CN114865666A - New energy power station energy storage capacity configuration method considering thermodynamic system cloud energy storage service - Google Patents

New energy power station energy storage capacity configuration method considering thermodynamic system cloud energy storage service Download PDF

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CN114865666A
CN114865666A CN202210794921.4A CN202210794921A CN114865666A CN 114865666 A CN114865666 A CN 114865666A CN 202210794921 A CN202210794921 A CN 202210794921A CN 114865666 A CN114865666 A CN 114865666A
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energy storage
power
representing
energy
power station
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苏一博
刘昱良
张险峰
张世旭
周登科
李姚旺
李雨欣
张宁
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Tsinghua University
China Three Gorges Corp
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China Three Gorges Corp
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a new energy power station energy storage capacity configuration method considering a thermodynamic system cloud energy storage service, which comprises the following steps: establishing a new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service, wherein the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is composed of an objective function and constraint conditions; and linearizing the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service to obtain the new energy power station energy storage capacity optimization configuration model. According to the method provided by the invention, the thermodynamic system is used as a new energy power station cloud energy storage service provider, the equivalent energy storage capacity of the thermodynamic system can be fully utilized, the energy storage configuration and use cost of the new energy power station is effectively reduced, the consumption of renewable energy is increased, and the method has a high application value.

Description

New energy power station energy storage capacity configuration method considering thermodynamic system cloud energy storage service
Technical Field
The invention relates to the technical field of energy storage system optimization configuration, in particular to a new energy power station energy storage capacity configuration method considering thermodynamic system cloud energy storage service.
Background
The electric power department is a foundation stone supporting the development of society and economy, and is also the largest fossil energy consumption and carbon emission industry. Therefore, a high-proportion renewable energy power system is constructed, the power system is transformed from fossil energy as a main energy to low-carbon clean energy, and a key effect is played on the realization of a double-carbon target. The power generation forms of renewable energy sources such as wind power, photovoltaic and the like have strong intermittence and randomness, and with the gradual increase of the permeability of the renewable energy sources, great challenges are brought to the safe and stable operation of a power system. The energy storage technology has strong flexible adjustment capability, can well deal with uncertainty caused by renewable energy, and is acknowledged as an optimal means for solving the problem of high-proportion renewable energy access of a power system. In recent years, a plurality of provincial outbound documents require new energy stations to be configured with energy storage in a corresponding proportion (5% -20%), and the trend of configuring energy storage devices with certain capacity at the new energy stations is great. On the other hand, the energy storage cost is high, the energy storage configured in the new energy station faces high cost pressure, and the economic benefit brought by the energy storage is not optimistic.
Cloud energy storage is a business model based on energy storage resource sharing, and it fuses sharing economic model and electric power system degree of depth, can share energy storage cost and income jointly between a plurality of operation subjects, effectively promotes energy storage utilization efficiency. The cloud energy storage can concentrate the energy storage devices originally dispersed on the user side to the cloud end, and the virtual energy storage capacity of the cloud end is used for replacing the entity energy storage of the user side. The specific definition and related concepts of cloud energy storage are detailed in the new forms of the energy storage of the future power system: cloud energy storage [ J ], power system automation, 2017, 41(21), 2-8 ". Currently, in the related research aiming at the cloud energy storage technology, the energy storage resources of the power system are mostly considered, and the distributed utilization of the centralized energy storage device is researched, so that the centralized energy storage device can provide energy storage service for a plurality of users at the same time; and the aggregation utilization of a large number of distributed energy storage devices and the idle energy storage resources in the active system are controlled. From the perspective of a power system, energy storage resources which can be used in a converged manner by the new energy station are very limited. However, under the view of multi-energy coordination, as comprehensive energy systems such as a thermodynamic system and the like have the energy storage characteristic, the comprehensive energy systems can be introduced into a multi-energy cloud energy storage mode as generalized energy storage resources to provide energy storage service for new energy stations, and the mode can effectively reduce the energy storage use cost of the new energy stations and reduce the requirement of newly increased energy storage capacity. However, no research report that the new energy power station aggregates and shares the equivalent energy storage resources in the integrated energy system is seen at present, and no relevant research about energy storage capacity optimal configuration of the new energy power station after the new energy power station considers the equivalent energy storage resources of the integrated energy system is seen.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides a new energy power station energy storage capacity configuration method considering the thermodynamic system cloud energy storage service. The method can effectively integrate and utilize equivalent energy storage resources of the comprehensive energy system, reduce the requirement of new energy power station on energy storage capacity configuration, reduce the energy storage use cost and improve the economic benefits of all operation subjects.
