CN112446546A - Comprehensive energy system two-stage optimal configuration method considering energy reliability - Google Patents

Comprehensive energy system two-stage optimal configuration method considering energy reliability Download PDF

Info

Publication number
CN112446546A
CN112446546A CN202011392849.XA CN202011392849A CN112446546A CN 112446546 A CN112446546 A CN 112446546A CN 202011392849 A CN202011392849 A CN 202011392849A CN 112446546 A CN112446546 A CN 112446546A
Authority
CN
China
Prior art keywords
energy
power
period
energy storage
storage device
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.)
Granted
Application number
CN202011392849.XA
Other languages
Chinese (zh)
Other versions
CN112446546B (en
Inventor
于常乐
赵庆杞
刘军
朱远达
徐明虎
徐浩然
赵晓娜
李文文
肖傲
杨林
金硕巍
杨东升
李广地
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
Northeastern University China
State Grid Liaoning Electric Power Co Ltd
Original Assignee
State Grid Liaoning Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by State Grid Liaoning Electric Power Co Ltd filed Critical State Grid Liaoning Electric Power Co Ltd
Priority to CN202011392849.XA priority Critical patent/CN112446546B/en
Publication of CN112446546A publication Critical patent/CN112446546A/en
Application granted granted Critical
Publication of CN112446546B publication Critical patent/CN112446546B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/067—Enterprise or organisation modelling
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a two-stage optimal configuration method of a comprehensive energy system considering energy reliability, which comprises the following steps of: establishing an electricity-gas combined comprehensive energy system model; establishing a two-stage optimization configuration model of the electricity-gas combined comprehensive energy system; according to the Rayleigh distribution of the wind speed, obtaining a probability distribution model of the power generation amount and adding the probability distribution model into an optimization model constraint; determining the influence of equipment random faults on a system, establishing two energy reliability indexes and adding the two energy reliability indexes into an optimization model constraint; and solving the optimal configuration scheme of the electricity-gas combined comprehensive energy system by using a CPLEX solver. The method can reduce the influence of uncertainty of the energy of the comprehensive energy system, effectively improve the utilization of the system energy, reduce the input cost of the system and ensure that the system has sustainability and economy.

Description

Comprehensive energy system two-stage optimal configuration method considering energy reliability
Technical Field
The invention belongs to the technical field of comprehensive energy optimization, and relates to a two-stage optimization configuration method of a comprehensive energy system in consideration of energy reliability.
Background
Energy is an important basis for human survival and development, along with social development, the energy demand is gradually increased, the price of non-renewable energy such as petroleum, coal, natural gas and the like is increased, environmental problems such as environmental pollution, greenhouse effect and the like threaten the healthy development of the human society, and the energy and environmental problems faced by the development of the economic society become important challenges for human beings.
In order to relieve energy pressure and improve environmental problems, a comprehensive energy system combining different forms of energy and energy storage and power generation systems is constructed, so that the utilization efficiency of energy can be effectively improved, and the energy supply cost is reduced, so that many researches are developed around the optimization planning and design of the comprehensive energy system. In the planning and design of the comprehensive energy system, the uncertainty and relevance of the output of renewable energy such as air, light and the like are researched to bring great complexity to the design of the system, and if the factors are neglected, suboptimal decision risk is introduced in the system planning stage; meanwhile, it is generally considered that the interruption of the operation of the system components is the main reason for the unreliable energy supply of the energy equipment.
For the optimal configuration considering the reliability of energy, the prior art mainly focuses on two aspects: on one hand, the uncertainty of the output of wind power or photovoltaic is analyzed, a wind power or photovoltaic prediction model is established by more technologies, and the influence on the system operation under a typical scene is obtained through scene reduction; on the other hand, the method is used for analyzing the uncertainty of energy supply caused by equipment failure, and more technologies provide relevant indexes for measuring system instability caused by equipment failure. But there are fewer techniques related to optimizing the system for device capacity and operating strategy that take into account both renewable energy uncertainty and device failure uncertainty factors.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a two-stage optimal configuration method of an integrated energy system considering energy reliability, and simultaneously considers the uncertainty of wind power generation and the uncertainty of equipment fault function, so as to effectively improve the utilization of system energy.
The invention discloses a two-stage optimal configuration method of a comprehensive energy system considering energy reliability, which comprises the following steps:
step 1: establishing an electricity-gas combined comprehensive energy system model;
the electric-gas combined integrated energy system model comprises an energy conversion model without considering the energy storage module and an energy conversion model with considering the energy storage module;
the energy storage module comprises: an electrical energy storage device, a cold energy storage device, a hot energy storage device;
the energy conversion comprises: electric energy, cold energy and heat energy converted by the energy conversion equipment; the energy conversion apparatus includes: the system comprises a wind driven generator, an electric refrigerator, an electric boiler, a combined cooling heating and power device and a gas boiler;
the energy conversion model without considering the energy storage module is as follows:
Figure BDA0002811492250000021
wherein ,PELE,tElectric power output by the system for a period t;
Figure BDA0002811492250000022
the electricity purchasing power and the electricity selling power of the system in the time period t are respectively;
Figure BDA0002811492250000023
electric power input to the electric refrigerator and the electric boiler respectively in the period of t;
Figure BDA0002811492250000024
inputting the natural gas power of the combined cooling heating and power generation device for the t time period;
Figure BDA0002811492250000025
the power generation efficiency of the combined cooling heating and power generation device; pWT,tThe generated power of the wind driven generator;
Figure BDA0002811492250000026
wherein ,PCOOL,tCold power output by the system for a period t;
Figure BDA0002811492250000027
inputting electric power of the electric refrigerator and natural gas power of the combined cooling heating and power device in a time period t;
Figure BDA0002811492250000028
the refrigeration efficiency of the electric refrigerator and the combined cooling heating and power device is respectively;
Figure BDA0002811492250000029
wherein ,PHEAT,tThermal power output by the system in the period t;
Figure BDA00028114922500000210
inputting the natural gas power of the gas boiler for a time period t;
Figure BDA00028114922500000211
the heat efficiency of an electric boiler, a combined cooling heating and power device and a gas boiler;
Figure BDA00028114922500000212
wherein ,
Figure BDA00028114922500000213
the gas purchasing quantity of the system in the period t; hLA low calorific value for natural gas combustion;
the energy conversion model considering the energy storage module is as follows:
Figure BDA0002811492250000031
ΔSm,t＝Sm,t-Sm,t-1
Em,t＝Pm,tΔt-ΔSm,t
wherein m belongs to { ele, cool, heat }, wherein m represents electricity when ele, cold when cool and heat when heat;
Figure BDA0002811492250000032
respectively charging and discharging energy power of the energy storage device in the time period of t;
Figure BDA0002811492250000033
respectively charging and discharging energy to and from the energy storage device in the t time period m; delta Sm,tThe variation of the energy stored in the energy storage device for the t time period m; sm,t、Sm,t-1M energy stored by the energy storage device m at t and t-1 time periods respectively; pm,tM power, P output by the system in t period of the energy storage module is not consideredm,t∈{PELE,t,PCOOL,t,PHEAT,t}；
Step 2: establishing a two-stage optimization configuration model of the electricity-gas combined comprehensive energy system;
step 2.1: the method aims at the lowest operation and maintenance cost of configuration installation and put into use of energy equipment in engineering, and establishes a two-stage optimization configuration model objective function of the electricity-gas combined comprehensive energy system as follows:
Figure BDA0002811492250000034
Figure BDA0002811492250000035
wherein ,CTThe total input cost of the comprehensive energy system; cTIInvestment cost for installing equipment for the system once; cTOThe annual operating cost of the system within the service life; p is a radical ofsProbability of occurrence for a typical day; s is the number of typical days; tau is the coefficient of the equivalent annual investment cost change value, r is the discount rate; y is the age of the system in use;
step 2.2: taking the capacity of system installation equipment as a decision variable optimized in the first stage, and taking the total input cost minimization of the electricity-gas combined comprehensive energy system as an objective function:
f1＝min CTI
Figure BDA0002811492250000036
wherein n belongs to { AC, EB, CCHP, GB, WT }; n denotes an energy conversion device, n is a WT time tableA wind power generator, an electric refrigerator when n is AC, an electric boiler when n is EB, a combined cooling heating and power generation device when n is CCHP, and a gas boiler when n is GB;
Figure RE-GDA0002886747420000037
the installation states of the kth energy conversion device n and the qth energy storage means m respectively,
Figure RE-GDA0002886747420000041
1 denotes that the device is installed, 0 denotes that the device is not installed;
Figure RE-GDA0002886747420000042
the installation cost of the kth energy conversion device n;
Figure RE-GDA0002886747420000043
the installation cost of the qth m energy storage device;
step 2.3: and (2) optimizing decision variables by taking the operating conditions of energy conversion equipment and an energy storage device of the system as a second stage, and minimizing the typical daily operating cost in the service life of the electricity-gas combined comprehensive energy system as an objective function:
f2＝min CTO
CTO＝CELE+CGAS+CFO+CTAX
wherein ,CELEThe difference between the electricity purchasing cost and the electricity selling cost of the system; cGASThe gas purchase cost for the system; cFOOperating and maintaining costs for system equipment; cPENPenalizing costs for system load loss;
Figure BDA0002811492250000041
Figure BDA0002811492250000042
Figure BDA0002811492250000043
Figure BDA0002811492250000044
wherein ,
Figure RE-GDA0002886747420000048
the prices of the electric energy purchased and sold in the time period t are respectively;
Figure RE-GDA0002886747420000049
the price of purchasing natural gas for a period of time t;
Figure RE-GDA00028867474200000410
the operation and maintenance cost for the kth energy conversion device n;
Figure RE-GDA00028867474200000411
the power of the kth energy conversion device n is input for the period t,
Figure RE-GDA00028867474200000412
Figure RE-GDA00028867474200000413
the operating maintenance cost for the qth m energy storage device;
Figure RE-GDA00028867474200000414
respectively charging and discharging m energy power of the qth m energy storage device in the t time period; c. CmPenalty cost for load penalty for m energy;
Figure RE-GDA00028867474200000415
the expected shortage of m energy;
step 2.4: the two-stage optimization configuration model for constructing the electricity-gas combined comprehensive energy system has the following constraint conditions:
Figure BDA00028114922500000413
Figure BDA00028114922500000414
Figure BDA00028114922500000415
wherein ,
Figure BDA00028114922500000416
maximum values of electric power purchased and sold for the system respectively;
Figure BDA00028114922500000417
respectively the electricity purchasing state and the electricity selling state of the system in the time period t,
Figure BDA00028114922500000418
ensuring that the system cannot buy and sell electricity at the same time;
Figure BDA00028114922500000419
wherein ,
Figure BDA00028114922500000420
purchasing the maximum value of natural gas power for the system;
Figure BDA00028114922500000421
wherein ,
Figure RE-GDA0002886747420000053
for the operation state of the kth energy conversion device n,
Figure RE-GDA0002886747420000054
1 indicates that the apparatus is in operationState, 0 indicates that the plant is in a shutdown state;
Figure RE-GDA0002886747420000055
the power of m energy output by the kth energy conversion device n in the t period;
Figure RE-GDA0002886747420000056
respectively outputting upper and lower limits of m power for the kth energy conversion equipment n in the t period;
Figure BDA0002811492250000053
Figure BDA0002811492250000054
Figure BDA0002811492250000055
Figure BDA0002811492250000056
wherein ,
Figure RE-GDA00028867474200000511
respectively charging and discharging energy for the qth m energy storage device in the t time period;
Figure RE-GDA00028867474200000512
respectively charge and discharge energy to/from the qth m energy storage device,
Figure RE-GDA00028867474200000513
1, the energy storage device is in a working state, so that the energy storage device can not charge and discharge energy at the same time;
Figure RE-GDA00028867474200000514
maximum of q m energy storage devices in t periodMinimum charging power;
Figure RE-GDA00028867474200000515
the maximum and minimum discharge power of the qth m energy storage device in the t time period are respectively;
Figure RE-GDA00028867474200000516
energy stored by the qth m energy storage device for the t period;
Figure RE-GDA00028867474200000517
the upper limit and the lower limit of the energy stored by the qth m energy storage device in the t time period are respectively set;
Figure BDA00028114922500000514
Figure BDA00028114922500000515
wherein ：
Figure RE-RE-GDA00028867474200001114
m energy reserve power for the kth energy conversion device n during the t period;
Figure RE-RE-GDA00028867474200001115
reserve power of the qth m energy storage device for the t period;
and step 3: according to Rayleigh distribution of wind speed, obtaining a probability distribution model of power generation and adding the probability distribution model into a constraint condition of a two-stage optimization configuration model;
the power generation capacity probability distribution model is as follows:
Figure BDA00028114922500000518
wherein ：
Figure BDA00028114922500000519
for wind power generationRated output power of the motor; v. ofr、vin、voutRated wind speed and cut-in wind speed and cut-out wind speed respectively;
and 4, step 4: determining the influence of random faults of equipment on a system, establishing two energy reliability indexes and adding the two energy reliability indexes into constraint conditions of a two-stage optimization configuration model;
the two energy reliability indexes are energy shortage rate and energy supplement rate;
the energy shortage rate is as follows:
Figure BDA0002811492250000061
Figure BDA0002811492250000062
Figure BDA0002811492250000063
wherein ,
Figure BDA0002811492250000064
is the energy shortage rate of m energy,
Figure BDA0002811492250000065
the installation state of a fault device gamma outputting m energy for a period t;
Figure BDA0002811492250000066
probability of failure of a device gamma outputting m energy for a period of t;
Figure BDA0002811492250000067
respectively outputting m-energy power and standby power for the fault equipment gamma in the t period; rm,tReserve power for energy of m in t period;
the energy supplement rate is as follows:
Figure BDA0002811492250000068
wherein ,
Figure BDA0002811492250000069
energy supplement rate of m energy;
the two energy reliability indexes are added into an optimization model to be constrained as follows:
Figure BDA00028114922500000610
Figure BDA00028114922500000611
wherein ,
Figure BDA00028114922500000612
respectively the upper limit of the energy shortage rate and the lower limit of the energy supplement rate.
And 5: and solving the optimal configuration scheme of the electricity-gas combined comprehensive energy system by utilizing a particle swarm algorithm.
Step 5.1: inputting electricity, cold and heat load data and equipment parameters of the comprehensive energy system, setting the number of particles and the number of iterations of the algorithm, and generating an initial population with optimized configuration;
step 5.2: calculating the configuration cost of the system, calling a CPLEX solver to calculate the operation cost of the system, and updating the particle population and the optimal solution;
step 5.3: and when the upper limit of the iteration times is reached or the iteration times is converged, stopping calculating and outputting the optimal solution.
According to the two-stage optimal configuration method of the comprehensive energy system considering the energy reliability, the established two-stage optimal configuration model simultaneously considers the uncertainty of wind power generation and the uncertainty of equipment fault function, the influence of the uncertainty of system energy on the system output can be effectively reduced, the utilization of the system energy is effectively improved, and the system has sustainability and economy.
Drawings
FIG. 1 is a flow chart of a two-stage optimal configuration method of an integrated energy system considering energy reliability according to the present invention;
FIG. 2 is a block diagram of an electric-gas combined energy system;
FIG. 3 is a flow chart of solving an optimal configuration scheme of the electricity-gas combined energy system by utilizing a particle swarm algorithm;
FIG. 4a is a spring and autumn load curve;
FIG. 4b is a winter load curve;
FIG. 4c is a summer load curve;
FIG. 4d is a typical seasonal wind power output curve.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention and are a part, but not all, of the examples herein.
As shown in fig. 1, a flow chart of a two-stage optimal configuration method of an integrated energy system considering energy reliability according to the present invention is provided, where the method includes the following steps:
step 1: establishing an electricity-gas combined integrated energy system model shown in figure 2;
the electric-gas combined integrated energy system model comprises: the energy conversion model of the energy storage module is not considered, and the energy conversion model of the energy storage module is considered;
the energy storage module comprises: an electrical energy storage device, a cold energy storage device, a hot energy storage device;
the energy conversion comprises: electric energy, cold energy and heat energy converted by the energy conversion equipment; the energy conversion apparatus includes: the system comprises a wind driven generator, an electric refrigerator, an electric boiler, a combined cooling heating and power device and a gas boiler;
the energy conversion model without considering the energy storage module is as follows:
Figure BDA0002811492250000071
wherein ,PELE,tElectric power output by the system for a period t;
Figure BDA0002811492250000072
the electricity purchasing power and the electricity selling power of the system in the time period t are respectively;
Figure BDA0002811492250000073
electric power input to the electric refrigerator and the electric boiler respectively in the period of t;
Figure BDA0002811492250000074
inputting natural gas power of CCHP for a period t;
Figure BDA0002811492250000075
the power generation efficiency of CCHP; pWT,tThe generated power of the wind driven generator;
Figure BDA0002811492250000081
wherein ,PCOOL,tCold power output by the system for a period t;
Figure BDA0002811492250000082
inputting electric power of the electric refrigerator and natural gas power of CCHP for a period t respectively;
Figure BDA0002811492250000083
the refrigeration efficiencies of the electric refrigerator and the CCHP respectively;
Figure BDA0002811492250000084
wherein ,PHEAT,tThermal power output by the system in the period t;
Figure BDA0002811492250000085
natural gas fed to the gas boiler for a period of time tPower;
Figure BDA0002811492250000086
the heat efficiency of an electric boiler, the heat efficiency of a CCHP boiler and the heat efficiency of a gas boiler are respectively set;
Figure BDA0002811492250000087
wherein ,
Figure BDA0002811492250000088
the gas purchasing quantity of the system in the period t; hLA low calorific value for natural gas combustion;
the energy conversion model considering the energy storage module is as follows:
Figure BDA0002811492250000089
ΔSm,t＝Sm,t-Sm,t-1
Em,t＝Pm,tΔt-ΔSm,t
wherein m belongs to { ele, cool, heat }, wherein m represents electricity when ele, cold when cool and heat when heat;
Figure BDA00028114922500000810
respectively charging and discharging energy power of the energy storage device in the time period of t;
Figure BDA00028114922500000811
respectively charging and discharging energy for the energy storage device in the time period m of t; delta Sm,tThe variation of the energy stored in the energy storage device for the t time period m; sm,t、Sm,t-1M energy stored by the energy storage device m at t and t-1 time periods respectively; pm,tM power, P output by the system in t period of the energy storage module is not consideredm,t∈{PELE,t,PCOOL,t,PHEAT,t}；
Step 2: establishing a two-stage optimization configuration model of the electricity-gas combined comprehensive energy system;
step 2.1: the method aims at the lowest operation and maintenance cost of configuration installation and put into use of energy equipment in engineering, and establishes a two-stage optimization configuration model objective function of the electricity-gas combined comprehensive energy system as follows:
Figure BDA00028114922500000812
Figure BDA00028114922500000813
wherein ,CTThe total input cost of the comprehensive energy system; cTIInvestment cost for installing equipment for the system once; cTOThe annual operating cost of the system within the service life; p is a radical ofsProbability of occurrence for a typical day; s is the number of typical days; tau is the coefficient of the equivalent annual investment cost change value, r is the discount rate; y is the age of the system in use;
step 2.2: taking the capacity of system installation equipment as a decision variable optimized in the first stage, and taking the total input cost minimization of the electricity-gas combined comprehensive energy system as an objective function:
f1＝min CTI
Figure BDA0002811492250000091
wherein n belongs to { AC, EB, CCHP, GB, WT }; n represents an energy conversion device, a wind power generator when n is WT, an electric refrigerator when n is AC, an electric boiler when n is EB, a combined cooling, heating and power generation device when n is CCHP, and a gas boiler when n is GB;
Figure RE-GDA0002886747420000094
the installation states of the kth energy conversion device n and the qth energy storage means m respectively,
Figure RE-GDA0002886747420000095
1 denotes that the device is installed, 0 denotes that the device is not installed;
Figure RE-GDA0002886747420000096
the installation cost of the kth energy conversion device n;
Figure RE-GDA0002886747420000097
the installation cost of the qth m energy storage device;
step 2.3: and (2) optimizing decision variables by taking the operating conditions of energy conversion equipment and an energy storage device of the system as a second stage, and minimizing the typical daily operating cost in the service life of the electricity-gas combined comprehensive energy system as an objective function:
f2＝min CTO
CTO＝CELE+CGAS+CFO+CTAX
wherein ,CELEThe difference between the electricity purchasing cost and the electricity selling cost of the system; cGASThe gas purchase cost for the system; cFOOperating and maintaining costs for system equipment; cPENPenalizing costs for system load loss;
Figure BDA0002811492250000096
Figure BDA0002811492250000097
Figure BDA0002811492250000098
Figure BDA0002811492250000099
wherein ,
Figure RE-GDA0002886747420000105
are respectively provided withThe price of the electric energy purchased and sold for the time period t;
Figure RE-GDA0002886747420000106
the price of purchasing natural gas for a period of time t;
Figure RE-GDA0002886747420000107
the operation and maintenance cost for the kth energy conversion device n;
Figure RE-GDA0002886747420000108
the power of the kth energy conversion device n is input for the period t,
Figure RE-GDA0002886747420000109
Figure RE-GDA00028867474200001010
the operating maintenance cost for the qth m energy storage device;
Figure RE-GDA00028867474200001011
respectively charging and discharging m energy power of the qth m energy storage device in the t time period; c. CmPenalty cost for load penalty for m energy;
Figure RE-GDA00028867474200001012
the expected shortage of m energy;
step 2.4: the two-stage optimization configuration model for constructing the electricity-gas combined comprehensive energy system has the following constraint conditions:
Figure BDA0002811492250000101
Figure BDA0002811492250000102
Figure BDA0002811492250000103
wherein ,
Figure BDA0002811492250000104
maximum values of electric power purchased and sold for the system respectively;
Figure BDA0002811492250000105
respectively the electricity purchasing state and the electricity selling state of the system in the time period t,
Figure BDA0002811492250000106
ensuring that the system cannot buy and sell electricity at the same time;
Figure BDA0002811492250000107
wherein ,
Figure BDA0002811492250000108
purchasing the maximum value of natural gas power for the system;
Figure BDA0002811492250000109
wherein ,
Figure RE-GDA00028867474200001022
for the operation state of the kth energy conversion device n,
Figure RE-GDA00028867474200001023
1 represents that the equipment is in a working state, and 0 represents that the equipment is in a shutdown state;
Figure RE-GDA00028867474200001024
the power of m energy output by the kth energy conversion device n in the t period;
Figure RE-GDA00028867474200001025
respectively outputting upper and lower limits of m power for the kth energy conversion equipment n in the t period;
Figure BDA00028114922500001014
Figure BDA00028114922500001015
Figure BDA00028114922500001016
Figure BDA00028114922500001017
wherein ,
Figure RE-GDA0002886747420000115
respectively charging and discharging energy for the qth m energy storage device in the t time period;
Figure RE-GDA0002886747420000116
respectively charge and discharge energy to/from the qth m energy storage device,
Figure RE-GDA0002886747420000117
1, the energy storage device is in a working state, so that the energy storage device can not charge and discharge energy at the same time;
Figure RE-GDA0002886747420000118
the maximum energy charging power and the minimum energy charging power of the qth m energy storage device in the t time period are respectively;
Figure RE-GDA0002886747420000119
the maximum and minimum discharge power of the qth m energy storage device in the t time period are respectively;
Figure RE-GDA00028867474200001110
energy stored by the qth m energy storage device for the t period;
Figure RE-GDA00028867474200001111
the upper limit and the lower limit of the energy stored by the qth m energy storage device in the t time period are respectively set;
Figure BDA00028114922500001025
Figure BDA00028114922500001026
wherein ：
Figure RE-GDA00028867474200001114
m energy reserve power for the kth energy conversion device n during the t period;
Figure RE-GDA00028867474200001115
reserve power of the qth m energy storage device for the t period;
and step 3: according to Rayleigh distribution of wind speed, obtaining a probability distribution model of power generation and adding the probability distribution model into a constraint condition of a two-stage optimization configuration model;
the power generation capacity probability distribution model is as follows:
Figure BDA0002811492250000111
wherein ：
Figure BDA0002811492250000112
the rated output power of the wind driven generator; v. ofr、vin、voutRated wind speed and cut-in wind speed and cut-out wind speed respectively;
and 4, step 4: determining the influence of random faults of equipment on a system, establishing two energy reliability indexes and adding the two energy reliability indexes into constraint conditions of a two-stage optimization configuration model;
the two energy reliability indexes are energy shortage rate and energy supplement rate;
the energy shortage rate is as follows:
Figure BDA0002811492250000113
Figure BDA0002811492250000114
Figure BDA0002811492250000115
wherein ,
Figure BDA0002811492250000116
is the energy shortage rate of m energy,
Figure BDA0002811492250000117
the installation state of a fault device gamma outputting m energy for a period t;
Figure BDA0002811492250000118
probability of failure of a device gamma outputting m energy for a period of t;
Figure BDA0002811492250000119
respectively outputting m-energy power and standby power for the fault equipment gamma in the t period; rm,tReserve power for energy of m in t period;
the energy supplement rate is as follows:
Figure BDA00028114922500001110
wherein ,
Figure BDA00028114922500001111
energy supplement rate of m energy;
the two energy reliability indexes are added into an optimization model to be constrained as follows:
Figure BDA00028114922500001112
Figure BDA00028114922500001113
wherein ,
Figure BDA00028114922500001114
respectively the upper limit of the energy shortage rate and the lower limit of the energy supplement rate.
And 5: the optimal configuration scheme of the electricity-gas combined integrated energy system is solved by utilizing a particle swarm algorithm, and the specific solving flow is shown in figure 3. The method comprises the following steps:
step 5.1: inputting electricity, cold and heat load data and equipment parameters of the comprehensive energy system, setting the number of particles and the number of iterations of the algorithm, and generating an initial population with optimized configuration;
step 5.2: calculating the configuration cost of the system, calling a CPLEX solver to calculate the operation cost of the system, and updating the particle population and the optimal solution;
step 5.3: and when the upper limit of the iteration times is reached or the iteration times is converged, stopping calculating and outputting the optimal solution.
FIG. 4 is a typical seasonal wind power output versus load curve for the combined electric and gas energy system of FIG. 2.
Taking a certain area in the north as an example, explaining the two-stage optimization configuration method of the comprehensive energy system considering energy reliability, which is provided by the invention, according to the steps 1-4, a two-stage optimization configuration model of the electricity-gas combined comprehensive energy system is constructed, electricity is purchased by adopting time-of-use electricity price, the peak time period is 8: 00-14: 00, 19: 00-22: 00, the price is 1.2 yuan/kW.h, the average time period is 14: 00-19: 00, the price is 0.8 yuan/kW.h, the valley time period is 22: 00-8: 00, and the price is 0.4 yuan/kW.h; the price of electricity sold is 0.64 yuan/kW.h; the price of purchasing natural gas is 2.93 yuan/m3. The penalty cost of the loss of the electric energy, the cold energy and the heat energy is 8.05, 3.04 and 3.59 yuan/kW.h respectively. The service life of the comprehensive energy system constructed by the method is 20 years, the discount rate is 1 percent, and each energy deviceThe installation and operating costs associated with the energy storage device are shown in appendix table 1. The probability of occurrence in spring and autumn, winter and summer on typical days is 0.4, 0.4 and 0.2 respectively.
TABLE 1 capital cost of equipment and operating parameters
Figure BDA0002811492250000121
And (3) respectively considering and not considering the energy storage module according to the step 1, respectively introducing the reliability index in the step 4 into or not introducing the constraint, and setting a scene as shown in a table 2.
TABLE 2 scene settings
Figure BDA0002811492250000122
Figure BDA0002811492250000131
According to the step 5, the algorithm shown in fig. 3 is adopted, and the solution optimization results are shown in tables 3 and 4.
Table 3 equipment configuration number and capacity results in each scene
Figure BDA0002811492250000132
TABLE 4 optimization results under various scenarios
Figure BDA0002811492250000133
As can be seen from tables 3 and 4, compared with scene 1, scene 2 considers two reliability indexes, the number of installed electric boilers, electric refrigerators and energy storage modules is increased, and the number of combined cooling, heating and power generation devices, gas boilers and wind power generators is reduced, compared with the configuration scheme of scene 1, scene 2 reduces the investment cost of 2490 ten thousand yuan, the annual operation cost is slightly higher than that of scene 1 by 22.13 ten thousand yuan, but the shortage of electric, cold and heat energy is greatly reduced and is about one tenth of scene 1, the annual penalty cost of 35.59 ten thousand yuan is reduced, the annual penalty cost accounts for 91.23% of the annual penalty cost of scene 1, and the optimization result is that compared with the configuration scheme of scene 2, scene 1, the total investment cost of the system is reduced by 2732.89 ten thousand yuan; compared with the scenario 3, the scenario 4 considers two reliability indexes, the number of the installed electric refrigerators, the gas boilers and the energy storage devices is increased, the number of the electric boilers and the wind power generators is reduced, the investment cost of the scenario 4 is increased by 630 ten thousand compared with the configuration scheme of the scenario 3, the annual operation cost is reduced by 161.05 ten thousand, the shortage of electricity, cold and heat energy is effectively reduced, the annual penalty cost of 37.9 ten thousand yuan is reduced, the annual penalty cost accounts for 86.74% of the annual penalty cost of the scenario 3, and the configuration of the scenario 4 and the operation scheme have the optimization result that the total investment cost of the system is reduced by 2960.16 thousand yuan compared with the scenario 3. Therefore, after the reliability index is added into the optimized model constraint, the shortage energy of the system can be effectively reduced, the system is more stable and reliable, the total input cost of the system can be reduced, and the stability and the economical efficiency of the system are ensured.
The technical solutions and the accompanying drawings provided in the embodiments of the present invention are used for further illustrating the present invention and are not limited thereto, and it should be noted that a person skilled in the art should know that the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced, and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the present invention.

Claims (6)

1. A two-stage optimal configuration method of an integrated energy system considering energy reliability is characterized by comprising the following steps:
step 1: establishing an electricity-gas combined comprehensive energy system model;
step 2: establishing a two-stage optimization configuration model of the electricity-gas combined comprehensive energy system;
and step 3: according to Rayleigh distribution of wind speed, obtaining a probability distribution model of power generation and adding the probability distribution model into a constraint condition of a two-stage optimization configuration model;
and 4, step 4: determining the influence of random faults of equipment on a system, establishing two energy reliability indexes and adding the two energy reliability indexes into constraint conditions of a two-stage optimization configuration model;
and 5: and solving the optimal configuration scheme of the electricity-gas combined comprehensive energy system by utilizing a particle swarm algorithm.
2. The two-stage optimal configuration method of integrated energy system considering energy reliability according to claim 1, wherein the electric-gas combined integrated energy system model in step 1 comprises: the energy conversion model of the energy storage module is not considered, and the energy conversion model of the energy storage module is considered;
the energy storage module comprises: an electrical energy storage device, a cold energy storage device, a hot energy storage device;
the energy conversion comprises: electric energy, cold energy and heat energy converted by the energy conversion equipment; the energy conversion apparatus includes: the system comprises a wind driven generator, an electric refrigerator, an electric boiler, a combined cooling heating and power device and a gas boiler;
the energy conversion model without considering the energy storage module is as follows:
Figure FDA0002811492240000011
wherein ,PELE,tElectric power output by the system for a period t;
Figure FDA0002811492240000012
the electricity purchasing power and the electricity selling power of the system in the time period t are respectively;
Figure FDA0002811492240000013
electric power input to the electric refrigerator and the electric boiler respectively in the period of t;
Figure FDA0002811492240000014
inputting the natural gas power of the combined cooling heating and power generation device for the t time period;
Figure FDA0002811492240000015
the power generation efficiency of the combined cooling heating and power generation device; pWT,tThe generated power of the wind driven generator;
Figure FDA0002811492240000016
wherein ,PCOOL,tCold power output by the system for a period t;
Figure FDA0002811492240000017
inputting electric power of the electric refrigerator and natural gas power of the combined cooling heating and power device in t time period;
Figure FDA0002811492240000018
the refrigeration efficiencies of the electric refrigerator and the combined cooling heating and power device are respectively;
Figure FDA0002811492240000021
wherein ,PHEAT,tThermal power output by the system in the period t;
Figure FDA0002811492240000022
inputting the natural gas power of the gas boiler for a time period t;
Figure FDA0002811492240000023
the heat efficiency of an electric boiler, a combined cooling heating and power device and a gas boiler;
Figure FDA0002811492240000024
wherein ,
Figure FDA0002811492240000025
the gas purchasing quantity of the system in the period t; hLA low calorific value for natural gas combustion;
the energy conversion model considering the energy storage module is as follows:
Figure FDA0002811492240000026
ΔSm,t＝Sm,t-Sm,t-1
Em,t＝Pm,tΔt-ΔSm,t
wherein m belongs to { ele, cool, heat }, wherein m represents electricity when ele, cold when cool and heat when heat;
Figure FDA0002811492240000027
respectively charging and discharging energy power of the energy storage device in the time period of t;
Figure FDA0002811492240000028
respectively charging and discharging energy for the energy storage device in the time period m of t; delta Sm,tThe variation of the energy stored in the energy storage device for the t time period m; sm,t、Sm,t-1M energy stored by the energy storage device m at t and t-1 time periods respectively; pm,tM power, P output by the system in t period of the energy storage module is not consideredm,t∈{PELE,t,PCOOL,t,PHEAT,t}。
3. The two-stage optimal configuration method of integrated energy system considering energy reliability according to claim 1, wherein the step 2 comprises:
step 2.1: aiming at the lowest operation and maintenance cost of configuration installation and put into use of energy equipment in engineering, a two-stage optimization configuration model objective function of the electricity-gas combined comprehensive energy system is established as follows:
Figure RE-FDA00028867474100000210
Figure RE-FDA00028867474100000211
wherein ,CTThe total input cost of the comprehensive energy system; cTIInvestment cost for installing equipment for the system once; cTOThe annual operating cost of the system within the service life; p is a radical ofsProbability of occurrence for a typical day; s is the number of typical days; tau is the coefficient of the equivalent annual investment cost change value, r is the discount rate; y is the age of the system in use;
step 2.2: taking the capacity of system installation equipment as a decision variable optimized in the first stage, and taking the total input cost minimization of the electricity-gas combined comprehensive energy system as an objective function:
f1＝minCTI
Figure RE-FDA0002886747410000031
wherein n belongs to { AC, EB, CCHP, GB, WT }; n represents an energy conversion device, a wind power generator when n is WT, an electric refrigerator when n is AC, an electric boiler when n is EB, a combined cooling, heating and power generation device when n is CCHP, and a gas boiler when n is GB;
Figure RE-FDA0002886747410000032
respectively the installation states of the kth energy conversion device n and the qth energy storage device m,
Figure RE-FDA0002886747410000033
1 denotes that the device is installed, 0 denotes that the device is not installed;
Figure RE-FDA0002886747410000034
the installation cost for the kth energy conversion apparatus n;
Figure RE-FDA0002886747410000035
the installation cost of the qth m energy storage device;
step 2.3: and (2) optimizing decision variables by taking the operating conditions of energy conversion equipment and an energy storage device of the system as a second stage, and minimizing the typical daily operating cost in the service life of the electricity-gas combined comprehensive energy system as an objective function:
f2＝minCTO
CTO＝CELE+CGAS+CFO+CTAX
wherein ,CELEThe difference between the electricity purchasing cost and the electricity selling cost of the system; cGASThe gas purchase cost for the system; cFOOperating and maintaining costs for system equipment; cPENPenalizing costs for system load loss;
Figure RE-FDA0002886747410000036
Figure RE-FDA0002886747410000037
Figure RE-FDA0002886747410000038
Figure RE-FDA0002886747410000039
wherein ,
Figure RE-FDA00028867474100000310
the prices of the electric energy purchased and sold in the time period t are respectively;
Figure RE-FDA00028867474100000311
the price of purchasing natural gas for a period of time t;
Figure RE-FDA00028867474100000312
the operation and maintenance cost for the kth energy conversion device n;
Figure RE-FDA00028867474100000313
the power of the kth energy conversion device n is input for the period t,
Figure RE-FDA00028867474100000314
Figure RE-FDA00028867474100000315
the operating maintenance cost for the qth m energy storage device;
Figure RE-FDA0002886747410000041
respectively charging and discharging m energy power of the qth m energy storage device in the t time period; c. CmPenalty cost for m energy loss;
Figure RE-FDA0002886747410000042
the expected shortage of m energy;
step 2.4: the two-stage optimization configuration model for constructing the electricity-gas combined comprehensive energy system has the following constraint conditions:
Figure RE-FDA0002886747410000043
Figure RE-FDA0002886747410000044
Figure RE-FDA0002886747410000045
wherein ,
Figure RE-FDA0002886747410000046
maximum values of electric power purchased and sold for the system respectively;
Figure RE-FDA0002886747410000047
respectively the electricity purchasing state and the electricity selling state of the system in the time period t,
Figure RE-FDA0002886747410000048
ensuring that the system cannot buy and sell electricity at the same time;
Figure RE-FDA0002886747410000049
wherein ,
Figure RE-FDA00028867474100000410
purchasing the maximum value of natural gas power for the system;
Figure RE-FDA00028867474100000411
wherein ,
Figure RE-FDA00028867474100000412
for the operation state of the kth energy conversion device n,
Figure RE-FDA00028867474100000413
1 represents that the equipment is in a working state, and 0 represents that the equipment is in a shutdown state;
Figure RE-FDA00028867474100000414
outputting power of m energy for the kth energy conversion device n in the t period;
Figure RE-FDA00028867474100000415
respectively, is a period of tThe kth energy conversion equipment n outputs upper and lower limits of m power;
Figure RE-FDA00028867474100000416
Figure RE-FDA00028867474100000417
Figure RE-FDA00028867474100000418
Figure RE-FDA00028867474100000419
wherein ,
Figure RE-FDA00028867474100000420
respectively charging and discharging energy for the qth m energy storage device in the t time period;
Figure RE-FDA00028867474100000421
respectively charge and discharge energy to/from the qth energy storage device,
Figure RE-FDA00028867474100000422
1, the energy storage device is in a working state, so that the energy storage device can not charge and discharge energy at the same time;
Figure RE-FDA00028867474100000423
the maximum energy charging power and the minimum energy charging power of the qth m energy storage device in the t time period are respectively;
Figure RE-FDA00028867474100000424
the maximum and minimum discharge power of the qth m energy storage device in the t period are respectively;
Figure RE-FDA00028867474100000425
energy stored by the qth m energy storage device for the t period;
Figure RE-FDA00028867474100000426
the upper limit and the lower limit of the energy stored by the qth m energy storage device in the t time period are respectively set;
Figure RE-FDA0002886747410000051
Figure RE-FDA0002886747410000052
wherein ：
Figure RE-FDA0002886747410000053
m energy reserve power for the kth energy conversion device n during the t period;
Figure RE-FDA0002886747410000054
reserve power for the qth m energy storage device for the t period.
4. The two-stage optimal configuration method of the integrated energy system considering energy reliability according to claim 1, wherein the wind power generation capacity probability distribution model added with the optimization model constraint in the step 3 is:
Figure FDA0002811492240000051
wherein ：
Figure FDA0002811492240000052
the rated output power of the wind driven generator; v. ofr、vin、voutRespectively rated wind speedAnd cut-in and cut-out wind speeds.
5. The two-stage optimal configuration method of integrated energy system considering energy reliability of claim 1, wherein the two energy reliability indicators of the step 4 added with the optimization model constraint are energy shortage rate and energy supplement rate;
the energy shortage rate is as follows:
Figure FDA0002811492240000053
Figure FDA0002811492240000054
Figure FDA0002811492240000055
wherein ,
Figure FDA0002811492240000056
is the energy shortage rate of m energy,
Figure FDA0002811492240000057
the installation state of a fault device gamma outputting m energy for a period of t;
Figure FDA0002811492240000058
probability of failure of a device gamma outputting m energy for a period of t;
Figure FDA0002811492240000059
respectively outputting m-energy power and standby power for the fault equipment gamma in the t period; rm,tReserve power for energy of m in t period;
the energy supplement rate is as follows:
Figure FDA00028114922400000510
wherein ,
Figure FDA00028114922400000511
energy supplement rate of m energy;
the two energy reliability indexes are added into an optimization model to be constrained as follows:
Figure FDA00028114922400000512
Figure FDA00028114922400000513
wherein ,
Figure FDA00028114922400000514
respectively the upper limit of the energy shortage rate and the lower limit of the energy supplement rate.
6. The two-stage optimal configuration method of the integrated energy system considering energy reliability according to claim 1, wherein the step 5 is specifically:
step 5.1: inputting electricity, cold and heat load data and equipment parameters of the comprehensive energy system, and setting the number of particles and iteration times of the algorithm to generate an initial population with optimized configuration;
step 5.2: calculating the configuration cost of the system, calling a CPLEX solver to calculate the operation cost of the system, and updating the particle population and the optimal solution;
step 5.3: and when the upper limit of the iteration times is reached or the iteration times is converged, stopping calculating and outputting the optimal solution.
CN202011392849.XA 2020-12-02 2020-12-02 Comprehensive energy system two-stage optimal configuration method considering energy reliability Active CN112446546B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011392849.XA CN112446546B (en) 2020-12-02 2020-12-02 Comprehensive energy system two-stage optimal configuration method considering energy reliability

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011392849.XA CN112446546B (en) 2020-12-02 2020-12-02 Comprehensive energy system two-stage optimal configuration method considering energy reliability

Publications (2)

Publication Number Publication Date
CN112446546A true CN112446546A (en) 2021-03-05
CN112446546B CN112446546B (en) 2023-10-10

Family

ID=74740226

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011392849.XA Active CN112446546B (en) 2020-12-02 2020-12-02 Comprehensive energy system two-stage optimal configuration method considering energy reliability

Country Status (1)

Country Link
CN (1) CN112446546B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113158547A (en) * 2021-03-11 2021-07-23 上海电力大学 Regional comprehensive energy system optimal configuration method considering economy and reliability
CN113159985A (en) * 2021-03-26 2021-07-23 东北大学 Two-stage optimal scheduling method for electric heating comprehensive energy system
CN114142460A (en) * 2021-11-17 2022-03-04 浙江华云电力工程设计咨询有限公司 Energy storage double-layer target optimization configuration method and terminal in comprehensive energy system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160092622A1 (en) * 2013-05-20 2016-03-31 State Grid Corporation Of China Method for modeling medium and long term wind power output model of medium and long term optimal operationof power system
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN111489020A (en) * 2020-03-31 2020-08-04 杭州鸿晟电力设计咨询有限公司 Independent type comprehensive energy grid electricity-gas energy storage system optimal configuration solving method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160092622A1 (en) * 2013-05-20 2016-03-31 State Grid Corporation Of China Method for modeling medium and long term wind power output model of medium and long term optimal operationof power system
CN110417006A (en) * 2019-07-24 2019-11-05 三峡大学 Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization
CN111489020A (en) * 2020-03-31 2020-08-04 杭州鸿晟电力设计咨询有限公司 Independent type comprehensive energy grid electricity-gas energy storage system optimal configuration solving method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
陈灵敏;吴杰康;唐惠玲;茅云寿;黎昌杰;: "计及综合需求响应的冷热电联供独立微网容量优化配置模型", 南方电网技术, no. 10 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113158547A (en) * 2021-03-11 2021-07-23 上海电力大学 Regional comprehensive energy system optimal configuration method considering economy and reliability
CN113158547B (en) * 2021-03-11 2022-10-18 上海电力大学 Regional comprehensive energy system optimal configuration method considering economy and reliability
CN113159985A (en) * 2021-03-26 2021-07-23 东北大学 Two-stage optimal scheduling method for electric heating comprehensive energy system
CN113159985B (en) * 2021-03-26 2023-10-31 东北大学 Two-stage optimal scheduling method for electric heating comprehensive energy system
CN114142460A (en) * 2021-11-17 2022-03-04 浙江华云电力工程设计咨询有限公司 Energy storage double-layer target optimization configuration method and terminal in comprehensive energy system
CN114142460B (en) * 2021-11-17 2024-03-15 浙江华云电力工程设计咨询有限公司 Energy storage double-layer target optimal configuration method and terminal in comprehensive energy system

Also Published As

Publication number Publication date
CN112446546B (en) 2023-10-10

Similar Documents

Publication Publication Date Title
CN109919478B (en) Comprehensive energy microgrid planning method considering comprehensive energy supply reliability
CN111445090B (en) Double-layer planning method for off-grid type comprehensive energy system
CN109193626B (en) Unified solving method for model selection and constant volume optimization planning of distributed energy station
CN112446546B (en) Comprehensive energy system two-stage optimal configuration method considering energy reliability
Deng et al. System modeling and optimization of microgrid using genetic algorithm
CN112529244B (en) Comprehensive energy system collaborative optimization operation method considering electric load demand response
CN111859683B (en) Optimal configuration method of park comprehensive energy system based on dynamic energy concentrator
Liu et al. Thermo-economic comparison of heat–power decoupling technologies for combined heat and power plants when participating in a power-balancing service in an energy hub
CN113850409A (en) Comprehensive energy system optimized operation method considering renewable energy access
CN115099007B (en) Comprehensive energy system optimized operation method based on comprehensive cost-energy consumption curve
Zhao et al. Optimization and analysis of an integrated energy system based on wind power utilization and on-site hydrogen refueling station
Zhao et al. Integrated unit commitment and economic dispatch of combined heat and power system considering heat-power decoupling retrofit of CHP unit
Yu et al. Complementary configuration research of new combined cooling, heating, and power system driven by renewable energy under energy management modes
CN114255137A (en) Low-carbon comprehensive energy system optimization planning method and system considering clean energy
Zhang et al. Economic and Optimal Dispatch Model of Electricity, Heat and Gas for Virtual Power Plants in Parks Considering Low Carbon Targets.
Mohamed et al. Modelling and environmental/economic power dispatch of microgrid using multiobjective genetic algorithm optimization
CN112836957A (en) Regional comprehensive energy system planning method considering power supply reliability
CN111625961A (en) Comprehensive energy system collaborative optimization operation regulation and control method
CN110783917A (en) Configuration method of multi-energy hub containing new energy consumption
CN114330835A (en) Optimal configuration method of electricity/heat hybrid energy storage system in comprehensive energy microgrid
CN113988392A (en) Microgrid optimization planning method considering reliability demand response
Fu et al. Optimal Operation strategy of low-carbon integrated energy system considering power to gas and carbon capture technology
CN112149339A (en) Capacity optimization model of wind power-photovoltaic-photothermal-electric heater complementary power generation system
Qi et al. A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation.
Ming et al. Optimization strategy of hybrid AC-DC power supply for regional integrated energy system

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
TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20210408

Address after: 121011 No.128 Songpo Road, Linghe District, Jinzhou City, Liaoning Province

Applicant after: SKILLS TRAINING CENTER, STATE GRID LIAONING ELECTRIC POWER Co.,Ltd.

Applicant after: Northeastern University

Applicant after: STATE GRID CORPORATION OF CHINA

Address before: 121011 No.128 Songpo Road, Linghe District, Jinzhou City, Liaoning Province

Applicant before: SKILLS TRAINING CENTER, STATE GRID LIAONING ELECTRIC POWER Co.,Ltd.

Applicant before: Northeastern University

GR01 Patent grant
GR01 Patent grant