CN112039079A - Active power distribution network energy storage optimization system configuration method considering voltage safety - Google Patents

Active power distribution network energy storage optimization system configuration method considering voltage safety Download PDF

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CN112039079A
CN112039079A CN202010891523.5A CN202010891523A CN112039079A CN 112039079 A CN112039079 A CN 112039079A CN 202010891523 A CN202010891523 A CN 202010891523A CN 112039079 A CN112039079 A CN 112039079A
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李雪
宋彦龙
周歧斌
杜大军
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University of Shanghai for Science and Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention provides an energy storage optimal configuration method of an active power distribution network considering voltage safety, which comprises the following steps: inputting the stored energy capacity, the charge and discharge power, the access position and random variables in the active power distribution network into a lower-layer optimization model as known conditions, determining the stored energy working state and the charge and discharge power in each time period by adopting an improved interval control method, and performing peak clipping and valley filling on daily loads; carrying out probability load flow calculation, and carrying out voltage out-of-limit risk assessment and reverse load flow probability calculation according to the obtained node voltage and line load flow data; and respectively calculating energy storage configuration and maintenance cost, energy storage operation income and line loss cost in the upper layer model, and evaluating a configuration result by combining the calculated voltage out-of-limit risk and the reverse power flow probability. And repeating the processes to obtain an optimal energy storage configuration scheme. The invention aims to obtain an energy storage configuration scheme beneficial to voltage safety and tide distribution of an active power distribution network.

Description

Active power distribution network energy storage optimization system configuration method considering voltage safety
Technical Field
The invention relates to an energy storage optimal configuration method for an active power distribution network, in particular to an energy storage optimal configuration method for an active power distribution network considering voltage safety and reverse power flow.
Background
The rapid development of economy leads to rapid increase of power consumption, but due to the fact that the construction level of a power distribution system is lagged behind compared with a power generation system and a power transmission system, and due to the structural characteristics of radial distribution, large R/X value of a line and the like of a power distribution network, the voltage level of a long branch end node is low, and the voltage problem of the power distribution network is prominent; on the other hand, distributed power generation with random characteristics and large-scale access of electric vehicles to a power distribution network enable power flow to be converted from traditional unidirectional flow to bidirectional flow, and voltage instability and voltage out-of-limit risks are aggravated. In order to eliminate/reduce the voltage out-of-limit risk, an energy storage system with the advantages of low storage and high power is gradually adopted, and the peak clipping and valley filling of the energy storage system can stabilize the fluctuation of the load and the voltage in time, so that the voltage out-of-limit risk is reduced, and the tide distribution is improved.
In the aspect of optimal configuration of an energy storage system, the existing research mainly considers the influences of an energy storage access position and capacity on the voltage, the trend, the system operation economy and the like of a power distribution network, for example, an upper-layer multi-objective optimization model of energy storage location and capacity of the power distribution network is established by considering energy storage investment cost, line loss cost, main network electricity purchasing cost and overall node voltage deviation, so that the voltage quality and the economic benefit of the power distribution network are improved. Or a multi-objective optimization model of energy storage location and volume is established by considering node voltage fluctuation, load fluctuation and total energy storage capacity, so that adverse effects caused by the fact that a large amount of distributed power generation is connected to a power distribution network are improved. However, the research on the influence of the access of the energy storage system on the voltage safety of the system is not deep enough, and the possibility and the severity of voltage out-of-limit are not considered. According to the national standard 'power quality supply voltage allowable deviation', the supply voltage of the system has a certain allowable fluctuation range, and the voltage exceeding the allowable fluctuation value can influence the normal work of the electric equipment, so that the static safety of the system is reduced. The node voltage can be changed when the energy storage system is connected to the power distribution network, and the voltage fluctuation, deviation and voltage distribution of the system in different time are further influenced, so that the influence of energy storage configuration on the voltage safety of the power distribution network needs to be researched from a more comprehensive and deep perspective. In addition, the output of the distributed generation in the active power distribution network at different moments is not completely matched with the load demand, and the output of the distributed generation cannot be consumed on the spot, so that the flow direction of the power flow in some branches is changed at the moment when the output of the distributed generation is larger than the load demand, and a reverse power flow is generated, which affects the normal operation of the directional protection device and is not beneficial to the line safety of the active power distribution network, therefore, the reverse power flow is necessarily introduced into the target of the optimized configuration of the energy storage of the active power distribution network.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to overcome the defects in the prior art and provide an energy storage optimal configuration method for an active power distribution network considering voltage safety.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
a configuration method of an active power distribution network energy storage optimization system considering voltage safety is used for establishing a double-layer optimization model of active power distribution network energy storage, and comprises an upper layer model and a lower layer model:
the upper layer model is responsible for configuring parameters of the energy storage system, the maximum capacity, the maximum charge and discharge power and the access installation position of the energy storage system are planned by taking energy storage configuration and maintenance cost, energy storage operation income, line loss cost, voltage out-of-limit risk and reverse power flow occurrence probability as the minimum targets, and the constraints of the upper layer model comprise power balance constraint, energy storage allowable configuration capacity constraint and energy storage allowable configuration maximum charge and discharge power constraint;
the lower layer model optimizes the operation strategy of energy storage by taking the minimum daily load peak-valley difference of the active power distribution network as a target, and the constraints of the lower layer model comprise energy storage capacity constraint, energy storage maximum charging and discharging power constraint and energy storage charging and discharging balance constraint;
in a double-layer optimization model of the energy storage of the active power distribution network, an upper layer model corresponds to an energy storage multi-objective optimization configuration sub-problem, and a lower layer model corresponds to an energy storage operation strategy optimization sub-problem of performing peak clipping and valley filling on the energy storage according to daily load fluctuation; the upper layer model transmits a configuration result comprising the maximum energy storage capacity, the maximum charge-discharge power and the access position parameter to the lower layer model, the lower layer model obtains the output of the energy storage system at each time period according to the configuration result and the original load, and system node voltage and line tide information data after the energy storage access are obtained through probability tide calculation and are transmitted to the upper layer model; and solving the upper layer model and the lower layer model by respectively adopting an improved multi-target particle swarm algorithm and an improved interval control method.
Preferably, in the upper layer model, the distributed power generation and the energy storage of the power distribution network are subjected to combined optimization configuration, the operation strategy of the energy storage in the lower layer model is used as a known quantity, a 2m +1 point estimation method is adopted to perform probability power flow calculation, the voltage out-of-limit risks of all nodes in the active power distribution network and the reverse power flow occurrence probability of the line are evaluated according to the probability power flow calculation result, the maximum value of the system voltage out-of-limit risks in all time periods is used as the voltage safety index of the energy storage optimization configuration, and the reverse power flow of the line is considered in the configuration target.
Preferably, in the lower model, the energy storage operation strategy is optimized, based on the configuration result of the upper model, an interval control method is adopted for solving the operation strategy optimization problem of the energy storage system under different load scenes aiming at the condition that the daily load has a plurality of peak-valley periods, and the optimization result is returned to the upper model.
Preferably, after the lower-layer model determines that the original load obtains the output of the energy storage system at each time period, the correlation problem of the energy storage output and the load is processed by adopting Nataf conversion and elementary conversion.
Preferably, the distributed power generation in the active power distribution network comprises a fan and a photovoltaic, the load comprises an electric vehicle charging load and an original load of the power distribution network, different probability models are adopted to describe the probability distribution of variables, wherein the charging starting moment of the electric vehicle obeys normal distribution, the daily driving mileage obeys lognormal distribution, the charging and discharging power of each energy storage period and the original load of the power distribution network obey normal distribution, and the probability distribution of wind speed and illumination is respectively described by adopting two parameters, namely Weibull distribution and Beta distribution.
Preferably, the probability models of the wind speed, the illumination intensity, the charging amount of the electric automobile, the load and the energy storage output are used as input variables of the probability tidal current, and the influence of the energy storage access on the voltage of the active power distribution network and the line tidal current is further analyzed by calculating the probability tidal current. Because correlation exists among all input variables, the preferred correlation comprises the regional correlation of wind speed and illumination intensity and the correlation among loads, the correlation of variables is processed by combining Nataf transformation and elementary transformation, the node voltage and the line power flow are assumed to be in normal distribution, the probability power flow is solved by using a 2m +1 point estimation method, and the voltage threshold risk and the inverse power flow probability of the system are further analyzed according to the calculation result of the probability power flow.
Preferably, the active power distribution network energy storage optimization system configuration method considering voltage safety adopts the following steps:
initializing parameters of a multi-target particle swarm optimization algorithm, statistics of random variables including wind speed, illumination intensity and load, and randomly generating N groups of energy storage capacity, maximum charge and discharge power and access positions as initial populations within a constraint condition range;
inputting the energy storage capacity, the maximum charge and discharge power, the access position and the daily load into a lower-layer optimization model as known conditions, determining the upper limit and the lower limit of a control interval by utilizing improved interval control, further obtaining the working state and the charge and discharge power of energy storage in each time period, and performing peak clipping and valley filling on the daily load;
step three, updating node information of the system, processing correlation among wind speed, illumination intensity, electric vehicle charging amount, load and energy storage output by combining Nataf transformation and elementary transformation, and performing probability load flow calculation by using a 2m +1 point estimation method;
performing voltage out-of-limit risk assessment according to node voltage data obtained by probability load flow calculation, and calculating the probability of reverse load flow occurring at each moment of each branch according to line load flow data;
step five, respectively calculating investment cost, line loss cost and energy storage operation income of energy storage configuration in an upper model, and evaluating individuals in the population by combining the calculated voltage out-of-limit risk evaluation and the reverse power flow occurrence probability result to generate a group of new optimal solution sets;
step six, judging whether iteration is terminated:
if so, outputting an optimal solution set, and selecting an optimal solution according to the weight;
otherwise, returning to the second step, and iteratively updating a group of new populations according to the multi-target particle swarm optimization algorithm, wherein the populations comprise energy storage capacity, maximum charge and discharge power and access positions.
Preferably, the voltage out-of-limit risk is calculated based on the probability load flow, the probability and the severity of the system voltage out-of-limit are considered, and the product of the probability and the severity is obtained to represent the voltage safety of the active power distribution network; wherein the calculation formulas of the likelihood index, the severity index, and the voltage out-of-limit risk index of the system are respectively shown in the following formulas (1) to (3):
Figure BDA0002657167210000031
Figure BDA0002657167210000041
Figure BDA0002657167210000042
in the formula (I), the compound is shown in the specification,
Figure BDA0002657167210000043
P(V i) The probability of the voltage crossing the upper and lower limits, Vmax、VminRespectively an upper limit and a lower limit of a voltage allowable fluctuation range, ViFor the voltage of node i, the probability density function f (V) of the voltage of node ii) Can be obtained by probabilistic power flow calculation, Sev: (V i)、
Figure BDA0002657167210000044
The severity of the upper and lower limits of the voltage respectively,
Figure BDA0002657167210000045
Vi. 3σ respectively mu +3 sigma and mu-3 sigma of the node voltage value,
Figure BDA0002657167210000046
S(V i) The severity of the upper and lower limits of the normalized voltage, Vlvp、VovpRespectively, under-voltage, over-voltage protected action threshold, NbusThe number of total nodes of the system;
the occurrence probability of the reverse power flow is obtained based on a probability power flow calculation result, the power flow direction flowing from a bus end to a tail end node in the power grid is set to be positive, and the occurrence probability of the reverse power flow is the accumulated probability of the line power flow direction possibly being a negative part, as shown in a formula (4):
Figure BDA0002657167210000047
in the formula, PlIn order to be a line flow,
Figure BDA0002657167210000048
probability density function f (P) of line flow for random events with line flow in reverse directionl) Obtaining a probability load flow calculation result;
the calculation formula of the energy storage configuration and maintenance cost of the upper model is shown as formula (5):
Figure BDA0002657167210000049
in the formula, Cp、CeCost of allocation, P, of unit power and capacity of stored energy, respectivelyess.k、Sess.kConfiguring power and capacity, N, respectively for the kth energy storage deviceESSThe number of the energy storage devices is shown, tau is the annual rate, and y is the service life of the energy storage;
the calculation formula of the energy storage operation yield of the upper model is shown as the formula (6):
Figure BDA00026571672100000410
in the formula, CpriceTo the electricity price, Pk(t) is the charge and discharge quantity of the kth stored energy at the moment t, P during dischargek(t) is a positive value, P on chargingk(t) is a negative value,. DELTA.t is the unit time;
the calculation formula of the line loss cost of the upper layer model is shown as formula (7):
Figure BDA0002657167210000051
in the formula, CepCost per line loss, Ploss.k(t) line loss at time t of kth branch after energy storage configuration, NlineThe number of the system bus lines is; Δ t is a unit time;
the upper layer model adopts the maximum value of the out-of-limit risk of the system voltage in each time period as the standard for evaluating the voltage safety, and the formula (8) is as follows:
Figure BDA0002657167210000052
in the formula, V1，V2，......V24The system voltage level is in each period within 24 hours every day;
Figure BDA0002657167210000053
the risk of system voltage out-of-limit for each time period;
the upper layer model considers the maximum probability of reverse power flow generated by branches in different time periods, as shown in formula (9):
Figure BDA0002657167210000054
in the formula (I), the compound is shown in the specification,
Figure BDA0002657167210000055
the probability of reverse power flow on the first line in the t period is shown;
the upper layer model comprehensively considers the configuration cost of energy storage, the energy storage operation income and the system line loss cost as economic indexes of energy storage optimization configuration, and simultaneously considers node voltage out-of-limit risks and line reverse power flow probability, and the multi-objective optimization function of the energy storage configuration is shown as a formula (10):
min F＝[F1,F2,F3]＝[f1-f2+f3,f4,f5] (10)。
and (4) calculating the multi-objective optimization function as the solution of the multi-objective optimization configuration subproblem according to the formula (10).
Preferably, after the lower-layer model determines the capacity, the maximum charge-discharge power and the access installation position of the energy storage system according to the upper-layer optimization model, considering the condition that daily loads have a plurality of peak-valley periods, performing peak clipping and valley filling on the loads by using an interval control method, and optimizing an energy storage operation strategy; the interval control method obtains corresponding charge and discharge control intervals according to different daily load curves, energy storage capacity and maximum charge and discharge power so as to obtain the charge and discharge power of energy storage in each time interval, and the charge and discharge time interval of the energy storage system and the charge and discharge amount in different time intervals are determined by the difference value between the upper limit and the lower limit of the preset interval and the original load:
when the load value is within the interval, the energy storage system does not work;
when the load value is higher than the upper limit of the interval, the energy storage system discharges outwards;
when the load value is lower than the lower limit of the interval, the energy storage system is charged;
the lower layer model aims to minimize the daily load peak-valley difference of the active power distribution network, namely the difference between the upper limit and the lower limit of the control interval of energy storage, and the required constraint of the interval calculation is shown in formulas (11) to (13):
Figure BDA0002657167210000061
Figure BDA0002657167210000062
Figure BDA0002657167210000063
in the formula (I), the compound is shown in the specification,
Figure BDA0002657167210000064
is the total charge amount of the ith charging period;
Figure BDA0002657167210000065
the total discharge amount of the ith discharge period;
Figure BDA0002657167210000067
the difference value of the total charging quantity and the total discharging quantity in the previous time period is represented, namely the residual capacity at the current moment; sess.kConfiguring capacity for the kth stored energy; eta is energy conversion efficiency; lambda and mu are respectively reliable coefficients of the energy storage system for preventing overcharge and overdischarge; n and M are the number of charging and discharging time periods respectively;
the above equations (11) and (12) are capacity and charge-discharge balance constraints respectively, the first equation in the equation (11) represents that the sum of the total charge amount and the residual capacity in any time period needs to satisfy the overcharge protection constraint of energy storage, and the second equation in the equation (11) represents that the total discharge amount in any time period needs to satisfy the overdischarge protection constraint of an energy storage power station; the first formula in the formula (12) indicates that the sum of the charge amount and the residual capacity of each time interval needs to meet the discharge demand of the next time interval, and the second formula in the formula (12) indicates that the total charge amount and the discharge amount in one energy storage operation cycle need to be kept balanced, namely the energy storage electric quantity after one day needs to be kept at the initial electric quantity before working, so that the normal working of the energy storage in the next cycle is ensured, and one cycle is one day; average daily load P between upper and lower limits of intervaldAs an initial value, an iterative method is adopted to obtain:
if the operation strategy of the energy storage system does not meet the capacity constraint or the charge-discharge balance constraint in the iteration process, the upper limit of the up-shifting interval or the lower limit of the down-shifting interval is reached until the formulas (11) - (12) meet the conditions;
the charge-discharge power of each time interval in the lower model is obtained by the difference between a preset interval and the original load, and besides the capacity constraint and the power balance constraint of the formulas (11) to (12), the power constraint of the energy storage system also needs to be checked according to a formula (14):
if the charging/discharging power in a certain time interval exceeds a threshold value, taking the corresponding upper limit/lower limit as the actual charging/discharging power in the time interval, and checking the capacity constraint of the energy storage power station in the time interval and the subsequent time interval according to the formula (15);
if the energy storage system electric quantity in a certain time interval exceeds the threshold value according to the formula (16), the charge and discharge power in the time interval is re-established according to the formula (17), specifically as follows:
-Pess.k≤Pk(t)≤Pess.k (14)
Figure BDA0002657167210000066
μSess.k≤Sk(t)≤λSess.k (16)
Figure BDA0002657167210000071
in the formula, Pess.kFor the maximum charge-discharge power, P, of the stored energy configured in the upper modelk(t) is the charge and discharge power at time t; p at dischargek(t) is a positive value, P on chargingk(t) is a negative value, Sk(t)、SkAnd (t-1) respectively representing the electric quantity of the energy storage system at the current moment and the last moment.
Preferably, the energy storage multi-objective optimization configuration problem of the upper model is solved by adopting an improved multi-objective particle swarm algorithm, the multi-objective particle swarm algorithm combines a Pareto sorting mechanism and a basic particle swarm, an individual optimal and global non-inferior solution set is determined through a domination relation among particles, the non-inferior solution set is updated according to dynamic dense distances, and then the global optimal is selected;
in order to improve the population diversity and prevent the particle swarm algorithm from prematurely converging to the local optimum, cross and variation factors in the genetic algorithm are introduced into the particle swarm, and the processing method of crossing the boundary of the particle is changed according to the iteration times, as shown in formulas (18) to (19):
Figure BDA0002657167210000072
Figure BDA0002657167210000073
wherein rnd (a, b) is [ a, b ]]Random number of (2), xmax、xminRespectively an upper boundary and a lower boundary of inequality constraint, iter is the current iteration number, gen is the maximum iteration number, and x is the position of the particle in the solution space;
the output result of the multi-target particle swarm algorithm is a group of Pareto non-inferior solution sets, due to the fact that different dimensions exist among a plurality of target values, all the targets are normalized, and after weighted summation, an optimal solution is selected according to the fitness value of each scheme, wherein the formula (20) is as follows:
Figure BDA0002657167210000074
in the formula, λjIs the weight value corresponding to the jth target value, and λ1+λ2+λ 31, n population; biFitness value, x, of weighted sum after normalization for different targets of each scenariokFor the scheme with the maximum fitness value in the Pareto non-inferior solution set, xgbest is the optimal position where the particles in the whole population arrive once, and represents that the population is optimal.
Compared with the prior art, the invention has the following obvious and prominent substantive characteristics and remarkable advantages:
1. on the basis that the economy of energy storage configuration is considered in an active power distribution network, voltage out-of-limit risks are introduced to serve as indexes for evaluating the voltage safety of the system, reverse power flow of a line is considered at the same time, an improved interval control method is provided based on the configuration result of the energy storage, the problem of operation strategy optimization of the energy storage system in a multi-peak-valley load scene is solved, and the operation strategy is returned to the upper layer to further evaluate the configuration of the energy storage system;
2. the invention is an energy storage configuration beneficial to the voltage safety and the tide distribution of an active power distribution network;
3. the method is simple and easy to implement, low in cost and suitable for popularization and application.
Drawings
Fig. 1 is an architecture diagram of an energy storage double-layer optimization model of an active power distribution network considering voltage safety.
Fig. 2 is a flow chart of the overall solution of the active power distribution network energy storage double-layer optimization model considering voltage safety.
Fig. 3 is an IEEE-33 node distribution network for an energy storage system to be planned.
Fig. 4 is Pareto optimal solution set distribution of the energy storage configuration results.
FIG. 5 is the system voltage out-of-limit risk for scenario 1.
FIG. 6 is the system voltage out-of-limit risk for scenario 5.
Fig. 7 is the system reverse trend probability of scheme 1.
Fig. 8 is the system reverse trend probability of scenario 5.
Fig. 9 is a daily load characteristic curve in a single-peak valley scene.
Fig. 10 is a daily load characteristic curve in a multiple peak-valley scenario.
Detailed Description
The above-described scheme is further illustrated below with reference to specific embodiments, which are detailed below:
the first embodiment is as follows:
in this embodiment, referring to fig. 1 and fig. 2, a method for configuring an energy storage optimization system of an active power distribution network in consideration of voltage safety is implemented by establishing a double-layer optimization model of energy storage of the active power distribution network, which includes an upper layer model and a lower layer model:
the upper layer model is responsible for configuring parameters of the energy storage system, the maximum capacity, the maximum charge and discharge power and the access installation position of the energy storage system are planned by taking energy storage configuration and maintenance cost, energy storage operation income, line loss cost, voltage out-of-limit risk and reverse power flow occurrence probability as the minimum targets, and the constraints of the upper layer model comprise power balance constraint, energy storage allowable configuration capacity constraint and energy storage allowable configuration maximum charge and discharge power constraint;
the lower layer model optimizes the operation strategy of energy storage by taking the minimum daily load peak-valley difference of the active power distribution network as a target, and the constraints of the lower layer model comprise energy storage capacity constraint, energy storage maximum charging and discharging power constraint and energy storage charging and discharging balance constraint;
in a double-layer optimization model of the energy storage of the active power distribution network, an upper layer model corresponds to an energy storage multi-objective optimization configuration sub-problem, and a lower layer model corresponds to an energy storage operation strategy optimization sub-problem of performing peak clipping and valley filling on the energy storage according to daily load fluctuation; the upper layer model transmits a configuration result comprising the maximum energy storage capacity, the maximum charge-discharge power and the access position parameter to the lower layer model, the lower layer model obtains the output of the energy storage system at each time period according to the configuration result and the original load, and system node voltage and line tide information data after the energy storage access are obtained through probability tide calculation and are transmitted to the upper layer model; and solving the upper layer model and the lower layer model by respectively adopting an improved multi-target particle swarm algorithm and an improved interval control method.
According to the method, on the basis that the economy of energy storage configuration of the active power distribution network is considered, voltage out-of-limit risks are introduced to serve as indexes for evaluating the voltage safety of the system, the reverse trend of the line is considered, an improved interval control method is provided based on the configuration result of the energy storage, the problem of operation strategy optimization of the energy storage system under the multi-peak-valley load scene is solved, and the operation strategy is returned to the upper layer to further evaluate the configuration of the energy storage system.
Example two:
this embodiment is substantially the same as the first embodiment, and is characterized in that:
in the embodiment, in the upper layer model, joint optimization configuration is performed on distributed power generation and energy storage of the power distribution network, a probability power flow calculation is performed by adopting a 2m +1 point estimation method by using an operation strategy of the energy storage in the lower layer model as a known quantity, the voltage out-of-limit risk of each node in the active power distribution network and the reverse power flow occurrence probability of a line are evaluated according to the probability power flow calculation result, the maximum value of the system voltage out-of-limit risk in each period is used as a voltage safety index of the energy storage optimization configuration, and the reverse power flow of the line is considered in a configuration target.
In this embodiment, in the lower model, the energy storage operation strategy is optimized, based on the configuration result of the upper model, an interval control method is adopted to solve the operation strategy optimization problem of the energy storage system in different load scenes aiming at the situation that daily loads have a plurality of peak-valley periods, and the optimization result is returned to the upper model.
In this embodiment, after the lower layer model determines that the original load obtains the output of the energy storage system at each time interval, the correlation problem between the energy storage output and the load is processed by adopting the Nataf transformation and the elementary transformation.
The active power distribution network energy storage optimal configuration method considering voltage safety mainly aims to further introduce a voltage out-of-limit risk index and a reverse power flow probability on the basis of considering energy storage configuration economy to obtain an optimal configuration scheme beneficial to voltage safety and power flow direction of a power distribution network.
Example three:
this embodiment is substantially the same as the previous embodiment, and is characterized in that:
in the embodiment, the voltage safety-considered active power distribution network energy storage optimization system configuration method includes that the voltage out-of-limit risk is calculated based on a probability power flow calculation result, the possibility and the severity of system voltage out-of-limit are considered, and the product of the possibility and the severity is obtained to represent the voltage safety of the active power distribution network; wherein the calculation formulas of the likelihood index, the severity index, and the voltage out-of-limit risk index of the system are respectively shown in the following formulas (1) to (3):
Figure BDA0002657167210000101
Figure BDA0002657167210000102
Figure BDA0002657167210000103
in the formula (I), the compound is shown in the specification,
Figure BDA0002657167210000104
P(V i) The probability of the voltage crossing the upper and lower limits, Vmax、VminRespectively an upper limit and a lower limit of a voltage allowable fluctuation range, ViFor the voltage of node i, the probability density function f (V) of the voltage of node ii) Can be obtained by probabilistic power flow calculation, Sev: (V i)、
Figure BDA0002657167210000105
The severity of the upper and lower limits of the voltage respectively,
Figure BDA0002657167210000106
Vi. 3σ respectively mu +3 sigma and mu-3 sigma of the node voltage value,
Figure BDA0002657167210000107
S(V i) The severity of the upper and lower limits of the normalized voltage, Vlvp、VovpRespectively, under-voltage, over-voltage protected action threshold, NbusThe number of total nodes of the system;
the occurrence probability of the reverse power flow is obtained based on a probability power flow calculation result, the power flow direction flowing from a bus end to a tail end node in the power grid is set to be positive, and the occurrence probability of the reverse power flow is the accumulated probability of the line power flow direction possibly being a negative part, as shown in a formula (4):
Figure BDA0002657167210000108
in the formula, PlIn order to be a line flow,
Figure BDA0002657167210000109
probability density function f (P) of line flow for random events with line flow in reverse directionl) Obtaining a probability load flow calculation result;
the calculation formula of the energy storage configuration and maintenance cost of the upper model is shown as formula (5):
Figure BDA00026571672100001010
in the formula, Cp、CeCost of allocation, P, of unit power and capacity of stored energy, respectivelyess.k、Sess.kConfiguring power and capacity, N, respectively for the kth energy storage deviceESSThe number of the energy storage devices is shown, tau is the annual rate, and y is the service life of the energy storage;
the calculation formula of the energy storage operation yield of the upper model is shown as the formula (6):
Figure BDA0002657167210000111
in the formula, CpriceTo the electricity price, Pk(t) is the charge and discharge quantity of the kth stored energy at the moment t, P during dischargek(t) is a positive value, P on chargingk(t) is a negative value,. DELTA.t is the unit time;
the calculation formula of the line loss cost of the upper layer model is shown as formula (7):
Figure BDA0002657167210000112
in the formula, CepCost per line loss, Ploss.k(t) line loss at time t of kth branch after energy storage configuration, NlineThe number of the system bus lines is; Δ t is a unit time;
the upper layer model adopts the maximum value of the out-of-limit risk of the system voltage in each time period as the standard for evaluating the voltage safety, and the formula (8) is as follows:
Figure BDA0002657167210000113
in the formula, V1，V2，......V24The system voltage level is in each period within 24 hours every day;
Figure BDA0002657167210000114
the risk of system voltage out-of-limit for each time period;
the upper layer model considers the maximum probability of reverse power flow generated by branches in different time periods, as shown in formula (9):
Figure BDA0002657167210000115
in the formula (I), the compound is shown in the specification,
Figure BDA0002657167210000116
the probability of reverse power flow on the first line in the t period is shown;
the upper layer model comprehensively considers the configuration cost of energy storage, the energy storage operation income and the system line loss cost as economic indexes of energy storage optimization configuration, and simultaneously considers node voltage out-of-limit risks and line reverse power flow probability, and the multi-objective optimization function of the energy storage configuration is shown as a formula (10):
min F＝[F1,F2,F3]＝[f1-f2+f3,f4,f5] (10)。
and (4) calculating the multi-objective optimization function as the solution of the multi-objective optimization configuration subproblem according to the formula (10).
In this embodiment, after the lower-layer model determines the capacity, the maximum charge-discharge power and the access installation position of the energy storage system according to the upper-layer optimization model, considering the situation that daily loads have a plurality of peak-valley periods, the interval control method is used for peak clipping and valley filling of the loads, and the energy storage operation strategy is optimized; the interval control method obtains corresponding charge and discharge control intervals according to different daily load curves, energy storage capacity and maximum charge and discharge power so as to obtain the charge and discharge power of energy storage in each time interval, and the charge and discharge time interval of the energy storage system and the charge and discharge amount in different time intervals are determined by the difference value between the upper limit and the lower limit of the preset interval and the original load:
when the load value is within the interval, the energy storage system does not work;
when the load value is higher than the upper limit of the interval, the energy storage system discharges outwards;
when the load value is lower than the lower limit of the interval, the energy storage system is charged;
the lower layer model aims to minimize the daily load peak-valley difference of the active power distribution network, namely the difference between the upper limit and the lower limit of the control interval of energy storage, and the required constraint of the interval calculation is shown in formulas (11) to (13):
Figure BDA0002657167210000121
Figure BDA0002657167210000122
Figure BDA0002657167210000123
in the formula (I), the compound is shown in the specification,
Figure BDA0002657167210000124
is the total charge amount of the ith charging period;
Figure BDA0002657167210000125
the total discharge amount of the ith discharge period;
Figure BDA0002657167210000126
the difference value of the total charging quantity and the total discharging quantity in the previous time period is represented, namely the residual capacity at the current moment; sess.kConfiguring capacity for the kth stored energy; eta is energy conversion efficiency; lambda and mu are respectively reliable coefficients of the energy storage system for preventing overcharge and overdischarge; n and M are the number of charging and discharging time periods respectively;
the above equations (11) and (12) are capacity and charge-discharge balance constraints respectively, the first equation in the equation (11) represents that the sum of the total charge amount and the residual capacity in any time period needs to satisfy the overcharge protection constraint of energy storage, and the second equation in the equation (11) represents that the total discharge amount in any time period needs to satisfy the overdischarge protection constraint of an energy storage power station; the first formula in the formula (12) indicates that the sum of the charge amount and the residual capacity of each time interval needs to meet the discharge demand of the next time interval, and the second formula in the formula (12) indicates that the total charge amount and the discharge amount in one energy storage operation cycle need to be kept balanced, namely the energy storage electric quantity after one day needs to be kept at the initial electric quantity before working, so that the normal working of the energy storage in the next cycle is ensured, and one cycle is one day; average daily load P between upper and lower limits of intervaldAs an initial value, an iterative method is adopted to obtain:
if the operation strategy of the energy storage system does not meet the capacity constraint or the charge-discharge balance constraint in the iteration process, the upper limit of the up-shifting interval or the lower limit of the down-shifting interval is reached until the formulas (11) - (12) meet the conditions;
the charge-discharge power of each time interval in the lower model is obtained by the difference between a preset interval and the original load, and besides the capacity constraint and the power balance constraint of the formulas (11) to (12), the power constraint of the energy storage system also needs to be checked according to a formula (14):
if the charging/discharging power in a certain time interval exceeds a threshold value, taking the corresponding upper limit/lower limit as the actual charging/discharging power in the time interval, and checking the capacity constraint of the energy storage power station in the time interval and the subsequent time interval according to the formula (15);
if the energy storage system electric quantity in a certain time interval exceeds the threshold value according to the formula (16), the charge and discharge power in the time interval is re-established according to the formula (17), specifically as follows:
-Pess.k≤Pk(t)≤Pess.k (14)
Figure BDA0002657167210000131
μSess.k≤Sk(t)≤λSess.k (16)
Figure BDA0002657167210000132
in the formula, Pess.kFor the maximum charge-discharge power, P, of the stored energy configured in the upper modelk(t) is the charge and discharge power at time t; p at dischargek(t) is a positive value, P on chargingk(t) is a negative value, Sk(t)、SkAnd (t-1) respectively representing the electric quantity of the energy storage system at the current moment and the last moment.
In this embodiment, the energy storage multi-objective optimization configuration problem of the upper model is solved by adopting an improved multi-objective particle swarm algorithm, the multi-objective particle swarm algorithm combines a Pareto sorting mechanism and a basic particle swarm, an individual optimal and global non-inferior solution set is determined through a domination relation among particles, the non-inferior solution set is updated according to dynamic dense distances, and then the global optimal is selected;
in order to improve the population diversity and prevent the particle swarm algorithm from prematurely converging to the local optimum, cross and variation factors in the genetic algorithm are introduced into the particle swarm, and the processing method of crossing the boundary of the particle is changed according to the iteration times, as shown in formulas (18) to (19):
Figure BDA0002657167210000133
Figure BDA0002657167210000134
wherein rnd (a, b) is [ a, b ]]Random number of (2), xmax、xminRespectively an upper boundary and a lower boundary of inequality constraint, iter is the current iteration number, gen is the maximum iteration number, and x is the position of the particle in the solution space;
the output result of the multi-target particle swarm algorithm is a group of Pareto non-inferior solution sets, due to the fact that different dimensions exist among a plurality of target values, all the targets are normalized, and after weighted summation, an optimal solution is selected according to the fitness value of each scheme, wherein the formula (20) is as follows:
Figure BDA0002657167210000135
in the formula, λjIs the weight value corresponding to the jth target value, and λ1+λ2+λ 31, n population; biFitness value, x, of weighted sum after normalization for different targets of each scenariokFor the scheme with the maximum fitness value in the Pareto non-inferior solution set, xgbest is the optimal position where the particles in the whole population arrive once, and represents that the population is optimal.
According to the method, on the basis that the economy of energy storage configuration of the active power distribution network is considered, voltage out-of-limit risks are introduced to serve as indexes for evaluating the voltage safety of the system, the reverse trend of the line is considered, an improved interval control method is provided based on the configuration result of the energy storage, the problem of operation strategy optimization of the energy storage system under the multi-peak-valley load scene is solved, and the operation strategy is returned to the upper layer to further evaluate the configuration of the energy storage system.
Example four:
this embodiment is substantially the same as the previous embodiment, and is characterized in that:
in this embodiment, an active power distribution network energy storage double-layer optimization model considering voltage safety is shown in fig. 1-2, and the active power distribution network energy storage optimization system configuration method considering voltage safety in this embodiment includes the following steps:
initializing parameters of a multi-target particle swarm optimization algorithm, statistics of random variables including wind speed, illumination intensity and load, and randomly generating N groups of energy storage capacity, maximum charge and discharge power and access positions as initial populations within a constraint condition range;
inputting the energy storage capacity, the maximum charge and discharge power, the access position and the daily load into a lower-layer optimization model as known conditions, determining the upper limit and the lower limit of a control interval by utilizing improved interval control, further obtaining the working state and the charge and discharge power of energy storage in each time period, and performing peak clipping and valley filling on the daily load;
step three, updating node information of the system, processing correlation among wind speed, illumination intensity, electric vehicle charging amount, load and energy storage output by combining Nataf transformation and elementary transformation, and performing probability load flow calculation by using a 2m +1 point estimation method;
performing voltage out-of-limit risk assessment according to node voltage data obtained by probability load flow calculation, and calculating the probability of reverse load flow occurring at each moment of each branch according to line load flow data;
step five, respectively calculating investment cost, line loss cost and energy storage operation income of energy storage configuration in an upper model, and evaluating individuals in the population by combining the calculated voltage out-of-limit risk evaluation and the reverse power flow occurrence probability result to generate a group of new optimal solution sets;
step six, judging whether iteration is terminated:
if so, outputting an optimal solution set, and selecting an optimal solution according to the weight;
otherwise, returning to the second step, and iteratively updating a group of new populations according to the multi-target particle swarm optimization algorithm, wherein the populations comprise energy storage capacity, maximum charge and discharge power and access positions.
In this embodiment, taking the active power distribution network of the energy storage system to be planned shown in fig. 3 as an example, the energy storage system is optimally configured, a Pareto optimal solution set for improving multi-target particle swarm output is shown in fig. 4, and a weight λ is determined1、λ2、λ3And obtaining an optimal solution according to the formula (20), wherein the table 1 lists energy storage optimal configuration schemes under different weight combinations.
Table 1. energy storage optimal configuration scheme under different weight combinations
Figure BDA0002657167210000151
Since the variation of the energy storage system peak clipping and valley filling yield and the line loss cost is lower than the configuration cost of the energy storage system, the option of not configuring the energy storage system is chosen in scheme 1, which only considers the system economy. The data of the schemes 2-6 show that the energy storage system is connected to the active power distribution network, so that the voltage out-of-limit risk can be effectively reduced, the voltage safety of the system is improved, and the influence on the reverse power flow occurrence probability of the line depends on the specific configuration scheme of the energy storage system. The schemes 4-6 comprehensively consider the influence of energy storage access on system economic indexes, voltage out-of-limit risks and reverse tide probability, and optimal energy storage configuration results with different preferences can be obtained by setting different weight configuration schemes. Taking the weight setting and configuration results in the scheme 5 as an example, the comparison is performed with the scheme 1 without introducing the voltage out-of-limit risk and the reverse power flow probability index, wherein the weight of the voltage out-of-limit risk and the reverse power flow probability in the scheme 1 is 0. Comparing fig. 5 and fig. 6, it can be seen that the voltage out-of-limit risk of each node in each period of the system of the scheme 5 is smaller than that of the scheme 1, because the voltage of the end node of the long line in the active power distribution network is lower in the peak load period, there is a greater risk of voltage out-of-limit, and the stored energy is discharged in the peak load period, so as to increase the voltage level of the end node in the peak load period. Comparing fig. 7 and 8, it can be seen that the maximum reverse power flow occurrence probability of each time period and each line of the system of scheme 5 is smaller than that of scheme 1, because the output of the distributed fan is large in the early morning time period, and the time period is in the load valley period, and the distributed power generation output is larger than the total load requirements of the access node and the downstream nodes thereof, so that the output of the fan cannot be consumed on the spot, and the fan transmits power to the bus side of the power distribution network instead.
In order to verify the effectiveness of the energy storage improvement interval control method provided in the lower model, the following 3 scenes are selected for comparison: scene 1: and (4) the optimization of an energy storage operation strategy in a lower model is not considered, and constant-power charging and discharging are carried out at a fixed period according to a typical daily load curve. Scene 2: and (4) controlling and optimizing an energy storage operation strategy in the lower layer model by using an energy storage interval only considering the daily load characteristics of the single peak and the valley. Scene 3: the energy storage operation strategy in the lower model is optimized by applying the improved energy storage interval control provided by the invention.
The configuration results in different scenarios are shown in table 2, and the configured correlation index pairs are shown in table 3.
Table 2. configuration results under different scenarios of the present invention
Figure BDA0002657167210000161
TABLE 3 comparison of objective function under different scenarios of the invention
Figure BDA0002657167210000162
As can be seen from the data in table 3, the real-time operation strategy optimization of energy storage is considered in the scenarios 2 and 3, the line loss of the active power distribution network is reduced, and the energy storage operation yield is improved compared with the scenario 1, because the real-time operation strategy optimization of energy storage is considered, so that the load fluctuation of the system at each moment is further stabilized, the corresponding energy storage operation yield is improved, and the total operation cost of the system is reduced. In addition, the voltage out-of-limit risk and the reverse power flow probability are reduced to a greater extent after the real-time operation strategy optimization considering the energy storage is carried out. Therefore, the superiority of the double-layer optimization model considering the energy storage real-time operation strategy optimization in the aspects of system operation economy and safety is verified. Comparing scenes 2 and 3 in table 3, it can be seen that under the condition that the energy storage configuration parameters are the same, compared with interval control, the energy storage operation yield can be further improved by adopting improved interval control, the line loss cost and the voltage out-of-limit risk are reduced, and the influence on the voltage out-of-limit risk is most obvious. As can be seen from the load curves in different configurations of fig. 9-10, the peak clipping and valley filling effects in the scenarios 2 and 3 under consideration of the real-time operation strategy optimization are better, and the peak-valley difference is smaller compared with the daily load characteristic curve in the scenario 1. In fig. 9, the original load only has a single charge-discharge cycle in one day, so that the peak clipping and valley filling effects of scenes 2 and 3 adopting interval control and improved interval control are the same, while the daily load curve in fig. 10 has a plurality of peaks and valleys, the energy storage interval control only considers a single charge-discharge period, the sum of the charge/discharge amount in one day is used as the capacity constraint, and the charge-discharge working modes alternately appear under the actual condition, so that the electric quantity of the energy storage power station after each charge period is continuously consumed, and the actual capacity at each time does not exceed the upper limit of the capacity, therefore, the preset interval of the improved interval control is smaller, and the configuration capacity of the energy storage is fully utilized. Meanwhile, the discharge electric quantity at any moment needs to be taken from the residual quantity in the previous time period, the energy storage interval is controlled to default that only one charge-discharge cycle exists in one day, the precedence relationship of a plurality of charge-discharge time periods is not considered, so that the residual electric quantity in partial time periods is insufficient, the stored energy cannot be discharged, and the actual peak clipping and valley filling effect is influenced. Therefore, the improved energy storage interval control can obtain a better energy storage operation strategy, the operation strategy is transmitted to the upper layer model to further obtain a better energy storage configuration result, and the effectiveness of the improved energy storage interval control is proved.
To sum up, the above embodiment considers the energy storage optimization configuration method of the active power distribution network with voltage safety, and the implementation main steps include: (1) inputting the stored energy capacity, the charge and discharge power, the access position and random variables in the active power distribution network into a lower-layer optimization model as known conditions, determining the stored energy working state and the charge and discharge power in each time period by adopting an improved interval control method, and performing peak clipping and valley filling on daily loads; (2) carrying out probability load flow calculation, and carrying out voltage out-of-limit risk assessment and reverse load flow probability calculation according to the obtained node voltage and line load flow data; (3) and respectively calculating energy storage configuration and maintenance cost, energy storage operation income and line loss cost in the upper layer model, and evaluating a configuration result by combining the calculated voltage out-of-limit risk and the reverse power flow probability. And (3) repeating the processes (1), (2) and (3) until an optimal energy storage configuration scheme is obtained. The invention aims to obtain an energy storage configuration scheme beneficial to voltage safety and tide distribution of an active power distribution network.
The embodiments of the present invention have been described with reference to the accompanying drawings, but the present invention is not limited to the embodiments, and various changes and modifications can be made according to the purpose of the invention, and any changes, modifications, substitutions, combinations or simplifications made according to the spirit and principle of the technical solution of the present invention shall be equivalent substitutions, as long as the purpose of the present invention is met, and the present invention shall fall within the protection scope of the present invention without departing from the technical principle and inventive concept of the present invention.

Claims (8)

1. An active power distribution network energy storage optimization system configuration method considering voltage safety is characterized in that: establishing a double-layer optimization model of the energy storage of the active power distribution network, which comprises an upper layer model and a lower layer model:
the upper layer model is responsible for configuring parameters of the energy storage system, the maximum capacity, the maximum charge and discharge power and the access installation position of the energy storage system are planned by taking energy storage configuration and maintenance cost, energy storage operation income, line loss cost, voltage out-of-limit risk and reverse power flow occurrence probability as the minimum targets, and the constraints of the upper layer model comprise power balance constraint, energy storage allowable configuration capacity constraint and energy storage allowable configuration maximum charge and discharge power constraint;
the lower layer model optimizes the operation strategy of energy storage by taking the minimum daily load peak-valley difference of the active power distribution network as a target, and the constraints of the lower layer model comprise energy storage capacity constraint, energy storage maximum charging and discharging power constraint and energy storage charging and discharging balance constraint;
in a double-layer optimization model of the energy storage of the active power distribution network, an upper layer model corresponds to an energy storage multi-objective optimization configuration sub-problem, and a lower layer model corresponds to an energy storage operation strategy optimization sub-problem of performing peak clipping and valley filling on the energy storage according to daily load fluctuation; the upper layer model transmits a configuration result comprising the maximum energy storage capacity, the maximum charge-discharge power and the access position parameter to the lower layer model, the lower layer model obtains the output of the energy storage system at each time period according to the configuration result and the original load, and system node voltage and line tide information data after the energy storage access are obtained through probability tide calculation and are transmitted to the upper layer model; and solving the upper layer model and the lower layer model by respectively adopting an improved multi-target particle swarm algorithm and an improved interval control method.
2. The active power distribution network energy storage optimization system configuration method considering voltage safety according to claim 1, is characterized in that: in the upper layer model, the distributed power generation and energy storage of the power distribution network are subjected to combined optimization configuration, the operation strategy of the energy storage in the lower layer model is used as a known quantity, a 2m +1 point estimation method is adopted for probability load flow calculation, the voltage out-of-limit risks of all nodes in the active power distribution network and the reverse load flow generation probability of lines are evaluated according to the probability load flow calculation result, the maximum value of the system voltage out-of-limit risks in all time periods is used as a voltage safety index of the energy storage optimization configuration, and the reverse load flow of the lines is considered in the configuration target.
3. The active power distribution network energy storage optimization system configuration method considering voltage safety according to claim 1, is characterized in that: and optimizing the energy storage operation strategy in the lower model, solving the operation strategy optimization problem of the energy storage system under different load scenes by adopting an interval control method aiming at the condition that the daily load has a plurality of peak-valley time periods based on the configuration result of the upper model, and returning the optimization result to the upper model.
4. The active power distribution network energy storage optimization system configuration method considering voltage safety according to claim 1, is characterized in that: and after the lower-layer model determines that the original load obtains the output of the energy storage system at each time period, the correlation problem of the energy storage output and the load is processed by adopting Nataf conversion and elementary conversion.
5. The method for configuring the energy storage optimization system of the active power distribution network considering the voltage safety as claimed in claim 1 is characterized by comprising the following steps:
initializing parameters of a multi-target particle swarm optimization algorithm, statistics of random variables including wind speed, illumination intensity and load, and randomly generating N groups of energy storage capacity, maximum charge and discharge power and access positions as initial populations within a constraint condition range;
inputting the energy storage capacity, the maximum charge and discharge power, the access position and the daily load into a lower-layer optimization model as known conditions, determining the upper limit and the lower limit of a control interval by utilizing improved interval control, further obtaining the working state and the charge and discharge power of energy storage in each time period, and performing peak clipping and valley filling on the daily load;
step three, updating node information of the system, processing correlation among wind speed, illumination intensity, electric vehicle charging amount, load and energy storage output by combining Nataf transformation and elementary transformation, and performing probability load flow calculation by using a 2m +1 point estimation method;
performing voltage out-of-limit risk assessment according to node voltage data obtained by probability load flow calculation, and calculating the probability of reverse load flow occurring at each moment of each branch according to line load flow data;
step five, respectively calculating investment cost, line loss cost and energy storage operation income of energy storage configuration in an upper model, and evaluating individuals in the population by combining the calculated voltage out-of-limit risk evaluation and the reverse power flow occurrence probability result to generate a group of new optimal solution sets;
step six, judging whether iteration is terminated:
if so, outputting an optimal solution set, and selecting an optimal solution according to the weight;
otherwise, returning to the second step, and iteratively updating a group of new populations according to the multi-target particle swarm optimization algorithm, wherein the populations comprise energy storage capacity, maximum charge and discharge power and access positions.
6. The active power distribution network energy storage optimization system configuration method considering voltage safety according to claim 1, is characterized in that: the voltage out-of-limit risk is obtained by taking the possibility and the severity of system voltage out-of-limit into consideration based on a probability load flow calculation result and taking the product of the possibility and the severity of the system voltage out-of-limit as a product to represent the voltage safety of the active power distribution network; wherein the calculation formulas of the likelihood index, the severity index, and the voltage out-of-limit risk index of the system are respectively shown in the following formulas (1) to (3):
Figure FDA0002657167200000021
Figure FDA0002657167200000022
Figure FDA0002657167200000023
in the formula (I), the compound is shown in the specification,
Figure FDA0002657167200000024
P(V i) The probability of the voltage crossing the upper and lower limits, Vmax、VminRespectively an upper limit and a lower limit of a voltage allowable fluctuation range, ViFor the voltage of node i, the probability density function f (V) of the voltage of node ii) Can be obtained by probabilistic power flow calculation, Sev: (V i)、
Figure FDA0002657167200000025
The severity of the upper and lower limits of the voltage respectively,
Figure FDA0002657167200000026
Vi. 3σ respectively mu +3 sigma and mu-3 sigma of the node voltage value,
Figure FDA0002657167200000031
S(V i) The severity of the upper and lower limits of the normalized voltage, Vlvp、VovpRespectively, under-voltage, over-voltage protected action threshold, NbusThe number of total nodes of the system;
the occurrence probability of the reverse power flow is obtained based on a probability power flow calculation result, the power flow direction flowing from a bus end to a tail end node in the power grid is set to be positive, and the occurrence probability of the reverse power flow is the accumulated probability of the line power flow direction possibly being a negative part, as shown in a formula (4):
Figure FDA0002657167200000032
in the formula, PlIn order to be a line flow,
Figure FDA0002657167200000033
probability density function f (P) of line flow for random events with line flow in reverse directionl) Obtaining a probability load flow calculation result;
the calculation formula of the energy storage configuration and maintenance cost of the upper model is shown as formula (5):
Figure FDA0002657167200000034
in the formula, Cp、CeCost of allocation, P, of unit power and capacity of stored energy, respectivelyess.k、Sess.kConfiguring power and capacity, N, respectively for the kth energy storage deviceESSThe number of the energy storage devices is shown, tau is the annual rate, and y is the service life of the energy storage;
the calculation formula of the energy storage operation yield of the upper model is shown as the formula (6):
Figure FDA0002657167200000035
in the formula, CpriceTo the electricity price, Pk(t) is the charge and discharge quantity of the kth stored energy at the moment t, P during dischargek(t) is a positive value, P on chargingk(t) is a negative value,. DELTA.t is the unit time;
the calculation formula of the line loss cost of the upper layer model is shown as formula (7):
Figure FDA0002657167200000036
in the formula, CepCost per line loss, Ploss.k(t) the kth branch after energy storage configurationLine loss at time t, NlineThe number of the system bus lines is; Δ t is a unit time;
the upper layer model adopts the maximum value of the out-of-limit risk of the system voltage in each time period as the standard for evaluating the voltage safety, and the formula (8) is as follows:
Figure FDA0002657167200000037
in the formula, V1，V2，......V24The system voltage level is in each period within 24 hours every day;
Figure FDA0002657167200000038
the risk of system voltage out-of-limit for each time period;
the upper layer model considers the maximum probability of reverse power flow generated by branches in different time periods, as shown in formula (9):
Figure FDA0002657167200000041
in the formula (I), the compound is shown in the specification,
Figure FDA0002657167200000042
the probability of reverse power flow on the first line in the t period is shown;
the upper layer model comprehensively considers the configuration cost of energy storage, the energy storage operation income and the system line loss cost as economic indexes of energy storage optimization configuration, and simultaneously considers node voltage out-of-limit risks and line reverse power flow probability, and the multi-objective optimization function of the energy storage configuration is shown as a formula (10):
min F＝[F1,F2,F3]＝[f1-f2+f3,f4,f5] (10)。
and (4) calculating the multi-objective optimization function as the solution of the multi-objective optimization configuration subproblem according to the formula (10).
7. The active power distribution network energy storage optimization system configuration method considering voltage safety according to claim 1, is characterized in that:
after the lower-layer model determines the capacity, the maximum charge-discharge power and the access installation position of the energy storage system according to the upper-layer optimization model, considering the condition that daily load has a plurality of peak-valley periods, performing peak clipping and valley filling on the load by using an interval control method, and optimizing an energy storage operation strategy; the interval control method obtains corresponding charge and discharge control intervals according to different daily load curves, energy storage capacity and maximum charge and discharge power so as to obtain the charge and discharge power of energy storage in each time interval, and the charge and discharge time interval of the energy storage system and the charge and discharge amount in different time intervals are determined by the difference value between the upper limit and the lower limit of the preset interval and the original load:
when the load value is within the interval, the energy storage system does not work;
when the load value is higher than the upper limit of the interval, the energy storage system discharges outwards;
when the load value is lower than the lower limit of the interval, the energy storage system is charged;
the lower layer model aims to minimize the daily load peak-valley difference of the active power distribution network, namely the difference between the upper limit and the lower limit of the control interval of energy storage, and the required constraint of the interval calculation is shown in formulas (11) to (13):
Figure FDA0002657167200000043
Figure FDA0002657167200000044
Figure FDA0002657167200000045
in the formula (I), the compound is shown in the specification,
Figure FDA0002657167200000046
for total charging of the ith charging periodAn amount of electricity;
Figure FDA0002657167200000047
the total discharge amount of the ith discharge period;
Figure FDA0002657167200000048
the difference value of the total charging quantity and the total discharging quantity in the previous time period is represented, namely the residual capacity at the current moment; sess.kConfiguring capacity for the kth stored energy; eta is energy conversion efficiency; lambda and mu are respectively reliable coefficients of the energy storage system for preventing overcharge and overdischarge; n and M are the number of charging and discharging time periods respectively;
the above equations (11) and (12) are capacity and charge-discharge balance constraints respectively, the first equation in the equation (11) represents that the sum of the total charge amount and the residual capacity in any time period needs to satisfy the overcharge protection constraint of energy storage, and the second equation in the equation (11) represents that the total discharge amount in any time period needs to satisfy the overdischarge protection constraint of an energy storage power station; the first formula in the formula (12) indicates that the sum of the charge amount and the residual capacity of each time interval needs to meet the discharge demand of the next time interval, and the second formula in the formula (12) indicates that the total charge amount and the discharge amount in one energy storage operation cycle need to be kept balanced, namely the energy storage electric quantity after one day needs to be kept at the initial electric quantity before working, so that the normal working of the energy storage in the next cycle is ensured, and one cycle is one day; average daily load P between upper and lower limits of intervaldAs an initial value, an iterative method is adopted to obtain:
if the operation strategy of the energy storage system does not meet the capacity constraint or the charge-discharge balance constraint in the iteration process, the upper limit of the up-shifting interval or the lower limit of the down-shifting interval is reached until the formulas (11) - (12) meet the conditions;
the charge-discharge power of each time interval in the lower model is obtained by the difference between a preset interval and the original load, and besides the capacity constraint and the power balance constraint of the formulas (11) to (12), the power constraint of the energy storage system also needs to be checked according to a formula (14):
if the charging/discharging power in a certain time interval exceeds a threshold value, taking the corresponding upper limit/lower limit as the actual charging/discharging power in the time interval, and checking the capacity constraint of the energy storage power station in the time interval and the subsequent time interval according to the formula (15);
if the energy storage system electric quantity in a certain time interval exceeds the threshold value according to the formula (16), the charge and discharge power in the time interval is re-established according to the formula (17), specifically as follows:
-Pess.k≤Pk(t)≤Pess.k (14)
Figure FDA0002657167200000051
μSess.k≤Sk(t)≤λSess.k (16)
Figure FDA0002657167200000052
in the formula, Pess.kFor the maximum charge-discharge power, P, of the stored energy configured in the upper modelk(t) is the charge and discharge power at time t; p at dischargek(t) is a positive value, P on chargingk(t) is a negative value, Sk(t)、SkAnd (t-1) respectively representing the electric quantity of the energy storage system at the current moment and the last moment.
8. The active power distribution network energy storage optimization system configuration method considering voltage safety according to claim 1, is characterized in that:
the energy storage multi-target optimization configuration problem of the upper model is solved by adopting an improved multi-target particle swarm algorithm, the multi-target particle swarm algorithm combines a Pareto sorting mechanism and a basic particle swarm, an individual optimal non-inferior solution set and a global non-inferior solution set are determined through a domination relation among particles, the non-inferior solution set is updated according to a dynamic dense distance, and then the global optimal is selected;
in order to improve the population diversity and prevent the particle swarm algorithm from prematurely converging to the local optimum, cross and variation factors in the genetic algorithm are introduced into the particle swarm, and the processing method of crossing the boundary of the particle is changed according to the iteration times, as shown in formulas (18) to (19):
Figure FDA0002657167200000061
Figure FDA0002657167200000062
wherein rnd (a, b) is [ a, b ]]Random number of (2), xmax、xminRespectively an upper boundary and a lower boundary of inequality constraint, iter is the current iteration number, gen is the maximum iteration number, and x is the position of the particle in the solution space;
the output result of the multi-target particle swarm algorithm is a group of Pareto non-inferior solution sets, due to the fact that different dimensions exist among a plurality of target values, all the targets are normalized, and after weighted summation, an optimal solution is selected according to the fitness value of each scheme, wherein the formula (20) is as follows:
Figure FDA0002657167200000063
in the formula, λjIs the weight value corresponding to the jth target value, and λ1+λ2+λ31, n population; biFitness value, x, of weighted sum after normalization for different targets of each scenariokFor the scheme with the maximum fitness value in the Pareto non-inferior solution set, xgbest is the optimal position where the particles in the whole population arrive once, and represents that the population is optimal.
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