CN114204549A - Wind-solar-storage cluster joint optimization operation method considering energy storage sharing - Google Patents

Wind-solar-storage cluster joint optimization operation method considering energy storage sharing Download PDF

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CN114204549A
CN114204549A CN202111390769.5A CN202111390769A CN114204549A CN 114204549 A CN114204549 A CN 114204549A CN 202111390769 A CN202111390769 A CN 202111390769A CN 114204549 A CN114204549 A CN 114204549A
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new energy
power station
energy storage
wind
energy power
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郑传良
伍仰金
郭茜婷
陈峥
叶家玮
林晨杰
陈岳晟
黄丁捷
郑涛
魏兰兰
付馨慧
涂承谦
王超君
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State Grid Fujian Electric Power Co Ltd
Ningde Power Supply Co of State Grid Fujian Electric Power Co Ltd
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State Grid Fujian Electric Power Co Ltd
Ningde Power Supply Co of State Grid Fujian 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • 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
    • H02J3/008—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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—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
    • 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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic 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
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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 relates to a wind-solar-energy-storage cluster combined optimization operation method considering energy storage sharing, which comprises the following steps of: establishing a wind-solar energy storage cluster comprising a plurality of new energy power stations and an energy storage sharing aggregator, wherein the resource transaction behavior of the cluster and an external system is carried out through the energy storage sharing aggregator; establishing a resource scheduling strategy inside the wind-solar storage cluster by considering the complementary characteristics of the new energy power station; establishing a revenue model for a single new energy power station in the wind and light storage cluster based on the resource scheduling strategy in the wind and light storage cluster; modeling uncertainty of market electricity price and new energy power station output by adopting a scene-based random planning method; establishing an objective function which considers a profit model, market electricity price and new energy power station output of a single new energy power station and takes the maximum overall profit of the wind, light and energy storage cluster as a target; solving the objective function by improving a particle swarm algorithm, and outputting an optimal wind-solar-storage cluster combined operation scheme.

Description

Wind-solar-storage cluster joint optimization operation method considering energy storage sharing
Technical Field
The invention relates to a wind-solar energy storage cluster combined optimization operation method considering energy storage sharing, and belongs to the technical field of power system shared energy storage.
Background
Efforts to improve energy utilization efficiency, improve energy utilization structures, and reduce the degree of dependence on fossil energy have become the energy development trend in various countries throughout the world. Renewable energy sources such as wind power and solar energy are considered to be sustainable and environment-friendly energy sources, and theoretically, the renewable energy sources such as the wind power and the solar energy have economic benefits with zero marginal cost and environmental benefits with zero emission. But from a system perspective, the uncertainty and limited predictability of wind and solar power, with insufficient flexibility, requires more reserve power to be deployed to reduce the occurrence of "wind curtailment" or "power limit" situations. Therefore, the renewable energy with random fluctuation is utilized on a large scale, on one hand, many challenges are brought to the dispatching operation of the power system, and the operation cost of the system is increased: on the other hand, a large amount of energy is wasted, and the cost benefit of renewable energy is reduced.
Therefore, the introduction of shared energy storage is necessary to be considered, the output characteristics and the output complementary capacity of each new energy power station are fully utilized, and the operation of the new energy power station is optimized.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a wind-solar-energy-storage cluster joint optimization operation method considering energy storage sharing, which can coordinate the output and energy storage resources of each new energy power station, fully exert the cooperative complementary capacity in a cluster, reduce the uncertainty of new energy power generation and effectively improve the operation flexibility of the new energy power station.
The technical scheme of the invention is as follows:
in one aspect, the invention provides a wind-solar-energy-storage cluster joint optimization operation method considering energy storage sharing, which comprises the following steps:
establishing a wind and light storage cluster comprising a plurality of new energy power stations and an energy storage sharing aggregator, wherein the new energy power stations are wind power plants or photovoltaic power stations; the resource transaction behavior of the cluster and the external system is carried out through the energy storage sharing aggregator;
establishing a resource scheduling strategy inside the wind-solar storage cluster by considering the complementary characteristics of the new energy power station;
establishing a revenue model for a single new energy power station in the wind and light storage cluster based on the resource scheduling strategy in the wind and light storage cluster;
modeling uncertainty of market electricity price and new energy power station output by adopting a scene-based random planning method;
establishing an objective function which considers a profit model, market electricity price and new energy power station output of a single new energy power station and takes the maximum overall profit of the wind, light and energy storage cluster as a target;
solving the objective function by improving a particle swarm algorithm, and outputting an optimal wind-solar-storage cluster combined operation scheme.
As a preferred embodiment, the resource scheduling policy in the wind-solar energy storage cluster specifically includes:
when the actual output of the wind power plant or the photovoltaic power plant is larger than the planned output, preferentially providing charging power for the energy storage sharing aggregator;
if the excess power of the new energy power station exceeds the charging power requirement of the energy storage sharing aggregator, the surplus power is used for self-distribution energy storage charging; if the excess power of the new energy power station cannot meet the charging power requirement of the energy storage sharing aggregator, the self-distribution energy storage continues to provide charging power for the energy storage sharing aggregator;
when the actual output of the wind power plant or the photovoltaic power station is smaller than the planned output, the self-distribution energy storage is used for making up the generation shortage of the new energy power station preferentially;
if the self-distribution energy storage can completely make up the power generation shortage, the surplus power of the self-distribution energy storage provides charging power for the energy storage sharing aggregator;
and if the self-distribution energy storage cannot completely make up the power generation shortage, the energy storage sharing aggregator provides discharge power for the new energy power station.
As a preferred embodiment, the revenue model for a single new energy power station in the wind-solar energy storage cluster specifically includes:
Figure BDA0003368748850000031
in the formula: a represents the type of a new energy power station, and A belongs to { PW, PV }; cA i,tRepresenting the total profit of the new energy power station i in the time period t;
Figure BDA0003368748850000032
representing the income obtained by the new energy power station i participating in the day-ahead competitive bidding of the energy market at the time t; csh_Ac,i,tRepresenting the income obtained by the new energy power station i in the time period t for providing charging power for the energy storage sharing aggregator; csh_Ad,i,tRepresenting the cost required by the energy storage sharing aggregator to provide discharge power for the new energy power station i in the time period t; cApuni,i,tRepresenting the output deviation punishment cost of the new energy power station i at the time t;
Figure BDA0003368748850000033
representing the power transmission cost of the new energy power station i in the t period;
Figure BDA0003368748850000034
representing the operation and maintenance cost of the energy storage equipment of the new energy power station i in the time period t;
wherein the content of the first and second substances,
Figure BDA0003368748850000035
expressed as:
Figure BDA0003368748850000036
in the formula: pitMarket electricity prices for time period t; pAe,i,tIndicating the competitive bidding output of the new energy power station i in the time period t; Δ t is a single period length;
Csh_Ac,i,tand Csh_Ad,i,tExpressed as:
Figure BDA0003368748850000041
Figure BDA0003368748850000042
in the formula:
Figure BDA0003368748850000043
charging power of the energy storage sharing aggregator for the new energy power station i in a time period t;
Figure BDA0003368748850000044
charging power of the energy storage sharing aggregator for self-distribution energy storage of the new energy power station i in a time period t;
Figure BDA0003368748850000045
the self-distributed stored energy discharging power of the energy storage sharing aggregator to the new energy power station i and the new energy power station i in the t time period is respectively obtained;
CApuni,i,texpressed as:
Figure BDA0003368748850000046
in the formula: alpha is alphaup、αdwPenalty factors, alpha, for overproduction and underproduction, respectivelyupValue less than 1, alphadwThe value is greater than 1;
PA i,trepresenting the actual output of the new energy power station i in the time period t;
Figure BDA0003368748850000047
respectively charging and discharging power of the new energy power station i to the energy storage sharing aggregator at the time period t;
Figure BDA0003368748850000048
respectively charging and discharging power of the new energy power station i between the time period t and the self-distribution energy storage;
Figure BDA0003368748850000049
expressed as:
Figure BDA00033687488500000410
Figure BDA00033687488500000411
in the formula: gamma raytrIs a transmission cost coefficient;
Figure BDA00033687488500000412
charging power of the new energy power station i to the energy storage sharing aggregator at the time t;
Figure BDA00033687488500000413
expressed as:
Figure BDA0003368748850000051
in the formula: lambda [ alpha ]sfESThe cost coefficient of operation and maintenance is stored for the self-distribution energy storage unit power.
As a preferred embodiment, the step of modeling the uncertainty of the market electricity price and the new energy power station output by using the scene-based stochastic programming method specifically comprises:
and (3) making the actual value of the output of the new energy power station and the actual value of the market electricity price be the sum of the predicted value and the prediction error:
Figure BDA0003368748850000052
in the formula: pit、
Figure BDA0003368748850000056
Respectively representing an actual value and a predicted value of the market electricity price in the t period;
Figure BDA0003368748850000053
a prediction error representing a market price; pA i,t、PA,forct i,tRespectively representing an actual value and a predicted value of the output of the new energy power station; e.g. of the typeA i,tThe output prediction error of the new energy power station is obtained;
the power price of the market and the wind and light prediction error are both subjected to normal distribution with the mean value of 0:
Figure BDA0003368748850000054
taking the standard deviations of prediction errors of market electricity price and new energy power station output as follows:
Figure BDA0003368748850000055
in the formula: sigmaπt、
Figure BDA0003368748850000057
Respectively the standard deviation of the prediction errors of the market electricity price and the new energy power station output in the time period t; pA,N i,tRated capacity of the ith new energy electric field;
random sampling is carried out on the prediction error by adopting Latin hyper-legislation sampling, a large number of scenes which are likely to appear are generated, the scenes are reduced into a plurality of typical scenes based on k-means clustering, and uncertain models containing random variables are converted into deterministic models under each typical scene.
As a preferred embodiment, the objective function is specifically:
Figure BDA0003368748850000061
wherein, γsThe probability of occurrence of each scene; pis,tThe price of the day-ahead energy market at the time t under the scene s; alpha is alphaup、αdwPunishment coefficients of over-production and under-production are respectively; gamma raytrIs a transmission cost coefficient; lambda [ alpha ]sfESStoring, operating and maintaining cost coefficients for self-distribution energy storage unit power;
Figure BDA0003368748850000062
the variables of 0 and 1 of the new energy production state under the scene s are respectively 1 when the production is excessive and 0 when the production is insufficient;
Figure BDA0003368748850000063
the competitive bidding output of the new energy power station i in the time period t is achieved; pA s,i,tRepresenting the actual output of the new energy power station i in the time period t under the scene s;
Figure BDA0003368748850000064
and
Figure BDA0003368748850000065
charging and discharging power of the new energy power station i to the energy storage sharing aggregator in the time period t under the scene s;
Figure BDA0003368748850000066
and
Figure BDA0003368748850000067
charging and discharging power of the energy storage sharing aggregator to self-distribution energy storage of the ith new energy power station in a scene s at a time period t;
Figure BDA0003368748850000068
and
Figure BDA0003368748850000069
representing the charging and discharging power of the new energy power generation of the new energy power station i to the self-distribution energy storage in the scene s at the time t;
adding constraints to the objective function, including:
the bidding output of the new energy power station cannot exceed the predicted value of the output of the new energy power station:
Figure BDA00033687488500000610
and (3) charge-discharge power balance constraint between new energy power stations:
Figure BDA00033687488500000611
when the actual output of the new energy power station is greater than the bidding output, the charging power of the new energy power station to the energy storage sharing aggregator cannot be greater than the surplus electric quantity; when the actual output of the new energy power station is smaller than the competitive bidding output, the discharge power of the energy storage sharing aggregator to the new energy power station cannot be larger than the power shortage of the new energy power station:
Figure BDA0003368748850000071
Figure BDA0003368748850000072
the charge and discharge power constraint of the self-distribution energy storage of the new energy power station is as follows:
Figure BDA0003368748850000073
Figure BDA0003368748850000074
Figure BDA0003368748850000075
Figure BDA0003368748850000076
the state of charge of the self-distribution energy storage of the new energy power station needs to be within a self-allowable range, and the state of charge of the self-distribution energy storage is kept unchanged before and after each period:
Figure BDA0003368748850000077
Figure BDA0003368748850000078
Figure BDA0003368748850000079
as a preferred embodiment, the step of solving the objective function by improving the particle swarm optimization specifically includes:
randomly initializing particle positions, and respectively corresponding to different wind-solar energy storage cluster combined operation schemes;
constructing an evaluation function, calculating the fitness value of each particle based on the evaluation function, and initializing an individual optimal value and a global optimal value according to the fitness value;
updating the learning factor;
calculating the average fitness value of the particles and updating an inertia weight factor;
updating the speed and position of each particle, and processing the particles beyond the boundary;
randomly compiling part of the particle positions;
calculating the fitness value of the particle after the position is updated, and updating the individual optimal value and the global optimal value;
and judging whether an iteration termination condition is reached, if so, outputting a wind-solar-energy storage cluster combined operation scheme with an optimal position of the global optimal value, and if not, continuing iteration.
As a preferred embodiment, the evaluation function is constructed by an outlier method of a penalty function, which is specifically as follows:
F(x)＝f(x)-λ(k)H(x)；
Figure BDA0003368748850000081
wherein f (x) is an initial objective function with a constrained optimization problem; k is the currentThe number of iterations;
Figure BDA0003368748850000082
is a penalty function factor; h (x) is a penalty image; n, M is equality constraint and inequality constraint number respectively; h (x), g (x) are equality constraint function and inequality constraint function respectively; alpha is a penalty progression.
As a preferred embodiment, in order to improve the optimizing capability of the conventional particle swarm algorithm, an adaptive inertial weight factor ω shown below is introducedi：
Figure BDA0003368748850000083
Wherein, ω isiAn inertial weight factor representing the ith iteration; omegamin、ωmaxRespectively representing the maximum value and the minimum value of the inertia weight factor; f. ofiA fitness function value representing the ith iteration; f. ofmax、faverageRespectively representing the maximum and average values of the fitness function.
As a preferred embodiment, a variable learning factor is used to replace a fixed learning factor of a traditional particle swarm algorithm, and the formula is as follows:
Figure BDA0003368748850000091
in the formula: c. C1,i、c2,iRespectively representing self learning factors and group learning factors of the ith iteration; k. k is a radical ofmaxRespectively representing the current iteration number and the maximum iteration number.
In another aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for jointly optimizing the operation of the wind-solar-storage cluster according to any embodiment of the present invention.
The invention has the following beneficial effects:
1. the wind-solar energy storage cluster combined optimization operation method considering energy storage sharing establishes a resource scheduling strategy inside the wind-solar energy storage cluster, can coordinate the output and energy storage resources of each new energy power station, fully exerts the cooperative complementary capacity in the cluster, reduces the uncertainty of new energy power generation, and effectively improves the operation flexibility of the new energy power station.
2. The wind-solar-energy-storage-cluster combined optimization operation method considering energy storage sharing designs a profit model of a new energy power station and an objective function taking cluster profit maximization as a target, solves the objective function by improving a particle swarm algorithm, can effectively improve market bidding capability of the wind-solar-energy-storage cluster, and increases market profit of the new energy power station.
Drawings
FIG. 1 is a flowchart of a method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an energy exchange relationship among a wind farm, a self-distribution energy storage, a shared energy storage aggregator and a large power grid according to an embodiment of the invention;
FIG. 3 is a flowchart of solving an objective function by improving a particle swarm optimization in the embodiment of the present invention;
FIG. 4 is a graph of market price for exemplary scenarios provided in embodiments of the present invention;
fig. 5 is a new energy power station output curve diagram of each typical scenario of the calculation example provided in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
The first embodiment is as follows:
referring to fig. 1, a method for wind-solar-energy-storage cluster joint optimization operation considering energy storage sharing includes the following steps:
step S1: establishing a wind and light storage cluster comprising a plurality of new energy power stations and an energy storage sharing aggregator, wherein the new energy power stations are wind power plants or photovoltaic power stations; the resource transaction behavior of the cluster and the external system is carried out through the energy storage sharing aggregator; each new energy power station is self-provided with a certain proportion of energy storage systems, and the energy storage sharing aggregator is formed by each new energy power station in the cluster; and determining the output plan of each new energy power station in the cluster in the day-ahead market through bidding, and trading the surplus power resources and the energy storage resources through the energy storage sharing aggregator the next day.
Step S2: considering complementary characteristics of a new energy power station and a wind and light storage cluster cooperative operation mode of energy storage sharing, and establishing a resource scheduling strategy in a wind and light storage cluster;
step S3: establishing a revenue model for a single new energy power station in the wind and light storage cluster based on the resource scheduling strategy in the wind and light storage cluster;
step S4: modeling uncertainty of market electricity price and new energy power station output by adopting a scene-based random planning method;
step S5: establishing an objective function which considers a profit model, market electricity price and new energy power station output of a single new energy power station and takes the maximum overall profit of the wind, light and energy storage cluster as a target;
step S6: solving the objective function by improving a particle swarm algorithm, and outputting an optimal wind-solar-storage cluster combined operation scheme.
The energy storage shared wind and light storage cluster combined optimization operation method provided by the embodiment can effectively improve the utilization rate of self-distribution energy storage, increase the benefits of the wind and light storage cluster, and play a positive role in market absorption of new energy.
Example two:
on the basis of the first embodiment, the present embodiment further proposes, for step S2, a resource scheduling policy inside the wind-solar energy storage cluster, specifically:
s2.1: in a market environment, submitting a day-ahead bidding output plan by each new energy power station in the cluster at the day-ahead stage according to a prediction curve of the new energy output;
s2.2: the resource scheduling principle inside the wind-solar storage cluster is as follows:
when the actual output of the wind power plant or the photovoltaic power plant is larger than the planned output, preferentially providing charging power for the energy storage sharing aggregator;
if the excess power of the new energy power station exceeds the charging power requirement of the energy storage sharing aggregator, the surplus power is used for self-distribution energy storage charging; if the excess power of the new energy power station cannot meet the charging power requirement of the energy storage sharing aggregator, the self-distribution energy storage continues to provide charging power for the energy storage sharing aggregator;
when the actual output of the wind power plant or the photovoltaic power station is smaller than the planned output, the self-distribution energy storage is used for making up the generation shortage of the new energy power station preferentially;
if the self-distribution energy storage can completely make up the power generation shortage, the surplus power of the self-distribution energy storage provides charging power for the energy storage sharing aggregator;
and if the self-distribution energy storage cannot completely make up the power generation shortage, the energy storage sharing aggregator provides discharge power for the new energy power station.
Example three:
on the basis of the first embodiment, the embodiment provides a profit model specifically for a single new energy power station inside a wind-light storage cluster in step S3, and for a single wind farm and a single photovoltaic power station, the actual profit thereof is composed of market profit, energy storage sharing aggregator transaction profit and cost, output deviation punishment, transmission cost, energy storage operation maintenance cost, and can be expressed as:
Figure BDA0003368748850000131
in the formula: a represents the type of a new energy power station, and A belongs to { PW, PV };
Figure BDA0003368748850000137
representing the total profit of the new energy power station i in the time period t;
Figure BDA0003368748850000132
representing the income obtained by the new energy power station i participating in the day-ahead competitive bidding of the energy market at the time t; csh_Ac,i,tRepresenting the income obtained by the new energy power station i in the time period t for providing charging power for the energy storage sharing aggregator; csh_Ad,i,tRepresenting the cost required by the energy storage sharing aggregator to provide discharge power for the new energy power station i in the time period t; cApuni,i,tRepresenting the output deviation punishment cost of the new energy power station i at the time t;
Figure BDA0003368748850000133
representing the power transmission cost of the new energy power station i in the t period;
Figure BDA0003368748850000134
representing the operation and maintenance cost of the energy storage equipment of the new energy power station i in the time period t;
wherein, the new energy power station participates in the revenue obtained by competitive bidding of the day-ahead energy market
Figure BDA0003368748850000135
Can be expressed as:
Figure BDA0003368748850000136
in the formula: pitMarket electricity prices for time period t; pAe,i,tIndicating the competitive bidding output of the new energy power station i in the time period t; Δ t is a single period length;
the power interactive gain and cost of the new energy power station and the energy storage sharing aggregator are Csh_Ac,i,tAnd Csh_Ad,i,tRespectively expressed as:
Figure BDA0003368748850000141
Figure BDA0003368748850000142
in the formula:
Figure BDA0003368748850000143
charging power of the energy storage sharing aggregator for the new energy power station i in a time period t;
Figure BDA0003368748850000144
charging power of the energy storage sharing aggregator for self-distribution energy storage of the new energy power station i in a time period t;
Figure BDA0003368748850000145
the self-distributed stored energy discharging power of the energy storage sharing aggregator to the new energy power station i and the new energy power station i in the t time period is respectively obtained;
deviation punishment cost C of new energy power stationApuni,i,tExpressed as:
Figure BDA0003368748850000146
in the formula: alpha is alphaup、αdwPenalty factors, alpha, for overproduction and underproduction, respectivelyupValue less than 1, alphadwThe value is greater than 1;
PA i,trepresenting the actual output of the new energy power station i in the time period t;
Figure BDA0003368748850000147
respectively charging and discharging power of the new energy power station i to the energy storage sharing aggregator at the time period t;
Figure BDA0003368748850000148
respectively charging and discharging power of the new energy power station i between the time period t and the self-distribution energy storage;
certain power transmission cost exists when the new energy power station and the energy storage sharing aggregator transmit energy
Figure BDA0003368748850000149
Expressed as:
Figure BDA00033687488500001410
Figure BDA00033687488500001411
in the formula: gamma raytrIs a transmission cost coefficient;
Figure BDA00033687488500001412
charging power of the new energy power station i to the energy storage sharing aggregator at the time t;
operation and maintenance cost of self-distribution energy storage system of new energy power station
Figure BDA00033687488500001413
Expressed as:
Figure BDA0003368748850000151
in the formula: lambda [ alpha ]sfESThe cost coefficient of operation and maintenance is stored for the self-distribution energy storage unit power.
To assist those skilled in the art to understand, the present embodiment illustrates the energy exchange relationship between one wind farm and its self-distributed energy storage and shared energy storage aggregator and the large power grid in fig. 2.
Example four:
the embodiment provides a random planning method based on a scene specifically for step S4, where the step of modeling uncertainty of market electricity price and new energy power station output specifically includes:
step S4.1: and (3) making the actual value of the output of the new energy power station and the actual value of the market electricity price be the sum of the predicted value and the prediction error:
Figure BDA0003368748850000152
in the formula: pit、
Figure BDA0003368748850000156
Respectively representing an actual value and a predicted value of the market electricity price in the t period;
Figure BDA0003368748850000153
a prediction error representing a market price; pA i,t、PA,forct i,tRespectively representing an actual value and a predicted value of the output of the new energy power station; e.g. of the typeA i,tThe output prediction error of the new energy power station is obtained;
step S4.2: the power price of the market and the wind and light prediction error are both subjected to normal distribution with the mean value of 0:
Figure BDA0003368748850000154
taking the standard deviations of prediction errors of market electricity price and new energy power station output as follows:
Figure BDA0003368748850000155
in the formula: sigmaπt、
Figure BDA00033687488500001610
Respectively the standard deviation of the prediction errors of the market electricity price and the new energy power station output in the time period t; pA,N i,tRated capacity of the ith new energy electric field;
step S4.3: random sampling is carried out on the prediction error by adopting Latin super-legislation sampling, a large number of scenes with different new energy output and market electricity prices are generated, the scenes are reduced into a plurality of typical scenes on the basis of a k-means clustering method, and uncertain models containing random variables are converted into deterministic models under each typical scene.
Example five:
on the basis of the first embodiment, the present embodiment provides an objective function specifically for step S5, specifically including:
Figure BDA0003368748850000161
wherein, γsThe probability of occurrence of each scene; pis,tThe price of the day-ahead energy market (market electricity price) for a time period t under a scene s; alpha is alphaup、αdwPunishment coefficients of over-production and under-production are respectively; gamma raytrIs a transmission cost coefficient; lambda [ alpha ]sfESStoring, operating and maintaining cost coefficients for self-distribution energy storage unit power;
Figure BDA0003368748850000162
the variables of 0 and 1 of the new energy production state under the scene s are respectively 1 when the production is excessive and 0 when the production is insufficient;
Figure BDA0003368748850000163
the competitive bidding output of the new energy power station i in the time period t is achieved; pA s,i,tRepresenting the actual output of the new energy power station i in the time period t under the scene s;
Figure BDA0003368748850000164
and
Figure BDA0003368748850000165
charging and discharging power of the new energy power station i to the energy storage sharing aggregator in the time period t under the scene s;
Figure BDA0003368748850000166
and
Figure BDA0003368748850000167
charging and discharging power of the energy storage sharing aggregator to self-distribution energy storage of the ith new energy power station in a scene s at a time period t;
Figure BDA0003368748850000168
and
Figure BDA0003368748850000169
representing the charging and discharging power of the new energy power generation of the new energy power station i to the self-distribution energy storage in the scene s at the time t;
adding constraints to the objective function, including:
in the process that the new energy power station participates in market bidding, the bidding output of the new energy power station in unit time period cannot exceed the predicted value of the new energy power station output:
Figure BDA0003368748850000171
the energy storage sharing aggregator needs to maintain power exchange balance between new energy power stations, and needs to satisfy the following power balance constraints:
Figure BDA0003368748850000172
when the actual output of the new energy power station is greater than the bidding output, the charging power of the new energy power station to the energy storage sharing aggregator cannot be greater than the surplus electric quantity; when the actual output of the new energy power station is smaller than the competitive bidding output, the discharge power of the energy storage sharing aggregator to the new energy power station cannot be larger than the power shortage of the new energy power station:
Figure BDA0003368748850000173
Figure BDA0003368748850000174
the charge and discharge power constraint of the self-distribution energy storage of the new energy power station is as follows:
Figure BDA0003368748850000175
Figure BDA0003368748850000176
Figure BDA0003368748850000177
Figure BDA0003368748850000178
the state of charge of the self-distribution energy storage of the new energy power station needs to be within a self-allowable range, and the state of charge of the self-distribution energy storage is kept unchanged before and after each period:
Figure BDA0003368748850000181
Figure BDA0003368748850000182
Figure BDA0003368748850000183
example six:
referring to fig. 3, on the basis of the first embodiment, the present embodiment proposes a step specifically directed to solving the objective function by improving the particle swarm algorithm in step S6, specifically including:
step S6.1: randomly initializing particle positions, and respectively corresponding to different wind-solar energy storage cluster combined operation schemes;
step S6.2: constructing an evaluation function, calculating the fitness value of each particle based on the evaluation function, and initializing an individual optimal value and a global optimal value according to the fitness value;
updating the learning factor;
step S6.3: calculating the average fitness value of the particles and updating an inertia weight factor;
step S6.4: updating the speed and position of each particle, and processing the particles beyond the boundary;
step S6.5: randomly compiling part of the particle positions;
step S6.6: calculating the fitness value of the particle after the position is updated, and updating the individual optimal value and the global optimal value;
step S6.7: judging whether an iteration termination condition is met, if so, outputting a wind-solar-energy-storage cluster combined operation scheme with an optimal position of a global optimal value, and if not, continuing iteration; and the iteration termination condition is that the maximum iteration times are met to meet the precision requirement.
As a preferred embodiment of this embodiment, the evaluation function is constructed by an outlier method of a penalty function, which is specifically as follows:
F(x)＝f(x)-λ(k)H(x)； (24)
Figure BDA0003368748850000191
wherein f (x) is an initial objective function with a constrained optimization problem; k is the current iteration number;
Figure BDA0003368748850000192
is a penalty function factor; h(x) Is a punishment image; n, M is equality constraint and inequality constraint number respectively; h (x), g (x) are equality constraint function and inequality constraint function respectively; alpha is a penalty progression.
As a preferred implementation manner of this embodiment, in order to improve the optimization capability of the conventional particle swarm algorithm, an adaptive inertia weight factor ω shown below is introducedi：
Figure BDA0003368748850000193
Wherein, ω isiAn inertial weight factor representing the ith iteration; omegamin、ωmaxRespectively representing the maximum value and the minimum value of the inertia weight factor; f. ofiA fitness function value representing the ith iteration; f. ofmax、faverageRespectively representing the maximum and average values of the fitness function.
As a preferred implementation manner of this embodiment, a variable learning factor is used to replace a fixed learning factor of a conventional particle swarm algorithm, and its formula is:
Figure BDA0003368748850000194
in the formula: c. C1,i、c2,iRespectively representing self learning factors and group learning factors of the ith iteration; k. k is a radical ofmaxRespectively representing the current iteration number and the maximum iteration number.
The algorithm parameter settings for the improved particle swarm algorithm are shown in table 1:
TABLE 1 improved particle swarm algorithm parameter settings
Figure BDA0003368748850000201
In order to verify the effectiveness and feasibility of the wind-solar-storage cluster combined optimization operation method provided by the invention, the following calculation examples are provided:
the wind, light and reservoir groups in the present example comprise 2 wind farms PW1, PW2 and one photovoltaic power plant PV1, with installed capacities of 50MW, 40MW and 20MW, respectively. The wind power plant and the photovoltaic power station are both configured with self-distribution energy storage capacity according to 20% of installed capacity of the wind power plant and the photovoltaic power station, and the initial value of the energy storage charge state is 20%. The model parameters are shown in table 2.
TABLE 2 model specific parameters
Figure BDA0003368748850000202
The embodiment is based on historical electricity price data and wind and light output prediction data of a trading center, random variables are sampled according to formulas (15) and (16) by adopting a Latin super-legislation sampling technology, 1000 real-time electricity price scenes and wind and light output scenes are generated, and all scenes are reduced into 4 typical scenes based on k-means clustering. The market electricity price curve and the corresponding probability of each typical scene are shown in fig. 4, and the new energy power station output curve and the corresponding scene probability of each typical scene are shown in fig. 5. Based on the data, 1h is taken as a single scheduling period, and 24h is optimally scheduled for one day.
To verify the effectiveness of the proposed method, a comparison scheme was added for analysis. In the comparison scheme, the operation result of the new energy power station which is used for bidding alone is compared with the operation result of the combined operation method provided by the invention for analysis, and the comparison result is shown in the following table 3.
TABLE 3 comparison of economics of protocols
Figure BDA0003368748850000211
As can be seen from table 3, the combined operation improves the energy market yield to a certain extent in the future compared with the single operation, and because the combined operation can provide certain charging and discharging power capacities under the conditions of excess production or insufficient production of each power station, the combined operation has a great advantage in the aspect of output deviation penalty cost, the penalty cost is reduced by 57.45%, and the net yield is improved by 2.472% compared with the single bidding mode.
Example seven:
the present embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and is characterized in that the processor, when executing the program, implements the method for jointly optimizing the operation of the wind-solar-storage cluster according to any embodiment of the present invention.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A wind-solar-energy-storage cluster joint optimization operation method considering energy storage sharing is characterized by comprising the following steps:
establishing a wind and light storage cluster comprising a plurality of new energy power stations and an energy storage sharing aggregator, wherein the new energy power stations are wind power plants or photovoltaic power stations; the resource transaction behavior of the cluster and the external system is carried out through the energy storage sharing aggregator;
establishing a resource scheduling strategy inside the wind-solar storage cluster by considering the complementary characteristics of the new energy power station;
establishing a revenue model for a single new energy power station in the wind and light storage cluster based on the resource scheduling strategy in the wind and light storage cluster;
modeling uncertainty of market electricity price and new energy power station output by adopting a scene-based random planning method;
establishing an objective function which considers a profit model, market electricity price and new energy power station output of a single new energy power station and takes the maximum overall profit of the wind, light and energy storage cluster as a target;
solving the objective function by improving a particle swarm algorithm, and outputting an optimal wind-solar-storage cluster combined operation scheme.
2. The method for wind, photovoltaic and energy storage cluster joint optimization operation considering energy storage sharing according to claim 1, wherein the resource scheduling policy inside the wind, photovoltaic and energy storage cluster is specifically as follows:
when the actual output of the wind power plant or the photovoltaic power plant is larger than the planned output, preferentially providing charging power for the energy storage sharing aggregator;
if the excess power of the new energy power station exceeds the charging power requirement of the energy storage sharing aggregator, the surplus power is used for self-distribution energy storage charging; if the excess power of the new energy power station cannot meet the charging power requirement of the energy storage sharing aggregator, the self-distribution energy storage continues to provide charging power for the energy storage sharing aggregator;
when the actual output of the wind power plant or the photovoltaic power station is smaller than the planned output, the self-distribution energy storage is used for making up the generation shortage of the new energy power station preferentially;
if the self-distribution energy storage can completely make up the power generation shortage, the surplus power of the self-distribution energy storage provides charging power for the energy storage sharing aggregator;
and if the self-distribution energy storage cannot completely make up the power generation shortage, the energy storage sharing aggregator provides discharge power for the new energy power station.
3. The method for wind, photovoltaic and energy storage cluster joint optimization operation considering energy storage sharing according to claim 1, wherein the revenue model for a single new energy power station inside the wind, photovoltaic and energy storage cluster is specifically as follows:
Figure FDA0003368748840000021
in the formula: a represents the type of a new energy power station, and A belongs to { PW, PV };
Figure FDA0003368748840000022
representing the total profit of the new energy power station i in the time period t;
Figure FDA0003368748840000023
representing the income obtained by the new energy power station i participating in the day-ahead competitive bidding of the energy market at the time t; csh_Ac,i,tRepresenting the income obtained by the new energy power station i in the time period t for providing charging power for the energy storage sharing aggregator; csh_Ad,i,tRepresenting the cost required by the energy storage sharing aggregator to provide discharge power for the new energy power station i in the time period t; cApuni,i,tRepresenting the output deviation punishment cost of the new energy power station i at the time t;
Figure FDA0003368748840000024
representing the power transmission cost of the new energy power station i in the t period;
Figure FDA0003368748840000025
representing the operation and maintenance cost of the energy storage equipment of the new energy power station i in the time period t;
wherein the content of the first and second substances,
Figure FDA0003368748840000026
expressed as:
Figure FDA0003368748840000027
in the formula: pitMarket electricity prices for time period t; pAe,i,tIndicating the competitive bidding output of the new energy power station i in the time period t; Δ t is a single period length;
Csh_Ac,i,tand Csh_Ad,i,tExpressed as:
Figure FDA0003368748840000031
Figure FDA0003368748840000032
in the formula:
Figure FDA0003368748840000033
for new energy power station i to share aggregator for energy storage in t time periodA charging power;
Figure FDA0003368748840000034
charging power of the energy storage sharing aggregator for self-distribution energy storage of the new energy power station i in a time period t;
Figure FDA0003368748840000035
the self-distributed stored energy discharging power of the energy storage sharing aggregator to the new energy power station i and the new energy power station i in the t time period is respectively obtained;
CApuni,i,texpressed as:
Figure FDA0003368748840000036
in the formula: alpha is alphaup、αdwPenalty factors, alpha, for overproduction and underproduction, respectivelyupValue less than 1, alphadwThe value is greater than 1;
PA i,trepresenting the actual output of the new energy power station i in the time period t;
Figure FDA0003368748840000037
respectively charging and discharging power of the new energy power station i to the energy storage sharing aggregator at the time period t;
Figure FDA0003368748840000038
respectively charging and discharging power of the new energy power station i between the time period t and the self-distribution energy storage;
Figure FDA0003368748840000039
expressed as:
Figure FDA00033687488400000310
Figure FDA00033687488400000311
in the formula: gamma raytrIs a transmission cost coefficient;
Figure FDA00033687488400000312
charging power of the new energy power station i to the energy storage sharing aggregator at the time t;
Figure FDA0003368748840000041
expressed as:
Figure FDA0003368748840000042
in the formula: lambda [ alpha ]sfESThe cost coefficient of operation and maintenance is stored for the self-distribution energy storage unit power.
4. The method for wind-solar-energy-storage-cluster joint optimization operation considering energy storage sharing according to claim 3, wherein the step of modeling uncertainty of market electricity price and new energy power station output by using a scene-based stochastic programming method specifically comprises:
and (3) making the actual value of the output of the new energy power station and the actual value of the market electricity price be the sum of the predicted value and the prediction error:
Figure FDA0003368748840000043
in the formula: pit、
Figure FDA0003368748840000047
Respectively representing an actual value and a predicted value of the market electricity price in the t period;
Figure FDA0003368748840000044
a prediction error representing a market price; pA i,t、PA,forct i,tRespectively representing an actual value and a predicted value of the output of the new energy power station; e.g. of the typeA i,tThe output prediction error of the new energy power station is obtained;
the power price of the market and the wind and light prediction error are both subjected to normal distribution with the mean value of 0:
Figure FDA0003368748840000045
taking the standard deviations of prediction errors of market electricity price and new energy power station output as follows:
Figure FDA0003368748840000046
in the formula: sigmaπt、σAi,tRespectively the standard deviation of the prediction errors of the market electricity price and the new energy power station output in the time period t; pA,Ni,tRated capacity of the ith new energy electric field;
random sampling is carried out on the prediction error by adopting Latin hyper-legislation sampling, a large number of scenes which are likely to appear are generated, the scenes are reduced into a plurality of typical scenes based on k-means clustering, and uncertain models containing random variables are converted into deterministic models under each typical scene.
5. The method for wind, light and storage cluster joint optimization operation considering energy storage sharing according to claim 4, wherein the objective function is specifically as follows:
Figure FDA0003368748840000051
wherein, γsThe probability of occurrence of each scene; pis,tThe price of the day-ahead energy market at the time t under the scene s; alpha is alphaup、αdwPunishment coefficients of over-production and under-production are respectively; gamma raytrIs a transmission cost coefficient; lambda [ alpha ]sfESFor self-distribution of stored energyA unit power storage, operation and maintenance cost coefficient;
Figure FDA0003368748840000052
the variables of 0 and 1 of the new energy production state under the scene s are respectively 1 when the production is excessive and 0 when the production is insufficient;
Figure FDA0003368748840000053
the competitive bidding output of the new energy power station i in the time period t is achieved; pA s,i,tRepresenting the actual output of the new energy power station i in the time period t under the scene s;
Figure FDA0003368748840000054
and
Figure FDA0003368748840000055
charging and discharging power of the new energy power station i to the energy storage sharing aggregator in the time period t under the scene s;
Figure FDA0003368748840000056
and
Figure FDA0003368748840000057
charging and discharging power of the energy storage sharing aggregator to self-distribution energy storage of the ith new energy power station in a scene s at a time period t;
Figure FDA0003368748840000058
and
Figure FDA0003368748840000059
representing the charging and discharging power of the new energy power generation of the new energy power station i to the self-distribution energy storage in the scene s at the time t;
adding constraints to the objective function, including:
the bidding output of the new energy power station cannot exceed the predicted value of the output of the new energy power station:
Figure FDA00033687488400000510
and (3) charge-discharge power balance constraint between new energy power stations:
Figure FDA0003368748840000061
when the actual output of the new energy power station is greater than the bidding output, the charging power of the new energy power station to the energy storage sharing aggregator cannot be greater than the surplus electric quantity; when the actual output of the new energy power station is smaller than the competitive bidding output, the discharge power of the energy storage sharing aggregator to the new energy power station cannot be larger than the power shortage of the new energy power station:
Figure FDA0003368748840000062
Figure FDA0003368748840000063
the charge and discharge power constraint of the self-distribution energy storage of the new energy power station is as follows:
Figure FDA0003368748840000064
Figure FDA0003368748840000065
Figure FDA0003368748840000066
Figure FDA0003368748840000067
the state of charge of the self-distribution energy storage of the new energy power station needs to be within a self-allowable range, and the state of charge of the self-distribution energy storage is kept unchanged before and after each period:
Figure FDA0003368748840000068
Figure FDA0003368748840000069
Figure FDA00033687488400000610
6. the method of claim 5, wherein the step of solving the objective function by improving the particle swarm optimization specifically comprises:
randomly initializing particle positions, and respectively corresponding to different wind-solar energy storage cluster combined operation schemes;
constructing an evaluation function, calculating the fitness value of each particle based on the evaluation function, and initializing an individual optimal value and a global optimal value according to the fitness value;
updating the learning factor;
calculating the average fitness value of the particles and updating an inertia weight factor;
updating the speed and position of each particle, and processing the particles beyond the boundary;
randomly compiling part of the particle positions;
calculating the fitness value of the particle after the position is updated, and updating the individual optimal value and the global optimal value;
and judging whether an iteration termination condition is reached, if so, outputting a wind-solar-energy storage cluster combined operation scheme with an optimal position of the global optimal value, and if not, continuing iteration.
7. The method of claim 6, wherein the wind-solar-energy-storage-cluster joint optimization operation considering energy storage sharing is characterized in that:
the evaluation function is constructed by adopting an outlier method of a penalty function, and specifically comprises the following steps:
F(x)＝f(x)-λ(k)H(x)；
Figure FDA0003368748840000071
wherein f (x) is an initial objective function with a constrained optimization problem; k is the current iteration number;
Figure FDA0003368748840000072
is a penalty function factor; h (x) is a penalty image; n, M is equality constraint and inequality constraint number respectively; h (x), g (x) are equality constraint function and inequality constraint function respectively; alpha is a penalty progression.
8. The method of claim 6, wherein the wind-solar-energy-storage-cluster joint optimization operation considering energy storage sharing is characterized in that:
in order to improve the optimizing capability of the traditional particle swarm algorithm, the following self-adaptive inertia weight factor omega is introducedi：
Figure FDA0003368748840000081
Wherein, ω isiAn inertial weight factor representing the ith iteration; omegamin、ωmaxRespectively representing the maximum value and the minimum value of the inertia weight factor; f. ofiA fitness function value representing the ith iteration; f. ofmax、faverageRespectively representing the maximum and average values of the fitness function.
9. The method of claim 6, wherein the wind-solar-energy-storage-cluster joint optimization operation considering energy storage sharing is characterized in that:
a variable learning factor is adopted to replace a fixed learning factor of a traditional particle swarm algorithm, and the formula is as follows:
Figure FDA0003368748840000082
in the formula: c. C1,i、c2,iRespectively representing self learning factors and group learning factors of the ith iteration; k. k is a radical ofmaxRespectively representing the current iteration number and the maximum iteration number.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of wind-solar-storage cluster joint optimization operation according to any one of claims 1 to 9 when executing the program.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116979586A (en) * 2023-09-19 2023-10-31 中国电力科学研究院有限公司 Shared energy storage power station energy management method and system considering cluster division

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200143489A1 (en) * 2018-11-01 2020-05-07 Battelle Memorial Institute Flexible allocation of energy storage in power grids
CN112003268A (en) * 2020-07-27 2020-11-27 四川大学 Intelligent building group electric energy optimization sharing system and method based on model prediction control
CN113315144A (en) * 2021-05-24 2021-08-27 清华大学 Operating method of energy storage system for new energy power plant sharing energy storage to participate in frequency modulation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200143489A1 (en) * 2018-11-01 2020-05-07 Battelle Memorial Institute Flexible allocation of energy storage in power grids
CN112003268A (en) * 2020-07-27 2020-11-27 四川大学 Intelligent building group electric energy optimization sharing system and method based on model prediction control
CN113315144A (en) * 2021-05-24 2021-08-27 清华大学 Operating method of energy storage system for new energy power plant sharing energy storage to participate in frequency modulation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨晓萍;刘浩杰;黄强;: "考虑分时电价的风光储联合优化调度研究", 西安理工大学学报, no. 04 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116979586A (en) * 2023-09-19 2023-10-31 中国电力科学研究院有限公司 Shared energy storage power station energy management method and system considering cluster division
CN116979586B (en) * 2023-09-19 2023-12-15 中国电力科学研究院有限公司 Shared energy storage power station energy management method and system considering cluster division

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