CN110119886B - Active distribution network dynamic planning method - Google Patents
Active distribution network dynamic planning method Download PDFInfo
- Publication number
- CN110119886B CN110119886B CN201910312494.XA CN201910312494A CN110119886B CN 110119886 B CN110119886 B CN 110119886B CN 201910312494 A CN201910312494 A CN 201910312494A CN 110119886 B CN110119886 B CN 110119886B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power
- storage system
- charging
- load
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention provides a dynamic planning method for an active power distribution network, which comprises the following steps: s1, establishing a demand side load power prediction model, a distributed power supply power prediction model and a charge and discharge model of an energy storage system; s2, establishing an objective function in a social comprehensive economic benefit maximization mode according to the demand side load power prediction model, the distributed power supply power prediction model and the charge and discharge model of the energy storage system; s3, planning the energy storage systems of the active power distribution network by adopting a chaotic particle swarm hierarchical optimization algorithm to obtain the optimal position and power of each energy storage system; and S4, obtaining a charge-discharge strategy of each energy storage system according to the optimal position and power of each energy storage system. The economic efficiency, the reliability and the stability of the power distribution network are comprehensively considered, and the optimization effect of demand side management and an energy storage battery on the active power distribution network is considered. The stability and the reliability of the power distribution network are considered while the maximization of the economic benefit of operators is guaranteed.
Description
Technical Field
The invention relates to the technical field of dynamic planning of active power distribution networks, in particular to a dynamic planning method of an active power distribution network.
Background
With the pulling of a new turn of electric power system reformation sequence in China, the reformation on the electricity selling side and the electricity transmission and distribution price can generate profound influence on a power plant, an electric network and an electricity user. The more and more the various types of controllable devices such as distributed power supplies, energy storage systems, demand side response loads, flexible loads and the like of photovoltaic, wind energy, hydrogen energy and the like are applied to the power distribution network, and due to the uncertainty of multiple factors such as the distributed power supplies, the energy storage systems and the demand side response loads, the planning of the power distribution network needs to deal with various factors, such as the comprehensive utilization of various resources, the mutual coordination of various load types and the cooperative win-win problem of different stakeholders, and the passive control and passive management mode of the existing power distribution network is difficult to adapt to the requirement of the efficient utilization of distributed energy sources, so that the research of active power distribution network planning considering the active control and active management of demand side response loads and energy storage systems is urgently needed to be developed, the active elimination and multistage coordinated utilization of various renewable energy sources are realized, the uncertain factors such as the flexible loads are reasonably regulated, and the network topology is changed to improve the reliability and stability of the system. The demand side response load and the energy storage system are important interactive resources under the framework of the active power distribution network, and if the demand side response load and the energy storage system can be fully utilized, the purposes of optimizing an energy utilization mode, improving the power supply reliability and realizing energy conservation and emission reduction can be achieved.
In a patent document (application number: CN 201410749514.7), a power demand prediction planning and reliability-based power distribution network construction method is proposed, which is used for performing power demand prediction planning aiming at the characteristics of different regions, setting different reliable targets, optimizing weak links of a power distribution network, and reasonably designing power distribution network projects.
In another patent document (application number: 201710235551. X), a dynamic planning method for an active power distribution network energy storage system is provided, wherein an active power distribution network containing distributed photovoltaic and energy storage systems is taken as a research object, the dynamic planning method is based on a double-layer planning model and combines a shortest path idea to optimize an output strategy of the energy storage system, so that installed capacities of the distributed photovoltaic and energy storage systems under an optimal output strategy of the energy storage system are determined, in the double-layer planning model, the upper-layer planning determines the installed capacities of the distributed photovoltaic and energy storage systems by taking the minimum annual comprehensive cost of the power distribution network as a target, a planning scheme is generated, and the lower-layer planning optimizes the output strategy of the energy storage system in one operating cycle and one day by taking the minimum typical daily operating cost and load peak-valley difference of the power distribution network as a target. Compared with the prior art, the method can effectively improve the operating characteristics of the active power distribution network and the optimal configuration of the distributed power supply and the energy storage system.
In another patent document (application number: CN 201810170942.2), a multi-objective hierarchical planning method for an active power distribution network is proposed, which takes demand side management and energy storage into consideration. The upper-layer planning model takes various economic costs of a power distribution company and the reliability and stability of a power distribution network as upper-layer targets in a planning process in a comprehensive consideration mode, considers investment and benefits of demand side management and energy storage equipment, and determines a target function and constraint conditions; the lower layer planning model takes the minimum cut-off amount of the distributed power supply in the planning process as an objective function, considers the maximum effective utilization of the distributed power supply, reduces the output of the distributed power supply through measures of wind abandoning, light abandoning and the like, and adjusts a transformer tap, reactive power compensation equipment and the like to perform active control.
None of the above patents considers the element of maximizing social benefit.
Disclosure of Invention
The invention aims to provide a dynamic planning method for an energy storage system of an active power distribution network, so as to solve the problem that the maximization of social benefits is not considered in the prior art.
In order to solve the technical problem, the invention provides a dynamic planning method for an active power distribution network, which comprises the following steps:
s1, establishing a demand side load power prediction model, a distributed power supply power prediction model and a charge and discharge model of an energy storage system;
s2, establishing an objective function according to the demand side load power prediction model, the distributed power supply power prediction model and the charge and discharge model of the energy storage system in a comprehensive social economic benefit maximization mode;
s3, planning an energy storage system of the active power distribution network by adopting a chaotic particle swarm hierarchical optimization algorithm to obtain the optimal position and power of the energy storage system;
and S4, obtaining a charge-discharge strategy of the energy storage system according to the optimal position and the optimal power of the energy storage system.
