CN111181154A - Interconnected micro-grid energy storage capacity optimal configuration method - Google Patents
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Abstract
The invention relates to a double-layer optimization method for energy storage capacity configuration of interconnected micro-grids, which is used for researching energy storage optimization configuration in each micro-grid and operation strategy optimization problems in each operation scene of the interconnected micro-grids. The upper layer model takes the optimum annual net income after the energy storage is additionally arranged in the interconnected micro-grid as a target, the rated capacity E and the rated power P of the energy storage configured in each micro-grid are optimized respectively, and the lower layer model takes the optimum net income of operation under each scene as a target to formulate the optimum operation strategy of the interconnected micro-grid. Under different operation scenes, the multi-microgrid interconnection operation mode has certain advantages compared with an independent operation mode: under a normal operation scene, the photovoltaic consumption rate can be improved, the electricity purchasing cost of a higher-level power grid is reduced, and the operation economy is improved; when general faults occur, the load recovery amount and recovery time can be improved, and the power supply reliability is improved; when extreme faults occur, the recovery amount and recovery time of the key load can be improved, and the toughness is improved.
Description
Technical Field
The present invention relates to the field of energy storage systems. And more particularly, to an energy storage capacity configuration method for comprehensively improving the planning economy and the power supply capacity of interconnected micro-grids.
Background
In recent years, with the fact that more and more distributed generator sets such as wind turbine generators and photovoltaic units are connected to a power distribution network, due to the fact that power consumption requirements of partial regions are relieved, a market mechanism for promoting new energy consumption is lacked, the problems of wind abandonment and light abandonment are caused, and the development of new energy power generation industry in China is hindered. The energy storage system is used as an important component in the microgrid, and by means of the characteristic that the energy can flow in two directions and can meet the requirements of power and capacity at the same time, the power generation utilization rate of renewable energy sources can be improved, the output power fluctuation of a distributed power supply is stabilized, the schedulability of the microgrid system is improved, and important guarantee is provided for the stable and economic operation of a power system after large-scale renewable energy source power generation grid connection, the energy storage configuration capacity is an important factor influencing the functional performance of the microgrid system, and the operation stability and the economy of the microgrid system are influenced by the reasonable configuration scheme or not.
With the continuous development of the economic society and the continuous improvement of the power consumption demand, the reliability problem of the microgrid system becomes the focus of the increasing attention of the power system, and the key load can cause significant economic and political influences even if the power is cut off in a short time, so that the improvement of the power supply reliability is one of the important research problems of microgrid planning and operation decision making. Large-scale power failure accidents caused by extreme events such as important equipment faults, manual operation, extreme natural disasters and the like can cause large-scale political and economic loss, casualties and major social influences, so that the scenes with the most serious influence when the extreme faults occur need to be researched aiming at vulnerable weak links in the topology of the micro-grid system. In the micro-grid energy storage planning stage, toughness indexes of the system when extreme disasters occur are taken into consideration, and the energy storage unit is used as an emergency power supply when extreme faults occur and is used for recovering key loads directly connected to the micro-grid and a power distribution network feeder line, so that the power failure time of the key loads can be shortened, and the rapidity of fault recovery is improved.
When an extreme disaster occurs, the micro-grid autonomous system interconnects a plurality of micro-grids with similar geographic positions to form an interconnected micro-grid, and the interconnected micro-grid autonomous system can be also used for improving the toughness of a power distribution network system. Each microgrid is regarded as an independent individual, and through coordinated operation in the interconnected microgrid system, each microgrid supplies power to other microgrids and key loads accessed by feeder lines of the power distribution network on the premise of ensuring normal power supply of the key loads of the microgrid, so that the overall operation economy and toughness level of the interconnected microgrid system can be improved, and the self-healing capability of the power distribution network system in extreme events is improved.
Disclosure of Invention
The invention aims to provide an energy storage capacity optimal configuration method for interconnected micro-grids, which can realize energy mutual aid among the micro-grids and comprehensively improve the power supply reliability and toughness.
Based on the idea of hierarchical optimization, a double-layer optimization model for energy storage capacity configuration of the interconnected micro-grids is provided, and the problems of energy storage optimization configuration in each micro-grid and operation strategy optimization in each operation scene of the interconnected micro-grids are researched. The upper layer model respectively optimizes the rated capacity E and the rated power P of the energy storage configured in each microgrid by taking the annual net income optimization after the energy storage is additionally arranged in the interconnected microgrid as a target, and the energy storage optimization configuration result in the upper layer model is not only related to an objective function F (X, P, E), an equality constraint G (X, P, E) and an inequality constraint H (X, P, E) of the upper layer optimization problem, but also related to a lower layer optimization result X, wherein X refers to the value of each variable in the function when the objective function of the lower layer model reaches the optimum, and is expressed by the variable X as a function expression formula; based on analysis of interconnection operation modes in different types of operation scenes, the lower layer model takes the operation net income optimization in each scene as a target to make an optimal operation strategy of the interconnection microgrid, the lower layer optimization problem is composed of an objective function f (X, P, E), an equality constraint g (X, P, E) and an inequality constraint h (X, P, E), and the optimization result is simultaneously influenced by the energy storage configuration P, E in the upper layer optimization model problem.
In order to achieve the purpose, the invention adopts the following technical scheme:
an interconnected microgrid energy storage capacity optimal configuration method comprises the following steps:
step 1: assuming the configuration capacities of a non-schedulable distributed generation (NDDG) unit and a schedulable distributed generation (DDG) unit in each microgrid, the required power of each level of load is a known quantity; wherein the variable subscripts I, II, III represent the load class, s,
represents an operational scenario and
respectively representing a normal operation scene, a general fault scene and an extreme fault scene, wherein the duration of each scene is 24h, i,
the responses of the representative piconets, t,
representing the study period in hours.
Step 2: and establishing an upper layer model objective function of the interconnected micro-grid energy storage capacity configuration double-layer optimization model, wherein the upper layer model objective function optimizes the rated capacity E and the rated power P of the energy storage configured in each micro-grid by taking the annual net income f optimal after the energy storage is additionally arranged in the interconnected micro-grid as a target.