The invention provides a new energy power station energy storage capacity optimal configuration method considering thermodynamic system cloud energy storage service, which comprises the following steps: establishing a new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system, wherein an objective function of the new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system is that the total abandoned wind recovery of the new energy power station in an optimization period of a typical day is the maximum on-line benefit, and constraint conditions comprise the operation condition constraint of a cogeneration unit, the heat supply node and pipeline constraint of the thermodynamic system, the water temperature limit constraint of the thermodynamic system, the maximum abandoned wind which can be utilized by the system, the acceptable electric power increment constraint and the configured energy storage system operation constraint; and linearizing the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service to obtain the new energy power station energy storage capacity optimization configuration model.
Optionally, the objective function of the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure 100002_DEST_PATH_IMAGE002
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE004
represents the total revenue of the system increase;
Figure 100002_DEST_PATH_IMAGE006
the daily average equal annual investment cost for configuring the energy storage power station to the new energy station is shown;
Figure 100002_DEST_PATH_IMAGE008
to representThe daily operation and maintenance cost of the energy storage power station is increased for the new energy field station;
the calculation expressions are respectively:
Figure 100002_DEST_PATH_IMAGE010
Figure 100002_DEST_PATH_IMAGE012
Figure 100002_DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE016
represents a collection of all periods of a typical day;
Figure 100002_DEST_PATH_IMAGE018
indicating a period of time
Figure 100002_DEST_PATH_IMAGE020
The wind power grid-connection electricity price;
Figure 100002_DEST_PATH_IMAGE022
indicating energy storage power station is in time period
Figure 996071DEST_PATH_IMAGE020
The magnitude of the charging power of (c);
Figure 100002_DEST_PATH_IMAGE024
indicating energy storage power station is in time period
Figure 193835DEST_PATH_IMAGE020
The magnitude of the discharge power of (c);
Figure 100002_DEST_PATH_IMAGE026
representing that cogeneration units share self energy storage capacity to abandon windIn a time period after consumption
Figure 256337DEST_PATH_IMAGE020
Increased generated power;
Figure 100002_DEST_PATH_IMAGE028
representing the cost of unit energy storage capacity of the new energy station configuration energy storage;
Figure 100002_DEST_PATH_IMAGE030
representing the unit energy storage power cost of the new energy station configuration energy storage;
Figure 100002_DEST_PATH_IMAGE032
representing the capacity of the energy storage configured by the new energy station;
Figure 100002_DEST_PATH_IMAGE034
representing the power of the new energy station configuration energy storage;
Figure 100002_DEST_PATH_IMAGE036
expressing the discount rate;
Figure 100002_DEST_PATH_IMAGE038
indicating the service life of energy storage;
Figure 100002_DEST_PATH_IMAGE040
the operation and maintenance cost of the energy storage system in unit operation hour is represented;
Figure 100002_DEST_PATH_IMAGE042
representing the number of hours the energy storage system is operating.
Optionally, the constraint of the operation condition of the cogeneration unit in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure 100002_DEST_PATH_IMAGE044
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE046
indicating the cogeneration unit in time
Figure 114834DEST_PATH_IMAGE020
The magnitude of the electrical power of;
Figure 100002_DEST_PATH_IMAGE048
indicating the cogeneration unit in time
Figure 873099DEST_PATH_IMAGE020
The thermal power of (2);
Figure 100002_DEST_PATH_IMAGE050
representing a coefficient reflecting the incidence relation of the electric power and the thermal power of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE052
representing the fuel consumption of the cogeneration unit per unit of electric power;
Figure 100002_DEST_PATH_IMAGE054
the fuel consumption of the unit thermal power of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE056
representing the lower limit of fuel consumption of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE058
representing the upper limit of fuel consumption of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE060
representing the lower limit of the thermal power of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE062
representing the upper limit of the thermal power of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE064
representing the lower limit of the electric power of the cogeneration unit;
Figure 100002_DEST_PATH_IMAGE066
represents the upper limit of the electric power of the cogeneration unit.
Optionally, the thermodynamic system heat supply node and pipeline constraint in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure 100002_DEST_PATH_IMAGE068
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE070
is shown as
Figure 135322DEST_PATH_IMAGE070
A plurality of heating network nodes; 0 represents a heat source node;
Figure 100002_DEST_PATH_IMAGE072
represents a collection of all heating network nodes;
Figure 100002_DEST_PATH_IMAGE074
representing nodes
Figure 614233DEST_PATH_IMAGE070
In a period of time
Figure 100002_DEST_PATH_IMAGE076
The temperature of the supplied water;
Figure 100002_DEST_PATH_IMAGE078
representing nodes
Figure 419246DEST_PATH_IMAGE070
In a period of time
Figure 768319DEST_PATH_IMAGE076
The temperature of the return water is controlled;
Figure 100002_DEST_PATH_IMAGE080
representing nodes
Figure 189942DEST_PATH_IMAGE070
The equivalent thermal insulation coefficient of the heat supply pipeline;
Figure 100002_DEST_PATH_IMAGE082
representing nodes
Figure 735193DEST_PATH_IMAGE070
The transmission of the heat supply pipeline is delayed;
Figure 100002_DEST_PATH_IMAGE084
represents the ambient temperature;
Figure 100002_DEST_PATH_IMAGE086
representing flow through a node
Figure 550307DEST_PATH_IMAGE070
Mass flow of the heat supply pipeline;
Figure 100002_DEST_PATH_IMAGE088
represents the specific heat capacity of water;
Figure 100002_DEST_PATH_IMAGE090
representing nodes
Figure 53970DEST_PATH_IMAGE070
In a period of time
Figure 510359DEST_PATH_IMAGE076
The thermal load of (2).
Optionally, the constraint of the thermodynamic system water temperature limit in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure 100002_DEST_PATH_IMAGE092
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE094
represents a lower limit of the supply water temperature;
Figure 100002_DEST_PATH_IMAGE096
represents an upper limit of the supply water temperature;
Figure 100002_DEST_PATH_IMAGE098
the lower limit of the backwater water temperature is shown;
Figure 100002_DEST_PATH_IMAGE100
and representing the upper limit of the backwater water temperature.
Optionally, the maximum wind curtailment that can be utilized by the system and the acceptable electric power increment constraint in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service are as follows:
Figure 100002_DEST_PATH_IMAGE102
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE104
indicating the cogeneration unit in time
Figure 100002_DEST_PATH_IMAGE106
The down-regulated power generation power is consumed for increasing the wind power;
Figure 100002_DEST_PATH_IMAGE108
representing wind farm in time period
Figure 314760DEST_PATH_IMAGE106
The abandoned wind power;
Figure 100002_DEST_PATH_IMAGE110
indicating that the system is in time
Figure 313940DEST_PATH_IMAGE106
An upper acceptable electrical power limit.
Optionally, the energy storage system operation constraint in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure 100002_DEST_PATH_IMAGE112
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE114
a state variable indicating whether the energy storage system is operating in a charging state;
Figure 100002_DEST_PATH_IMAGE116
a state variable indicating whether the energy storage system is operating in a discharge state;
Figure 100002_DEST_PATH_IMAGE118
representing an energy storage charging power lower limit;
Figure 100002_DEST_PATH_IMAGE120
representing an upper energy storage charging power limit;
Figure 100002_DEST_PATH_IMAGE122
indicating the period of stored energy
Figure 378717DEST_PATH_IMAGE106
Storing the electric energy;
Figure 100002_DEST_PATH_IMAGE124
representing the charging efficiency of the energy storage system;
Figure 100002_DEST_PATH_IMAGE126
indicating the discharge efficiency of the energy storage system;
Figure 100002_DEST_PATH_IMAGE128
representing the lower limit of the stored electric quantity of the energy storage system;
Figure 100002_DEST_PATH_IMAGE130
representing the upper limit of the stored electric quantity of the energy storage system;
Figure 100002_DEST_PATH_IMAGE132
representing the amount of electricity stored by the energy storage system during the optimization initial period;
Figure 100002_DEST_PATH_IMAGE134
indicating that the amount of power stored by the energy storage system is optimized for the last period of time.
Optionally, the linearizing the nonlinear constraint in the constraint condition includes:
determining a nonlinear term in a new energy power station energy storage capacity optimization configuration model considering thermodynamic system cloud energy storage service:
Figure 100002_DEST_PATH_IMAGE136
the nonlinear term is linearized by using a large M method, and the expression after the linearization is as follows:
Figure 100002_DEST_PATH_IMAGE138
in the formula (I), the compound is shown in the specification,
Figure 100002_DEST_PATH_IMAGE140
is a constant that is approximately infinite.
The invention has the characteristics and beneficial effects that:
1. the invention provides a new energy power station energy storage capacity optimal configuration method considering a thermodynamic system cloud energy storage service, which can give full play to the equivalent energy storage characteristic of the thermodynamic system, enables the thermodynamic system to serve as a generalized cloud energy storage resource to provide energy storage service for a new energy station, effectively improves the energy storage utilization rate, reduces the energy storage configuration and use cost of the new energy power station, increases the renewable energy consumption capacity, and improves the economic benefits of the new energy station and the thermodynamic system.
2. The invention considers the coordination interaction between the new energy power station and the thermodynamic system, provides an energy storage sharing mechanism of the cross-energy system, completes the optimal energy storage capacity and power configuration of the new energy power station by taking the maximum benefit as the target, and realizes the mutual benefit and win-win of the cross-energy system.
Drawings
Fig. 1 is a schematic diagram of a system architecture and a basic operation mechanism of a thermodynamic system for providing an equivalent energy storage service for a new energy station according to the present invention.
Fig. 2 is a flowchart of a new energy power station energy storage capacity configuration method considering a thermodynamic system cloud energy storage service according to the present invention.