The method for establishing the demand side load power prediction model specifically comprises the following steps:
acquiring historical load data of a demand side based on weather forecast, and extracting load variables related to meteorological factors;
and establishing a demand side load power prediction model according to the historical load data and the load variables related to meteorological factors.
The method for establishing the distributed power supply power prediction model specifically comprises the following steps:
acquiring the output characteristics of the historical load data of weather forecast on wind power generation, and modeling to obtain the output probability density distribution and the output expected value of the wind power generation;
acquiring output characteristics of photovoltaic power generation by historical load data of weather forecast and modeling to obtain output probability density distribution and output expected value of the photovoltaic power generation;
and establishing a distributed power supply power prediction model according to the output probability density distribution and the processing expected value of the wind power generation, the processing probability density distribution and the output expected value of the photovoltaic power generation.
Wherein, the charge-discharge model of the energy storage system is as follows:
and (3) discharging:
and (3) charging process:
SOC (t) represents the remaining charge level of the ESS at time t; epsilon represents the self-discharge rate per hour of the residual electric quantity of the energy storage system,%/h; p ess，dis (t)、P ess，c (t) respectively showing the discharge power and the charging power of the energy storage system; alpha and beta respectively represent the discharging and charging efficiencies of the energy storage system; e e Is the capacity of the energy storage system; t is the sampling interval, and 1h is taken.
The constraint conditions of the charge and discharge model of the energy storage system are as follows:
charge and discharge power constraint: -P ess，c，max (t)≤P ess (t)≤P ess，dis，max (t)
Depth of discharge constraint and charge quantity constraint: SOC min ≤SOC(t)≤SOC max
And (3) charge time constraint: h is less than or equal to h max
P ess (t) charging or discharging power at time t; p ess，c，max (t)、P ess，dis，max (t) represents the maximum charge power and discharge power allowed by the battery energy storage system, respectively, and the "-" number represents the battery charge, SOC min 、SOC max Respectively the minimum residual capacity and the maximum residual capacity of the battery system, h is the charge and discharge times of the battery in one day, h is max Is a limit value of the number of times of charge and discharge in one day;
wherein, the node voltage constraint conditions are as follows: u shape imin ≤U i ≤U imax
i＝1，2，3，...，n，U imin 、U imax Lower and upper limits of the voltage at the ith node, respectively; n is the total number of nodes of the power distribution network;
wherein, the trend constraint is:
wherein, P G，i 、Q G，i Active and reactive power of the power supply at the i node respectively; p is L，i 、Q L，i Respectively the active and reactive loads at the i node; u shape i 、U j The voltage amplitudes of the i node and the j node are respectively; g ij 、B ij Respectively a real part and an imaginary part of a node admittance matrix element; theta ij Is the voltage phase angle difference of node i and node j;
P G，i 、Q G，i active and reactive power output of the power supply at the i node respectively; p L，i 、Q L，i Respectively the active and reactive loads at the i node; u shape i 、U j The voltage amplitudes of the i node and the j node respectively; g ij 、B ij Respectively a real part and an imaginary part of a node admittance matrix element; theta.theta. ij Is the voltage phase angle difference of node i and node j;
wherein the branch current constraints are: i is i ≤I imax
I nmax The upper limit of the current of the nth branch circuit is set; d is the total number of branches;
demand response management constraints:
P TL，s，t ＝P TLO，s，t -P TLI，s，t
wherein, P TLO，s，t 、P TLI，s，t Load shifting out and load shifting in the tth period of the s-th scene,
respectively transferring the load out of the ith scene in the t period and transferring the load into the upper limit value and the lower limit value of the load response system;
and (4) load interruption power upper and lower limit values for the tth time period of the s scene.
Wherein the objective function is:
max f 1 ＝(C s +C loss +C kw +C b -C ess -C ess，m -C l，y -C DG -C MA -C Line -C sub -C S )
C s ＝k s ·(C s，dis -C s，c )
C kw ＝λ·p t ·C k
C e ＝[r·(1+r) h ]/[(1+r) h -1]
wherein, C s 、C loss And C kw Respectively sleeving low-storage high-discharge profit for the energy storage system, saving electric energy loss annual profit and clipping peak annual profit, C b Subsidizing the costs for the government of wind or photovoltaic power generation, C ess Annual investment cost for energy storage system C ess，m For annual operating maintenance costs of the energy storage system, C l，y For compensation of interruptible loads, C DG For investment and maintenance costs of the distributed power supply, C MA Management of expenses for integrated load demand response projects, C Line For distribution line investment and operating maintenance costs, C sub Operating electricity purchase cost of transformer substation, k s Converting the arbitrage of the typical daily active power distribution network into a conversion coefficient of arbitrage of one year, C s，dis 、C s，c Charging income expense and charging expense for one day of the active distribution network respectively, p dis 、p ch Respectively the discharge electricity price and the charge electricity price, P, of the active power distribution network ess，j，dis (t′)、P ess，j，c (t) and the discharge power at time t' and the charging power at time t, p, of the active distribution network, respectively t Lambda is the comprehensive investment cost of unit capacity of the transformer substation for peak clipping value after the active power distribution network is introduced, C k The annual value coefficients of the capital of the transformer substation are equal, and r and h are respectively the current rate and the service life of the active power distribution network; k is a radical of e Investment cost per unit volume, E e，j 、C ress Capacity and residual value, k, of the jth active distribution network m For the annual operation and maintenance cost of unit capacity of the active power distribution network,
wherein, the S3 specifically includes:
s31, coding the position and the power of the energy storage system;
s32, initializing seed position and speed variables, calculating a target function of each particle, and putting the target function into a non-inferior solution set;
s33, calculating and determining a historical optimal solution and a population global optimal solution of each particle;
s34, calculating the difference value between each particle and the optimal particle, updating the inertia weight of each particle, and performing cross variation calculation on the particles;
s35, calculating the objective function value of each particle, updating the particle historical optimal solution according to the domination relation, forming a new non-inferior solution set, and then updating the non-inferior solution set;
s36, selecting a population global optimal solution, and if a final condition is met, turning to S37; otherwise, go to S33:
and S37, outputting the optimal solution set to obtain the optimal position and power of the energy storage system.
Wherein, the step S4 specifically includes:
the charge and discharge time period is divided according to the time-of-use electricity price,
and if a plurality of energy storage systems exist, determining the magnitude of the charging and discharging power of each energy storage system at each moment in the charging time period according to the sampling point load value at each moment in the charging time period.
The determining of the charging and discharging power of each energy storage system at each moment in the charging time period specifically includes:
sequencing the load values of sampling points at all times in a charging time period from small to large, and determining the charging power of the energy storage system at the sampling points at all times corresponding to the loads;
charging the sampling point with the minimum load according to the maximum power, charging other sampling points according to variable power smaller than the maximum power, sequentially calculating an SOC value, if the SOC value is out of limit, discharging the energy storage system at the sampling point with the maximum load according to the maximum power in the sequence from the maximum load to the minimum load, discharging the energy storage systems at the other sampling points according to the variable power smaller than the maximum power, otherwise, charging the sampling points with undetermined power by using zero power.
Wherein, the charging at the other sampling points according to the variable power smaller than the maximum power specifically comprises:
wherein, P L (T) is T c，i The load value at the internal t moment; t is c，i Is a charging period; p L，min Is T c，i The minimum load value at all sampling points;P C，max 、P e，c respectively providing maximum charging power and rated charging power of the energy storage system; p C，max +P L，min The index which should be reached after the energy storage charging;
the step of discharging the energy storage systems at other sampling points according to the variable power smaller than the maximum power specifically comprises the following steps:
wherein, P' L (T') is T dis，i The load value at the inner t' sampling point; t is dis，i Is a discharge period; p L，max Is T dis，i The maximum load value at all sampling points; p is DIS，max 、P e，dis Respectively the maximum discharge power and the rated discharge power of the energy storage system; η is the discharge power weight.
The embodiment of the invention has the beneficial effects that: the invention optimally plans the energy storage system accessed to the active power distribution network, provides an energy storage system optimal planning model, optimally considers the peak clipping and valley filling and voltage regulation capabilities of the energy storage in the short-term energy storage optimization, considers the investment cost of an energy storage device and the operation and reliability cost of the active power distribution network in the economic cost objective function of the long-term energy storage planning, and determines the position, the capacity and the rated power of the battery by minimizing the cost objective function. The economic efficiency, the reliability and the stability of the power distribution network are comprehensively considered, and the optimization effect of demand side management and an energy storage battery on the active power distribution network is considered. The stability and the reliability of the power distribution network are considered while the maximization of the economic benefits of operators is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a dynamic planning method for an active power distribution network according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the present invention provides a method for dynamically planning an active power distribution network, where the method includes the following steps:
s1, establishing a demand side load power prediction model, a distributed power supply power prediction model and a charge and discharge model of an energy storage system.
And S2, establishing an objective function according to the demand side load power prediction model, the distributed power supply power prediction model and the charge and discharge model of the energy storage system in a comprehensive social economic benefit maximization mode.
And S3, planning the energy storage systems of the active power distribution network by adopting a chaotic particle swarm hierarchical optimization algorithm to obtain the optimal position and power of each energy storage system.
And S4, obtaining the charge and discharge strategy of each energy storage system according to the optimal position and power of each energy storage system.
In the dynamic planning method for the energy storage system of the active power distribution network, the active power distribution network comprises a demand side response system, a distributed power supply and an energy storage system. The dynamic planning method is based on a double-layer planning model to carry out dynamic planning, wherein the first layer of planning takes the comprehensive economic benefits of the whole society as a target function to be maximized, and the second layer of planning takes the daily comprehensive cost and the load peak-valley difference in the response of the demand side of the power distribution network as targets to optimize the output of the energy storage system in one operating period and one day.
Specifically, in step S1, a load model is established that takes into account the photovoltaic power generation cost, the wind power generation cost, and the consumer electricity time-sharing electricity consumption cost. The demand side load prediction is based on historical load data information of numerical weather forecast, load variation related to meteorological factors is extracted, a demand side load power prediction model considering meteorological influence and time-sharing electricity consumption cost factors is established according to the historical load data of the weather forecast, then the output characteristics of wind power generation and photovoltaic power generation are modeled based on the historical load data of the weather forecast, probability density distribution of output of wind and light and expected value output are obtained, and a distributed power supply power prediction model is established.
Specifically, in step S1, the energy storage system charge-discharge model is:
and (3) discharging:
and (3) charging process:
in the formula: SOC (t) represents the remaining charge level of the ESS at time t; epsilon represents the self-discharge rate per hour of the residual electric quantity of the energy storage system,%/h; p ess，dis (t)、P ess，c (t) respectively showing the discharge power and the charging power of the energy storage system; alpha and beta respectively represent the discharging and charging efficiencies of the energy storage system; e e Is the capacity of the energy storage system; for the sampling interval, take 1h.
Specifically, in step S2, the constraint conditions of the energy storage system are:
(1) And (3) charge and discharge restraint:
charge and discharge power constraint: -P ess，c，max (t)≤P ess (t)≤P ess，dis，max (t)
Depth of discharge constraint and charge quantity constraint: SOC min ≤SOC(t)≤SOC max
And (3) charge time constraint: h is less than or equal to h max
In the formula: p ess (t) charging or discharging power at time t; p ess，c，max (t)、P ess，dis，max (t) represents the maximum charge power and discharge power allowed by the battery energy storage system, respectively, and the "-" number represents the battery charge, SOC min 、SOC max Respectively the minimum residual capacity and the maximum residual capacity of the battery system, h is the charge and discharge times of the battery in one day, h is max Is a limit value of the number of times of charge and discharge in one day;
(2) Node voltage constraint
U imin ≤U i ≤U imax (i＝1，2，3，...，n)
In the formula: u shape imin 、U imax Lower and upper limits, respectively, of the voltage at the first node; n is the total number of nodes of the power distribution network;
(3) Flow equation constraints
In the formula: p G，i 、Q G，i Active and reactive power output of the power supply at the i node respectively; p is L，i 、Q L，i Respectively the active and reactive loads at the i node; u shape i 、U j The voltage amplitudes of the i node and the j node are respectively; g ij 、B ij Respectively a real part and an imaginary part of a node admittance matrix element; theta ij Is the voltage phase angle difference of node i and node j;
(4) Branch current constraint
I n ≤I nmax (n＝1，2，3，...，d)
In the formula: i is nmax The upper limit of the current of the nth branch circuit; d is the total number of branches.
(5) Demand response management constraints
P TL，s，t ＝P TLO，s，t -P TLI，s，t
In the formula: p is TLO，s，t 、P TLI，s，t Load shifting out and load shifting in the tth period of the s-th scene,
respectively transferring the load out of the ith scene in the t period and transferring the load into the upper limit value and the lower limit value of the load response system;
and (4) loading interruption power upper and lower limit values for the t-th time period of the s-th scene.
Specifically, in S2, the overall economic benefit of the society is maximized as an objective function.
max f 1 ＝(C s +C loss +C kw +C b -C ess -C ess，m -C l，y -C DG -C MA -C Line -C sub -C s )
In the formula: c s 、C loss And C kw The annual profit, the annual profit of electric energy loss saving and the annual profit of peak clipping are respectively set for the low storage and high discharge of the energy storage system; c b Subsidizing the cost for the government of wind power generation or photovoltaic power generation; c ess Annual investment cost for energy storage system C ess，m The annual operating and maintaining cost of the energy storage system is saved; c l，y A compensation fee for interruptible loads; c DG For investment and maintenance costs of the distributed power supply, C MA Responding to project management costs for the integrated load demand; c Line Investment and operation maintenance cost for the distribution line; c sub The electricity purchasing cost of the transformer substation is calculated; c S Reduced annual electricity sales revenue after implementing demand responses.
The low storage and high release annual income is as follows:
C s ＝k s ·(C s，dis -C s，c )
in the formula, k s Is a typical day ESS discountCalculating to obtain a conversion coefficient of the set profit for one year; c s，dis 、C s，c The specific calculation method is as follows for the ESS one-day charging income charge and charging expenditure charge respectively:
in the formula, p dis 、p ch The ESS discharge electricity price and the charging electricity price are respectively; p ess，j，dis (t′)、P ess，j，c (t) and discharging power of the ESS at time t' and charging power at time t, respectively;
the peak clipping and valley filling annual income is as follows:
C kw ＝λ·p t ·C k
in the formula: p is a radical of formula t Lambda is the comprehensive investment cost of unit capacity of the substation for peak clipping value after the introduction of the energy storage system, C k For the annual value coefficient of the substation capital and the like,
C e ＝[r·(1+r) h ]/[(1+r) h -1]
in the formula, r and h are respectively the current rate and the service life of the energy storage system;
the annual investment cost is as follows:
in the formula, k e Investment cost per unit volume; e e，j 、C ress Capacity and residual value of jth ESS respectively; the annual operation and maintenance cost is as follows:
in the formula, k m For energy storage systemAnnual operating maintenance costs per unit capacity of the system.
Specifically, in step S3, a multi-target particle swarm algorithm of the adaptive inertia right is adopted to plan the active power distribution network energy storage system.
Step S31: the location and power of the energy storage system is encoded,
C＝[x 1 ，x 2 ，...，x N ，y 1 ，...，y n ，...，y j+1 ，...，y T，N ]
in the formula, x i The access position of the ith energy storage is pointed; n is the number of the energy storage systems; finger y j+1 The power of the ith stored energy at the moment; t is the total number of times.
Step S32, initializing seed position and speed variables, calculating a target function of each particle, and putting the target function into a non-inferior solution set;
s33, calculating and determining a historical optimal solution and a population global optimal solution of each particle;
step S34, calculating the difference value between each particle and the optimal particle, updating the inertia weight of each particle, and performing cross variation calculation on the particles;
step S35, calculating objective function values of all particles, updating historical optimal solutions of the particles according to the domination relation, forming a new non-inferior solution set, and then updating the non-inferior solution set;
s36, selecting a population global optimal solution, and if a final condition is met, turning to S7; otherwise, go to step S33;
and step S37, outputting the optimal solution set.
Specifically, in step S104, the planning of the distributed power source in the active power distribution network and the operation of energy storage cooperation are combined, and the optimal scheduling of the active power distribution network for the energy storage device is considered in the planning of the distributed power source, so that the scheduling of the energy storage system within a day can reduce the planning error caused by uncertainty of the output of the distributed power source, and the planning is more effective. The formulation of the charging and discharging strategy of the energy storage system comprises the following steps: the energy storage system is fully utilized, the effect of reducing the peak-valley difference is exerted to the maximum extent by a certain index, and the smoothness of the equivalent load curve after the energy storage system is configured is considered, so that the equivalent load curve after the energy storage system is configured is as smooth as possible, and the adverse effect on the system caused by sudden charging and discharging of the energy storage system is avoided.
