market transaction strategy;
s4, determining comprehensive weight values of all indexes influencing the enterprise value of the thermal power plant in a comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process-entropy weight method, and comprehensively evaluating the values of all indexes by adopting a fuzzy comprehensive evaluation method, wherein the comprehensive weight values are divided into non-added value, low-added value, medium-added value and high-added value operation;
and S5, performing value-added measure analysis on non-value-added and low-value-added operations in the transverse value chain of the thermal power plant enterprise, the internal value chain of the thermal power plant enterprise and the longitudinal value chain of the thermal power plant enterprise to form value-added scheduling schemes of each operation, and solving and outputting the optimal value-added scheduling schemes on each value chain of the thermal power plant enterprise with the aim of maximizing the value added of the thermal power plant enterprise.
In this embodiment, in the S1, the thermal power plant enterprise production operation value chain includes a combustion supply operation value chain, a combustion system operation value chain, a steam-water system operation value chain, an electrical system operation value chain, a power supply system operation value chain, a heating system operation value chain, a gas supply system operation value chain, and an enterprise indirect production operation value chain;
the combustion supply operation value chain comprises a fuel loading and unloading operation, a fuel conveying operation, a fuel storage operation and a fuel blending operation; the combustion system operation value chain comprises boiler combustion operation, slag dehydration operation, slag unloading and ash removal operation and desulfurization and denitrification operation; the steam-water system operation value chain comprises water taking operation, chemical water treatment operation, steam turbine operation, temperature and pressure reduction operation and steam-water conveying operation; the electric system operation value chain comprises electric operation, self-power-consumption operation and external power-supply operation; the operation value chain of the power supply system comprises the operation of converting steam heat energy of a steam turbine into mechanical energy and converting mechanical energy of a generator into electric energy; the heating system operation chain comprises heat energy operation of converting fuel combustion chemical energy into steam; the operation value chain of the air supply system comprises the operation of converting electric energy into air pressure energy by a compressor; the enterprise indirect production operation value chain comprises environmental protection equipment operation, overhaul maintenance and inspection and checking;
the thermal power plant enterprise auxiliary operation value chain comprises manpower resource management, information system management, material management, equipment transformation management and marketing.
In this embodiment, the S2 specifically includes:
Predicting the electricity consumption, heat consumption and gas consumption of users in a downstream multi-functional product vendor value chain, and obtaining predicted values of the annual, monthly, daily and ultra-short-term electricity consumption demands, the predicted values of the heat consumption demands and the predicted values of the gas consumption demands of the users;
Introducing an electric power market transaction strategy to electric energy sales in a downstream multi-energy product sales value chain, publishing an annual electricity consumption prediction range to manufacturers including thermal power plant enterprises and competitors by a transaction center, carrying out annual data declaration and bidding of electric quantity of contract transaction by manufacturers participating in annual contract market transaction bidding, carrying out market clearing according to annual data declaration information and an annual electricity consumption prediction value, checking by combining unit operation constraint and safety constraint conditions of each manufacturer, formulating an annual transaction contract, and forming annual transaction electric quantity and transaction electric price of the thermal power plant enterprise;
the method comprises the steps of performing month decomposition of annual electricity consumption and day decomposition of the monthly electricity consumption, sequentially performing month data reporting, day data reporting, real-time data reporting and bidding on contract transaction electricity quantity by a manufacturer participating in the month, day and real-time transaction bidding according to month, day and ultra-short term electricity consumption prediction range by a transaction center, and performing market clearing and checking to obtain month, day and real-time contract transaction electricity quantity and transaction electricity price;
Wherein, in the electric power market trading strategy, further comprising: annual contract market transaction rolling optimization and monthly contract market transaction rolling optimization: measuring external factors of a power grid maintenance plan and starting of a unit in a thermal power plant, adjusting a power generation plan and a power transaction plan of the future month of the year, ensuring the electric quantity in the annual contract transaction to be completed when the external factors change by adjusting the formed annual contract transaction month by month, adjusting the power generation plan and the power transaction plan of each day of the future of the month, and ensuring the electric quantity in the current month contract transaction to be completed when the external factors change by adjusting the formed monthly contract transaction day by day;
And after the dynamic transaction electric quantity and the transaction electric price under the full-cycle multi-transaction market mechanism of the thermal power plant enterprise are formed, further comprising: based on the heat consumption and gas consumption demand in the downstream multi-functional product vendor value chain, the unit operation constraint of the thermal power plant enterprise and the safety operation constraint of the heat supply network and the gas network are combined to form transaction heat quantity, transaction gas quantity, transaction heat price and transaction gas price.
