CN115313508A - Microgrid energy storage optimal configuration method, device and storage medium - Google Patents

Microgrid energy storage optimal configuration method, device and storage medium Download PDF

Info

Publication number
CN115313508A
CN115313508A CN202210842011.9A CN202210842011A CN115313508A CN 115313508 A CN115313508 A CN 115313508A CN 202210842011 A CN202210842011 A CN 202210842011A CN 115313508 A CN115313508 A CN 115313508A
Authority
CN
China
Prior art keywords
energy storage
microgrid
power
constraint
calculating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210842011.9A
Other languages
Chinese (zh)
Inventor
郑建平
李力
杨银国
陆秋瑜
谭力强
朱誉
吴杰康
雷振
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Original Assignee
Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Power Grid Co Ltd, Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd filed Critical Guangdong Power Grid Co Ltd
Priority to CN202210842011.9A priority Critical patent/CN115313508A/en
Publication of CN115313508A publication Critical patent/CN115313508A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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

Abstract

The invention discloses a microgrid energy storage optimal configuration method, a device and a storage medium, wherein the method comprises the following steps: calculating according to the wind power, the photovoltaic power generation power and the power load to obtain the supply and demand deviation of the microgrid, and establishing energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the microgrid; based on energy storage capacity constraint, constructing an objective function with the maximum operation benefit of the energy storage equipment, and constructing an energy storage optimization configuration model according to the objective function; and solving an energy storage capacity optimization configuration model by adopting a particle swarm algorithm to obtain the optimal configuration capacity. According to the method, the objective function is constructed according to the maximum operation benefit of the energy storage equipment, the energy storage optimal configuration model is constructed according to the objective function, and the objective function is constructed by comprehensively considering various data influencing the benefit of the energy storage equipment so as to construct a reliable energy storage optimal configuration model based on source charge uncertainty, so that the accuracy of the energy storage optimal configuration of the microgrid can be effectively improved.