An energy storage system charge-discharge strategy is formulated based on a typical daily equivalent load curve (a curve obtained by superposing daily load demand and distributed power supply output), the strategy formulation process can be summarized into two steps, and the first step is charging time T c，i And discharge time T dis，i In a second step of determining T c，i 、T dis，i The specific process of the charge and discharge power at each moment in the time period is as follows:
the first step is as follows: dividing charging and discharging time periods according to the time-of-use electricity price, wherein the high electricity price time period is the discharging time T dis，i (ii) a The low electricity price period is the charging time T c，i . In order to improve the utilization rate of the energy storage system, when the electricity price is flat, if the time periods before and after the period are high electricity price time periods, the charging time T is c，i (ii) a If the time periods before and after the time period are low electricity price time periods, the time period is discharge time T dis，i (ii) a The battery is in an idle state or is considered to be charged and discharged at zero power in other cases.
The second step is that: if a plurality of energy storage systems exist, the energy storage systems are coordinated with each other to determine the respective charging and discharging power, and a single energy storage system is determined to be in T c，i The process of the charging power at each moment (sampling point) in the time period is as follows:
1) The smaller the load of the sampling point is, the larger the peak-valley difference is, the more energy storage and charging are needed, and T is measured c，i And (4) sequencing the load values of all sampling points of the time period, and respectively determining the charging power of the energy storage system at the sampling points corresponding to the loads according to the sequence from small to large.
2) In order to ensure that the fluctuation of a load curve of the energy storage system after charging is as small as possible, except that the sampling point with the minimum load is charged according to the maximum power, other sampling points are charged according to variable power smaller than the maximum power, and the specific power calculation method comprises the following steps:
in the formula: p L (T) is T c，i The load value at the internal t moment; p L，min Is T c，i The minimum load value at all sampling points; p C，max 、P e，c Respectively providing maximum charging power and rated charging power of the energy storage system; p C，max +P L，min The index which is required to be reached after the energy storage charging is adopted, and if the index which is required to be reached after the energy storage charging is adopted, the energy storage charging is carried out at the rated power in the optimal state; gamma is the charging power weight of the jth energy storage system;
in the formula: n is a radical of hydrogen e The number of energy storage systems; e e，j The energy storage system capacity of jth;
3): determining T in sequence according to the methods in the step 1) and the step 2) c，i The charging power at each sampling point of the time period is increased correspondingly when the charging power at one sampling point is determined, if the SOC is out of limit, the step 4) is carried out, and the sampling points with undetermined power can be regarded as being charged with zero power;
and step 4): the process of the discharge power magnitude at various sampling points of the output period is similar to the above process, and the difference is as follows: the larger the load at the sampling point is, the larger the peak-valley difference is, the more energy storage and discharge are needed, and the discharge power of the energy storage system at the corresponding sampling point is determined according to the sequence of the load from large to small; except that the energy storage system at the maximum sampling point of the load discharges according to the maximum power, other sampling points discharge according to the variable power of the small maximum power, and the specific power calculation method is shown as the following formula
Of formula (II) to (III)' L (T') is T dis，i The load value at the inner t' sampling point; p L，max Is T dis，i The maximum load value at all sampling points; p is DIS，max 、P e，dis Respectively the maximum discharge power and the rated discharge power of the energy storage system; eta is the weight of the discharge power, and the gamma is the same as the discharge power; each time the discharge power at a sampling point is determined, SOC decreases P accordingly ess，dis (Δt)/E e ·β；P L，max -P DIS，max The index is the index which should be reached after the energy storage discharge, and if the index is reached before the discharge, the discharge is carried out at the rated power in the optimal state.
And respectively determining the magnitude of the charge and discharge power at each sampling point of other time periods according to the method, and finally obtaining the charge and discharge strategy of the energy storage system in the daily scheduling period.
According to the dynamic planning method for the active power distribution and energy storage system, the energy storage system accessed to the active power distribution network is optimized and planned, an energy storage system optimization planning model is provided, the peak clipping and valley filling and voltage regulation capabilities of energy storage are considered in short-term energy storage optimization, the investment cost of an energy storage device and the operation and reliability cost of the active power distribution network are considered in the economic cost objective function of long-term energy storage planning, and the position, the capacity and the rated power of a battery are determined by minimizing the cost objective function. The economic efficiency, the reliability and the stability of the power distribution network are comprehensively considered, and the optimization effect of demand side management and an energy storage battery on the active power distribution network is considered. The stability and the reliability of the power distribution network are considered while the maximization of the economic benefit of operators is guaranteed.
It should be noted that the above embodiments are only used for illustrating the structure and the working effect of the present invention, and are not used for limiting the protection scope of the present invention. Modifications and adaptations to the above-described embodiments may occur to one skilled in the art without departing from the spirit and scope of the present invention and are intended to be covered by the following claims.
Claims (5)
1. A dynamic planning method for an active power distribution network is disclosed, wherein the active power distribution network comprises a demand side response system, a distributed power supply and an energy storage system, and is characterized by comprising the following steps:
s1, establishing a demand side load power prediction model, a distributed power supply power prediction model and a charge and discharge model of an energy storage system;
s2, establishing an objective function according to the demand side load power prediction model, the distributed power supply power prediction model and the charge and discharge model of the energy storage system in a comprehensive social economic benefit maximization mode;
s3, planning the energy storage systems of the active power distribution network by adopting a chaotic particle swarm hierarchical optimization algorithm to obtain the optimal position and power of each energy storage system;
s4, obtaining a charge-discharge strategy of each energy storage system according to the optimal position and power of each energy storage system;
wherein, the objective function in the step S2 is:
maxf 1 ＝(C s +C loss +C kw +C b -C ess -C ess，m -C l，y -C DG -C MA -C Line -C sub -C S‘ )
C s ＝k s ·(C s，dis -C s，c )
C kw ＝λ·p t ·C k
C e ＝[r·(1+r) h ]/[(1+r) h -1]
wherein, C s 、C loss And C kw Respectively sleeving the low-storage high-discharge profit, the electric energy loss saving profit and the peak clipping profit for the energy storage system C b Subsidizing the costs for the government of wind or photovoltaic power generation, C ess Annual investment cost for energy storage system C ess，m For annual operating maintenance costs of the energy storage system, C l，y For compensation of interruptible loads, C DG Investment and operating maintenance costs for distributed power sources, C MA Management of expenses for integrated load demand response projects, C Line For distribution line investment and operating maintenance costs, C sub Operating electricity purchase cost of transformer substation, k s Converting the arbitrage of a typical daily energy storage system into a conversion coefficient of arbitrage of one year, C s，dis 、C s，c Charging income charge and charging expenditure charge of the energy storage system for one day respectively, p dis 、p ch For discharging and charging tariff, P, respectively, of the energy storage system ess，j，dis (t′)、P ess，j，c (t ') is the discharge power of the energy storage system at time t ' and the charge power at time t ', p, respectively t Lambda is the comprehensive investment cost of unit capacity of the substation for peak clipping value after the introduction of the energy storage system, C k The annual value coefficient of the capital of the transformer substation is equal, and r and h are respectively the current rate and the service life of the energy storage system; k is a radical of e Investment cost per unit volume, E e，j 、C ress Capacity and residual value, k, of the jth energy storage system, respectively m Annual operating maintenance cost per unit capacity of the energy storage system, N e For the number of energy storage systems,. DELTA.t is the sampling interval, C S‘ Annual revenue reduction after implementing demand response;
the S3 specifically includes:
s31, coding the position and the power of the energy storage system;
s32, initializing seed position and speed variables, calculating a target function of each particle, and putting the target function into a non-inferior solution set;
s33, calculating and determining a historical optimal solution and a population global optimal solution of each particle;
s34, calculating the difference value between each particle and the optimal particle, updating the inertia weight of each particle, and performing cross variation calculation on the particles;
s35, calculating the objective function value of each particle, updating the particle historical optimal solution according to the domination relation, forming a new non-inferior solution set, and then updating the non-inferior solution set;
s36, selecting a population global optimal solution, and if a termination condition is met, turning to S37; otherwise, turning to S33;
s37, outputting an optimal solution set to obtain the optimal position and power of the energy storage system;
wherein, the step S4 specifically includes:
dividing a charging and discharging time period according to the time-of-use electricity price, and if a plurality of energy storage systems exist, determining the charging and discharging power of each energy storage system at each moment in the charging time period according to the sampling point load value at each moment in the charging time period;
the determining of the charging and discharging power of each energy storage system at each moment in the charging time period specifically includes:
sequencing the load values of sampling points at all times in a charging time period from small to large, and determining the charging power of the energy storage system at the sampling points at all times corresponding to the loads;
charging the sampling point with the minimum load according to the maximum power, charging other sampling points according to variable power smaller than the maximum power, sequentially calculating the energy storage residual electric quantity value of the battery, and if the energy storage residual electric quantity value of the battery is out of limit, discharging the energy storage system at the sampling point with the maximum load according to the maximum discharge power in the sequence from large load to small load, and discharging the energy storage systems at other sampling points according to the variable power smaller than the maximum power, otherwise, charging the sampling points with undetermined power by using zero power;
wherein, the charging at the other sampling points according to the variable power smaller than the maximum power specifically comprises:
wherein, P L (T) is T c，i The load value at the internal t moment; t is a unit of c，i Is a charging time period; p L，min Is T c，i The minimum load value at all sampling points; p C，max 、P e，c Respectively providing maximum charging power and rated charging power of the energy storage system; p is C，max +P L，min Gamma is the charging power weight of the jth energy storage system;
in the formula: n is a radical of e The number of energy storage systems; e e，j The capacity of the jth energy storage system;
the step of discharging the energy storage systems at other sampling points according to the variable power smaller than the maximum power specifically comprises the following steps:
wherein, P' L (T') is T dis，i The load value at the inner t' sampling point; t is a unit of dis，i Is a discharge time period; p L，max Is T dis，i The maximum load value at all sampling points; p DIS，max 、P e，dis Respectively the maximum discharge power and the rated discharge power of the energy storage system; and eta is the discharge power weight of the jth energy storage system.
2. The method of claim 1, wherein building the demand side load power prediction model specifically comprises:
acquiring historical load data of a demand side based on weather forecast, and extracting load variables related to meteorological factors;
and establishing a demand side load power prediction model according to the historical load data and the load variables related to meteorological factors.
3. The method of claim 2, wherein building the distributed power source power prediction model specifically comprises:
acquiring the output characteristics of the historical load data of weather forecast on wind power generation, and modeling to obtain the output probability density distribution and the output expected value of the wind power generation;
acquiring the output characteristics of the photovoltaic power generation by historical load data of weather forecast and modeling to obtain output probability density distribution and output expected value of the photovoltaic power generation;
and establishing a distributed power supply power prediction model according to the output probability density distribution and the output expected value of the wind power generation, the output probability density distribution and the output expected value of the photovoltaic power generation.