It should be noted that, in the operation and management process, the electric power trade market needs to pay attention to the benign interaction of both supply and demand sides to reach balance. Starting from three different transaction mechanisms of long-term contract transaction, spot and futures, respectively providing an electric power transaction mechanism suitable for electric quantity requirements of thermal power plant enterprises and future users, establishing a full-period multi-mode transaction market mechanism of medium-long term (annual, monthly) +short term+spot (daily, real-time) +futures, encouraging market subjects to develop transactions in months such as week and day on the basis of annual and monthly transactions, promoting continuous refinement of electric quantity, and increasing transaction flexibility and price elasticity. In the existing medium-and-long-term contract transaction mechanism, the annual and monthly contract transaction electric quantity is estimated to conduct the fine design of contract transaction through the decomposition and refinement of the electric quantity demand predicted value.
The electric futures refer to the fact that a preset number of electric power buys and sells are carried out at the time points appointed in the future, the electric power futures are about targets, the electric power futures are novel market trading mechanisms based on long-term electric power contract trading, the electric power futures are highly normalized long-term contracts, because electric power is different from general commodities, electric power demands have certain rigidity, production and consumption cannot be disjointed, a certain risk exists in an electric power market, and the problem can be effectively solved by building the electric power futures.
The current price of the electric power reflects the value and scarcity degree of the electric energy in time and space, and is influenced by multiple factors such as geographic position, installed capacity, power generation cost, user load and the like, and the change of any factor directly or indirectly changes the supply and demand conditions to cause the price change. In the electric power market, the spot market and the medium-and-long-term market complement each other to be linked. When the spot price is predicted to be high, the power generation side thermal power plant enterprise is more willing to generate power according to the spot market price, and accordingly the long-term already-achieved power in the unit is reduced.
Elements of the medium-to-long term trading strategy include price, electricity, and electricity split curves. The contracted price of the middle-and-long-term contract electric quantity is gradually formed in the electric power market with other manufacturers according to the measuring and calculating results of the thermal power plant on annual fuel cost, unit operation cost and the like. The electric quantity refers to the actual annual transaction electric quantity of the unit, and the situation that the cost may be higher than the spot price can be caused by information such as fuel cost, fuel price fluctuation and the like, and the thermal power plant needs to determine the expected annual transaction total electric quantity according to the affordable risk range. The electricity decomposing curve refers to that the electricity generating side and the electricity selling side need to agree on an electricity curve, and month, day and time sharing curves are defined, wherein the electricity dividing curve comprises the steps of reducing the specific gravity of electricity during the period of peak-to-peak summer in the month dividing curve, increasing the specific gravity of electricity on holidays in the day dividing curve, and increasing the specific gravity of electricity on valley in the time sharing curve so as to increase the value of contract electricity in medium-long-term markets under the same electricity and price.
In this embodiment, in the step S3, a comprehensive evaluation index system that affects the enterprise value of the thermal power plant is constructed, and specifically includes:
setting first-level indexes influencing the enterprise value of the thermal power plant, wherein the first-level indexes comprise a transverse value index, an internal value index and a longitudinal value index;
the secondary indexes corresponding to the transverse value indexes comprise competitor value indexes; the three-level indexes corresponding to the competitor value indexes comprise the price quantity attribute of the competitor enterprise products, the technical development of the competitor enterprise, the purchasing and selling mode of the competitor enterprise and the competitor product service;
The secondary indexes corresponding to the internal value indexes comprise a combustion supply operation value index, a combustion system operation value index, a steam-water system operation value index, an electric system operation value index, a power supply system operation value index, a heat supply system operation value index, a gas supply system operation value index, an enterprise indirect production operation value index and an enterprise auxiliary operation value index; setting three-level indexes corresponding to each two-level index in the internal value indexes according to the content corresponding to each operation value chain;
The secondary indexes corresponding to the longitudinal value indexes comprise provider value indexes and multi-functional product vendor value indexes; the three-level indexes corresponding to the provider value indexes comprise a raw material provider cost index, an outsourcing electricity provider cost index, a standby capacity cost index and a carbon emission trading index; the multi-energy product seller value index comprises electric power transaction amount, electric power transaction electricity price, transaction heat, transaction air quantity, transaction heat price, transaction air price, multi-energy product transaction period, electric quantity demand forecast values, heat demand forecast values and air quantity demand forecast values of different periods of users.
