US 10,833,508 B2Granted

Active power distribution network multi-time scale coordinated optimization scheduling method and storage medium

AssigneesChina Electric Power Research Institute, State Grid Corporation of China, North China Electric Power University, State Grid Beijing Electric Power Company, State Grid Tianjin Electric Power Company

InventorsXiaohui Wang, Tianjiao Pu, Lei Dong, Naishi Chen, Ye Li, Jiancheng Yu, Renle Huang, Xianjun Ge, Zhihong Li, Minghao Ai, Fumin Qu, Liqiang Zhao

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Abstract

An active power distribution network multi-time scale coordinated optimization scheduling method and storage medium are provided. The method includes the following steps: performing long-time scale optimization scheduling of an active power distribution network; performing short-time scale rolling optimization scheduling on the basis of MPC of the active power distribution network according to an optimization result of the long-time scale optimization scheduling of the active power distribution network. By using a model prediction control method, taking the long-time scale optimization scheduling result as a reference, and performing short-time scale rolling correction, the optimization scheduling of the active power distribution network is achieved, thereby reducing unfavorable influences of prediction precision of a distributed power supply and a low-voltage load on the optimization scheduling.

Claims12

Dependent claims

A multi-time-scale coordinated optimization scheduling method for an active power distribution network, comprising the following steps:

(1) performing long-time-scale optimization scheduling on the active power distribution network (1);
and
(2) performing Model Predictive Control (MPC)-based short-time-scale rolling optimization scheduling on the active power distribution network according to an optimization result of long-time-scale optimization scheduling of the active power distribution network,
wherein Step (1) comprises:
(1-1) acquiring a distributed power supply predicted value {circumflex over (P)}k(t), load predicted value {circumflex over (P)}load(t), adjustable energy storage capacity and adjustable flexible load capacity of the active power distribution network under a long time scale;
(1-2) constructing a long-time-scale optimization scheduling model for the active power distribution network;
and
(1-3) acquiring the optimization result of long-time-scale optimization scheduling of the active power distribution network, the optimization result comprising:
energy storage output power Pstori(t) and flexible load output power ΔPLj (t),
wherein the long-time-scale optimization scheduling model for the active power distribution network in Step (1-2) comprises:
a target function for long-time-scale optimization scheduling and a constraint condition;
the target function for long-time-scale optimization scheduling is:
min
⁢
⁢
F
=
min
⁢
∑
t
=
1
T
⁢
⁢
1
⁢
⁢
{
c
grid
⁡
(
t
)
⁢
P
grid
⁡
(
t
)
+
∑
i
=
1
NL
⁢
⁢
C
loadi
⁡
(
t
)
+
∑
j
=
1
NS
⁢
⁢
C
storj
⁡
(
t
)
}
,
(
1
)
in formula (1), min F being a minimum value of cost in long-time-scale optimization scheduling of the active power distribution network, cgrid(t) representing a time-of-use electricity price of a power grid, Pgrid(t) being output power of a tie line under the long time scale, Cloadi(t) being flexible load scheduling cost, Cstorj(t) being energy storage equipment scheduling cost, T1 representing a long-time-scale optimization scheduling period, NL being a number of flexible loads in the active power distribution network and NS being a number of energy storage devices;
and
the constraint condition comprises:
a power constraint of the tie line of the active power distribution network and a main network, a power balance constraint, an energy storage battery constraint, a flexible load constraint and a network power flow security constraint.

77 more elements

A non-transitory storage medium having stored therein a computer-executable instruction configured to execute a multi-time-scale coordinated optimization scheduling method for an active power distribution network, the method comprising steps:

(1) performing long-time-scale optimization scheduling on the active power distribution network (1);
and
(2) performing Model Predictive Control (MPC)-based short-time-scale rolling optimization scheduling on the active power distribution network according to an optimization result of long-time-scale optimization scheduling of the active power distribution network,
wherein Step (1) comprises:
(1-1) acquiring a distributed power supply predicted value {circumflex over (P)}k(t), load predicted value {circumflex over (P)}load(t), adjustable energy storage capacity and adjustable flexible load capacity of the active power distribution network under a long time scale;
(1-2) constructing a long-time-scale optimization scheduling model for the active power distribution network;
and
(1-3) acquiring the optimization result of long-time-scale optimization scheduling of the active power distribution network, the optimization result comprising:
energy storage output power Pstori(t) and flexible load output power ΔPLj(t),
wherein the long-time-scale optimization scheduling model for the active power distribution network in Step (1-2) comprises:
a target function for long-time-scale optimization scheduling and a constraint condition;
the target function for long-time-scale optimization scheduling is:
min
⁢
⁢
F
=
min
⁢
∑
t
=
1
T
⁢
⁢
1
⁢
⁢
{
c
grid
⁡
(
t
)
⁢
P
grid
⁡
(
t
)
+
∑
i
=
1
NL
⁢
⁢
C
loadi
⁡
(
t
)
+
∑
j
=
1
NS
⁢
⁢
C
storj
⁡
(
t
)
}
,
(
1
)
in formula (1), min F being a minimum value of cost in long-time-scale optimization scheduling of the active power distribution network, cgrid(t) representing a time-of-use electricity price of a power grid, Pgrid(t) being output power of a tie line under the long time scale, Cloadi(t) being flexible load scheduling cost, Cstorj(t) being energy storage equipment scheduling cost, T1 representing a long-time-scale optimization scheduling period, NL being a number of flexible loads in the active power distribution network and NS being a number of energy storage devices;
and
the constraint condition comprises:
a power constraint of the tie line of the active power distribution network and a main network, a power balance constraint, an energy storage battery constraint, a flexible load constraint and a network power flow security constraint.

77 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/740,847Filed 27 Jun 2016
Priority: CN 2015 1 037357330 Jun 2015
Published: US 2020/0033933 A130 Jan 2020
Granted: 10 Nov 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification19 codes

H02J 3/48
Controlling the sharing of generated power between the generators, sources or networks: controlling the sharing of active power
G06F 1/3209
Monitoring of events, devices or parameters that trigger a change in power modality: monitoring remote activity, e.g. over telephone lines or…
G06F 1/3228
Monitoring of events, devices or parameters that trigger a change in power modality: monitoring task completion, e.g. by use of idle timers, stop…
G06F 1/329
Power saving characterised by the action undertaken: by task scheduling