4. The method of claim 3, wherein the charge-discharge model of the energy storage system is:
and (3) discharging:
and (3) charging process:
the state of charge SOC (t) of the battery represents the residual capacity level of the energy storage system at the moment t; epsilon represents the self-discharge rate per hour of the residual electric quantity of the energy storage system,%/h; p ess，dis (t)、P ess，c (t) respectively representing the discharge power and the charging power of the energy storage system; alpha and beta respectively represent the discharging and charging efficiencies of the energy storage system; e e Is the capacity of the energy storage system; and delta t is a sampling interval and is taken as 1h.
5. The method according to claim 4, wherein the constraints of the charge and discharge model of the energy storage system are:
charge and discharge power constraint: -P ess，c，max (t)≤P ess (t)≤P ess，dis，max (t)
Depth of discharge constraint and charge quantity constraint: SOC min ≤SOC(t)≤SOC max
Number of chargesNumber constraint: h is less than or equal to h max
P ess (t) charging or discharging power at time t; p ess，c，max (t)、P ess，dis，max (t) represents the maximum charge power and discharge power allowed by the battery energy storage system, respectively, and the "-" number represents the battery charge, SOC min 、SOC max Respectively the minimum residual capacity and the maximum residual capacity of the battery energy storage system, h is the charging and discharging times of the battery in one day max Is a limit value of the number of times of charge and discharge in one day;
wherein, the node voltage constraint conditions are as follows: u shape imin ≤U i ≤U imax
i＝1，2，3，...，n，U imin 、U imax Lower and upper limits of the voltage at the ith node, respectively; n is the total number of nodes of the power distribution network;
wherein, the trend constraint is:
wherein, P G，i 、Q G，i Active and reactive power output of the power supply at the i node respectively; p L，i 、Q L，i Respectively the active and reactive loads at the i node; u shape i 、U j The voltage amplitudes of the i node and the j node are respectively; g ij 、B ij Respectively a real part and an imaginary part of a node admittance matrix element; theta ij Is the voltage phase angle difference of node i and node j;
wherein the branch current constraints are: I.C. A n ≤I nmax (n＝1，2，3，...，d)
I nmax The upper limit of the current of the nth branch circuit; d is the total number of branches;
demand response management constraints:
P TL，s，t ＝P TLO，s，t -P TLI，s，t
wherein, P TLO，s，t 、P TLI，s，t Load transfer-out and load transfer-in for the tth scene period respectively,
respectively transferring the load out of the sth scene at the tth time period and transferring the load into the upper limit value and the lower limit value of the load response system;
and (4) loading interruption power upper and lower limit values for the t-th time period of the s-th scene.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910312494.XA CN110119886B (en) | 2019-04-18 | 2019-04-18 | Active distribution network dynamic planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910312494.XA CN110119886B (en) | 2019-04-18 | 2019-04-18 | Active distribution network dynamic planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110119886A CN110119886A (en) | 2019-08-13 |
| CN110119886B true CN110119886B (en) | 2022-11-25 |
Family
ID=67521219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910312494.XA Active CN110119886B (en) | 2019-04-18 | 2019-04-18 | Active distribution network dynamic planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110119886B (en) |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108988390A (en) * | 2018-08-13 | 2018-12-11 | 国网江西省电力有限公司电力科学研究院 | A kind of distribution network voltage control method based on best abandoning light rate |
| CN110837912A (en) * | 2019-09-17 | 2020-02-25 | 万克能源科技有限公司 | Energy storage system capacity planning method based on investment benefits |
| CN111049171B (en) * | 2019-12-25 | 2024-03-22 | 深圳供电局有限公司 | Active power distribution network energy storage configuration method |
| CN111509743B (en) * | 2020-04-16 | 2023-06-30 | 国网江苏省电力有限公司淮安供电分公司 | Control method for improving stability of power grid by using energy storage device |
| CN111753471B (en) * | 2020-07-07 | 2022-07-12 | 国网江苏省电力有限公司经济技术研究院 | Optimized operation method of energy storage battery |
| CN111786411A (en) * | 2020-07-14 | 2020-10-16 | 上海大学 | Power distribution network distributed generation and energy storage combined optimization configuration method considering voltage safety |
| CN111969598B (en) * | 2020-08-04 | 2023-07-07 | 广西电网有限责任公司电力科学研究院 | User side energy storage optimization method and system considering excitation type demand side response |
| CN112039129B (en) * | 2020-09-22 | 2022-06-14 | 广东电网有限责任公司 | Wind power output guarantee rate determination method and system based on energy storage optimization configuration |
| CN112821451B (en) * | 2021-01-11 | 2023-05-09 | 国网福建省电力有限公司泉州供电公司 | Urban power distribution network photovoltaic access response method based on demand side management and energy storage |
| CN112994092B (en) * | 2021-02-24 | 2022-07-29 | 中南大学 | Independent wind-solar storage micro-grid system size planning method based on power prediction |
| CN113255991B (en) * | 2021-05-21 | 2024-02-02 | 长沙理工大学 | On-load voltage regulating transformer optimal configuration method, electronic equipment and readable storage medium |
| CN114243694B (en) * | 2021-12-15 | 2023-09-15 | 东北电力大学 | Grid-connected micro-grid optimal configuration method considering ladder carbon transaction and demand response |
| CN115062811B (en) * | 2022-04-24 | 2024-03-12 | 哈尔滨华盛能源科技有限公司 | Optimizing method and system for new energy planning scheme integrating economic factors and energy factors |
| CN115579871B (en) * | 2022-10-13 | 2024-02-23 | 国网河北省电力有限公司营销服务中心 | Distribution network with distributed power source penetration and photovoltaic digestion method |
| CN115759375B (en) * | 2022-11-09 | 2023-06-02 | 浙江咸亨创新产业中心有限公司 | Modularized mobile battery energy storage configuration method applied to fault distribution network |
| CN116151436B (en) * | 2023-01-09 | 2023-08-18 | 广州汇锦能效科技有限公司 | Household-user-oriented photovoltaic building energy planning method and system |
| CN115800275B (en) * | 2023-02-08 | 2023-06-30 | 国网浙江省电力有限公司宁波供电公司 | Power balance regulation and control distribution method, system, equipment and storage medium |
| CN116526567A (en) * | 2023-06-26 | 2023-08-01 | 深圳智电时代科技有限公司 | Energy management method and device for household photovoltaic power supply system and storage medium |
| CN117374942A (en) * | 2023-10-13 | 2024-01-09 | 国网安徽省电力有限公司芜湖供电公司 | Energy storage economy optimization scheduling method based on production plan and load prediction |
| CN117254497A (en) * | 2023-11-20 | 2023-12-19 | 国网山东省电力公司枣庄供电公司 | Method and system for processing electric digital data for user side distributed energy system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107591794A (en) * | 2016-07-08 | 2018-01-16 | 南京理工大学 | Active distribution network source storage capacity configuration optimizing method based on load classification |
| CN107688879A (en) * | 2017-10-20 | 2018-02-13 | 云南电网有限责任公司 | A kind of active distribution network distributed power source planing method of consideration source lotus matching degree |
| CN108470239A (en) * | 2018-03-01 | 2018-08-31 | 国网福建省电力有限公司 | The active distribution network multi objective layered programming method of meter and demand side management and energy storage |
| CN109508499A (en) * | 2018-11-15 | 2019-03-22 | 国网江苏省电力有限公司经济技术研究院 | Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method |
-
2019
- 2019-04-18 CN CN201910312494.XA patent/CN110119886B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107591794A (en) * | 2016-07-08 | 2018-01-16 | 南京理工大学 | Active distribution network source storage capacity configuration optimizing method based on load classification |
| CN107688879A (en) * | 2017-10-20 | 2018-02-13 | 云南电网有限责任公司 | A kind of active distribution network distributed power source planing method of consideration source lotus matching degree |
| CN108470239A (en) * | 2018-03-01 | 2018-08-31 | 国网福建省电力有限公司 | The active distribution network multi objective layered programming method of meter and demand side management and energy storage |
| CN109508499A (en) * | 2018-11-15 | 2019-03-22 | 国网江苏省电力有限公司经济技术研究院 | Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method |
Non-Patent Citations (5)
| Title |
|---|
| 一种同时考虑DG和ESS选址定容的主动配电网规划方法;盛四清等;《电力科学与工程》;20151031;第31卷(第10期);全文 * |
| 主动配电网中储能和需求侧响应的联合优化规划;李秀磊 等;《电网技术》;20161231;第40卷(第12期);全文 * |
| 含风电接入的配电网储能系统优化配置;田录林 等;《西北水电》;20181031(第05期);全文 * |
| 基于分布式电源和需求响应的主动配电网规划;李金炀 等;《自动化与仪表》;20170430;第32卷(第04期);全文 * |
| 基于概率潮流的主动配电网储能配置与控制设计;肖园园 等;《电工电能新技术》;20160630;第35卷(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110119886A (en) | 2019-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110119886B (en) | Active distribution network dynamic planning method | |
| CN107301470B (en) | Double-layer optimization method for power distribution network extension planning and optical storage location and volume fixing | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| CN112467722B (en) | Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station | |
| CN103490410B (en) | Micro-grid planning and capacity allocation method based on multi-objective optimization | |
| Chakraborty et al. | PV-microgrid operational cost minimization by neural forecasting and heuristic optimization | |
| CN111934360B (en) | Virtual power plant-energy storage system energy collaborative optimization regulation and control method based on model predictive control | |
| CN111476407B (en) | Medium-and-long-term hidden random scheduling method for cascade hydropower station of combined wind power photovoltaic power station | |
| CN110633854A (en) | Full life cycle optimization planning method considering energy storage battery multiple segmented services | |
| CN112383086B (en) | Island micro-grid day-ahead energy-standby combined optimization scheduling method | |
| CN103715688A (en) | Grid-connected micro-grid optimizing configuration method and device | |
| Wang et al. | A hierarchical control algorithm for managing electrical energy storage systems in homes equipped with PV power generation | |
| CN114970191A (en) | Power grid traffic system day-ahead distribution robust scheduling method based on potential game | |
| CN114462854A (en) | Hierarchical scheduling method and system containing new energy and electric vehicle grid connection | |
| CN113364043A (en) | Micro-grid group optimization method based on condition risk value | |
| CN117154778A (en) | Distributed energy storage optimal configuration method and system for power distribution network | |
| CN115841216A (en) | Distribution network energy storage optimization configuration method considering distributed photovoltaic absorption rate | |
| CN115375032A (en) | Multi-time scale optimization scheduling method for regional power grid with distributed energy storage | |
| CN114912735A (en) | Demand response optimization method considering participation of 5G base station energy storage in power grid interaction | |
| CN113255957A (en) | Quantitative optimization analysis method and system for uncertain factors of comprehensive service station | |
| CN112418950A (en) | Photovoltaic-related microgrid short-term load prediction and control method | |
| Mahmud et al. | Load forecasting based dynamic pricing model for power sharing in solar home systems | |
| CN110690719B (en) | Micro-grid battery energy storage configuration method and readable storage medium | |
| Tan et al. | Distributed demand response for plug-in electrical vehicles in the smart grid | |
| CN109038624B (en) | Household energy scheduling method based on double-storage-battery capacity dynamic allocation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |