CN117117937A - Network failure recovery methods for coordinated network reconfiguration and mobile energy storage system fleets - Google Patents
Network failure recovery methods for coordinated network reconfiguration and mobile energy storage system fleets Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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- H—ELECTRICITY
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
本发明提供一种协调网络重构和移动储能系统车队的网络故障恢复方法，包括构建MESS车队行驶模型；根据MESS车队行驶模型构建协调网络恢复模型；构建考虑负荷和DG输出预测不确定性的协调网络恢复模型，得到协调网络恢复模型的鲁棒变体，以恢复主动配电网的网络供电。本发明协调停电地区的MESS、SESS、网络恢复、DG调度和需求响应资源，以恢复电力供应，在协调网络恢复模型的鲁棒变体中考虑了负荷和DG输出预测的不确定性，为配电系统灾后恢复供电提供指导与建议。
The invention provides a network fault recovery method for coordinated network reconstruction and mobile energy storage system fleet, which includes constructing a MESS fleet driving model; constructing a coordinated network recovery model based on the MESS fleet driving model; and constructing a network fault recovery method that considers load and DG output prediction uncertainty. Coordinated Network Restoration Model, A robust variant of the coordinated network restoration model is obtained to restore network power supply in active distribution networks. This invention coordinates MESS, SESS, network recovery, DG dispatching and demand response resources in blackout areas to restore power supply. The uncertainty of load and DG output predictions is taken into account in the robust variant of the coordinated network recovery model to provide distribution solutions. Provide guidance and advice on restoring power supply to the electrical system after a disaster.
Description
Technical Field
The invention belongs to the technical field of network reconstruction of an active power distribution network, and particularly relates to a network fault recovery method for coordinating network reconstruction and a mobile energy storage system vehicle team.
Background
An Active Distribution Network (ADN) is capable of actively controlling network topology, distributed Generation (DG) output, and Demand Response (DR) resources. However, due to the geographical dispersion of DG and DR resources, their utility may be limited when applied to natural disasters such as storms, earthquakes, and floods. Furthermore, increased use of renewable energy can create fluctuations and uncertainties that prevent ADNs from achieving reliable energy scheduling during disasters. To address these issues, a mobile energy storage system (Mobile Energy Storage System, mes) fleet may provide flexible emergency power for network recovery services, and may also hedge loads and DG output predictive risk.
The MESS is a common scale repository (e.g., lithium ion battery). When a catastrophic failure occurs, a power distribution system operator (DNO) can schedule the MESS to move between different locations for service restoration. Compared to an aggregate Electric Vehicle (EV) owned by a resident or third party, the mes is more reliable and easier to dispatch, which is critical to network restoration.
However, the uncertainty sets a significant hurdle to the recovery task. Fluctuating DG output, time-varying load demand and load estimation errors are three major sources of ADN recovery uncertainty. For the corresponding uncertainty risk, poor recovery performance may lead to condition uncertainty when using a deterministic model, and even failure of recovery strategies due to violations of security constraints.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a network fault recovery method for coordinating network reconstruction and a mobile energy storage system vehicle team.
In a first aspect, the present invention provides a network failure recovery method for coordinating network reconfiguration and a mobile energy storage system fleet, comprising:
constructing a MESS motorcade driving model;
constructing a coordinated network recovery model according to the MESS motorcade driving model;
and constructing a coordinated network recovery model considering the load and the DG output prediction uncertainty, and obtaining a robust variant of the coordinated network recovery model to recover the network power supply of the active power distribution network.
Further, the method for coordinating network reconfiguration and network failure recovery of a mobile energy storage system fleet according to claim 1, wherein the constructing a MESS fleet travel model comprises:
constructing an MESS fleet transportation delay model expression:
wherein D is a zero diagonal element distance matrix; each element D in the distance matrix D ij Representing the distance between node i and node j; τ ij Running time for the MESS fleet; v (V) avg Average speed for the MESS fleet; t is t ins Representing the installation time of the MESS motorcade; tc (tc) ij Is traffic congestion time; t (T) s Sampling time for collecting state information and adjusting strategy in minutes;
building MESS motorcade position constraint:
wherein, when z m,i,t When=1, the mth MESS is located at node i at time t; when z m,i,t When=0, the mth MESS is not located at node i at time t;is the initial state of the MESS; psi MESS Is the total number of MESS; psi N Is the total number of nodes;Indicating that the initial position of the mth MESS is at node i;Indicating that the mth MESS does not move at the initial time;Indicating that the mth MESS does not move at the final moment; psi T Is the total time; omega m,t The movement state of the mth MESS at time t;
building MESS running constraint:
wherein,indicating that the mth MESS starts to travel at time t;Indicating that the mth MESS stops traveling at time t;Indicating that the mth MESS stops traveling at time t;Indicating that the mth MESS starts to travel at time t;Is the maximum value of the travelling frequency of the MESS;Energy supplied at time t for the mth MESS; z m,t' Is the running state of MESSM at the time point t'; t is the set of all time points T;
building a MESS power output constraint:
wherein,an upper limit for the power of the mth MESS;The power of the mth MESS at the time t;The total output power of the MESS is the time t of the node i;The battery charge state of the mth MESS at the initial moment;the battery charge state of the mth MESS at a set moment;The battery charge state of the mth MESS at the time t;The charge and discharge efficiency of the mth MESS;Battery capacity for the mth MESS; Δt is the time interval;is the lower limit of the charge state of the m-th MESS battery;Is the upper limit of the charge state of the m-th MESS battery.
Further, the constructing a coordinated network recovery model according to the MESS fleet driving model includes:
calculating the maximum value of the sum of the predicted powers of each node in the active distribution network according to the following formula:
wherein P is i Is the maximum value of the sum of the predicted powers of the ith node; psi N The total number of nodes in the active power distribution network; b i A weight factor for the i-th node;active power consumed by the actual load of the ith node at the moment t;
constructing DG power output constraints:
wherein,active power of the i1 st DG at the t moment;The power generation efficiency of the ith 1 DG at the t moment;Rated complex power for the i1 st DG; psi DG Is the total number of DGs; psi T Is the total time;φminimum power factor angle for DG；Reactive power of the i1 st DG at the t moment;Is the maximum power factor angle of DG;
building reactive power output constraints of SVC:
wherein,the reactive power of the i2 th static reactive compensator at the t moment;The lower limit of the reactive power of the i2 th static reactive compensator at the t moment;The upper limit of reactive power of the ith 2 static reactive compensator at the moment t; psi SVC The total number of static var compensators;
constructing a power output constraint of a fixed energy storage system:
wherein,indicating whether the ith 3 stationary energy storage system is in a charged state at time t, < >>Indicating state of charge +.>Indicating a non-charged state;Indicating whether the ith 3 fixed energy storage system is in a discharge state at the moment t,indicating the discharge state +.>Indicating a non-discharge state; psi SESS Total number of stationary energy storage systems;The actual charging power of the ith 3 fixed energy storage system at the t moment;The lower limit of the charging power of the ith 3 fixed energy storage system at the t moment;The upper limit of the charging power of the ith 3 fixed energy storage system at the moment;The actual discharge power of the ith 3 fixed energy storage systems at the t moment;The lower limit of the discharge power of the ith 3 fixed energy storage system at the t moment;the upper limit of the discharge power of the ith 3 fixed energy storage systems at the moment t; SOC (State of Charge) i3,0 The energy storage state of the ith 3 fixed energy storage system at the initial moment; SOC (State of Charge) i3,set The energy storage state preset for the ith 3 fixed energy storage system at the initial moment; SOC (State of Charge) i3,t The energy storage state of the ith 3 fixed energy storage system at the t moment;Charging efficiency for the i3 rd stationary energy storage system; Δt is the time interval of charging and discharging of the fixed energy storage system;Discharge efficiency for the i3 rd stationary energy storage system;Rated energy storage for the i3 th stationary energy storage system; i3 SOCa lower limit for the energy storage state of the i3 rd stationary energy storage system;an upper limit for the energy storage state of the i3 rd stationary energy storage system;
constructing a tide constraint:
wherein P is ki,t Active power flowing from the kth node to the ith node at time t; b i A weight factor for the i-th node; p (P) ij,t Active power flowing from the ith node to the jth node at time t; pi (i) and gamma (i) each represent a set of nodes connected to the ith node; psi E Is the total number of branches from node i to node j; q (Q) ij,t Reactive power flowing out from the ith node to the jth node at time t; q (Q) ki,t Reactive power flowing out from the kth node to the kth node i at time t; c ij Forming a closed or open state of a branch for the ith node and the jth node; c ij =1, indicating branch closure; c ij =0, indicating branch opening; v (V) i,t The voltage of the ith node at the moment t; v (V) j,t The voltage of the j node at the t moment; r is (r) ij A resistance between the i node and the j node; x is x ij Is the reactance between the i node and the j node; m is a constant; phi is the power factor of the load of node i;
constructing network security constraints:
wherein,is the maximum complex power capacity from node i to node j; i Va lower voltage limit for the i-th node;An upper voltage limit for the i-th node;
constructing a demand response constraint:
wherein,the maximum load capacity of the I path which can be interrupted at the moment t;For the interrupt state of the j1 st load at time t, -/->The i4 th load at the moment t is in an interrupt state;The i4 th load at the moment t is in a non-interrupt state; psi DR Is the total number of loads;The starting state of the i4 th load at the t moment;The i4 th load at the moment t is indicated as a starting state;Indicating that the i4 th load is in a non-starting state at the moment t;The stop state of the ith 4 th load at the moment t;Indicating that the i4 th load is in a stop state at the moment t;Indicating that the i4 th load is in a non-stop state at the time t;Minimum interrupt time for the load;Maximum interruption time for the load; num (Num) DR Representing a maximum number of loads that can be interrupted;
constructing topology constraints:
n MG ≥1；
wherein F is ki Active power for line ki; f (F) ij Active power for line ij; h i For DG to payload at Sub node i; psi Sub A set of substation nodes; n is n MG For the micro-grid quantity, when n MG When the micro-grid is equal to 1, no micro-grid exists in the power distribution network; c i,j Is the capacity between the lines ij; the || represents the cardinality of the collection.
Further, the constructing a coordinated network recovery model that considers load and DG output prediction uncertainty, to obtain a robust variant of the coordinated network recovery model, to recover network power of the active power distribution network, includes:
calculating the load demand and the DG output predicted value according to the following formula:
wherein,the average value of active power consumed by the actual load of the ith node at the moment t;The prediction error of the active power consumed by the actual load of the ith node at the moment t;The average value of the active power of the i1 DG at the t moment;the prediction error of the active power of the i1 st DG at the t moment;
building an ellipsoid uncertainty set of load prediction errors and DG prediction errors:
wherein Ω N An ellipsoid uncertainty set that is a load prediction error; omega shape DG An ellipsoid uncertainty set that is a DG prediction error;an uncertain budget for a load;An uncertain budget for DG; theta (theta) L,i Covariance matrix of load prediction error; theta (theta) DG,i1 Covariance matrix for i1 st DG prediction error; zeta type L Radial uncertainty for the load; zeta type DG Radial uncertainty for DG;
building a robust variant expression of a coordinated network recovery model:
in a second aspect, the present invention provides a computer device comprising a processor and a memory; the processor executes the computer program stored in the memory to implement the method for coordinating network reconstruction and recovering network faults of a fleet of mobile energy storage systems according to the first aspect.
In a third aspect, the present invention provides a computer-readable storage medium storing a computer program; the computer program when executed by a processor implements the steps of the method for coordinating network reconfiguration and network failure recovery for a fleet of mobile energy storage systems according to the first aspect.
The invention provides a network fault recovery method for coordinating network reconstruction and a mobile energy storage system vehicle team, which comprises the steps of constructing a MESS vehicle team driving model; constructing a coordinated network recovery model according to the MESS motorcade driving model; and constructing a coordinated network recovery model considering the load and the DG output prediction uncertainty, and obtaining a robust variant of the coordinated network recovery model to recover the network power supply of the active power distribution network. The invention coordinates MESS, SESS, network recovery, DG scheduling and demand response resources in the power outage area to recover power supply, considers the uncertainty of load and DG output prediction in the robust variant of the coordinated network recovery model, and provides guidance and suggestion for recovering power supply after power distribution system disaster.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a network failure recovery method for coordinating network reconfiguration and a mobile energy storage system fleet according to an embodiment of the present invention;
fig. 2 is a diagram of a test model on a rural power distribution network of 59 nodes according to an embodiment of the present invention;
FIG. 3 is a graph of predicted load and photovoltaic output provided by an embodiment of the present invention;
FIG. 4 is a 59 node distance matrix diagram provided by an embodiment of the present invention;
fig. 5 is an island division result diagram of case a according to an embodiment of the present invention;
fig. 6 is a graph of recovered load and PV output results in case a provided by an embodiment of the present invention;
fig. 7 is a diagram of island division and MESS allocation results of case B according to an embodiment of the present invention;
FIG. 8 is a graph of the recovery load, PV output and SOC of the SESS in case B provided by an embodiment of the present invention;
FIG. 9 is a diagram of SOC results for the MESS in case B provided by an embodiment of the present invention;
FIG. 10 is a diagram of the island division and MESS distribution results of RO10 according to the embodiment of the present invention;
FIG. 11 is a graph showing the recovery load, PV output and SOC of the SESS in RO10 according to the embodiment of the present invention;
FIG. 12 is a diagram showing the result of SOC of the MESS in RO10 according to the embodiment of the present invention;
FIG. 13 is a graph of the island division and MESS distribution results of RO90 provided by an embodiment of the present invention;
FIG. 14 is a graph showing the recovery load, PV output and SOC of the SESS in RO90 according to an embodiment of the present invention;
fig. 15 is a graph of calculated time for each case provided by an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In an embodiment, as shown in fig. 1, an embodiment of the present invention provides a network fault recovery method for coordinating network reconfiguration and a mobile energy storage system fleet, including:
and step 101, constructing a MESS motorcade driving model.
Illustratively, a MESS fleet transportation delay model expression is constructed:
wherein D is a zero diagonal element distance matrix; distance ofEach element D in matrix D ij Representing the distance between node i and node j; τ ij Running time for the MESS fleet; v (V) avg Average speed for the MESS fleet; t is t ins Representing the installation time of the MESS motorcade; tc (tc) ij Is traffic congestion time; t (T) s The sampling time of the state information acquisition and strategy adjustment is performed in minutes.
Building MESS motorcade position constraint:
wherein, when z m,i,t When=1, the mth MESS is located at node i at time t; when z m,i,t When=0, the mth MESS is not located at node i at time t;is the initial state of the MESS; psi MESS Is the total number of MESS; psi N Is the total number of nodes;Indicating that the initial position of the mth MESS is at node i;Indicating that the mth MESS does not move at the initial time;Indicating that the mth MESS does not move at the final moment, unnecessary movement during service restoration power supply can be prevented; psi T Is the total time; omega m,t Is the movement state of the mth MESS at time t.
Building MESS running constraint:
wherein,indicating that the mth MESS starts to travel at time t;Indicating that the mth MESS stops traveling at time t;Indicating that the mth MESS stops traveling at time t;Indicating that the mth MESS starts to travel at time t;Is the maximum value of the travelling frequency of the MESS;Energy supplied at time t for the mth MESS; z m,t' Is the running state of MESSM at the time point t'; t is the set of all time points T.
Building a MESS power output constraint:
wherein,an upper limit for the power of the mth MESS;The power of the mth MESS at the time t;The total output power of the MESS is the time t of the node i;The battery charge state of the mth MESS at the initial moment;the battery charge state of the mth MESS at a set moment;The battery charge state of the mth MESS at the time t;The charge and discharge efficiency of the mth MESS;Battery capacity for the mth MESS; Δt is the time interval;is the lower limit of the charge state of the m-th MESS battery;Is the upper limit of the charge state of the m-th MESS battery.
And 102, constructing a coordinated network recovery model according to the MESS vehicle team driving model.
Illustratively, the maximum value of the sum of the predicted powers for each node in the active distribution network is calculated according to the following formula:
wherein P is i Is the maximum value of the sum of the predicted powers of the ith node; psi N The total number of nodes in the active power distribution network; b i A weight factor for the i-th node;active power consumed for the actual load of the ith node at time t.
Constructing DG power output constraints:
wherein,active power of the i1 st DG at the t moment;The power generation efficiency of the ith 1 DG at the t moment;Rated complex power for the i1 st DG; psi DG Is the total number of DGs; psi T Is the total time;φis the minimum power factor angle of DG;reactive power of the i1 st DG at the t moment;Is the maximum power factor angle of DG.
Building reactive power output constraints of SVC:
wherein,the reactive power of the i2 th static reactive compensator at the t moment;The lower limit of the reactive power of the i2 th static reactive compensator at the t moment;The upper limit of reactive power of the ith 2 static reactive compensator at the moment t; psi SVC Is the total number of static var compensators.
A Stationary Energy Storage System (SESS) provides local support for load demand in a fault region. The SESS has a plurality of functions, firstly, it is an energy supply unit for important loads of the micro-grid. Furthermore, it can smooth fluctuations in DG output, delivering high quality power to the load.
Constructing a power output constraint of a fixed energy storage system:
wherein,indicating whether the ith 3 stationary energy storage system is in a charged state at time t, < >>Indicating state of charge +.>Indicating a non-charged state;Indicating whether the ith 3 fixed energy storage system is in a discharge state at the moment t,indicating the discharge state +.>Indicating a non-discharge state; psi SESS Total number of stationary energy storage systems;The actual charging power of the ith 3 fixed energy storage system at the t moment;The lower limit of the charging power of the ith 3 fixed energy storage system at the t moment;The upper limit of the charging power of the ith 3 fixed energy storage system at the moment;The actual discharge power of the ith 3 fixed energy storage systems at the t moment;The lower limit of the discharge power of the ith 3 fixed energy storage system at the t moment;The upper limit of the discharge power of the ith 3 fixed energy storage systems at the moment t; SOC (State of Charge) i3,0 The energy storage state of the ith 3 fixed energy storage system at the initial moment; SOC (State of Charge) i3,set The energy storage state preset for the ith 3 fixed energy storage system at the initial moment; SOC (State of Charge) i3,t The energy storage state of the ith 3 fixed energy storage system at the t moment;Charging efficiency for the i3 rd stationary energy storage system; Δt is the time interval of charging and discharging of the fixed energy storage system;Discharge efficiency for the i3 rd stationary energy storage system;Rated energy storage for the i3 th stationary energy storage system; i3 SOCa lower limit for the energy storage state of the i3 rd stationary energy storage system;an upper limit for the energy storage state of the i3 rd stationary energy storage system;
constructing a tide constraint:
wherein P is ki,t Active power flowing from the kth node to the ith node at time t; b i A weight factor for the i-th node; p (P) ij,t Active power flowing from the ith node to the jth node at time t; pi (i) and gamma (i) each represent a set of nodes connected to the ith node; psi E Is the total number of branches from node i to node j; q (Q) ij,t Reactive power flowing out from the ith node to the jth node at time t; q (Q) ki,t Reactive power flowing out from the kth node to the kth node i at time t; c ij Forming a closed or open state of a branch for the ith node and the jth node; c ij =1, indicating branch closure; c ij =0, indicating branch opening; v (V) i,t The voltage of the ith node at the moment t; v (V) j,t The voltage of the j node at the t moment; r is (r) ij For the ith sectionResistance between the point and the j-th node; x is x ij Is the reactance between the i node and the j node; m is a constant;the power factor of the load for node i;
constructing network security constraints:
wherein,is the maximum complex power capacity from node i to node j; i Va lower voltage limit for the i-th node;An upper voltage limit for the i-th node;
constructing a demand response constraint:
wherein,the maximum load capacity of the I path which can be interrupted at the moment t;For the interrupt state of the j1 st load at time t, -/->The i4 th load at the moment t is in an interrupt state;The i4 th load at the moment t is in a non-interrupt state; psi DR Is the total number of loads;The starting state of the i4 th load at the t moment;The i4 th load at the moment t is indicated as a starting state;Indicating that the i4 th load is in a non-starting state at the moment t;Stopping the ith 4 th load for time tA state;Indicating that the i4 th load is in a stop state at the moment t;Indicating that the i4 th load is in a non-stop state at the time t;Minimum interrupt time for the load;Maximum interruption time for the load; num (Num) DR Indicating the maximum number of loads that can be interrupted.
Constructing topology constraints:
n MG ≥1；
wherein F is ki Active power for line ki; f (F) ij Active power for line ij; h i For DG to payload at Sub node i; psi Sub A set of substation nodes; n is n MG For the micro-grid quantity, when n MG When the micro-grid is equal to 1, no micro-grid exists in the power distribution network; c i,j Is the capacity between the lines ij; the || represents the cardinality of the collection.
And step 103, constructing a coordinated network recovery model considering the load and the DG output prediction uncertainty, and obtaining a robust variant of the coordinated network recovery model to recover the network power supply of the active power distribution network.
Illustratively, the load demand and DG output predicted values are calculated according to the following formulas:
wherein,the average value of active power consumed by the actual load of the ith node at the moment t;The prediction error of the active power consumed by the actual load of the ith node at the moment t;The average value of the active power of the i1 DG at the t moment;the prediction error of the active power of the i1 st DG at the t moment.
Building an ellipsoid uncertainty set of load prediction errors and DG prediction errors:
wherein Ω N An ellipsoid uncertainty set that is a load prediction error; omega shape DG An ellipsoid uncertainty set that is a DG prediction error;an uncertain budget for a load;An uncertain budget for DG; theta (theta) L,i Covariance matrix of load prediction error; theta (theta) DG,i1 Covariance matrix for i1 st DG prediction error; zeta type L Radial uncertainty for the load; zeta type DG Radial uncertainty of DG. />
Building a robust variant expression of a coordinated network recovery model:
the invention can coordinate MESS, SESS, network recovery, DG scheduling and demand response resources in the power outage area to recover power supply, considers the uncertainty of load and DG output prediction in a robust variant of a coordinated network recovery model, and provides guidance and suggestion for recovering power supply after power distribution system disaster.
In order to better demonstrate the technical effect of the invention, a 59-node rural power distribution system is selected for testing. The structure of a 59bus rural power distribution system is shown in fig. 2. There are 5 photovoltaic sites in the test system, each site having a capacity of 1.5 megawatts. Two 300kVar SVCs are provided at nodes 18 and 42. Two MESS are positioned on the bus 16 and the bus 38, the capacity is 1MWh, the maximum charge and discharge power is 0.5MW, and the charge and discharge efficiency is 0.9. The total load requirements of the system were 3.85MW and 0.97MVar. The load is divided into three types: residential, industrial, and commercial loads. The location of each load is shown in fig. 2. Flexible loads that react positively to DNO are located on bus bars 4, 16, 22 and 30. Their load profile corresponds to the load on the bus. The maximum scheduling frequency is set to twice, and the maximum hour of load scheduling is set to 20 minutes each time. With reference to the chinese standard, example analysis was performed at 5 minute intervals with a total recovery time of 2 hours. The substation bus voltage amplitude is set to 10.5kV, the distribution system base power is set to 50MW, the SESS base power is set to 1MW, and all node voltage ranges are set to [0.93,1.07] p.u.. In the present embodiment, the loads are classified into residential, commercial and industrial loads. During recovery, the load and predicted photovoltaic output curves are shown in figure 3. Assuming that the covariance of the prediction error is 1, there is no correlation between time and space. This example analysis was run in a workstation with a CPU of 2.20GHz and a memory of 16 GB.
The distance between each node is shown in fig. 4. The maximum distance between node 20 and node 50 in fig. 4 is about 10500 meters, while the average distance is about 4110 meters. False, falseAssuming a running speed of 60 km/h (1 km/min) for the MESS truck, the maximum running time should be 10 minutes and the corresponding average running speed should be 4.1 minutes. Time t of MESS installation ins Set to 2 minutes, sampling time T s Set to 5 minutes.
We assume that a fault occurs between bus bars 2 and 24 and between bus bars 1 and 10. The deterministic and robust models are then tested in turn to demonstrate their performance.
(1) Deterministic model calculation results
1) Case a: without MESS connection
Based on case a no MESS connected to ADN. In this case, only the ADNM scheme is used and two SESSs at nodes 16 and 38 are used to aid in network recovery.
Fig. 5 illustrates the network operation state in case a. As shown in fig. 5, the entire network is divided into two micro-grids, and only 45 nodes and 43 lines are connected to the grid.
Fig. 6 shows the recovery load, PV output and SOC of the SESS in case a. As shown in fig. 6, the total recovery load was 24.16MWh and the pv output energy was 1.77MWh. According to the SOC curve in fig. 6, SESS is not fully used for network recovery. And in particular SESS1 at 16 nodes, receives one hour of charge from the PV. Meanwhile, the SESS2 at the 38 node is continuously charged and discharged to keep the regional energy balance. Thus, even if an ADNM scheme is implemented, the proposed model can be used for network recovery.
2) Case B: connecting MESS
In case B, the ADN uses a mes with a capacity of 1MWh, a power input of 1MW, and an output limit. The initial allocation of the MESS is at node 1. The initial SOC of the MESS was set to 0.8.
Fig. 7 illustrates the network operation state of case B. Unlike the results of case a, the integrity of the distribution network is preserved, with no micro-grid separated from the original network. In case B, 48 nodes and 47 lines are connected to the grid.
Fig. 8 shows the recovery load, PV output and SOC of the SESS in case B. As shown in fig. 8, the total recovery load was 26.04MWh and the pv output energy was 1.56MWh. It is readily found that with the aid of the MESS, the load recovered in the ADN is significantly increased and more PV resources are used to recover the network. FIG. 9 shows the SOC curves of the MESS. As can be seen in conjunction with fig. 7 and 9, the mes supports nodes 1, 19, 28 and 10 in time periods 1, 16 and 24. However, problems such as uncertainty of PV in actual scenes inevitably lead to a certain degree of error in analysis of the whole system; thus, the introduction of uncertainty analysis translates the problem into a robust optimization problem.
(2) Robust model calculation results
First, we consider the case where the uncertainty budget is 10% (denoted RO 10), where the system provides a lower degree of consideration for possible uncertainties. Island division and MESS movement trajectories are shown in fig. 10. Load, photovoltaic output and SOC of the recovered SESS as shown in fig. 11. FIG. 12 shows the SOC of the MESS. In RO10, the network is divided into two parts: 42 nodes and 40 lines in the system are connected to the micro grid. Compared with the deterministic model result in case B, the number of unconnected nodes and unconnected lines is slightly increased, but the method is more in line with the actual operation condition of the power distribution network. Thus, this method is more practical. The SOC of the SESS indicates that the energy storage system is still in a fully utilized state. MESS charges and discharges more actively and deeper than case B.
If the uncertainty budget of the system is further increased to 90% (noted RO 90), more conservative recovery results can be obtained under extremely severe conditions. The network state of RO90 is shown in fig. 13, and the load, PV, and SOC curves are shown in fig. 14.
As can be seen from fig. 13, the network is divided into three micro-grids; the whole system only has nine nodes and seven lines to access the network, and most of the load is cut off. RO90 only recovers 4.52MWh load and 1.09MWh PV. This is because photovoltaic output is severely limited under the most conservative conditions; thus, even if the SESS is fully used, only a few loads will be restored.
Based on the analysis, the cooperative work of the MESS and the network reconstruction can greatly improve the fault recovery capability of the power distribution network. However, the load recovery rate is also reduced in consideration of uncertainty in random output of renewable energy. It is readily apparent from the results of the robust optimization model that as the uncertainty budget increases, so does the conservation of the computation results, the system must sacrifice economics and part load to handle the potentially extremely harsh conditions. In the actual operation process of the power distribution network, a decision maker can select different uncertainty budgets according to the actual needs of the system.
(3) Computing performance
All algorithms were performed on an HP Z840 workstation equipped with an Intel (R) Xeon (R) E5-2650v4 CPU operating at 2.20GHz and 16GB RAM. The proposed model was programmed and solved using Generic Algebraic Modeling System (GAMS) software and a commercial solver CPLEX 20.1. The CPU time of the proposed model is shown in fig. 15. As shown in fig. 15, the deterministic model without MESS (case a) exhibited the best computational performance. When MESS is considered (case B), the calculation time increases significantly. This is because the MESS model contains a large number of binary variables, requiring more time to process. The uncertainty set in the robust model reduces the feasible area, thereby significantly reducing the computation time. Furthermore, the calculation time generally decreases with increasing conservation level.
In another embodiment, the invention provides a computer device comprising a processor and a memory; the processor executes the computer program stored in the memory to realize the steps of the network fault recovery method for the coordination network reconstruction and the mobile energy storage system vehicle team.
For more specific processes of the above method, reference may be made to the corresponding contents disclosed in the foregoing method embodiments, and no further description is given here.
In another embodiment, the present invention provides a computer-readable storage medium storing a computer program; the computer program when executed by the processor implements the steps of the network fault recovery method of the coordinated network reconstruction and mobile energy storage system fleet described above.
For more specific processes of the above method, reference may be made to the corresponding contents disclosed in the foregoing method embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. The apparatus and storage medium disclosed in the embodiments are described more simply because they correspond to the methods disclosed in the embodiments, and the description thereof will be made with reference to the method section.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or what contributes to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
The invention has been described in detail in connection with the specific embodiments and exemplary examples thereof, but such description is not to be construed as limiting the invention. It will be understood by those skilled in the art that various equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its embodiments without departing from the spirit and scope of the present invention, and these fall within the scope of the present invention. The scope of the invention is defined by the appended claims.
Claims (6)
1. A network failure recovery method for coordinating network reconfiguration and mobile energy storage system fleet, comprising:
constructing a MESS motorcade driving model;
constructing a coordinated network recovery model according to the MESS motorcade driving model;
and constructing a coordinated network recovery model considering the load and the DG output prediction uncertainty, and obtaining a robust variant of the coordinated network recovery model to recover the network power supply of the active power distribution network.
2. The method for coordinating network reconfiguration and network failure recovery of a fleet of mobile energy storage systems of claim 1, wherein the constructing a MESS fleet travel model comprises:
constructing an MESS fleet transportation delay model expression:
wherein D is a zero diagonal element distance matrix; each element D in the distance matrix D ij Representing the distance between node i and node j; τ ij Running time for the MESS fleet; v (V) avg Average speed for the MESS fleet; t is t ins Representing the installation time of the MESS motorcade; tc (tc) ij Is traffic congestion time; t (T) s Sampling time for collecting state information and adjusting strategy in minutes;
building MESS motorcade position constraint:
wherein, when z m,i,t When=1, the mth MESS is located at node i at time t; when z m,i,t When=0, the mth MESS is not located at node i at time t;is the initial state of the MESS; psi MESS Is the total number of MESS; psi N Is the total number of nodes;Indicating that the initial position of the mth MESS is at node i;Indicating that the mth MESS does not move at the initial time;indicating that the mth MESS does not move at the final moment; psi T Is the total time; omega m,t The movement state of the mth MESS at time t;
building MESS running constraint:
wherein,indicating that the mth MESS starts to travel at time t;Indicating that the mth MESS stops traveling at time t;Indicating that the mth MESS stops traveling at time t;Indicating that the mth MESS starts to travel at time t;Is the maximum value of the travelling frequency of the MESS;Energy supplied at time t for the mth MESS; z m,t' Is the running state of MESSM at the time point t'; t is the set of all time points T;
building a MESS power output constraint:
wherein,an upper limit for the power of the mth MESS;The power of the mth MESS at the time t;The total output power of the MESS is the time t of the node i;The battery charge state of the mth MESS at the initial moment;the battery charge state of the mth MESS at a set moment;The battery charge state of the mth MESS at the time t;The charge and discharge efficiency of the mth MESS;Battery capacity for the mth MESS; Δt is the time interval;is the lower limit of the charge state of the m-th MESS battery;Is the upper limit of the charge state of the m-th MESS battery.
3. The method for coordinating network reconfiguration and network failure recovery of a fleet of mobile energy storage systems of claim 1, wherein the constructing a coordinated network recovery model from a MESS fleet travel model comprises:
calculating the maximum value of the sum of the predicted powers of each node in the active distribution network according to the following formula:
wherein P is i Is the maximum value of the sum of the predicted powers of the ith node; psi N The total number of nodes in the active power distribution network; b i A weight factor for the i-th node;active power consumed by the actual load of the ith node at the moment t;
constructing DG power output constraints:
wherein,active power of the i1 st DG at the t moment;The power generation efficiency of the ith 1 DG at the t moment;Rated complex power for the i1 st DG; psi DG Is the total number of DGs; psi T Is the total time; phi is the minimum power factor angle of DG;Reactive power of the i1 st DG at the t moment;Is the maximum power factor angle of DG;
building reactive power output constraints of SVC:
wherein,the reactive power of the i2 th static reactive compensator at the t moment;The lower limit of the reactive power of the i2 th static reactive compensator at the t moment;The upper limit of reactive power of the ith 2 static reactive compensator at the moment t; psi SVC The total number of static var compensators;
constructing a power output constraint of a fixed energy storage system:
wherein,indicating whether the ith 3 stationary energy storage system is in a charged state at time t, < >>Indicating the state of charge of the battery,indicating a non-charged state;Indicating whether the ith 3 fixed energy storage system is in a discharge state at time t, < >>Indicating the discharge state +.>Indicating a non-discharge state; psi SESS Total number of stationary energy storage systems;For the ith 3 th stationary energy storage at time tThe actual charging power of the system;The lower limit of the charging power of the ith 3 fixed energy storage system at the t moment;The upper limit of the charging power of the ith 3 fixed energy storage system at the moment;The actual discharge power of the ith 3 fixed energy storage systems at the t moment;The lower limit of the discharge power of the ith 3 fixed energy storage system at the t moment;The upper limit of the discharge power of the ith 3 fixed energy storage systems at the moment t; SOC (State of Charge) i3,0 The energy storage state of the ith 3 fixed energy storage system at the initial moment; SOC (State of Charge) i3,set The energy storage state preset for the ith 3 fixed energy storage system at the initial moment; SOC (State of Charge) i3,t The energy storage state of the ith 3 fixed energy storage system at the t moment;Charging efficiency for the i3 rd stationary energy storage system; Δt is the time interval of charging and discharging of the fixed energy storage system;Discharge efficiency for the i3 rd stationary energy storage system;Rated energy storage for the i3 th stationary energy storage system; i3 SOCis the ith 3 fixed type storageA lower limit of the energy storage state of the energy storage system;An upper limit for the energy storage state of the i3 rd stationary energy storage system;
constructing a tide constraint:
wherein P is ki,t Active power flowing from the kth node to the ith node at time t; b i A weight factor for the i-th node; p (P) ij,t Active power flowing from the ith node to the jth node at time t; pi (i) and gamma (i) each represent a set of nodes connected to the ith node; psi E Is the total number of branches from node i to node j; q (Q) ij,t Reactive power flowing out from the ith node to the jth node at time t; q (Q) ki,t Reactive power flowing out from the kth node to the kth node i at time t; c ij Forming a closed or open state of a branch for the ith node and the jth node; c ij =1, indicating branch closure; c ij =0, indicating branch opening; v (V) i,t The voltage of the ith node at the moment t; v (V) j,t The voltage of the j node at the t moment; r is (r) ij A resistance between the i node and the j node; x is x ij Is the reactance between the i node and the j node; m is a constant;power factor for load of node iA number;
constructing network security constraints:
wherein,is the maximum complex power capacity from node i to node j; i Va lower voltage limit for the i-th node; v (V) i An upper voltage limit for the i-th node;
constructing a demand response constraint:
wherein,the maximum load capacity of the I path which can be interrupted at the moment t;The interrupt state of the j1 st load at the time t,the i4 th load at the moment t is in an interrupt state;The i4 th load at the moment t is in a non-interrupt state; psi DR Is the total number of loads;The starting state of the i4 th load at the t moment;The i4 th load at the moment t is indicated as a starting state;Indicating that the i4 th load is in a non-starting state at the moment t;The stop state of the ith 4 th load at the moment t;Indicating that the i4 th load is in a stop state at the moment t;Indicating that the i4 th load is in a non-stop state at the time t;Minimum interrupt time for the load;Maximum interruption time for the load; num (Num) DR Representing a maximum number of loads that can be interrupted;
constructing topology constraints:
n MG ≥1；
wherein F is ki Active power for line ki; f (F) ij Active power for line ij; h i For DG to payload at Sub node i; psi Sub A set of substation nodes; n is n MG For the micro-grid quantity, when n MG When the micro-grid is equal to 1, no micro-grid exists in the power distribution network; c i,j Is the capacity between the lines ij; the || represents the cardinality of the collection.
4. The method for recovering network faults of a fleet of coordinated network reconfiguration and mobile energy storage systems according to claim 3, wherein said constructing a coordinated network recovery model taking into account load and DG output prediction uncertainty results in a robust variant of the coordinated network recovery model to recover network power of an active power distribution network, comprising:
calculating the load demand and the DG output predicted value according to the following formula:
wherein,the average value of active power consumed by the actual load of the ith node at the moment t;The prediction error of the active power consumed by the actual load of the ith node at the moment t;The average value of the active power of the i1 DG at the t moment;the prediction error of the active power of the i1 st DG at the t moment;
building an ellipsoid uncertainty set of load prediction errors and DG prediction errors:
wherein Ω N An ellipsoid uncertainty set that is a load prediction error; omega shape DG An ellipsoid uncertainty set that is a DG prediction error;an uncertain budget for a load;An uncertain budget for DG; theta (theta) L,i Covariance matrix of load prediction error; theta (theta) DG,i1 Covariance matrix for i1 st DG prediction error; zeta type L Radial uncertainty for the load; zeta type DG Radial uncertainty for DG;
building a robust variant expression of a coordinated network recovery model:
5. a computer device comprising a processor and a memory; wherein the processor, when executing the computer program stored in the memory, implements the steps of the network failure recovery method of the fleet of coordinated network reconfiguration and mobile energy storage systems of any one of claims 1-4.
6. A computer-readable storage medium storing a computer program; the computer program, when executed by a processor, implements the steps of the method for coordinating network reconstruction and network failure recovery for a fleet of mobile energy storage systems as defined in any one of claims 1-4.
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