CN116842678A - Energy storage configuration method for low-voltage distribution transformer area - Google Patents

Energy storage configuration method for low-voltage distribution transformer area Download PDF

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Publication number
CN116842678A
CN116842678A CN202310170747.0A CN202310170747A CN116842678A CN 116842678 A CN116842678 A CN 116842678A CN 202310170747 A CN202310170747 A CN 202310170747A CN 116842678 A CN116842678 A CN 116842678A
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energy storage
power
configuration
representing
optimization
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陈卫东
韩帅
姚知洋
孙乐平
卢健斌
郭小璇
阮诗雅
吴宁
龚文兰
郭敏
陈恭宇
蒋权辉
董丽芝
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Electric Power Research Institute of Guangxi Power Grid 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/16—Cables, cable trees or wire harnesses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • 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

Abstract

The application belongs to the technical field of energy storage configuration, and particularly relates to an energy storage configuration method of a low-voltage distribution transformer area, which aims at a normal state, establishes an energy storage optimization configuration double-layer model, and an upper-layer optimization model generates various energy storage configuration schemes through a genetic algorithm, and transmits a generated energy storage configuration result to a lower-layer model with the minimum total planning cost as a target; and the lower layer optimization model fully considers the effect of the configuration scheme on the photovoltaic output, and performs optimal power flow calculation of the power distribution network with the minimum total operation cost as a target, so as to optimize the output of energy storage. The energy storage optimization configuration model is also built under the fault scene of the distribution area, so that the power supply capacity of the distribution area under the fault scene is effectively ensured. The final planning result can improve the economy and the light Fu Xiaona capacity of the power distribution network in a normal state, and meanwhile, the continuous power supply capacity of the photovoltaic transformer area in the island operation under the fault can be improved.

Description

Energy storage configuration method for low-voltage distribution transformer area
Technical Field
The application belongs to the technical field of energy storage configuration, and particularly relates to an energy storage configuration method for a low-voltage distribution transformer area.
Background
The climate change caused by fossil energy consumption has profound effects on the world, a low-carbon energy system is a necessary choice for energy development in the future, and along with a large number of distributed power supplies connected to a power distribution network, a series of economic, technical and environmental benefits are brought to the power distribution network, and meanwhile, high-proportion renewable energy access also brings a series of problems to the operation and planning of the power distribution network system. The output of the distributed photovoltaic power generation (distributed photovoltaic, DPV) has the characteristics of randomness and intermittence, and meanwhile, the uncertainty characteristic of the power load of the power distribution network is combined, so that the output of the DPV has strong mismatch with the time sequence characteristic of the load. Therefore, large-scale photovoltaic grid connection can not be consumed in situ, local voltage of the power distribution network is increased or even out of limit due to power layer-by-layer backflow, network loss of the power distribution network is increased, the problem of mismatch of DPV output and load time sequence reflects insufficient flexibility of the power distribution network, and the flexibility refers to the capability of effectively scheduling available resources in an overall mode, coping with uncertainty and random change of a system and maintaining normal operation of the system.
The energy storage system is used as an efficient flexible resource, and has the characteristics of high charge and discharge speed and flexible and adjustable charge and discharge state. When the power distribution network operates normally, the energy storage equipment can stabilize fluctuation and intermittence of the output of the distributed photovoltaic, promote the distributed photovoltaic to be absorbed, and improve the electric energy quality of a photovoltaic platform area. The energy storage device may provide short-term backup energy to the user during scheduled maintenance or medium voltage line faults when operating in the photovoltaic cell island mode. Meanwhile, the energy storage equipment can enable intermittent photovoltaic power generation to serve as a schedulable energy source, balance between energy output and demand of the photovoltaic power generation is guaranteed, and continuous and stable power supply during operation of the photovoltaic transformer area island mode is achieved. Therefore, the method comprehensively considers the normal economy and the reliability under the fault, and has important significance for reasonably optimizing and configuring the energy storage. At present, most of researches on energy storage configuration only consider normal operation scenes or fault scenes of a power distribution network, and a planning scheme cannot simultaneously consider normal economy and power supply reliability under faults.
Disclosure of Invention
The application provides a low-voltage power distribution station energy storage configuration method, which aims at the research of the energy storage configuration of the current low-voltage power distribution station, does not comprehensively consider the normal operation scene and the fault scene of the power distribution station, and has limited applicability. The specific technical scheme is as follows:
a low-voltage power distribution station energy storage configuration method comprises the following steps:
step S1, an energy storage optimization configuration double-layer model under normal operation of a power distribution area is established, wherein the energy storage optimization configuration double-layer model comprises an upper-layer optimization model and a lower-layer optimization model; the upper optimization model takes the minimum total cost of energy storage configuration annual planning as an objective function, and specifically comprises the following steps:
wherein: f represents the total planning cost of energy storage configuration year;representing annual investment cost of concentrated energy storage of the transformer substation; c (C) o Representing the annual running total cost of the power distribution network, and obtaining the annual running total cost by a lower-layer optimization model;
the lower layer optimization model takes the lowest annual running total cost of the power distribution network as an objective function, and is specifically as follows:
wherein:representing annual operation maintenance cost of energy storage; c (C) light Representing cut-off compensation of a photovoltaic user by a power grid with reduced photovoltaic power generation capacity due to configuration energy storage; c (C) inc Price arbitrage annual income for charging energy storage to peak discharge valley of upper-level power grid is represented; />Representing power distributionAnnual network loss cost of the line;
constraint conditions of the lower optimization model comprise energy storage charge and discharge power constraint, energy storage state of charge constraint, power foldback constraint and distribution line power flow constraint;
step S2, an energy storage optimization configuration model under a power distribution area fault scene is established, wherein the energy storage optimization configuration model under the power distribution area fault scene aims at the lowest total cost of energy storage configuration annual planning, and the method specifically comprises the following steps:
constraint conditions of the energy storage optimization configuration model in the fault scene of the distribution area comprise energy storage charging and discharging power constraint, energy storage charge state constraint, distribution line power flow constraint and area electricity protection constraint in the fault state;
step S3, a genetic algorithm and a CPLEX solver are called to solve the energy storage optimization configuration double-layer model under the normal operation of the power distribution area, which is proposed in the step S1, so as to obtain an energy storage configuration scheme with optimal economy, and the energy storage configuration scheme is recorded as a scheme I;
step S4, taking a typical day of a photovoltaic platform area as an example, substituting the fault scene with the maximum power deficiency into the energy storage optimal configuration model under the fault scene of the distribution platform area, which is proposed in the step S2, solving to obtain an energy storage configuration scheme with the strongest power supply protection capability, and marking the energy storage configuration scheme as a scheme II;
and S5, comprehensively comparing the energy storage configuration schemes of the first scheme and the second scheme, and selecting the maximum energy storage capacity of the two schemes as the final concentrated energy storage configuration capacity.
Preferably, the energy storage optimization configuration double-layer model optimization step in the normal operation of the distribution area in the step S1 is as follows:
s11, the upper optimizing model generates various energy storage configuration schemes through a genetic algorithm, and the generated energy storage configuration schemes are transmitted to the lower optimizing model by taking the lowest total cost of energy storage configuration annual planning as a total objective function;
step S12, fully considering the effect of the energy storage configuration scheme generated in the upper optimization model on the absorption of photovoltaic output in the lower optimization model, calculating the optimal power flow of the power distribution network with the minimum total annual running cost of the power distribution network as a target, optimizing the output of energy storage, and returning the optimization result to the upper optimization model;
and 13, calculating a total objective function by the upper optimization model, updating the energy storage configuration scheme, and finally obtaining the optimal energy storage configuration scheme through iteration of the upper optimization model and the lower optimization model.
Preferably, the annual investment cost of the concentrated energy storage of the transformer substationThe method comprises the following steps:
wherein:representing the cost of energy storage unit capacity; />Representing the energy storage rated capacity; />Representing the cost of energy storage unit power; />Representing the rated power of the stored energy; r represents the discount rate; t (T) S Representing the full life cycle of the stored energy;
preferably, the annual investment cost of the concentrated energy storage of the transformer substationThe highest investment cost constraint needs to be met, specifically as follows:
wherein:representing the maximum allowable value of the energy storage investment cost.
Preferably, annual operating maintenance cost of the stored energyThe calculation mode of (2) is as follows:
wherein: y represents the year;representing the operation maintenance cost of the concentrated energy storage unit charging/discharging power; />Representing the maximum charge/discharge power of the concentrated energy storage; i.e r Representing the inflation rate; d, d r Represent the rate of discount, T S Representing the full life cycle of the stored energy;
cut-off compensation C of photovoltaic users by the power grid with reduced photovoltaic power generation capacity due to configuration of energy storage light The calculation mode of (2) is as follows:
in the method, in the process of the application,representing the unit compensation of the photovoltaic power grid to the photovoltaic user; p (P) loss,t The power of the photovoltaic cutting machine is reduced at the moment t after the energy storage is installed;
price arbitrage annual income C for energy storage to charge to peak discharge valley of upper-level power grid inc The calculation mode of (2) is as follows:
wherein:the time-sharing electricity price of electricity purchase at the moment t is shown; />The power purchasing power from an upper power grid at t moment before the transformer substation is installed for centralized energy storage is shown; />The power purchasing power from the upper power grid at the moment t after the transformer substation is installed and concentrated to store energy is shown;
annual network loss cost of the distribution lineThe calculation mode of (2) is as follows:
preferably, the energy storage charging and discharging power is constrained as follows:
wherein P is dis (t) represents the discharge power of the stored energy at time t; p (P) ch (t) represents the charging power of the stored energy at time t; p (P) out Representing the maximum charge/discharge power of the stored energy; b (B) dis (t)、B ch (t) represents a 0-1 variable and is satisfied with B at any time dis (t)+B ch (t)≤1。
Preferably, the stored state of charge constraint is: the state of charge of the stored energy must be greater than the minimum charge amount SOC at any one time min And is smaller than the maximum charge amount SOC max The method comprises the steps of carrying out a first treatment on the surface of the The energy stored at the beginning and the end of each scheduling period of energy storage is consistent so as to ensure continuous operation; the method comprises the following steps:
wherein: SOC (t) and SOC (t-1) respectively represent the real-time electricity storage quantity of the stored energy at the time t and the time t-1; η (eta) dis 、η ch The energy conversion efficiency during discharging and charging of the stored energy is shown respectively.
Preferably, the power foldback constraint is as follows:
wherein, in the formula: p (P) fs Representing the power of the head node of the outgoing line side of the transformer in the transformer area; s is S b Is the transformer capacity of the transformer area.
Preferably, the distribution line power flow constraint is as follows:
U i,min ≤U i ≤U i,max ； (15)
wherein: p (P) i 、Q i Respectively representing the active power and the reactive power injected by the node i; g ij 、B ij 、δ ij Respectively representing the conductance, susceptance and voltage phase angle difference between the nodes i and j; n is the total number of system nodes; u (U) i 、U j The voltage amplitudes of the nodes i and j are respectively represented; u (U) i,min 、U i,max Respectively the voltage minimum value and the voltage maximum value allowed by the node i; p (P) ij The transmission power for branch ij;is the upper limit of the transmission power of the branch ij.
Preferably, the power protection constraint of the platform area in the fault state is specifically:
wherein t is g Representing the shortest time of off-network operation of a station area, P 0 And the power of the outgoing line head measuring node of the transformer substation in the transformer area is represented, and alpha represents the minimum load proportion of off-grid power supply of the transformer area.
The beneficial effects of the application are as follows: the application provides an energy storage configuration method of a low-voltage distribution station, which is characterized in that an energy storage optimization configuration double-layer model is established aiming at a normal state, an upper-layer optimization model generates various energy storage configuration schemes through a genetic algorithm, and the generated energy storage configuration result is transmitted to a lower-layer model with the lowest total planning cost as a target; and the lower layer optimization model fully considers the effect of the configuration scheme on the photovoltaic output, and performs optimal power flow calculation of the power distribution network with the minimum total operation cost as a target, so as to optimize the output of energy storage. According to the energy storage optimization configuration model, the energy storage optimization configuration model is built under the fault scene of the distribution transformer area, two indexes of the transformer area off-grid operation time and the transformer area off-grid power supply capacity are fully considered, and the power supply capacity of the distribution transformer area under the fault scene is effectively ensured. The final planning result can effectively ensure the safe and economic operation of the power grid and improve the photovoltaic digestion capability of the power distribution network. The energy storage configuration method can improve the economy and the light Fu Xiaona capacity of the power distribution network in a normal state, and can also improve the continuous power supply capacity of the photovoltaic transformer area in the island operation under the fault.