And step 3: establishing a lower-layer model objective function of an interconnected micro-grid energy storage capacity configuration double-layer optimization model, wherein the lower-layer model objective function is calculated according to the running net income I under each scenesOptimizing X to obtain optimal operation strategy of interconnected microgrid, wherein X refers to the value of each variable in the function when the objective function of the underlying model is optimal and comprises binary variable Bin, integer variable Int, continuous variable Con, semi-continuous variable Scon and semi-integer variable Sint。
And 4, step 4: and (3) calling a cplexmill function to solve X by adopting MATLAB to the interconnected microgrid energy storage capacity configuration double-layer optimization model established in the step (2) and the step (3).
On the basis of the above scheme, the upper layer model objective function in step 2 is as follows:
wherein, each micro-grid i is used in the upper layer model objective function,
rated capacity of internal energy storage
And rated power
As a decision variable, the annual net income f after the energy storage is additionally arranged in each microgrid is optimized, wherein the f comprises the annual equivalent investment cost of the energy storage in each microgrid
Annual operating maintenance costs
Annual installation costs
Optimizing under each operation scene s to obtain the optimal operation net income expectation NE of the years(Is)；
Initial investment cost of energy storage and investment cost c of unit power thereofP(yuan/kW), investment cost per unit volume cERelated to (yuan/kWh), and converting the conversion rate r and the planning age L of the interconnected micro-grid into equivalent investment cost of energy storage year
Annual operating maintenance costs
And unit operation maintenance cost co(yuan/kW) is in direct proportion; annual installation costs
And unit installation cost cs(yuan/kWh) is proportional; net operating revenue expectation, N · Es(Is) The optimal operation net income I of the interconnected micro-grid in the operation days N (days) and each operation scene s of the lower model objective function contained in the yearsAnd the occurrence probability Pr(s) thereof.
The constraints of the upper layer model include:
the upper limit of investment in the initial stage of energy storage is restricted; energy storage rated power and rated capacity lower limit constraint; reliability index constraint; constraint of toughness indexes;
on the basis of the scheme, the upper limit of the initial investment of the energy storage in the step 2 is restricted as follows:
wherein, STIF is the upper limit value of the energy storage investment of the microgrid in a short period;
the lower limit constraints of the energy storage rated power and the rated capacity are as follows:
wherein, when the formula (7) shows that the fault occurs, the DDG unit and the stored energy in the microgrid i can respectively output rated output thereof
Supporting the peak loads of class I and II
Normal operation is carried out;
the formula (8) shows that when a fault occurs, the DDG unit and the energy storage unit in the microgrid I can at least support the peak loads of the I level and the II level in the microgrid to operate for T hours,
SoCthe upper and lower limits of the State of Charge (SoC) of the energy storage unit are respectively.
The reliability index constraints are:
the reliability index is defined as a general fault scenario
Load power failure time expectation, T, of interconnected micro-gridsi.sFor the load power failure time of the microgrid i in the scene s,
is the upper limit of the reliability index;
the toughness index constraint is as follows:
setting the toughness index as an extreme fault scene
In the interior, the weight coefficient gamma for quantifying the load criticality of level I, II is consideredI、γIIAnd then, the obtained expected power failure time T of the key load of the interconnected microgridI.i.s、TII.i.sThe power failure time of the I, II-grade load of the microgrid i in the scene s is respectively, and RS is the upper limit of the toughness index.
On the basis of the above scheme, the lower layer model objective function in step 3 is as follows:
running net profit I by lower layer model objective function under each running scene ssOptimal target, operation strategy for each scene
Is optimized, IsSelling electricity revenue from interconnected micro-grids
Extreme fault scenario
Revenue from recovering critical loads on feeder lines of distribution network
DDG Unit Fuel cost
Start-stop costHigher-level power grid electricity purchasing cost
General fault scenarios and extreme fault scenarios
Lower shedding load penalty cost
Energy storage decay cost
Composition is carried out;
g in the lower model object function formulat(yuan/kWh) is the price of the unit micro-grid load electricity selling,
load power of the microgrid i in a time period t under a scene s;
load power is inscribed for the microgrid i in a time period t under a scene s;
power of an NDDG unit of the microgrid i in a time period t under a scene s is cut off; in the formula, ct(yuan/kWh) is the unit profit for restoring the power supply of the key load of the distribution network,
recovering the key load power of the power distribution network within a time period t under a scene s for the microgrid i; in the formula, cf(yuan/kW) is the unit fuel cost of the DDG unit,
outputting power for the DDG unit in a time period t under a scene s for the microgrid i; in the formula, cU、cD(Yuan/time) is unit startup and shutdown cost, delta P of the uniti.s.tIs the output unbalanced power of the microgrid i in the period t under the scene s, delta P'i.s.tto account for the actual outlet imbalance power after the line loss rate ηi.s.t、bξi.s.tThe method comprises the steps that binary variables describing the on and off states of a DDG unit in a time period t of a micro-grid i under a scene s are respectively described; in the formula mt(yuan/kWh) is the electricity purchasing cost of a unit power grid,
purchasing power for a superior power grid of the microgrid i within a time period t under a scene s; in the formula cl(yuan/kWh) is the penalty cost for load blackout,
for load shedding power of the microgrid i in a time period t under a scene s,
respectively representing the discharge power of the stored energy and the charging power of the stored energy,
respectively representing the time period t of stored energy in the microgrid i under the scene sThe stored electric quantity, the binary variable of the DDG unit operation state and the binary variable of the energy storage operation state, Ti.sFor the load power off time of the microgrid i under the scene s, cPThe unit price of the degradation cost of the energy storage battery is shown, h is the slope of a linear approximate function of the degradation of the energy storage life and the number of charge and discharge cycles, and delta t is shown as a time interval.
The constraints of the underlying model include: distributed power supply operation constraints; energy storage charging and discharging operation constraint; inter-microgrid power exchange constraints; load shedding power constraint; recovering power constraints from critical loads on the feeder of the distribution network; a controllable distributed power supply total fuel amount constraint;
on the basis of the scheme, the operation constraint of the distributed power supply is as follows (1):
wherein, equation (19) is the NDDG unit to remove the power constraint,
outputting power for an NDDG unit of the microgrid i within a time period t under a scene s;
power of an NDDG unit of the microgrid i in a time period t under a scene s is cut off; equations (21) to (24) are the operating constraints of the DDG unit;
the rated output of the DDG unit in the microgrid i is obtained.
Equation (20) is the DDG unit output upper and lower limit constraints,
for the DDG unit output power of the microgrid i within a time period t under a scene s,
and
the running states of the DDG units in the time period t and the time period t-1 in the scene s of the microgrid i are respectively binary variables; equations (21) and (22) are the DDG unit shortest on-off time constraint, UTi、DTiThe shortest startup and shutdown time; equations (23), (24) are constraints describing the relationship between three binary variables.
(2) Energy storage charge and discharge operation constraint
Wherein,
in order to store the binary variables of the running state in the microgrid i in the scene s within the time period t,
when the energy storage state is 1, the energy storage is in a discharging state, otherwise, the energy storage is in a charging or idle state; the formula (27) is charge-discharge cycle number constraint, the charge-discharge conversion number of the stored energy in the microgrid i in the scene s should not exceed K, namely the charge-discharge cycle number of the stored energy in the scene should not exceed K/2, and the highest charge-discharge cycle number in one day is 2 times specified in the application; ei.s.tAnd Ei.s.t-1respectively storing the stored electric quantity in a time period t and a time period t-1 of the microgrid i in a scene s, wherein eta is the energy storage and discharge efficiency, and the formula (30) represents that the microgrid i is in a normal operation scene
Next, the influence of energy storage charge-discharge cycles on the operation life of the energy storage device needs to be considered, so that the state of charge of the energy storage device at the starting time and the ending time of a scene is the same; due to the fault scene
Since the return of the power supply to the load is preferably ensured, the initial state of charge of the stored energy is defined in equation (31) as the setpoint Ei.s.0。
And the power exchange constraint between the micro-grids is as follows:
this is an inter-piconet power exchange constraint under three types of operating scenarios.
Wherein, Δ Pi.s.tThe method comprises the steps that unbalanced power is output by a microgrid i within a time period t under a scene s, when the unbalanced power is a positive value, it is indicated that surplus power can be transmitted to other microgrids for power supply in the microgrid, and when the unbalanced power is a negative value, it is indicated that power shortage exists in the microgrid, and electric energy needs to be transmitted to the microgrid by a superior microgrid or other microgrids for load recovery; due to the existence of the distributed power source energy storage units in the micro-grids, power transmission among the micro-grids has bidirectionality, and the line loss in the power transmission process among the micro-grids is considered to be delta P 'in order to avoid the power loss caused by unnecessary power transmission among the micro-grids'i.s.tin order to account for the actual outlet unbalanced power after the line loss rate η', the power transmission between the microgrid and the upper power grid is specified to have unidirectionality in the formula (34).
The three types of operation scenes comprise: under normal operation scene, general fault scene and extreme fault scene;
the micro-grid power exchange constraint in the normal operation scene is
The formulas (35) and (36) show that in a normal operation scene s, when the microgrid with power shortage and the microgrid with excess power form the interconnected microgrid within a time period t
Then, first, at
Internally satisfying power balance, secondly
When the whole power is still in shortage, the power is supplied by the superior power grid; micro-grid not affected by faults and still in independent grid-connected operation state
The power balance is formed between the independent power grid and the superior power grid,
the method is a microgrid set in an interconnected microgrid formed in a time period t under a scene s.
The microgrid power exchange constraint under the general fault scene is
This is the period of no failure under the general failure scenario s
The microgrid power exchange constraint is the same as the operation mode in the normal operation scene, and represents the failure time period
Inside, receive each microgrid of fault influence conversion to island operation
After interconnection, the microgrid gc is close to the geographic position, is not influenced by faults and normally operates in a grid-connected modesInterconnection, namely integrally converting the interconnected micro-grid into grid-connected operation by forming the interconnected micro-grid to finish load recovery in a failure period; in the fault period, other micro-grids which are not influenced by the fault and are not interconnected with the fault micro-grid are subjected to fault treatment
Still operating in an independent grid-connected state.
The microgrid power exchange constraint under the extreme fault scene is as follows:
this is the period of no failure under the extreme failure scenario s
The microgrid power exchange constraint is the same as the operation mode in the normal operation scene, and represents the period of extreme fault occurrence
Because the fault of the upper-level power grid is caused, all the micro-grids are converted into isolated island operation after being interconnected, and the load on the feeder line of the power distribution network can be recovered when the surplus power is still available on the premise of ensuring the power supply of the key load in the interconnected micro-grids,
and (4) the power of the key load of the feeder line of the power distribution network recovered by the microgrid i within a time period t under the scene s.
The load shedding power constraint is as follows:
the upper and lower limits of each level of load shedding power are constrained under a fault scene s;
the key load recovery power constraint on the feeder line of the power distribution network is as follows:
this is a critical load recovery power constraint on the distribution feeder under extreme fault scenario s,
power to be recovered for the key load of the power distribution network within the time period t;
the total fuel quantity constraint of the controllable distributed power supply is as follows:
when extreme disasters occur, fuel storage shortage and difficult replenishment in the micro-grid can be caused due to damage of gas transportation pipelines and ground traffic paralysis, so that the power generation amount of the DDG unit is limited, which is the restriction of the total fuel amount upper limit of the DDG unit in an extreme fault scene,
the power generation amount of the DDG unit in the microgrid i under the extreme fault scene s is the upper limit value.
On the basis of the scheme, the solving of the interconnected microgrid energy storage capacity configuration double-layer optimization model in the step 4 comprises the following steps:
step 4-1: arranging the energy storage capacity configured double-layer optimization model into a single-layer model, which is shown in the following formula;
step 4-2: converting nonlinear constraint conditions appearing in the converted single-layer model into linear constraint conditions by a large M method, wherein the linear constraint conditions are shown as the following formula;
wherein: m is an arbitrarily large penalty factor, and M > 0.
Step 4-3: and (3) after r, Aineq, bineq, Aeq, beq, lb and ub which are required by model solution are constructed, adopting MATLAB to call a cplexmill function to solve X.
The variable x to be optimized may include a binary variable (Bin), an integer variable (Int), a continuous variable (Con), a semi-continuous variable (Scon), and a semi-integer variable (Sint);
aineq and Aeq refer to coefficient matrixes before variables to be optimized in constraint conditions of objective functions of upper and lower layers of models, bineq and beq refer to constant column vectors on the right side of inequality constraint conditions of the objective functions of the upper and lower layers of models and constant column vectors on the right side of equal signs of the equality constraint conditions of the objective functions of the upper and lower layers of models respectively, and lb and ub refer to constant column vectors on the left and right sides of the inequality signs of the variables to be optimized in the constraint conditions of the objective functions of the upper and lower layers of models respectively.
The invention has the beneficial effects that:
the invention establishes an energy storage capacity configuration double-layer optimization model for comprehensively improving the reliability and toughness of power supply in a short period. Compared with an independent operation mode, the energy storage capacity configuration in the internet micro-network is lower; under different operation scenes, the multi-microgrid has certain advantages compared with an independent operation mode under an interconnected operation mode: under a normal operation scene, the photovoltaic consumption rate can be improved, the electricity purchasing cost of a superior power grid is reduced, and the operation economy is improved through mutual electric energy coordination among micro grids; when a general fault occurs, load recovery amount and recovery time can be improved and power supply reliability is improved through interconnection and grid-connected operation among the micro grids; when extreme faults occur, the stored energy can be used as an emergency power supply, and the key load recovery amount and recovery time can be improved through mutual assistance of electric energy among micro-grids, so that the toughness is improved.
Drawings
The invention has the following drawings:
fig. 1 is a block diagram of a two-layer optimization model for energy storage capacity configuration.
Fig. 2 is a schematic structural diagram of an interconnected microgrid including three microgrids as an interconnected microgrid.
Detailed Description
In order to more clearly illustrate the present invention, the present invention will be further described with reference to the following examples and the accompanying drawings 1-2.
Assuming the configuration capacity of a non-schedulable distributed generation (NDDG) unit and a schedulable distributed generation (DDG) unit in each microgrid, the required power of each stage of load is a known quantity. The variable indices I, II, III represent the load classes,
represents an operational scenario and
respectively representing a normal operation scene, a general fault scene and an extreme fault scene, wherein the duration of each scene is 24h,
the micro-grid is represented by,
representing the study period in hours.
1. An upper layer model objective function of the interconnected microgrid energy storage capacity configuration double-layer optimization model is established as follows:
wherein, each microgrid is arranged in the upper layer model
Rated capacity of internal energy storage
And rated power
As a decision variable, the annual net income f after the energy storage is additionally arranged in each microgrid is optimized, wherein the f comprises the annual equivalent investment cost of the energy storage in each microgrid
Annual operating maintenance costs
Annual installation costs
Optimizing under each operation scene s to obtain the optimal operation net income expectation NE of the years(Is)；
Initial energy storage throwingCapital cost and investment cost per unit power cP(yuan/kW), investment cost per unit volume cERelated to (yuan/kWh), and converting the conversion rate r and the planning age L of the interconnected micro-grid into equivalent investment cost of energy storage year
Annual operating maintenance costs
And unit operation maintenance cost co(yuan/kW) is in direct proportion; annual installation costs
And unit installation cost cs(yuan/kWh) is proportional; net operating revenue expectation, N · Es(Is) The optimal operation net income I under each operation scene s of the lower layer is determined by the operation days N (days) of the interconnected micro-grids contained in one yearsAnd the occurrence probability Pr(s) thereof.
The constraints of the upper layer model include:
the upper limit of investment in the initial stage of energy storage is restricted; energy storage rated power and rated capacity lower limit constraint; reliability index constraint; constraint of toughness indexes;
(1) the upper limit of the investment in the initial energy storage stage is restricted as follows:
and the STIF is an upper limit value of the micro-grid energy storage investment in a short period.
(2) The lower limit constraints of the energy storage rated power and the rated capacity are as follows:
wherein, the formula (7) represents the DDG unit in the microgrid i when the fault occursAnd the stored energy can be rated at its output
Supporting the peak loads of class I and II
Normal operation is carried out;
the formula (8) shows that when a fault occurs, the DDG unit and the energy storage unit in the microgrid I can at least support the peak loads of the I level and the II level in the microgrid to operate for T hours,
SoCthe upper and lower limits of the State of Charge (SoC) of the energy storage unit are respectively.
(3) The reliability index constraints are:
the reliability index is defined as a general fault scenario
Load power failure time expectation, T, of interconnected micro-gridsi.sFor the load power failure time of the microgrid i in the scene s,
is the upper limit of the reliability index;
(4) the toughness index constraint is as follows:
setting the toughness index as an extreme fault scene
In the interior, the weight coefficient gamma for quantifying the load criticality of level I, II is consideredI、γIILater, the obtained expectation of the power failure time of the key load of the interconnected microgrid，TI.i.s、TII.i.sRespectively representing the power failure time of I, II-grade load of the microgrid i in the scene s,
the upper limit of the toughness index.
2. The lower layer model objective function of the interconnected microgrid energy storage capacity configuration double-layer optimization model is established as follows:
running the lower layer model under each running scene s to obtain net profit IsOptimal target, operation strategy for each scene
Is optimized, IsInterconnected micro-grid electricity selling methodGain of
Extreme fault scenario
Revenue from recovering critical loads on feeder lines of distribution network
DDG Unit Fuel cost
Start-stop cost
Higher-level power grid electricity purchasing cost
General fault scenarios and extreme fault scenarios
Lower shedding load penalty cost
Energy storage decay cost
And (4) forming.
Lower layer objective function formula gt(yuan/kWh) is the price of the unit micro-grid load electricity selling,
load power of the microgrid i in a time period t under a scene s;
load power is inscribed for the microgrid i in a time period t under a scene s;
time period under scene s for microgrid ithe NDDG unit within t cuts off power; in the formula, ct(yuan/kWh) is the unit profit for restoring the power supply of the key load of the distribution network,
recovering the key load power of the power distribution network within a time period t under a scene s for the microgrid i; in the formula, cf(yuan/kW) is the unit fuel cost of the DDG unit,
outputting power for the DDG unit in a time period t under a scene s for the microgrid i; in the formula cU、cD(Yuan/time) is unit startup and shutdown cost, delta P of the uniti.s.tIs the output unbalanced power of the microgrid i in the period t under the scene s, delta P'i.s.tto account for the actual outlet imbalance power after the line loss rate ηi.s.t、bξi.s.tThe method comprises the steps that binary variables describing the on and off states of a DDG unit in a time period t of a micro-grid i under a scene s are respectively described; in the formula mt(yuan/kWh) is the electricity purchasing cost of a unit power grid,
purchasing power for a superior power grid of the microgrid i within a time period t under a scene s; in the formula cl(yuan/kWh) is the penalty cost for load blackout,
for load shedding power of the microgrid i in a time period t under a scene s,
respectively representing the discharge power of the stored energy and the charging power of the stored energy,
respectively representing the stored electric quantity of the stored energy in a period T of the micro-grid i in a scene s, the binary variable of the operation state of the DDG unit and the binary variable of the energy storage operation state, Ti.sFor the load power off time of the microgrid i under the scene s, cPThe unit price of the declining cost of the energy storage battery is shown, and h is the energy storage lifeThe slope of a linear approximation function of the life decay and the number of charge-discharge cycles, Δ t, is expressed as a time interval.
The constraints of the underlying model include: distributed power supply operation constraints; energy storage charging and discharging operation constraint; inter-microgrid power exchange constraints; load shedding power constraint; recovering power constraints from critical loads on the feeder of the distribution network; recovering power constraints from critical loads on the feeder of the distribution network;
(1) the distributed power supply operation constraints are as follows:
wherein, equation (19) is the NDDG unit to remove the power constraint,
outputting power for an NDDG unit of the microgrid i within a time period t under a scene s;
power of an NDDG unit of the microgrid i in a time period t under a scene s is cut off; equations (21) to (24) are the operating constraints of the DDG unit;
the rated output of the DDG unit in the microgrid i is obtained.
Equation (20) is the DDG unit output upper and lower limit constraints,
for the DDG unit output power of the microgrid i within a time period t under a scene s,
and
the running states of the DDG units in the time period t and the time period t-1 in the scene s of the microgrid i are respectively binary variables; equations (21) and (22) are the DDG unit shortest on-off time constraint, UTi、DTiThe shortest startup and shutdown time; equations (23), (24) are constraints describing the relationship between three binary variables.
(2) Energy storage charge and discharge operation constraint
Wherein,
in order to describe the binary variables of the energy storage operation state in the micro grid i in the scene s within the time period t,
when the energy storage state is 1, the energy storage is in a discharging state, otherwise, the energy storage is in a charging or idle state; the formula (27) is charge-discharge cycle number constraint, the charge-discharge conversion number of the stored energy in the microgrid i in the scene s should not exceed K, namely the charge-discharge cycle number of the stored energy in the scene should not exceed K/2, and the highest charge-discharge cycle number in one day is 2 times specified in the application; ei.s.tAnd Ei.s.t-1respectively storing the stored electric quantity in a time period t and a time period t-1 of the microgrid i in a scene s, wherein eta is the energy storage and discharge efficiency, and the formula (30) represents that the microgrid i is in a normal operation scene
Next, the influence of energy storage charge-discharge cycles on the operation life of the energy storage device needs to be considered, so that the state of charge of the energy storage device at the starting time and the ending time of a scene is the same; due to the fault scene
Since the return of the power supply to the load is preferably ensured, the initial state of charge of the stored energy is defined in equation (31) as the setpoint Ei.s.0。
(3) Inter-piconet power exchange constraints
This is an inter-piconet power exchange constraint under three types of operating scenarios.
Wherein, Δ Pi.s.tThe method comprises the steps that unbalanced power is output by a microgrid i within a time period t under a scene s, when the unbalanced power is a positive value, it is indicated that surplus power can be transmitted to other microgrids for power supply in the microgrid, and when the unbalanced power is a negative value, it is indicated that power shortage exists in the microgrid, and electric energy needs to be transmitted to the microgrid by a superior microgrid or other microgrids for load recovery; due to the existence of the distributed power source energy storage units in the micro-grids, power transmission among the micro-grids has bidirectionality, and the line loss in the power transmission process among the micro-grids is considered to be delta P 'in order to avoid the power loss caused by unnecessary power transmission among the micro-grids'i.s.tin order to account for the actual outlet unbalanced power after the line loss rate η', the power transmission between the microgrid and the upper power grid is specified to have unidirectionality in the formula (34).
The three types of operation scenes comprise: under normal operation scene, general fault scene and extreme fault scene;
the micro-grid power exchange constraint in the normal operation scene is
The formulas (35) and (36) show that in a normal operation scene s, when the microgrid with power shortage and the microgrid with excess power form the interconnected microgrid within a time period t
Then, first, at
Internally satisfying power balance, secondly
When the whole power is still in shortage, the power is supplied by the superior power grid; micro-grid not affected by faults and still in independent grid-connected operation state
The power balance is formed between the independent power grid and the superior power grid,
the method is a microgrid set in an interconnected microgrid formed in a time period t under a scene s.
The microgrid power exchange constraint under the general fault scene is
This is the period of no failure under the general failure scenario s
The microgrid power exchange constraint is the same as the operation mode in the normal operation scene, and represents the failure time period
Inside, receive each microgrid of fault influence conversion to island operation
After interconnection, the microgrid gc is close to the geographic position, is not influenced by faults and normally operates in a grid-connected modesInterconnection by forming interconnectionThe whole microgrid is converted into grid-connected operation, and load recovery in a failure period is completed; in the fault period, other micro-grids which are not influenced by the fault and are not interconnected with the fault micro-grid are subjected to fault treatment
Still operating in an independent grid-connected state.
The microgrid power exchange constraint under the extreme fault scene is
This is the period of no failure under the extreme failure scenario s
The microgrid power exchange constraint is the same as the operation mode in the normal operation scene, and represents the period of extreme fault occurrence
Because the fault of the upper-level power grid is caused, all the micro-grids are converted into isolated island operation after being interconnected, and the load on the feeder line of the power distribution network can be recovered when the surplus power is still available on the premise of ensuring the power supply of the key load in the interconnected micro-grids,
and (4) the power of the key load of the feeder line of the power distribution network recovered by the microgrid i within a time period t under the scene s.
(4) Load shedding power constraint
This is the upper and lower limit constraints of each level of switching load power under the fault scene s.
(5) Critical load recovery power constraints on power distribution network feeders
This is a critical load recovery power constraint on the distribution feeder under extreme fault scenario s,
and (4) power to be recovered for the key load of the power distribution network in the time period t.
(6) Controllable distributed power total fuel amount constraint
When extreme disasters occur, fuel storage shortage and difficult replenishment in the micro-grid can be caused due to damage of gas transportation pipelines and ground traffic paralysis, so that the power generation amount of the DDG unit is limited, which is the restriction of the total fuel amount upper limit of the DDG unit in an extreme fault scene,
the power generation amount of the DDG unit in the microgrid i under the extreme fault scene s is the upper limit value.
The model established by the invention is a double-layer mixed integer linear programming problem, the upper layer is a linear programming problem only containing continuous variables, and the lower layer decision variables comprise continuous variables of various units such as output, energy storage charging and discharging power, load shedding power, power failure time and the like, and binary variables describing energy storage charging and discharging states, DDG unit starting and stopping and running states and whether load loss occurs or not, so that the problem is a complex mixed integer linear programming problem.
A typical mixed integer linear programming problem can be expressed as:
minr·x (47)
s.t. Aineq·x≤bineq (48)
Aeq·x＝beq (49)
lb≤x≤ub (50)
x∈{Bin,Int,Con,Scon,Sint} (51)
where r, bineq, beq, lb, and ub are column vectors, Aineq and Aeq are matrices, and the variable x to be optimized may include a binary variable (Bin), an integer variable (Int), a continuous variable (Con), a semi-continuous variable (Scon), and a semi-integer variable (Sint). Wherein Aineq and Aeq refer to coefficient matrixes before variables to be optimized in constraint conditions of target functions of upper and lower layers of models, bineq and beq refer to constant column vectors on the right side of inequality constraint conditions of the target functions of the upper and lower layers of models and constant column vectors on the right side of equal signs of the equality constraint conditions of the target functions of the upper and lower layers of models respectively, and lb and ub refer to constant column vectors on the left and right sides of the inequality signs of the variables to be optimized in the constraint conditions of the target functions of the upper and lower layers of models respectively.
And 4, solving the interconnected microgrid energy storage capacity configuration double-layer optimization model, which comprises the following steps:
step 4-1: arranging the energy storage capacity configured double-layer optimization model into a single-layer model, which is shown in the following formula;
step 4-2: converting nonlinear constraint conditions appearing in the converted single-layer model into linear constraint conditions by a large M method, wherein the linear constraint conditions are shown as the following formula;
wherein: m is an arbitrarily large penalty factor, and M > 0.
Step 4-3: and (3) after r, Aineq, bineq, Aeq, beq, lb and ub which are required by model solution are constructed, adopting MATLAB to call a cplexmill function to solve X.
The above examples are given for the purpose of illustrating the invention clearly and not for the purpose of limiting the same, and it will be apparent to those skilled in the art that, in light of the foregoing description, numerous modifications and variations can be made in the form and details of the embodiments of the invention described herein, and it is not intended to be exhaustive or to limit the invention to the precise forms disclosed.
Claims (6)
1. An interconnected microgrid energy storage capacity optimal configuration method is characterized by comprising the following steps:
step 1: assuming the configuration capacity of the non-schedulable distributed power supply unit and the schedulable distributed power supply unit in each microgrid, the required power of each level of load is a known quantity; wherein the variable subscripts I, II, III represent the load class, s,
represents an operational scenario and
respectively representing a normal operation scene, a general fault scene and an extreme fault scene, wherein the duration of each scene is 24h, i,
the responses of the representative piconets, t,
represents the study period in hours;
step 2: establishing an upper layer model objective function of an interconnected microgrid energy storage capacity configuration double-layer optimization model, wherein the upper layer model objective function takes the annual net income f after the energy storage is additionally arranged in the interconnected microgrid as a target, and optimizes the rated capacity E and the rated power P of the energy storage configured in each microgrid respectively;
and step 3: establishing a lower-layer model objective function of an interconnected micro-grid energy storage capacity configuration double-layer optimization model, wherein the lower-layer model objective function is calculated according to the running net income I under each scenesOptimizing X to obtain an optimal operation strategy of the interconnected microgrid, wherein X refers to the value of each variable in the function when the objective function of the lower-layer model is optimal and comprises a binary variable Bin, an integer variable Int, a continuous variable Con, a semi-continuous variable Scon and a semi-integer variable Sint;
and 4, step 4: and (3) calling a cplexmill function to solve X by adopting MATLAB to the interconnected microgrid energy storage capacity configuration double-layer optimization model established in the step (2) and the step (3).
2. The interconnected microgrid energy storage capacity optimization configuration method of claim 1, characterized in that the upper layer model objective function in step 2 is as follows:
wherein, each micro-grid i is used in the upper layer model objective function,
rated capacity of internal energy storage
And rated power Pi EAs a decision variable, the annual net income f after the energy storage is additionally arranged in each microgrid is optimized, wherein the f comprises the annual equivalent investment cost of the energy storage in each microgrid
Annual operating maintenance costs
Annual installation costs
Optimizing under each operation scene s to obtain the optimal operation net income expectation NE of the years(Is)；
Initial investment cost of energy storage and investment cost c of unit power thereofPInvestment cost per unit volume cEIn connection with, cPUnit of (A) is Yuan/kW, cEThe unit of (a) is yuan/kWh, and the conversion rate r and the planning age limit L of the interconnected micro-grid are converted into the equivalent investment cost of the energy storage year
Annual operating maintenance costs
And unit operation maintenance cost coIs in direct proportion; annual installation costs
And unit installation cost csIs in direct proportion; c is mentionedoIn units of yuan/kW, csUnit of (d) is yuan/kWh; net operating revenue expectation, N · Es(Is) The optimal operation net income I of each operation scene s of the lower model objective function is determined by the number of operation days N of the interconnected micro-grids contained in one yearsAnd the occurrence probability Pr(s) of the same;
the constraints of the upper layer model include:
the upper limit of investment in the initial stage of energy storage is restricted; energy storage rated power and rated capacity lower limit constraint; reliability index constraint; and (5) restricting toughness indexes.
3. The interconnected microgrid energy storage capacity optimal configuration method of claim 2, characterized in that the energy storage initial investment upper limit constraint of step 2 is:
wherein, STIF is the upper limit value of the energy storage investment of the microgrid in a short period;
the lower limit constraints of the energy storage rated power and the rated capacity are as follows:
wherein, when the fault occurs, the distributable power supply unit and the energy storage unit in the microgrid i which can be dispatched are respectively rated to output P according to the formula (7)i G.R、Pi ESupporting the peak loads of class I and II
Normal operation is carried out;
equation (8) shows that in the microgrid i when a fault occursThe schedulable distributed power supply unit and the energy storage unit at least support I-level and II-level peak loads in the microgrid for operation for T hours,
SoCrespectively the upper limit and the lower limit of the charge state of the energy storage unit;
the reliability index constraints are:
the reliability index is defined under a general fault scene s
Interconnected microgrid load power failure time expectation, Ti.sFor the load power failure time of the microgrid i in the scene s,
is the upper limit of the reliability index;
the toughness index constraint is as follows:
the toughness index is set within an extreme fault scene s
Weight coefficient gamma for quantifying load criticality of level I, III、γIIAnd then, the obtained expected power failure time T of the key load of the interconnected microgridI.i.s、TII.i.sRespectively representing the power failure time of I, II-grade load of the microgrid i in the scene s,
the upper limit of the toughness index.
4. The interconnected microgrid energy storage capacity optimization configuration method of claim 3, characterized in that the lower layer model objective function in step 3 is as follows:
running net profit I by lower layer model objective function under each running scene ssOptimal target, operation strategy for each scene
Is optimized, IsSelling electricity revenue from interconnected micro-grids
Extreme fault scenario s
Revenue from restoration of critical loads on distribution feeder
Schedulable distributed power unit fuel cost
Start-stop cost
Higher-level power grid electricity purchasing cost
General fault scenario and extreme fault scenario s
Penalty cost of load shedding
Energy storage decay cost
Composition is carried out;
g in the lower model object function formulatThe unit micro-grid load electricity selling price is unit of yuan/kWh,
load power of the microgrid i in a time period t under a scene s;
load power is inscribed for the microgrid i in a time period t under a scene s;
cutting off power for the non-dispatchable distributed power supply unit of the microgrid i in a time period t under a scene s; in the formula, ctThe unit income for recovering the key load power supply of the power distribution network is unit/kWh,
recovering the key load power of the power distribution network within a time period t under a scene s for the microgrid i; in the formula, cfIs the unit fuel cost of the schedulable distributed power generating set, the unit is yuan/kW,
outputting power for a distributed power supply unit which can be dispatched in a time period t under a scene s for the microgrid i; in the formula, cU、cDRespectively unit startup and shutdown cost of the unit, the unit is Yuan/time, delta Pi.s.tIs the output unbalanced power of the microgrid i in the period t under the scene s, delta P'i.s.tto account for the actual outlet imbalance power after the line loss rate ηi.s.t、bξi.s.tThe method comprises the steps that binary variables describing the on and off states of a schedulable distributed power supply unit of a microgrid i in a time period t under a scene s are respectively described; in the formula mtThe unit of the unit power grid electricity purchasing cost is yuan/kWh,
purchasing power for a superior power grid of the microgrid i within a time period t under a scene s; in the formula clPenalty cost for load blackout, in units of dollars/kWh,
for load shedding power of the microgrid i in a time period t under a scene s,
representing the discharge power of the stored energy and the charge power of the stored energy, respectively, Ei.s.t,
Respectively representing the stored electric quantity of the stored energy in a time period T of the microgrid i in a scene s, the binary variable of the operating state of the schedulable distributed power supply unit and the binary variable of the energy storage operating state, Ti.sFor the load power off time of the microgrid i under the scene s, cPThe cost unit price of the energy storage battery decline is represented, h is the slope of a linear approximate function of the energy storage life decline and the number of charge-discharge cycles, and delta t is represented as a time interval;
the constraints of the underlying model include: distributed power supply operation constraints; energy storage charging and discharging operation constraint; inter-microgrid power exchange constraints; load shedding power constraint; recovering power constraints from critical loads on the feeder of the distribution network; a controllable distributed power total fuel constraint.
5. The interconnected microgrid energy storage capacity optimization configuration method of claim 4, wherein the distributed power supply operation constraints are:
wherein, formula(19) To remove the power constraint for the non-dispatchable distributed power supply unit,
outputting power for the non-schedulable distributed power supply unit of the microgrid i in the time period t under the scene s;
cutting off power for the non-dispatchable distributed power supply unit of the microgrid i in a time period t under a scene s; equations (21) to (24) are operation constraints of the schedulable distributed power supply unit; pi G.RRated output of a schedulable distributed power supply unit in the microgrid i;
equation (20) is the upper and lower limit constraints on the output of the schedulable distributed power supply unit,
for the schedulable distributed power supply unit output power of the microgrid i within a time period t under a scene s,
and
the binary variables are respectively the operating states of the schedulable distributed power supply units in the time period t and the time period t-1 in the scene s of the microgrid i; equations (21), (22) are the shortest on-off time constraints for schedulable distributed Power units, UTi、DTiThe shortest startup and shutdown time; equations (23), (24) are constraints describing the relationship between three binary variables;
the energy storage charging and discharging operation constraints are as follows:
wherein,
in order to store the binary variables of the running state in the microgrid i in the scene s within the time period t,
when the energy storage state is 1, the energy storage is in a discharging state, otherwise, the energy storage is in a charging or idle state; the formula (27) is charge-discharge cycle number constraint, the charge-discharge conversion number of the stored energy in the microgrid i in the scene s should not exceed K, namely the charge-discharge cycle number of the stored energy in the scene should not exceed K/2, and the highest charge-discharge cycle number in one day is 2 times specified in the application; ei.s.tAnd Ei.s.t-1respectively storing the stored electric quantity in a time period t and a time period t-1 of the microgrid i under a scene s, wherein eta is the energy storage and discharge efficiency, and the formula (30) shows that under a normal operation scene s,
the charge states at the initial and end of energy storage are the same; due to the failure scenario s,
The return of the power supply to the load should preferably be ensured, so that the initial state of charge of the stored energy is defined in equation (31) as the setpoint value Ei.s.0；
And the power exchange constraint between the micro-grids is as follows:
this is the power exchange constraint between the micro-networks under three types of operation scenarios;
wherein, Δ Pi.s.tThe method comprises the steps that unbalanced power is output by a microgrid i within a time period t under a scene s, when the unbalanced power is a positive value, it is indicated that surplus power can be transmitted to other microgrids for power supply in the microgrid, and when the unbalanced power is a negative value, it is indicated that power shortage exists in the microgrid, and electric energy needs to be transmitted to the microgrid by a superior microgrid or other microgrids for load recovery; delta P'i.s.tthe power transmission between the microgrid and the upper-level power grid is specified to have unidirectionality in the formula (34);
the three types of operation scenes comprise: under normal operation scene, general fault scene and extreme fault scene;
the microgrid power exchange constraint in the normal operation scene is as follows:
the formulas (35) and (36) show that in a normal operation scene s, when the microgrid with power shortage and the microgrid with excess power form the interconnected microgrid within a time period t
Then, first, at
Internally satisfying power balance, secondly
When the whole power is still in shortage, the power is supplied by the superior power grid; but not affected by faults and still in an independent grid-connected operation state,
the power balance is formed between the independent power grid and the superior power grid,
the method comprises the steps of (1) forming a microgrid set in an interconnected microgrid formed in a time period t under a scene s;
the microgrid power exchange constraint under the general fault scene is as follows:
this is the period of no failure under the general failure scenario s
The microgrid power exchange constraint is the same as the operation mode in the normal operation scene, and represents the failure time period
In the interior, each microgrid i which is changed into island operation under the influence of faults,
after interconnection, the microgrid gc is close to the geographic position, is not influenced by faults and normally operates in a grid-connected modesInterconnection, namely integrally converting the interconnected micro-grid into grid-connected operation by forming the interconnected micro-grid to finish load recovery in a failure period; in the failure period, for other microgrids i which are not affected by the failure and are not interconnected with the failed microgrid at the same time,
the system still operates in an independent grid-connected state;
the microgrid power exchange constraint under the extreme fault scene is as follows:
this is the period of no failure under the extreme failure scenario s
The microgrid power exchange constraint is the same as the operation mode in the normal operation scene, and represents the period of extreme fault occurrence
Because of the fault of the upper-level power grid, all the micro-grids are converted into isolated island operation after being interconnected, and the load on the feeder line of the power distribution network is recovered when the surplus power still exists on the premise of ensuring the power supply of the key load in the interconnected micro-grids,
the method comprises the steps of obtaining power distribution network feeder key load power recovered by a microgrid i within a time period t under a scene s;
the load shedding power constraint is as follows:
the upper and lower limits of each level of load shedding power are constrained under a fault scene s;
the key load recovery power constraint on the feeder line of the power distribution network is as follows:
this is a critical load recovery power constraint on the distribution network feeder, P, under extreme fault scenario st LDPower to be recovered for the key load of the power distribution network within the time period t;
the total fuel quantity constraint of the controllable distributed power supply is as follows:
this is a total fuel upper limit constraint for a dispatchable distributed power unit in an extreme fault scenario,
the method is the upper limit value of the generated energy of the schedulable distributed power supply unit in the microgrid i under the extreme fault scene s.
6. The interconnected microgrid energy storage capacity optimization configuration method of claim 5, wherein the solving of the interconnected microgrid energy storage capacity configuration double-layer optimization model in the step 4 comprises the following steps:
step 4-1: arranging the energy storage capacity configured double-layer optimization model into a single-layer model, which is shown in the following formula;
step 4-2: converting nonlinear constraint conditions appearing in the converted single-layer model into linear constraint conditions by a large M method, wherein the linear constraint conditions are shown as the following formula;
wherein: m is an arbitrarily large penalty factor, and M > 0;
step 4-3: constructing r, Aineq, bineq, Aeq, beq, lb and ub required by model solving, and calling cplexmill function by using MATLAB to solve X;
wherein Aineq and Aeq refer to coefficient matrixes before variables to be optimized in constraint conditions of objective functions of upper and lower layers of models, bineq and beq refer to constant column vectors on the right side of inequality constraint conditions of the objective functions of the upper and lower layers of models and constant column vectors on the right side of equal signs of the equality constraint conditions of the objective functions of the upper and lower layers of models respectively, and lb and ub refer to constant column vectors on the left and right sides of the inequality signs of the variables to be optimized in the constraint conditions of the objective functions of the upper and lower layers of models respectively.
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