CN115622144A - Multi-microgrid energy sharing method - Google Patents
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- 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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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- 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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- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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
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- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
- H02J2300/26—The renewable source being solar energy of photovoltaic origin involving maximum power point tracking control for photovoltaic sources
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
The invention provides a multi-microgrid energy sharing method, which comprises the following steps: s1, modeling internal composition units of the micro-grid, and establishing mathematical models of wind power generation, photovoltaic power generation and energy storage batteries; s2, determining constraint limits of the internal composition units of the microgrid; designing a multi-microgrid system topological structure based on the mathematical model established in the step S1 and the constraint limit determined in the step S2; s4, in the multi-microgrid system topological structure designed in the step S3, the scheduling problems of different time scales in the actual scene are solved, a double-time scale control strategy is applied, and a hierarchical optimization method is provided; and S5, according to the optimization method provided in the step S4, adopting a scheduling strategy of a ladder coordination scheduling idea to perform energy optimization on each subnet control level. Based on the method, the electric quantity of transaction between each micro-grid and the distribution network can be reduced, and the operation stability of the distribution network and the economy of each micro-grid are improved.
Description
Technical Field
The invention relates to the technical field of power, in particular to a multi-microgrid energy sharing method.
Background
At present, the mature development of energy conversion technology is accelerating the transformation of the propulsion energy structure. Meanwhile, the social requirements on flexibility and safety of energy requirements are continuously improved, so that the traditional centralized large-scale power system is changed into a distributed combined supply system. The cooperative scheduling research of multiple micro-grids and a power distribution network becomes an important gripper for assisting in carbon peak reaching and carbon neutralization.
And for the microgrid with renewable energy supply larger than the demand, redundant renewable energy is directly sold to the power distribution network by the microgrid, and due to the uncertainty of the renewable energy, the direct selling to the power distribution network may cause the operation stability of the power distribution network to be reduced, the loss of remote transmission of electric energy is increased, and the utilization rate of the renewable energy is reduced.
Renewable energy sharing among multiple micro-grids is considered, and due to the fact that the types and load characteristics of distributed power generation of the micro-grids are different, the primary task of each micro-grid is to meet the requirements of the micro-grid. If not, it may purchase power from the distribution grid. When the microgrid has residual electric quantity, the residual electric quantity is preferentially given to other microgrid lacking electricity, and if the microgrid has residual electric quantity, the residual electric quantity is sold to the power distribution network. Based on the method, the electric quantity of transaction between each micro-grid and the distribution network can be reduced, and the operation stability of the distribution network and the economy of each micro-grid are improved.
Disclosure of Invention
The invention aims to provide a multi-microgrid energy sharing method, which can reduce the electric quantity of transaction between each microgrid and a distribution network and improve the operation stability of the distribution network and the economy of each microgrid.
The purpose of the invention is realized by the following technical scheme:
a multi-microgrid energy sharing method comprises the following steps:
s1, modeling internal composition units of the micro-grid, and establishing mathematical models of wind power generation, photovoltaic power generation and energy storage batteries;
s2, determining constraint limits of internal component units of the microgrid, wherein the constraint limits comprise corresponding limits of load demands, energy storage battery limits and renewable energy source limits;
s3, designing a multi-microgrid system topological structure based on the mathematical model established in the step S1 and the constraint limit determined in the step S2;
s4, in the multi-microgrid system topological structure designed in the step S3, the scheduling problems of different time scales in the actual scene are solved, a double-time scale control strategy is applied, and a hierarchical optimization method is provided;
and S5, according to the optimization method provided by the step S4, a scheduling strategy of a ladder coordination scheduling idea is adopted to optimize energy of each subnet control level, the middle layer regulates controllable load demand through managing charging and discharging of the energy storage battery, the upper layer meets the optimization target of meeting the power consumption demand of each user on the premise of minimizing economic cost according to the priority and real-time market trading power price, and the bottom layer load integrated controller optimizes the power consumption demand of the user load on a fast time scale.
Further, modeling is performed on internal composition units of the microgrid in the step S1, and mathematical models of wind power generation, photovoltaic power generation and energy storage batteries are established, wherein the mathematical models are as follows:
s11, wind power generation model
The wind speed is simulated by adopting a Weibull distribution model, and the distribution function is as follows:
the probability density function is as follows:
where v denotes the wind speed, k denotes the shape parameter, c denotes the scale parameter, and the wind turbine output function is expressed as follows:
in the formula, PWGr represents a rated power of the wind turbine generator system, PWG represents an actual output power of the wind turbine generator system, and v i Indicating the cut-in wind speed, v 0 Representing cut-out wind speed, v representing actual wind speed, v r Representing a rated wind speed;
s12: photovoltaic power generation model
In a certain time range, the actual sunlight intensity on the ground meets the Beat distribution, and the probability density is as follows:
wherein e and e max The actual and maximum illumination intensities in the time interval are respectively, alpha and beta are both beta distribution parameters, the photovoltaic cell converts light energy into electric energy, and the output power of the photovoltaic cell is as follows:
P PV ＝η PV eS
in the formula eta PV Converting the efficiency of the photovoltaic cell; s is the effective irradiation area of the photovoltaic cell;
the output power of the photovoltaic cell is also in a Beat distribution, and is represented as:
in the formula, P PV,max ＝η PV e max S is the maximum power of the photovoltaic cell;
s13: energy storage battery model
Selecting a storage battery as an energy storage unit of the microgrid, and using E i (k) The energy storage capacity of the i-th microgrid system at the time k is represented, and the energy storage capacity at the next time is related to the energy storage capacity at the previous time, the charge/discharge capacity at the current time and the exchange efficiency, and the mathematical model is as follows:
in the formula, P i BESS (k) The total energy exchanged between the energy storage battery of the ith microgrid system and the current microgrid system at the kth time is represented as follows:
P i BESS (k)＝P i RES (k)-P i L (k)
in the formula, P i BESS (k) The power storage battery of the ith microgrid system is charged at the moment k when the voltage is more than 0, and P i BESS (k) < 0 indicates that the energy storage battery of the ith microgrid system is discharged at the time k,
the charging efficiency of the energy storage battery of the ith microgrid system at the moment k is shown,
represents the discharge efficiency of the energy storage battery of the ith microgrid system at the moment k, and
further, the constraint limits in step S2 are specifically as follows:
s21: load demand response limit:
P i L (k)＝P i DR (k)+ΔP i L (k)
in the formula (I), the compound is shown in the specification,P L being the lowest active power demanded by the total load,
the maximum active power required by the total load, namely the demand response P of the total load of the ith microgrid system i L (k) Should satisfy the power flow constraint, P i DR In order to be an important load,
scheduling the maximum energy of the controllable load;
s22, limiting the energy storage battery:
in the formula (I), the compound is shown in the specification,
is the upper limit of the energy stored in the energy storage battery,P BESS and
respectively representing the maximum charge-discharge limit of the electric quantity exchanged between the energy storage battery and the local system;
s23, renewable energy source limitation:
in the formula (I), the compound is shown in the specification,
for the maximum output power generated by the renewable power generation source, the renewable power generation source of the microgrid should meet the minimum and maximum power flow limits at time k.
Further, the multi-microgrid system topology structure in the step S3 is composed of M microgrid systems, each microgrid system includes renewable energy sources represented by photovoltaic power generation and wind power generation, important loads represented by a refrigerator and an air conditioner, controllable loads represented by charging piles, and energy storage batteries, and electric energy is transmitted through power transmission lines inside the microgrid and among the microgrids;
the multi-microgrid system topology structure is a distributed control structure, each microgrid system is provided with a controller, each microgrid preferentially meets the own energy supply requirement, if the renewable energy sources which can be supplied by the microgrid exceed the system load requirement, the rest renewable energy sources are preferentially supplied to other microgrid lacking electricity under the condition of meeting the own supply requirement, and if the renewable energy sources are left, the rest renewable energy sources are sold to a power distribution network; if the energy which can be supplied by the microgrid is difficult to meet the system load demand and cannot be met under the condition of receiving the renewable energy resources of other microgrids, the microgrid purchases electric energy from the power distribution network.
Further, step S4 is to deal with the scheduling problem of different time scales in the actual scene in the multi-microgrid system topology structure designed in step S3, apply a dual-time scale control strategy, and provide a hierarchical optimization method, which specifically includes:
in an actual scene, the energy coordination layer and the load regulation and control layer are executed in the microgrid at different sampling periods. In addition, information and energy exchange exists among different layers, a double-time-scale control strategy is adopted for the scheduling problem of different time scales, a three-layer optimization structure comprising an upper layer, a middle layer and a bottom layer is provided, and the power distribution network may not have enough electric quantity to trade with the microgrid aiming at the condition that the whole system possibly has supply and demand shortage, so that the middle-layer optimization problem is firstly solved under the slow time scale, the energy exchange between the microgrid and the power distribution network and the energy distribution in the microgrid are optimized based on load demand, renewable energy prediction information and energy storage charging and discharging, the rest renewable energy is preferentially provided for other microgrids and then sold to the power distribution network, and the unbalance information of the local microgrid is uploaded to the power distribution network; secondly, the optimization problem at the top layer coordinates energy exchange between the power distribution network and the microgrid in the same time scale as that of the middle layer; and finally, according to the optimization of the upper layer, the middle layer and the lower layer, the third optimization is realized on the bottom layer, the controllable load requirement of the user is adjusted by a faster sampling period, and the energy distribution of the user is optimized.
Further, the method for hierarchical optimization in step S4 specifically includes:
s41 local AMG optimization
The local AMG optimization goal of the middle layer is to preliminarily meet the principles of balance of supply and demand and minimum cost by managing charging and discharging of the energy storage battery model in the S1, adjusting the controllable load demand, sharing renewable energy electric quantity with other micro-grids and purchasing/selling electricity with DNO, and providing a local optimization objective function as follows:
in the formula, N s Optimized time domain (24 h), l representing energy scheduling s Representing slow sampling periods, ξ ij I =1, …, M, j =1, …,3 is a weight coefficient, λ i (k) Market trading power price, P, representing AMG and DNO at time k i B (k+l s I k) represents the AMG purchasing power from DNO, P i S (k+l s | k) represents that the AMG sells power to the DNO;
the actual physical meaning of the first term of the objective function is the punishment of the over-high charge/discharge capacity of the energy storage battery, if the battery is frequently charged/discharged in a large quantity, certain loss is caused to the capacity of the battery, and the punishment function is defined as follows:
J i,bat (k+l s ∣k)＝P i BESS (k+l s ∣k) 2
according to the autonomous microgrid characteristics, P i BSEE (k+l s I k) is written as:
P i BESS (k+l s ∣k)＝P i RES (k+l s ∣k)-P i L (k+l s ∣k)
the actual physical meaning of the second term of the objective function represents the dissatisfaction cost brought by reduction of the controllable load of the user, the cost is regarded as the cost of the virtual power generation unit by adopting the incentive-based demand response management plan, and the cost function is as follows:
in the formula, a i And b i Is an independent positive coefficient between the demand reduction and the cost of delivery of comfort;
the third item of the objective function represents the electricity purchasing/selling cost of the local microgrid, and the fourth item represents the renewable energy electric quantity shared by the local microgrid and other microgrids;
according to the above expression, the decision variables of the local AMG optimization problem are given as:
U i (k+l s |k)＝[ΔP i L (k+l s |k)P i B (k+l s |k)P i S (k+l s |k)P i out (k+l s |k)P i in (k+l s |k),U i (k+l s |k)∈Ω i
wherein omega i Is a micro-grid subsystem i allowable control quantity set, delta P i L (k+l s If | k) is the adjustable load prediction of the microgrid subsystem i, the optimization problem 1 is written as:
if P is i BESS (k+l s | k) is greater than or equal to 0, then:
0≤P i out (k+l s ∣k )≤P i BESS (k+l s ∣k)
if P is i BESS (k+l s | k) is less than or equal to 0, then:
0≤P i in (k+l s ∣k)≤|P i BESS (k+l s ∣k)|
and is
The energy conservation is constrained as follows:
P i RES (k+l s ∣k)-P i S (k+l s ∣k)+P i B (k+l s ∣k)-P i out (k+l s ∣k)+ P i in (k+l s ∣k)-pre_P i BESS (k+l s ∣k)＝pre_P i L (k+l s ∣k)
in the formula, pre _ P i BESS (k+l s ∣k),pre_P i L (k+l s | k) is the optimized value;
s42 Global DNO optimization
The DNO is used as a common coupling node for energy transaction with each AMG subsystem in the multi-microgrid system and is a control entity of the global level of the system, the supply and demand information of each microgrid is collected in each slow sampling period, the optimized value is distributed to each AMG after the energy distribution is optimized, the supply and demand balance of the whole multi-microgrid system is met as far as possible, the system benefit and the electricity utilization comfort level of users are guaranteed, the DNO meets the optimized target of meeting the electricity utilization requirement of each user on the premise of minimum economic cost according to the priority and the real-time market transaction electricity price, and the objective function of DNO optimization is provided as follows:
where λ (k) is the average trade electricity price for all microgrid subsystems, as follows:
in the formula (I), the compound is shown in the specification,
the predicted value of the power purchased by the DNO to all the microgrid subsystems,
optimizing the problem if predicted values of electric quantity are sold to all micro-grid subsystems for DNOWrite 2 to:
s43 load collective controller optimization
After the optimization problem of the upper and middle layers is solved, the optimization solution of the slow sampling period is obtained
After receiving the optimization information of the upper layer and the lower layer of the slow time scale, the bottom layer load integrated controller optimizes the power consumption demand of the user load on the fast time scale;
the optimized values on the load side are set as:
wherein l f Represents a slow sampling period;
the optimization target of the bottom layer load integrated controller gives an optimization reference solution delta P for tracking the upper and top layer DNO optimization problem i L* (k+l s I k), based on this, the optimization objective function of the load aggregation controller is proposed as follows:
then optimization problem 3 is written as:
the decision variable for optimization problem 3 is Z i (k+l s +nl f ∣k)＝[ΔP i L (k+l s +nl f ∣k)] T The optimal solution of which can be expressed as
Further, step S5 specifically includes the following steps:
the scheduling strategy of the idea of step coordination scheduling is adopted, the control level of the middle-layer subnet is preferentially optimized for energy, then the other two layers are optimized,
the algorithm design flow is as follows: starting an optimization cycle each time, firstly solving an optimization problem 1, and uploading optimized supply and demand balance information to a top DNO; then, the DNO control layer solves the optimization problem 2 to obtain an energy trading optimization value under global supply and demand balance; and finally, issuing the obtained optimization instruction to a bottom layer collector, and optimally distributing related controllable loads by solving an optimization problem 3.
The invention provides a multi-microgrid energy sharing method aiming at the condition that renewable energy sources in a microgrid are over-demand, so that the electric quantity purchased by other microgrids from a distribution network can be further reduced, the influence of renewable grid connection is reduced, and the economy of the microgrid is improved.
Drawings
Fig. 1 is a flowchart of a multi-piconet energy sharing method according to an embodiment of the present invention;
FIG. 2 is a graph of the output characteristics of a generator according to an embodiment of the present invention;
fig. 3 is a diagram of an operation mode of a microgrid alliance and a distribution network according to an embodiment of the present invention;
FIG. 4 is a flowchart of an algorithm of a step coordination scheduling idea according to an embodiment of the present invention;
fig. 5 is a microgrid renewable energy and load graph of an embodiment of the present invention;
FIG. 6 is a graph of the transaction price of electricity for AMG and DNO according to the embodiment of the present invention;
FIG. 7 is a graph of the unbalanced energy distribution of AMGs in accordance with embodiments of the invention;
fig. 8 is a charging and discharging curve diagram of the microgrid energy storage battery after local optimization according to the embodiment of the invention;
fig. 9 is a graph of the capacity of the energy storage battery of the microgrid system after local optimization according to the embodiment of the present invention;
FIG. 10 is a comparative graph of the load before and after load optimization of an embodiment of the present invention;
FIG. 11 is a graph of DNO optimized transaction power according to an embodiment of the present invention;
FIG. 12 is a comparison of load response curves at two time scales for an embodiment of the present invention;
FIG. 13 is a graph comparing EBR curves according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a multi-microgrid energy sharing method, which considers that a supply and demand situation may occur inside a microgrid, and establishes an AMG energy coordination management scheme including microgrid energy sharing. Firstly, the middle layer focuses on the partition autonomy of a local micro-grid subsystem, preferentially coordinates the self supply and demand balance, and provides supply and demand information and renewable energy source shared electric quantity between micro-grids for the top layer DNO; then DNO optimizes the electric quantity transacted with AMGs; and finally, the bottom layer integrated controller makes corresponding load interruption according to the optimization results of the upper layer and the middle layer. The method specifically comprises the following steps:
s1), modeling internal composition units of the micro-grid, and establishing mathematical models of wind power generation, photovoltaic power generation and energy storage batteries; the established mathematical model is concretely as follows:
s11, wind power generation model
The mainstream wind power generation modeling method at present is a probability method, wherein the distribution model with the most simple and flexible characteristics is Weibull distribution. Because the Weibull distribution model has better fitting performance on the actual wind speed and can flexibly adjust the relevant parameters of the change of the wind speed, the wind speed is simulated by adopting the Weibull distribution model. The distribution function is as follows:
the probability density function is as follows:
where v denotes a wind speed, k denotes a shape parameter, and c denotes a scale parameter. The wind turbine output function is expressed as follows:
in the formula, PWGr represents a rated power of the wind turbine generator system, PWG represents an actual output power of the wind turbine generator system, and v i Indicating cut-in wind speed, v 0 Representing cut-out wind speed, v representing actual wind speed, v r Indicating the rated wind speed. The force profile is shown in figure 2.
S12, photovoltaic power generation model
The generated power of the photovoltaic power generation system is mainly determined by the illumination intensity and is limited by various factors, but because of the randomness of the ground illumination intensity, the generated power of the photovoltaic power generation system is also random, the generated power has larger fluctuation, and ideally, within a certain time range, the actual ground sunlight intensity meets the Beat distribution, and the probability density is as follows:
wherein e and e max The actual and maximum illumination intensities in the time interval are respectively, and both alpha and beta are Beat distribution parameters. The photovoltaic cell converts light energy into electric energy, and the output power of the photovoltaic cell is as follows:
P PV ＝η PV eS
in the formula eta PV Converting the efficiency of the photovoltaic cell; and S is the effective irradiation area of the photovoltaic cell.
From this, the output power of the photovoltaic cell is also Beat distribution, which is expressed as
In the formula, P PV,max ＝η PV e max And S is the maximum power of the photovoltaic cell.
The invention selects the storage battery as the energy storage unit of the microgrid and uses E i (k) And the electric quantity stored by the energy storage battery of the ith microgrid system at the moment k is represented. The energy storage capacity at the next moment is related to the energy storage capacity at the previous moment, the charge/discharge capacity at the current moment and the exchange efficiency, and the mathematical model is as follows:
in the formula, P i BESS (k) The total energy of the energy storage battery of the ith microgrid system exchanged with the current microgrid system at the kth time is represented as follows:
P i BESS (k)＝P i RES (k)-P i L (k)
in the formula, P i BESS (k) The power storage battery of the ith microgrid system is charged at the moment k when the voltage is more than 0, and P i BESS (k) < 0 indicates that the energy storage battery of the ith microgrid system is discharged at the time k,
the charging efficiency of the energy storage battery of the ith microgrid system at the moment k is shown,
represents the discharge efficiency of the energy storage battery of the ith microgrid system at the moment k, and
s2, determining constraint limits of internal composition units of the microgrid, wherein the constraint limits comprise corresponding limits of load requirements, energy storage battery limits and renewable energy source limits;
load demand response limit:
P i L (k)＝P i DR (k)+ΔP i L (k)
in the formula (I), the compound is shown in the specification,P L being the lowest active power demanded by the total load,
the maximum active power required for the total load. Namely the demand response P of the total load of the ith microgrid system i L (k) The power flow constraint should be satisfied. P is i DR In order to be an important load,
and scheduling the maximum energy of the controllable load.
And (4) limiting the energy storage battery:
in the formula (I), the compound is shown in the specification,
is the upper limit of the energy stored in the energy storage battery,P BESS and
respectively representing the maximum charge and discharge limits of the energy storage battery and the local system for exchanging electric quantity.
Limitation of renewable energy sources:
in the formula (I), the compound is shown in the specification,
for maximum output power generated by the renewable power generation source, the renewable power generation source of the microgrid should meet minimum and maximum power flow limits at time k.
And S3), designing a multi-microgrid system topological structure based on the mathematical model established in the step S1 and the constraint limit determined in the step S2.
The multi-microgrid system topology structure provided by the invention is shown in fig. 3 and consists of M microgrid systems. Each micro-grid system comprises renewable energy sources represented by photovoltaic power generation and wind power generation, important loads represented by refrigerators and air conditioners, controllable loads represented by charging piles and energy storage batteries. Electric energy is transmitted inside the microgrid and between the microgrids through power transmission lines.
From the topological point of view, the multi-microgrid system is a distributed control structure, and each microgrid system has a controller of the microgrid system. Each microgrid preferentially meets the energy supply requirement of the microgrid, if the renewable energy sources which can be supplied by the microgrid exceed the system load requirement, the rest renewable energy sources preferentially supply other microgrid lacking electricity under the condition of meeting the supply requirement of the microgrid, and if the rest renewable energy sources exist, the rest renewable energy sources are sold to the power distribution network. If the energy which can be supplied by the microgrid is difficult to meet the system load demand and cannot be met under the condition of receiving the renewable energy resources of other microgrids, the microgrid purchases electric energy from the power distribution network.
And S4, in the multi-microgrid system topological structure designed in the step S3, solving the scheduling problem of different time scales in the actual scene, applying a double-time scale control strategy and providing a hierarchical optimization method.
Fig. 5, 6 and 7 show the real curves of the load demand, RESS output curve and market price of electricity in a certain area. As can be seen from FIG. 6, during 7-9, AMG1 has excess power for DNO due to RESS being higher than the load demand. However, AMG2 needs to purchase some power from DNO because the load demand is greater than RESS supply, while the supply demand of AMG3 is nearly balanced. Fig. 6 shows the energy trade price versus energy supply, and the market price increases when energy is scarce and fluctuates according to the microgrid decision
The simulation optimization results of all the micro grids in the middle layer in the three-layer energy management architecture are shown in fig. 8, 9 and 10. Fig. 8 shows the charging/discharging conditions of the energy storage batteries of each microgrid subsystem in one day. As can be seen from the figure, the energy storage device charge/discharge optimization values of the micro-grid subsystems are within a given constraint range. Fig. 9 shows the variation of the stored energy of each microgrid subsystem during a day, and it can be seen that the optimized values of the stored energy capacities are within the given constraint range. The two were analyzed in comparison, and at time period 0:00-5: in 00, the microgrid 1 is always in a condition of over supply and over demand, on one hand, the microgrid 1 charges the stored energy, and on the other hand, part of energy is shared by other microgs 2, so that the energy storage battery of the microgrid 1 is not fully charged. If the microgrid 2 is always in a state of supply less than demand, the energy storage battery is rapidly discharged at the beginning because of a large shortage. And the microgrid 3 keeps the supply and demand balance all the time in the period, and the capacity of the energy storage battery of the microgrid 3 keeps 45p.u. In the following step 6:00-11:00, the microgrid 1 is always over-demand, the microgrid 2 is always less-than-demand, and the microgrid 3 is the same as the microgrid 2. The renewable energy electric quantity of the microgrid 1 is used for charging the energy storage and also shared by other microgrids, so that the energy storage battery of the microgrid 1 is not fully charged. And the other time periods also adjust the energy storage capacity according to the supply and demand relation.
After the charging/discharging of the energy storage devices of the subsystems in the middle layer is optimized, the supply and demand imbalance information of the subsystems in the middle layer is uploaded to the DNO in the top layer. And then, DNO comprehensively analyzes supply and demand information of all micro-grid subsystems, and all energy is scheduled by solving the optimization problem of the level. The DNO optimized transaction power is shown in fig. 10.
As shown in fig. 11, a curve being positive indicates that the local microgrid subsystem purchases power from the DNO, and a curve being negative indicates that the microgrid subsystem sells power to the DNO, and it can be seen that the power for DNO transactions is within the constraint range. According to the optimized transaction electric quantity curve, in a time period of 0:00-10:00 and 17:00-23:00, the electricity trading of the three micro-grids is very active, so that the electricity price of market trading reaches the peak value in the two time periods. After the energy sharing between the piconets is considered, the invention greatly reduces the transaction electric quantity with the DNO, for example, the energy sharing ratio is 8:00, after receiving the energy sharing of the microgrid 1, the microgrid 2 of the present invention purchases 10p.u. from DNO. In time period 19:00-23:00, after energy sharing is considered, all microgrid subsystems of the invention are reduced compared with the electricity quantity of microgrid transaction [14], but in consideration of economy of the microgrid, a part of redundant electricity quantity of renewable energy sources is sold to the DNO in the whole DNO optimization process. As can be seen from the DNO transaction power, there is still a supply-demand balance between AMGs. Therefore, the DNO issues the supply and demand information of the micro-grids to the controller on the bottom layer, the result is further optimized, the electricity consumption of the controllable load is reduced, and the demand response of the load is optimally scheduled.
The optimization of the upper layer and the middle layer can realize the optimal scheduling of energy, the optimal solution is sent to the load set controller at the bottom layer, and the energy scheduling is further optimized by adjusting the controllable load. FIG. 12 shows a comparison of load demand response profiles at a fast time scale and a slow time scale. It can be seen that although the user demand changes in real time, the load response curve in the fast sampling period can always track the load response curve in the slow sampling period in real time.
In order to evaluate the effectiveness of the microgrid Energy scheduling policy, an Energy Balance rate index (EBR) is defined, and the formula is as follows:
wherein, P i ES (k) The total energy supply for the ith microgrid system at time k may be calculated by the following equation:
P i ES (k)＝P i RES (k)+P i b (k)+P i B (k)-P i S (k)+P i in (k)+P i out (k)
fig. 13 shows a comparison of EBR effectiveness before and after the control strategy proposed by the present invention. As can be seen from the figure, the EBR of the control method provided by the invention is above 95% at any time. From 11 to 14, the three microgrid subsystems are balanced in supply and demand, so that the curve is gentle, and after 19, the photovoltaic power generation output is 0, so that the EBR curve has large fluctuation.
And S5, preferentially performing energy optimization on the control level of the middle-layer subnet by adopting a scheduling strategy of a ladder coordination scheduling idea according to the optimization method provided by the step S4. Firstly, the local micro-grid system in the middle layer acquires the prediction information of the renewable power generation source, the total load power consumption demand and the energy storage system power storage amount in a slow sampling period, and optimizes the power supply to the load by adjusting the charging and discharging of the energy storage equipment and then changing the local system power supply amount. And then, the supply and demand imbalance information of the system, namely how much renewable energy electric quantity that the system can share for other microgrid subsystems/can accept the renewable energy electric quantity transmitted by other microgrid subsystems, and how much electric quantity that the system needs to buy/sell from the DNO is uploaded to the top-level DNO. And then, after receiving supply and demand information and energy storage capacity transmitted by the micro-grid of all the subsystems, the DNO makes an optimization decision in the same sampling period as the middle layer to obtain the electric quantity which should be traded with each subsystem, and then sends out an instruction. And finally, the load collection controller positioned at the bottom layer executes the optimization instruction issued by the upper layer at a faster time scale, and optimizes the power consumption requirement of the intelligent controllable load of the user in each micro-grid subsystem so as to match the upper power supply balance.
The algorithm design flow is as follows: and (3) at the beginning of each optimization period, firstly solving an optimization problem 1, and uploading optimized supply and demand balance information to a top layer DNO. Then, the DNO control layer solves the optimization problem 2 to obtain an energy trading optimization value under the global supply and demand balance. And finally, issuing the obtained optimization instruction to a bottom layer collector, and optimizing and distributing related controllable loads by solving an optimization problem 3. The algorithm flow is shown in fig. 4.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A multi-microgrid energy sharing method is characterized by comprising the following steps:
s1, modeling internal composition units of the micro-grid, and establishing mathematical models of wind power generation, photovoltaic power generation and energy storage batteries;
s2, determining constraint limits of internal component units of the microgrid, wherein the constraint limits comprise corresponding load demand limits, energy storage battery limits and renewable energy source limits;
s3, designing a multi-microgrid system topological structure based on the mathematical model established in the step S1 and the constraint limits determined in the step S2;
s4, in the multi-microgrid system topological structure designed in the step S3, scheduling problems of different time scales in an actual scene are solved, a double-time scale control strategy is applied, and a hierarchical optimization method is provided;
and S5, according to the optimization method provided by the step S4, a scheduling strategy of a ladder coordination scheduling idea is adopted to perform energy optimization on each subnet control level, the middle layer regulates controllable load demand by managing charging and discharging of the energy storage battery, the upper layer meets the optimization target of meeting the power consumption demand of each user on the premise of minimizing economic cost according to the priority and real-time market transaction electricity price, and the bottom layer load integrated controller optimizes the power consumption demand of the user load on a fast time scale.
2. The multi-microgrid energy sharing method according to claim 1, characterized in that in step S1, modeling is performed on internal constituent units of the microgrid, and mathematical models of wind power generation, photovoltaic power generation and energy storage batteries are established, specifically as follows:
s11, wind power generation model
The wind speed is simulated by adopting a Weibull distribution model, and the distribution function is as follows:
the probability density function is as follows:
where v denotes the wind speed, k denotes the shape parameter, c denotes the scale parameter, and the wind turbine output function is expressed as follows:
in the formula, PWGr represents a rated power of the wind turbine generator system, PWG represents an actual output power of the wind turbine generator system, and v i Indicating cut-in wind speed, v 0 Representing cut-out wind speed, v representing actual wind speed, v r Representing a rated wind speed;
s12: photovoltaic power generation model
Within a certain time range, the actual sunlight intensity on the ground meets the Beat distribution, and the probability density is as follows:
wherein e and e max The actual and maximum illumination intensities in the time interval are respectively, alpha and beta are both beta distribution parameters, the photovoltaic cell converts light energy into electric energy, and the output power of the photovoltaic cell is as follows:
P PV ＝η PV eS
in the formula eta PV Converting the efficiency of the photovoltaic cell; s is the effective irradiation area of the photovoltaic cell;
the output power of the photovoltaic cell is also in a Beat distribution, and is represented as:
in the formula, P PV,max ＝η PV e max S is the maximum power of the photovoltaic cell;
s13: energy storage battery model
Selecting a storage battery as an energy storage unit of the microgrid by using E i (k) The energy storage capacity of the i-th microgrid system at the time k is represented, and the energy storage capacity at the next time is related to the energy storage capacity at the previous time, the charge/discharge capacity at the current time and the exchange efficiency, and the mathematical model is as follows:
in the formula, P i BESS (k) The total energy exchanged between the energy storage battery of the ith microgrid system and the current microgrid system at the kth time is represented as follows:
P i BESS (k)＝P i RES (k)-P i L (k)
in the formula, P i BESS (k) The power storage battery of the ith microgrid system is charged at the moment k when the voltage is more than 0, and P i BESS (k) < 0 indicates that the energy storage battery of the ith microgrid system is discharged at the time k,
the charging efficiency of the energy storage battery of the ith microgrid system at the moment k is shown,
represents the discharge efficiency of the energy storage battery of the ith microgrid system at the moment k, and
3. the method according to claim 1, wherein the constraint limits in step S2 are specifically as follows:
s21: load demand response limit:
P i L (k)＝P i DR (k)+ΔP i L (k)
in the formula (I), the compound is shown in the specification,P L being the lowest active power demanded by the total load,
is the total loadThe maximum active power required, namely the demand response P of the total load of the ith microgrid system i L (k) Should satisfy the power flow constraint, P i DR In order to be an important load,
scheduling the maximum energy of the controllable load;
s22, limiting the energy storage battery:
in the formula (I), the compound is shown in the specification,
is the upper limit of the energy stored in the energy storage battery,P BESS and
respectively representing the maximum charge-discharge limit of the electric quantity exchanged between the energy storage battery and the local system;
s23, renewable energy source limitation:
in the formula (I), the compound is shown in the specification,
for the maximum output power generated by the renewable power generation source, the renewable power generation source of the microgrid should meet the minimum and maximum power flow limits at time k.
4. The multi-microgrid energy sharing method according to claim 1, wherein the multi-microgrid system topology in the step S3 consists of M microgrid systems, wherein each microgrid system comprises renewable energy sources represented by photovoltaic power generation and wind power generation, important loads represented by refrigerators and air conditioners, controllable loads represented by charging piles and energy storage batteries, and electric energy is transmitted inside the microgrid and among the microgrid systems through power transmission lines;
the multi-microgrid system topological structure is a distributed control structure, each microgrid system is provided with a controller, each microgrid preferentially meets the own energy supply requirement, if the renewable energy sources which can be supplied by the microgrid exceed the system load requirement, the rest renewable energy sources are preferentially supplied to other microgrid lacking electricity under the condition of meeting the own supply requirement, and if the rest renewable energy sources are available, the rest microgrid is sold to a power distribution network; if the energy which can be supplied by the microgrid is difficult to meet the system load demand and cannot be met under the condition of receiving the renewable energy resources of other microgrids, the microgrid purchases electric energy from the power distribution network.
5. The multi-microgrid energy sharing method according to claim 4, wherein step S4 is implemented in a multi-microgrid system topology designed in S3, in response to scheduling problems of different time scales in an actual scene, and a double-time scale control strategy is applied to provide a hierarchical optimization method, specifically as follows:
in an actual scene, an energy coordination layer and a load regulation and control layer are executed in a microgrid in different sampling periods, in addition, information and energy exchange exists among different layers, and aiming at scheduling problems of different time scales, a double-time scale control strategy is adopted, a three-layer optimization structure comprising an upper layer, a middle layer and a bottom layer is provided, and aiming at the condition that supply and demand are possibly insufficient, a power distribution network possibly has insufficient electric quantity to trade with the microgrid, so that the middle-layer optimization problem is firstly solved under a slow time scale, energy exchange of the microgrid and energy distribution in the microgrid are optimized on the basis of load requirements, renewable energy prediction information and energy storage charging and discharging, the rest renewable energy is preferentially provided for other power distribution networks and sold to the power distribution network, and local unbalance information of the microgrid is uploaded to the power distribution network; secondly, the optimization problem at the top layer coordinates the energy exchange between the power distribution network and the microgrid in the same time scale as the middle layer; and finally, according to the optimization of the upper layer, the middle layer and the lower layer, the third optimization is realized on the bottom layer, the controllable load requirement of the user is adjusted by a faster sampling period, and the energy distribution of the user is optimized.
6. The method for energy sharing among multiple piconets according to claim 5, wherein the hierarchical optimization method in step S4 is specifically:
s41 local AMG optimization
The local AMG optimization goal of the middle layer is to preliminarily meet the principles of balance of supply and demand and minimum cost by managing charging and discharging of the energy storage battery model in the S1, adjusting the controllable load demand, sharing renewable energy electric quantity with other micro-grids and purchasing/selling electricity with DNO, and providing a local optimization objective function as follows:
in the formula, N s Optimized time domain (24 h), l representing energy scheduling s Representing the slow sampling period, ξ ij I =1, …, M, j =1, …,3 is a weight coefficient, λ i (k) Indicating the market-trade price of electricity, P, for AMG and DNO at time k i B (k+l s I k) represents the AMG purchasing power from DNO, P i S (k+l s | k) represents that the AMG sells power to the DNO;
the actual physical meaning of the first term of the objective function is the punishment of the over-high charge/discharge capacity of the energy storage battery, if the battery is frequently charged/discharged in a large quantity, certain loss is caused to the capacity of the battery, and the punishment function is defined as follows:
J i,bat (k+l s ∣k)＝P i BESS (k+l s ∣k) 2
according to the autonomous microgrid characteristics, P i BSEE (k+l s I k) is written as:
P i BESS (k+l s ∣k)＝P i RES (k+l s ∣k)-P i L (k+l s ∣k)
the actual physical meaning of the second term of the objective function represents the dissatisfaction cost brought by reduction of the controllable load of the user, the cost is regarded as the cost of the virtual power generation unit by adopting the incentive-based demand response management plan, and the cost function is as follows:
in the formula, a i And b i Is an independent positive coefficient between the demand reduction amount and the payment cost of the comfort level;
the third item of the objective function represents the electricity purchasing/selling cost of the local microgrid, and the fourth item represents the renewable energy electric quantity shared by the local microgrid and other microgrids;
according to the expression, the decision variable of the local AMG optimization problem is set to be U i (k+l s |k)＝[ΔP i L (k+l s |k)P i B (k+l s |k)P i S (k+l s |k)P i out (k+l s |k)P i in (k+l s |k),U i (k+l s |k)∈Ω i
Wherein omega i Is a micro-grid subsystem i allowable control quantity set, delta P i L (k+l s If | k) is the adjustable load prediction amount of the microgrid subsystem i, the optimization problem 1 is written as:
if P is i BESS (k+l s | k) is greater than or equal to 0, then:
0≤P i out (k+l s ∣k)≤P i BESS (k+l s ∣k)
if P is i BESS (k+l s | k) is less than or equal to 0, then:
0≤P i in (k+l s ∣k)≤|P i BESS (k+l s ∣k)|
and is
The energy conservation is constrained as follows:
P i RES (k+l s ∣k)-P i S (k+l s ∣k)+P i B (k+l s ∣k)-P i out (k+l s ∣k)+P i in (k+l s ∣k)-pre_P i BESS (k+l s ∣k)＝pre_P i L (k+l s ∣k)
in the formula, pre _ P i BESS (k+l s ∣k),pre_P i L (k+l s | k) is the optimized value;
s42 Global DNO optimization
The DNO is used as a common coupling node for energy transaction with each AMG subsystem in the multi-microgrid system and is a control entity of the global level of the system, the supply and demand information of each microgrid is collected in each slow sampling period, the optimized value is distributed to each AMG after the energy distribution is optimized, the supply and demand balance of the whole multi-microgrid system is met as far as possible, the system benefit and the electricity utilization comfort level of users are guaranteed, the DNO meets the optimized target of meeting the electricity utilization requirement of each user on the premise of minimum economic cost according to the priority and the real-time market transaction electricity price, and the objective function of DNO optimization is provided as follows:
where λ (k) is the average trade electricity price for all microgrid subsystems, as follows:
in the formula (I), the compound is shown in the specification,
the predicted value of the power purchased by the DNO to all the microgrid subsystems,
for the DNO to sell the predicted value of the electric quantity to all the microgrid subsystems, the optimization problem 2 is written as:
s43 load collective controller optimization
After the optimization problem of the upper and middle layers is solved, the optimization solution of the slow sampling period is obtained
After receiving the optimization information of the upper layer and the lower layer of the slow time scale, the bottom layer load integrated controller optimizes the power consumption demand of the user load on the fast time scale;
the optimized values on the load side are set as:
wherein l f Represents a slow sampling period;
the optimization target of the bottom layer load integrated controller gives an optimization reference solution delta P for tracking the upper and top layer DNO optimization problem i L* (k+l s I k), based on which the optimization objective function of the load set controller is proposed as follows:
then optimization problem 3 is written as:
the decision variable for optimization problem 3 is Z i (k+l s +nl f ∣k)＝[ΔP i L (k+l s +nl f ∣k)] T The optimization solution can be expressed as
7. The method for energy sharing among multiple piconets according to claim 6, wherein the step S5 is as follows:
the scheduling strategy of the idea of step coordination scheduling is adopted, the control level of the middle-layer subnet is preferentially optimized for energy, then the other two layers are optimized,
the algorithm design flow is as follows: at the beginning of each optimization period, firstly solving an optimization problem 1, and uploading optimized supply and demand balance information to a top layer DNO; then, the DNO control layer solves the optimization problem 2 to obtain an energy trading optimization value under global supply and demand balance; and finally, issuing the obtained optimization instruction to a bottom layer collector, and optimally distributing related controllable loads by solving an optimization problem 3.
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| 堵益高: "基于预测模型的孤岛微电网系统能量调度与优化", 《CNKI中国优秀博士学位论文》 * |
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