Detailed Description
All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated 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.
It is also to be understood that the terminology used in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification 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.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In order to improve the economy of normal operation of a distribution area and the power supply reliability in a fault scene, the application respectively establishes an energy storage optimal configuration model of normal operation and an energy storage optimal configuration model of the fault scene, and further provides an energy storage configuration scheme which takes the economy and the power supply reliability into consideration. The specific provides a low-voltage distribution station energy storage configuration method, which comprises the following steps:
step S1, in order to enhance the photovoltaic digestion capability of a power distribution station in a normal operation scene, and fully consider the economical efficiency of power distribution network operation, an energy storage optimization configuration double-layer model in the normal operation of the power distribution station is established, wherein the energy storage optimization configuration double-layer model comprises an upper-layer optimization model and a lower-layer optimization model; the economic requirement of energy storage configuration of the power distribution network is fully considered, and the upper optimization model takes the minimum total cost of annual planning of the energy storage configuration as an objective function, and is specifically as follows:
wherein: f represents the total planning cost of energy storage configuration year;representing annual investment cost of concentrated energy storage of the transformer substation; c (C) o The total annual running cost of the distribution network is represented and is obtained by a lower-layer optimization model.
Annual investment cost of centralized energy storage of transformer substationThe method comprises the following steps:
wherein:representing the cost of energy storage unit capacity; />Representing the energy storage rated capacity; />Representing the cost of energy storage unit power; />Representing the rated power of the stored energy; r represents the discount rate; t (T) S Representing the full life cycle of the stored energy;
the constraint condition mainly considered by the upper optimization model is equipment investment cost constraint, the investment limit of each equipment in the power distribution network is considered, and the annual investment cost of concentrated energy storage of the transformer substation is consideredThe highest investment cost constraint needs to be met, specifically as follows:
wherein:representing the maximum allowable value of the energy storage investment cost.
The lower layer optimization model comprehensively considers the influence of time-of-use electricity price, equipment operation maintenance and network loss on the optimization scheduling, and the lower layer optimization model takes the lowest annual operation total cost of the power distribution network as an objective function, and is specifically as follows:
wherein:representing annual operation maintenance cost of energy storage; c (C) light Representing cut-off compensation of a photovoltaic user by a power grid with reduced photovoltaic power generation capacity due to configuration energy storage; c (C) inc Price arbitrage annual income for charging energy storage to peak discharge valley of upper-level power grid is represented; />Representing annual network loss costs of the distribution line;
annual operation maintenance cost of energy storageThe calculation mode of (2) is as follows:
wherein: y represents the year;representing the operation maintenance cost of the concentrated energy storage unit charging/discharging power; />Representing maximum charge/discharge power of concentrated energy storage；i r Representing the inflation rate; d, d r Represent the rate of discount, T S Representing the full life cycle of the stored energy;
cut-off compensation C of photovoltaic users by power grids with reduced photovoltaic power generation capacity due to configuration of stored energy light The calculation mode of (2) is as follows:
in the method, in the process of the application,representing the unit compensation of the photovoltaic power grid to the photovoltaic user; p (P) loss,t The power of the photovoltaic cutting machine is reduced at the moment t after the energy storage is installed;
price arbitrage annual income C for energy storage to charge to peak discharge valley of upper-level power grid inc The calculation mode of (2) is as follows:
wherein:the time-sharing electricity price of electricity purchase at the moment t is shown; />The power purchasing power from an upper power grid at t moment before the transformer substation is installed for centralized energy storage is shown; />The power purchasing power from the upper power grid at the moment t after the transformer substation is installed and concentrated to store energy is shown;
annual network loss cost of distribution linesThe calculation mode of (2) is as follows:
constraint conditions of the lower-layer optimization model comprise energy storage charge and discharge power constraint, energy storage charge state constraint, power foldback constraint and distribution line flow constraint.
The energy storage charging and discharging power is constrained as follows:
wherein P is dis (t) represents the discharge power of the stored energy at time t; p (P) ch (t) represents the charging power of the stored energy at time t; p (P) out Representing the maximum charge/discharge power of the stored energy; b (B) dis (t)、B ch (t) represents a 0-1 variable and is satisfied with B at any time dis (t)+B ch (t)≤1。
The energy storage state of charge constraint is: the state of charge of the stored energy must be greater than the minimum charge amount SOC at any one time min And is smaller than the maximum charge amount SOC max The method comprises the steps of carrying out a first treatment on the surface of the The energy stored at the beginning and the end of each scheduling period of energy storage is consistent so as to ensure continuous operation; the method comprises the following steps:
wherein: SOC (t) and SOC (t-1) respectively represent the real-time electricity storage quantity of the stored energy at the time t and the time t-1; η (eta) dis 、η ch The energy conversion efficiency during discharging and charging of the stored energy is shown respectively.
The power foldback constraint is as follows:
wherein, in the formula: p (P) fs Representing the power of the head node of the outgoing line side of the transformer in the transformer area; s is S b Is the transformer capacity of the transformer area.
The distribution line power flow constraints are as follows:
U i,min ≤U i ≤U i,max ； (14)
wherein: p (P) i 、Q i Respectively representing the active power and the reactive power injected by the node i; g ij 、B ij 、δ ij Respectively representing the conductance, susceptance and voltage phase angle difference between the nodes i and j; n is the total number of system nodes; u (U) i 、U j The voltage amplitudes of the nodes i and j are respectively represented; u (U) i,min 、U i,max Respectively the voltage minimum value and the voltage maximum value allowed by the node i; p (P) ij The transmission power for branch ij;is the upper limit of the transmission power of the branch ij.
The energy storage optimization configuration double-layer model optimization steps under normal operation of the power distribution station area are as follows:
s11, the upper optimizing model generates various energy storage configuration schemes through a genetic algorithm, and the generated energy storage configuration schemes are transmitted to the lower optimizing model by taking the lowest total cost of energy storage configuration annual planning as a total objective function;
step S12, fully considering the effect of the energy storage configuration scheme generated in the upper optimization model on the absorption of photovoltaic output in the lower optimization model, calculating the optimal power flow of the power distribution network with the minimum total annual running cost of the power distribution network as a target, optimizing the output of energy storage, and returning the optimization result to the upper optimization model;
and 13, calculating a total objective function by the upper optimization model, updating the energy storage configuration scheme, and finally obtaining the optimal energy storage configuration scheme through iteration of the upper optimization model and the lower optimization model.
Step S2, in order to ensure the power supply capacity of the distribution area under the fault scene, an energy storage optimization configuration model is established aiming at the fault scene with the maximum power shortage. And fully considering two indexes of the off-grid running time of the station area and the off-grid electricity-saving capacity of the station area, and modeling by taking the lowest planning cost of the energy storage configuration year as a target. Comprising the following steps: an energy storage optimization configuration model under a power distribution area fault scene is established, wherein the energy storage optimization configuration model under the power distribution area fault scene aims at the lowest total cost of energy storage configuration annual planning, and the energy storage optimization configuration model specifically comprises the following steps:
constraint conditions of the energy storage optimization configuration model in the fault scene of the distribution area comprise energy storage charging and discharging power constraint, energy storage charge state constraint, distribution line power flow constraint and area electricity protection constraint in the fault state; the energy storage charging and discharging power constraint is shown in the formula (9), the energy storage state of charge constraint is shown in the formula (10), and the distribution line power flow constraint is shown in the formulas (12) - (15).
The power protection constraint of the station area under the fault state is specifically as follows:
wherein t is g Representing the shortest time of off-network operation of a station area, P 0 And the power of the outgoing line head measuring node of the transformer substation in the transformer area is represented, and alpha represents the minimum load proportion of off-grid power supply of the transformer area.
Step S3, a genetic algorithm and a CPLEX solver are called to solve the energy storage optimization configuration double-layer model under the normal operation of the power distribution area, which is proposed in the step S1, so as to obtain an energy storage configuration scheme with optimal economy, and the energy storage configuration scheme is recorded as a scheme I;
step S4, taking a typical day of a photovoltaic platform area as an example, substituting the fault scene with the maximum power deficiency into the energy storage optimal configuration model under the fault scene of the distribution platform area, which is proposed in the step S2, solving to obtain an energy storage configuration scheme with the strongest power supply protection capability, and marking the energy storage configuration scheme as a scheme II;
and S5, comprehensively comparing the energy storage configuration schemes of the first scheme and the second scheme, and selecting the maximum energy storage capacity of the two schemes as the final concentrated energy storage configuration capacity.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the elements of the examples have been described generally in terms of functionality in the foregoing description to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the division of the units is merely a logic function division, and there may be other division manners in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.

Claims (10)

1. The energy storage configuration method for the low-voltage power distribution station area is characterized by comprising the following steps of:
step S1, an energy storage optimization configuration double-layer model under normal operation of a power distribution area is established, wherein the energy storage optimization configuration double-layer model comprises an upper-layer optimization model and a lower-layer optimization model; the upper optimization model takes the minimum total cost of energy storage configuration annual planning as an objective function, and specifically comprises the following steps:
wherein: f represents the total planning cost of energy storage configuration year;representing annual investment cost of concentrated energy storage of the transformer substation; c (C) o Representing the annual running total cost of the power distribution network, and obtaining the annual running total cost by a lower-layer optimization model;
the lower layer optimization model takes the lowest annual running total cost of the power distribution network as an objective function, and is specifically as follows:
wherein:representing annual operation maintenance cost of energy storage; c (C) light Representing cut-off compensation of a photovoltaic user by a power grid with reduced photovoltaic power generation capacity due to configuration energy storage; c (C) inc Price arbitrage annual income for charging energy storage to peak discharge valley of upper-level power grid is represented; />Representing annual network loss costs of the distribution line;
constraint conditions of the lower optimization model comprise energy storage charge and discharge power constraint, energy storage state of charge constraint, power foldback constraint and distribution line power flow constraint;
step S2, an energy storage optimization configuration model under a power distribution area fault scene is established, wherein the energy storage optimization configuration model under the power distribution area fault scene aims at the lowest total cost of energy storage configuration annual planning, and the method specifically comprises the following steps:
constraint conditions of the energy storage optimization configuration model in the fault scene of the distribution area comprise energy storage charging and discharging power constraint, energy storage charge state constraint, distribution line power flow constraint and area electricity protection constraint in the fault state;
step S3, a genetic algorithm and a CPLEX solver are called to solve the energy storage optimization configuration double-layer model under the normal operation of the power distribution area, which is proposed in the step S1, so as to obtain an energy storage configuration scheme with optimal economy, and the energy storage configuration scheme is recorded as a scheme I;
step S4, taking a typical day of a photovoltaic platform area as an example, substituting the fault scene with the maximum power deficiency into the energy storage optimal configuration model under the fault scene of the distribution platform area, which is proposed in the step S2, solving to obtain an energy storage configuration scheme with the strongest power supply protection capability, and marking the energy storage configuration scheme as a scheme II;
and S5, comprehensively comparing the energy storage configuration schemes of the first scheme and the second scheme, and selecting the maximum energy storage capacity of the two schemes as the final concentrated energy storage configuration capacity.
2. The energy storage configuration method of the low-voltage distribution transformer substation according to claim 1, wherein the energy storage optimization configuration double-layer model optimization step under the normal operation of the distribution transformer substation in the step S1 is as follows:
s11, the upper optimizing model generates various energy storage configuration schemes through a genetic algorithm, and the generated energy storage configuration schemes are transmitted to the lower optimizing model by taking the lowest total cost of energy storage configuration annual planning as a total objective function;
step S12, fully considering the effect of the energy storage configuration scheme generated in the upper optimization model on the absorption of photovoltaic output in the lower optimization model, calculating the optimal power flow of the power distribution network with the minimum total annual running cost of the power distribution network as a target, optimizing the output of energy storage, and returning the optimization result to the upper optimization model;
and 13, calculating a total objective function by the upper optimization model, updating the energy storage configuration scheme, and finally obtaining the optimal energy storage configuration scheme through iteration of the upper optimization model and the lower optimization model.
3. The method for energy storage configuration of a low voltage distribution substation according to claim 1, wherein the annual investment cost of the centralized energy storage of the substationThe method comprises the following steps:
wherein:representing the cost of energy storage unit capacity; />Representing the energy storage rated capacity; />Representing the cost of energy storage unit power;representing the rated power of the stored energy; r represents the discount rate; t (T) S Indicating the full life cycle of the stored energy.
4. A low voltage distribution substation energy storage configuration method according to claim 3, wherein the annual investment cost of the centralized energy storage of the substationThe highest investment cost constraint needs to be met, specifically as follows:
wherein:representing the maximum allowable value of the energy storage investment cost.
5. The method for configuring energy storage in a low voltage distribution block of claim 1, wherein the annual operation maintenance cost of the energy storageThe calculation mode of (2) is as follows:
wherein: y represents the year;representing the operation maintenance cost of the concentrated energy storage unit charging/discharging power; />Representing the maximum charge/discharge power of the concentrated energy storage; i.e r Representing the inflation rate; d, d r Represent the rate of discount, T S Representing the full life cycle of the stored energy;
cut-off compensation C of photovoltaic users by the power grid with reduced photovoltaic power generation capacity due to configuration of energy storage light The calculation mode of (2) is as follows:
in the method, in the process of the application,representing the unit compensation of the photovoltaic power grid to the photovoltaic user; p (P) loss,t The power of the photovoltaic cutting machine is reduced at the moment t after the energy storage is installed;
price arbitrage annual income C for energy storage to charge to peak discharge valley of upper-level power grid inc The calculation mode of (2) is as follows:
wherein:the time-sharing electricity price of electricity purchase at the moment t is shown; />The power purchasing power from an upper power grid at t moment before the transformer substation is installed for centralized energy storage is shown; />The power purchasing power from the upper power grid at the moment t after the transformer substation is installed and concentrated to store energy is shown;
annual network loss cost of the distribution lineThe calculation mode of (2) is as follows:
6. the method for configuring energy storage in a low voltage distribution area according to claim 1, wherein the energy storage charging and discharging power constraint is as follows:
wherein P is dis (t) represents the discharge power of the stored energy at time t; p (P) ch (t) represents the charging power of the stored energy at time t; p (P) out Representing the maximum charge/discharge power of the stored energy; b (B) dis (t)、B ch (t) represents a 0-1 variable and is satisfied with B at any time dis (t)+B ch (t)≤1。
7. The method of claim 1, wherein the energy storage state of charge constraint is: the state of charge of the stored energy must be greater than the minimum charge amount SOC at any one time min And is smaller than the maximum charge amount SOC max The method comprises the steps of carrying out a first treatment on the surface of the The energy stored at the beginning and the end of each scheduling period of energy storage is consistent so as to ensure continuous operation; the method comprises the following steps:
wherein: SOC (t) and SOC (t-1) respectively represent the real-time electricity storage quantity of the stored energy at the time t and the time t-1; η (eta) dis 、η ch The energy conversion efficiency during discharging and charging of the stored energy is shown respectively.
8. A low voltage distribution block energy storage configuration method according to claim 1, characterized in that the power foldback constraints are as follows:
wherein, in the formula: p (P) fs Representing the power of the head node of the outgoing line side of the transformer in the transformer area; s is S b Is the transformer capacity of the transformer area.
9. The method of claim 1, wherein the distribution line power flow constraints are as follows:
U i,min ≤U i ≤U i,max ； (15)
wherein: p (P) i 、Q i Respectively representing the active power and the reactive power injected by the node i; g ij 、B ij 、δ ij Respectively representing the conductance, susceptance and voltage phase angle difference between the nodes i and j; n is the total number of system nodes; u (U) i 、U j The voltage amplitudes of the nodes i and j are respectively represented; u (U) i,min 、U i,max Respectively the voltage minimum value and the voltage maximum value allowed by the node i; p (P) ij The transmission power for branch ij;is the upper limit of the transmission power of the branch ij.
10. The method for configuring energy storage in a low-voltage distribution transformer area according to claim 1, wherein the transformer area electricity-protecting constraint in the fault state is specifically:
wherein t is g Representing the shortest time of off-network operation of a station area, P 0 And the power of the outgoing line head measuring node of the transformer substation in the transformer area is represented, and alpha represents the minimum load proportion of off-grid power supply of the transformer area.
CN202310170747.0A 2023-02-27 2023-02-27 Energy storage configuration method for low-voltage distribution transformer area Pending CN116842678A (en)

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