US 2025/0054081 A1 · COMPUTER-IMPLEMENTED METHOD FOR COMMUNITY ENERGY MANAGEMENT USING MIXED-INTEGER LINEAR PROGRAMMING, MODEL PREDICTIVE CONTROL, NON-INVASIVE LOAD MONITORING TECHNIQUES, ROBUST FORECASTING MODELS AND RELATED SYSTEM

Abstract

The present invention relates to a computer-implemented method and respective system (100) for community energy management in a community comprising energy consumers and energy producers. Such a community energy management method and system (100) allow optimal energy sharing within energy communities. The method of the present invention is based on mixed-Integer linear programming, operating under the receding horizon concept of Model Predictive Control. The system (100) comprises an aggregator computational unit (110) for the acquisition of data, a cloud-based server (115), a weather station (120), a database comprising tariff-related data (125), data acquisition subsystems (140) and a user interface (145).

A computer implemented method for community energy management (1000), characterized in that the method comprises the following steps:

a) acquisition, by an aggregator computational unit (110), of data d0 that will be used for designing prediction models and appliance profiles, during a period of time, preferably during at least one month;
b) design, using a cloud-based server (115) of Radial Basis Function prediction models to use for predicting selected from the group consisting of:
global solar radiation and atmospheric air temperature of the community, power consumption and power generation in each house h of the community, as well as operation and power consumption for all monitored appliances by Non-Invasive Load Monitoring models, wherein said design comprises the following substeps:
b1) selection of the data available for design from the data do acquired in step a);
b2) minimization of the model objectives through a Multi-Objective Genetic Algorithm;
b3) analysis of the results of the first design, restricting some of the objectives minimized in step b2) and redefinition of the input space and/or the model topology;
b4) utilization of the preferential set of models obtained from a second Multi-Objective Genetic Algorithm execution;
b5) determination of a second set of the preferred models mepref obtained in the previous substep through the following equation:
m
e
pref
=
{
m
pref
:
nw
⁡
(
m
pref
)
≤
m
˘
nw
pref
}
⋂
{
m
pref
:
fore
⁢
(
m
pref
)
≤
m
˘
fore
pref
}
wherein mpref is the set of preferred models obtained in the second Multi-Objective Genetic Algorithm execution, m̆nwpref is the median of the linear weight norm for each model in mpref and m̆forepref the median of the sum of the forecasts for the prediction time-series obtained by models mpref;
b6) obtaining of a third set of models, mepar from the previous step by using the following equation:
m
e
par
=
{
nd
⁡
(
fore
⁢
(
m
e
pref
)
,
nw
⁡
(
m
e
pref
)
)
:
#
⁢
nd
=
NS
}
,
wherein nd(.) stands for non-dominated solutions and NS is its number, preferably NS=25;
b7) if the model that should be designed is a Nonlinear Auto-Regressive model with Exogeneous inputs, usage, by the server of mepar for the exogenous variables;
c) determination of the power profiles for all monitored, manually controlled and computer-controlled appliances in the community, by the cloud-based server (115);
d) reception, by the aggregator computational unit (110), of all models designed in b), all power profiles determined in c), initial values v0 and initial control actions, through a user-interface, as well as data history for all models supplied by a data acquisition subsystem (140) installed in each house h, one or more consumption energy meters (130) installed in at least one house h, one or more inverter energy meters (160) installed in at least one house h, one or more sensors (150) installed in at least one house h;
e) reception, by a data acquisition subsystem (140), of data related to house h, wherein said data is sent wirelessly and/or wired and wherein said reception includes the following substeps:
e1) reception of electric consumption data d1 sent by a consumption energy meter (130) installed at each house h, wherein data d1 comprise active power being consumed in house h at all instants kl within the sampling interval Δk, Ph,kl, and reactive power being consumed in house h at all instants k1 within the sampling interval Δk, Rh,k1;
