CN115051361A - Shared energy storage regulation and control method and management system considering load characteristics of 5G base station - Google Patents
Shared energy storage regulation and control method and management system considering load characteristics of 5G base station Download PDFInfo
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Abstract
The invention discloses a shared energy storage regulation and control method and a management system considering the load characteristics of a 5G base station, and the load demand characteristics of a large-scale photovoltaic integration 5G base station and the net load peak-valley characteristics of an active power distribution network are determined according to the communication flow load characteristics; acquiring shared energy storage charge-discharge demand and charge-discharge state bit characteristic data, constructing an optimal planning upper-layer optimization model of the shared energy storage system, and acquiring optimal planning data of the capacity of the shared energy storage system; acquiring capacity optimal planning data of a shared energy storage system, constructing a peak clipping and valley filling lower-layer combined optimization model of a large-scale photovoltaic integrated 5G base station and an active power distribution network by combining the load demand characteristic of the large-scale photovoltaic integrated 5G base station and the net load peak valley characteristic of the active power distribution network, and updating the charge and discharge demand and the charge and discharge state bit characteristic of the shared energy storage; and (3) iteratively solving the optimal planning upper-layer optimization model and the lower-layer combined optimization model of the shared energy storage system by using a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
Description
Technical Field
The invention belongs to the technical field of shared energy storage planning and configuration, and particularly relates to a shared energy storage regulation and control method and a management system considering the load characteristics of a 5G base station.
Background
The photovoltaic integrated 5G base station is expected to become an effective mode for responding low-carbon green development and reducing the operation cost of communication operators, the photovoltaic power generation unit and the 5G base station are integrated, the power supply dependence of the 5G base station on an active power distribution network can be effectively reduced, and the influence of large-scale 5G base station loads on the safe and stable operation of the active power distribution network is reduced. However, the photovoltaic energy has intermittence and fluctuation, so that the photovoltaic power generation unit still cannot be used as an independent power supply to supply power to the 5G base station, and a light abandon phenomenon often occurs along with the low-peak period of the communication load to ensure the safe and stable operation of the active power distribution network. Therefore, the energy storage system has become a flexible adjustment resource for planning in the power grid and the communication grid together as an important means for effectively stabilizing the imbalance between the random energy generation and the load demand.
The photovoltaic integrated 5G base station can utilize the rapid charge-discharge characteristic of the energy storage system to stabilize the power fluctuation of the 5G base station, fully absorb photovoltaic energy and reduce the impact frequency to the active power distribution network; the active power distribution network can reduce the load peak-valley difference of the active power distribution network by relying on the space-time translation characteristic of energy storage, and improve the peak regulation capability. Currently, a 5G base station is usually configured with a backup energy storage battery to ensure uninterrupted power supply, which can be regarded as the inherent energy storage of the 5G base station and exists as a distributed energy storage form. However, in a normal power supply state, the inherent energy storage of the 5G base station is used as a standby power supply to ensure that the 5G base station supplies power uninterruptedly as a primary task, so that the base station is always in an idle state, and the schedulable capacity is limited. Secondly, in the process of regulating and controlling the inherent energy storage of the 5G base station, because the load of the 5G base station is directly influenced by the communication load and has the time-varying property and the geographical difference of service demands, the battery energy storage charging and discharging behaviors with the same capacity configuration are disordered, and the energy storage operation benefit under the whole view angle is low. Therefore, optimal planning and optimal regulation of energy storage of the large-scale photovoltaic integrated 5G base station still remain to be solved.
Compared with the distributed energy storage system of the inherent energy storage of the 5G base station, the shared energy storage system has higher superiority in the aspects of reducing investment and construction cost, maintaining cost, exerting energy storage utilization rate and the like. For the large-scale photovoltaic integrated 5G base station and the active power distribution network, energy storage capacity leasing service can be provided by introducing a third-party energy storage operator by relying on a shared energy storage regulation and control platform, the complementation and the complementation of the large-scale 5G base station energy are promoted, auxiliary service is provided for the active power distribution network, the low energy utilization cost of the large-scale 5G base station is realized, and the electric energy quality of the active power distribution network is improved.
In view of the defects of effectively stabilizing the imbalance between the random energy generation and the load demand, the optimal energy storage planning and optimal regulation of the large-scale photovoltaic integrated 5G base station, higher investment and construction cost, higher maintenance cost and exertion of the energy storage utilization rate, the invention aims to create a shared energy storage regulation and control system and a management platform considering the load characteristic of the 5G base station, so that the shared energy storage regulation and control system and the management platform have industrial utilization value.
Disclosure of Invention
In order to solve the technical problem, the invention provides a shared energy storage regulation and control method and a management system considering the load characteristics of a 5G base station.
The shared energy storage regulation and control method considering the load characteristics of the 5G base station comprises the following steps of:
according to the communication flow load characteristic data, determining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network;
acquiring charge-discharge requirements and charge-discharge state bit characteristic data of a large-scale photovoltaic integrated 5G base station and an active power distribution network shared energy storage system, constructing an optimal planning upper-layer optimization model of the shared energy storage system, and acquiring optimal planning data of capacity of the shared energy storage system;
acquiring capacity optimal planning data of a shared energy storage system, combining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network, constructing a peak clipping and valley filling lower-layer combined optimization model of the large-scale photovoltaic integration 5G base station and the active power distribution network, and updating charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system;
and (3) iteratively solving the optimal planning upper-layer optimization model and the lower-layer combined optimization model of the shared energy storage system by using a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
Further, the charging and discharging requirements and charging and discharging state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system are collected and transmitted to the active power distribution network collaborative interaction information management platform through the communication gateway, an optimal planning upper layer optimization model of the shared energy storage system is constructed, and optimal planning data of the capacity of the shared energy storage system are obtained through the optimal planning upper layer optimization model.
Further, collecting the charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integrated 5G base station and the active power distribution network shared energy storage system, and constructing an optimal planning upper-layer optimization model of the shared energy storage system by taking the daily average capacity planning cost of the shared energy storage system in a minimized planning period as an optimization target; an objective function expression F of the optimal planning upper layer optimization model of the shared energy storage system UL Comprises the following steps:
F UL ＝C inv -C ser -C BS -C GRID -C en
in the formula:
C inv -representing the shared energy storage system daily average investment and maintenance costs;
C ser -representing a shared energy storage system capacity rental service revenue;
C BS representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the large-scale 5G base station;
C GRID representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the active power distribution network;
C en representing the equivalent environmental benefit improvement income obtained by the energy storage operator participating in the peak shaving scheduling of the active power distribution network;
average daily investment and maintenance costs C for a shared energy storage system inv Expressed as:
in the formula:
O SES ,M SES -respectively representing the daily average investment cost and the daily average maintenance cost of the shared energy storage system;
η p ,η e -representing the shared energy storage system per power configuration cost and per capacity configuration cost, respectively;
η m -representing a shared energy storage system unit power maintenance cost;
T E -representing the expected number of years of use of the shared energy storage system;
shared energy storage system capacity lease income C ser Expressed as:
in the formula:
n represents the number of the 5G base stations of the large-scale photovoltaic integration;
t is the total scheduling period in the planning cycle;
δ ser -representing a shared energy storage system rental cost per unit capacity;
Δ t — scheduling period length;
the energy storage operator and the large-scale 5G base station generate income C for electricity trading BS Expressed as:
in the formula:
δ dis (t),δ c (t) -respectively representing the unit electricity selling price of the shared energy storage system and the large-scale 5G base station in the time period t;
income C generated by trading electric quantity between energy storage operator and active power distribution network GRID Expressed as:
in the formula:
δ sell (t),δ buy (t) -respectively representing the power price of the active power distribution network on the internet and the power price of unit electric quantity sold in the time period t;
equivalent environmental benefit improvement income C obtained by energy storage operator participating in active power distribution network peak shaving scheduling en ；
In the formula:
δ en -representing an environmental benefit improvement benefit parameter;
the upper-layer optimization model needs to meet dynamic capacity leasing constraint, charging and discharging power constraint, power balance constraint and SOC constraint of the shared energy storage system, and is represented as follows:
in the formula:
t-set of scheduling periods;
E SES (t),E SES (t +1) -respectively representing the real-time electric quantity of the shared energy storage system in the t time period and the t +1 time period;
η max ,η min respectively representing the real-time electric quantity upper and lower limit coefficients of the shared energy storage system;
α SES (t),α SES,GRID (t) -respectively representing the charge and discharge state bit of the shared energy storage system and the electricity purchasing and selling state bit for carrying out electricity quantity transaction with the active power distribution network, wherein the charge and discharge state bit is a variable of 0-1.
Further, capacity optimal planning data of the shared energy storage system is collected and transmitted to an active power distribution network collaborative interaction information management platform through a communication gateway, a large-scale photovoltaic integrated 5G base station load demand characteristic data and an active power distribution network net load peak valley characteristic data are combined, a large-scale photovoltaic integrated 5G base station and active power distribution network peak valley filling lower layer combined optimization model is constructed, and the charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integrated 5G base station and the active power distribution network shared energy storage system are updated through the lower layer combined optimization model.
Further, the large-scale photovoltaic integrated 5G base station and the active power distribution network peak clipping and valley filling lower layer combined optimization model is based on capacity optimal planning data of a shared energy storage system, load demand characteristic data of the large-scale photovoltaic integrated 5G base station and net load peak and valley characteristic data of the active power distribution network, and the optimization target is that the total operation cost of the large-scale photovoltaic integrated 5G base station and the net load peak and valley adjustment operation cost of the active power distribution network are minimized;
total operation cost expression F of large-scale photovoltaic integrated 5G base station BS Comprises the following steps:
F BS ＝C G +C cv +C ser +C BS
in the formula:
C G the payment cost required by the large-scale photovoltaic integrated 5G base station for purchasing electricity from the power grid is represented;
C cv representing the light abandonment penalty of the large-scale distributed photovoltaic power generation unit;
electricity purchase cost C of large-scale photovoltaic integrated 5G base station power grid G Expressed as:
in the formula:
P GRID,i (t) watch5G base station BS i Purchasing power from the active power distribution network in a time period t;
light abandonment punishment C of large-scale distributed photovoltaic power generation units cv Expressed as:
in the formula:
δ cv -representing a photovoltaic curtailment penalty coefficient;
P cv,i (t) -representing a 5G base station BS i Photovoltaic clipping power at time t;
active power distribution network net load peak regulation operation cost expression F ADN Comprises the following steps:
in the formula:
L net (t) -represents the net load power of the active distribution grid during time period t;
L net,ever -representing the mean net load of the active distribution network over the planning period;
target function expression F of combined optimization model of peak clipping and valley filling lower layer of large-scale photovoltaic integrated 5G base station and active power distribution network LL Comprises the following steps:
F LL ＝αF BS +(1-α)F ADN ,α∈(0,1)
in the formula:
alpha-represents the weight coefficient of the lower model objective function;
the lower-layer optimization model needs to meet the SOC constraint, the charging and discharging power constraint and the power balance constraint of the large-scale 5G base station of the shared energy storage system lease capacity, and is expressed as follows:
in the formula:
i, large-scale photovoltaic integration 5G base station set;
E SES,i (t),E SES,i (t +1) — respectively representing 5G base stations BS i Sharing the energy storage leasing electric quantity in real time in the t time period and the t +1 time period;
P PV,i (t) respectively representing 5G base stations BS i Actual output of the distributed photovoltaic power generation unit in a time period t;
L i (t) -respectively representing 5G base stations BS i Load demand over time t.
Further, the optimal planning upper-layer optimization model and the optimal planning lower-layer combined optimization model of the shared energy storage system are iteratively solved by utilizing a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation scheme;
the method comprises the following substeps:
s1: the iteration number q is set to be 1, the convergence precision epsilon is set to be 0.001, and the upper bound of the upper optimization model objective function of the optimal planning of the shared energy storage system is as follows: UB is- ∞, and the lower bound of the objective function of the optimal planning upper layer optimization model of the shared energy storage system is as follows: LB ∞;
s2: solving an optimal planning upper-layer optimization model of the shared energy storage system, obtaining the optimal planning capacity of the shared energy storage system, and updating an objective function lower bound of the optimal planning upper-layer optimization model of the shared energy storage system;
s3: bringing and solving the optimal planning capacity of the shared energy storage system to obtain the optimal large-scale photovoltaic integration 5G base station operation cost and the active power distribution network net load peak regulation operation cost, and updating the upper bound of the optimal planning upper optimization model objective function of the shared energy storage system;
s4: until the absolute value of the difference between the upper bound of the optimal planning upper-layer optimization model objective function of the shared energy storage system and the lower bound of the optimal planning upper-layer optimization model objective function of the shared energy storage system meets the convergence condition, outputting the planning capacity in the optimal planning upper-layer optimization model of the shared energy storage system and the optimal value of the optimal planning upper-layer optimization model objective function of the shared energy storage system;
s5: and if the convergence condition is not met, updating the variables of the optimal planning upper-layer optimization model of the shared energy storage system, bringing the optimal value of the optimal planning lower-layer optimization model objective function of the shared energy storage system into the optimal planning upper-layer optimization model of the shared energy storage system, updating the iteration number q which is q +1, and returning to the step S2.
A shared energy storage regulation and control management system considering load characteristics of a 5G base station, the system comprising:
the determining module is used for determining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network according to the communication flow load characteristic data;
the first construction module is used for acquiring charge-discharge requirements and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system, constructing an optimal planning upper-layer optimization model of the shared energy storage system and acquiring optimal planning data of the capacity of the shared energy storage system;
the second construction module is used for collecting capacity optimal planning data of the shared energy storage system, constructing a large-scale photovoltaic integration 5G base station and active power distribution network peak load and valley load shifting lower-layer combined optimization model by combining the large-scale photovoltaic integration 5G base station load demand characteristic data and the active power distribution network net load peak and valley characteristic data, and updating the charge and discharge demand and charge and discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system;
and the solving module is used for iteratively solving the upper-layer optimization model and the lower-layer combined optimization model of the optimal planning of the shared energy storage system by using a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
Further, the first construction module is used for collecting charge-discharge requirements and charge-discharge state bit characteristic data of the large-scale photovoltaic integrated 5G base station and the active power distribution network shared energy storage system, transmitting the data to the active power distribution network collaborative interaction information management platform through the communication gateway, constructing an optimal planning upper-layer optimization model of the shared energy storage system, and acquiring capacity optimal planning data of the shared energy storage system by using the optimal planning upper-layer optimization model.
Further, the second construction module is used for collecting capacity optimal planning data of the shared energy storage system, transmitting the capacity optimal planning data to the active power distribution network collaborative interaction information management platform through the communication gateway, constructing a large-scale photovoltaic integrated 5G base station and active power distribution network peak-valley load shifting lower-layer combined optimization model by combining large-scale photovoltaic integrated 5G base station load demand characteristic data and active power distribution network net load peak-valley characteristic data, and updating charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integrated 5G base station and active power distribution network shared energy storage system by using the lower-layer combined optimization model.
Further, the first building block includes: a first building element;
the first construction unit is used for constructing an optimal planning upper-layer optimization model of the shared energy storage system;
the method specifically comprises the following steps: acquiring charge-discharge requirements and charge-discharge state bit characteristic data of a large-scale photovoltaic integrated 5G base station and an active power distribution network shared energy storage system, and constructing an optimal planning upper-layer optimization model of the shared energy storage system by taking the daily average capacity planning cost of the shared energy storage system in a minimized planning period as an optimization target;
an objective function expression F of the optimal planning upper layer optimization model of the shared energy storage system UL Comprises the following steps:
F UL ＝C inv -C ser -C BS -C GRID -C en
in the formula:
C inv -representing the shared energy storage system daily average investment and maintenance costs;
C ser -representing a shared energy storage system capacity rental service revenue;
C BS representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the large-scale 5G base station; c GRID Representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the active power distribution network;
C en representing the equivalent environmental benefit improvement income obtained by the energy storage operator participating in the peak shaving scheduling of the active power distribution network;
average daily investment and maintenance costs C for a shared energy storage system inv Expressed as:
in the formula:
O SES ,M SES respectively representing the daily average investment cost and the daily average maintenance cost of the shared energy storage system;
η p ,η e -representing the shared energy storage system per power configuration cost and per capacity configuration cost, respectively;
η m -representing a shared energy storage system unit power maintenance cost;
T E -representing the expected number of years of use of the shared energy storage system;
shared energy storage system capacity lease income C ser Expressed as:
in the formula:
n is the number of 5G base stations of the large-scale photovoltaic integration;
t is the total scheduling period in the planning cycle;
δ ser -representing a shared energy storage system rental cost per unit capacity;
Δ t — scheduling period length;
the energy storage operator and the large-scale 5G base station generate income C for electricity trading BS Expressed as:
in the formula:
δ dis (t),δ c (t) -respectively representing the unit electricity selling price of the shared energy storage system and the large-scale 5G base station in the time period t;
income C generated by trading electric quantity between energy storage operator and active power distribution network GRID Expressed as:
in the formula:
δ sell (t),δ buy (t) -respectively representing the power price of the active power distribution network on the internet and the power selling price of unit electric quantity in a time period t;
-representing the power purchase power and the power sale power of the shared energy storage system from the active power distribution network respectively at time t;
equivalent environmental benefit improvement income C obtained by energy storage operator participating in active power distribution network peak shaving scheduling en ；
In the formula:
δ en -representing an environmental benefit improvement benefit parameter;
the upper-layer optimization model needs to meet dynamic capacity leasing constraint, charging and discharging power constraint, power balance constraint and SOC constraint of the shared energy storage system, and is represented as follows:
in the formula:
t-set of scheduling periods;
E SES (t),E SES (t +1) -respectively representing the real-time electric quantity of the shared energy storage system in the t time period and the t +1 time period;
η max ,η min respectively representing the real-time electric quantity upper and lower limit coefficients of the shared energy storage system;
α SES (t),α SES,GRID (t) -respectively representing the charge and discharge state bit of the shared energy storage system and the electricity purchasing and selling state bit for carrying out electricity quantity transaction with the active power distribution network, wherein the charge and discharge state bit is a variable of 0-1.
Further, the second building block comprises: the second construction unit is used for constructing a combined optimization model of the large-scale photovoltaic integrated 5G base station and the peak clipping and valley filling lower layer of the active power distribution network;
the method specifically comprises the following steps: the large-scale photovoltaic integration 5G base station and active power distribution network peak clipping and valley filling lower layer combined optimization model is based on capacity optimal planning data of a shared energy storage system and large-scale photovoltaic integrationThe method comprises the steps that 5G base station load demand characteristic data and active power distribution network net load peak-valley characteristic data are obtained, the total operation cost of a large-scale photovoltaic integration 5G base station and the net load peak-load regulation operation cost of an active power distribution network are minimized as optimization targets, and the large-scale photovoltaic integration 5G base station optimization operation cost expression F BS Comprises the following steps:
F BS ＝C G +C cv +C ser +C BS
in the formula:
C G the payment cost required by the large-scale photovoltaic integrated 5G base station for purchasing electricity from the power grid is represented;
C cv representing the light abandonment penalty of the large-scale distributed photovoltaic power generation unit;
electricity purchase cost C of large-scale photovoltaic integrated 5G base station power grid G Expressed as:
in the formula:
P GRID,i (t) -representing a 5G base station BS i Purchasing power from the active power distribution network in a time period t;
light abandonment punishment C of large-scale distributed photovoltaic power generation units cv Expressed as:
in the formula:
δ cv -representing a photovoltaic curtailment penalty coefficient;
P cv,i (t) -representing a 5G base station BS i Photovoltaic clipping power at time t;
active power distribution network net load peak regulation operation cost expression F ADN Comprises the following steps:
in the formula:
L net (t) -representing the net load power of the active distribution network at time period t;
L net,ever -representing the mean net load of the active distribution network over the planning period;
target function expression F of combined optimization model of peak clipping and valley filling lower layer of large-scale photovoltaic integrated 5G base station and active power distribution network LL Comprises the following steps:
F LL ＝αF BS +(1-α)F ADN ,α∈(0,1)
in the formula:
alpha-represents the weight coefficient of the lower model objective function;
the lower-layer optimization model needs to meet SOC constraints, charge-discharge power constraints and power balance constraints of the large-scale 5G base station of the shared energy storage system leasing capacity, and is expressed as follows:
in the formula:
i, large-scale photovoltaic integration 5G base station set;
E SES,i (t),E SES,i (t +1) — respectively representing 5G base stationsBS i Sharing the energy storage lease electric quantity in real time at the t time and the t +1 time;
P PV,i (t) respectively representing 5G base stations BS i Actual output of the distributed photovoltaic power generation unit in a time period t;
L i (t) respectively representing 5G base stations BS i Load demand during time t.
The solving module comprises:
the setting unit is used for setting the iteration number q to be 1, the convergence precision epsilon to be 0.001, and the upper bound of the upper optimization model objective function of the optimal planning of the shared energy storage system is as follows: UB is- ∞, and the lower bound of the objective function of the optimal planning upper layer optimization model of the shared energy storage system is as follows: LB ∞;
the first solving unit is used for solving an optimal planning upper-layer optimization model of the shared energy storage system, obtaining the optimal planning capacity of the shared energy storage system and updating an optimal planning upper-layer optimization model target function lower bound of the shared energy storage system;
the second solving unit is used for bringing and solving the optimal planning capacity of the shared energy storage system, obtaining the optimal large-scale photovoltaic integration 5G base station operation cost and the active power distribution network net load peak shaving operation cost, and updating the upper bound of the optimal planning upper optimization model objective function of the shared energy storage system;
the output unit is used for outputting the planning capacity in the optimal planning upper-layer optimization model of the shared energy storage system and the optimal value of the objective function of the optimal planning upper-layer optimization model of the shared energy storage system when the absolute value of the difference between the upper bound of the objective function of the optimal planning upper-layer optimization model of the shared energy storage system and the lower bound of the objective function of the optimal planning upper-layer optimization model of the shared energy storage system meets the convergence condition;
and the updating unit is used for updating the variables of the optimal planning upper-layer optimization model of the shared energy storage system when the convergence condition is not met, bringing the optimal value of the optimal planning lower-layer optimization model of the shared energy storage system into the optimal planning upper-layer optimization model of the shared energy storage system, updating the iteration number q which is q +1, and returning to the first solving unit.
A computer readable storage medium having stored therein computer executable instructions for performing the method of any one of the above when executed by a processor.
The shared energy storage regulation and control method and the management system considering the load characteristics of the 5G base station have the following technical effects:
1. compared with the prior art, the optimal planning upper-layer optimization model of the shared energy storage system can monitor and collect the multi-main-body state information of the energy storage demand side in real time.
2. Considering the differentiated time-varying requirements of different shared energy storage system optimal planning upper-layer optimization models on shared energy storage resources, and providing energy storage leasing service with quick response and accurate regulation and control for energy storage demanders by means of an active power distribution network collaborative interaction information management platform according to the centralized planning shared energy storage capacity and power in the load demand characteristics of the large-scale photovoltaic integrated 5G base station.
3. And a large-scale photovoltaic integration 5G base station optimization module is arranged, so that low-carbon economic operation of the large-scale photovoltaic integration 5G base station and the active power distribution network is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate a certain embodiment of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a block diagram of a shared energy storage optimal planning and control scheme solving step considering the load characteristics of a 5G base station according to the present invention;
FIG. 2 is a simulation diagram of a load curve of a large-scale photovoltaic integrated 5G base station according to the present invention;
FIG. 3 is a calculation flow chart of an optimal planning and control scheme of the collaborative interaction information management platform of the active power distribution network according to the invention;
fig. 4 is an overall framework diagram of the shared energy storage regulation and control device considering the load characteristics of the 5G base station.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Example 1:
taking an active power distribution network comprising a large-scale 5G base station and a shared energy storage system as an example, the steps of solving the shared energy storage optimal planning and regulation scheme considering the load characteristics of the 5G base station shown in fig. 1 are specifically explained, and the technical scheme provided by the invention mainly comprises the following four steps: (1) the method comprises the steps of collecting communication flow load characteristic data of a communication load dense area, simulating a large-scale 5G base station load curve, (2) collecting charging and discharging power and charging and discharging state bit data information of a shared energy storage system used by an energy storage demander, transmitting the data information to an active power distribution network collaborative interaction information management platform through a communication gateway, and constructing an optimal planning upper layer optimization model of the shared energy storage system, (3) collecting optimal planning capacity data of the shared energy storage, transmitting the optimal planning capacity data to the active power distribution network collaborative interaction information management platform through the communication gateway, and constructing a large-scale photovoltaic integrated 5G base station optimized operation and active power distribution network peak clipping and valley filling lower layer model, (4) adopting a reconstruction and decomposition (R & D) algorithm to iteratively solve the upper layer optimization model and the lower layer optimization model so as to obtain an optimal shared energy storage planning scheme and a regulation scheme.
