CN112668755A - Optimized operation strategy of multi-energy complementary distributed energy system - Google Patents
Optimized operation strategy of multi-energy complementary distributed energy system Download PDFInfo
- Publication number
- CN112668755A CN112668755A CN202011446911.9A CN202011446911A CN112668755A CN 112668755 A CN112668755 A CN 112668755A CN 202011446911 A CN202011446911 A CN 202011446911A CN 112668755 A CN112668755 A CN 112668755A
- Authority
- CN
- China
- Prior art keywords
- energy
- representing
- constraint
- formula
- complementary distributed
- 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
- 230000000295 complement effect Effects 0.000 title claims abstract description 40
- 238000013486 operation strategy Methods 0.000 title claims abstract description 18
- 238000004146 energy storage Methods 0.000 claims abstract description 25
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 238000000034 method Methods 0.000 claims abstract description 16
- 230000007613 environmental effect Effects 0.000 claims abstract description 10
- 230000002068 genetic effect Effects 0.000 claims abstract description 9
- 239000007789 gas Substances 0.000 claims description 18
- 239000004698 Polyethylene Substances 0.000 claims description 12
- 238000001816 cooling Methods 0.000 claims description 12
- 238000010521 absorption reaction Methods 0.000 claims description 9
- 239000003344 environmental pollutant Substances 0.000 claims description 9
- 231100000719 pollutant Toxicity 0.000 claims description 9
- 238000010248 power generation Methods 0.000 claims description 7
- 230000005611 electricity Effects 0.000 claims description 6
- -1 polyethylene Polymers 0.000 claims description 6
- 229920000573 polyethylene Polymers 0.000 claims description 6
- 239000002918 waste heat Substances 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 claims description 3
- 238000005338 heat storage Methods 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000005057 refrigeration Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to operation optimization of a multi-energy complementary distributed energy system, in particular to an optimization operation strategy considering comprehensive performance of the system. Aiming at a multi-energy complementary distributed energy system, three indexes such as primary energy saving rate representing energy efficiency, cost annual value saving rate representing economy, pollution gas emission reduction rate representing environmental protection and the like are respectively established, the three indexes are subjected to weighting treatment to obtain comprehensive performance indexes, and the indexes are used as optimization objective functions; determining decision variables and constraint conditions in the optimized operation process, wherein the decision variables and the constraint conditions comprise system load balance constraint, equipment capacity constraint and energy storage device operation constraint; solving the optimization model by using a genetic algorithm to obtain an operation strategy with optimal comprehensive performance indexes; thereby providing scientific theoretical guidance for the optimal operation of the multi-energy complementary distributed energy system.
Description
Technical Field
The invention relates to operation optimization of a multi-energy complementary distributed energy system, in particular to an optimization operation strategy of the multi-energy complementary distributed energy system.
Background
The multi-energy complementary distributed energy system is a novel energy supply system with high energy utilization rate, safe and reliable energy supply and good environmental protection performance, and the system comprises sub-units such as a wind power generation unit, a distributed photovoltaic unit, a photo-thermal unit, a gas-steam combined cycle unit and an energy storage system. It can provide various types of load products such as cold, heat, electricity and the like, the structure of the system is complex, various types and a plurality of numbers of devices are involved, and the coupling degree of the devices is high. In the actual operation process, in order to meet the load requirements of users such as cold, heat and electricity, the operation strategy provides guidance for the coordination and cooperation operation among all the devices of the system, and a large optimization space exists in the process.
In the current research on the operation strategy of an energy supply system, most researchers select the traditional operation strategies of 'fixing power by heat' and 'fixing heat by electricity', the two strategies cannot show the superiority of the multi-energy complementary distributed energy system to the greatest extent, in addition, the research also aims at optimizing the operation of the energy efficiency and the economy of the system, and the optimization cannot be carried out from the comprehensive performance perspective of the system. In order to embody the excellent comprehensive performance of the multi-energy complementary distributed energy system in the operation process, a system optimization operation strategy needs to be researched based on the comprehensive performance of the system, so that scientific theoretical guidance is provided for the optimized operation of the multi-energy complementary distributed energy system.
Disclosure of Invention
The invention aims to provide an optimal operation strategy of a multi-energy complementary distributed energy system, and scientific guidance is provided for the optimal operation of the system. The method comprises the following steps:
respectively establishing performance indexes representing energy efficiency, economy and environmental protection for a multi-energy complementary distributed energy system, weighting the three indexes to obtain a comprehensive performance index, and taking the index as an optimization objective function;
determining decision variables and constraint conditions in the optimized operation process, wherein the decision variables and the constraint conditions comprise system load balance constraint, equipment capacity constraint, equipment operation constraint and energy storage device operation constraint;
thirdly, solving the optimization model by using a genetic algorithm to obtain an operation strategy with optimal comprehensive performance indexes;
in the step (I), in a multi-energy complementary distributed energy system, a gas-steam combined cycle unit, a fan and a photovoltaic are matched to generate power and are connected with commercial power in a grid mode; an electric refrigerator, an absorption refrigerator and a ground source heat pump are matched for cooling; the waste heat of the combined cycle unit, the solar heat collector and the ground source heat pump are matched for heating; two energy storage modes of electricity storage and heat storage are adopted;
aiming at the system, establishing the energy efficiency of a primary energy saving rate index representation system, establishing a cost annual value saving rate table aiming at the economy of the system, and establishing the environmental protection of a pollutant emission reduction rate representation system;
(1) primary energy saving rate
In the formula: PESR represents the primary energy saving rate of the multi-energy complementary distributed energy system; PERSP、PERDMESRespectively representing the primary energy utilization rate of a traditional separate supply system and a multi-energy complementary distributed energy system; fSP、FDMESRespectively representing the primary energy consumption of the traditional separate supply system and the multi-energy complementary distributed energy system.
(2) Annual cost savings
In the formula: ACSR represents the annual cost savings rate of the multi-energy complementary distributed energy system; ACSPRepresenting the annual value of the cost of the distribution system, Yuan; ACDMESRepresenting the annual cost value, dollar, of the multi-energy complementary distributed energy system.
(3) Emission reduction rate of polluted gas
In the formula: PERR represents the pollution gas emission reduction rate of the multi-energy complementary distributed energy system; PE (polyethylene)SPRepresents the discharge amount of the polluted gas of the branch supply system, g; PE (polyethylene)DMESAnd the emission of pollutant gas, g, of the multi-energy complementary distributed energy system.
And weighting the three types of indexes to construct a comprehensive index, and determining the index as an objective function for optimizing operation.
Fun＝w1*PESR+w2*ACSR+w3*PERR
In the formula: fun is the objective function for optimal operation, w1，w2，w3The system comprises a primary energy saving rate, a cost annual value saving rate and a pollutant emission reduction rate, wherein the weights are selected by operation scheduling personnel according to consideration on three aspects of energy efficiency, economy and environmental protection of the system, and the weight constraint conditions are as follows: w is a1+w2+w3＝1。
In the step (II), determining the decision variables in the optimized operation process as the operation states U of each energy supply device and each energy storage devicei(t)，Ui(t) satisfies:
0≤Ui(t)≤1
in the formula: u shapei(t) represents the operating state of the device i at time t.
Determining constraint conditions in the optimized operation process, wherein the constraint conditions comprise system load balance constraint, equipment capacity constraint, equipment operation constraint and energy storage device operation constraint;
(1) electrical load balancing constraints
Edmn＝E+EP+EEC≤EPV+EWT+Epgu+Egrid
In the formula: edmnRepresenting the system electrical load demand/kW.h; e represents the power consumption/kW.h of the user; ePRepresents the power consumption of auxiliary equipment/kW.h; eECThe power consumption/kW.h of the electric refrigeration equipment is represented; ePVRepresenting solar photovoltaic power generation capacity/kW.h; eWTRepresenting the generated energy/kW.h of the wind turbine generator; epguRepresenting the generated energy/kW.h of the power generation equipment of the gas turbine unit; egridAnd the electric quantity purchased by the power grid/kW.h is shown.
(2) Thermal load balancing constraints
Hdmn＝H+HAC≤HSHC+HRE+HGB
In the formula: hdmnRepresents the system thermal load demand/kW.h; h represents the heat used by the user/kW.h; hACRepresenting the consumed heat/kW.h of the absorption refrigerating unit; hSHCRepresenting solar heat collection/kW.h; hRERepresenting the recovery of waste heat/kW.h; hGBThe afterburning heat/kW.h of the gas boiler is shown.
(3) Cold load balancing constraints
Cdmn＝C≤CAC+CEC
In the formula: cdmnRepresenting the system cold load demand/kW.h; c represents the cooling capacity/kW.h used by a user; cACThe cooling capacity/kW.h of the absorption refrigerator is shown; cECThe cooling capacity/kW.h of the electric refrigerator is shown.
(4) Capacity constraints for energy supply equipment
The energy supply equipment needs to meet capacity constraint in the process of outputting power:
Pi,min≤Pi(t)≤Pi,max
in the formula Pi(t) represents the output power of the energy supply device i at time t, Pi,min、Pi，maxRespectively representing the minimum output power and the maximum output power of the energy supply device i.
(5) Energy storage device operational constraints
Qi,min≤Qi(t)≤Qi,max
In the formula Qi(t) represents the energy storage capacity of the energy storage device i at time t, Qi,min、Qi，maxRepresenting the minimum and maximum stored energy of the energy storage device i, respectively.
In the step (III), the established optimization target is set as a fitness function of a genetic algorithm, the performance parameters, constraint conditions and user load requirements of each device are input, the optimization problem is solved by using the genetic algorithm, and an operation strategy with optimal comprehensive performance indexes is obtained.
Drawings
FIG. 1 is a flow chart of an optimized operation strategy based on a multi-energy complementary distributed energy system
Detailed Description
The invention is described in further detail below with reference to the following figures and detailed description:
the first step is as follows: establishing a multi-energy complementary distributed energy system, generating power by matching a gas-steam combined cycle unit, a fan and a photovoltaic unit, and connecting the power with commercial power; an electric refrigerator, an absorption refrigerator and a ground source heat pump are matched for cooling; the waste heat of the combined cycle unit, the solar heat collector and the ground source heat pump are matched for heating; two energy storage modes of electricity storage and heat storage are adopted;
aiming at the system, establishing the energy efficiency of a primary energy saving rate index representation system, establishing a cost annual value saving rate table aiming at the economy of the system, and establishing the environmental protection of a pollutant emission reduction rate representation system;
(1) primary energy saving rate
In the formula: PESR represents the primary energy saving rate of the multi-energy complementary distributed energy system; PERSP、PERDMESRespectively representing the primary energy utilization rate of a traditional separate supply system and a multi-energy complementary distributed energy system; fSP、FDMESRespectively representing the primary energy consumption of the traditional separate supply system and the multi-energy complementary distributed energy system.
(2) Annual cost savings
In the formula: ACSR represents the annual cost savings rate of the multi-energy complementary distributed energy system; ACSPRepresenting the annual value of the cost of the distribution system, Yuan; ACDMESRepresenting the annual cost value, dollar, of the multi-energy complementary distributed energy system.
(3) Emission reduction rate of polluted gas
In the formula: PERR represents the pollution gas emission reduction rate of the multi-energy complementary distributed energy system; PE (polyethylene)SPRepresents the discharge amount of the polluted gas of the branch supply system, g; PE (polyethylene)DMESAnd the emission of pollutant gas, g, of the multi-energy complementary distributed energy system.
And weighting the three types of indexes to construct a comprehensive index, and determining the index as an objective function for optimizing operation.
Fun＝w1*PESR+w2*ACSR+w3*PERR
In the formula: fun is the objective function for optimal operation, w1，w2，w3The weights are respectively primary energy saving rate, cost annual value saving rate and pollutant emission reduction rate, and are selected by operation scheduling personnel according to consideration on three aspects of energy efficiency, economy and environmental protection of the systemThe heavy constraint conditions are as follows: w is a1+w2+w3＝1。
The second step is that: determining decision variables in the optimized operation process as the operation states U of each energy supply device and each energy storage devicei(t)，Ui(t) satisfies:
0≤Ui(t)≤1
in the formula: u shapei(t) represents the operating state of the device i at time t.
Determining constraint conditions in the optimized operation process, wherein the constraint conditions comprise system load balance constraint, equipment capacity constraint, equipment operation constraint and energy storage device operation constraint;
(1) electrical load balancing constraints
Edmn＝E+EP+EEC≤EPV+EWT+Epgu+Egrid
In the formula: edmnRepresenting the system electrical load demand/kW.h; e represents the power consumption/kW.h of the user; ePRepresents the power consumption of auxiliary equipment/kW.h; eECThe power consumption/kW.h of the electric refrigeration equipment is represented; ePVRepresenting solar photovoltaic power generation capacity/kW.h; eWTRepresenting the generated energy/kW.h of the wind turbine generator; epguRepresenting the generated energy/kW.h of the power generation equipment of the gas turbine unit; egridAnd the electric quantity purchased by the power grid/kW.h is shown.
(2) Thermal load balancing constraints
Hdmn＝H+HAC≤HSHC+HRE+HGB
In the formula: hdmnRepresents the system thermal load demand/kW.h; h represents the heat used by the user/kW.h; hACRepresenting the consumed heat/kW.h of the absorption refrigerating unit; hSHCRepresenting solar heat collection/kW.h; hRERepresenting the recovery of waste heat/kW.h; hGBThe afterburning heat/kW.h of the gas boiler is shown.
(3) Cold load balancing constraints
Cdmn＝C≤CAC+CEC
In the formula: cdmnRepresenting the system cold load demand/kW.h; c represents the cooling capacity/kW.h used by a user; cACThe cooling capacity/kW.h of the absorption refrigerator is shown; cECThe cooling capacity/kW.h of the electric refrigerator is shown.
(4) Capacity constraints for energy supply equipment
The energy supply equipment needs to meet capacity constraint in the process of outputting power:
Pi,min≤Pi(t)≤Pi,max
in the formula Pi(t) represents the output power of the energy supply device i at time t, Pi,min、Pi，maxRespectively representing the minimum output power and the maximum output power of the energy supply device i.
(5) Energy storage device operational constraints
Qi,min≤Qi(t)≤Qi,max
In the formula Qi(t) represents the energy storage capacity of the energy storage device i at time t, Qi,min、Qi，maxRepresenting the minimum and maximum stored energy of the energy storage device i, respectively.
The third step: setting the established optimization target as a fitness function of a genetic algorithm, inputting performance parameters, constraint conditions and user load requirements of each device, and solving the optimization problem by using the genetic algorithm to obtain an operation strategy with optimal comprehensive performance indexes.
It should be noted that: the method is used for optimizing the operation of the multi-energy complementary distributed energy system, the comprehensive performance index capable of representing the energy efficiency, the economy and the environmental protection of the system is established, the index is taken as an optimization objective function, the system load balance constraint, the equipment capacity constraint, the equipment operation constraint, the energy storage device operation constraint, the decision variable and the constraint thereof are established, a complete operation optimization model is established, the optimization problem is solved by adopting a genetic algorithm, the operation strategy with the optimal comprehensive performance can be obtained, and the operation strategy can provide scientific and accurate guidance for the operation of the system.
All modifications, equivalents and the like which come within the spirit of the invention are desired to be protected.
Claims (1)
1. An optimized operation strategy of a multi-energy complementary distributed energy system is characterized by comprising the following steps:
the first step is as follows: in the multi-energy complementary distributed energy system, a gas-steam combined cycle unit, a fan and a photovoltaic are matched to generate power and are connected with commercial power in a grid mode; an electric refrigerator, an absorption refrigerator and a ground source heat pump are matched for cooling; the waste heat of the combined cycle unit, the solar heat collector and the ground source heat pump are matched for heating; two energy storage modes of electricity storage and heat storage are adopted;
aiming at the system, establishing the energy efficiency of a primary energy saving rate index representation system, establishing a cost annual value saving rate table aiming at the economy of the system, and establishing the environmental protection of a pollutant emission reduction rate representation system;
(1) primary energy saving rate
In the formula: PESR represents the primary energy saving rate of the multi-energy complementary distributed energy system; PERSP、PERDMESRespectively representing the primary energy utilization rate of a traditional separate supply system and a multi-energy complementary distributed energy system; fSP、FDMESRespectively representing the primary energy consumption of the traditional separate supply system and the multi-energy complementary distributed energy system.
(2) Annual cost savings
In the formula: ACSR represents the annual cost savings rate of the multi-energy complementary distributed energy system; ACSPRepresenting the annual value of the cost of the distribution system, Yuan; ACDMESRepresenting the annual cost value, dollar, of the multi-energy complementary distributed energy system.
(3) Emission reduction rate of polluted gas
In the formula: PERR represents the pollution gas emission reduction rate of the multi-energy complementary distributed energy system; PE (polyethylene)SPRepresents the discharge amount of the polluted gas of the branch supply system, g; PE (polyethylene)DMESAnd the emission of pollutant gas, g, of the multi-energy complementary distributed energy system.
And weighting the three types of indexes to construct a comprehensive index, and determining the index as an objective function for optimizing operation.
Fun＝w1*PESR+w2*ACSR+w3*PERR
In the formula: fun is the objective function for optimal operation, w1，w2，w3The system comprises a primary energy saving rate, a cost annual value saving rate and a pollutant emission reduction rate, wherein the weights are selected by operation scheduling personnel according to consideration on three aspects of energy efficiency, economy and environmental protection of the system, and the weight constraint conditions are as follows: w is a1+w2+w3＝1。
The second step is that: determining decision variables in the optimized operation process as the operation states U of each energy supply device and each energy storage devicei(t)，Ui(t) satisfies:
0≤Ui(t)≤1
in the formula: u shapei(t) represents the operating state of the device i at time t.
Determining constraint conditions in the optimized operation process, wherein the constraint conditions comprise system load balance constraint, equipment capacity constraint, equipment operation constraint and energy storage device operation constraint;
(1) electrical load balancing constraints
Edmn＝E+EP+EEC≤EPV+EWT+Epgu+Egrid
In the formula: edmnRepresenting the system electrical load demand/kW.h; e represents the power consumption/kW.h of the user; ePRepresents the power consumption of auxiliary equipment/kW.h; eECThe power consumption/kW.h of the electric refrigeration equipment is represented; ePVRepresenting solar photovoltaic power generation capacity/kW.h; eWTRepresenting the generated energy/kW.h of the wind turbine generator; epguRepresenting the generated energy/kW.h of the power generation equipment of the gas turbine unit; egridTo representThe electric quantity purchased by the power grid/kW.h.
(2) Thermal load balancing constraints
Hdmn＝H+HAC≤HSHC+HRE+HGB
In the formula: hdmnRepresents the system thermal load demand/kW.h; h represents the heat used by the user/kW.h; hACRepresenting the consumed heat/kW.h of the absorption refrigerating unit; hSHCRepresenting solar heat collection/kW.h; hRERepresenting the recovery of waste heat/kW.h; hGBThe afterburning heat/kW.h of the gas boiler is shown.
(3) Cold load balancing constraints
Cdmn＝C≤CAC+CEC
In the formula: cdmnRepresenting the system cold load demand/kW.h; c represents the cooling capacity/kW.h used by a user; cACThe cooling capacity/kW.h of the absorption refrigerator is shown; cECThe cooling capacity/kW.h of the electric refrigerator is shown.
(4) Capacity constraints for energy supply equipment
The energy supply equipment needs to meet capacity constraint in the process of outputting power:
Pi,min≤Pi(t)≤Pi,max
in the formula Pi(t) represents the output power of the energy supply device i at time t, Pi,min、Pi，maxRespectively representing the minimum output power and the maximum output power of the energy supply device i.
(5) Energy storage device operational constraints
Qi,min≤Qi(t)≤Qi,max
In the formula Qi(t) represents the energy storage capacity of the energy storage device i at time t, Qi,min、Qi，maxRepresenting the minimum and maximum stored energy of the energy storage device i, respectively.
The third step: setting the established optimization target as a fitness function of a genetic algorithm, inputting performance parameters, constraint conditions and user load requirements of each device, and solving the optimization problem by using the genetic algorithm to obtain an operation strategy with optimal comprehensive performance indexes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011446911.9A CN112668755A (en) | 2020-12-09 | 2020-12-09 | Optimized operation strategy of multi-energy complementary distributed energy system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011446911.9A CN112668755A (en) | 2020-12-09 | 2020-12-09 | Optimized operation strategy of multi-energy complementary distributed energy system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112668755A true CN112668755A (en) | 2021-04-16 |
Family
ID=75402156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011446911.9A Pending CN112668755A (en) | 2020-12-09 | 2020-12-09 | Optimized operation strategy of multi-energy complementary distributed energy system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112668755A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113326631A (en) * | 2021-06-16 | 2021-08-31 | 东南大学 | Distributed energy supply system optimization design method considering complementary characteristics of renewable energy sources |
| CN113592200A (en) * | 2021-08-30 | 2021-11-02 | 东北大学 | Low-carbon optimized operation method for regional comprehensive energy system containing water source heat pump |
| CN114035434A (en) * | 2021-11-22 | 2022-02-11 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
| CN114091973A (en) * | 2021-12-06 | 2022-02-25 | 国网山东省电力公司枣庄供电公司 | Method and device for improving energy efficiency of comprehensive energy system and terminal equipment |
| CN116611199A (en) * | 2023-02-24 | 2023-08-18 | 中国市政工程华北设计研究总院有限公司 | Genetic algorithm-based capacity optimization configuration method for multi-energy complementary heating system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109004686A (en) * | 2018-08-29 | 2018-12-14 | 三峡大学 | A kind of supply of cooling, heating and electrical powers type micro-grid system considering ice-storage air-conditioning multi-mode |
| CN111340359A (en) * | 2020-02-25 | 2020-06-26 | 西安交通大学 | Comprehensive evaluation method for multi-energy complementary distributed energy system |
| CN111815025A (en) * | 2020-06-09 | 2020-10-23 | 国网山东省电力公司经济技术研究院 | Flexible optimization scheduling method for comprehensive energy system considering uncertainty of wind, light and load |
-
2020
- 2020-12-09 CN CN202011446911.9A patent/CN112668755A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109004686A (en) * | 2018-08-29 | 2018-12-14 | 三峡大学 | A kind of supply of cooling, heating and electrical powers type micro-grid system considering ice-storage air-conditioning multi-mode |
| CN111340359A (en) * | 2020-02-25 | 2020-06-26 | 西安交通大学 | Comprehensive evaluation method for multi-energy complementary distributed energy system |
| CN111815025A (en) * | 2020-06-09 | 2020-10-23 | 国网山东省电力公司经济技术研究院 | Flexible optimization scheduling method for comprehensive energy system considering uncertainty of wind, light and load |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113326631A (en) * | 2021-06-16 | 2021-08-31 | 东南大学 | Distributed energy supply system optimization design method considering complementary characteristics of renewable energy sources |
| CN113326631B (en) * | 2021-06-16 | 2024-04-09 | 东南大学 | Distributed energy supply system optimization design method considering renewable energy complementary characteristics |
| CN113592200A (en) * | 2021-08-30 | 2021-11-02 | 东北大学 | Low-carbon optimized operation method for regional comprehensive energy system containing water source heat pump |
| CN113592200B (en) * | 2021-08-30 | 2023-08-22 | 东北大学 | Low-carbon optimized operation method for regional comprehensive energy system of water-containing source heat pump |
| CN114035434A (en) * | 2021-11-22 | 2022-02-11 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
| CN114035434B (en) * | 2021-11-22 | 2023-09-01 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
| CN114091973A (en) * | 2021-12-06 | 2022-02-25 | 国网山东省电力公司枣庄供电公司 | Method and device for improving energy efficiency of comprehensive energy system and terminal equipment |
| CN116611199A (en) * | 2023-02-24 | 2023-08-18 | 中国市政工程华北设计研究总院有限公司 | Genetic algorithm-based capacity optimization configuration method for multi-energy complementary heating system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112668755A (en) | Optimized operation strategy of multi-energy complementary distributed energy system | |
| CN111445090B (en) | Double-layer planning method for off-grid type comprehensive energy system | |
| CN109242350B (en) | Capacity optimization configuration method for combined cooling heating and power system considering translatable load | |
| CN108154309B (en) | Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity | |
| CN102563959B (en) | Integrated energy matching system and control method thereof | |
| CN107358345B (en) | Distributed combined cooling heating and power system optimization operation method considering demand side management | |
| CN109474025B (en) | Optimized dispatching model of park level comprehensive energy system | |
| CN103426032A (en) | Method for economically and optimally dispatching cogeneration units | |
| CN108808663B (en) | Industrial user heat demand response method based on multi-energy complementation | |
| CN110333660A (en) | A kind of cooling heating and power generation system Multipurpose Optimal Method | |
| CN108625988A (en) | A kind of CCHP microgrids structure and its operation method containing compressed-air energy storage | |
| CN111244939A (en) | Two-stage optimization design method for multi-energy complementary system considering demand side response | |
| CN113592200B (en) | Low-carbon optimized operation method for regional comprehensive energy system of water-containing source heat pump | |
| CN109543889A (en) | A kind of regional complex energy resource system cooperates with optimizing operation method a few days ago | |
| CN106920177A (en) | A kind of economic operation strategy of micro- energy of providing multiple forms of energy to complement each other | |
| CN113541598A (en) | Multi-stage utilization type cooling, heating and power energy supply system and system capacity configuration optimization method thereof | |
| CN104899659A (en) | Multistage energy consumption transmission model for intelligent area | |
| CN113201366A (en) | Cooperative operation method and system of biomass gasification poly-generation system | |
| CN214540448U (en) | Phosgene electricity stores up district comprehensive energy supply system of geothermal coupling | |
| CN114330835A (en) | Optimal configuration method of electricity/heat hybrid energy storage system in comprehensive energy microgrid | |
| CN114066056A (en) | Optimal scheduling method and system considering flexibility of thermoelectric cooperative comprehensive energy system | |
| CN113410855A (en) | Hydrogen energy storage and hydrogen supply chain equipment configuration method based on comprehensive energy system | |
| CN112347628B (en) | Cold and hot double-storage type heat pump equipment planning method | |
| Li et al. | Performance Analysis of Multi-energy Complementary Heating System Based on Photo-voltaic/thermal collectors and Heat Pumps | |
| Xie et al. | Planning Method for Power-Gas-Heat-Steam Integrated Energy System in Industrial Park Considering Annual Comprehensive Cost |
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 |