The three-level indexes corresponding to the combustion supply operation value indexes comprise fuel quality, fuel storage and transportation and fuel blending; the three-level indexes corresponding to the operation value indexes of the combustion system comprise main steam parameters of a unit, a heat and electricity load ratio and unit operation consumption of equipment in a plant; the three-level indexes corresponding to the steam-water system operation value indexes comprise a steam-machine operation parameter, a water supplementing parameter and a water supply cooling water parameter; the three-level indexes corresponding to the operation value indexes of the electrical system comprise electrical operation parameters, self-power consumption and external power supply information; the three-level index corresponding to the operation value index of the power supply system comprises a thermal energy-to-mechanical energy parameter and a mechanical energy-to-electric energy parameter; the three-level index corresponding to the operation value index of the heating system comprises a heat energy parameter for converting chemical energy into steam; the three-level index corresponding to the operation value index of the air supply system comprises an electric energy-to-air energy parameter; the three-level indexes corresponding to the indirect production operation value indexes of the enterprise comprise environment-friendly equipment operation parameters, maintenance plans and maintenance cost; the three-level indexes corresponding to the enterprise auxiliary operation value indexes comprise manpower resource management, information system management, material management, equipment transformation management and marketing.
In this embodiment, in S4, a fuzzy analytic hierarchy process-entropy weight method is used to determine a comprehensive weight value of each index affecting the enterprise value of the thermal power plant in the comprehensive evaluation index system, and specifically includes:
a fuzzy analytic hierarchy process is adopted to obtain subjective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system;
An entropy weight method is adopted to obtain objective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system;
and fusing the subjective weight and the objective weight of each index to obtain the comprehensive weight value of each index.
In this embodiment, the method for obtaining subjective weights of the indexes affecting the enterprise value of the thermal power plant in the comprehensive evaluation index system by using a fuzzy analytic hierarchy process specifically includes:
Constructing a fuzzy complementary judgment matrix by comparing the importance degrees of the indexes, wherein the fuzzy complementary judgment matrix is expressed as follows:
n is the number of indexes; b ij is the importance degree of the index a i compared with the index a j, and the larger the value is, the more important the index a i is;
A fuzzy consistency decision matrix B f＝(fij)n×n is established, B i is the sum of the elements of the i-th row in matrix B; bj is the sum of the j-th row elements in matrix B;
the subjective weight of each index is calculated and expressed as:
w zi is the subjective weight of index a i; alpha is a parameter, and meets alpha not less than (n-1)/2;
the method for solving the objective weight of each index affecting the enterprise value of the thermal power plant in the comprehensive evaluation index system by adopting the entropy weight method comprises the following steps:
Establishing a comprehensive evaluation matrix X= (X ij)n×m;xij is an index value, i=1, 2, …, n, j=1, 2, …, m, n is an index number, and m is a sample number;
after normalization processing is performed on the comprehensive evaluation matrix X, the method is expressed as follows:
Calculating entropy of each index
The objective weights of the various indicators are calculated and expressed as:
w ki is the objective weight of index a i;
the method for obtaining the comprehensive weight value of each index comprises the following steps of: the subjective weight and the objective weight of each index are fused by adopting a linear weighting method, and the comprehensive weight of each index is obtained and expressed as follows:
beta is a balance factor between subjective weights; μ is a balance factor between objective weights.
In this embodiment, in S4, the value of each index is comprehensively evaluated by using a fuzzy comprehensive evaluation method, and is divided into non-added value, low-added value, medium-added value and high-added value operations, which specifically include:
Establishing an index set and a corresponding weight set which influence the enterprise value of the thermal power plant;
Establishing a comment set according to the actual value-added situation and expert opinion of the thermal power plant enterprise;
Calculating the membership degree of each index by adopting a triangular membership degree function;
Establishing fuzzy evaluation vectors of the indexes according to membership degrees of the indexes in the comment set, and combining the fuzzy evaluation vectors of the indexes to establish a fuzzy evaluation matrix;
performing fuzzy calculation on the fuzzy evaluation matrix and the comprehensive weight of each index to obtain a comprehensive evaluation result of each index value;
And dividing the operation under each index into non-increment, low-increment, medium-increment and high-increment operation according to the comprehensive evaluation result of the value of each index and a preset operation increment interval.
