CN111463809A - Light and electricity storage coordination control method considering source charge uncertainty - Google Patents

Light and electricity storage coordination control method considering source charge uncertainty Download PDF

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Publication number
CN111463809A
CN111463809A CN202010127037.6A CN202010127037A CN111463809A CN 111463809 A CN111463809 A CN 111463809A CN 202010127037 A CN202010127037 A CN 202010127037A CN 111463809 A CN111463809 A CN 111463809A
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power
photovoltaic
energy storage
charging
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周丹
童伟
谭将军
管敏渊
曹建伟
王函韵
蒋建杰
周丽华
刘业伟
汪蕾
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Zhejiang University of Technology ZJUT
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Zhejiang University of Technology ZJUT
Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Power Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract

A light and electricity storage coordination control method considering source charge uncertainty comprises the following steps: 1) the regional system comprises a photovoltaic system, an energy storage system, a charging pile and an electric automobile; 2) to avoid discharging during off-peak periods, penalty costs will be introduced, selecting between partial peaks and off-peaks; 3) a multi-stage randomly optimized SDDP algorithm. The invention adopts a random dual dynamic programming algorithm, considers the power generation and power utilization characteristics of the energy storage and the electric automobile, and optimizes the charge-discharge curves of the energy storage and the electric automobile by using the SDDP algorithm, thereby reducing the power purchasing cost from the power grid.

Description

Light and electricity storage coordination control method considering source charge uncertainty
Technical Field
The invention relates to a method for coordinately controlling Photovoltaic (PV), electric vehicle (PEV) and stored energy by adopting a random dual dynamic programming (SDDP) method, so that the electricity purchasing cost is reduced to the minimum.
Background
With the wide development of photovoltaic and electric vehicles in various countries of the world, the planning and construction problems of photovoltaic and charging infrastructures have received important attention from governments in China. At present, the primary energy at the power generation side of the power system in China is mainly coal, the electric automobile is directly connected to a power grid through a charging infrastructure for charging, the actually generated indirect carbon emission is not obviously superior to that of a fuel-fired automobile, and the dependence on fossil fuel is difficult to reduce. In this case, there are two ways to realize low carbon in the true sense: firstly, a renewable energy power generation system is vigorously developed, and the consumption capacity of a power grid on available renewable energy is improved; and secondly, the association between the charging and discharging facility and the distributed renewable energy power generation system is directly established, so that the on-site consumption and utilization of renewable energy are realized. In view of the current development, it is very difficult to adjust the primary energy structure of the power grid, but with the rapid development of renewable energy power generation technology, it is possible to associate charging and discharging facilities with a distributed renewable energy power generation system. Photovoltaic, energy storage, fill electric pile and electric automobile's integration on the spot can effectively improve renewable energy utilization ratio to full play fills the benefit of electric pile, photovoltaic and energy storage.
At present, the coordinated control of light, storage and electricity is already carried out: power balance, load transfer, power cost reduction, peak regulation, and the like. However, uncertainty such as photovoltaic power generation and load distribution is rarely considered when building mathematical models for some uncertain variables, in some cases, investment in photovoltaic plants needs to be considered, power limiting requirements are considered, and photovoltaic power generation has considerable uncertainty due to weather changes. On the user side, with the gradual increase of electric vehicles in recent years, the types of users are increasingly diversified, which causes uncertainty of charging time of the electric vehicles, and thus the location and volume of the charging pile also become a critical problem. While some scholars consider uncertainties in photovoltaic power generation and power demand, these uncertainties are evaluated separately rather than for coordinated control schemes. Partial scholars consider the uncertainty of the movement of the electric automobile in the random control method of photovoltaic power generation, but do not consider the problem of site selection and volume fixing of the charging pile.
Therefore, for any optimal energy management control framework, it is necessary to consider the randomness of the photovoltaic generation and load requirements and the relative structure between them. The method takes the randomness of photovoltaic power generation and load requirements and related structures among the photovoltaic power generation and the load requirements into consideration during modeling, provides a random dual dynamic programming algorithm, avoids the problem of high dimensional number of dynamic programming on calculation, and utilizes the algorithm to coordinate and control the light, storage and electric vehicles so as to effectively improve the energy utilization rate of the light, storage and electric vehicles.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a light-electricity storage coordination control method considering source charge uncertainty, which adopts a random dual dynamic programming algorithm, considers the power generation and electricity utilization characteristics of the energy storage and electric automobile, and optimizes the charge-discharge curves of the energy storage and the electric automobile by using an SDDP algorithm, thereby reducing the electricity purchasing cost from a power grid.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a light and electricity storage coordination control method considering source charge uncertainty comprises the following steps:
1) the regional system comprises a photovoltaic, an energy storage, a charging pile and an electric automobile, wherein the photovoltaic transmits energy to the park and the energy storage through a direct current bus and an alternating current-direct current converter, the energy storage is connected to the same direct current bus through a bidirectional direct current-direct current converter, the charging pile is connected to the park through a bidirectional converter and another direct current bus, a direct current-alternating current inverter supplies power to the park from the direct current bus, the energy storage and the charging pile are set to only provide power for the park, and the park load can receive power from a power grid, the photovoltaic and the energy storage; the photovoltaic, the storage and charging pile and the electric automobile can be in information communication with the controller through a communication network;
2) to avoid discharging during off-peak periods, which would introduce penalty costs, a choice is made between partial peaks and off-peaks, and the objective function J and model constraints are as follows:
Figure BDA0002394709020000031
wherein, CtThe time-of-use electricity price is displayed; pg,tThe electric quantity required by the power grid at the moment t;
Figure BDA0002394709020000032
respectively as a starting target electric vehicle charge state and a non-charging electric vehicle charge state; k is a penalty factor of the objective function; cdA penalty factor to avoid PEV discharge signals;
Figure BDA0002394709020000033
representing the electric vehicle discharge power at the time t;
constraint conditions are as follows:
2.1) Power balance constraints
Figure BDA0002394709020000034
Pdef,tThe photovoltaic power generation power delayed in the time period t;
Figure BDA0002394709020000035
the photovoltaic energy storage instantaneous charging power and the photovoltaic energy storage discharging instantaneous discharging power;
Figure BDA0002394709020000036
the instantaneous charge and discharge power of the electric automobile;
2.2) Charge balance constraints
Figure BDA0002394709020000037
Figure BDA0002394709020000038
Figure BDA0002394709020000039
Respectively representing the energy storage state and the charge state of the electric automobile within the time t; qPVAnd QPEVη PV and η PEV are respectively the storage charging efficiency and the efficiency of electric vehicle charging;
2.3) photovoltaic and load based charging and discharging operation Limit
Figure BDA00023947090200000310
Figure BDA00023947090200000311
Figure BDA00023947090200000312
Figure BDA00023947090200000313
k≥0 (9)
Figure BDA00023947090200000314
Is the price incurred during off-peak hours;
Figure BDA00023947090200000315
is the price for part of the peak time period;
2.4) non-negative requirements for purchase from the grid
Pg,t≥0 (10)
2.5) Upper and lower bounds of model decision variables
Figure BDA00023947090200000316
Figure BDA0002394709020000041
Figure BDA0002394709020000042
Figure BDA0002394709020000043
Figure BDA0002394709020000044
Figure BDA0002394709020000045
Wherein,
Figure BDA0002394709020000046
if the load demand exceeds photovoltaic power generation, the additional power required to meet the campus demand can be from stored energy discharged power or power purchased from the grid; in this case, the efficiency of charging the storage according to (2) to (4) is low; on the other hand, if the photovoltaic power generation amount is higher than the required amount, the remaining amount of power will be stored in the battery, and there will be no discharge; therefore, based on the above conditions, two basic assumptions are proposed:
the grid can only deliver power to the park, and no net metering compensation is provided;
the energy storage and electric automobile can only be charged through photovoltaic power generation, and the discharge of the two devices can only transmit power to the park,
representing the problems as a multi-stage stochastic programming model, and then solving by adopting an SDDP algorithm;
3) SDDP algorithm with multi-stage random optimization
The formula for a general t-order random linear programming to solve the problem is given:
Figure BDA0002394709020000047
constraint conditions are as follows:
Atxt＝Btxt-1+bt:Πt(9)
xt≥0 (10)
the decision variables for a particular phase t are treated as a vector xtIncluding purchasing power from the grid, charging and discharging, and stored SOC capacity; btRepresenting the random photovoltaic power generation and load of the t stage; equation (8) represents a model objective function designed to minimize the total cost including the current and expected future costs; the formula (9) represents structural constraints (2) to (4) and (6) to (7). Dual variable pitFrom the results obtained in the transition constraint, the piecewise linearity of the future cost function is constructed according to the decomposition scheme of the curved segmentation to be approximated, and the equation (10) represents the simple limits of the decision variables (5) and (8) - (16);
starting a process for forward pass by sampling the highlighted forward path; in the forward process, a simplex method is used for solving a series of models at each time stage, and in the solving process, a curved cutting method of iterative accumulation at the previous stage is used as an additional constraint condition to better approach the future cost and improve the decision process; in the final stage of forward transmission, estimating the total expected cost, and taking the total expected cost as the upper limit of the problem, calculating the lower limit of the problem by solving the first-stage problem of forward transmission under the condition of considering the current expected cost and the future expected cost, and stopping the SDDP process if the lower limit cost reaches the stopping standard; otherwise, the iterative process will continue until a desired level of convergence is reached.
Further, in said step 3), in each iteration, a new forward path is independently sampled in the scene tree, and in order to achieve a desired level of convergence, the algorithm continues to perform a backward traversal in which the algorithm calculates the amount of curved cuts in the previous stage to improve the approximation of the future cost function in each stage.
The invention has the following beneficial effects: and a random dual dynamic programming algorithm is adopted, the power generation and power utilization characteristics of the energy storage and the electric automobile are considered, and the charging and discharging curves of the energy storage and the electric automobile are optimized by utilizing the SDDP algorithm, so that the power purchasing cost from the power grid is reduced.
Drawings
Fig. 1 is a schematic diagram of a regional system of photovoltaic, energy storage, electric vehicles.
Fig. 2 is an SDDP solution process.
Fig. 3 is a flow chart of the SDDP algorithm.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a method for coordinating and controlling light and electricity storage considering uncertainty of source charge includes the following steps:
1) the regional system comprises a photovoltaic, an energy storage, a charging pile and an electric automobile, wherein the photovoltaic transmits energy to the park and the energy storage through a direct current bus and an alternating current-direct current converter, the energy storage is connected to the same direct current bus through a bidirectional direct current-direct current converter, the charging pile is connected to the park through a bidirectional converter and another direct current bus, a direct current-alternating current inverter supplies power to the park from the direct current bus, the energy storage and the charging pile are set to only provide power for the park, and the park load can receive power from a power grid, the photovoltaic and the energy storage; the photovoltaic, the storage and charging pile and the electric automobile can be in information communication with the controller through a communication network;
2) during the time of day, peak and off-peak electricity usage charging criteria are different, and off-peak hours may be selected to recharge stored energy due to the economics of charging during off-peak hours. Charging and discharging during off-peak periods can increase electricity costs and can result in energy losses. Thus, to avoid discharging during off-peak periods, which would introduce penalty costs, a choice is made between partial peaks and off-peaks, and the objective function J and model constraints are as follows:
Figure BDA0002394709020000061
wherein, CtThe time-of-use electricity price is displayed; pg,tThe electric quantity required by the power grid at the moment t;
Figure BDA0002394709020000062
respectively as a starting target electric vehicle charge state and a non-charging electric vehicle charge state; k is a penalty factor of the objective function; cdA penalty factor to avoid PEV discharge signals;
Figure BDA0002394709020000063
representing the electric vehicle discharge power at the time t;
constraint conditions are as follows:
2.1) Power balance constraints
Figure BDA0002394709020000064
Pdef,tThe photovoltaic power generation power delayed in the time period t;
Figure BDA0002394709020000065
the photovoltaic energy storage instantaneous charging power and the photovoltaic energy storage discharging instantaneous discharging power;
Figure BDA0002394709020000066
the instantaneous charge and discharge power of the electric automobile;
2.2) Charge balance constraints
Figure BDA0002394709020000067
Figure BDA0002394709020000068
Figure BDA0002394709020000069
Respectively representing the energy storage state and the charge state of the electric automobile within the time t; qPVAnd QPEVη PV and η PEV are respectively the storage charging efficiency and the efficiency of electric vehicle charging;
2.3) photovoltaic and load based charging and discharging operation Limit
Figure BDA00023947090200000610
Figure BDA00023947090200000611
Figure BDA0002394709020000071
Figure BDA0002394709020000072
k≥0 (9)
Figure BDA0002394709020000073
Is the price incurred during off-peak hours;
Figure BDA0002394709020000074
is the price for part of the peak time period;
2.4) non-negative requirements for purchase from the grid
Pg,t≥0 (10)
2.5) Upper and lower bounds of model decision variables
Figure BDA0002394709020000075
Figure BDA0002394709020000076
Figure BDA0002394709020000077
Figure BDA0002394709020000078
Figure BDA0002394709020000079
Figure BDA00023947090200000710
Wherein,
Figure BDA00023947090200000711
if the load demand exceeds photovoltaic power generation, the additional power required to meet the campus demand can come from power discharged from the stored energy or purchased from the power grid. In this case, the efficiency of charging the storage according to (2) to (4) is low. On the other hand, if the photovoltaic power generation amount is higher than the required amount, the remaining amount of power will be stored in the battery (if there is available storage capacity) and there will be no discharge. Therefore, it is impossible to charge and discharge the storage at the same time. For similar reasons, it is not possible for the electric vehicle to be charged and discharged simultaneously. Based on the above conditions, two basic assumptions are proposed:
the grid can only deliver power to the park, and no net metering compensation is provided;
the energy storage and electric automobile can only be charged through photovoltaic power generation, and the discharge of the two devices can only transmit power to the park,
representing the problems as a multi-stage stochastic programming model, and then solving by adopting an SDDP algorithm;
3) SDDP algorithm with multi-stage random optimization
The SDDP algorithm avoids the well-known high-dimensional problem of dynamic programming, and increases iteratively as the algorithm is iterated by constructing an approximate future cost function and representing a piecewise linear function by using a curved cut method. When the stop condition is reached, the process stops. To facilitate the solution, a general t-order random linear programming formula for solving the problem is given:
Figure BDA0002394709020000081
constraint conditions are as follows:
Atxt＝Btxt-1+bt:Πt(9)
xt≥0 (10)
the decision variables for a particular phase t are treated as a vector xtIncluding purchasing power from the grid, charging and discharging, and stored SOC capacity; btRepresenting the random photovoltaic power generation and load of the t stage; equation (8) represents a model objective function designed to minimize the total cost including the current and expected future costs; the formula (9) represents structural constraints (2) to (4) and (6) to (7). Dual variable pitFrom the results obtained in the transition constraint, the piecewise linearity of the future cost function is constructed according to the decomposition scheme of the curved segmentation to be approximated, and the equation (10) represents the simple limits of the decision variables (5) and (8) - (16);
once the SDDP has the sample scene tree as shown in FIG. 2, the process starts by sampling the highlighted forward path for forward pass;
in the forward process, a series of models are solved by using a simplex method at each time stage, and in the solving process, a curved cutting method of iterative accumulation of the previous stage is used as an additional constraint condition to better approach the future cost and improve the decision process. At the final stage of forward transfer, the total expected cost is estimated and used as the upper bound for the problem. The lower bound for the problem is computed by solving the forward-propagating first-stage problem, taking into account the current and future expected costs. If the lower cost meets the stopping criteria (defined herein as being within the 95% confidence interval of the upper cost), then the SDDP process stops. Otherwise, the iterative process will continue until a desired level of convergence is reached. In each iteration, a new forward path is sampled independently in the scene tree. To achieve the desired level of convergence, the algorithm proceeds with the reverse traversal shown in FIG. 3. In backward traversal, the algorithm calculates the amount of curved cuts for the previous stage to improve the approximation of the future cost function for each stage.
As shown in fig. 1, this is a regional system consisting of photovoltaic, energy storage, charging pile and electric vehicle. And the photovoltaic transmits energy to the park and the stored energy through the direct current bus and the alternating current-direct current converter. The stored energy is connected to the same DC bus through a bidirectional DC-DC converter. The charging pile is connected with another direct current bus to the park through a bidirectional converter. The dc-ac inverter supplies power from the dc bus to the campus. In this configuration, it is assumed that the energy storage and charging poles can only provide power to the campus. The campus load may receive power from the grid, photovoltaic, and stored energy. Photovoltaic, storage and charging stake, electric automobile can carry out information communication through communication network and controller.

Claims (2)

1. An optical storage coordination control method considering source charge uncertainty, which is characterized by comprising the following steps:
1) the regional system comprises a photovoltaic, an energy storage, a charging pile and an electric automobile, wherein the photovoltaic transmits energy to the park and the energy storage through a direct current bus and an alternating current-direct current converter, the energy storage is connected to the same direct current bus through a bidirectional direct current-direct current converter, the charging pile is connected to the park through a bidirectional converter and another direct current bus, a direct current-alternating current inverter supplies power to the park from the direct current bus, the energy storage and the charging pile are set to only provide power for the park, and the park load can receive power from a power grid, the photovoltaic and the energy storage; the photovoltaic, the storage and charging pile and the electric automobile can be in information communication with the controller through a communication network;
2) to avoid discharging during off-peak periods, which would introduce penalty costs, a choice is made between partial peaks and off-peaks, and the objective function J and model constraints are as follows:
Figure FDA0002394709010000011
wherein, CtThe time-of-use electricity price is displayed; pg,tThe electric quantity required by the power grid at the moment t;
Figure FDA0002394709010000012
respectively as a starting target electric vehicle charge state and a non-charging electric vehicle charge state; k is a penalty factor of the objective function; cdA penalty factor to avoid PEV discharge signals;
Figure FDA0002394709010000013
representing the electric vehicle discharge power at the time t;
constraint conditions are as follows:
2.1) Power balance constraints
Figure FDA0002394709010000014
Pdef，tThe photovoltaic power generation power delayed in the time period t;
Figure FDA0002394709010000015
the photovoltaic energy storage instantaneous charging power and the photovoltaic energy storage discharging instantaneous discharging power;
Figure FDA0002394709010000016
the instantaneous charge and discharge power of the electric automobile;
2.2) Charge balance constraints
Figure FDA0002394709010000017
Figure FDA0002394709010000018
Figure FDA0002394709010000019
Respectively representing the energy storage state and the charge state of the electric automobile within the time t; qPVAnd QPEVη PV and η PEV are respectively the storage charging efficiency and the efficiency of electric vehicle charging;
2.3) photovoltaic and load based charging and discharging operation Limit
Figure FDA00023947090100000110
Figure FDA00023947090100000111
Figure FDA00023947090100000112
Figure FDA00023947090100000113
k≥0 (9)
Figure FDA00023947090100000114
Is the price incurred during off-peak hours;
Figure FDA00023947090100000115
is the price for part of the peak time period;
2.4) non-negative requirements for purchase from the grid
Pg,t≥0 (10)
2.5) Upper and lower bounds of model decision variables
Figure FDA0002394709010000021
Figure FDA0002394709010000022
Figure FDA0002394709010000023
Figure FDA0002394709010000024
Figure FDA0002394709010000025
Figure FDA0002394709010000026
Wherein, ω isL,T∈ΩL,T,
Figure FDA0002394709010000027
ωt∈ΩPV,t；
If the load demand exceeds photovoltaic power generation, the additional power required to meet the campus demand can be from stored energy discharged power or power purchased from the grid; in this case, the efficiency of charging the storage according to (2) to (4) is low; on the other hand, if the photovoltaic power generation amount is higher than the required amount, the remaining amount of power will be stored in the battery, and there will be no discharge; therefore, based on the above conditions, two basic assumptions are proposed:
the grid can only deliver power to the park, and no net metering compensation is provided;
the energy storage and electric automobile can only be charged through photovoltaic power generation, and the discharge of the two devices can only transmit power to the park,
representing the problems as a multi-stage stochastic programming model, and then solving by adopting an SDDP algorithm;
3) SDDP algorithm with multi-stage random optimization
The formula for a general t-order random linear programming to solve the problem is given:
Figure FDA0002394709010000028
constraint conditions are as follows:
Atxt＝Btxt-1+bt:Πt(9)
xt≥0 (10)
the decision variables for a particular phase t are treated as a vector xtIncluding purchasing power from the grid, charging and discharging, and stored SOC capacity; btRepresenting the random photovoltaic power generation and load of the t stage; equation (8) represents a model objective function designed to minimize the total cost including the current and expected future costs; equation (9) represents structural constraints (2) - (4) and (6) - (7), dual variables ΠtFrom the results obtained in the transition constraint, the piecewise linearity of the future cost function is constructed according to the decomposition scheme of the curved segmentation to be approximated, and the equation (10) represents the simple limits of the decision variables (5) and (8) - (16);
starting a process for forward pass by sampling the highlighted forward path; in the forward process, a simplex method is used for solving a series of models at each time stage, and in the solving process, a curved cutting method of iterative accumulation at the previous stage is used as an additional constraint condition to better approach the future cost and improve the decision process; in the final stage of forward transmission, estimating the total expected cost, and taking the total expected cost as the upper limit of the problem, calculating the lower limit of the problem by solving the first-stage problem of forward transmission under the condition of considering the current expected cost and the future expected cost, and stopping the SDDP process if the lower limit cost reaches the stopping standard; otherwise, the iterative process will continue until a desired level of convergence is reached.
2. The method as claimed in claim 1, wherein in step 3), a new forward path is independently sampled in the scene tree in each iteration, and the algorithm continues to perform a backward traversal in order to achieve a desired convergence level, and in the backward traversal, the algorithm calculates a curved cut amount of a previous stage to improve the approximation of a future cost function of each stage.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104071033A (en) * 2013-12-07 2014-10-01 西南交通大学 Method for matching and optimizing parameters of mixed power locomotive with fuel cell and super capacitor
CN105528674A (en) * 2015-12-04 2016-04-27 中国电力科学研究院 Grid planning method based on stochastic dual dynamic theory
CN106339778A (en) * 2016-09-30 2017-01-18 安徽工程大学 Optical storage microgrid operation optimization method considering multiple objectives
CN107196294A (en) * 2017-06-16 2017-09-22 国网江苏省电力公司电力科学研究院 Micro-capacitance sensor Multiple Time Scales adaptive energy dispatching method under the net load interaction pattern of source
CN110474367A (en) * 2019-08-05 2019-11-19 广东工业大学 A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104071033A (en) * 2013-12-07 2014-10-01 西南交通大学 Method for matching and optimizing parameters of mixed power locomotive with fuel cell and super capacitor
CN105528674A (en) * 2015-12-04 2016-04-27 中国电力科学研究院 Grid planning method based on stochastic dual dynamic theory
CN106339778A (en) * 2016-09-30 2017-01-18 安徽工程大学 Optical storage microgrid operation optimization method considering multiple objectives
CN107196294A (en) * 2017-06-16 2017-09-22 国网江苏省电力公司电力科学研究院 Micro-capacitance sensor Multiple Time Scales adaptive energy dispatching method under the net load interaction pattern of source
CN110474367A (en) * 2019-08-05 2019-11-19 广东工业大学 A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
FAEZA HAFIZ ET: "Coordinated Control of PEV and PV-Based Storages in Residential Systems Under Generation and Load Uncertainties", 《IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS》 *
王先齐 等: "基于分时电价机制的并网型微网多目标动态优化调度", 《电力***保护与控制》 *
王成山 等: "不同场景下的光蓄微电网调度策略", 《电网技术》 *
石庆均 等: "包含蓄电池储能的微网实时能量优化调度", 《电力自动化设备》 *

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