Collecting communication traffic load characteristic data of a communication load dense area, and simulating a large-scale 5G base station load curve;
the active power distribution network comprising the large-scale photovoltaic integrated 5G base stations and the shared energy storage system is provided with 150 5G 5G base stations which are evenly distributed in 3 different functional areas including residential areas, business areas and working areas. By investigating and simulating the change trend of the communication traffic of the large-scale 5G base station in each functional area, the daily load curve of the large-scale 5G base station in each functional area can be obtained as shown in fig. 2.
Secondly, collecting the charge and discharge power and charge and discharge state bit data of the shared energy storage system used by the energy storage demander, transmitting the charge and discharge power and charge and discharge state bit data to the active power distribution network collaborative interaction information management platform through the communication gateway, and constructing a large-scale photovoltaic integrated 5G base station optimized operation and active power distribution network peak clipping valley filling lower layer model;
in the upper-layer optimization model, the operation cost of the large-scale photovoltaic integrated 5G base station and the peak clipping and valley filling effect of the active power distribution network in the lower-layer optimization model are directly influenced by the shared energy storage system optimal capacity planning decision, and in the lower-layer optimization model, the economy of the upper-layer shared energy storage optimal planning can be influenced by changing the shared energy storage use decision, namely the upper-layer optimization model depends on the optimal solution of the lower-layer optimization model, and the optimal solution of the lower-layer optimization model is directly influenced by the decision variables of the upper-layer optimization model.
A. Upper optimization model objective function
The invention defines a large-scale photovoltaic integration 5G base station and a distributed energy index set thereof as I belonging to I [ identical to ] { 1.,. N }, and a scheduling time period set as T belonging to T [ identical to ] { 1.,. T }. The operation and maintenance cost, the capacity lease service income, the equivalent environment improvement income and the electric quantity trade income of the shared energy storage system are comprehensively considered by the upper optimization model, the daily average capacity planning cost of the shared energy storage system in the minimized planning period is taken as an optimization target, and an objective function expression F of the upper model UL Comprises the following steps:
F UL ＝C inv -C ser -C BS -C GRID -C en
in the formula:
C inv -representing the shared energy storage system daily average investment and maintenance costs;
C ser -representing a shared energy storage system capacity rental service revenue;
C BS representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the large-scale 5G base station;
C GRID representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the active power distribution network;
C en representing the equivalent environmental benefit improvement income obtained by the energy storage operator participating in the peak shaving scheduling of the active power distribution network;
average daily investment and maintenance costs C for a shared energy storage system inv Expressed as:
in the formula:
O SES ,M SES -respectively representing the daily average investment cost and the daily average maintenance cost of the shared energy storage system;
η p ,η e -representing the shared energy storage system per power configuration cost and per capacity configuration cost, respectively;
η m -representing a shared energy storage system unit power maintenance cost;
T E -representing the expected number of years of use of the shared energy storage system;
shared energy storage system capacity lease revenue C ser Expressed as:
in the formula:
n is the number of 5G base stations of the large-scale photovoltaic integration;
t is the total scheduling period in the planning cycle;
δ ser -representing a shared energy storage system rental cost per unit capacity;
delta t-scheduling period Length
Revenue C generated by electric quantity trading between energy storage operators and large-scale 5G base station BS Expressed as:
in the formula:
δ dis (t),δ c (t) -respectively representing the unit electricity selling price of the shared energy storage system and the large-scale 5G base station in the time period t.
Income C generated by trading electric quantity between energy storage operator and active power distribution network GRID Expressed as:
in the formula:
δ sell (t),δ buy (t) -respectively representing the power price of the active power distribution network on the internet and the power selling price of unit electric quantity in a time period t;
equivalent environmental benefit improvement income C obtained by energy storage operator participating in active power distribution network peak shaving scheduling en ；
In the formula:
δ en -representing an environmental benefit improvement benefit parameter;
B. constraint conditions of upper optimization model
The upper-layer optimization model needs to satisfy the dynamic capacity leasing constraint, the charge-discharge power constraint, the power balance constraint and the state of charge (SOC) constraint of the shared energy storage system, and is expressed as follows:
in the formula:
t-set of scheduling periods;
E SES (t),E SES (t +1) -respectively representing the real-time electric quantity of the shared energy storage system in the t time period and the t +1 time period;
η max ,η min respectively representing the real-time electric quantity upper and lower limit coefficients of the shared energy storage system;
α SES (t),α SES,GRID (t) -respectively representing the charge and discharge state bit of the shared energy storage system and the electricity purchasing and selling state bit for carrying out electricity quantity transaction with the active power distribution network, wherein the charge and discharge state bit is a variable of 0-1.
Thirdly, collecting the shared energy storage optimal planning capacity data, transmitting the data to an active power distribution network collaborative interaction information management platform through a communication gateway, constructing a large-scale photovoltaic integration 5G base station optimized operation and active power distribution network peak clipping and valley filling lower layer model,
A. lower layer optimization model objective function
The optimization model of the lower layer comprehensively considers the optimization operation economy of the large-scale photovoltaic integration 5G base station and the peak clipping and valley filling effect of the active power distribution network, data are acquired based on the shared energy storage optimal planning capacity, the optimization target is that the total operation cost of the large-scale photovoltaic integration 5G base station and the net load peak valley difference of the active power distribution network are minimized, and the expression F of the total operation cost of the large-scale photovoltaic integration 5G base station BS Comprises the following steps:
F BS ＝C G +C cv +C ser +C BS
in the formula:
C G the payment cost required by the large-scale photovoltaic integrated 5G base station for purchasing electricity from the power grid is represented;
C cv representing the light abandonment penalty of the large-scale distributed photovoltaic power generation unit;
electricity purchase cost C of large-scale photovoltaic integrated 5G base station power grid G Expressed as:
in the formula:
P GRID,i (t) -representing a 5G base station BS i Purchasing power from the active power distribution network in a time period t;
light abandonment punishment C of large-scale distributed photovoltaic power generation units cv Expressed as:
in the formula:
δ cv -representing a photovoltaic curtailment penalty coefficient;
P cv,i (t) -representing a 5G base station BS i Photovoltaic clipping power at time t;
active power distribution network net load peak valley difference expression F ADN Comprises the following steps:
in the formula:
L net (t) -represents the net load power of the active distribution grid during time period t;
L net,avg -representing the mean net load of the active distribution network over the planning period;
lower model objective function expression F LL Comprises the following steps:
F LL ＝αF BS +(1-α)F ADN ,α∈(0,1)
in the formula:
alpha-represents the underlying model objective function weight coefficient.
B. Constraint conditions of lower optimization model
The lower layer optimization model needs to meet the state of charge (SOC) constraint, the charge and discharge power constraint and the power balance constraint of the large-scale 5G base station of the shared energy storage system leasing capacity, and is represented as follows:
in the formula:
i, large-scale photovoltaic integration 5G base station set;
E SES,i (t),E SES,i (t +1) — respectively representing 5G base stations BS i Sharing the energy storage leasing electric quantity in real time in the t time period and the t +1 time period;
P PV,i (t) respectively representing 5G base stations BS i Actual output of the distributed photovoltaic power generation unit in a time period t; l is i (t) respectively representing 5G base stations BS i Load demand during time t.
Fourthly, iterative solution is carried out on the upper-layer optimization model and the lower-layer optimization model by adopting a reconstruction and decomposition (R & D) algorithm
An embedded reconstruction and decomposition (R & D) algorithm of the active power distribution network collaborative interaction information management platform can perform iterative solution on an upper layer optimization model and a lower layer optimization model to obtain an optimal shared energy storage planning scheme and a shared energy storage regulation and control scheme, and the calculation flow is shown in figure 3 and comprises the following steps:
(1) setting the iteration number q to be 1, the convergence precision epsilon to be 0.001, and setting an upper bound and a lower bound of an objective function of an upper-layer optimization model: UB ═ infinity, LB ═ infinity;
(2) solving an upper-layer optimization model to obtain the optimal planning capacity of the shared energy storage system, and updating the lower bound of the objective function of the upper-layer optimization model;
(3) bringing the optimal planning capacity of the shared energy storage system into a lower-layer optimization model and solving to obtain the optimal large-scale photovoltaic integrated 5G base station operation cost and the peak clipping and valley filling effect of the active power distribution network, and updating an upper bound of an objective function of an upper-layer optimization model;
(4) outputting the optimal planning capacity of the shared energy storage system and the optimal value of the objective function until the absolute value of the difference between the upper bound of the objective function of the upper optimization model and the lower bound of the objective function of the upper optimization model meets the convergence condition;
(5) and if the convergence condition is not met, substituting the optimal value of the objective function of the lower-layer optimization model into the upper-layer optimization model, updating the iteration number q to be q +1, and returning to the step (2).
The embodiment further provides a shared energy storage regulation and control device considering the load characteristics of a 5G base station, and an overall framework is shown in fig. 4, and includes: the system comprises an active power distribution network collaborative interaction information management platform, a shared energy storage monitoring terminal and a large-scale photovoltaic integrated 5G base station monitoring terminal.
The power monitoring terminal is mainly divided into a shared energy storage monitoring terminal and a large-scale photovoltaic integrated 5G base station monitoring terminal, mainly comprises an intelligent ammeter and a high-definition camera, and is used for collecting large-scale 5G base station moving loop monitoring data, distributed photovoltaic output data, active power distribution network net load dynamic data and shared energy storage charge state data;
the communication gateway is connected with the power terminal and used for transmitting the data information to the active power distribution network collaborative interaction information management platform;
the active power distribution network collaborative interaction information management platform is connected with a communication gateway, data information transmitted by the communication gateway is preprocessed through multi-main-body shared energy storage double-layer mixed integer programming model construction, iterative solution is conducted on the multi-main-body shared energy storage double-layer mixed integer programming model through a reconstruction and decomposition (R & D) algorithm, and specific regulation and control instructions are generated.
The active power distribution network collaborative interaction information management platform further comprises an electric power terminal information management module, a statistics and analysis module and an application and regulation module.
The power terminal information management module is used for detecting and managing data information such as the online state, the geographic position and the online performance of the power communication terminal, the statistics and analysis module is connected with the power terminal management module and used for storing, exchanging, counting and fault alarm identifying the data information of the power terminals belonging to different main bodies, the preprocessing of establishing a multi-main-body shared energy storage double-layer mixed integer programming model is carried out, the regulation and control management module is connected with the statistics and analysis module, the multi-main-body shared energy storage double-layer mixed integer programming model is received and solved, a specific regulation and control instruction is generated and sent to the shared energy storage system.
Example 2:
in order to better implement the method, the embodiment of the invention also provides a shared energy storage regulation and control system considering the load characteristics of the 5G base station;
for example, a shared energy storage regulation and control management system considering load characteristics of a 5G base station, the system comprising:
the determining module is used for determining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network according to the communication flow load characteristic data;
the first construction module is used for acquiring charge-discharge requirements and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system, constructing an optimal planning upper-layer optimization model of the shared energy storage system and acquiring optimal planning data of the capacity of the shared energy storage system;
the second construction module is used for collecting capacity optimal planning data of the shared energy storage system, constructing a large-scale photovoltaic integration 5G base station and active power distribution network peak load and valley load shifting lower-layer combined optimization model by combining the large-scale photovoltaic integration 5G base station load demand characteristic data and the active power distribution network net load peak and valley characteristic data, and updating the charge and discharge demand and charge and discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system;
and the solving module is used for iteratively solving the upper-layer optimization model and the lower-layer combined optimization model of the optimal planning of the shared energy storage system by using a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
Example 3:
it will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present invention provides a storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to perform the steps in any one of the methods for controlling shared energy storage according to the load characteristics of a 5G base station provided in the embodiments of the present invention.
For example, the instructions may perform the steps of: according to the communication flow load characteristic data, determining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network; acquiring charge-discharge requirements and charge-discharge state bit characteristic data of a large-scale photovoltaic integrated 5G base station and an active power distribution network shared energy storage system, constructing an optimal planning upper-layer optimization model of the shared energy storage system, and acquiring optimal planning data of capacity of the shared energy storage system; acquiring capacity optimal planning data of a shared energy storage system, combining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network, constructing a peak clipping and valley filling lower-layer combined optimization model of the large-scale photovoltaic integration 5G base station and the active power distribution network, and updating charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system; and (3) utilizing a reconstruction and decomposition algorithm to iteratively solve the upper-layer optimization model and the lower-layer combined optimization model of the optimal planning of the shared energy storage system to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. The shared energy storage regulation and control method considering the load characteristics of the 5G base station is characterized by comprising the following steps of:
according to the communication flow load characteristic data, determining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network;
acquiring charge-discharge requirements and charge-discharge state bit characteristic data of a large-scale photovoltaic integrated 5G base station and an active power distribution network shared energy storage system, constructing an optimal planning upper-layer optimization model of the shared energy storage system, and acquiring optimal planning data of capacity of the shared energy storage system;
acquiring capacity optimal planning data of a shared energy storage system, combining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network, constructing a peak clipping and valley filling lower-layer combined optimization model of the large-scale photovoltaic integration 5G base station and the active power distribution network, and updating charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system;
and (3) iteratively solving the optimal planning upper-layer optimization model and the lower-layer combined optimization model of the shared energy storage system by using a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
2. The shared energy storage regulation and control method considering the load characteristics of the 5G base station according to claim 1, wherein: the method comprises the steps of collecting charge-discharge demand and charge-discharge state bit characteristic data of a large-scale photovoltaic integrated 5G base station and an active power distribution network shared energy storage system, transmitting the data to an active power distribution network collaborative interaction information management platform through a communication gateway, constructing an optimal planning upper layer optimization model of the shared energy storage system, and obtaining capacity optimal planning data of the shared energy storage system by utilizing the optimal planning upper layer optimization model.
3. The shared energy storage regulation and control method considering the load characteristics of the 5G base station according to claim 2, wherein: acquiring charge-discharge requirements and charge-discharge state bit characteristic data of a large-scale photovoltaic integrated 5G base station and an active power distribution network shared energy storage system, and constructing an optimal planning upper-layer optimization model of the shared energy storage system by taking the daily average capacity planning cost of the shared energy storage system in a minimized planning period as an optimization target; an objective function expression F of the optimal planning upper layer optimization model of the shared energy storage system UL Comprises the following steps:
F UL ＝C inv -C ser -C BS -C GRID -C en
in the formula:
C inv -representing the shared energy storage system daily average investment and maintenance costs;
C ser -representing a shared energy storage system capacity rental service revenue;
C BS representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the large-scale 5G base station; c GRID Representing the electricity purchasing and selling income generated by the electricity quantity transaction between the energy storage operator and the active power distribution network;
C en representing the equivalent environmental benefit improvement income obtained by the energy storage operator participating in the peak shaving scheduling of the active power distribution network;
average daily investment and maintenance costs C for a shared energy storage system inv Expressed as:
in the formula:
O SES ,M SES -respectively representing the daily average investment cost and the daily average maintenance cost of the shared energy storage system;
η p ,η e -representing the shared energy storage system per power configuration cost and per capacity configuration cost, respectively;
η m -representing a shared energy storage system unit power maintenance cost;
T E -representing the expected number of years of use of the shared energy storage system;
shared energy storage system capacity lease income C ser Expressed as:
in the formula:
n is the number of 5G base stations of the large-scale photovoltaic integration;
t is the total scheduling period in the planning cycle;
δ ser -representing a shared energy storage system sheetA bit capacity rental fee;
Δ t — scheduling period length;
the energy storage operator and the large-scale 5G base station generate income C for electricity trading BS Expressed as:
in the formula:
income C generated by trading electric quantity between energy storage operator and active power distribution network GRID Expressed as:
in the formula:
δ sell (t),δ buy (t) -respectively representing the power price of the active power distribution network on the internet and the power selling price of unit electric quantity in a time period t;
the energy storage operator participates in the peak regulation scheduling of the active power distribution networkEquivalent environmental benefit improvement gain C en ；
In the formula:
δ en -representing an environmental benefit improvement benefit parameter;
the upper-layer optimization model needs to meet dynamic capacity leasing constraint, charging and discharging power constraint, power balance constraint and SOC constraint of the shared energy storage system, and is represented as follows:
in the formula:
t-set of scheduling periods;
E SES (t),E SES (t +1) -respectively representing the real-time electric quantity of the shared energy storage system in the t time period and the t +1 time period;
η max ,η min respectively representing the upper and lower limit coefficients of the real-time electric quantity of the shared energy storage system;
α SES (t),α SES,GRID (t) -respectively representing the charge and discharge state bit of the shared energy storage system and the electricity purchasing and selling state bit for carrying out electricity quantity transaction with the active power distribution network, wherein the charge and discharge state bit is a variable of 0-1.
4. The shared energy storage regulation and control method considering the load characteristics of the 5G base station according to claim 1, wherein: the method comprises the steps of collecting capacity optimal planning data of a shared energy storage system, transmitting the capacity optimal planning data to an active power distribution network collaborative interaction information management platform through a communication gateway, combining load demand characteristic data of a large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network, constructing a large-scale photovoltaic integration 5G base station and active power distribution network peak-clipping valley-filling lower-layer combined optimization model, and updating charge-discharge demand and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system through the lower-layer combined optimization model.