In practical application, an index set A= { a 1,a2,…,an } which influences the enterprise value of the thermal power plant is established, and a corresponding weight set W= { W 1,w2,…,wn };
Establishing a comment set Y= { Y 1,y2,…,ym }, according to the actual value-added situation and expert opinion of the thermal power plant enterprise;
Calculating the membership degree of each index by adopting a triangular membership degree function, and setting For the membership of index a j to the ith evaluation level, e j is the actual value of index a j,/>Is the upper limit of the ith evaluation level of index a j,/>Is the lower limit of the ith evaluation level of index a j,/>The average value of the upper limit and the lower limit of the jth index;
According to the membership degree of each index in the comment set, establishing a fuzzy evaluation vector of each index And establishing a fuzzy evaluation matrix by combining fuzzy evaluation vectors of all indexes, wherein the fuzzy evaluation matrix is expressed as:
performing fuzzy calculation on the fuzzy evaluation matrix and the comprehensive weight of each index to obtain a comprehensive evaluation result of each index value, wherein the comprehensive evaluation result is expressed as:
Is a fuzzy synthesis operator;
according to the comprehensive evaluation result of the value of each index and a preset operation increment interval, the operation under each index is divided into non-increment, low-increment, medium-increment and high-increment operation, which are expressed as follows:
d f,max、df,min is the upper limit and the lower limit of the preset non-value-added operation respectively; d l,max、dl,min is the upper limit and the lower limit of the preset low-value-added operation respectively; d m,max、dm,min is the upper limit and the lower limit of the preset medium value-added operation respectively; d h,max、dh,min is the upper limit and the lower limit of the preset high-value-added operation respectively; the upper limit and the lower limit of a value-added section of different operations under each index on different value chains are different, and the value-added section is set according to the operation type; for example, the upper and lower intermediate value limits of different operations under the lateral value chain and the longitudinal value chain are different.
In this embodiment, in S5, value-added measure analysis is performed on non-value-added and low-value-added operations in the thermal power plant enterprise lateral value chain, the thermal power plant enterprise internal value chain, and the thermal power plant enterprise longitudinal value chain, so as to form an operation value-added scheduling scheme, which specifically includes:
And carrying out value-added measure analysis aiming at non-value-added and low-value-added operation in a transverse value chain of a thermal power plant enterprise, wherein the value-added measure analysis at least comprises the following steps: analyzing the value advantages and disadvantages of the thermal power plant enterprises and competitors, and carrying out the development and upgrading of enterprise equipment and technology, the improvement of purchasing and selling links and the collaborative energy supply among other enterprises;
And carrying out value-added measure analysis aiming at non-value-added and low-value-added operation in an enterprise internal value chain of the thermal power plant, wherein the value-added measure analysis at least comprises the following steps: performing dynamic simulation, parameter optimization adjustment and intelligent diagnosis and inspection by adopting a machine learning algorithm on operations in a production operation value chain of a thermal power plant enterprise; staff training and system management improvement are carried out on auxiliary operation of the thermal power plant enterprise;
And carrying out value-added measure analysis on non-value-added and low-value-added operations in a longitudinal value chain of the thermal power plant enterprise, wherein the value-added measure analysis at least comprises the following steps: enhancing cooperation with fuel suppliers, enhancing electricity consumption during valley time by using peak-valley flat electricity price charge of a power grid, reducing external purchase electricity quantity by energy storage equipment, and introducing photovoltaic power generation and optimizing an electricity market transaction model.
In this embodiment, the optimizing of the electric power market transaction model includes: and (3) performing game bidding by thermal power plant enterprises and other competitor manufacturers, based on enterprise electric quantity data reporting information and user electricity consumption requirements, taking the maximum gain of the thermal power plant enterprises as a target and the minimum electricity consumption cost of the users as a target, establishing an electric power market transaction optimization model by combining constraint conditions of electric power market transaction and constraint conditions of electricity purchasing of the users, and outputting optimal transaction electric quantity and transaction electric price.
In this embodiment, in the step S5, with the goal of maximizing the added value of the thermal power plant enterprise, the optimal added value scheduling scheme on each value chain of the output thermal power plant enterprise is solved, and the method specifically includes:
And calculating the value added result of the operation of the thermal power plant enterprise under each scheduling scheme aiming at each operation value added scheduling scheme, establishing a thermal power plant enterprise value added optimization scheduling model aiming at the maximum value added of the thermal power plant enterprise and combining constraint conditions of each operation, and determining the optimal value added scheduling scheme on each value chain of the thermal power plant enterprise by utilizing an intelligent optimization algorithm based on a simulation platform.
It should be noted that, the value-added maximization of the thermal power plant enterprise performs comprehensive calculation according to the cost minimization, the value-added maximization of non-value-added operation and low value-added operation of the thermal power plant, and the minimization of the power supply, heat supply and air supply missing rate.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other manners as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The value-added optimal scheduling method for the thermal power plant based on the value chain and the electric power market transaction is characterized by comprising the following steps of:
s1, taking a thermal power plant enterprise taking fuel as input and electric, thermal and gas multifunctional products as output as a profit main body, and dividing a thermal power plant enterprise value chain model into a thermal power plant enterprise transverse value chain, a thermal power plant enterprise internal value chain and a thermal power plant enterprise longitudinal value chain based on a value chain theory;
The thermal power plant enterprise transverse value chain is a competitor value chain; the thermal power plant enterprise internal value chain comprises a thermal power plant enterprise production operation value chain and a thermal power plant enterprise auxiliary operation value chain; the longitudinal value chain of the thermal power plant enterprise comprises an upstream supplier value chain and a downstream multi-functional product vendor value chain of the thermal power plant enterprise;
s2, forecasting the demands of users of electric, heat and gas multifunctional products, introducing an electric market trading strategy for electric energy sales in a downstream multifunctional product seller value chain, bidding by thermal power plant enterprises and competitor manufacturers and electric power users, and carrying out market clearing by taking quotations of all manufacturers and electricity purchasing demands of the users as judgment, and forming dynamic trading electric quantity and trading electric price under a full-period multi-trading market mechanism of the thermal power plant enterprises by combining operation constraint;
s3, constructing a comprehensive evaluation index system influencing the enterprise value of the thermal power plant according to the thermal power plant enterprise transverse value chain, the thermal power plant enterprise internal value chain and the thermal power plant enterprise longitudinal value chain and combining with an electric power market transaction strategy;
s4, determining comprehensive weight values of all indexes influencing the enterprise value of the thermal power plant in a comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process-entropy weight method, and comprehensively evaluating the values of all indexes by adopting a fuzzy comprehensive evaluation method, wherein the comprehensive weight values are divided into non-added value, low-added value, medium-added value and high-added value operation;
and S5, performing value-added measure analysis on non-value-added and low-value-added operations in the transverse value chain of the thermal power plant enterprise, the internal value chain of the thermal power plant enterprise and the longitudinal value chain of the thermal power plant enterprise to form value-added scheduling schemes of each operation, and solving and outputting the optimal value-added scheduling schemes on each value chain of the thermal power plant enterprise with the aim of maximizing the value added of the thermal power plant enterprise.
2. The value added optimization scheduling method of a thermal power plant according to claim 1, wherein in S1, the thermal power plant enterprise production operation value chain includes a combustion supply operation value chain, a combustion system operation value chain, a steam-water system operation value chain, an electrical system operation value chain, a power supply system operation value chain, a heating system operation value chain, a gas supply system operation value chain, and an enterprise indirect production operation value chain;
the combustion supply operation value chain comprises a fuel loading and unloading operation, a fuel conveying operation, a fuel storage operation and a fuel blending operation; the combustion system operation value chain comprises boiler combustion operation, slag dehydration operation, slag unloading and ash removal operation and desulfurization and denitrification operation; the steam-water system operation value chain comprises water taking operation, chemical water treatment operation, steam turbine operation, temperature and pressure reduction operation and steam-water conveying operation; the electric system operation value chain comprises electric operation, self-power-consumption operation and external power-supply operation; the operation value chain of the power supply system comprises the operation of converting steam heat energy of a steam turbine into mechanical energy and converting mechanical energy of a generator into electric energy; the heating system operation chain comprises heat energy operation of converting fuel combustion chemical energy into steam; the operation value chain of the air supply system comprises the operation of converting electric energy into air pressure energy by a compressor; the enterprise indirect production operation value chain comprises environmental protection equipment operation, overhaul maintenance and inspection and checking;
the thermal power plant enterprise auxiliary operation value chain comprises manpower resource management, information system management, material management, equipment transformation management and marketing.
3. The value-added optimization scheduling method of the thermal power plant according to claim 1, wherein the S2 specifically comprises:
Predicting the electricity consumption, heat consumption and gas consumption of users in a downstream multi-functional product vendor value chain, and obtaining predicted values of the annual, monthly, daily and ultra-short-term electricity consumption demands, the predicted values of the heat consumption demands and the predicted values of the gas consumption demands of the users;
Introducing an electric power market transaction strategy to electric energy sales in a downstream multi-energy product sales value chain, publishing an annual electricity consumption prediction range to manufacturers including thermal power plant enterprises and competitors by a transaction center, carrying out annual data declaration and bidding of electric quantity of contract transaction by manufacturers participating in annual contract market transaction bidding, carrying out market clearing according to annual data declaration information and an annual electricity consumption prediction value, checking by combining unit operation constraint and safety constraint conditions of each manufacturer, formulating an annual transaction contract, and forming annual transaction electric quantity and transaction electric price of the thermal power plant enterprise;
the method comprises the steps of performing month decomposition of annual electricity consumption and day decomposition of the monthly electricity consumption, sequentially performing month data reporting, day data reporting, real-time data reporting and bidding on contract transaction electricity quantity by a manufacturer participating in the month, day and real-time transaction bidding according to month, day and ultra-short term electricity consumption prediction range by a transaction center, and performing market clearing and checking to obtain month, day and real-time contract transaction electricity quantity and transaction electricity price;
Wherein, in the electric power market trading strategy, further comprising: annual contract market transaction rolling optimization and monthly contract market transaction rolling optimization: measuring external factors of a power grid maintenance plan and starting of a unit in a thermal power plant, adjusting a power generation plan and a power transaction plan of the future month of the year, ensuring the electric quantity in the annual contract transaction to be completed when the external factors change by adjusting the formed annual contract transaction month by month, adjusting the power generation plan and the power transaction plan of each day of the future of the month, and ensuring the electric quantity in the current month contract transaction to be completed when the external factors change by adjusting the formed monthly contract transaction day by day;
And after the dynamic transaction electric quantity and the transaction electric price under the full-cycle multi-transaction market mechanism of the thermal power plant enterprise are formed, further comprising: based on the heat consumption and gas consumption demand in the downstream multi-functional product vendor value chain, the unit operation constraint of the thermal power plant enterprise and the safety operation constraint of the heat supply network and the gas network are combined to form transaction heat quantity, transaction gas quantity, transaction heat price and transaction gas price.
4. The value-added optimization scheduling method for the thermal power plant according to claim 1, wherein in S3, a comprehensive evaluation index system affecting the enterprise value of the thermal power plant is constructed, and specifically comprises:
setting first-level indexes influencing the enterprise value of the thermal power plant, wherein the first-level indexes comprise a transverse value index, an internal value index and a longitudinal value index;
the secondary indexes corresponding to the transverse value indexes comprise competitor value indexes; the three-level indexes corresponding to the competitor value indexes comprise the price quantity attribute of the competitor enterprise products, the technical development of the competitor enterprise, the purchasing and selling mode of the competitor enterprise and the competitor product service;
The secondary indexes corresponding to the internal value indexes comprise a combustion supply operation value index, a combustion system operation value index, a steam-water system operation value index, an electric system operation value index, a power supply system operation value index, a heat supply system operation value index, a gas supply system operation value index, an enterprise indirect production operation value index and an enterprise auxiliary operation value index; setting three-level indexes corresponding to each two-level index in the internal value indexes according to the content corresponding to each operation value chain;
The secondary indexes corresponding to the longitudinal value indexes comprise provider value indexes and multi-functional product vendor value indexes; the three-level indexes corresponding to the provider value indexes comprise a raw material provider cost index, an outsourcing electricity provider cost index, a standby capacity cost index and a carbon emission trading index; the multi-energy product seller value index comprises electric power transaction amount, electric power transaction electricity price, transaction heat, transaction air quantity, transaction heat price, transaction air price, multi-energy product transaction period, electric quantity demand forecast values, heat demand forecast values and air quantity demand forecast values of different periods of users.
5. The value-added optimization scheduling method of the thermal power plant according to claim 1, wherein in S4, a fuzzy analytic hierarchy process-entropy weight method is adopted to determine a comprehensive weight value of each index affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system, and the method specifically comprises the following steps:
a fuzzy analytic hierarchy process is adopted to obtain subjective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system;
An entropy weight method is adopted to obtain objective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system;
and fusing the subjective weight and the objective weight of each index to obtain the comprehensive weight value of each index.
6. The value-added optimization scheduling method for the thermal power plant according to claim 5, wherein the method for obtaining subjective weights of all indexes affecting the enterprise value of the thermal power plant in a comprehensive evaluation index system by adopting a fuzzy analytic hierarchy process comprises the following steps:
Constructing a fuzzy complementary judgment matrix by comparing the importance degrees of the indexes, wherein the fuzzy complementary judgment matrix is expressed as follows:
n is the number of indexes; b ij is the importance degree of the index a i compared with the index a j, and the larger the value is, the more important the index a i is;
A fuzzy consistency decision matrix B f＝(fij)n×n is established, B i is the sum of the elements of the i-th row in matrix B; bj is the sum of the j-th row elements in matrix B;
the subjective weight of each index is calculated and expressed as:
w zi is the subjective weight of index a i; alpha is a parameter, and meets alpha not less than (n-1)/2;
the method for solving the objective weight of each index affecting the enterprise value of the thermal power plant in the comprehensive evaluation index system by adopting the entropy weight method comprises the following steps:
establishing a comprehensive evaluation matrix X= (X ij)n×m; xij is an index value, i=1, 2, …, n, j=1, 2, …, m, n is an index number, and m is a sample number;
after normalization processing is performed on the comprehensive evaluation matrix X, the method is expressed as follows:
Calculating entropy of each index
The objective weights of the various indicators are calculated and expressed as:
w ki is the objective weight of index a i;
the method for obtaining the comprehensive weight value of each index comprises the following steps of: the subjective weight and the objective weight of each index are fused by adopting a linear weighting method, and the comprehensive weight of each index is obtained and expressed as follows:
beta is a balance factor between subjective weights; μ is a balance factor between objective weights.
7. The value-added optimization scheduling method of the thermal power plant according to claim 1, wherein in S4, the value of each index is comprehensively evaluated by a fuzzy comprehensive evaluation method and is divided into non-value-added, low-value-added, medium-value-added and high-value-added operations, and the method specifically comprises the following steps:
Establishing an index set and a corresponding weight set which influence the enterprise value of the thermal power plant;
Establishing a comment set according to the actual value-added situation and expert opinion of the thermal power plant enterprise;
Calculating the membership degree of each index by adopting a triangular membership degree function;
Establishing fuzzy evaluation vectors of the indexes according to membership degrees of the indexes in the comment set, and combining the fuzzy evaluation vectors of the indexes to establish a fuzzy evaluation matrix;
performing fuzzy calculation on the fuzzy evaluation matrix and the comprehensive weight of each index to obtain a comprehensive evaluation result of each index value;
And dividing the operation under each index into non-increment, low-increment, medium-increment and high-increment operation according to the comprehensive evaluation result of the value of each index and a preset operation increment interval.
8. The value-added optimizing dispatching method for thermal power plant according to claim 1, wherein in S5, value-added measure analysis is performed on non-value-added and low-value-added operations in a thermal power plant enterprise lateral value chain, a thermal power plant enterprise internal value chain and a thermal power plant enterprise longitudinal value chain to form each operation value-added dispatching scheme, and the method specifically comprises the following steps:
And carrying out value-added measure analysis aiming at non-value-added and low-value-added operation in a transverse value chain of a thermal power plant enterprise, wherein the value-added measure analysis at least comprises the following steps: analyzing the value advantages and disadvantages of the thermal power plant enterprises and competitors, and carrying out the development and upgrading of enterprise equipment and technology, the improvement of purchasing and selling links and the collaborative energy supply among other enterprises;
And carrying out value-added measure analysis aiming at non-value-added and low-value-added operation in an enterprise internal value chain of the thermal power plant, wherein the value-added measure analysis at least comprises the following steps: performing dynamic simulation, parameter optimization adjustment and intelligent diagnosis and inspection by adopting a machine learning algorithm on operations in a production operation value chain of a thermal power plant enterprise; staff training and system management improvement are carried out on auxiliary operation of the thermal power plant enterprise;
And carrying out value-added measure analysis on non-value-added and low-value-added operations in a longitudinal value chain of the thermal power plant enterprise, wherein the value-added measure analysis at least comprises the following steps: enhancing cooperation with fuel suppliers, enhancing electricity consumption during valley time by using peak-valley flat electricity price charge of a power grid, reducing external purchase electricity quantity by energy storage equipment, and introducing photovoltaic power generation and optimizing an electricity market transaction model.
9. The value-added optimization scheduling method of a thermal power plant according to claim 8, wherein the optimization of the electric power market transaction model comprises: and (3) performing game bidding by thermal power plant enterprises and other competitor manufacturers, based on enterprise electric quantity data reporting information and user electricity consumption requirements, taking the maximum gain of the thermal power plant enterprises as a target and the minimum electricity consumption cost of the users as a target, establishing an electric power market transaction optimization model by combining constraint conditions of electric power market transaction and constraint conditions of electricity purchasing of the users, and outputting optimal transaction electric quantity and transaction electric price.
10. The value-added optimization scheduling method for the thermal power plant according to claim 1, wherein in S5, the optimal value-added scheduling scheme on each value chain of the output thermal power plant enterprise is solved with the goal of maximizing the value added of the thermal power plant enterprise, and the method specifically comprises:
And calculating the value added result of the operation of the thermal power plant enterprise under each scheduling scheme aiming at each operation value added scheduling scheme, establishing a thermal power plant enterprise value added optimization scheduling model aiming at the maximum value added of the thermal power plant enterprise and combining constraint conditions of each operation, and determining the optimal value added scheduling scheme on each value chain of the thermal power plant enterprise by utilizing an intelligent optimization algorithm based on a simulation platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410163745.3A CN117973617A (en) | 2024-02-05 | 2024-02-05 | Thermal power plant value-added optimization scheduling method based on value chain and electric power market transaction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410163745.3A CN117973617A (en) | 2024-02-05 | 2024-02-05 | Thermal power plant value-added optimization scheduling method based on value chain and electric power market transaction |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117973617A true CN117973617A (en) | 2024-05-03 |
Family
ID=90852779
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410163745.3A Pending CN117973617A (en) | 2024-02-05 | 2024-02-05 | Thermal power plant value-added optimization scheduling method based on value chain and electric power market transaction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117973617A (en) |
-
2024
- 2024-02-05 CN CN202410163745.3A patent/CN117973617A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Multi-objective optimal scheduling model with IGDT method of integrated energy system considering ladder-type carbon trading mechanism | |
| Zhou et al. | Analysis of risk priorities for renewable energy investment projects using a hybrid IT2 hesitant fuzzy decision-making approach with alpha cuts | |
| Poplavskaya et al. | Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets | |
| Morales et al. | Short-term trading for a wind power producer | |
| Delarue et al. | Effect of the accuracy of price forecasting on profit in a price based unit commitment | |
| Li et al. | Comprehensive effectiveness assessment of energy storage incentive mechanisms for PV-ESS projects based on compound real options | |
| Li et al. | General Nash bargaining based direct P2P energy trading among prosumers under multiple uncertainties | |
| Wang et al. | Bidding strategy optimization for power generation company in carbon emission rights and electricity market | |
| Bai et al. | Supplier portfolio selection and order allocation under carbon neutrality: Introducing a “Cool” ing model | |
| Zhang et al. | Assessing the policy synergy among power, carbon emissions trading and tradable green certificate market mechanisms on strategic GENCOs in China | |
| Motamedisedeh et al. | A novel optimization model for biding in the deregulated power market with pay as a bid settlement mechanism, based on the stochastic market clearing price | |
| Wang et al. | Study on the bi-level optimal clearing model of electricity market considering the weight of consumption responsibility | |
| Li et al. | Analysis of the compound differential evolution game of new energy manufacturers’ two-stage market behavior under the weight of consumption responsibility | |
| Guo et al. | Incentive-oriented power‑carbon emissions trading-tradable green certificate integrated market mechanisms using multi-agent deep reinforcement learning | |
| Liu et al. | Optimal bid-offer strategy for a virtual energy storage merchant: A stochastic bi-level model with all-scenario feasibility | |
| Liu et al. | A comprehensive modeling framework for coupled electricity and carbon markets | |
| Flores et al. | Investment planning in energy considering economic and environmental objectives | |
| Halynska et al. | Combined model of optimal electricity production: Evidence from Ukraine | |
| CN117973617A (en) | Thermal power plant value-added optimization scheduling method based on value chain and electric power market transaction | |
| Hallegatte et al. | The Macroeconomic Implications of a Transition to Zero Net Emissions: A Modeling Framework | |
| Alizamir et al. | Investment in wind energy: The role of subsidies | |
| Zhang et al. | Cooperative operation strategy of electricity-carbon market considering randomness and correlation of electricity-carbon price | |
| Rezek et al. | Abatement cost heterogeneity in phase I electric utilities | |
| Li et al. | Optimal Power Generation Portfolio Considering the Trading of Generation Right and Carbon Emission Allowance | |
| Ma et al. | The tradable flexibility certificate market policy for flexible investment incentive |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |