CN111431179B - Grid-connected hybrid energy system capacity configuration optimization method - Google Patents
Grid-connected hybrid energy system capacity configuration optimization method Download PDFInfo
- Publication number
- CN111431179B CN111431179B CN202010321577.8A CN202010321577A CN111431179B CN 111431179 B CN111431179 B CN 111431179B CN 202010321577 A CN202010321577 A CN 202010321577A CN 111431179 B CN111431179 B CN 111431179B
- Authority
- CN
- China
- Prior art keywords
- station
- pump
- capacity
- power station
- small hydropower
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000005611 electricity Effects 0.000 claims abstract description 43
- 238000011084 recovery Methods 0.000 claims abstract description 16
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 71
- 238000011144 upstream manufacturing Methods 0.000 claims description 68
- 230000006870 function Effects 0.000 claims description 40
- 239000002245 particle Substances 0.000 claims description 30
- 238000010248 power generation Methods 0.000 claims description 17
- 238000012423 maintenance Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 14
- 230000002441 reversible effect Effects 0.000 claims description 13
- 230000008901 benefit Effects 0.000 claims description 9
- 230000000295 complement effect Effects 0.000 claims description 7
- 238000007599 discharging Methods 0.000 claims description 7
- 238000005086 pumping Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 3
- 239000008239 natural water Substances 0.000 claims description 3
- 230000005855 radiation Effects 0.000 claims description 3
- 238000004064 recycling Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011234 economic evaluation Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- ZZUFCTLCJUWOSV-UHFFFAOYSA-N furosemide Chemical compound C1=C(Cl)C(S(=O)(=O)N)=CC(C(O)=O)=C1NCC1=CC=CO1 ZZUFCTLCJUWOSV-UHFFFAOYSA-N 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000004513 sizing Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Power Engineering (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a capacity allocation optimization method for a grid-connected hybrid energy system, which is based on an output model, actual conditions and actual market electricity price of the grid-connected hybrid energy system, provides a double-layer planning model with the minimum recovery age of system investment cost as an upper-layer capacity optimization objective function and the maximum electricity selling profit obtained by the system as a lower-layer operation optimization objective function, and solves the upper and lower layers of the model by an SQP algorithm to obtain an optimal capacity allocation scheme for investment of each main power station of the hybrid energy system.
Description
Technical Field
The invention belongs to the technical field of hybrid new energy, and particularly relates to a capacity configuration optimization method of a hybrid energy system with complementation of photovoltaic power, small hydropower stations and pumped storage power stations under a market mechanism.
Background
Since the birth of the power industry of China in the Shanghai in 1882, development and change for over a century pushed the power system industry of China to maturity step by step. In the construction and maintenance of various aspects of power systems, the Chinese power industry has undergone a dramatic change from nothing to nothing, and from quantitative change to qualitative change. In the long run, power reformation in a market environment is an important method for dealing with problems such as the rigidity of power price and frequent failure of power generation in a power system. In recent years, due to the outstanding advantages of clean and environment-friendly new energy, the trend of adding new energy power generation technology to the power market is more and more obvious. Under the current new power reform and the large background that market competition mechanisms are introduced in power trading to further gradually promote the power market reformation process, in order to enhance the economic benefit of new energy power generation and improve the overall income and competitiveness of new energy units participating in the market, new technologies represented by hybrid energy systems are likely to become adjusting means and ways matched with the new technologies for use, so that the joint optimization operation of the new energy units under the hybrid energy systems is realized, and the maximum investment return obtained by investment of new energy power generation enterprises and new energy is realized. Therefore, under the condition of the power market, the strategy problem and the capacity planning problem of the research on the hybrid energy system consisting of new energy units such as photovoltaic, hydropower and the like are undoubtedly significant to new energy power generation enterprises and the investment of new energy.
In recent years, a great deal of research has been conducted at home and abroad aiming at the problems of operation, capacity optimization configuration and the like of a hybrid energy system, and not only is the hybrid energy system composed of new energy units for wind power generation and photovoltaic power generation researched, but also optimization methods of the hybrid energy system under various grid-connected conditions are provided. For example, the document "Tao Ma, Hongxing Yang, Lin Lu, et al, optimal design of an autonomous solar-wind-pumped storage power supply system [ J ]. Applied Energy, vol.160, pp.728-736,2015" proposes the system design of a hybrid Energy system combining photovoltaic, wind and pumped storage power stations on the basis of an isolated micro-grid system of several kW scale, introduces the design process of the main components of the system, and evaluates according to the technical and economic evaluation indexes to further study the optimal system configuration of the hybrid Energy system; the literature "Xiao Xu, Weihao Hu, Di Cao, et al, optimized sizing of a stationary PV-with-hydraulic station with a pumped-storage isolation hybrid system [ J ]. Renewable Energy, vol.147, pp.1418-1431,2020" adopts technical and economic indexes to design a hybrid Energy system composed of Energy sources such as photovoltaic, wind Energy and the like, and aims to find the optimal capacity configuration with the maximum power supply reliability and the minimum investment cost, and the literature also considers the reduction rate of wind power generation and photovoltaic power generation due to policy requirements. It is contemplated that photovoltaic/small hydropower/pumped storage power stations can utilize both solar and hydro power generation, wherein pumped storage power stations can store electrical energy to benefit from power market price fluctuations. Therefore, it is necessary to provide a technical solution for planning capacity optimization configuration of a hybrid energy system in consideration of investment cost and income of the hybrid energy system composed of three power stations.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for optimizing the capacity allocation of a grid-connected hybrid energy system, which explores the relationship between the investment cost and the electricity selling income of the hybrid energy system while considering the investment cost of a new energy generator set and finally obtains the system capacity optimal allocation with the maximum economic benefit in a project period.