5. The shared energy storage regulation and control method considering the load characteristics of the 5G base station according to claim 4, wherein: the large-scale photovoltaic integrated 5G base station and active power distribution network peak clipping and valley filling lower layer combined optimization model is based on shared energy storage system capacity optimal planning data, large-scale photovoltaic integrated 5G base station load demand characteristic data and active power distribution network net load peak valley characteristic data, and takes the total operation cost of the large-scale photovoltaic integrated 5G base station and the net load peak clipping operation cost of the active power distribution network as optimization targets;
total operation cost expression F of large-scale photovoltaic integrated 5G base station BS Comprises the following steps:
F BS ＝C G +C cv +C ser +C BS
in the formula:
C G the payment cost required by the large-scale photovoltaic integrated 5G base station for purchasing electricity from the power grid is represented;
C cv representing the light abandoning punishment of the large-scale distributed photovoltaic power generation units;
electricity purchase cost C of large-scale photovoltaic integrated 5G base station power grid G Expressed as:
in the formula:
P GRID,i (t) -representing a 5G base station BS i Purchasing power from the active power distribution network in a time period t;
light abandonment punishment C of large-scale distributed photovoltaic power generation units cv Expressed as:
in the formula:
δ cv -representing a photovoltaic curtailment penalty coefficient;
P cv,i (t) -representing a 5G base station BS i At a time period tPhotovoltaic reduction of power;
active power distribution network net load peak regulation operation cost expression F ADN Comprises the following steps:
in the formula:
L net (t) -represents the net load power of the active distribution grid during time period t;
L net,ever -representing a net load mean of the active distribution network over a planning period;
target function expression F of combined optimization model of peak clipping and valley filling lower layer of large-scale photovoltaic integrated 5G base station and active power distribution network LL Comprises the following steps:
F LL ＝αF BS +(1-α)F ADN ,α∈(0,1)
in the formula:
alpha-represents the weight coefficient of the lower model objective function;
the lower-layer optimization model needs to meet SOC constraints, charge-discharge power constraints and power balance constraints of the large-scale 5G base station of the shared energy storage system leasing capacity, and is expressed as follows:
in the formula:
i, large-scale photovoltaic integration 5G base station set;
E SES,i (t),E SES,i (t +1) — respectively representing 5G base stations BS i Sharing the energy storage leasing electric quantity in real time in the t time period and the t +1 time period;
P PV,i (t) respectively representing 5G base stations BS i Actual output of the distributed photovoltaic power generation unit in a time period t;
L i (t) respectively representing 5G base stations BS i Load demand during time t.
6. The shared energy storage regulation and control method considering the load characteristics of the 5G base station according to claim 1, wherein: the optimal planning upper-layer optimization model and the lower-layer combined optimization model of the shared energy storage system are iteratively solved by utilizing a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation scheme;
the method comprises the following substeps:
s1: the iteration number q is set to be 1, the convergence precision epsilon is set to be 0.001, and the upper bound of the upper optimization model objective function of the optimal planning of the shared energy storage system is as follows: UB is- ∞, and the lower bound of the objective function of the optimal planning upper layer optimization model of the shared energy storage system is as follows: LB ∞;
s2: solving an optimal planning upper-layer optimization model of the shared energy storage system, obtaining the optimal planning capacity of the shared energy storage system, and updating an optimal planning upper-layer optimization model target function lower bound of the shared energy storage system;
s3: bringing and solving the optimal planning capacity of the shared energy storage system to obtain the optimal large-scale photovoltaic integration 5G base station operation cost and the active power distribution network net load peak regulation operation cost, and updating the upper bound of the optimal planning upper optimization model objective function of the shared energy storage system;
s4: until the absolute value of the difference between the upper bound of the optimal planning upper-layer optimization model objective function of the shared energy storage system and the lower bound of the optimal planning upper-layer optimization model objective function of the shared energy storage system meets the convergence condition, outputting the planning capacity in the optimal planning upper-layer optimization model of the shared energy storage system and the optimal value of the optimal planning upper-layer optimization model objective function of the shared energy storage system;
s5: and if the convergence condition is not met, updating variables of the upper optimization model of the optimal planning of the shared energy storage system, bringing the optimal value of the objective function of the lower optimization model of the optimal planning of the shared energy storage system into the upper optimization model of the optimal planning of the shared energy storage system, updating the iteration number q which is q +1, and returning to the step S2.
7. A shared energy storage regulation and control management system considering load characteristics of a 5G base station is characterized by comprising:
the determining module is used for determining load demand characteristic data of the large-scale photovoltaic integration 5G base station and net load peak-valley characteristic data of the active power distribution network according to the communication flow load characteristic data;
the first construction module is used for acquiring charge-discharge requirements and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system, constructing an optimal planning upper-layer optimization model of the shared energy storage system and acquiring optimal planning data of the capacity of the shared energy storage system;
the second construction module is used for collecting capacity optimal planning data of the shared energy storage system, constructing a large-scale photovoltaic integration 5G base station and active power distribution network peak load and valley load shifting lower-layer combined optimization model by combining the large-scale photovoltaic integration 5G base station load demand characteristic data and the active power distribution network net load peak and valley characteristic data, and updating the charge and discharge demand and charge and discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system;
and the solving module is used for iteratively solving the upper-layer optimization model and the lower-layer combined optimization model of the optimal planning of the shared energy storage system by using a reconstruction and decomposition algorithm to obtain a shared energy storage planning scheme and a shared energy storage regulation and control scheme.
8. The shared energy storage regulation and control management system considering 5G base station load characteristics according to claim 7, wherein: the first construction module is used for collecting charge-discharge requirements and charge-discharge state bit characteristic data of the large-scale photovoltaic integration 5G base station and the active power distribution network shared energy storage system, transmitting the data to the active power distribution network collaborative interaction information management platform through the communication gateway, constructing an optimal planning upper layer optimization model of the shared energy storage system, and acquiring capacity optimal planning data of the shared energy storage system by using the optimal planning upper layer optimization model.
9. The shared energy storage regulation and control management system considering 5G base station load characteristics according to claim 7, wherein: the second construction module is used for collecting capacity optimal planning data of the shared energy storage system, transmitting the capacity optimal planning data to the active power distribution network collaborative interaction information management platform through the communication gateway, constructing a large-scale photovoltaic integrated 5G base station and active power distribution network peak load and valley load shifting lower-layer combined optimization model by combining large-scale photovoltaic integrated 5G base station load demand characteristic data and active power distribution network net load peak and valley characteristic data, and updating charge-discharge demand and charge-discharge state position characteristic data of the large-scale photovoltaic integrated 5G base station and the active power distribution network shared energy storage system by using the lower-layer combined optimization model.
10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1 to 6.
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