e2) reception of data d2 related to at least one energy producing photovoltaic subsystem installed in at least one house h, sent by an inverter energy meter (160) wherein data d2 comprise the power generated by at least one energy producing photovoltaic subsystem of house h at an instant k, Ph,kPV;
the power imported from the grid Ph,kGrid−, and the power exported to the grid Ph,kGrid+, wherein PV refers to photovoltaic;
e3) reception of data d3 related to at least one battery bank installed in at least one house h, sent by an inverter energy meter (160) wherein data d3 comprises at least the state of charge of the battery;
f) sending of the control actions determined in the previous step, by the aggregator computational unit (110) to each data acquisition subsystem (140), which, on its turn, sends the respective control actions to the computer-controlled devices in each house of the community;
g) reception, by an aggregator computational unit (110), of the data d1, d2 and d3 related to house h at an instant k, sent by the data acquisition subsystem (140);
h) reception, by the aggregator computational unit (110), of weather data d4, related to the community, wherein said weather data d4 comprises air temperature data and global solar radiation data;
i) reception of tariff-related data d5 of each house h in the community, wherein said data d5 is retrieved from a database (125) by the aggregator computational unit (110);
j) reception, by the aggregator computational unit (110), of occupation-related data d6 of each house h, inserted in a database by each user via a user interface (145);
k) determination of the averaged active power consumption of each house:
P
h
,
k
=
P
h
,
k
1
N
,
N
=
Δ
⁢
k
k
1
;
l) calculation of forecasts for a user-specified Prediction Horizon PH of both the energy consumption of each individual house h and the energy generation of each energy producing photovoltaic apparatus in the community over the prediction horizon, by using one or more of the methods of the group consisting of naive forecasting methods, multi-step Nonlinear Auto-Regressive Exogenous forecasting models, AutoRegressive Integrated Moving Average, ARIMA, Classification and Regression Trees, CART, Wavelet, Fuzzy or Neuro-Fuzzy models, and an hybrid combination thereof;
wherein the present calculation step comprises the following substeps:
l1) calculation of forecasts of consumption for the devices in each house that are monitored and not controlled NMC, which is given as the sum of the active power of corresponding appliances in each house, given by:
P
^
h
,
{
k
+
1
⁢
…
⁢
k
+
PH
}
NMC
=
∑
j
=
1
#
⁢
(
{
NM
i
}
⋃
{
NC
i
}
)
P
^
h
,
j
,
{
k
+
1
⁢
…
⁢
k
+
PH
}
;
(
I
)
for the devices in each house h that are monitored but not controlled NC by means of:
P
^
h
,
{
k
+
1
⁢
…
⁢
k
+
PH
}
NMC
=
P
^
h
,
{
k
+
1
⁢
…
⁢
k
+
PH
NARX
}
NMC
⋃
P
^
h
,
{
k
+
PH
NARX
+
1
⁢
…
⁢
k
+
PH
}
NMC
(
II
)
wherein the first term in the right-hand-side of (II) refers to the use of the Nonlinear Auto-Regressive model with Exogeneous NARX model and the second term to the naïve forecast technique;
wherein PHNARK denotes the first steps within PH whose forecasts are obtained as the median of the outputs of the models mepref:
P
^
h
,
{
k
+
1
⁢
…
⁢
k
+
PH
NARX
}
NMC
=
{
M
h
P
(
P
_
h
,
l
NMC
,
T
_
l
,
O
h
,
l
,
D
l
)
,
l
=
k
+
1
⁢
…
⁢
k
+
PH
NARX
}
;
(
III
)
wherein the second term is given as:
P
^
h
,
{
k
+
PH
NARX
+
1
⁢
…
⁢
k
+
PH
}
NMC
=
P
h
,
{
k
+
PH
NARX
+
1
-
24
Δ
⁢
k
⁢
…
⁢
k
+
PH
-
24
Δ
⁢
k
}
NMC
,
(
IV
)
i.e., the value obtained one day ago;
wherein Ph,lNMC denotes a set of past values relative to/of Ph,lNMC, Tl denotes a set of past values relative to l of Tl Oh,l denotes the daily occupation relative to instant l of house h, and Dl corresponds to a codification of the day, within the week:
wherein as the multi-step iteration of (III) involves predictions of the atmospheric temperature, these forecasts will be given as a multi-step iteration of a NAR model:
T
^
{
k
+
1
...
⁢
k
+
PH
NARX
}
=
{
M
T
(
T
_
l
)
,
l
=
k
+
1
⁢
…
⁢
k
+
PH
NARX
}
;
(
V
)
l2) calculation of forecasts of consumption for the devices in each house that are monitored but not controlled NC, which is given as the sum of the active power of corresponding appliances in each house, given by:
P
^
h
,
{
k
+
1
...
⁢
k
+
PH
}
NC
=
∑
j
=
1
#
⁢
(
{
M
i
}
⋂
{
NC
i
}
)
P
^
h
,
j
,
{
k
+
1
...
⁢
k
+
PH
}
;
(
VI
)
wherein the forecasts of consumption of each device, {circumflex over (P)}h,j,{k+1. . .k+PH}, are given by the joint application of Non-Invasive Load Monitoring NILM classification models configured to determine if the appliance is operating or is off for every instant in PH and, if the appliance is on, the estimate of consumption given by the NILM forecasting model, and the power profile of the appliance, determined offline and updated on-line;
l3) calculation of forecasts of consumption for the devices in each house that are manually controlled MC, which is given as the sum of the active power of corresponding appliances in each house, given by:
P
^
h
,
{
k
+
1
...
⁢
k
+
PH
}
MC
=
∑
m
=
1
#
⁢
(
{
MC
i
}
)
P
^
h
,
m
,
{
k
+
1
...
⁢
k
+
PH
}
;
(
VII
)
wherein the forecasts of consumption of each device
∑
m
=
1
#
⁢
(
{
MC
i
}
)
P
^
h
,
m
,
{
k
+
1
...
⁢
k
+
PH
}
are obtained using the power profiles determined off-line;
l4) calculation of forecasts of consumption for the devices in each house that are computer controlled CC, which is given as the sum of the active power of corresponding appliances in each house, given by:
P
^
h
,
{
k
+
1
...
⁢
k
+
PH
}
CC
=
∑
n
=
1
#
⁢
(
{
CC
i
}
)
P
^
h
,
n
,
{
k
+
1
...
⁢
k
+
PH
}
;
(
VIII
)
wherein the operation of each appliance is given as the solution of the Mixed-Integer Linear Programming MILP problem carried out in step m), and whose consumption estimation can be given using the power profiles determined off-line;
l5) calculation of forecasts of photovoltaic power generation by means of a combination of the naive forecasting method and multi-step Nonlinear Auto-Regressive Exogenous NARX forecasting models for each energy generation photovoltaic subsystem in each house h comprising an energy generation photovoltaic subsystem;
the NARX forecasts are given by:
i.
PV
h
,
{
k
+
1
...
⁢
k
+
PH
NARX
}
=
{
M
h
PV
(
P
_
⁢
V
_
h
,
l
,
T
_
l
,
R
_
l
)
,
l
=
k
+
1
⁢
…
⁢
k
+
PH
NARX
}
;
(
IX
)
wherein R denotes global solar radiation,
wherein an additional forecasting model is needed for solar radiation over PH, to determine its evolution is used:
ii.
R
{
k
+
1
...
⁢
k
+
36
}
=
{
M
R
(
R
_
l
)
,
l
=
k
+
1
⁢
…
⁢
k
+
36
}
(
X
)
m) formulation, by the aggregator computational unit (110), of a Mixed-Integer Linear Programming problem as indicated in formula (XI) below
min
⁢
C
^
Com
=
min
⁢
∑
h
=
1
Nh
∑
s
=
1
PH
(
P
^
h
,
l
Grid
-
+
P
^
h
,
l
Com
-
)
⁢
π
l
Grid
-
⁢
Δ
⁢
k
-
(
P
^
h
,
l
Grid
+
+
P
^
h
,
l
Com
+
)
⁢
π
l
Grid
+
⁢
Δ
⁢
k
,
l
=
k
+
s
(
XI
)
wherein
Nh denotes the number of houses in the community;
PH is a prediction horizon, and
Δk is the sampling interval;
{circumflex over (P)}h,lGrid−/{circumflex over (P)}h,lGrid+ are estimates of the active power P bought−/sold+ from/to an electrical energy grid at a sampling instant/by house h in the community;
{circumflex over (P)}h,lCom−/{circumflex over (P)}h,lCom+ are estimates of the active power P bought−/sold+ from/to the community at the sampling instant/by house h;
πlGrid−/πlGrid + are the price of the energy bought−/sold+ from/to the community or the electrical energy grid at the sampling instant l;
wherein the following constraints (XII) and (XIII) related to the electrical energy grid are satisfied:
0
≤
P
^
h
,
l
Grid
+
≤
P
_
h
Grid
+
(
1
-
v
h
,
l
Grid
)
(
XII
)
0
≤
P
^
h
,
l
Grid
-
≤
P
_
h
Grid
-
⁢
v
h
,
l
Grid
;
(
XIII
)
wherein
{circumflex over (P)}h,lGrid+/{circumflex over (P)}h,lGrid− are positive variables with a maximum value equal to the contracted power form the electrical energy grid, PhGrid+/PhGrid− by house h,
wherein vh,lGrid is the binary variable modelling the selling/purchasing energy from house h to/from the electrical energy grid, assuming 1 for purchasing and 0 for selling;
and wherein the following constraints (XIV) and (XV) related to the community are satisfied:
0
≤
P
^
h
,
l
Com
+
≤
P
_
h
Com
+
(
1
-
v
h
,
l
Com
)
(
XIV
)
0
≤
P
^
h
,
l
Com
-
≤
P
_
h
Com
-
⁢
v
h
,
l
Com
(
XV
)
wherein
{circumflex over (P)}h,lCom+/{circumflex over (P)}h,lCom− are positive variables with a maximum value equal to the maximum value exchangeable with the community, PhCom+/PhCom− by house h, wherein vh,lCom is the binary variable modelling the selling/purchasing energy from house h to/from the community, assuming 1 for selling and 0 for purchasing, and considering that it is not allowed to buy energy form the grid and sell energy to the community at the same time:
vh,lGrid+vh,lCom≤1;
wherein the energy balance of the community is given as (XVI):
∑
h
=
1
Nh
P
^
k
,
l
Com
-
=
∑
h
=
1
Nh
P
^
k
,
l
Com
+
;
(
XVI
)
wherein the energy balance of house h in the community can be obtained by several ways, selected by the community:
m1) if there are no restrictions on the transfer of energy between the community, the energy balance of house h is given by the equality restriction:
P
^
h
,
l
PV
+
P
^
h
,
l
B
+
+
P
^
h
,
l
Grid
-
+
P
^
h
,
l
Com
-
=
P
^
h
,
l
B
-
+
P
^
h
,
l
Grid
+
+
P
^
h
,
l
NMC
+
P
^
h
,
l
NC
+
P
^
h
,
l
MC
+
P
^
h
,
l
CC
+
P
^
h
,
l
Com
+
(
XVII
)
wherein {circumflex over (P)}h,lPV is the estimate of the alternate current power generated by the PV installation of house h at instant l;
wherein {circumflex over (P)}h,lB+/{circumflex over (P)}h,lB− is the estimate of the discharged or charged power of the battery bank of house h at instant l;
wherein the {circumflex over (P)}h,lNMC term relates to the estimate of the active power of house h being consumed at instant l by devices that are not monitored neither controlled, the {circumflex over (P)}h,lNC term relates to the estimate of the active power of house h being consumed at instant l by devices that are monitored but not controlled, the {circumflex over (P)}h,lMC term relates to the estimate of the active power of house h being consumed at instant/by devices manually controlled, and the {circumflex over (P)}CC term relates to the estimate of the active power of house h being consumed at instant l by devices that are computer controlled;
m2) energy balance of house h (VIII) in the community, if it is not allowed the transfer of energy between the community, is given by the equality restriction:
P
^
h
,
l
Com
-
=
P
h
,
l
Com
+
=
0
⇒
⇒
P
^
h
,
l
PV
+
P
^
h
,
l
B
+
+
P
^
h
,
l
Grid
-
=
P
^
h
,
l
B
-
+
P
^
h
,
l
Grid
+
+
P
^
h
,
l
NMC
+
P
^
h
,
l
NC
+
P
^
h
,
l
MC
+
P
^
h
,
l
CC
(
XVIII
)
m3) wherein the energy is shared between the houses using energy sharing coefficients βh,l as shown in the formulas (XIX) e (XX):
P
^
h
,
l
Com
+
=
P
^
h
,
l
PV
⇒
⇒
P
^
h
,
l
B
+
+
P
^
h
,
l
Grid
-
+
P
^
h
,
l
Com
-
=
P
^
h
,
l
B
-
+
P
^
h
,
l
Grid
+
+
P
^
h
,
l
NMC
+
P
^
h
,
l
NC
+
P
^
h
,
l
MC
+
P
^
h
,
l
CC
(
XIX
)
P
^
h
,
l
Com
-
=
β
h
,
l
⁢
∑
h
=
1
Nh
P
^
h
,
l
PV
,
∑
h
=
1
Nh
β
h
,
l
=
1
,
l
=
1
⁢
…
⁢
PH
,
(
XX
)
wherein said coefficients follow one of the following different alternatives (XXI):
β
h
,
l
=
{
FC
h
,
l
∈
[
0
,
1
]
;
Fixed
VC
h
,
l
∈
[
0
,
1
]
;
Variable
P
^
h
,
l
∑
l
=
1
Nh
P
^
h
,
l
;
Consumption
-
based
FC
h
,
l
⁢
P
^
h
,
l
∑
l
=
1
Nh
FC
h
,
l
⁢
P
^
h
,
l
;
Hybrid
;
(
XXI
)
wherein FCh,l represent fixed coefficients, and VCh,l variable coefficients which are obtained through an optimization problem;
n) solution, by the aggregator computational unit (110), of the problem formulated in step m), using the receding horizon principle, wherein said solution includes the determination of all control actions, for the whole prediction horizon PH, but where only the ones calculated for the first step within PH will be sent to the corresponding data-acquisition systems (140), in step f);
o) update of the history for every model used;
wherein steps e) through o) are repeated every sampling interval Δk.

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Computer-implemented method for community energy management using mixed-integer linear programming, model predictive control, non-invasive load monitoring techniques, robust forecasting models and related system

Life of this patent

Computer-implemented method for community energy management using mixed-integer linear programming, model predictive control, non-invasive load monitoring techniques, robust forecasting models and related system

Life of this patent

Abstract

Claims14