Description

Microgrid energy storage optimal configuration method, device and storage medium
Technical Field
The invention relates to the technical field of power systems, in particular to a microgrid energy storage optimal configuration method, a microgrid energy storage optimal configuration device and a storage medium.
Background
At present, the problems of energy crisis, climate change, environmental pollution and the like are increasingly prominent, and the prior development and utilization of renewable energy sources such as hydroenergy, wind energy, solar energy and the like is an important way for protecting the environment and reducing the dependence on fossil energy, and is a consensus of the international society. Under the promotion of relevant national policies, a plurality of hydropower-dominated high-proportion renewable energy power grids appear in China, and due to the characteristic that new energy (wind power and photovoltaic) is not schedulable, when a new energy day-ahead plan is made, a deterministic scheduling mode that a forecast value is a plan value is adopted in most cases. The mode is highly dependent on the prediction level of new energy, and once a large prediction deviation occurs, the reliability of the hydropower plan is influenced. With the annual improvement of the permeability of new energy, the reliability of the power grid hydropower plan is reduced day by day, and the situation of electricity abandon or electricity limitation can occur in severe cases. With the wide application of distributed power generation, controllable load, energy storage devices and the like in the power distribution network, the novel characteristics of distributed energy provide higher requirements for the operation and control of the traditional power distribution network, the traditional power distribution network for passively accepting the distributed power does not meet the future technical development requirements any more, and an active power distribution network for actively accepting the access of the distributed energy needs to be established, so that the available capacity of the distributed energy is effectively improved, the utilization rate of the assets of the power distribution network is improved, and the power utilization quality and the power supply reliability of users are improved. The energy storage device in the microgrid is used for solving the problem of large-scale wind power grid connection in recent years due to the characteristics of flexible operation mode, capability of charging and discharging, environmental compatibility and the like.
The existing microgrid energy storage optimization configuration method is generally used for establishing an objective function with the lowest cost of an energy storage device, and only the most direct cost of the energy storage device is considered, so that the microgrid energy storage is difficult to be accurately optimized and configured.
Disclosure of Invention
The invention provides a microgrid energy storage optimal configuration method, a microgrid energy storage optimal configuration device and a storage medium, and aims to solve the technical problem that the microgrid energy storage optimal configuration method is difficult to accurately optimize.
The embodiment of the invention provides a micro-grid energy storage optimal configuration method, which comprises the following steps:
calculating according to wind power, photovoltaic power generation power and power load to obtain the supply and demand deviation of the micro-grid, and establishing energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the micro-grid;
based on the energy storage capacity constraint, constructing an objective function with the maximum operating benefit of the energy storage equipment, and constructing an energy storage optimization configuration model according to the objective function, wherein the constraint conditions of the energy storage optimization configuration model comprise: node power balance constraint, line power flow constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint;
and solving the energy storage capacity optimal configuration model by adopting a particle swarm algorithm to obtain the optimal configuration capacity.
Further, before calculating the supply and demand deviation of the microgrid according to the wind power, the photovoltaic power generation power and the power load, the method further comprises the following steps:
calculating to obtain the wind power of the microgrid based on the wind power uncertainty;
calculating to obtain the photovoltaic power generation power of the microgrid based on the photovoltaic power generation uncertainty;
and calculating the power load of the microgrid based on the uncertainty of the energy demand of the user.
Further, the calculating the wind power of the microgrid based on the wind power uncertainty includes:
simulating a wind speed probability distribution function of wind power uncertainty by adopting Weibull distribution;
and calculating the wind power of the microgrid according to the relation between the wind power and the wind speed and based on the wind speed probability distribution function.
Further, the calculating the photovoltaic power generation power of the microgrid based on the photovoltaic power generation uncertainty includes:
simulating a sunshine intensity probability distribution function of photovoltaic power generation uncertainty by adopting beta distribution;
and calculating the photovoltaic power generation sunshine intensity of the microgrid based on the sunshine intensity probability distribution function according to the relation between the photovoltaic power generation power and the sunshine intensity.
Further, the calculating of the power load of the microgrid based on the uncertainty of the user energy demand includes:
simulating a power load distribution function of uncertainty of energy demand of a user by normal distribution;
and calculating the power load of the microgrid by adopting a weighted average method based on the power load distribution function.
Further, the calculating according to the wind power, the photovoltaic power generation power and the power load to obtain the supply and demand deviation of the microgrid, and establishing the energy storage capacity constraint based on the source load uncertainty according to the supply and demand deviation of the microgrid comprises:
the supply and demand deviation of the micro-grid is as follows:
Figure BDA0003751506740000031
wherein the content of the first and second substances,
Figure BDA0003751506740000032
for micro-grids at time tDeviation of supply and demand; p W,m (t) is the wind power of the mth wind turbine generator at the tth moment; p is PV,n (t) is the photovoltaic power generation power of the nth wind turbine generator at the t moment; p L (t) the power loads of all users of the microgrid at the t moment; m is the number of all wind turbine generators of the micro-grid; n is the number of all photovoltaic power stations of the micro-grid;
and calculating to obtain the maximum supply and demand deviation of the microgrid within 24 hours a day according to the supply and demand deviation of the microgrid, and setting energy storage capacity constraint based on source load uncertainty according to the maximum supply and demand deviation.
Further, the constructing an objective function of an energy storage optimization configuration model with maximum energy storage device operation benefit based on the energy storage capacity constraint includes:
based on the energy storage capacity constraint, according to the peak-valley profit extension of the energy storage system, the energy storage system delays the investment income of the power grid, the energy storage system reduces the network loss income, the energy storage policy subsidy income, the energy storage investment construction cost and the objective function is constructed for the operation maintenance cost and the fault decommissioning cost.
One embodiment of the present invention provides a microgrid energy storage optimal configuration apparatus, including:
the energy storage capacity constraint building module is used for calculating the supply and demand deviation of the microgrid according to the wind power, the photovoltaic power generation power and the power load, and establishing energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the microgrid;
the energy storage optimization configuration model building module is used for building an objective function with the maximum operation benefit of the energy storage equipment based on the energy storage capacity constraint, and building an energy storage optimization configuration model according to the objective function, wherein the constraint conditions of the energy storage optimization configuration model include: node power balance constraint, line power flow constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint;
and the optimal configuration capacity solving module is used for solving the energy storage capacity optimal configuration model by adopting a particle swarm algorithm to obtain the optimal configuration capacity.
An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the microgrid energy storage optimization configuration method as described above.
According to the method and the device, the energy storage capacity constraint based on the source load uncertainty is established according to the supply and demand deviation of the microgrid, the objective function is established according to the energy storage capacity constraint, the energy storage equipment operation benefit is the maximum, the energy storage optimal configuration model is established according to the objective function, the energy storage system peak-valley profit and profit margin influencing the energy storage equipment benefit are comprehensively considered, the power grid investment profit is delayed, the network loss profit is reduced, the policy subsidy profit, the investment construction cost, the operation maintenance cost and the failure decommissioning cost are comprehensively considered, the objective function is established, the reliable energy storage optimal configuration model based on the source load uncertainty is established, and the accuracy of the microgrid energy storage optimal configuration is improved.
Furthermore, the energy storage optimization configuration model is solved by adopting the machine-changing particle swarm optimization algorithm, the machine-changing particle swarm optimization algorithm is used for solving on the basis of constructing the energy storage optimization configuration model based on the source charge uncertainty, the accurate and reliable optimal configuration capacity of the microgrid can be obtained, and therefore the accuracy and reliability of the energy storage optimization configuration of the microgrid can be further improved.
Drawings
Fig. 1 is a schematic flowchart of a microgrid energy storage optimization configuration method according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a process of calculating parameters of a microgrid according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a microgrid energy storage optimization configuration device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in this application will be understood to be a specific case for those of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present invention provides a method for optimizing configuration of energy storage of a microgrid, including:
s1, calculating to obtain the supply and demand deviation of a microgrid according to wind power, photovoltaic power generation power and power load, and establishing energy storage capacity constraint based on source charge uncertainty according to the supply and demand deviation of the microgrid;
in the embodiment of the invention, before calculating the supply and demand deviation of the microgrid, a diverse data set is constructed:
the method comprises the steps of collecting historical data of the microgrid, wherein the historical data comprises historical data of wind speed, wind power, sunlight intensity, photovoltaic power generation power and power load, and constructing various data sets for subsequent calculation according to the historical data.
S2, based on energy storage capacity constraint, constructing an objective function with the maximum operation benefit of the energy storage equipment, and constructing an energy storage optimization configuration model according to the objective function, wherein the constraint conditions of the energy storage optimization configuration model comprise: node power balance constraint, line power flow constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint;
and S3, solving an energy storage capacity optimization configuration model by adopting a particle swarm algorithm to obtain the optimal configuration capacity.
According to the embodiment of the invention, the energy storage capacity constraint based on the source charge uncertainty is established according to the supply and demand deviation of the microgrid, the objective function is established according to the energy storage capacity constraint, the maximum energy storage equipment operation benefit is established, the energy storage optimization configuration model is established according to the objective function, the peak-valley profit and profit overlap of the energy storage system influencing the energy storage equipment benefit is comprehensively considered, the power grid investment benefit is delayed, the network loss benefit is reduced, the policy subsidy benefit, the investment construction cost, the operation maintenance cost and the fault retirement cost are established to establish the objective function, the reliable energy storage optimization configuration model based on the source charge uncertainty is established, and the accuracy of the microgrid energy storage optimization configuration is improved.
Furthermore, the energy storage optimization configuration model is solved by the machine-changing particle swarm optimization algorithm, the machine-changing particle swarm optimization algorithm is used for solving on the basis of constructing the energy storage optimization configuration model based on the source charge uncertainty, the accurate and reliable optimal configuration capacity of the micro-grid can be obtained, and therefore the accuracy and reliability of the energy storage optimization configuration of the micro-grid can be further improved.
Referring to fig. 2, before calculating the supply and demand deviation of the microgrid according to the wind power, the photovoltaic power generation power and the power load, the method further includes:
s101, calculating to obtain wind power of the microgrid based on wind power uncertainty;
optionally, calculating the wind power of the microgrid based on the wind power uncertainty includes:
simulating a wind speed probability distribution function of wind power uncertainty by adopting Weibull distribution;
it can be understood that the actual wind speed is a continuous time sequence and is greatly influenced by external natural factors, and the wind speed measuring method and the wind speed measuring device can be accurately fitted by adopting double-parameter Weibull distribution with a simple structureAnd obtaining a wind speed probability distribution function of wind power qualification. Specifically, the method comprises the following steps: let the moving wind speed sequence be V = { V 1 ,V 1 ,...,V j ,...,V J The variation of the wind speed in different intervals obeys a Weibull distribution function as follows:
