CN113794241A - Optimized scheduling method and device for low-voltage smart grid user side source load storage - Google Patents
Optimized scheduling method and device for low-voltage smart grid user side source load storage Download PDFInfo
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
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- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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Abstract
The application discloses a method and a device for optimizing and scheduling source storage and load of a low-voltage smart grid user side, wherein the method comprises the following steps: establishing an energy storage system model according to the charging and discharging characteristics of the energy storage system, and establishing a load model according to the mode that local loads participate in scheduling; obtaining predicted photovoltaic output data and load data according to a load model; establishing a source storage coordination optimization model according to the electricity consumption cost, the voltage deviation and the power failure time length in the fault of the user side of the intelligent power grid; and substituting the photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model, and calculating to obtain a strategy result of source storage optimization scheduling of the user side of the smart grid. Through the mode, the phenomenon that the peak load causes low voltage to affect the power supply quality and reliability is considered, the three aspects of the power consumption cost, the voltage deviation and the power failure time duration during the fault of a user are comprehensively considered to formulate a scheduling strategy, and therefore the power supply economy, the power supply quality and the reliability are improved.
Description
Technical Field
The application relates to the technical field of smart power grids, in particular to a method and a device for optimally scheduling source storage and load of a low-voltage smart power grid user side.
Background
With the continuous development of the smart power grid, the distributed photovoltaic power supply, the energy storage system and the smart electrical appliances are gradually introduced to the low-voltage user side, and the quantity and the capacity of the smart electrical appliances are increased rapidly, so that the load of the user side is increased continuously, and the safe and stable operation of the power grid is threatened. Therefore, under the background that the loads on the user side of the low-voltage smart grid are increasingly complex, attention needs to be paid to a user side source load coordination optimization scheduling method.
At present, research on a low-voltage intelligent power grid user side source storage coordination optimization scheduling method is deepened continuously, a document 'consider user side intelligent electricity utilization optimization strategy research of an electric vehicle' (stum. consider user side intelligent electricity utilization optimization strategy research [ D ] Tianjin university, 2018.) of the electric vehicle analyzes working characteristics of power adjustable equipment, time adjustable equipment and intelligent power generation equipment, and constructs an intelligent electricity utilization optimization management model aiming at minimizing total electricity utilization cost of resident users; in the document, "family energy management system optimized scheduling algorithm in smart grid environment" (spreading, great peng, zang zhang. home energy management system optimized scheduling algorithm in smart grid environment [ J ]. power system protection and control, 2016,44(02):18-26.) a family energy management system optimized scheduling algorithm is proposed with the aim of minimizing the electricity consumption of users; according to the literature, "urban residential user flexible load demand response strategy research" (southern Sibo. urban residential user flexible load demand response strategy research [ D ]. North China Power university; North China Power university (Beijing), 2019.), a two-stage demand response random optimization model is established aiming at a specific demand response structure of a residential load in an intelligent power grid environment and considering the comfort level of residential users and aiming at reducing load curve peak-valley difference and electricity consumption cost; the document ' demand response strategy optimization method taking user participation uncertainty into account ' (1 ' Penghao, land Jun, Von Yongjun, and the like.) the demand response strategy optimization method taking user participation uncertainty into account [ J ] power grid technology, 2018,42(05):1588 + 1594.) takes user participation uncertainty into account, and a demand response strategy is formulated by taking the load fluctuation degree as an objective function.
The above documents take into account demand-side responses of low-voltage smart grid users, but the coordination optimization model focuses mainly on reducing electricity costs and reducing the fluctuation degree of the load curve. In practical situations, when the load is large, especially peak load in summer, the low voltage phenomenon is very easy to occur in the low voltage network, the daily life of a user is affected by large voltage deviation, and power failure is caused by accidental faults such as tripping operation and the like.
Therefore, a method for coordinating and optimizing the source and the load of the user side of the low-voltage smart grid is urgently needed, and a scheduling strategy is formulated by comprehensively considering the electricity utilization cost, the voltage deviation and the power failure time duration during the fault of the user, so that the economy, the power supply quality and the reliability of power supply are improved.
Disclosure of Invention
The application provides an optimal scheduling method and device for source load storage of a user side of a low-voltage smart grid, and aims to solve the problems that low voltage is easy to occur and large voltage deviation is generated on the user side in the prior art.
In order to solve the technical problem, the application provides an optimal scheduling method for low-voltage smart grid user side source storage, which comprises the following steps: establishing an energy storage system model according to the charging and discharging characteristics of the energy storage system, and establishing a load model according to the mode that local loads participate in scheduling; obtaining predicted photovoltaic output data and load data according to a load model; establishing a source storage coordination optimization model according to the electricity consumption cost, the voltage deviation and the power failure time length in the fault of the user side of the intelligent power grid; and substituting the photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model, and calculating to obtain a strategy result of source storage optimization scheduling of the user side of the smart grid.
In order to solve the technical problem, the present application provides an optimized scheduling device for low-voltage smart grid user side source load storage, including: the energy storage system model module is used for establishing an energy storage system model according to the charging and discharging characteristics of the energy storage system; the load model module is used for establishing a load model according to a local load participation scheduling mode and acquiring load data according to the load model; the source storage and load coordination optimization model module is used for establishing a source storage and load coordination optimization model according to the power consumption cost, the voltage deviation and the power failure time length in the fault of the user side of the intelligent power grid; and the strategy result module is used for substituting the obtained predicted photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model and calculating to obtain a strategy result of source storage optimization scheduling of the user side of the intelligent power grid.
The application provides an optimized scheduling method and device for source storage of a low-voltage smart grid user side, which can establish a source storage coordination optimization model according to the power utilization cost, voltage deviation and power failure time of the smart grid user side; and substituting the photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model, and calculating to obtain a strategy result of source storage optimization scheduling of the user side of the smart grid. According to the method and the device, the phenomenon that the peak load causes low voltage to affect the power supply quality and reliability is considered, and the three aspects of the power consumption cost, the voltage deviation and the power failure time duration during the fault of a user are comprehensively considered to formulate a scheduling strategy, so that the power supply economy, the power supply quality and the reliability are improved.
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In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of an embodiment of a method for optimally scheduling source storage of a low-voltage smart grid user side according to the present application;
FIG. 2 is a flowchart illustrating an embodiment of step S140 in FIG. 1;
FIG. 3 is a schematic structural diagram of an embodiment of a low voltage network topology of the present application;
FIG. 4 is a load curve diagram in one embodiment;
FIG. 5 is a schematic graph of a power output curve for an embodiment of a distributed photovoltaic power source;
FIG. 6 is a graph comparing an embodiment of local load before and after optimization;
fig. 7 is a schematic structural diagram of an embodiment of an optimized scheduling device for low-voltage smart grid user-side source storage according to the present application.
Detailed Description
In order to enable those skilled in the art to better understand the technical solution of the present application, the following describes in detail the method and apparatus for optimally scheduling the source storage of the low-voltage smart grid user side provided by the present application with reference to the accompanying drawings and the detailed description.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of an optimized scheduling method for low-voltage smart grid user side source storage, where in this embodiment, the optimized scheduling method for low-voltage smart grid user side source storage may include steps S110 to S140, and each step is specifically as follows:
s110: and establishing an energy storage system model according to the charging and discharging characteristics of the energy storage system.
In this embodiment, the energy storage system model is:
in the formula, SOCESS(t) state of charge of the battery for the tth time period; SOCESS,0Is the initial state of charge of the battery; pESS,c(t) charging power for the tth time period; etaESS,cThe charging efficiency of the storage battery; pESS,d(t) the discharge power of the t-th time period; etaESS,dThe discharge efficiency of the storage battery; eESS,nThe rated capacity of the storage battery; Δ t is the time interval from the calculation time to the initial time. Preferably, Δ t may take an integer multiple of one hour; t is a scheduling period.
Further, the local load can be divided into an unscheduled load, a reducible load, a translatable load and an electric vehicle load according to the manner of participation of the local load in scheduling, and models are respectively established, that is, the load models include an unscheduled load model, a reducible load model, a translatable load model and an electric vehicle load model.
It should be noted that the non-dispatchable load model is only used for acquiring the input parameters and participating in the calculation process, but does not perform any processing on the input parameters.
(a) The load model can be reduced:
0≤PCut(t)≤PCut,max；
0≤ΔTCut(t)≤ΔTCut,max；
fCut(t)＝CCom*PCut(t)*ΔTCut(t)；
in the formula, PCut(t) reducing the load power at time t; pCut,maxThe maximum load reduction power allowable value is obtained; delta TCut(t) time reduction at time t; delta TCut,maxThe maximum load reduction time allowed value is obtained; cComThe compensation price is reduced in unit electricity quantity unit time; f. ofCut(t) compensation cost reduction for the user;
(b) translatable load model:
TTra＝TTra0；
PTra(ΔtTra+tTras)＝PTra(ΔtTra+tTras0)；
in the formula, TTraThe operation period after the translational load optimization; t isTra0The original duty cycle of the translatable load; pTra(ΔtTra+tTras) The working power at any moment after optimizing the translatable load; pTra(ΔtTra+tTras0) The working power of the translatable load at the corresponding moment of the original working cycle;
(c) electric automobile load model:
in the formula, SOCEV(t) is the state of charge of the electric vehicle at time t; pEV(t) is the charging power of the electric automobile at the moment t; etaEVThe charging efficiency of the electric vehicle is improved; eEVIs the battery capacity of the electric automobile.
S120: and obtaining the predicted photovoltaic output data and obtaining the load data according to the load model.
Optionally, the photovoltaic output data may be obtained from a photovoltaic prediction module.
S130: and establishing a source storage coordination optimization model according to the electricity utilization cost, the voltage deviation and the power failure time length in the fault of the user side of the intelligent power grid.
And comprehensively considering the reduction of the electricity consumption cost of a user, the reduction of voltage deviation and the shortening of the power failure time in the case of fault, and establishing a source storage and load coordination optimization model, wherein the objective function of the source storage and load coordination optimization model is a multi-objective function. Namely, a multi-objective function with the objectives of reducing electricity consumption cost, reducing voltage offset and shortening power failure time in fault is established, which is specifically as follows:
(1) reducing electricity costs f1：
Cost of electricity f1Including the electricity purchase charge C of the userL(t) remaining Internet surfing expense Csell(t) reduction of Compensation cost fCut(t)：
Wherein, the electricity purchasing cost CL(t) the following:
PNet(t)＝-(PPV(t)-PESS(t)-PL(t))；
PESS(t)＝PESS,c(t)+PESS,d(t)；
PL(t)＝PUnsch(t)+PCut(t)+PTra(t)+PEV(t)；
in the formula, c0(t) is the electricity purchasing price (yuan/(kWh)) at the time t; pNet(t) is the net load at time t; pPV(t) power generated by the distributed photovoltaic power supply at the moment t; pESS(t) power of the energy storage system at time t; pL(t) is the local load of the user side at time t; pUnschAnd (t) is the power of the non-dispatchable load at time t.
Surplus internet fee Csell(t) the following:
in the formula, csellThe remaining net electricity price (yuan/(kW h)) is obtained.
(2) Reducing voltage offset f2：
f2＝min(max|V(t)-VN|) t＝1,...,T；
Wherein, V (t) is the actual voltage of the user at the time t; vNIs a rated voltage; t is a scheduling period.
(3) Shortening the power failure time f in fault3：
In addition, the source storage load coordination optimization model needs to satisfy the following constraints when solving the policy result of the optimization scheduling:
(1) power balance constraint
In the formula, N is a node set; for any (i, j) ∈ N, line lijHas an impedance of zij＝rij+jxijAnd has yij＝1/zij＝gij-jbij；vj(t) is the square of the voltage amplitude of the node j at time t; pj(t)、Qj(t) injecting active power and reactive power into the node j at the moment t respectively;
respectively outputting active power and reactive power of the distributed photovoltaic at the time t;
respectively the active power and the reactive power of the energy storage system at the moment t;
respectively the active power and the reactive power of the local load at the moment t; pij(t)、Qij(t) respectively the active power and the reactive power of the line flowing from the node i to the node j at the moment t;
(2) output power constraint for distributed photovoltaic power supply
PPV,min≤PPV(t)≤PPV,max；
In the formula, PPV,max、PPV,minRespectively representing the maximum value and the minimum value of the output of the distributed photovoltaic power supply;
(3) energy storage charge and discharge power limit constraint
In the formula, PESS,maxThe maximum charge-discharge power of the storage battery; SOCESS,max、SOCESS,minRespectively the maximum value and the minimum value of the state of charge allowed by the storage battery;
(4) energy storage charge-discharge state constraint
The energy storage device can only be in a charged state or a discharged state at any time, i.e.:
PESS,c(t)PESS,d(t)＝0；
(5) energy storage charge-discharge balance constraint
The stored charging power and discharging power in one scheduling period should be equal, namely:
(6) constraint capable of reducing load cutting power and cutting time
In the formula, PCut,maxThe maximum load reduction power allowable value is obtained; delta TCut(t) time reduction at time t; delta TCut,maxThe maximum load shedding time allowed value is obtained.
(7) Electric vehicle charging restraint
SOCEV,min≤SOCEV(t)≤SOCEV,max；
In the formula, SOCEV,max、SOCEV,minRespectively the maximum value and the minimum value of the state of charge allowed by the battery of the electric automobile.
(8) Node voltage constraint
Vmin≤Vi(t)≤Vmax；
In the formula, Vmax、VminThe maximum and minimum values of the node voltage, respectively.
S140: and substituting the photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model, and calculating to obtain a strategy result of source storage optimization scheduling of the user side of the smart grid.
Preferably, the source storage load coordination optimization model is solved by using a SPEA2 algorithm and a multi-target fuzzy comprehensive evaluation decision method. The method comprises the steps of calculating to obtain a Pareto optimal solution set of a model by using a SPEA2 algorithm, screening from the Pareto optimal solution set by using a multi-target fuzzy comprehensive evaluation decision method to obtain an optimal solution, and outputting the optimal solution as a strategy result.
Specifically, a multi-objective function value of the source and load storage coordination optimization model is normalized by a multi-objective fuzzy comprehensive evaluation decision method, and each objective function value is normalized to a range [0,1 ]; and calculating the average satisfaction degree according to the number of the objective functions and by utilizing an analytic hierarchy process, wherein the individual with the maximum average satisfaction degree in the Pareto optimal solution set is used as a strategy result of the source storage load optimization scheduling of the smart grid user side. Namely:
in the formula, mufiFor the ith individualiA normalized value; f. ofi max、fi minAre respectively an objective function fiMaximum and minimum values of; n is the number of the objective functions; omegaiThe weight value of the ith objective function; mu.siIs the average satisfaction.
Further, step S140 may further include steps S141 to S146, please refer to fig. 2, and fig. 2 is a schematic flowchart of an embodiment of step S140 in fig. 1. Steps S141 to S146 are specifically as follows:
s141: and (5) initializing.
Randomly generating a size N according to the upper and lower limits of the reducible power and the reduction time of the reducible load, the upper and lower limits of the starting operation time of the translatable load and the upper and lower limits of the starting charging time of the electric automobile loadPReal value initial population P of0Creating an empty external archive A0Setting t to be 0;
s142: and (4) allocating the degree of adaptability.
Computing population PtAnd external archive AtThe electricity cost, the voltage deviation and the power failure time length in the fault time corresponding to all the individuals are obtained, and therefore an individual fitness value F (i):
S(i)＝|{j|xj∈Pt+At,xi＞xj}|；
F(i)＝R(i)+D(i)；
wherein S (i) is the intensity value of the individual; x is the number ofi＞xjRepresenting an individual xiPareto dominate individuals xj(ii) a R (i) is an individual original fitness value; d (i) is individual density value;
the normalized Euclidean distance of the point k far away from the individual i to the individual i on the target space; n is a radical ofAIs the external file size;
s143: and (4) selecting the environment.
The population PtAnd external archive AtAll Pareto optimal solutions in (A) are copied to (A)t+1In, if At+1Is greater than NAThen to At+1Performing a pruning operation tot+1The individual with the smallest distance in i is eliminated, namely:
otherwise, P is selectedtAnd AtPareto in (A) dominates solution additiont+1Performing the following steps;
s144: and (5) terminating the judgment.
If t>MaxIt or other termination condition is satisfied, A is outputt+1The solution is a Pareto optimal solution set, otherwise, the algorithm continues to run;
s145: a new individual is generated.
According to At+1The fitness value of the medium is selected by the championship selection mechanism to obtain NPUsing the individual as parent, and using the expanded intermediate crossover operator and uniform mutation operator to form the filial generation population Pt+1；
S146: and (6) iterating the algorithm.
When t is t +1, the process proceeds to step S142.
In this embodiment, a 75-node 0.4kV low-voltage network is adopted and source-storage coordinated optimization scheduling calculation is performed for the user B. Referring to fig. 3-6, fig. 3 is a schematic structural diagram of an embodiment of a low voltage network topology according to the present application; FIG. 4 is a load curve diagram in one embodiment; FIG. 5 is a schematic graph of a power output curve for an embodiment of a distributed photovoltaic power source; FIG. 6 is a graph comparing local load before and after optimization.
In this embodiment, a per unit value is used for calculation, assuming that the power factor of the node at all times is 0.95, the reference power used for calculation is 100, the distribution transformer is a Yyn0 type connection method, the reference voltages on the high and low voltage sides are 10kV and 0.4kV respectively, and node 1 is taken as a balanced node.
Where each user has a home load (sum of non-dispatchable load, cutable load, and translatable load) and an electric vehicle load, the load curve is shown in fig. 4.
The user A, B, C has a distributed photovoltaic power source and an energy storage system, the output curve of the distributed photovoltaic power source is shown in fig. 5, and the parameters of the energy storage system are shown in table 1.
TABLE 1 energy storage device parameters
The user side adopts time-of-use electricity price, and specific electricity price information in different time periods is shown in table 2.
TABLE 2 time of use electricity price
The surplus internet-surfing electricity price of the user side is 0.673 yuan/kWh, the reduction compensation electricity price of the reducible load is 1.258 yuan/kWh, the maximum reduction load power allowable value is 0.5kW, and the maximum reduction load time allowable value is 2 h; the weights of the three objective functions are assigned to omega by using an analytic hierarchy process1＝0.63，ω2＝0.28，ω3＝0.09。
When the SPEA2 algorithm is adopted for solving, the population quantity is set to be 50, and the external file A is set to be0The number of iterations is 100, and the final scheduling policy results are shown in table 3.
TABLE 3 strategic results
A comparison of the local load curves before and after optimization is shown in fig. 6, and the target function value pair ratio before and after optimization is shown in table 4.
TABLE 4 comparison of objective function values before and after optimization
Compared with the objective function values before and after the optimization in the table, the comparison shows that the electricity consumption cost is high, the voltage deviation is large before the optimization, the power failure time is long, the energy storage system cannot fully play a role, but the electricity consumption cost is reduced by 44.49% after the optimization, the voltage deviation is reduced by 21.77%, the power failure time is shortened by 3h when the fault occurs, the power supply economy, the power supply quality and the reliability are obviously improved, and the effectiveness of the model is proved.
Based on the foregoing method for optimizing and scheduling a low-voltage smart grid, the present application also provides an apparatus for optimizing and scheduling a low-voltage smart grid, please refer to fig. 7, and fig. 7 is a schematic structural diagram of an embodiment of the apparatus for optimizing and scheduling a low-voltage smart grid according to the present application. The optimized scheduling device of the low-voltage smart power grid comprises:
the energy storage system model module 710 is configured to establish an energy storage system model according to charge and discharge characteristics of the energy storage system, and obtain predicted photovoltaic output data according to the energy storage system model;
the load model module 720 is used for establishing a load model according to a local load participation scheduling mode and obtaining load data according to the load model;
the source storage and load coordination optimization model module 730 is used for establishing a source storage and load coordination optimization model according to the power consumption cost, the voltage deviation and the power failure duration of the user side of the smart grid;
and the strategy result module 740 is used for substituting the photovoltaic output data and the load data into the source storage and load coordination optimization model and calculating to obtain a strategy result of source storage and load optimization scheduling of the smart grid user side.
Optionally, the energy storage system model is:
in the formula, SOCESS(t) state of charge of the battery for the tth time period; SOCESS,0Is the initial state of charge of the battery; pESS,c(t) charging power for the tth time period; etaESS,cThe charging efficiency of the storage battery; pESS,d(t) the discharge power of the t-th time period; etaESS,dThe discharge efficiency of the storage battery; eESS,nThe rated capacity of the storage battery; Δ t is the time interval from the calculation time to the initial time.
Optionally, the source-storage coordination optimization model module 730 is further configured to:
and comprehensively considering the reduction of the electricity consumption cost of a user, the reduction of voltage deviation and the shortening of the power failure time in the case of fault, and establishing a source storage and load coordination optimization model, wherein the objective function of the source storage and load coordination optimization model is a multi-objective function.
Optionally, the policy result module 740 is further configured to:
calculating a source load storage coordination optimization model by using a SPEA2 algorithm to obtain a Pareto optimal solution set;
normalizing the multi-objective function values of the Pareto optimal solution set by using a multi-objective fuzzy comprehensive evaluation decision method, and normalizing each objective function value into an interval [0,1 ]; and calculating the average satisfaction degree according to the number of the objective functions and by utilizing an analytic hierarchy process, wherein the individual with the maximum average satisfaction degree in the Pareto optimal solution set is used as a strategy result of the source storage load optimization scheduling of the smart grid user side.
Optionally, the load models include a non-dispatchable load model, a reducible load model, a translatable load model, and an electric vehicle load model.
It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. In addition, for convenience of description, only a part of structures related to the present application, not all of the structures, are shown in the drawings. The step numbers used herein are also for convenience of description only and are not intended as limitations on the order in which the steps are performed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above embodiments are merely examples and are not intended to limit the scope of the present disclosure, and all modifications, equivalents, and flow charts using the contents of the specification and drawings of the present disclosure or those directly or indirectly applied to other related technical fields are intended to be included in the scope of the present disclosure.
Claims (10)
1. A method for optimally scheduling source storage and load of a low-voltage smart grid user side is characterized by comprising the following steps:
establishing an energy storage system model according to the charging and discharging characteristics of the energy storage system, and establishing a load model according to the mode that local loads participate in scheduling;
obtaining predicted photovoltaic output data and load data according to the load model;
establishing a source storage coordination optimization model according to the electricity consumption cost, the voltage deviation and the power failure time length in the fault of the user side of the intelligent power grid;
and substituting the photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model, and calculating to obtain a strategy result of source storage optimization scheduling of the smart grid user side.
2. The method for optimally scheduling the source storage of the user side of the low-voltage smart grid according to claim 1, wherein the energy storage system model is as follows:
in the formula, SOCESS(t) state of charge of the battery for the tth time period; SOCESS,0Is the initial state of charge of the battery; pESS,c(t) charging power for the tth time period; etaESS,cThe charging efficiency of the storage battery; pESS,d(t) the discharge power of the t-th time period; etaESS,dThe discharge efficiency of the storage battery; eESS,nThe rated capacity of the storage battery; delta t is the time interval between the calculation moment and the initial moment; t is a scheduling period.
3. The method for optimally scheduling the source storage of the low-voltage smart grid user side according to claim 2, wherein the establishing of the source storage coordination optimization model according to the electricity utilization cost, the voltage deviation and the power failure duration of the smart grid user side comprises the following steps:
and comprehensively considering the reduction of the electricity consumption cost of a user, the reduction of voltage deviation and the shortening of the power failure time in the case of a fault, and establishing the source and storage coordination optimization model, wherein the objective function of the source and storage coordination optimization model is a multi-objective function.
4. The method for optimally scheduling source storage of the low-voltage smart grid user side according to claim 3, wherein the step of substituting the photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model and performing calculation to obtain a policy result of the source storage optimal scheduling of the smart grid user side comprises the steps of:
calculating the source load storage coordination optimization model by using a SPEA2 algorithm to obtain a Pareto optimal solution set;
normalizing the multi-objective function values of the Pareto optimal solution set by using a multi-objective fuzzy comprehensive evaluation decision method, and normalizing each objective function value into an interval [0,1 ];
and calculating the average satisfaction degree according to the number of the objective functions and by utilizing an analytic hierarchy process, wherein the individual with the maximum average satisfaction degree in the Pareto optimal solution set is used as a strategy result of the source storage load optimization scheduling of the smart grid user side.
5. The method for optimizing and scheduling user-side source storage of the low-voltage smart grid according to claim 1,
the load models include a non-dispatchable load model, a reducible load model, a translatable load model, and an electric vehicle load model.
6. The utility model provides an optimization scheduling device of low pressure smart power grids user side source storage load which characterized in that includes:
the energy storage system model module is used for establishing an energy storage system model according to the charging and discharging characteristics of the energy storage system;
the load model module is used for establishing a load model according to a local load participation scheduling mode and acquiring load data according to the load model;
the source storage and load coordination optimization model module is used for establishing a source storage and load coordination optimization model according to the power consumption cost, the voltage deviation and the power failure time length in the fault of the user side of the intelligent power grid;
and the strategy result module is used for substituting the obtained predicted photovoltaic output data and the load data into the source storage coordination optimization model and the energy storage system model and calculating to obtain a strategy result of source storage optimization scheduling of the smart grid user side.
7. The low-voltage smart grid user side source load optimization scheduling device of claim 6, wherein the energy storage system model is:
in the formula, SOCESS(t) state of charge of the battery for the tth time period; SOCESS,0Is the initial state of charge of the battery; pESS,c(t) charging power for the t-th time period；ηESS,cThe charging efficiency of the storage battery; pESS,d(t) the discharge power of the t-th time period; etaESS,dThe discharge efficiency of the storage battery; eESS,nThe rated capacity of the storage battery; delta t is the time interval between the calculation moment and the initial moment; t is a scheduling period.
8. The low-voltage smart grid user-side source load optimal scheduling device of claim 7, wherein the source load coordination optimization model module is further configured to:
and comprehensively considering the reduction of the electricity consumption cost of a user, the reduction of voltage deviation and the shortening of the power failure time in the case of a fault, and establishing the source and storage coordination optimization model, wherein the objective function of the source and storage coordination optimization model is a multi-objective function.
9. The optimal scheduling device for low-voltage smart grid user-side source storage according to claim 8, wherein the policy result module is further configured to:
calculating the source load storage coordination optimization model by using a SPEA2 algorithm to obtain a Pareto optimal solution set;
normalizing the multi-objective function values of the Pareto optimal solution set by using a multi-objective fuzzy comprehensive evaluation decision method, and normalizing each objective function value into an interval [0,1 ];
and calculating the average satisfaction degree according to the number of the objective functions and by utilizing an analytic hierarchy process, wherein the individual with the maximum average satisfaction degree in the Pareto optimal solution set is used as a strategy result of the source storage load optimization scheduling of the smart grid user side.
10. The low-voltage smart grid user side source load optimized scheduling device of claim 6,
the load models include a non-dispatchable load model, a reducible load model, a translatable load model, and an electric vehicle load model.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116613821A (en) * | 2023-07-13 | 2023-08-18 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy cooperative operation method, operation platform, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150311710A1 (en) * | 2013-09-18 | 2015-10-29 | Jiangsu Nanjing Power Supply Company | A time based global optimization dispatching method |
| CN106803157A (en) * | 2017-02-17 | 2017-06-06 | 广东电网有限责任公司电力科学研究院 | A kind of quality of power supply ameliorative way of low-voltage network distributed energy storage system |
| CN109190799A (en) * | 2018-08-07 | 2019-01-11 | 广东电网有限责任公司 | A kind of the collaboration optimal control method and device of industry and commerce temperature control load |
| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
| CN113258581A (en) * | 2021-05-31 | 2021-08-13 | 广东电网有限责任公司佛山供电局 | Source-load coordination voltage control method and device based on multiple intelligent agents |
-
2021
- 2021-09-10 CN CN202111065826.2A patent/CN113794241B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150311710A1 (en) * | 2013-09-18 | 2015-10-29 | Jiangsu Nanjing Power Supply Company | A time based global optimization dispatching method |
| CN106803157A (en) * | 2017-02-17 | 2017-06-06 | 广东电网有限责任公司电力科学研究院 | A kind of quality of power supply ameliorative way of low-voltage network distributed energy storage system |
| CN109190799A (en) * | 2018-08-07 | 2019-01-11 | 广东电网有限责任公司 | A kind of the collaboration optimal control method and device of industry and commerce temperature control load |
| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
| CN113258581A (en) * | 2021-05-31 | 2021-08-13 | 广东电网有限责任公司佛山供电局 | Source-load coordination voltage control method and device based on multiple intelligent agents |
Non-Patent Citations (1)
| Title |
|---|
| 刘文颖;文晶;谢昶;王维洲;梁琛;: "考虑风电消纳的电力系统源荷协调多目标优化方法", 中国电机工程学报, no. 05, 5 March 2015 (2015-03-05) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116613821A (en) * | 2023-07-13 | 2023-08-18 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy cooperative operation method, operation platform, equipment and storage medium |
| CN116613821B (en) * | 2023-07-13 | 2023-10-24 | 国网浙江省电力有限公司宁波供电公司 | Multi-energy cooperative operation method, operation platform, equipment and storage medium |
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