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 PDFInfo
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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
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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
- H02J3/38—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
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- 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
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- 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
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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
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:
wherein, CtThe time-of-use electricity price is displayed; pg,tThe electric quantity required by the power grid at the moment t;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;representing the electric vehicle discharge power at the time t;
constraint conditions are as follows:
2.1) Power balance constraints
Pdef,tThe photovoltaic power generation power delayed in the time period t;the photovoltaic energy storage instantaneous charging power and the photovoltaic energy storage discharging instantaneous discharging power;the instantaneous charge and discharge power of the electric automobile;
2.2) Charge balance constraints
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
k≥0 (9)
2.4) non-negative requirements for purchase from the grid
Pg,t≥0 (10)
2.5) Upper and lower bounds of model decision variables
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:
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:
wherein, CtThe time-of-use electricity price is displayed; pg,tThe electric quantity required by the power grid at the moment t;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;representing the electric vehicle discharge power at the time t;
constraint conditions are as follows:
2.1) Power balance constraints
Pdef,tThe photovoltaic power generation power delayed in the time period t;the photovoltaic energy storage instantaneous charging power and the photovoltaic energy storage discharging instantaneous discharging power;the instantaneous charge and discharge power of the electric automobile;
2.2) Charge balance constraints
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
k≥0 (9)
2.4) non-negative requirements for purchase from the grid
Pg,t≥0 (10)
2.5) Upper and lower bounds of model decision variables
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:
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:
wherein, CtThe time-of-use electricity price is displayed; pg,tThe electric quantity required by the power grid at the moment t;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;representing the electric vehicle discharge power at the time t;
constraint conditions are as follows:
2.1) Power balance constraints
Pdef,tThe photovoltaic power generation power delayed in the time period t;the photovoltaic energy storage instantaneous charging power and the photovoltaic energy storage discharging instantaneous discharging power;the instantaneous charge and discharge power of the electric automobile;
2.2) Charge balance constraints
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
k≥0 (9)
2.4) non-negative requirements for purchase from the grid
Pg,t≥0 (10)
2.5) Upper and lower bounds of model decision variables
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:
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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