Figure BDA0003751506740000061
wherein, λ and k are two key parameters of Weibull distribution, λ is proportion parameter, k shape parameter.
The probability density function is:
Figure BDA0003751506740000062
and calculating the wind power of the microgrid according to the relation between the wind power and the wind speed and based on the wind speed probability distribution function.
In the embodiment of the invention, the wind power and the wind speed are in a cubic direct proportional relationship, and the specific relationship is as follows:
Figure BDA0003751506740000063
wherein A is the swept area of the wind wheel, C p Is the power coefficient of the fan, rho is the air density, V j Is the wind speed.
Based on the relationship between the wind power and the wind speed, the expected power of the wind turbine generator is calculated by adopting a weighted average method as follows:
Figure BDA0003751506740000064
in the formula, P W (t) is the power of the wind turbine generator at the t moment;
Figure BDA0003751506740000065
the power of the wind turbine generator at the t moment is P W,j Am (a)And (4) the ratio.
S102, calculating to obtain photovoltaic power generation power of the microgrid based on photovoltaic power generation uncertainty;
optionally, calculating the photovoltaic power generation power of the microgrid based on the photovoltaic power generation uncertainty includes:
simulating a sunshine intensity probability distribution function of the uncertainty of photovoltaic power generation by adopting beta distribution;
it can be understood that the actual sunlight intensity is a discontinuous time series, and is greatly influenced by external natural factors, especially solar radiation and the like. The embodiment of the invention adopts the beta distribution with complete structure for description so as to improve the accuracy. Specifically, the method comprises the following steps: let the solar intensity sequence be:
Figure BDA0003751506740000071
the change of the sunlight intensity in different intervals obeys the distribution function of beta distribution as follows:
Figure BDA0003751506740000072
wherein alpha and Beta are two key parameters of Beta distribution respectively,
Figure BDA0003751506740000073
is an incomplete B function.
The probability density function is:
Figure BDA0003751506740000074
wherein Γ is a gamma function, and is specifically represented as:
Figure BDA0003751506740000075
and calculating the photovoltaic power generation sunlight intensity of the microgrid based on the sunlight intensity probability distribution function according to the relation between the photovoltaic power generation power and the sunlight intensity.
In the embodiment of the invention, the photovoltaic power generation power and the sunlight intensity are in a direct proportional relation, and the specific relation is as follows:
P PV,j ＝ηSS PV,j [1-0.05(t 0 +25)]
wherein η is the conversion efficiency (%) of the photovoltaic cell; s is the area (m) of the photovoltaic panel 2 )；t 0 The temperature (DEG C) of the working environment of the photovoltaic panel.
Based on the fact that the photovoltaic power generation power and the sunlight intensity present a direct proportional relation, the expected power of the photovoltaic power station is calculated by adopting a weighted average method as follows:
Figure BDA0003751506740000076
in the formula, P PV (t) is the power of the wind turbine generator at the t moment;
Figure BDA0003751506740000077
the power of the wind turbine generator at the t moment is P PV,j The probability of (c).
And S103, calculating to obtain the power load of the microgrid based on the uncertainty of the energy demand of the user.
Optionally, calculating the power load of the microgrid based on the uncertainty of the energy demand of the user, including:
simulating a power load distribution function with uncertain energy demand for users by adopting normal distribution;
it will be appreciated that the electrical load is a continuous time series and is greatly influenced by external natural factors, particularly meteorological factors. The embodiment of the invention adopts normal distribution to describe the power load of the microgrid. The method specifically comprises the following steps:
the power load sequence is set as follows: p L ＝{P L,1 ,P L,2 ,...,P L,h ,...,P L,t And the probability density function of the normal distribution of the change of the power load in different intervals is as follows:
Figure BDA0003751506740000081
wherein μ and σ are two critical parameters of normal distribution respectively, μ is expectation of normal distribution, and σ is standard deviation of normal distribution.
And calculating the power load of the microgrid by adopting a weighted average method based on the power load distribution function.
In the embodiment of the invention, the expected power load of a user is calculated by adopting a weighted average method as follows:
Figure BDA0003751506740000082
wherein, P L (t) the active power of the power consumer at the t moment;
Figure BDA0003751506740000083
the active power of the electric load at the t moment is P L,j The probability of (c).
In one embodiment, calculating a supply and demand deviation of a microgrid according to wind power, photovoltaic power generation power and a power load, and establishing an energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the microgrid, including:
the supply and demand deviation of the microgrid is as follows:
Figure BDA0003751506740000084
wherein the content of the first and second substances,
Figure BDA0003751506740000085
supply and demand deviation of the microgrid at the t moment; p is W,m (t) is the wind power of the mth wind turbine at the t moment; p is PV,n (t) is the photovoltaic power generation power of the nth wind turbine generator at the t moment; p L (t) the power loads of all users of the microgrid at the t moment; m is the number of all wind turbine generators of the microgrid; n is the number of all photovoltaic power stations of the micro-grid;
and calculating the maximum supply and demand deviation of the microgrid within 24 hours a day according to the supply and demand deviation of the microgrid, and setting energy storage capacity constraint based on source load uncertainty according to the maximum supply and demand deviation.
In the embodiment of the invention, the maximum supply and demand deviation of the microgrid within 24 hours of a day based on the uncertainty of the source load is as follows:
Figure BDA0003751506740000091
in an embodiment of the invention, the energy storage capacity is constrained from a minimum capacity of the energy storage configuration to a maximum capacity of the energy storage configuration. The maximum capacity of the energy storage configuration is defined as the maximum supply and demand deviation of the micro-grid in one day considering source charge uncertainty, the maximum capacity of the energy storage configuration is defined as 1/2 of the maximum capacity of the energy storage configuration, namely the energy storage capacity constraint in the embodiment of the invention is expressed as:
Figure BDA0003751506740000092
wherein, E S And simulating the configured energy storage capacity for the micro-grid.
In one embodiment, based on the energy storage capacity constraint, constructing an objective function of an energy storage optimization configuration model with the maximum operation benefit of the energy storage device includes:
based on energy storage capacity constraint, according to the peak valley profit and profit extension of the energy storage system, the energy storage system delays the investment profit of the power grid, the energy storage system reduces the network loss profit, the energy storage policy subsidies the profit and the energy storage investment construction cost, and an objective function is constructed for the operation maintenance cost and the fault retirement cost.
In the embodiment of the present invention, the expression of the objective function is:
max[f 1 +f 2 +f 3 +f 4 -c 1 -c 2 -c 3 ]
wherein f is 1 Earnings for peak-valley arbitrage of energy storage systems, f 2 Delaying the grid investment gain for energy storage systems, f 3 The network loss profit is reduced for the energy storage system,f 4 subsidizing the earnings for energy storage policies, c 1 Investment and construction costs for energy storage, c 2 For operating maintenance costs, c 3 For the cost of decommissioning a fault.
1) Calculating the peak valley arbitrage yield of the energy storage system:
f 1 ＝(P peak section -P Millet section )×H Δ
Wherein, f 1 Earnings are conducted for peak valley profit of the energy storage system; p Peak section 、P Millet section Respectively storing the electricity prices of the energy in the peak time period and the valley time period; h Δ The electric quantity participating peak clipping from the peak time period to the valley time period.
2) The energy storage system delays the calculation of the investment income of the power grid:
f 2 ＝W×η
wherein f is 2 The investment income of the power grid is delayed for the energy storage system; w is the capacity of the power grid required to be expanded; η is the efficiency coefficient of the energy storage system.
3) And (3) reducing the network loss and profit calculation by the energy storage system:
f 3 ＝ΔP H ×P peak section -ΔP L ×P Millet section
Wherein f is 3 Network loss benefits are reduced for the energy storage system; delta P H 、ΔP L The active power reduced for the peak period load and the active power increased for the valley period load can be specifically expressed as:
ΔP H ＝(θ-θ H )×H Δ
ΔP L ＝(θ-θ L )×L Δ
wherein, theta H 、θ L Theta is the comprehensive line loss rate in the peak time period, the valley time period and the normal time period respectively; h Δ 、L Δ Respectively the electric quantity of the energy storage system participating in peak clipping and valley filling.
4) And (3) calculating the subsidy profit of the policy of the energy storage system:
Figure BDA0003751506740000101
wherein, f 4 Subsidizing the income for the energy storage policy;
Figure BDA0003751506740000102
the subsidy amount corresponding to the unit energy storage capacity.
5) And (3) calculating the investment construction cost of the energy storage system:
Figure BDA0003751506740000103
wherein, c 1 Investment and construction cost for an energy storage system; c t Investment and construction costs for unit capacity energy storage related to energy storage capacity; c w Investment and construction costs for energy storage unrelated to energy storage capacity; alpha is the current rate of the energy storage project; and T is the operating age of the energy storage project.
6) Calculating the operation and maintenance cost of the energy storage system:
c 2 ＝C as *E S +C T
wherein, c 2 Operating and maintaining costs for the energy storage system; c as The annual operation and maintenance cost corresponding to the unit capacity of the energy storage system; c T And annual lease cost of the energy storage system.
7) Calculating the failure decommissioning cost of the energy storage system:
c 3 ＝C gz *E S +C TY
wherein, c 3 The energy storage system failure decommissioning cost is calculated; c gz The fault maintenance cost corresponding to the unit capacity of the energy storage system; c TY And the cost is treated for the decommissioning of the energy storage system.
The constraint conditions of the energy storage optimization configuration model in the embodiment of the invention comprise: the method comprises the following steps of node power balance constraint, line tide constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint, and specifically comprises the following steps:
1) Node power balance constraint:
Figure BDA0003751506740000111
Figure BDA0003751506740000112
Figure BDA0003751506740000113
Figure BDA0003751506740000114
wherein, P i Active power injected for the ith node;
Figure BDA0003751506740000115
the active power is generated by the photovoltaic power station on the ith node;
Figure BDA0003751506740000116
active power generated by the wind power plant for the ith node;
Figure BDA0003751506740000117
active power injected into the energy storage system on the ith node;
Figure BDA0003751506740000118
the active load power on the ith node;
Figure BDA0003751506740000119
charging power for the energy storage system of the node i in the t time period;
Figure BDA00037515067400001110
discharging power of the energy storage system for the node i in the t time period; u is a state coefficient, and in general, u = [0,1]。
2) And (3) line power flow constraint:
P ij ≤α ij P ij,max
wherein, P ij Is the transmission power between the transmission lines ij; p ij,max The maximum transmission power between the transmission lines ij; alpha is alpha ij Is a load rate constraint parameter between transmission lines ij, in general, alpha ij ＝(0,1]。
If all transmission lines of the whole network adopt the same constraint value, alpha is obtained ij = α, the corresponding above equation can be simplified:
P ij ≤αP ij,max
3) Unit output restraint:
Figure BDA00037515067400001111
Figure BDA0003751506740000121
wherein the content of the first and second substances,
Figure BDA0003751506740000122
the actual output of the photovoltaic power station on the node i;
Figure BDA0003751506740000123
the minimum output value of the photovoltaic power station on the node i is obtained;
Figure BDA0003751506740000124
the maximum output value of the photovoltaic power station on the node i is obtained;
Figure BDA0003751506740000125
the actual output of the wind farm at node i;
Figure BDA0003751506740000126
the minimum output value of the wind power plant on the node i is obtained;
Figure BDA0003751506740000127
the maximum output value of the wind farm above the node i.
4) Phase angle constraint:
θ min ≤θ i ≤θ max
wherein, theta i Is the actual phase angle of node i; theta.theta. min Is the minimum value allowed by the phase angle on the node i; theta max Is the minimum allowable for the phase angle on node i.
5) Energy storage charge and discharge power constraint:
Figure BDA0003751506740000128
Figure BDA0003751506740000129
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00037515067400001210
the rated value of the energy storage converter on the node i.
6) Energy storage state of charge constraint:
Figure BDA00037515067400001211
Figure BDA00037515067400001212
wherein the content of the first and second substances,
Figure BDA00037515067400001213
the state of charge for energy storage at the t-1 th time period on the node i;
Figure BDA00037515067400001214
the minimum state of charge for energy storage on node i;
Figure BDA00037515067400001215
the maximum state of charge for energy storage on node i;
Figure BDA00037515067400001216
charging efficiency for energy storage on node i;
Figure BDA00037515067400001217
the discharge efficiency of the stored energy on the node i.
In one embodiment, solving the energy storage capacity optimization configuration model by using a particle swarm algorithm to obtain the optimal configuration capacity includes:
s31, a calculation formula of initial generation weight in the particle swarm optimization is as follows:
Figure BDA00037515067400001218
wherein u =0,n i C, C is a fixed value and is determined by the number of samples.
S32, a calculation formula of the inertia weight omega in the particle swarm optimization is as follows:
Figure BDA0003751506740000131
wherein, omega is the inertia weight in the particle swarm optimization, and omega is the weight of the inertia in the particle swarm optimization max Is the maximum inertial weight, ω min Is the minimum inertial weight, m t For the current number of iterations, m a Is the total number of iterations.
S33, in the particle swarm optimization deep confidence network process, a calculation formula of the initial weight of the network is as follows:
Figure BDA0003751506740000132
wherein the content of the first and second substances,
Figure BDA0003751506740000133
the updated network weight value;
Figure BDA0003751506740000134
is the initial weight value of the network,
Figure BDA0003751506740000135
the updated network weight speed value.
Figure BDA0003751506740000136
Wherein the content of the first and second substances,
Figure BDA0003751506740000137
for the network initial velocity value, ω i Is an initial weight value, x id As an initial position, pbest id For optimal positioning of individual particles, gbest id For the optimal position of the whole population of particles, u and v are coefficients, and the calculation formula of M is as follows.
Figure BDA0003751506740000138
ψ＝ψ 1 +ψ 2
Where ψ is normally greater than 4.
And S35, solving the energy storage capacity optimal configuration model by adopting an improved particle swarm algorithm according to the plurality of weights to obtain the most configured capacity of the micro-grid energy storage.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the energy storage capacity constraint based on the source charge uncertainty is established according to the supply and demand deviation of the microgrid, the objective function is established according to the energy storage capacity constraint, the maximum energy storage equipment operation benefit is established, the energy storage optimization configuration model is established according to the objective function, the peak-valley profit and profit overlap of the energy storage system influencing the energy storage equipment benefit is comprehensively considered, the power grid investment benefit is delayed, the network loss benefit is reduced, the policy subsidy benefit, the investment construction cost, the operation maintenance cost and the fault retirement cost are established to establish the objective function, the reliable energy storage optimization configuration model based on the source charge uncertainty is established, and the accuracy of the microgrid energy storage optimization configuration is improved.
Furthermore, the energy storage optimization configuration model is solved by adopting the machine-changing particle swarm optimization algorithm, the machine-changing particle swarm optimization algorithm is used for solving on the basis of constructing the energy storage optimization configuration model based on the source charge uncertainty, the accurate and reliable optimal configuration capacity of the microgrid can be obtained, and therefore the accuracy and reliability of the energy storage optimization configuration of the microgrid can be further improved.
Referring to fig. 3, based on the same invention company as the above embodiment, an embodiment of the present invention provides a microgrid energy storage optimal configuration apparatus, including:
the energy storage capacity constraint building module 10 is used for calculating the supply and demand deviation of the microgrid according to the wind power, the photovoltaic power generation power and the power load, and building energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the microgrid;
the energy storage optimization configuration model building module 20 is configured to build an objective function with the maximum operation benefit of the energy storage device based on energy storage capacity constraint, and build an energy storage optimization configuration model according to the objective function, where constraint conditions of the energy storage optimization configuration model include: node power balance constraint, line tide constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint;
and the optimal configuration capacity solving module 30 is configured to solve the energy storage capacity optimal configuration model by using a particle swarm algorithm to obtain the optimal configuration capacity.
In one embodiment, the apparatus further comprises a microgrid parameter calculation module configured to:
calculating to obtain the wind power of the microgrid based on the wind power uncertainty;
calculating to obtain the photovoltaic power generation power of the microgrid based on the photovoltaic power generation uncertainty;
and calculating the power load of the microgrid based on the uncertainty of the energy demand of the user.
In one embodiment, the calculating of the wind power of the microgrid based on the wind power uncertainty includes:
simulating a wind speed probability distribution function of wind power uncertainty by adopting Weibull distribution;
and calculating the wind power of the microgrid according to the relation between the wind power and the wind speed and based on the wind speed probability distribution function.
In one embodiment, calculating the photovoltaic power generation power of the microgrid based on photovoltaic power generation uncertainty comprises:
simulating a sunshine intensity probability distribution function of photovoltaic power generation uncertainty by adopting beta distribution;
and calculating the photovoltaic power generation sunlight intensity of the microgrid based on the sunlight intensity probability distribution function according to the relation between the photovoltaic power generation power and the sunlight intensity.
In one embodiment, calculating the power load of the microgrid based on the uncertainty of the energy demand of the user comprises:
simulating a power load distribution function of uncertainty of energy demand of a user by normal distribution;
and calculating the power load of the microgrid by adopting a weighted average method based on the power load distribution function.
In one embodiment, the energy storage capacity constraint building block 10 is further configured to:
the supply and demand deviation of the microgrid is as follows:
Figure BDA0003751506740000151
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003751506740000152
supply and demand deviation of the microgrid at the t moment; p W,m (t) is the wind power of the mth wind turbine generator at the tth moment; p is PV,n (t) is the photovoltaic power generation power of the nth wind turbine generator at the t moment; p L (t) the power loads of all users of the microgrid at the t moment; m is the number of all wind turbine generators of the micro-grid; n is the number of all photovoltaic power stations of the micro-grid;
and calculating to obtain the maximum supply and demand deviation of the microgrid within 24 hours a day according to the supply and demand deviation of the microgrid, and setting energy storage capacity constraint based on source load uncertainty according to the maximum supply and demand deviation.
In one embodiment, the energy storage optimization configuration model building module 20 is further configured to:
based on energy storage capacity constraint, according to the peak valley profit and profit extension of the energy storage system, the energy storage system delays the investment profit of the power grid, the energy storage system reduces the network loss profit, the energy storage policy subsidies the profit and the energy storage investment construction cost, and an objective function is constructed for the operation maintenance cost and the fault retirement cost.
An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the microgrid energy storage optimization configuration method as described above.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.

Claims (9)

1. A microgrid energy storage optimal configuration method is characterized by comprising the following steps:
calculating according to the wind power, the photovoltaic power generation power and the power load to obtain the supply and demand deviation of the microgrid, and establishing energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the microgrid;
based on the energy storage capacity constraint, constructing an objective function with the maximum operation benefit of the energy storage equipment, and constructing an energy storage optimization configuration model according to the objective function, wherein the constraint conditions of the energy storage optimization configuration model comprise: node power balance constraint, line tide constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint;
and solving the energy storage capacity optimal configuration model by adopting a particle swarm algorithm to obtain the optimal configuration capacity.
2. The microgrid energy storage optimization configuration method according to claim 1, wherein before calculating a supply and demand deviation of the microgrid according to wind power, photovoltaic power generation power and power load, the method further comprises:
calculating to obtain the wind power of the microgrid based on the wind power uncertainty;
calculating to obtain the photovoltaic power generation power of the microgrid based on the photovoltaic power generation uncertainty;
and calculating to obtain the power load of the microgrid based on the uncertainty of the energy demand of the user.
3. The microgrid energy storage optimization configuration method of claim 2, wherein the calculating wind power of the microgrid based on wind power uncertainty comprises:
simulating a wind speed probability distribution function of wind power uncertainty by adopting Weibull distribution;
and calculating the wind power of the microgrid according to the relation between the wind power and the wind speed and based on the wind speed probability distribution function.
4. The microgrid energy storage optimization configuration method of claim 2, wherein the calculating photovoltaic power generation power of the microgrid based on photovoltaic power generation uncertainty comprises:
simulating a sunshine intensity probability distribution function of photovoltaic power generation uncertainty by adopting beta distribution;
and calculating the photovoltaic power generation sunshine intensity of the microgrid based on the sunshine intensity probability distribution function according to the relation between the photovoltaic power generation power and the sunshine intensity.
5. The microgrid energy storage optimization configuration method of claim 2, wherein the calculating based on the uncertainty of the user energy demand to obtain the power load of the microgrid comprises:
simulating a power load distribution function with uncertain energy demand for users by adopting normal distribution;
and calculating the power load of the microgrid by adopting a weighted average method based on the power load distribution function.
6. The microgrid energy storage optimization configuration method of claim 1, wherein the calculating of supply and demand deviation of a microgrid according to wind power, photovoltaic power generation power and power load and the establishing of energy storage capacity constraint based on source load uncertainty with the supply and demand deviation of the microgrid comprises:
the supply and demand deviation of the microgrid is as follows:
Figure FDA0003751506730000021
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003751506730000022
supply and demand deviation of the microgrid at the t moment; p W,m (t) is the wind power of the mth wind turbine generator at the tth moment; p PV,n (t) is the photovoltaic power generation power of the nth wind turbine generator at the t moment; p L (t) the power loads of all users of the microgrid at the t moment; m is the number of all wind turbine generators of the micro-grid; n is the number of all photovoltaic power stations of the micro-grid;
and calculating the maximum supply and demand deviation of the microgrid within 24 hours a day according to the supply and demand deviation of the microgrid, and setting energy storage capacity constraint based on source load uncertainty according to the maximum supply and demand deviation.
7. The microgrid energy storage optimization configuration method of claim 1, wherein the constructing an objective function of an energy storage optimization configuration model with energy storage device operational effectiveness maximization based on the energy storage capacity constraint comprises:
based on the energy storage capacity constraint, according to the peak-valley profit extension of the energy storage system, the energy storage system delays the investment income of the power grid, the energy storage system reduces the network loss income, the energy storage policy subsidy income, the energy storage investment construction cost and the objective function is constructed for the operation maintenance cost and the fault decommissioning cost.
8. A microgrid energy storage optimal configuration device, comprising:
the energy storage capacity constraint building module is used for calculating the supply and demand deviation of the microgrid according to the wind power, the photovoltaic power generation power and the power load, and establishing energy storage capacity constraint based on source load uncertainty according to the supply and demand deviation of the microgrid;
the energy storage optimization configuration model building module is used for building an objective function with the maximum operation benefit of the energy storage equipment based on the energy storage capacity constraint, and building an energy storage optimization configuration model according to the objective function, wherein the constraint conditions of the energy storage optimization configuration model include: node power balance constraint, line tide constraint, unit output constraint, phase angle constraint, energy storage charging and discharging power constraint and energy storage charge state constraint;
and the optimal configuration capacity solving module is used for solving the energy storage capacity optimal configuration model by adopting a particle swarm algorithm to obtain the optimal configuration capacity.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the microgrid energy storage optimization configuration method according to any one of claims 1 to 7.
CN202210842011.9A 2022-07-18 2022-07-18 Microgrid energy storage optimal configuration method, device and storage medium Pending CN115313508A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210842011.9A CN115313508A (en) 2022-07-18 2022-07-18 Microgrid energy storage optimal configuration method, device and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210842011.9A CN115313508A (en) 2022-07-18 2022-07-18 Microgrid energy storage optimal configuration method, device and storage medium

Publications (1)

Publication Number Publication Date
CN115313508A true CN115313508A (en) 2022-11-08

Family

ID=83857376

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210842011.9A Pending CN115313508A (en) 2022-07-18 2022-07-18 Microgrid energy storage optimal configuration method, device and storage medium

Country Status (1)

Country Link
CN (1) CN115313508A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307304A (en) * 2023-05-24 2023-06-23 电力规划总院有限公司 Hybrid energy storage configuration information generation method, device, equipment and readable storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116307304A (en) * 2023-05-24 2023-06-23 电力规划总院有限公司 Hybrid energy storage configuration information generation method, device, equipment and readable storage medium
CN116307304B (en) * 2023-05-24 2023-09-29 电力规划总院有限公司 Hybrid energy storage configuration information generation method, device, equipment and readable storage medium

Similar Documents

Publication Publication Date Title
Yuan et al. Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming
CN102419394B (en) Wind/solar power prediction method with variable prediction resolution
CN103944175A (en) Wind-solar-storage combined power generation system output characteristic optimization method
Ru et al. Exact sizing of battery capacity for photovoltaic systems
CN110783959B (en) New forms of energy power generation system's steady state control system
Li et al. Optimized operation of hybrid system integrated with MHP, PV and PHS considering generation/load similarity
Natsheh Hybrid power systems energy management based on artificial intelligence
CN112072643A (en) Light-storage system online scheduling method based on depth certainty gradient strategy
CN115099590A (en) Active power distribution network economic optimization scheduling method and system considering light load uncertainty
CN115313508A (en) Microgrid energy storage optimal configuration method, device and storage medium
Zhang et al. Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response
Zahedi et al. Operational strategy optimization of a hybrid green power system based on fuzzy logic controller with considering for optimal sizing and analysis of different priorities for energy storage
Wang et al. Adequacy assessment of generating systems incorporating wind, PV and energy storage
Tsikalakis et al. Financial evaluation of renewable energy source production in microgrids markets using probabilistic analysis
Tianqi et al. Optimal scheduling of virtual power plants that ignore the cost of battery loss
Kim et al. On impact of randomly distributed PV systems on distribution networks
Brka Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques
CN115395579A (en) Photothermographic and photovoltaic configuration methods, systems, devices, and media considering confidence capacity
CN114188942A (en) Power grid dispatching method comprising large-scale new energy base
Taniguchi et al. Determination of optimal operation plans of fuel cell system in residential house with pv system
Wei et al. Distributed energy storage planning method considering the comprehensive value of energy storage
CN113054685B (en) Solar micro-grid scheduling method based on crow algorithm and pattern search algorithm
CN117436773B (en) Independent micro-grid resource planning method and system containing interruptible load
Zhang et al. Simulation Evaluation Method of Power Grid Operation Considering High Proportion New Energy Grid Connection
Al Shaikh et al. The Role of GCC Interconnection for Renewable Energy Penetration

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination