CN112366757A - Microgrid energy management and control method and device - Google Patents

Microgrid energy management and control method and device Download PDF

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Publication number
CN112366757A
CN112366757A CN202011040281.5A CN202011040281A CN112366757A CN 112366757 A CN112366757 A CN 112366757A CN 202011040281 A CN202011040281 A CN 202011040281A CN 112366757 A CN112366757 A CN 112366757A
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power
representing
constraint
time
load
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王剑晓
陈琳
钟海旺
李庚银
李鹏
张艺涵
李慧璇
王世谦
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Tsinghua University
North China Electric Power University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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Tsinghua University
North China Electric Power University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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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
    • H02J3/38—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
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    • 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
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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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
    • 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
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
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    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
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    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
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    • 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
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    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention discloses a microgrid energy management regulation and control method and device, and relates to the technical field of power dispatching, wherein the method comprises the following steps: acquiring photovoltaic output data, electricity price data and load data; establishing a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data and the load data; and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid. Therefore, the convergence of the net load demand and the total standby demand of the micro-grid and the convergence of the LMP and the ARC of the micro-grid are achieved, the combined optimization result is finally obtained, and theoretical guidance is provided for behavior decision of reducing the green energy reduction ratio.

Description

Microgrid energy management and control method and device
Technical Field
The invention relates to the technical field of power dispatching, in particular to a micro-grid energy management and control method and device.
Background
In the related art, the Tradable Green Certificate (TGC) in the prior art is restricted by non-bundled sales, which limits the sale of TGC separately from potential energy sources and can be used nationwide. In this case, while such TGCs provide a flexible approach to support renewable energy development, they do not alter the enterprise's existing power contracts and physical power delivery.
The spot-market clearing price is derived from economic dispatch in the power system, Green Electric (GE) takes into account zero marginal cost and therefore enjoys priority dispatch. However, no matter how high the quota ratio of Renewable energy Portfolio Standard (RPS) is set, the GE reduction is inevitable as the GE prevalence rate increases, and as the GE ratio increases, the RPS mechanism may gradually fail: the additional consumption of GE will greatly increase the ancillary service cost of thermal power, taking up a share of thermal power, making further consumption of GE impossible in cost-oriented economic dispatch.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, an object of the present invention is to provide a method for managing and controlling energy of a microgrid, which achieves convergence of a payload demand and a total Reserve demand of the microgrid, a marginal price (LMP) of a node of the microgrid and a Reserve capacity Cost (ARC), and finally obtains a joint optimization result, thereby providing a theoretical guidance for a behavior decision for reducing a green energy reduction ratio.
The invention also aims to provide a micro-grid energy management and control device.
In order to achieve the above object, an embodiment of the invention provides a microgrid energy management and control method, which includes:
acquiring photovoltaic output data, electricity price data and load data;
establishing a microgrid energy management scheduling model according to a first constraint condition, the photovoltaic output data, the electricity price data and the load data;
establishing a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data and the load data;
and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid.
In addition, the microgrid energy management and regulation method according to the above embodiment of the present invention may further have the following additional technical features:
according to an embodiment of the present invention, the building a microgrid energy management scheduling model according to a preset first constraint condition, the photovoltaic output data, the electricity price data and the load data includes:
the objective function for establishing the microgrid energy management scheduling model is as follows:
Figure BDA0002706410150000021
wherein the content of the first and second substances,
Figure BDA0002706410150000022
the cost of electricity for the microturbine;
Figure BDA0002706410150000023
a tradeable green certificate procurement cost;
Figure BDA0002706410150000024
a transaction cost for the utility grid;
Figure BDA0002706410150000025
for amortized spare capacity cost;
Figure BDA0002706410150000026
cost for load transfer;
the constraint conditions for constructing the microgrid energy management scheduling model comprise: a first power balance constraint, a first power supply constraint, and an energy storage system constraint;
wherein the first power balance constraint is:
Figure BDA0002706410150000027
wherein the content of the first and second substances,
Figure BDA0002706410150000028
represents the payload at time t in the s-th scenario;
Figure BDA0002706410150000029
representing the original load demand at time t in the s-th scenario;
Figure BDA00027064101500000210
and
Figure BDA00027064101500000211
respectively representing the charging and discharging power of the energy storage system at time t under the s-th scene;
Figure BDA00027064101500000212
and
Figure BDA00027064101500000213
respectively representing the up-regulation power and the down-regulation power of the transferable load at the time t in the s-th scene;
Figure BDA00027064101500000214
Figure BDA00027064101500000215
and
Figure BDA00027064101500000216
respectively representing wind energy, micro-turbine generated power and solar energy at time t in the s-th scene;
Figure BDA00027064101500000217
representing the TGC requirement in the s-th scenario; deltaRPSRepresenting the percentage of the minimum amount of electricity consumed from renewable energy by the power consumer required by the renewable energy portfolio standard RPS to the total load;
wherein the first power supply constraint is:
Figure BDA00027064101500000218
wherein the content of the first and second substances,
Figure BDA00027064101500000219
and
Figure BDA00027064101500000220
respectively representing wind energy and solar energy in the s-th sceneAn upper limit of available power at a lower time t;
Figure BDA0002706410150000031
represents the upper limit of the microturbine power generation;
Figure BDA0002706410150000032
and
Figure BDA0002706410150000033
respectively representing an up-regulation limit electric quantity and a down-regulation limit electric quantity of the transferable load at time t;
Figure BDA0002706410150000034
representing the running state of the distributed energy source on the node x;
wherein the energy storage system constraints are:
Figure BDA0002706410150000035
wherein the content of the first and second substances,
Figure BDA0002706410150000036
and
Figure BDA0002706410150000037
representing limits of charging power and discharging power of the energy storage system, respectively;
Figure BDA0002706410150000038
represents the amount of power stored in the energy storage system at time t in the s-th scenario;
Figure BDA0002706410150000039
and
Figure BDA00027064101500000310
representing the charging and discharging power of the energy storage system, respectively;
Figure BDA00027064101500000311
indicating an energy storage systemThe capacity of (a);
Figure BDA00027064101500000312
and
Figure BDA00027064101500000313
representing the upper and lower limits of the energy storage system state of charge, respectively.
According to one embodiment of the invention, the
Figure BDA00027064101500000314
Cost of electricity for microturbines, said
Figure BDA00027064101500000315
Tradable green certificate procurement costs
Figure BDA00027064101500000316
Cost of trading of a utility grid, said
Figure BDA00027064101500000317
Amortized spare capacity cost and said
Figure BDA00027064101500000318
The expression for the cost of load shifting is:
Figure BDA00027064101500000319
wherein the content of the first and second substances,
Figure BDA00027064101500000320
and
Figure BDA00027064101500000321
coefficients of the costs of power and load transfer of the microturbine, respectively;
Figure BDA00027064101500000322
representing the power generation amount of the micro turbine at time t in the s-th scene; lambda [ alpha ]GCRepresents the TGC price;
Figure BDA00027064101500000323
represents the TGC requirement of node x at time t in the s-th scenario;
Figure BDA00027064101500000324
represents the removed power of node x in the power system at time t in the s-th scenario;
Figure BDA00027064101500000325
represents the net load of node x at time t in the s-th scenario; lambda [ alpha ]fixedRepresents a constant export price between the microgrid and the utility grid;
Figure BDA00027064101500000326
representing the marginal price of electricity at node x at time t in the s-th scenario; Δ T represents a time interval; gamma raysRepresenting the probability of the scene s occurring.
According to an embodiment of the present invention, the establishing of the market clearing model is:
Figure BDA0002706410150000041
wherein the content of the first and second substances,
Figure BDA0002706410150000042
represents conventional generator cost;
Figure BDA0002706410150000043
represents the spare capacity cost;
the second constraint includes: a second power balance constraint, a line flow constraint, a second power constraint, and an RPS constraint;
wherein the second power balance constraint is:
Figure BDA0002706410150000044
wherein the content of the first and second substances,
Figure BDA0002706410150000045
and
Figure BDA0002706410150000046
respectively representing the power of the conventional generator i and the power of the renewable energy source i at time t in the s-th scenario;
Figure BDA0002706410150000047
representing the reserve capacity of a conventional generator;
Figure BDA0002706410150000048
represents the payload at time t in the s-th scenario;
Figure BDA0002706410150000049
and
Figure BDA00027064101500000410
respectively representing a portion of the net load demand and renewable energy reserve capacity;
wherein the line flow constraint is:
Figure BDA00027064101500000411
wherein G isk-iRepresenting a power generation transfer division factor of the line k;
Figure BDA00027064101500000412
and
Figure BDA00027064101500000413
respectively representing the power of a conventional generator i, the power of a renewable energy source i and the reserve capacity of the conventional generator i at time t in the s-th scene;
Figure BDA00027064101500000414
represents the payload at time t in the s-th scenario;
Figure BDA00027064101500000415
represents the transmission capacity of line k;
wherein the second power supply constraint is:
Figure BDA00027064101500000416
wherein, Pi G,minAnd Pi G,maxRespectively representing the minimum and maximum power of a conventional generator i;
Figure BDA00027064101500000417
indicating that the green generator can provide electricity; pi U,maxAnd Pi D,maxRespectively representing the rising and falling limits of a conventional generator i;
wherein the RPS constraint is:
Figure BDA00027064101500000418
wherein the content of the first and second substances,
Figure BDA0002706410150000051
representing the TGC demand charge under scenario s.
According to an embodiment of the present invention, the solving the microgrid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain a payload demand and a total spare demand of a microgrid includes:
s1, initializing the iteration index k to be 0, and setting the operation strategy of the microgrid
Figure BDA0002706410150000052
And will net the load
Figure BDA0002706410150000053
Setting to an initial value;
s2, optimizing the aim of minimizing the Tradable Green Certificate (TGC) cost and the operation cost of the micro-grid based on load balance, wind power, solar energy and energy storage operation constraints and RPS requirements according to the node electricity price LMP and the spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid;
and S3, judging whether the optimization result and the time between the LMP and the ARC and the last time satisfy convergence conditions. If the convergence condition is met, outputting an optimization result, LMP and ARC; if the convergence condition is not satisfied, go to step S4;
and S4, performing economic dispatching including RPS constraint and safety constraint according to the net load demand and the total standby demand, on the premise of meeting the constraints of a generator, renewable power generation and line power flow, aiming at minimizing the regional operation cost, adjusting output, updating the LMP and the ARC, and returning to the step S2.
According to one embodiment of the present invention, the calculation formula of the LMP is:
Figure BDA0002706410150000054
wherein λ ist,sRepresenting LMP passable Lagrange multiplier in the joint settlement model
Figure BDA0002706410150000055
To realize the operation;
the ARC calculation formula is:
Figure BDA0002706410150000056
according to the microgrid energy management and control method, photovoltaic output data, electricity price data and load data are obtained; establishing a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data and the load data; and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid. Therefore, the convergence of the net load demand and the total standby demand of the micro-grid and the convergence of the LMP and the ARC of the micro-grid are achieved, the combined optimization result is finally obtained, and theoretical guidance is provided for behavior decision of reducing the green energy reduction ratio.
In order to achieve the above object, an embodiment of another aspect of the present invention provides a microgrid energy management and control apparatus, including: the acquisition module is used for acquiring photovoltaic output data, electricity price data and load data;
the first establishing module is used for establishing a microgrid energy management scheduling model according to a first constraint condition, the photovoltaic output data, the electricity price data and the load data;
the second establishing module is used for establishing a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data and the load data;
and the solving module is used for solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid.
According to the microgrid energy management and control device provided by the embodiment of the invention, photovoltaic output data, electricity price data and load data are obtained; establishing a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data and the load data; and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid. Therefore, the convergence of the net load demand and the total standby demand of the micro-grid and the convergence of the LMP and the ARC of the micro-grid are achieved, the combined optimization result is finally obtained, and theoretical guidance is provided for behavior decision of reducing the green energy reduction ratio.
According to a third aspect of embodiments of the present invention, there is provided a server, including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to execute the instructions to implement the microgrid energy management regulation and control method of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a storage medium including:
the instructions in the storage medium, when executed by a processor of a server, enable the server to perform the microgrid energy management regulation and control method of the first aspect.
According to a fifth aspect of embodiments of the present invention, there is provided a computer program product, wherein instructions of the computer program product, when executed by a processor, enable a server to execute the microgrid energy management regulation and control method of the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flow diagram of a microgrid energy management regulation method according to one embodiment of the present invention;
FIG. 2 is a flow diagram of a microgrid energy management regulation method according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a microgrid energy management regulation and control device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a microgrid energy management regulation and control method and device provided by the embodiment of the invention with reference to the attached drawings.
The micro-grid energy management and control method introduces and analyzes the balance in the RPS-constrained spot market aiming at the green power market containing renewable energy sources, constructs a green power model with RPS constraint, and provides theoretical guidance for behavior decision of reducing the green energy source reduction rate.
Among them, the renewable energy portfolio standard RPS is one of the most popular and innovative renewable energy incentives, according to which a certain percentage of the total annual power supply for the region must come from renewable energy sources; on the other hand, successful implementation of RPS requires a corresponding and compelling strategy as an effective tool. Thus, a Tradable Green Certificate (TGC) as a matching policy is traded and redeemed for profit, representing a certain amount of Green Electricity (GE), GE producers can seek additional benefit by selling their TGC through a financial contract with a quota obligator.
Fig. 1 is a flow chart of a microgrid energy management regulation method according to one embodiment of the present invention. As shown in fig. 1, the microgrid energy management and regulation method comprises the following steps:
and S101, acquiring photovoltaic output data, electricity price data and load data.
In this embodiment, the IEEE 14 bus system will be simulated to verify the proposed model, with 3 conventional generators and 2 wind farms of 100 MW. 5 micro grids provided with transferable loads and distributed energy equipment are positioned at 5 different nodes, and the transferable load of each micro grid accounts for 20 percent of the initial load each time; each microgrid has solar energy, a micro gas turbine, a fan and stored energy, and the capacities of the micro gas turbine, the fan and the stored energy are respectively 10MW, 35MW, 40MW and 20 MW. From 2016 load and solar data, 10 typical scenes were generated using K-means, with a probability of 0.1 per scene, such as 7MW, 25MW, 36MW, and 12MW solar, micro gas turbine, wind turbine, and energy storage generation of the microgrid in a scene.
And S102, establishing a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data.
In this embodiment, the objective function for establishing the microgrid energy management scheduling model is as follows:
Figure BDA0002706410150000071
wherein the content of the first and second substances,
Figure BDA0002706410150000072
the cost of electricity for the microturbine;
Figure BDA0002706410150000073
a tradeable green certificate procurement cost;
Figure BDA0002706410150000074
a transaction cost for the utility grid;
Figure BDA0002706410150000075
for amortized spare capacity cost;
Figure BDA0002706410150000076
cost for load transfer;
the constraint conditions for constructing the microgrid energy management scheduling model comprise: a first power balance constraint, a first power supply constraint, and an energy storage system constraint;
wherein the first power balance constraint is:
Figure BDA0002706410150000081
wherein the content of the first and second substances,
Figure BDA0002706410150000082
represents the payload at time t in the s-th scenario;
Figure BDA0002706410150000083
representing the original load demand at time t in the s-th scenario;
Figure BDA0002706410150000084
and
Figure BDA0002706410150000085
respectively representing the charging and discharging power of the energy storage system at time t under the s-th scene;
Figure BDA0002706410150000086
and
Figure BDA0002706410150000087
respectively representing the up-regulation power and the down-regulation power of the transferable load at the time t in the s-th scene;
Figure BDA0002706410150000088
Figure BDA0002706410150000089
and
Figure BDA00027064101500000810
respectively representing wind energy, micro-turbine generated power and solar energy at time t in the s-th scene;
Figure BDA00027064101500000811
representing the TGC requirement in the s-th scenario; deltaRPSRepresenting the percentage of the minimum amount of electricity consumed from renewable energy by the power consumer required by the renewable energy portfolio standard RPS to the total load;
wherein the first power supply constraint is:
Figure BDA00027064101500000812
wherein the content of the first and second substances,
Figure BDA00027064101500000813
and
Figure BDA00027064101500000814
respectively representing the upper limit of the available power of wind energy and solar energy at the time t under the s-th scene;
Figure BDA00027064101500000815
represents the upper limit of the microturbine power generation;
Figure BDA00027064101500000816
and
Figure BDA00027064101500000817
respectively representing an up-regulation limit electric quantity and a down-regulation limit electric quantity of the transferable load at time t;
Figure BDA00027064101500000818
representing the running state of the distributed energy source on the node x;
wherein the energy storage system constraints are:
Figure BDA00027064101500000819
wherein the content of the first and second substances,
Figure BDA00027064101500000820
and
Figure BDA00027064101500000821
representing limits of charging power and discharging power of the energy storage system, respectively;
Figure BDA00027064101500000822
represents the amount of power stored in the energy storage system at time t in the s-th scenario;
Figure BDA00027064101500000823
and
Figure BDA00027064101500000824
representing the charging and discharging power of the energy storage system, respectively;
Figure BDA00027064101500000825
representing the capacity of the energy storage system;
Figure BDA00027064101500000826
and
Figure BDA00027064101500000827
representing the upper and lower limits of the energy storage system state of charge, respectively.
In the present embodiment, it is preferred that,
Figure BDA0002706410150000091
cost of electricity for microturbines, said
Figure BDA0002706410150000092
Tradable green certificate procurement costs
Figure BDA0002706410150000093
Cost of trading of a utility grid, said
Figure BDA0002706410150000094
Amortized spare capacity cost and said
Figure BDA0002706410150000095
The expression for the cost of load shifting is:
Figure BDA0002706410150000096
wherein the content of the first and second substances,
Figure BDA0002706410150000097
and
Figure BDA0002706410150000098
coefficients of the costs of power and load transfer of the microturbine, respectively;
Figure BDA0002706410150000099
representing the power generation amount of the micro turbine at time t in the s-th scene; lambda [ alpha ]GCRepresents the TGC price;
Figure BDA00027064101500000910
represents the TGC requirement of node x at time t in the s-th scenario;
Figure BDA00027064101500000911
represents the removed power of node x in the power system at time t in the s-th scenario;
Figure BDA00027064101500000912
represents the net load of node x at time t in the s-th scenario; lambda [ alpha ]fixedRepresents a constant export price between the microgrid and the utility grid;
Figure BDA00027064101500000913
representing the marginal price of electricity at node x at time t in the s-th scenario; Δ T represents a time interval; gamma raysRepresenting the probability of the scene s occurring.
Wherein the node margin electricity price
Figure BDA00027064101500000914
And spare capacity cost
Figure BDA00027064101500000915
The method is influenced by the operation strategy of the micro-grid and is given by a market clearing model taking lower level into account of RPS constraint.
And S103, establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data and the load data.
In this embodiment, the market clearing model is established as follows:
Figure BDA00027064101500000916
wherein the content of the first and second substances,
Figure BDA00027064101500000917
represents conventional generator cost;
Figure BDA00027064101500000918
represents the spare capacity cost;
the second constraint includes: a second power balance constraint, a line flow constraint, a second power constraint, and an RPS constraint;
wherein the second power balance constraint is:
Figure BDA0002706410150000101
wherein the content of the first and second substances,
Figure BDA0002706410150000102
and
Figure BDA0002706410150000103
respectively representing the power of the conventional generator i and the power of the renewable energy source i at time t in the s-th scenario;
Figure BDA0002706410150000104
representing the reserve capacity of a conventional generator;
Figure BDA0002706410150000105
represents the payload at time t in the s-th scenario;
Figure BDA0002706410150000106
and
Figure BDA0002706410150000107
respectively representing a portion of the net load demand and renewable energy reserve capacity;
wherein the line flow constraint is:
Figure BDA0002706410150000108
wherein G isk-iRepresenting a power generation transfer division factor of the line k;
Figure BDA0002706410150000109
and
Figure BDA00027064101500001010
respectively representing the power of a conventional generator i, the power of a renewable energy source i and the reserve capacity of the conventional generator i at time t in the s-th scene;
Figure BDA00027064101500001011
represents the payload at time t in the s-th scenario;
Figure BDA00027064101500001012
represents the transmission capacity of line k;
wherein the second power supply constraint is:
Figure BDA00027064101500001013
wherein, Pi G,minAnd Pi G,maxRespectively representing the minimum and maximum power of a conventional generator i;
Figure BDA00027064101500001014
indicating that the green generator can provide electricity; pi U,maxAnd Pi D,maxRespectively representing the rising and falling limits of a conventional generator i;
wherein the RPS constraint is:
Figure BDA00027064101500001015
wherein the content of the first and second substances,
Figure BDA00027064101500001016
representing the TGC demand charge under scenario s.
And step S104, solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid.
In this embodiment, in step S1, the initialization iteration index k is 0, and the operation strategy of the microgrid is set
Figure BDA00027064101500001017
And will net the load
Figure BDA00027064101500001018
Setting to an initial value; s2, optimizing the target of minimizing the TGC cost and the operation cost of the micro-grid based on load balance, wind power, solar energy and energy storage operation constraints and RPS requirements according to the node electricity price LMP and the spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid; and S3, judging whether the optimization result and the time between the LMP and the ARC and the last time satisfy convergence conditions. If the convergence condition is met, outputting an optimization result, LMP and ARC; if the convergence condition is not satisfied, go to step S4; s4, performing economic dispatch including RPS constraints and safety constraints according to the net load demand and the total backup demand, and adjusting the output and updating the LMP and ARC with the goal of minimizing the regional operating cost on the premise of satisfying the constraints of the generator, the renewable power generation and the line power flow, and returning to step S2, that is, as shown in fig. 2.
The net load demand quantity represents the inlet and outlet electric quantity of the public power grid on the node; the total spare demand indicates that when the demand is positive, TGC covering a certain number of REs needs to be purchased.
In this embodiment, the calculation formula of LMP is:
Figure BDA0002706410150000111
wherein λ ist,sRepresenting that LMP in the joint settlement model can pass LagrangeDaily multiplier
Figure BDA0002706410150000112
To realize the operation;
the ARC calculation formula is:
Figure BDA0002706410150000113
in this embodiment, the regional reserve requirement is a linear sum of the reserve capacity required for regional loads and green power, and the regional marginal reserve price is the lagrange multiplier λt，sAnd the reserve cost is the percentage contribution to the total payload by each microgrid.
Therefore, market balance in the RPS-constrained spot market is introduced and analyzed, a green power regulation and control technology model with RPS constraint is constructed, through iterative interaction between independent system operators and the microgrid, the net load requirement and the total standby requirement of the microgrid and convergence of the LMP and ARC of the microgrid are achieved, a joint optimization result is finally obtained, and theoretical guidance is provided for behavior decision of reducing green energy reduction ratio.
According to the microgrid energy management and regulation method provided by the embodiment of the invention, photovoltaic output data, electricity price data and load data are obtained; establishing a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data and the load data; and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid. Therefore, the convergence of the net load demand and the total standby demand of the micro-grid and the convergence of the LMP and the ARC of the micro-grid are achieved, the combined optimization result is finally obtained, and theoretical guidance is provided for behavior decision of reducing the green energy reduction ratio.
Fig. 3 is a diagram illustrating a structure of a microgrid energy management and control device according to an embodiment of the present invention. As shown in fig. 3, the microgrid energy management and control device includes: an acquisition module 100, a first building module 200, a second building module 300, and a solving module 400.
The acquiring module 100 is configured to acquire photovoltaic output data, electricity price data, and load data.
The first establishing module 200 is configured to establish a microgrid energy management scheduling model according to a first constraint condition, the photovoltaic output data, the electricity price data, and the load data.
A second establishing module 300, configured to establish a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data, and the load data.
And the solving module 400 is used for solving the microgrid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the microgrid.
It should be noted that the foregoing explanation of the embodiment of the microgrid energy management and control method is also applicable to the microgrid energy management and control device of the embodiment, and details are not repeated here.
According to the microgrid energy management and control device provided by the embodiment of the invention, photovoltaic output data, electricity price data and load data are obtained; establishing a microgrid energy management scheduling model according to the first constraint condition, the photovoltaic output data, the electricity price data and the load data; establishing a market clearing model according to the second constraint condition, the photovoltaic output data, the electricity price data and the load data; and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid. Therefore, the convergence of the net load demand and the total standby demand of the micro-grid and the convergence of the LMP and the ARC of the micro-grid are achieved, the combined optimization result is finally obtained, and theoretical guidance is provided for behavior decision of reducing the green energy reduction ratio.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (10)

1. A microgrid energy management and regulation method is characterized by comprising the following steps:
acquiring photovoltaic output data, electricity price data and load data;
establishing a microgrid energy management scheduling model according to a first constraint condition, the photovoltaic output data, the electricity price data and the load data;
establishing a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data and the load data;
and solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid.
2. The microgrid energy management regulation and control method of claim 1, wherein the building of a microgrid energy management scheduling model according to preset first constraints, the photovoltaic output data, the electricity price data and the load data comprises:
the objective function for establishing the microgrid energy management scheduling model is as follows:
Figure FDA0002706410140000011
wherein the content of the first and second substances,
Figure FDA0002706410140000012
the cost of electricity for the microturbine;
Figure FDA0002706410140000013
a tradeable green certificate procurement cost;
Figure FDA0002706410140000014
a transaction cost for the utility grid;
Figure FDA0002706410140000015
for amortized spare capacity cost;
Figure FDA0002706410140000016
cost for load transfer;
the constraint conditions for constructing the microgrid energy management scheduling model comprise: a first power balance constraint, a first power supply constraint, and an energy storage system constraint;
wherein the first power balance constraint is:
Figure FDA0002706410140000017
wherein the content of the first and second substances,
Figure FDA0002706410140000018
represents the payload at time t in the s-th scenario;
Figure FDA0002706410140000019
representing the original load demand at time t in the s-th scenario;
Figure FDA00027064101400000110
and
Figure FDA00027064101400000111
respectively representing the charging and discharging power of the energy storage system at time t under the s-th scene;
Figure FDA00027064101400000112
and
Figure FDA00027064101400000113
respectively representing the up-regulation power and the down-regulation power of the transferable load at the time t in the s-th scene;
Figure FDA00027064101400000114
Figure FDA00027064101400000115
and
Figure FDA00027064101400000116
respectively representing wind energy, micro-turbine generated power and solar energy at time t in the s-th scene;
Figure FDA00027064101400000117
representing the TGC requirement in the s-th scenario; deltaRPSRepresenting the percentage of the minimum amount of electricity consumed from renewable energy by the power consumer required by the renewable energy portfolio standard RPS to the total load;
wherein the first power supply constraint is:
Figure FDA0002706410140000021
wherein the content of the first and second substances,
Figure FDA0002706410140000022
and
Figure FDA0002706410140000023
respectively representing the upper limit of the available power of wind energy and solar energy at the time t under the s-th scene;
Figure FDA0002706410140000024
represents the upper limit of the microturbine power generation;
Figure FDA0002706410140000025
and
Figure FDA0002706410140000026
respectively representing an up-regulation limit electric quantity and a down-regulation limit electric quantity of the transferable load at time t;
Figure FDA0002706410140000027
representing the running state of the distributed energy source on the node x;
wherein the energy storage system constraints are:
Figure FDA0002706410140000028
wherein the content of the first and second substances,
Figure FDA0002706410140000029
and
Figure FDA00027064101400000210
representing limits of charging power and discharging power of the energy storage system, respectively;
Figure FDA00027064101400000211
represents the amount of power stored in the energy storage system at time t in the s-th scenario;
Figure FDA00027064101400000212
and
Figure FDA00027064101400000213
representing the charging and discharging power of the energy storage system, respectively;
Figure FDA00027064101400000214
representing the capacity of the energy storage system;
Figure FDA00027064101400000215
and
Figure FDA00027064101400000216
representing the upper and lower limits of the energy storage system state of charge, respectively.
3. The microgrid energy management regulation method of claim 2 wherein the microgrid energy management regulation method is characterized byIn the above, the
Figure FDA00027064101400000217
Cost of electricity for microturbines, said
Figure FDA00027064101400000218
Tradable green certificate procurement costs
Figure FDA00027064101400000219
Cost of trading of a utility grid, said
Figure FDA00027064101400000220
Amortized spare capacity cost and said
Figure FDA00027064101400000221
The expression for the cost of load shifting is:
Figure FDA0002706410140000031
wherein the content of the first and second substances,
Figure FDA0002706410140000032
and
Figure FDA0002706410140000033
coefficients of the costs of power and load transfer of the microturbine, respectively;
Figure FDA0002706410140000034
representing the power generation amount of the micro turbine at time t in the s-th scene; lambda [ alpha ]GCRepresents the TGC price;
Figure FDA0002706410140000035
represents the TGC requirement of node x at time t in the s-th scenario;
Figure FDA0002706410140000036
represents the removed power of node x in the power system at time t in the s-th scenario;
Figure FDA0002706410140000037
represents the net load of node x at time t in the s-th scenario; lambda [ alpha ]fixedRepresents a constant export price between the microgrid and the utility grid;
Figure FDA0002706410140000038
representing the marginal price of electricity at node x at time t in the s-th scenario; Δ T represents a time interval; gamma raysRepresenting the probability of the scene s occurring.
4. The microgrid energy management and regulation method of claim 1, wherein the establishment of a market clearing model is:
Figure FDA0002706410140000039
wherein the content of the first and second substances,
Figure FDA00027064101400000310
represents conventional generator cost;
Figure FDA00027064101400000311
represents the spare capacity cost;
the second constraint includes: a second power balance constraint, a line flow constraint, a second power constraint, and an RPS constraint;
wherein the second power balance constraint is:
Figure FDA00027064101400000312
wherein the content of the first and second substances,
Figure FDA00027064101400000313
and
Figure FDA00027064101400000314
respectively representing the power of the conventional generator i and the power of the renewable energy source i at time t in the s-th scenario;
Figure FDA00027064101400000315
representing the reserve capacity of a conventional generator;
Figure FDA00027064101400000316
represents the payload at time t in the s-th scenario;
Figure FDA00027064101400000317
and
Figure FDA00027064101400000318
respectively representing a portion of the net load demand and renewable energy reserve capacity;
wherein the line flow constraint is:
Figure FDA0002706410140000041
wherein G isk-iRepresenting a power generation transfer division factor of the line k;
Figure FDA0002706410140000042
and
Figure FDA0002706410140000043
respectively representing the power of a conventional generator i, the power of a renewable energy source i and the reserve capacity of the conventional generator i at time t in the s-th scene;
Figure FDA0002706410140000044
representing the net at time t in the s-th sceneA load;
Figure FDA0002706410140000045
represents the transmission capacity of line k;
wherein the second power supply constraint is:
Figure FDA0002706410140000046
wherein, Pi G,minAnd Pi G,maxRespectively representing the minimum and maximum power of a conventional generator i;
Figure FDA0002706410140000047
indicating that the green generator can provide electricity; pi U,maxAnd Pi D,maxRespectively representing the rising and falling limits of a conventional generator i;
wherein the RPS constraint is:
Figure FDA0002706410140000048
wherein the content of the first and second substances,
Figure FDA0002706410140000049
representing the TGC demand charge under scenario s.
5. The microgrid energy management regulation and control method of claim 4, wherein the solving of the microgrid energy management scheduling model and the market clearing model by a diagonalization algorithm, a node electricity price (LMP) and a reserve capacity cost (ARC) to obtain a payload demand and a total reserve demand of a microgrid comprises:
s1, initializing the iteration index k to be 0, and setting the operation strategy of the microgrid
Figure FDA00027064101400000410
And will net the load
Figure FDA00027064101400000411
Setting to an initial value;
s2, optimizing the aim of minimizing the Tradable Green Certificate (TGC) cost and the operation cost of the micro-grid based on load balance, wind power, solar energy and energy storage operation constraints and RPS requirements according to the node electricity price LMP and the spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid;
and S3, judging whether the optimization result and the time between the LMP and the ARC and the last time satisfy convergence conditions. If the convergence condition is met, outputting an optimization result, LMP and ARC; if the convergence condition is not satisfied, go to step S4;
and S4, performing economic dispatching including RPS constraint and safety constraint according to the net load demand and the total standby demand, on the premise of meeting the constraints of a generator, renewable power generation and line power flow, aiming at minimizing the regional operation cost, adjusting output, updating the LMP and the ARC, and returning to the step S2.
6. The microgrid energy management regulation method of claim 1,
the calculation formula of the LMP is as follows:
Figure FDA0002706410140000051
wherein λ ist,sRepresenting LMP passable Lagrange multiplier in the joint settlement model
Figure FDA0002706410140000052
To realize the operation;
the ARC calculation formula is:
Figure FDA0002706410140000053
7. a microgrid energy management regulation and control device, comprising:
the acquisition module is used for acquiring photovoltaic output data, electricity price data and load data;
the first establishing module is used for establishing a microgrid energy management scheduling model according to a first constraint condition, the photovoltaic output data, the electricity price data and the load data;
the second establishing module is used for establishing a market clearing model according to a second constraint condition, the photovoltaic output data, the electricity price data and the load data;
and the solving module is used for solving the micro-grid energy management scheduling model and the market clearing model through a diagonalization algorithm, a node electricity price LMP and a spare capacity cost ARC to obtain the net load demand and the total spare demand of the micro-grid.
8. The microgrid energy management regulation device of claim 7, wherein the first establishment module is configured to:
the objective function for establishing the microgrid energy management scheduling model is as follows:
Figure FDA0002706410140000054
wherein the content of the first and second substances,
Figure FDA0002706410140000055
the cost of electricity for the microturbine;
Figure FDA0002706410140000056
a tradeable green certificate procurement cost;
Figure FDA0002706410140000057
a transaction cost for the utility grid;
Figure FDA0002706410140000058
for amortized spare capacity cost;
Figure FDA0002706410140000059
cost for load transfer;
the constraint conditions for constructing the microgrid energy management scheduling model comprise: a first power balance constraint, a first power supply constraint, and an energy storage system constraint;
wherein the first power balance constraint is:
Figure FDA00027064101400000510
wherein the content of the first and second substances,
Figure FDA00027064101400000511
represents the payload at time t in the s-th scenario;
Figure FDA00027064101400000512
representing the original load demand at time t in the s-th scenario;
Figure FDA00027064101400000513
and
Figure FDA00027064101400000514
respectively representing the charging and discharging power of the energy storage system at time t under the s-th scene;
Figure FDA0002706410140000061
and
Figure FDA0002706410140000062
respectively representing the up-regulation power and the down-regulation power of the transferable load at the time t in the s-th scene;
Figure FDA0002706410140000063
Figure FDA0002706410140000064
and
Figure FDA0002706410140000065
respectively representing wind energy, micro-turbine generated power and solar energy at time t in the s-th scene;
Figure FDA0002706410140000066
representing the TGC requirement in the s-th scenario; deltaRPSRepresenting the percentage of the minimum amount of electricity consumed from renewable energy by the power consumer required by the renewable energy portfolio standard RPS to the total load;
wherein the first power supply constraint is:
Figure FDA0002706410140000067
wherein the content of the first and second substances,
Figure FDA0002706410140000068
and
Figure FDA0002706410140000069
respectively representing the upper limit of the available power of wind energy and solar energy at the time t under the s-th scene;
Figure FDA00027064101400000610
represents the upper limit of the microturbine power generation;
Figure FDA00027064101400000611
and
Figure FDA00027064101400000612
respectively representing an up-regulation limit electric quantity and a down-regulation limit electric quantity of the transferable load at time t;
Figure FDA00027064101400000613
representing the running state of the distributed energy source on the node x;
wherein the energy storage system constraints are:
Figure FDA00027064101400000614
wherein the content of the first and second substances,
Figure FDA00027064101400000615
and
Figure FDA00027064101400000616
representing limits of charging power and discharging power of the energy storage system, respectively;
Figure FDA00027064101400000617
represents the amount of power stored in the energy storage system at time t in the s-th scenario;
Figure FDA00027064101400000618
and
Figure FDA00027064101400000619
representing the charging and discharging power of the energy storage system, respectively;
Figure FDA00027064101400000620
representing the capacity of the energy storage system;
Figure FDA00027064101400000621
and
Figure FDA00027064101400000622
representing the upper and lower limits of the energy storage system state of charge, respectively.
9. The microgrid energy of claim 8A quantity management regulation device, characterized in that
Figure FDA00027064101400000623
Cost of electricity for microturbines, said
Figure FDA00027064101400000624
Tradable green certificate procurement costs
Figure FDA00027064101400000625
Cost of trading of a utility grid, said
Figure FDA00027064101400000626
Amortized spare capacity cost and said
Figure FDA00027064101400000627
The expression for the cost of load shifting is:
Figure FDA0002706410140000071
wherein the content of the first and second substances,
Figure FDA0002706410140000072
and
Figure FDA0002706410140000073
coefficients of the costs of power and load transfer of the microturbine, respectively;
Figure FDA0002706410140000074
representing the power generation amount of the micro turbine at time t in the s-th scene; lambda [ alpha ]GCRepresents the TGC price;
Figure FDA0002706410140000075
represents the TGC requirement of node x at time t in the s-th scenario;
Figure FDA0002706410140000076
represents the removed power of node x in the power system at time t in the s-th scenario;
Figure FDA0002706410140000077
represents the net load of node x at time t in the s-th scenario; lambda [ alpha ]fixedRepresents a constant export price between the microgrid and the utility grid;
Figure FDA0002706410140000078
representing the marginal price of electricity at node x at time t in the s-th scenario; Δ T represents a time interval; gamma raysRepresenting the probability of the scene s occurring.
10. The microgrid energy management regulation and control apparatus of claim 7 wherein the established market clearing model is:
Figure FDA0002706410140000079
wherein the content of the first and second substances,
Figure FDA00027064101400000710
represents conventional generator cost;
Figure FDA00027064101400000711
represents the spare capacity cost;
the second constraint includes: a second power balance constraint, a line flow constraint, a second power constraint, and an RPS constraint;
wherein the second power balance constraint is:
Figure FDA00027064101400000712
wherein the content of the first and second substances,
Figure FDA00027064101400000713
and
Figure FDA00027064101400000714
respectively representing the power of the conventional generator i and the power of the renewable energy source i at time t in the s-th scenario;
Figure FDA00027064101400000715
representing the reserve capacity of a conventional generator;
Figure FDA00027064101400000716
represents the payload at time t in the s-th scenario;
Figure FDA00027064101400000717
and
Figure FDA00027064101400000718
respectively representing a portion of the net load demand and renewable energy reserve capacity;
wherein the line flow constraint is:
Figure FDA0002706410140000081
wherein G isk-iRepresenting a power generation transfer division factor of the line k;
Figure FDA0002706410140000082
and
Figure FDA0002706410140000083
respectively representing the power of a conventional generator i, the power of a renewable energy source i and the reserve capacity of the conventional generator i at time t in the s-th scene;
Figure FDA0002706410140000084
is shown inPayload at time t in the s-th scenario;
Figure FDA0002706410140000085
represents the transmission capacity of line k;
wherein the second power supply constraint is:
Figure FDA0002706410140000086
wherein, Pi G,minAnd Pi G,maxRespectively representing the minimum and maximum power of a conventional generator i;
Figure FDA0002706410140000087
indicating that the green generator can provide electricity; pi U,maxAnd Pi D,maxRespectively representing the rising and falling limits of a conventional generator i;
wherein the RPS constraint is:
Figure FDA0002706410140000088
wherein the content of the first and second substances,
Figure FDA0002706410140000089
representing the TGC demand charge under scenario s.
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