Fig. 3 is a flowchart of a new energy power station energy storage capacity configuration method considering a thermodynamic system cloud energy storage service according to the present invention.
FIG. 4 is a schematic diagram of an exemplary system according to the present invention.
Fig. 5 is a schematic diagram of typical daily curtailment curves of two wind farms according to the present invention.
Detailed Description
The invention provides a new energy power station energy storage capacity optimal configuration method considering thermodynamic system cloud energy storage service, and the invention is further described in detail below by combining the accompanying drawings and embodiments.
The invention provides a new energy power station energy storage capacity optimal configuration method considering a thermodynamic system cloud energy storage service, wherein a system architecture and a basic operation mechanism of a thermodynamic system for providing an equivalent energy storage service for a new energy station are shown in figure 1. It should be noted that, in the system, the thermodynamic system first satisfies its heating load, and then provides equivalent energy storage service for the new energy power station, therefore, the thermodynamic system participates in cloud energy storage and does not influence the normal heat utilization of heating load. The new energy power station in the invention is a wind farm.
As shown in fig. 1, the new energy power station performs reasonable regulation and control on the thermodynamic system, exerts the equivalent energy storage utility of the thermodynamic system, stores the abandoned renewable energy electric quantity of the new energy power station, and sells the stored electric energy on the internet in a proper time period, thereby increasing the renewable energy consumption and the system profit. Meanwhile, the thermodynamic system shares equivalent energy storage to the new energy power station, so that the energy storage configuration and use cost of the new energy power station can be reduced.
The invention provides a new energy power station energy storage capacity configuration method considering thermodynamic system cloud energy storage service, the whole flow is shown as figure 2, and the method comprises the following steps:
s1: establishing a new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system, wherein an objective function of the new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system is that the total abandoned wind recovery of the new energy power station in an optimization period of a typical day is the maximum on-line benefit, and constraint conditions comprise the operation condition constraint of a cogeneration unit, the heat supply node and pipeline constraint of the thermodynamic system, the water temperature limit constraint of the thermodynamic system, the maximum abandoned wind which can be utilized by the system, the acceptable electric power increment constraint and the configured energy storage system operation constraint;
s2: and linearizing the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service to obtain the new energy power station energy storage capacity optimization configuration model.
The specific implementation process is shown in fig. 3, and includes the following steps:
1) and establishing a new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system to obtain the optimal configuration power and capacity of energy storage and the optimal solution of the overall benefit of the system. The model consists of an objective function and constraint conditions, and comprises the following specific steps:
1-1) determining an objective function of a new energy power station energy storage capacity optimization configuration model considering thermodynamic system cloud energy storage service, wherein the expression is as follows:
Figure DEST_PATH_IMAGE142
(1)
in the formula (I), the compound is shown in the specification,
Figure 997436DEST_PATH_IMAGE004
represents the total revenue of the system increase;
Figure 963118DEST_PATH_IMAGE006
presentation configurationThe energy storage power station increases the daily average annual investment cost for the new energy station;
Figure 534913DEST_PATH_IMAGE008
the daily operation and maintenance cost for configuring the energy storage power station to the new energy station is shown; the calculation expressions are respectively as follows:
Figure DEST_PATH_IMAGE144
(2)
Figure DEST_PATH_IMAGE146
(3)
Figure DEST_PATH_IMAGE148
(4)
in the formula (I), the compound is shown in the specification,
Figure 583641DEST_PATH_IMAGE016
represents a collection of all periods of a typical day;
Figure 130467DEST_PATH_IMAGE018
indicating a period of time
Figure 962157DEST_PATH_IMAGE020
The wind power grid-connection electricity price;
Figure 670350DEST_PATH_IMAGE022
indicating energy storage power station is in time period
Figure 562082DEST_PATH_IMAGE020
The magnitude of the charging power of (c);
Figure 401731DEST_PATH_IMAGE024
indicating energy storage power station is in time period
Figure 178057DEST_PATH_IMAGE020
The magnitude of the discharge power of (c);
Figure 599811DEST_PATH_IMAGE026
indicating that the cogeneration unit shares the self energy storage capacity to abandon wind and consume
Figure 787079DEST_PATH_IMAGE020
Increased generated power;
Figure 927074DEST_PATH_IMAGE028
representing the cost of unit energy storage capacity of the new energy station configuration energy storage;
Figure 507091DEST_PATH_IMAGE030
representing the unit energy storage power cost of the new energy station configuration energy storage;
Figure 970302DEST_PATH_IMAGE032
representing the capacity of the energy storage configured by the new energy station;
Figure 469416DEST_PATH_IMAGE034
representing the power of the new energy station configuration energy storage;
Figure 706494DEST_PATH_IMAGE036
expressing the discount rate;
Figure 949256DEST_PATH_IMAGE038
indicating the service life of energy storage;
Figure 473166DEST_PATH_IMAGE040
the operation and maintenance cost of the energy storage system in unit operation hour is represented;
Figure 143182DEST_PATH_IMAGE042
representing the number of hours the energy storage system is operating.
1-2) determining constraint conditions of a new energy power station energy storage capacity optimization configuration model considering thermodynamic system cloud energy storage service, wherein the constraint conditions are as follows:
1-2-1) constraint of operation condition of the cogeneration unit, expressed as follows:
Figure DEST_PATH_IMAGE150
(5)
in the formula (I), the compound is shown in the specification,
Figure 664293DEST_PATH_IMAGE046
indicating the cogeneration unit in time
Figure 366539DEST_PATH_IMAGE020
The magnitude of the electrical power of;
Figure 758337DEST_PATH_IMAGE048
indicating the cogeneration unit in time
Figure 599254DEST_PATH_IMAGE020
The thermal power of (2);
Figure 60191DEST_PATH_IMAGE050
representing a coefficient reflecting the incidence relation of the electric power and the thermal power of the cogeneration unit;
Figure 644756DEST_PATH_IMAGE052
representing the fuel consumption of the cogeneration unit per unit of electric power;
Figure 891061DEST_PATH_IMAGE054
the fuel consumption of the unit thermal power of the cogeneration unit;
Figure 824251DEST_PATH_IMAGE056
representing the lower limit of fuel consumption of the cogeneration unit;
Figure 647850DEST_PATH_IMAGE058
representing the upper limit of fuel consumption of the cogeneration unit;
Figure 911472DEST_PATH_IMAGE060
representing the lower limit of the thermal power of the cogeneration unit;
Figure 136917DEST_PATH_IMAGE062
indicating a thermal electric connectionThe upper limit of the thermal power of the generator set;
Figure 181621DEST_PATH_IMAGE064
representing the lower limit of the electric power of the cogeneration unit;
Figure 758096DEST_PATH_IMAGE066
represents the upper limit of the electric power of the cogeneration unit.
1-2-2) thermodynamic system heat supply node and pipeline constraints, expressed as follows:
Figure DEST_PATH_IMAGE152
(6)
in the formula (I), the compound is shown in the specification,
Figure 12360DEST_PATH_IMAGE070
is shown as
Figure 92311DEST_PATH_IMAGE070
A plurality of heating network nodes; wherein 0 represents a heat source node;
Figure 586878DEST_PATH_IMAGE072
represents a collection of all heating network nodes;
Figure 119490DEST_PATH_IMAGE074
representing nodes
Figure 974183DEST_PATH_IMAGE070
In a period of time
Figure 908641DEST_PATH_IMAGE076
The temperature of the supplied water;
Figure 370846DEST_PATH_IMAGE078
representing nodes
Figure 780968DEST_PATH_IMAGE070
In a period of time
Figure 49138DEST_PATH_IMAGE076
The temperature of the return water is controlled;
Figure 979048DEST_PATH_IMAGE080
representing nodes
Figure 940050DEST_PATH_IMAGE070
The equivalent thermal insulation coefficient of the heat supply pipeline;
Figure 309239DEST_PATH_IMAGE082
representing nodes
Figure 381101DEST_PATH_IMAGE070
The transmission of the heat supply pipeline is delayed;
Figure 431096DEST_PATH_IMAGE084
represents the ambient temperature;
Figure 218793DEST_PATH_IMAGE086
representing flow through a node
Figure 478873DEST_PATH_IMAGE070
Mass flow of the heat supply pipeline;
Figure 964212DEST_PATH_IMAGE088
represents the specific heat capacity of water;
Figure 727768DEST_PATH_IMAGE090
representing nodes
Figure 889628DEST_PATH_IMAGE070
In a period of time
Figure 637004DEST_PATH_IMAGE076
The thermal load of (2).
1-2-3) thermodynamic system water temperature limit constraints, the expression is as follows:
Figure DEST_PATH_IMAGE154
(7)
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE156
represents a lower limit of the supply water temperature;
Figure DEST_PATH_IMAGE158
represents an upper limit of the supply water temperature;
Figure DEST_PATH_IMAGE160
the lower limit of the backwater water temperature is shown;
Figure DEST_PATH_IMAGE162
and representing the upper limit of the backwater water temperature.
1-2-4) maximum wind curtailment that the system can utilize and acceptable electric power increment constraints, the expression is as follows:
Figure DEST_PATH_IMAGE164
(8)
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE166
indicating the cogeneration unit in time
Figure DEST_PATH_IMAGE168
The down-regulated power generation power is consumed for increasing the wind power;
Figure DEST_PATH_IMAGE170
representing wind farm in time period
Figure 173639DEST_PATH_IMAGE168
The abandoned wind power;
Figure DEST_PATH_IMAGE172
indicating that the system is in time
Figure 198227DEST_PATH_IMAGE168
An upper acceptable electrical power limit.
1-2-5) energy storage system operation constraints, the expression is as follows:
Figure DEST_PATH_IMAGE174
(9)
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE176
the state variable indicates whether the energy storage system works in a charging state, the charging working condition time value is 1, and the other working condition states are 0;
Figure DEST_PATH_IMAGE178
the state variable indicates whether the energy storage system works in a discharge state, the discharge working condition time value is 1, and the other working condition states are 0;
Figure DEST_PATH_IMAGE180
representing an energy storage charging power lower limit;
Figure DEST_PATH_IMAGE182
representing an upper limit of energy storage charging power;
Figure DEST_PATH_IMAGE184
indicating the period of stored energy
Figure DEST_PATH_IMAGE186
Storing the electric energy;
Figure DEST_PATH_IMAGE188
representing the charging efficiency of the energy storage system;
Figure DEST_PATH_IMAGE190
indicating the discharge efficiency of the energy storage system;
Figure DEST_PATH_IMAGE192
representing the lower limit of the stored electric quantity of the energy storage system;
Figure DEST_PATH_IMAGE194
representing the upper limit of the stored electric quantity of the energy storage system;
Figure DEST_PATH_IMAGE196
representing an optimized initial period of stored electrical quantity of the energy storage system；
Figure DEST_PATH_IMAGE198
Indicating that the amount of power stored by the energy storage system is optimized for the last period of time.
2) The method comprises the following steps of processing a nonlinear item in a new energy power station energy storage capacity optimization configuration model considering thermodynamic system cloud energy storage service, and converting a nonlinear programming problem into a mixed integer linear programming problem, wherein the specific steps are as follows:
2-1) determining a nonlinear item in a new energy power station energy storage capacity optimization configuration model considering a thermodynamic system cloud energy storage service, wherein the energy storage power is a planning result and can affect the upper and lower limit values of energy storage charging and discharging power, so that the nonlinear item exists in the energy storage operation constraint established in the step 1-2-5), and the expression is as follows:
Figure DEST_PATH_IMAGE200
(10)
2-2) carrying out linearization processing on the nonlinear terms by using a large M method, wherein the processed expression is as follows:
Figure DEST_PATH_IMAGE202
(11)
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE204
is a constant that is approximately infinite.
3) Solving the linearized new energy power station energy storage capacity optimization configuration model taking account of the thermodynamic system cloud energy storage service established in the steps 1) and 2) by adopting commercial optimization software IBM ILOG CPLEX, and outputting the energy storage capacity optimization configuration model as an energy storage planning configuration result.
Example (b):
the embodiment of the invention is mainly based on the example analysis of the Chinese Mongolian power grid data in the documents of N, Zhang, X, Lu, M.B. Mcoley, et al, "Reducing security of wind electric in Chinese by applying electric enclosures for heat and pumped hydro for Energy storage," Applied Energy, vol.184, pp.987-994, 2016 ". Suppose that two new energy power stations in the system are both wind power plants and a 6-node thermodynamic system is provided, and the structural schematic diagram of the example system in the embodiment is shown in fig. 4. Typical curtailment curves for two wind farms are shown in fig. 5. The two wind power plants increase the operation benefit for reducing the abandoned wind, and have the energy storage configuration requirement, and the configured energy storage type is compressed air energy storage. The thermodynamic system serves as a cloud energy storage providing direction to provide energy storage service for the wind power plant. The wind power grid-connection electricity price in the embodiment is selected by referring to the actual wind power grid-connection electricity price in China, and is 530 yuan/MWh.
The parameters of the thermodynamic system in the embodiment of the present invention are mainly based on the 6-node thermodynamic system in the document "s. Lu, w. Gu, k. Meng, et al," Thermal inertial aggregation model for integrated energy Systems, "IEEE Transactions on Power Systems, vol.3, No.35, pp. 2374-. The maximum heat supply power of the cogeneration unit is 50MW, and the maximum power supply power is 75 MW. The unit power cost of the advanced adiabatic compressed air energy storage configured in the embodiment of the invention is 443 ten thousand yuan/MW, and the unit capacity cost is 55 ten thousand yuan/MWh.
Based on the energy storage capacity optimal configuration model provided in the embodiment, the thermodynamic system in the formula is not considered, the energy storage capacity optimal configuration model for independently configuring the energy storage of the new energy power station is established as an embodiment comparison example, and the rest parameter settings are consistent with the embodiment.
Based on the new energy power station energy storage capacity optimization configuration model and embodiment parameters considering the cloud energy storage service of the thermodynamic system, and the embodiment comparative example model and parameters, the established energy storage capacity optimization configuration model is solved by using IBM ILOG CPLEX 12.10.0 commercial optimization software, and the energy storage optimization configuration result shown in Table 1 is obtained.
Table 1 energy storage optimization configuration results and income tables of examples and comparative examples
Figure DEST_PATH_IMAGE206
As can be seen from the table 1, after the thermodynamic system provides equivalent energy storage service for the new energy power station, the energy storage use and configuration cost of the new energy power station can be reduced, and the total income of the system is improved. The optimal energy storage configuration power obtained by solving the embodiment is 20.433MW, and the optimal energy storage configuration capacity is 95.356 MWh.

Claims (8)

1. A new energy power station energy storage capacity configuration method considering a thermodynamic system cloud energy storage service is characterized by comprising the following steps:
s1: establishing a new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system, wherein an objective function of the new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system is that the total abandoned wind recovery of the new energy power station in an optimization period of a typical day is the maximum on-line benefit, and constraint conditions comprise the operation condition constraint of a cogeneration unit, the heat supply node and pipeline constraint of the thermodynamic system, the water temperature limit constraint of the thermodynamic system, the maximum abandoned wind which can be utilized by the system, the acceptable electric power increment constraint and the configured energy storage system operation constraint;
s2: and linearizing the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service to obtain the new energy power station energy storage capacity optimization configuration model.
2. The method according to claim 1, wherein the objective function of the new energy plant energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure DEST_PATH_IMAGE002
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE004
represents the total revenue of the system increase;
Figure DEST_PATH_IMAGE006
the daily average equal annual investment cost for configuring the energy storage power station to the new energy station is shown;
Figure DEST_PATH_IMAGE008
the daily operation and maintenance cost for configuring the energy storage power station to the new energy station is shown;
the calculation expressions are respectively:
Figure DEST_PATH_IMAGE010
Figure DEST_PATH_IMAGE012
Figure DEST_PATH_IMAGE014
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE016
represents a collection of all periods of a typical day;
Figure DEST_PATH_IMAGE018
indicating a period of time
Figure DEST_PATH_IMAGE020
The wind power grid-connection electricity price;
Figure DEST_PATH_IMAGE022
indicating energy storage power station is in time period
Figure 387234DEST_PATH_IMAGE020
The magnitude of the charging power of (c);
Figure DEST_PATH_IMAGE024
indicating energy storage power station is in time period
Figure 369621DEST_PATH_IMAGE020
The magnitude of the discharge power of (c);
Figure DEST_PATH_IMAGE026
indicating that the cogeneration unit shares the self energy storage capacity to abandon wind and consume
Figure 477254DEST_PATH_IMAGE020
Increased generated power;
Figure DEST_PATH_IMAGE028
representing the cost of unit energy storage capacity of the new energy station configuration energy storage;
Figure DEST_PATH_IMAGE030
representing the unit energy storage power cost of the new energy station configuration energy storage;
Figure DEST_PATH_IMAGE032
representing the capacity of the energy storage configured by the new energy station;
Figure DEST_PATH_IMAGE034
representing the power of the new energy station configuration energy storage;
Figure DEST_PATH_IMAGE036
expressing the discount rate;
Figure DEST_PATH_IMAGE038
indicating the service life of energy storage;
Figure DEST_PATH_IMAGE040
the operation and maintenance cost of the energy storage system in unit operation hour is represented;
Figure DEST_PATH_IMAGE042
representing the number of hours the energy storage system is operating.
3. The method according to claim 1, wherein the constraint of the operation condition of the cogeneration unit in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure DEST_PATH_IMAGE044
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE046
indicating the period of time of the cogeneration unit
Figure DEST_PATH_IMAGE048
The magnitude of the electrical power of (c);
Figure DEST_PATH_IMAGE050
indicating the cogeneration unit in time
Figure 311611DEST_PATH_IMAGE048
The thermal power of (2);
Figure DEST_PATH_IMAGE052
representing a coefficient reflecting the incidence relation of the electric power and the thermal power of the cogeneration unit;
Figure DEST_PATH_IMAGE054
representing the fuel consumption of the cogeneration unit per unit of electric power;
Figure DEST_PATH_IMAGE056
the fuel consumption of the unit thermal power of the cogeneration unit;
Figure DEST_PATH_IMAGE058
representing the lower limit of fuel consumption of the cogeneration unit;
Figure DEST_PATH_IMAGE060
representing the upper limit of fuel consumption of the cogeneration unit;
Figure DEST_PATH_IMAGE062
representing the lower limit of the thermal power of the cogeneration unit;
Figure DEST_PATH_IMAGE064
representing the upper limit of the thermal power of the cogeneration unit;
Figure DEST_PATH_IMAGE066
representing the lower limit of the electric power of the cogeneration unit;
Figure DEST_PATH_IMAGE068
represents the upper limit of the electric power of the cogeneration unit.
4. The method according to claim 1, wherein the thermodynamic system heat supply node and pipeline constraints in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service are as follows:
Figure DEST_PATH_IMAGE070
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE072
is shown as
Figure 296623DEST_PATH_IMAGE072
A plurality of heating network nodes; 0 represents a heat source node;
Figure DEST_PATH_IMAGE074
represents a collection of all heating network nodes;
Figure DEST_PATH_IMAGE076
representing nodes
Figure 715490DEST_PATH_IMAGE072
In a period of time
Figure DEST_PATH_IMAGE078
The temperature of the supplied water;
Figure DEST_PATH_IMAGE080
representing nodes
Figure 841578DEST_PATH_IMAGE072
In a period of time
Figure 571637DEST_PATH_IMAGE078
The temperature of the return water is controlled;
Figure DEST_PATH_IMAGE082
representing nodes
Figure 302832DEST_PATH_IMAGE072
The equivalent thermal insulation coefficient of the heat supply pipeline;
Figure DEST_PATH_IMAGE084
representing nodes
Figure 624092DEST_PATH_IMAGE072
The transmission of the heat supply pipeline is delayed;
Figure DEST_PATH_IMAGE086
represents the ambient temperature;
Figure DEST_PATH_IMAGE088
representing flow through a node
Figure 971897DEST_PATH_IMAGE072
Mass flow of the heat supply pipeline;
Figure DEST_PATH_IMAGE090
represents the specific heat capacity of water;
Figure DEST_PATH_IMAGE092
representing nodes
Figure 293595DEST_PATH_IMAGE072
In a period of time
Figure 348139DEST_PATH_IMAGE078
The thermal load of (2).
5. The method according to claim 1, wherein the thermodynamic system water temperature limit constraint in the new energy plant energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service is as follows:
Figure DEST_PATH_IMAGE094
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE096
represents a lower limit of the supply water temperature;
Figure DEST_PATH_IMAGE098
represents an upper limit of the supply water temperature;
Figure DEST_PATH_IMAGE100
the lower limit of the backwater water temperature is shown;
Figure DEST_PATH_IMAGE102
and representing the upper limit of the backwater water temperature.
6. The method of claim 1, wherein the maximum wind curtailment that can be utilized by the system and the acceptable incremental constraints on electric power in the new energy power station energy storage capacity optimization configuration model considering the cloud energy storage service of the thermodynamic system are as follows:
Figure DEST_PATH_IMAGE104
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE106
indicating the period of time of the cogeneration unit
Figure 292830DEST_PATH_IMAGE078
The down-regulated power generation power is consumed for increasing the wind power;
Figure DEST_PATH_IMAGE108
representing wind farm in time period
Figure 596772DEST_PATH_IMAGE078
The abandoned wind power;
Figure DEST_PATH_IMAGE110
indicating that the system is in time
Figure 199792DEST_PATH_IMAGE078
An upper acceptable electrical power limit.
7. The method according to claim 1, wherein the energy storage system operation constraints in the new energy power station energy storage capacity optimization configuration model considering the thermodynamic system cloud energy storage service are as follows:
Figure DEST_PATH_IMAGE112
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE114
a state variable indicating whether the energy storage system is operating in a charging state;
Figure DEST_PATH_IMAGE116
indicating whether the energy storage system is operating in a discharge stateA state variable of (a);
Figure DEST_PATH_IMAGE118
representing an energy storage charging power lower limit;
Figure DEST_PATH_IMAGE120
representing an upper energy storage charging power limit;
Figure DEST_PATH_IMAGE122
indicating the period of stored energy
Figure 829881DEST_PATH_IMAGE078
Storing the electric energy;
Figure DEST_PATH_IMAGE124
representing the charging efficiency of the energy storage system;
Figure DEST_PATH_IMAGE126
indicating the discharge efficiency of the energy storage system;
Figure DEST_PATH_IMAGE128
representing the lower limit of the stored electric quantity of the energy storage system;
Figure DEST_PATH_IMAGE130
representing the upper limit of the stored electric quantity of the energy storage system;
Figure DEST_PATH_IMAGE132
representing the amount of electricity stored by the energy storage system during the optimization initial period;
Figure DEST_PATH_IMAGE134
indicating that the amount of power stored by the energy storage system is optimized for the last period of time.
8. The method of claim 7, wherein linearizing the non-linear constraint in the constraint condition comprises:
determining a nonlinear term in a new energy power station energy storage capacity optimization configuration model considering thermodynamic system cloud energy storage service:
Figure DEST_PATH_IMAGE136
the nonlinear term is linearized by using a large M method, and the expression after the linearization is as follows:
Figure DEST_PATH_IMAGE138
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE140
is a constant that is approximately infinite.
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CN112418537A (en) * 2020-11-29 2021-02-26 清华大学 Optimized scheduling method for multi-energy cloud energy storage system
CN113378374A (en) * 2021-06-08 2021-09-10 国网江苏省电力有限公司经济技术研究院 Optimal configuration method for park comprehensive energy system

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