CN116502822A - Virtual power plant economic dispatching method considering comprehensive demand response - Google Patents
Virtual power plant economic dispatching method considering comprehensive demand response Download PDFInfo
- Publication number
- CN116502822A CN116502822A CN202310184552.1A CN202310184552A CN116502822A CN 116502822 A CN116502822 A CN 116502822A CN 202310184552 A CN202310184552 A CN 202310184552A CN 116502822 A CN116502822 A CN 116502822A
- Authority
- CN
- China
- Prior art keywords
- power
- power plant
- thermal
- load
- demand response
- 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
- 230000004044 response Effects 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 22
- 239000002699 waste material Substances 0.000 claims abstract description 15
- 238000005485 electric heating Methods 0.000 claims abstract description 14
- 238000005457 optimization Methods 0.000 claims abstract description 14
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 6
- 239000002245 particle Substances 0.000 claims abstract description 6
- 238000005516 engineering process Methods 0.000 claims abstract description 5
- 238000004891 communication Methods 0.000 claims abstract description 4
- 238000002922 simulated annealing Methods 0.000 claims abstract description 4
- 238000010438 heat treatment Methods 0.000 claims description 25
- 238000010248 power generation Methods 0.000 claims description 12
- 238000010977 unit operation Methods 0.000 claims description 10
- 230000009194 climbing Effects 0.000 claims description 9
- 238000005338 heat storage Methods 0.000 claims description 9
- 238000012423 maintenance Methods 0.000 claims description 6
- 230000001174 ascending effect Effects 0.000 claims description 3
- 239000003245 coal Substances 0.000 claims description 3
- 239000012141 concentrate Substances 0.000 claims description 3
- 230000002776 aggregation Effects 0.000 claims description 2
- 238000004220 aggregation Methods 0.000 claims description 2
- 230000010354 integration Effects 0.000 claims description 2
- 230000008901 benefit Effects 0.000 abstract description 8
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000010259 detection of temperature stimulus Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Biophysics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Quality & Reliability (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Feedback Control In General (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a virtual power plant economic dispatching method considering comprehensive demand response, which comprises the following steps: the thermal power unit, the cogeneration unit, the wind power plant, the concentrated photovoltaic power plant, the electric heater and the electric heating load are aggregated into a virtual power plant, and the units are uniformly controlled and managed by utilizing an advanced communication coordination technology, so that the optimal scheduling of the electric-thermal joint operation is realized; analyzing the comprehensive demand response of the virtual power plant, and establishing a comprehensive demand response model of the virtual power plant; considering the comprehensive demand response of a user, determining an objective function and constraint conditions, and establishing an economic optimization model of the virtual power plant; and solving the economic optimization model of the virtual power plant by adopting an improved simulated annealing principle particle swarm optimization algorithm. The invention greatly reduces waste wind power on the basis of considering the electric heating integrated demand response, and obtains larger economic benefit and environmental benefit.
Description
Technical Field
The invention relates to the technical field of virtual power plants, in particular to a virtual power plant economic dispatching method considering comprehensive demand response.
Background
In recent years, the renewable energy distributed generation has the characteristics of low pollution, power transmission and transformation investment saving, convenient transmission, high efficiency and the like, and is rapidly developed, but as the access power of wind energy and light energy in a power grid is higher and higher, the fluctuation of the renewable energy also brings a certain challenge for the stable and safe operation of the power grid. In order to solve the problem of uncertain operation caused by wind-solar randomness, the renewable energy utilization efficiency is higher and more stable by constructing local supply and demand coordination and a random micro-grid or an active power distribution network, on the other hand, the cost of power consumption of users is always pursued to be reduced at a terminal in China, which is contradictory to the current high grid-connected cost of the distributed renewable energy. The exploration mode innovation is used for promoting more local materials and local consumption of the distributed power generation equipment, and accelerating the further construction of a low-carbon energy system becomes a research focus in the field.
Under the drive of the aggregation scale benefit, the virtual power plant aggregates independent distributed resources to uniformly participate in the power market, the coordinated interaction between the power system supply side and the power system demand side is enhanced by utilizing the power market, the mutual containing capacity between new energy and the system is enhanced, the cooperation of various resources on the two sides of a source load is effectively promoted, and the flexibility and the economy of resource scheduling on the power system demand side are improved. However, in complex application scenarios in different regions, there are still many problems to be solved. For example, the operation mode of the cogeneration unit in the three north areas greatly reduces the peak shaving capacity of the system, and the regional waste wind is serious. How to improve the wind power consumption capability of the system on the basis of ensuring the stable and safe operation of the system and meeting the power supply and heat supply requirements becomes a key problem to be solved urgently.
Disclosure of Invention
The invention provides a virtual power plant economic dispatching method considering comprehensive demand response, which aims at: on the basis of considering the electric heating integrated demand response, the waste wind power is greatly reduced, and larger economic benefit and environmental benefit are obtained.
The technical scheme of the invention is as follows:
a virtual power plant economic dispatching method considering comprehensive demand response comprises the following steps:
s1: the thermal power unit, the cogeneration unit, the wind power plant, the concentrated photovoltaic power plant, the electric heater and the electric heating load are aggregated into a virtual power plant, and the units are uniformly controlled and managed by utilizing an advanced communication coordination technology, so that the optimal scheduling of the electric-thermal joint operation is realized;
s2: analyzing the comprehensive demand response of the virtual power plant, and establishing a comprehensive demand response model of the virtual power plant;
s3: considering the comprehensive demand response of a user, determining an objective function and constraint conditions, and establishing an economic optimization model of the virtual power plant;
s4: and solving the economic optimization model of the virtual power plant by adopting an improved simulated annealing principle particle swarm optimization algorithm.
Further, the comprehensive demand response model of the virtual power plant in step S2 is as follows:
wherein,,respectively scheduling the cost, k for flexible electric load and thermal load EDR 、k HDR Compensation factors for the participation of the flexible electrical load and the thermal load in the scheduling respectively, < >>And the power is respectively the power of the scheduled electric load and the power of the heat load, and delta t is the unit scheduling time.
Further, the determining method of the objective function in step S3 is as follows: the total cost minimum in one operation period is taken as an objective function, and the formula is as follows:
wherein F is the total operation cost of the virtual power plant in one operation period, T is one scheduling period,for the running cost of the thermal power generating unit, < >>For the operating costs of the cogeneration unit +.>In order to concentrate the operational maintenance costs of the photovoltaic power plant,for the wind farm comprehensive cost, < >>For the operating cost of the electric heater, < > for>Scheduling costs for flexible loads;
wherein N1 is the number of thermal power generating units, A i 、B i 、C i The coal consumption coefficients of the ith thermal power generating unit are respectively,for the output power of the ith thermal power unit, < +.>And->Respectively the starting and stopping states of the ith thermal power generating unit at the time t and the time t-1, S i The starting and closing cost of the ith thermal power generating unit;
wherein N2 is the number of cogeneration units, a i 、b i 、c i Respectively the power generation cost coefficients of the cogeneration unit,and->Generating capacity and thermal generating capacity of the cogeneration unit, p v,i Representing a reduced power output coefficient due to additional heating;
wherein,,and->Electric and thermal power of concentrated photovoltaic power plant, <' > respectively>And->Respectively supplying power to the concentrated photovoltaic power plant,Heating cost factor, < >>And->S for concentrating the operation conditions of the generator set at the time t-1 and the time t of the photovoltaic power plant CSP The starting and stopping costs of the concentrated photovoltaic power plant are reduced;
wherein k is W And k qf The cost coefficients of wind power plant operation maintenance and waste wind power are respectively calculated,and->The method comprises the steps of respectively obtaining actual wind power and waste wind power;
wherein k is EH For the operating cost factor of the electrical heating,a power input for electrical heating;
wherein,,and respectively scheduling the cost for the flexible electric load and the thermal load.
Further, the constraint conditions in the step S3 comprise power balance constraint, thermal power unit operation constraint, cogeneration unit operation constraint, operation constraint of a centralized photovoltaic power plant, electric heating power constraint, power limit constraint of a wind power plant and integration demand response constraint;
the power balancing constraint is expressed as:
wherein,,heating power of the electric heater, < >>Represents the electrical load at time t,/->The thermal load at time t;
the thermal power generating unit operation constraint is expressed as:
wherein P is DG,i,min And P DG,i,max Respectively the minimum power generation power and the maximum power generation power of the thermal power generating unit,and->The upward and downward climbing rates of the thermal power generating unit are respectively;
the cogeneration unit operation constraint is expressed as:
wherein P is CHP,i,min 、P CHP,i,max 、H CHP,i,min 、H CHP,i,max Respectively the maximum and minimum generating power and the maximum and minimum thermal power of the cogeneration unit,and->The ascending and descending speeds of the cogeneration unit are respectively;
the operational constraints of the concentrated photovoltaic power plant are expressed as:
wherein P is CSP,min 、P CSP,max 、H CSP,min 、H CSP,max Maximum and minimum electric power and maximum and minimum thermal power of the concentrated photovoltaic power plant respectively,and->Power flows of load and heat storage tank respectively, < >>And->The upward climbing rate and the downward climbing rate of the concentrated photovoltaic power plant are respectively;
the power constraint of electrical heating is expressed as:
wherein H is EH,max Is the upper limit of the heating power of the electric heater,and->The electric heating flow is respectively the thermal power of the load and the heat storage tank;
the power limitation constraint of a wind farm is expressed as:
wherein,,to predict wind power generation;
integrating the demand response constraints is expressed as:
wherein S is e,max And S is h,max The maximum response capacity of the flexible electro-thermal load,represents the electrical load at time t,/->Indicating the thermal load at time t, L p In response to the upper limit of flexible load capacity involved, R represents the equivalent load resistance.
Further, the method for constructing the aggregate virtual power plant in the step S1 is as follows: the system power supply task is jointly born by a thermal power unit, a cogeneration unit, a wind farm and a concentrated photovoltaic power plant, the system heating task is jointly born by the cogeneration unit, a cogeneration device and an electric heater, part of waste wind is absorbed in the electric heater and converted into heat energy to be stored in a heat storage tank to participate in heat supply, and part of heat load of a virtual power plant is jointly born by the system power supply task and the concentrated photovoltaic power plant device, so that electric-thermal decoupling of the cogeneration unit is realized.
Compared with the prior art, the invention has the following beneficial effects: the virtual power plant optimal scheduling model with the wind power plant and the concentrated photovoltaic power plant is constructed by integrating a cogeneration unit, a wind power plant, a concentrated solar power plant and the like into a whole to participate in operation and fully considering the comprehensive demand response of electric heating load, so that flexible adjustment of system output and promotion of wind energy utilization are realized; the method further establishes an objective function for minimizing the total running cost, adopts an improved particle swarm optimization algorithm to solve, and finally verifies the effectiveness and rationality of the model through calculation examples, thereby realizing deep movement of a large amount of flexible load resources of the system on the basis of considering electrothermal integrated demand response, and greatly reducing waste wind power on the basis of ensuring power supply and heat supply, so as to obtain larger economic benefit and environmental benefit.
The foregoing description is only an overview of the present invention, and is intended to provide a better understanding of the present invention, as it is embodied in the following description, with reference to the preferred embodiments of the present invention and the accompanying drawings. Specific embodiments of the present invention are given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of a virtual power plant under investigation of the present invention;
FIG. 3 shows the heat output of the cogeneration unit studied in accordance with the present invention in different modes of operation;
FIG. 4 is a graph showing the "wind curtailment" of a virtual power plant under various modes of operation as studied by the present invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention. The invention is more particularly described by way of example in the following paragraphs with reference to the drawings. Advantages and features of the invention will become more apparent from the following description and from the claims. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for convenience and clarity in aiding in the description of embodiments of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Referring to FIG. 1, a virtual power plant economic dispatch method considering comprehensive demand response includes the steps of:
s1: the thermal power generating unit, the cogeneration unit, the wind power plant, the concentrated photovoltaic power plant, the electric heater and the electric heating load are aggregated into a virtual power plant, and the virtual power plant cooperative control center utilizes an advanced communication coordination technology to uniformly control and manage each unit, so that the optimal scheduling of the electric-thermal combined operation is realized.
Preferably, as shown in fig. 2, the method for constructing the aggregated virtual power plant is as follows: the system power supply task is jointly born by a thermal power unit, a cogeneration unit, a wind farm and a concentrated photovoltaic power plant, the system heating task is jointly born by the cogeneration unit, a cogeneration device and an electric heater, part of waste wind is absorbed in the electric heater and converted into heat energy to be stored in a heat storage tank to participate in heat supply, and part of heat load of a virtual power plant is jointly born by the system power supply task and the concentrated photovoltaic power plant device, so that electric-thermal decoupling of the cogeneration unit is realized. Meanwhile, the power regulation capability of the cogeneration unit is further improved, the wind power grid space can be increased, and the abandoned wind is further absorbed.
S2: and analyzing the comprehensive demand response of the virtual power plant, and establishing a comprehensive demand response model of the virtual power plant.
There are a large number of flexible power loads in the system that can be scheduled. The thermal load has the same flexible regulation capability as the flexible electrical load due to the thermal inertia of the heating and the ambiguity of the human body's perception of temperature. Therefore, the demand response stimulus can be used for adjusting the electricity-heat flexible load, optimizing the system output and improving the wind power consumption capacity of the virtual power plant. Thus, a corresponding electrical heating load demand response mathematical model, i.e. a comprehensive demand response model of the virtual power plant, may be established:
wherein,,respectively scheduling the cost, k for flexible electric load and thermal load EDR 、k HDR Compensation factors for the participation of the flexible electrical load and the thermal load in the scheduling respectively, < >>And respectively scheduling the electric-thermal load power and delta t as unit scheduling time.
S3: and (3) considering the comprehensive demand response of the user, determining an objective function and constraint conditions, and establishing an economic optimization model of the virtual power plant.
Preferably, the method for determining the objective function is as follows: the total cost minimum in one operation period is taken as an objective function, and the formula is as follows:
wherein F is the total operation cost of the virtual power plant in one operation period, T is one scheduling period,for the running cost of the thermal power generating unit, < >>For the operating costs of the cogeneration unit +.>In order to concentrate the operational maintenance costs of the photovoltaic power plant,for the wind farm comprehensive cost, < >>For the operating cost of the electric heater, < > for>Scheduling costs for flexible loads;
wherein N1 is the number of thermal power generating units, A i 、B i 、C i The coal consumption coefficients of the ith thermal power generating unit are respectively,for the output power of the ith thermal power unit, < +.>And->Respectively the starting and stopping states of the ith thermal power generating unit at the time t and the time t-1, S i The starting and closing cost of the ith thermal power generating unit;
wherein N2 is the number of cogeneration units, a i 、b i 、c i Respectively the power generation cost coefficients of the cogeneration unit,and->Generating capacity and thermal generating capacity of the cogeneration unit, p v,i Representing a reduced power output coefficient due to additional heating;
wherein,,and->Electric and thermal power of concentrated photovoltaic power plant, <' > respectively>And->The power supply and heat supply cost coefficients of the centralized photovoltaic power plant are respectively->And->S for concentrating the operation conditions of the generator set at the time t-1 and the time t of the photovoltaic power plant CSP The starting and stopping costs of the concentrated photovoltaic power plant are reduced;
wherein k is W And k qf The cost coefficients of wind power plant operation maintenance and waste wind power are respectively calculated,and->The method comprises the steps of respectively obtaining actual wind power and waste wind power;
wherein k is EH For the operating cost factor of the electrical heating,a power input for electrical heating;
wherein,,and respectively scheduling the cost for the flexible electric load and the thermal load.
Further preferably, the constraint conditions include a power balance constraint, a thermal power plant operation constraint, a cogeneration plant operation constraint, a concentrated photovoltaic power plant operation constraint, an electrical heating power constraint, a wind farm power limit constraint, and an integrated demand response constraint.
The power balancing constraint is expressed as:
wherein,,heating power of the electric heater, < >>Represents the electrical load at time t,/->The thermal load at time t;
the thermal power generating unit operation constraint is expressed as:
wherein P is DG,i,min And P DG,i,max Respectively the minimum power generation power and the maximum power generation power of the thermal power generating unit,and->The upward and downward climbing rates of the thermal power generating unit are respectively;
the cogeneration unit operation constraint is expressed as:
wherein P is CHP,i,min 、P CHP,i,max 、H CHP,i,min 、H CHP,i,max Respectively the maximum and minimum generating power and the maximum and minimum thermal power of the cogeneration unit,and->The ascending and descending speeds of the cogeneration unit are respectively;
the operational constraints of the concentrated photovoltaic power plant are expressed as:
wherein P is CSP,min 、P CSP,max 、H CSP,min 、H CSP,max Maximum and minimum electric power and maximum and minimum thermal power of the concentrated photovoltaic power plant respectively,and->Power flows of load and heat storage tank respectively, < >>And->The upward climbing rate and the downward climbing rate of the concentrated photovoltaic power plant are respectively;
the power constraint of electrical heating is expressed as:
wherein H is EH,max Is the upper limit of the heating power of the electric heater,and->The electric heating flow is respectively the thermal power of the load and the heat storage tank;
the power limitation constraint of a wind farm is expressed as:
wherein,,to predict wind power generation;
integrating the demand response constraints is expressed as:
wherein S is e,max And S is h,max The maximum response capacity of the flexible electro-thermal load,represents the electrical load at time t,/->Indicating the thermal load at time t, L p In response to the upper limit of flexible load capacity involved, R represents the equivalent load resistance.
S4: aiming at the problems of premature convergence and search stagnation of the traditional particle swarm optimization algorithm, an improved simulated annealing principle particle swarm optimization algorithm is adopted to solve the economic optimization model of the virtual power plant.
As shown in fig. 3 and 4, three operation modes of the system are set, and data are compared and analyzed: mode 1: the comprehensive demand response of the electric heating load is not considered; mode 2: only consider the electrical load demand response; mode 3: consider an electro-thermal integrated demand response. Simulation solution is carried out by Matlab software, and comparison of system operation results under different modes is shown in Table 1.
Table 1 data comparison under different operation mode types
Analysis based on experimental data: in mode 1, the concentrated photovoltaic power plant and the electric heater together reduce the heat output of the cogeneration unit. However, when the power load is low at night, the power regulation capability of the cogeneration unit is insufficient, resulting in large-scale wind curtailment.
In mode 2, only the electrical load demand response is considered. In addition, due to the user's response to the system schedule, the flexible power load is transferred to night in part of the day. The use of the concentrated photovoltaic power plant and the electric heater relieves the electric heating characteristic of the cogeneration unit, increases the space of the wind power grid and reduces the air discarding quantity. Compared with mode 1, the waste wind power is reduced by 347.99MW, and the waste wind rate is reduced by 7.26%. However, in the mode 2, due to the limitation of the flexible power load response capability, the good wind power consumption effect cannot be achieved only by considering the power demand response.
Mode 3 allows for the distribution of thermal load compared to mode 2. The total running cost of the mode 3 is 463.37 ten thousand yuan, which is reduced by 3.21 ten thousand yuan compared with the mode 2; the abandoned wind power is 142.62MW and 138.67MW lower than that of the mode 2, which shows that considering the heat demand response can also promote wind power consumption to a certain extent and improve the system economy.
The above description is only of the preferred embodiments of the present invention, and is not intended to limit the present invention in any way; those skilled in the art will readily appreciate that the present invention may be implemented as shown in the drawings and described above; however, those skilled in the art will appreciate that many modifications, adaptations, and variations of the present invention are possible in light of the above teachings without departing from the scope of the invention; meanwhile, any equivalent changes, modifications and evolution of the above embodiments according to the essential technology of the present invention still fall within the scope of the present invention.
Claims (5)
1. The virtual power plant economic dispatching method considering comprehensive demand response is characterized by comprising the following steps of:
s1: the thermal power unit, the cogeneration unit, the wind power plant, the concentrated photovoltaic power plant, the electric heater and the electric heating load are aggregated into a virtual power plant, and the units are uniformly controlled and managed by utilizing an advanced communication coordination technology, so that the optimal scheduling of the electric-thermal joint operation is realized;
s2: analyzing the comprehensive demand response of the virtual power plant, and establishing a comprehensive demand response model of the virtual power plant;
s3: considering the comprehensive demand response of a user, determining an objective function and constraint conditions, and establishing an economic optimization model of the virtual power plant;
s4: and solving the economic optimization model of the virtual power plant by adopting an improved simulated annealing principle particle swarm optimization algorithm.
2. The virtual power plant economic dispatch method considering integrated demand response of claim 1, wherein: the comprehensive demand response model of the virtual power plant in step S2 is as follows:
wherein,,respectively scheduling the cost, k for flexible electric load and thermal load EDR 、k HDR Compensation factors for the participation of the flexible electrical load and the thermal load in the scheduling respectively, < >>And the power is respectively the power of the scheduled electric load and the power of the heat load, and delta t is the unit scheduling time.
3. The virtual power plant economic dispatch method considering integrated demand response of claim 1, wherein: the method for determining the objective function in step S3 is as follows: the total cost minimum in one operation period is taken as an objective function, and the formula is as follows:
wherein F is the total operation cost of the virtual power plant in one operation period, T is one scheduling period,for the running cost of the thermal power generating unit, < >>For the operating costs of the cogeneration unit +.>To concentrate the operating maintenance costs of the photovoltaic power plant, < >>For the wind farm comprehensive cost, < >>For the operating cost of the electric heater, < > for>Scheduling costs for flexible loads;
wherein N1 is the number of thermal power generating units, A i 、B i 、C i The coal consumption coefficients of the ith thermal power generating unit are respectively,for the output power of the ith thermal power unit, < +.>And->Respectively the starting and stopping states of the ith thermal power generating unit at the time t and the time t-1, S i The starting and closing cost of the ith thermal power generating unit;
wherein N2 is the number of cogeneration units, a i 、b i 、c i Respectively generating power for the cogeneration unitThe coefficient of the present invention is that,andgenerating capacity and thermal generating capacity of the cogeneration unit, p v,i Representing a reduced power output coefficient due to additional heating;
wherein,,and->Electric and thermal power of concentrated photovoltaic power plant, <' > respectively>And->The power supply and heat supply cost coefficients of the centralized photovoltaic power plant are respectively->And->S for concentrating the operation conditions of the generator set at the time t-1 and the time t of the photovoltaic power plant CSP The starting and stopping costs of the concentrated photovoltaic power plant are reduced;
wherein k is W And k qf The cost coefficients of wind power plant operation maintenance and waste wind power are respectively calculated,and->The method comprises the steps of respectively obtaining actual wind power and waste wind power;
wherein k is EH For the operating cost factor of the electrical heating,a power input for electrical heating;
wherein,,and respectively scheduling the cost for the flexible electric load and the thermal load.
4. The virtual power plant economic dispatch method considering integrated demand response of claim 2, wherein: the constraint conditions in the step S3 comprise power balance constraint, thermal power unit operation constraint, cogeneration unit operation constraint, operation constraint of a centralized photovoltaic power plant, electric heating power constraint, power limit constraint of a wind power plant and integration demand response constraint;
the power balancing constraint is expressed as:
wherein,,heating power of the electric heater, < >>Represents the electrical load at time t,/->The thermal load at time t;
the thermal power generating unit operation constraint is expressed as:
wherein P is DG,i,min And P DG,i,max Respectively the minimum power generation power and the maximum power generation power of the thermal power generating unit,and->The upward and downward climbing rates of the thermal power generating unit are respectively;
the cogeneration unit operation constraint is expressed as:
wherein P is CHP,i,min 、P CHP,i,max 、H CHP,i,min 、H CHP,i,max Respectively the maximum and minimum generating power and the maximum and minimum thermal power of the cogeneration unit,and->The ascending and descending speeds of the cogeneration unit are respectively;
the operational constraints of the concentrated photovoltaic power plant are expressed as:
wherein P is CSP,min 、P CSP,max 、H CSP,min 、H CSP,max Maximum and minimum electric power and maximum and minimum thermal power of the concentrated photovoltaic power plant respectively,and->Power flows of load and heat storage tank respectively, < >>And->The upward climbing rate and the downward climbing rate of the concentrated photovoltaic power plant are respectively;
the power constraint of electrical heating is expressed as:
wherein H is EH,max Is the upper limit of the heating power of the electric heater,and->The electric heating flow is respectively the thermal power of the load and the heat storage tank;
the power limitation constraint of a wind farm is expressed as:
wherein,,to predict wind power generation;
integrating the demand response constraints is expressed as:
wherein S is e,max And S is h,max The maximum response capacity of the flexible electro-thermal load,the electrical load at time t is indicated,indicating the thermal load at time t, L p In response to the upper limit of flexible load capacity involved, R represents the equivalent load resistance.
5. The virtual power plant economic dispatch method considering integrated demand response of any one of claims 1 to 4, wherein: the method for constructing the aggregation virtual power plant in the step S1 comprises the following steps: the system power supply task is jointly born by a thermal power unit, a cogeneration unit, a wind farm and a concentrated photovoltaic power plant, the system heating task is jointly born by the cogeneration unit, a cogeneration device and an electric heater, part of waste wind is absorbed in the electric heater and converted into heat energy to be stored in a heat storage tank to participate in heat supply, and part of heat load of a virtual power plant is jointly born by the system power supply task and the concentrated photovoltaic power plant device, so that electric-thermal decoupling of the cogeneration unit is realized.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310184552.1A CN116502822A (en) | 2023-03-01 | 2023-03-01 | Virtual power plant economic dispatching method considering comprehensive demand response |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310184552.1A CN116502822A (en) | 2023-03-01 | 2023-03-01 | Virtual power plant economic dispatching method considering comprehensive demand response |
Publications (1)
Publication Number | Publication Date |
---|---|
CN116502822A true CN116502822A (en) | 2023-07-28 |
Family
ID=87317308
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310184552.1A Pending CN116502822A (en) | 2023-03-01 | 2023-03-01 | Virtual power plant economic dispatching method considering comprehensive demand response |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116502822A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117634682A (en) * | 2023-11-28 | 2024-03-01 | 国网吉林省电力有限公司经济技术研究院 | Electric-thermal combined supply type virtual power plant optimization regulation and control method in cold region |
-
2023
- 2023-03-01 CN CN202310184552.1A patent/CN116502822A/en active Pending
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117634682A (en) * | 2023-11-28 | 2024-03-01 | 国网吉林省电力有限公司经济技术研究院 | Electric-thermal combined supply type virtual power plant optimization regulation and control method in cold region |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108154309B (en) | Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity | |
CN109742812B (en) | Source-load-storage coordinated scheduling method for improving new energy consumption | |
CN101510686B (en) | Micro electric network coordination control system based on multi-proxy technology | |
CN108964128B (en) | Low-carbon economic dispatching solving method based on coordinated heat supply of electric boiler and heat storage device | |
CN102710013B (en) | Park energy-network energy optimizing management system based on microgrids and implementing method thereof | |
CN108092324B (en) | AGC control system and control method for wind power participating in peak shaving frequency modulation | |
CN111898806B (en) | Electric-thermal coupling source storage integrated multipotency flow park operation optimization method and system | |
CN103296682A (en) | Multiple spatial and temporal scale gradually-advancing load dispatching mode designing method | |
CN110361969B (en) | Optimized operation method of cooling, heating and power comprehensive energy system | |
CN109340904B (en) | Electric heating collaborative optimization operation method | |
CN107968443B (en) | AGC control method for wind-solar-fire bundled direct current delivery | |
CN113869679B (en) | Hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources | |
CN114336745A (en) | Multi-energy complementary intelligent energy system based on source network charge storage and control method | |
CN107749645A (en) | A kind of method for controlling high-voltage large-capacity thermal storage heating device | |
CN110048415A (en) | A kind of real-time distributed economic load dispatching method suitable for grid type micro-capacitance sensor | |
CN115170343A (en) | Distributed resource and energy storage collaborative planning method for regional comprehensive energy system | |
CN112488363A (en) | Generalized energy storage based optimal scheduling method for multi-energy power system | |
CN116502822A (en) | Virtual power plant economic dispatching method considering comprehensive demand response | |
CN115688448A (en) | Optimal scheduling method for multi-region comprehensive energy system considering shared energy storage | |
CN112583051B (en) | Optimized scheduling model construction method of variable-speed pumped storage unit in regional power grid | |
CN114362152B (en) | Multi-time scale scheduling method for comprehensive energy system | |
CN107528352A (en) | A kind of power distribution network active optimization method based on regenerative resource high permeability | |
CN107086579B (en) | It is a kind of based on the air conditioner user of echo effect to the response method of Spot Price | |
Xia et al. | Low carbon operation of integrated energy systems considering electric and thermal flexible loads | |
Xiaoting et al. | Optimal scheduling of CCHP microgrid considering real-time demand |
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 |