In order to achieve the above object, the invention provides a grid-connected hybrid energy system capacity configuration optimization method, which is characterized by comprising the following steps:
(1) constructing an output model of a hybrid energy system which is complementary to the photovoltaic power station, the small hydropower station and the pumped storage power station;
(1.1) constructing an output model of the photovoltaic power station;
wherein, PPV1Rated power for the photovoltaic cell; etainvTo the inverter efficiency, ηlossFor photovoltaic cell depletion efficiency, ηrefAs photovoltaic electricityCell module at reference temperature TrefEfficiency of the process; k is a radical ofTIs the power temperature coefficient, T, of the photovoltaic cell panel0A temperature at which the photovoltaic module is operated; gαThe method comprises the following steps of (1) obtaining an hourly mean value of the surface solar radiation intensity of a photovoltaic region to be built;
(1.2) constructing an output model of the small hydropower station;
PHS＝aηQH0
wherein Q is the flow of a natural water source; h0The height difference between the upstream and downstream is obtained; eta is the energy conversion efficiency of the small hydropower station, a is a constant, and the product of a and eta determines the scale of the small hydropower station;
(1.3) constructing a force model of the pumped storage power station
Wherein, Ppump1(t) input electric quantity of the pumped storage power station at the moment t, Ppump(t) is the power generation capacity of the water volume in the upstream reservoir capacity of the pumped storage power station at the moment t, wherein Ppump(t) is positive for the pumped storage power station being pumped, Ppump(t) negative indicates that the pumped storage power station is in a water discharge state; etachAnd ηdisThe efficiency of pumping water and discharging water of the pumped storage power station is respectively; c is the electricity purchasing gain, which indicates that the electricity purchasing price of the pumped storage power station to the power grid is C times of the current electricity selling price of the pumped storage power station to the power grid;
(2) constructing a cost function of a hybrid energy system which is complementary to the photovoltaic power station, the small hydropower station and the pumped storage power station;
(2.1) constructing a cost function of the photovoltaic power station;
wherein, NPCPVFor the total cost of the photovoltaic power plant, NphIs the number of photovoltaic cells in the photovoltaic power station, CphIs a unit price of a photovoltaic cell, Crep_phAs unit photovoltaic cellCost change, TphIs the life cycle of the photovoltaic cell, COM_phIs the operation and maintenance cost of a unit photovoltaic cell, r is the discount rate, TaThe service cycle of the hybrid energy system;
(2.2) constructing a cost function of the small hydropower station;
wherein, NPCHSFor the total cost of small hydropower stations, Nhg_capVolume of upstream storage capacity in small hydropower stations, Chg_capThe unit price of the upstream storage capacity of the small hydropower station per cubic meter, Chg_powerUnit price per kilowatt for installed capacity of water turbine of small hydropower station, Nhg_powerInstalled capacity of water turbine of small hydropower station, Crep_hcReplacement cost for water turbines in small hydropower stations, ThgIs the life cycle of the water turbine of a small hydropower station, COM_hcFor the operating maintenance cost per cubic meter of the upstream storage capacity of the small hydropower station, COM_hpThe operation and maintenance cost per kilowatt of the small hydropower station is calculated;
(2.3) constructing a cost function of the pumped storage power station;
wherein, NPCpumpFor the total cost of pumped storage power stations, Cpump_capThe unit price of the upstream storage capacity per cubic meter of the pumped storage power station is Npump_capVolume of the upstream storage capacity of pumped storage power station, Npump_powerInstalled capacity of reversible pump turbine for pumped storage power stations, Cpump_powerUnit price per kilowatt of installed capacity of water turbine for pumped storage power station, Crep_ppReplacement cost for water turbines in pumped storage power stations, TpumpFor the life cycle of the water turbine of pumped storage power stations, COM_pcFor the operating maintenance cost per cubic meter of the upstream storage capacity of the pumped storage power station, COM_ppThe operation and maintenance cost per kilowatt of the pumped storage power station is reduced;
(3) and constructing an upper layer of a double-layer planning model:
(3.1) constructing an input cost recovery year objective function of the hybrid energy system;
(3.2) constructing constraint conditions of an input cost recovery year objective function of the hybrid energy system;
wherein, min (roi) is the minimum recycling year limit of the input cost of the hybrid energy system in the process of maximizing the economic benefit under a certain capacity configuration, and I is the electric sales income obtained by the hybrid energy system through the optimized operation in the next year of the capacity configuration; n is a radical ofphmaxIs the upper limit of the number of photovoltaic cells in a photovoltaic power station, Nhg_capminLower limit of the volume of the upstream reservoir of the small hydropower station, Nhg_capmaxUpper limit of the volume of the upstream reservoir of the small hydropower station, Nhg_powermaxIs the upper limit of the installed capacity of the water turbine of the small hydropower station, Npump_capmaxIs the upper limit of the volume of the upstream storage capacity of the pumped storage power station, Npump_powermaxThe upper limit of the installed capacity of the water turbine of the pumped storage power station;
(4) constructing a lower layer of a double-layer planning model
(4.1) constructing a yearly electricity selling income target function of the hybrid energy system;
wherein EP (t) is the electricity price at time t, PPV(t) output electric quantity P sold for the photovoltaic power station on the Internet at the moment tHS1(t) is the output electric quantity sold on the Internet of the small hydropower station at the moment t, Ppump(t) the input electric quantity of the pumped storage power station at the time t, and I the electricity sales obtained by optimizing the operation in one yearEarnings, M being the number of days in a year, T being the number of hours in a day;
(4.2) constructing a constraint condition of a yearly electricity selling income objective function of the hybrid energy system;
(4.2.1) constructing constraint conditions of the small hydropower station in the operation process;
and (3) restricting the operation power of the small hydropower station:
wherein, PHSmaxMaximum installed capacity of water turbine of small hydropower station, EHSmaxThe generated energy is corresponding to the maximum volume of the upstream storage capacity of the small hydropower station;
upstream capacity constraint:
wherein, PHS2(t) the electric quantity stored in the upstream storage capacity of the small hydropower station at the time t;
and (3) water inlet and outlet restraint of the small hydropower station:
wherein, PHS(t) the power generation amount of the inlet water of the small hydropower station at the moment t;
(4.2.2) constructing constraint conditions of the operation process of the pumped storage power station;
and (3) restricting the operation power of the pumped storage power station:
wherein, PpumpmaxMaximum installed capacity, E, of reversible pump turbines for pumped storage power stationspumpmaxGenerating capacity corresponding to maximum volume of upstream storage capacity of pumped storage power station；
Upstream capacity constraint:
water inlet and outlet restraint of the pumped storage power station:
(5) and establishing a double-layer optimization model by adopting a particle swarm algorithm and a sequential quadratic programming algorithm based on an output model and a cost function of the grid-connected hybrid energy system, and optimizing corresponding target functions on the inner layer and the outer layer respectively to obtain a capacity configuration scheme of the grid-connected hybrid energy system.
The invention aims to realize the following steps:
the invention discloses a capacity allocation optimization method for a grid-connected hybrid energy system, which is based on an output model, actual conditions and actual market electricity price of the grid-connected hybrid energy system, provides a double-layer planning model with the minimum recovery age of system investment cost as an upper-layer capacity optimization objective function and the maximum electricity selling income obtained by the system as a lower-layer operation optimization objective function, and solves the upper layer and the lower layer of the model by adopting an LDIW algorithm and an SQP algorithm to obtain an optimal capacity allocation scheme for investment of each main power station of the hybrid energy system.
Meanwhile, the capacity optimization configuration scheme of the photovoltaic/small hydropower station/pumped storage power station complementary hybrid energy system based on the market mechanism has the following beneficial effects:
(1) the capacity planning of a hybrid energy system in an energy storage mode is considered, and the pumped storage power station is used for storing the electric power, so that the electricity abandoning condition of a photovoltaic power station and a small hydropower station is effectively relieved, and better social benefit is realized;
(2) the capacity optimization configuration method of the hybrid energy system is provided based on the electric power market mechanism, the capacity configuration is carried out on the hybrid energy system by fully combining the electricity price in the electric power market, and the fluctuation of the electricity price in the market is utilized to obtain greater economic benefit and reflect better economic performance of the system in a project period;
(3) the capacity allocation of the hybrid energy system complemented by the photovoltaic/small hydropower station/pumped storage power station is optimized by establishing a double-layer planning model, and an optimal investment scheme is obtained by solving the model through an algorithm, so that the effectiveness and superiority of the capacity allocation optimization method for the hybrid energy system model are shown.
Drawings
FIG. 1 is a flow chart of a method for optimizing capacity allocation of a grid-connected hybrid energy system according to the present invention;
FIG. 2 is a schematic structural diagram of a model of output of each unit in the hybrid energy system;
fig. 3 is a flowchart of solving the two-layer planning model according to the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
Fig. 1 is a flow chart of a capacity allocation optimization method of a grid-connected hybrid energy system according to the present invention.
In this embodiment, as shown in fig. 1, the method for optimizing capacity allocation of a grid-connected hybrid energy system according to the present invention includes the following steps:
s1, constructing an output model of the hybrid energy system complementary to the photovoltaic power station, the small hydropower station and the pumped storage power station;
in this embodiment, as shown in fig. 2, the hybrid energy system includes: the system comprises a photovoltaic power station, a small hydropower station, a pumped storage power station, a control center unit and a large power grid;
the control center unit is connected with the module unit in the photovoltaic power station, the water turbine of the small hydropower station and the reversible water pump water turbine of the pumped storage power station;
the control center unit is connected with the electric power market, and can directly generate electricity and sell the electricity on the internet;
if the current electricity selling price of the power grid is in a higher interval of the electricity price in one day, the photovoltaic power station and the small hydropower station can select to sell the electricity generated by the photovoltaic power station and the small hydropower station through direct internet access, and the pumped storage power station can release the water storage capacity of the pumped storage power station to generate electricity as far as possible under the condition that corresponding constraints are met. If the current electricity selling price of the power grid is in a lower interval of the electricity price in one day, the photovoltaic power station can transmit the power generated by the photovoltaic power station to the pumped storage power station for storage, and the part which cannot be stored can be directly sold on the internet for selling. The small hydropower station can store natural flow in an upstream reservoir capacity of the small hydropower station, the part which cannot be stored is selected to be transmitted to the pumped storage power station for storage or directly used for power generation and internet surfing for sale, and the pumped storage power station not only stores the power generated in the system, but also can purchase the power from the power grid and pump the power for storage.
In the following, we describe the output model of each sub-model in detail, specifically as follows:
s1.1, constructing an output model of the photovoltaic power station;
wherein, PPV1Rated power for the photovoltaic cell; etainvTo the inverter efficiency, ηlossFor photovoltaic cell depletion efficiency, ηrefFor photovoltaic cell modules at a reference temperature TrefEfficiency at 25 ℃; k is a radical ofTIs the power temperature coefficient, T, of the photovoltaic cell panel0A temperature at which the photovoltaic module is operated; gαThe method comprises the following steps of (1) obtaining an hourly mean value of the surface solar radiation intensity of a photovoltaic region to be built; most of the parameters in the above formula can be found in a data manual provided by the photovoltaic panel manufacturer;
s1.2, constructing an output model of the small hydropower station;
PHS＝9.81ηQH0＝EQH0
wherein Q is the flow of a natural water source; h0Is the difference in height between the upstream and downstream(ii) a Eta is the energy conversion efficiency of small hydropower stations, generally the efficiency eta of water turbines1And generator efficiency eta2Is expressed as a product of; the value of E is determined by the scale of the power station and the advanced degree of equipment, and the value of E of the small hydropower station is usually between 6.5 and 7.5;
s1.3, constructing a force output model of the pumped storage power station
Wherein, Ppump1(t) input electric quantity of the pumped storage power station at the moment t, Ppump(t) is the power generation capacity of the water volume in the upstream reservoir capacity of the pumped storage power station at the moment t, wherein Ppump(t) is positive for the pumped storage power station being pumped, Ppump(t) negative indicates that the pumped storage power station is in a water discharge state; etachAnd ηdisThe efficiency of pumping water and discharging water of the pumped storage power station is respectively; c is the electricity purchasing gain which means that the electricity purchasing price of the pumped storage power station to the power grid is 1.1 times of the electricity selling price of the pumped storage power station to the power grid at present;
s2, constructing a cost function of the hybrid energy system which is complementary to the photovoltaic power station, the small hydropower station and the pumped storage power station;
s2.1, constructing a cost function of the photovoltaic power station;
wherein, NPCPVFor the total cost of the photovoltaic power plant, NphIs the number of photovoltaic cells in the photovoltaic power station, CphIs a unit price of a photovoltaic cell, Crep_phReplacement cost per unit photovoltaic cell, TphIs the life cycle of the photovoltaic cell, COM_phIs the operation and maintenance cost of a unit photovoltaic cell, r is the discount rate, TaThe service cycle of the hybrid energy system;
s2.2, constructing a cost function of the small hydropower station;
wherein, NPCHSFor the total cost of small hydropower stations, Nhg_capVolume of upstream storage capacity in small hydropower stations, Chg_capThe unit price of the upstream storage capacity of the small hydropower station per cubic meter, Chg_powerUnit price per kilowatt for installed capacity of water turbine of small hydropower station, Nhg_powerInstalled capacity of water turbine of small hydropower station, Crep_hcReplacement cost for water turbines in small hydropower stations, ThgIs the life cycle of the water turbine of a small hydropower station, COM_hcFor the operating maintenance cost per cubic meter of the upstream storage capacity of the small hydropower station, COM_hpThe operation and maintenance cost per kilowatt of the small hydropower station is calculated;
s2.3, constructing a cost function of the pumped storage power station;
wherein, NPCpumpFor the total cost of pumped storage power stations, Cpump_capThe unit price of the upstream storage capacity per cubic meter of the pumped storage power station is Npump_capVolume of the upstream storage capacity of pumped storage power station, Npump_powerInstalled capacity of reversible pump turbine for pumped storage power stations, Cpump_powerUnit price per kilowatt of installed capacity of water turbine for pumped storage power station, Crep_ppReplacement cost for water turbines in pumped storage power stations, TpumpFor the life cycle of the water turbine of pumped storage power stations, COM_pcFor the operating maintenance cost per cubic meter of the upstream storage capacity of the pumped storage power station, COM_ppThe operation and maintenance cost per kilowatt of the pumped storage power station is reduced;
s3, constructing the upper layer of the double-layer planning model;
s3.1, constructing an input cost recovery year limit objective function of the hybrid energy system;
s3.2, constructing constraint conditions of an input cost recovery year objective function of the hybrid energy system;
wherein, min (roi) is the minimum recycling year limit of the input cost of the hybrid energy system in the process of maximizing the economic benefit under a certain capacity configuration, and I is the electric sales income obtained by the hybrid energy system through the optimized operation in the next year of the capacity configuration; n is a radical ofphmaxIs the upper limit of the number of photovoltaic cells in a photovoltaic power station, Nhg_capminLower limit of the volume of the upstream reservoir of the small hydropower station, Nhg_capmaxUpper limit of the volume of the upstream reservoir of the small hydropower station, Nhg_powermaxIs the upper limit of the installed capacity of the water turbine of the small hydropower station, Npump_capmaxIs the upper limit of the volume of the upstream storage capacity of the pumped storage power station, Npump_powermaxThe upper limit of the installed capacity of the water turbine of the pumped storage power station; considering the practical situation, on the premise of simplifying the model, the downstream reservoirs of the small hydropower station and the pumped storage power station are regarded as sea levels S4 with infinite reservoir capacity, and a double-layer planning model lower layer is constructed;
s4.1, constructing a yearly electricity selling income target function of the hybrid energy system;
wherein EP (t) is the electricity price at time t, PPV(t) output electric quantity P sold for the photovoltaic power station on the Internet at the moment tHS1(t) is the output electric quantity sold on the Internet of the small hydropower station at the moment t, Ppump(T) the input electric quantity of the pumped storage power station at the time T, I is the electricity selling income obtained by optimizing operation in one year, M is the number of days in one year, and T is the number of hours in one day;
s4.2, constructing a constraint condition of a yearly electricity selling income target function of the hybrid energy system;
s4.2.1, constructing constraint conditions of the small hydropower station operation process;
and (3) restricting the operation power of the small hydropower station:
wherein, PHSmaxMaximum installed capacity of water turbine of small hydropower station, EHSmaxThe generated energy is corresponding to the maximum volume of the upstream storage capacity of the small hydropower station;
upstream capacity constraint:
wherein, PHS2(t) the electric quantity stored in the upstream storage capacity of the small hydropower station at the time t; limiting the water storage capacity of the upstream storage capacity of the small hydropower station at any moment not to exceed the set upstream storage capacity volume, and setting the water quantity of the upstream storage capacity of the small hydropower station to be half of the storage capacity volume at the initial moment;
and (3) water inlet and outlet restraint of the small hydropower station:
wherein, PHS(t) the power generation amount of the inlet water of the small hydropower station at the moment t; limiting the water inflow and the water outflow of the small and medium hydropower stations to be the same in one day;
s4.2.2, constructing constraint conditions of the operation process of the pumped storage power station;
and (3) restricting the operation power of the pumped storage power station:
wherein, PpumpmaxMaximum installed capacity, E, of reversible pump turbines for pumped storage power stationspumpmaxCorresponding to the maximum volume of the upstream storage capacity of the pumped storage power stationAn amount of electricity; the pumping and discharging power of the pumped storage power station at any time is limited not to exceed the smaller value of the maximum installed capacity of the reversible pump turbine and the maximum electricity generation capacity of the upstream reservoir capacity.
Upstream capacity constraint:
the water storage capacity of the upstream storage capacity of the pumped storage power station is limited to be not more than the set upstream storage capacity volume at any time, and the water quantity of the upstream storage capacity of the pumped storage power station is set to be half of the storage capacity volume at the initial time.
Water inlet and outlet restraint of the pumped storage power station:
limiting the water inflow and the water outflow of the pumped storage power station to be the same in one day;
s5, based on the output model and the cost function of the grid-connected hybrid energy system, a double-layer optimization model is established by adopting a particle swarm algorithm and a sequential quadratic programming algorithm, and the target functions corresponding to the inner layer and the outer layer are optimized respectively to obtain a capacity configuration scheme of the grid-connected hybrid energy system.
In this embodiment, the inner layer is an operation optimization of the hybrid energy system, and adopts a Sequential Quadratic Programming (SQP) algorithm, where the optimization variables include: operating power P of photovoltaic power stationPVOperating power P of small hydropower stationsHSCharging and discharging power P of pumped storage power stationpumpAnd the maximum annual electricity selling income obtained by the electricity price of the actual market enters the outer layer to participate in optimization as the inner layer optimization result.
The capacity optimization of the hybrid energy system is carried out as the outer layer, and the optimization variables comprise: number of cells N of photovoltaic power stationph(ii) a Capacity specification of small hydropower station, including total installed capacity N of small hydropower station water turbinehg_capAnd upstream reservoir volumeNhg_power(ii) a The capacity specification of the pumped storage power station comprises a reversible pump turbine N of the pumped storage power stationpump_capTotal installed capacity and upstream storage capacity volume Npump_power。
As shown in fig. 3, the specific optimization process is as follows:
s5.1, setting the maximum iteration times G and other parameters of the PSO algorithm; the outer layer adopts PSO algorithm to carry out iterative optimization on the investment cost optimization objective function, and randomly initializes the position x of each particle in the particle population within the solution space range0And velocity v0；
S5.2, setting the minimum cell number N of the photovoltaic power stationphminMinimum total installed capacity N of small hydropower station water turbinehg_powerminMinimum upstream storage volume N of small hydropower stationhg_capminMinimum installed capacity N of reversible pump turbine of pumped storage power stationpump_powerminAnd a minimum upstream volume Npump_capminForm an array [ N ]phmin,Nhg_powermin,Nhg_capmin,Npump_powermin,Npump_capmin]Maximum number of cells N of photovoltaic power plantphmaxMaximum total installed capacity N of small hydropower station water turbinehg_powermaxMaximum upstream storage volume N of small hydropower stationhg_capmaxMaximum installed capacity N of reversible pump turbine of pumped storage power stationpump_powermaxAnd a maximum upstream volume Npump_capmaxJointly form an array [ Nphmax,Nhg_powermax,Nhg_capmax,Npump_powermax,Npump_capmax]During each iteration, the array is stored at the position of each particle, and during the initial iteration, the initial position of each particle should satisfy x0＝[Nphmin,Nhg_powermin,Nhg_capmin,Npump_powermin,Npump_capmin]To [ N ]phmax,Nhg_powermax,Nhg_capmax,Npump_powermax,Npump_capmax]To (c) to (d);
s5.3, optimizing the operation optimization objective function by the inner layer through an SQP algorithm, bringing the initial position of each particle into the inner layer, and performing one-dimensional optimization through the inner layerSearching an objective function, calculating the maximum annual electricity selling income and the optimal charging and discharging power P corresponding to the pumped storage power station at each momentpumpAnd the water storage capacity C of the upstream storage capacity of the pumped storage power stationpump；
S5.4, jumping to the outer layer, and calculating the optimal recovery age limit of the input cost of the outer layer according to the maximum annual income;
s5.5, judging whether the current iteration times reach the maximum iteration times of the PSO algorithm, and if not, entering the step S5.6; otherwise, jumping to step S5.8;
s5.6, updating the speed and the position of each particle in the particle swarm algorithm;
wherein,
the velocity at the kth' iteration of the ith particle,
for the position of the ith particle at the kth iteration,
the individual extremum for the kth' iteration of the ith particle,
the current optimal solution of the whole population corresponding to the ith particle during the k' th iteration, namely the optimal solution generated by searching the ith particle from the initial to the current iteration times, c1、c2Respectively is an individual learning factor and a group learning factor, and G represents the maximum iteration number; w represents an inertial weight coefficient; r is1、r2Random numbers respectively belonging to the range of 0 to 1;
s5.7, adding 1 to the current iteration times, iterating the speed and the position of each particle in the previous iteration by using the updated speed and the updated position of each particle, and then returning to the step S5.2;
and S5.8, selecting the optimal investment cost recovery year after G iterations as final output, and then outputting the optimal investment cost recovery year and the optimal capacity configuration of each main power station corresponding to the optimal investment cost recovery year.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.
Claims (2)
1. A capacity configuration optimization method for a grid-connected hybrid energy system is characterized by comprising the following steps:
(1) constructing an output model of a hybrid energy system which is complementary to the photovoltaic power station, the small hydropower station and the pumped storage power station;
(1.1) constructing an output model of the photovoltaic power station;
wherein, PPV1Rated power for the photovoltaic cell; etainvTo the inverter efficiency, ηlossFor photovoltaic cell depletion efficiency, ηrefFor photovoltaic cell modules at a reference temperature TrefEfficiency of the process; k is a radical ofTIs the power temperature coefficient, T, of the photovoltaic cell panel0A temperature at which the photovoltaic module is operated; gαThe method comprises the following steps of (1) obtaining an hourly mean value of the surface solar radiation intensity of a photovoltaic region to be built;
(1.2) constructing an output model of the small hydropower station;
PHS＝aηQH0
wherein Q is the flow of a natural water source; h0The height difference between the upstream and downstream is obtained; eta is the energy conversion efficiency of small hydropower stationsA is a constant, and the product of a and eta determines the scale of the small hydropower station;
(1.3) constructing a force model of the pumped storage power station
Wherein, Ppump1(t) input electric quantity of the pumped storage power station at the moment t, Ppump(t) is the power generation capacity of the water volume in the upstream reservoir capacity of the pumped storage power station at the moment t, wherein Ppump(t) is positive for the pumped storage power station being pumped, Ppump(t) negative indicates that the pumped storage power station is in a water discharge state; etachAnd ηdisThe efficiency of pumping water and discharging water of the pumped storage power station is respectively; c is the electricity purchasing gain, which indicates that the electricity purchasing price of the pumped storage power station to the power grid is C times of the current electricity selling price of the pumped storage power station to the power grid;
(2) constructing a cost function of a hybrid energy system which is complementary to the photovoltaic power station, the small hydropower station and the pumped storage power station;
(2.1) constructing a cost function of the photovoltaic power station;
wherein, NPCPVFor the total cost of the photovoltaic power plant, NphIs the number of photovoltaic cells in the photovoltaic power station, CphIs a unit price of a photovoltaic cell, Crep_phReplacement cost per unit photovoltaic cell, TphIs the life cycle of the photovoltaic cell, COM_phIs the operation and maintenance cost of a unit photovoltaic cell, r is the discount rate, TaThe service cycle of the hybrid energy system;
(2.2) constructing a cost function of the small hydropower station;
wherein, NPCHSFor the total cost of small hydropower stations, Nhg_capVolume of upstream storage capacity in small hydropower stations, Chg_capThe unit price of the upstream storage capacity of the small hydropower station per cubic meter, Chg_powerUnit price per kilowatt for installed capacity of water turbine of small hydropower station, Nhg_powerInstalled capacity of water turbine of small hydropower station, Crep_hcReplacement cost for water turbines in small hydropower stations, ThgIs the life cycle of the water turbine of a small hydropower station, COM_hcFor the operating maintenance cost per cubic meter of the upstream storage capacity of the small hydropower station, COM_hpThe operation and maintenance cost per kilowatt of the small hydropower station is calculated;
(2.3) constructing a cost function of the pumped storage power station;
wherein, NPCpumpFor the total cost of pumped storage power stations, Cpump_capThe unit price of the upstream storage capacity per cubic meter of the pumped storage power station is Npump_capVolume of the upstream storage capacity of pumped storage power station, Npump_powerInstalled capacity of reversible pump turbine for pumped storage power stations, Cpump_powerUnit price per kilowatt of installed capacity of water turbine for pumped storage power station, Crep_ppReplacement cost for water turbines in pumped storage power stations, TpumpFor the life cycle of the water turbine of pumped storage power stations, COM_pcFor the operating maintenance cost per cubic meter of the upstream storage capacity of the pumped storage power station, COM_ppThe operation and maintenance cost per kilowatt of the pumped storage power station is reduced;
(3) and constructing an upper layer of a double-layer planning model:
(3.1) constructing an input cost recovery year objective function of the hybrid energy system;
(3.2) constructing constraint conditions of an input cost recovery year objective function of the hybrid energy system;
wherein, min (roi) is the minimum recycling year limit of the input cost of the hybrid energy system in the process of maximizing the economic benefit under a certain capacity configuration, and I is the electric sales income obtained by the hybrid energy system through the optimized operation in the next year of the capacity configuration; n is a radical ofphmaxIs the upper limit of the number of photovoltaic cells in a photovoltaic power station, Nhg_capminLower limit of the volume of the upstream reservoir of the small hydropower station, Nhg_capmaxUpper limit of the volume of the upstream reservoir of the small hydropower station, Nhg_powermaxIs the upper limit of the installed capacity of the water turbine of the small hydropower station, Npump_capmaxIs the upper limit of the volume of the upstream storage capacity of the pumped storage power station, Npump_powermaxThe upper limit of the installed capacity of the water turbine of the pumped storage power station;
(4) constructing a lower layer of a double-layer planning model
(4.1) constructing a yearly electricity selling income target function of the hybrid energy system;
wherein EP (t) is the electricity price at time t, PPV(t) output electric quantity P sold for the photovoltaic power station on the Internet at the moment tHS1(t) is the output electric quantity sold on the Internet of the small hydropower station at the moment t, Ppump(T) the input electric quantity of the pumped storage power station at the time T, I is the electricity selling income obtained by optimizing operation in one year, M is the number of days in one year, and T is the number of hours in one day;
(4.2) constructing a constraint condition of a yearly electricity selling income objective function of the hybrid energy system;
(4.2.1) constructing constraint conditions of the small hydropower station in the operation process;
and (3) restricting the operation power of the small hydropower station:
wherein, PHSmaxMaximum installed capacity of water turbine of small hydropower station, EHSmaxThe generated energy is corresponding to the maximum volume of the upstream storage capacity of the small hydropower station;
upstream capacity constraint:
wherein, PHS2(t) the electric quantity stored in the upstream storage capacity of the small hydropower station at the time t;
and (3) water inlet and outlet restraint of the small hydropower station:
wherein, PHS(t) the power generation amount of the inlet water of the small hydropower station at the moment t;
(4.2.2) constructing constraint conditions of the operation process of the pumped storage power station;
and (3) restricting the operation power of the pumped storage power station:
wherein, PpumpmaxMaximum installed capacity, E, of reversible pump turbines for pumped storage power stationspumpmaxGenerating capacity corresponding to the maximum volume of the upstream storage capacity of the pumped storage power station;
upstream capacity constraint:
water inlet and outlet restraint of the pumped storage power station:
(5) and establishing a double-layer optimization model by adopting a particle swarm algorithm and a sequential quadratic programming algorithm based on an output model and a cost function of the grid-connected hybrid energy system, and optimizing corresponding target functions on the inner layer and the outer layer respectively to obtain a capacity configuration scheme of the grid-connected hybrid energy system.
2. The grid-connected hybrid energy system capacity configuration optimization method according to claim 1, wherein in the step (5), the process of optimizing the objective function corresponding to the inner layer and the outer layer through the double-layer planning model comprises:
1) setting the maximum iteration number G of the PSO algorithm and other parameters; the outer layer adopts PSO algorithm to carry out iterative optimization on the investment cost optimization objective function, and randomly initializes the position x of each particle in the particle population within the solution space range0And velocity v0；
2) The minimum number of cells N of the photovoltaic power stationphminMinimum total installed capacity N of small hydropower station water turbinehg_powerminMinimum upstream storage volume N of small hydropower stationhg_capminMinimum installed capacity N of reversible pump turbine of pumped storage power stationpump_powerminAnd a minimum upstream volume Npump_capminForm an array [ N ]phmin,Nhg_powermin,Nhg_capmin,Npump_powermin,Npump_capmin]Maximum number of cells N of photovoltaic power plantphmaxMaximum total installed capacity N of small hydropower station water turbinehg_powermaxMaximum upstream storage volume N of small hydropower stationhg_capmaxMaximum installed capacity N of reversible pump turbine of pumped storage power stationpump_powermaxAnd a maximum upstream volume Npump_capmaxJointly form an array [ Nphmax,Nhg_powermax,Nhg_capmax,Npump_powermax,Npump_capmax]During each iteration, the position of each particle is stored in the array, and the array is first iteratedInstead, the initial position of each particle should satisfy x0＝[Nphmin,Nhg_powermin,Nhg_capmin,Npump_powermin,Npump_capmin]To [ N ]phmax,Nhg_powermax,Nhg_capmax,Npump_powermax,Npump_capmax]To (c) to (d);
3) the inner layer optimizes an operation optimization objective function by adopting an SQP algorithm, the initial position of each particle is brought into the inner layer, the maximum annual electricity selling income is calculated by one-dimensional searching of the objective function, and the optimal charging and discharging power P corresponding to the pumped storage power station at each moment is calculatedpumpAnd the water storage capacity C of the upstream storage capacity of the pumped storage power stationpump；
4) Skipping to enter the outer layer, and calculating the optimal recovery year limit of the input cost of the outer layer according to the maximum annual income;
5) judging whether the current iteration times reach the maximum iteration times of the PSO algorithm, and if not, entering the step 6); otherwise, jumping to step 8);
6) updating the speed and the position of each particle in the particle swarm algorithm;
wherein,
the velocity at the kth' iteration of the ith particle,
for the position of the ith particle at the kth iteration,
the individual extremum for the kth' iteration of the ith particle,
for the k' th iteration of the ith particleThe current optimal solution of the whole population corresponding to the generation time, namely the optimal solution generated by searching the ith particle from the initial to the current iteration times, c1、c2Respectively is an individual learning factor and a group learning factor, and G represents the maximum iteration number; w represents an inertial weight coefficient; r is1、r2Random numbers respectively belonging to the range of 0 to 1;
7) adding 1 to the current iteration times, iterating the speed and the position of each particle in the previous iteration by using the updated speed and the updated position of each particle, and then returning to the step 2);
8) and selecting the optimal investment cost recovery year after G iterations as final output, and then outputting the optimal investment cost recovery year and the optimal capacity configuration of each main power station corresponding to the investment cost recovery year.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010321577.8A CN111431179B (en) | 2020-04-22 | 2020-04-22 | Grid-connected hybrid energy system capacity configuration optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010321577.8A CN111431179B (en) | 2020-04-22 | 2020-04-22 | Grid-connected hybrid energy system capacity configuration optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111431179A CN111431179A (en) | 2020-07-17 |
| CN111431179B true CN111431179B (en) | 2021-12-10 |
Family
ID=71554278
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010321577.8A Active CN111431179B (en) | 2020-04-22 | 2020-04-22 | Grid-connected hybrid energy system capacity configuration optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111431179B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117691641A (en) * | 2024-01-31 | 2024-03-12 | 西华大学 | Pumped storage power station optimization method and system for generating power by multiple new energy sources |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325608A (en) * | 2018-06-01 | 2019-02-12 | 国网上海市电力公司 | Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness |
| CN110391677A (en) * | 2019-08-26 | 2019-10-29 | 电子科技大学 | A kind of water light storage hybrid system running optimizatin method based on Power Market |
| CN110474330A (en) * | 2019-08-22 | 2019-11-19 | 电子科技大学 | A kind of solar energy investment optimization method of parallel net type energy mix system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10656990B2 (en) * | 2018-06-13 | 2020-05-19 | Nutanix, Inc. | Dynamically adjusting reserve portion and allocation portions of disaster recovery site in a virtual computing system |
-
2020
- 2020-04-22 CN CN202010321577.8A patent/CN111431179B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325608A (en) * | 2018-06-01 | 2019-02-12 | 国网上海市电力公司 | Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness |
| CN110474330A (en) * | 2019-08-22 | 2019-11-19 | 电子科技大学 | A kind of solar energy investment optimization method of parallel net type energy mix system |
| CN110391677A (en) * | 2019-08-26 | 2019-10-29 | 电子科技大学 | A kind of water light storage hybrid system running optimizatin method based on Power Market |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111431179A (en) | 2020-07-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kyriakarakos et al. | Polygeneration microgrids: A viable solution in remote areas for supplying power, potable water and hydrogen as transportation fuel | |
| CN107464010B (en) | Virtual power plant capacity optimal configuration method | |
| Kiehbadroudinezhad et al. | Optimization of wind/solar energy microgrid by division algorithm considering human health and environmental impacts for power-water cogeneration | |
| Zhang et al. | A multi-agent deep reinforcement learning approach enabled distributed energy management schedule for the coordinate control of multi-energy hub with gas, electricity, and freshwater | |
| Jing et al. | Sizing optimization for island microgrid with pumped storage system considering demand response | |
| Yan et al. | Effect of various design configurations and operating conditions for optimization of a wind/solar/hydrogen/fuel cell hybrid microgrid system by a bio-inspired algorithm | |
| Zhou et al. | Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China | |
| Wang et al. | Synergistic planning of an integrated energy system containing hydrogen storage with the coupled use of electric-thermal energy | |
| Fang et al. | Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid | |
| Jordehi | Two-stage stochastic programming for risk-aware scheduling of energy hubs participating in day-ahead and real-time electricity markets | |
| CN113792953A (en) | Virtual power plant optimal scheduling method and system | |
| Kong et al. | Optimization of the hybrid solar power plants comprising photovoltaic and concentrating solar power using the butterfly algorithm | |
| Zhai et al. | Optimal Design Method of a Hybrid CSP‐PV Plant Based on Genetic Algorithm Considering the Operation Strategy | |
| Lund et al. | EnergyPLAN | |
| Li et al. | Multi-objective capacity optimization of a hybrid energy system in two-stage stochastic programming framework | |
| Wang et al. | Energy management strategy of hybrid energy storage based on Pareto optimality | |
| CN111668878A (en) | Optimal configuration method and system for renewable micro-energy network | |
| CN110601260A (en) | Light-storage system capacity optimization method for limiting power fluctuation on interconnection line | |
| CN113159407A (en) | Multi-energy storage module capacity optimal configuration method based on regional comprehensive energy system | |
| Han et al. | Multi-stage distributionally robust optimization for hybrid energy storage in regional integrated energy system considering robustness and nonanticipativity | |
| Van et al. | A comprehensive review of direct coupled photovoltaic-electrolyser system: sizing techniques, operating strategies, research progress, current challenges, and future recommendations | |
| CN110391677B (en) | Water-light storage hybrid system operation optimization method based on electric power market environment | |
| CN116599148A (en) | Hydrogen-electricity hybrid energy storage two-stage collaborative planning method for new energy consumption | |
| CN111431179B (en) | Grid-connected hybrid energy system capacity configuration optimization method | |
| CN111969595B (en) | Operation optimization method of water-light-storage hybrid energy system under off-grid/grid-connected condition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |