# US 11,443,252 B2 · Multi-objective operation control method for micro energy grid

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- Number: US 11,443,252 B2
- Document ID: US11443252B2
- Status: Granted
- Kind: B2
- Application number: 17/042,948
- Filed: 2018-10-31
- Published: 2021-04-29
- Granted: 2022-09-13
- Earliest claimed: 2018-03-29
- Assignees: South China University of Technology
- Applicants: South China University of Technology
- Inventors: Xiaoming Lin; Yongjun Zhang
- Web page: https://patentgrep.com/patent/US11443252B2

## CPC

- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06Q 50/06: Energy or water supply
- H02J 3/06: Controlling the transfer of power between connected networks; Controlling load sharing between connected networks
- H02J 3/46: Controlling the sharing of generated power between the generators, sources or networks
- H02J 3/466: Scheduling or selectively controlling the operation of the generators or sources, e.g. connecting or disconnecting generators to meet a demand
- H02J 2101/28: Wind energy
- H02J 2103/30: Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
- Y02E 60/00: Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION

## Abstract

The present invention disclosed a multi-objective operation control method for a micro energy grid, comprising the specific steps of: (1) establishing optimization objectives of the micro energy grid, the optimization objectives comprising comprehensive income maximization and comprehensive energy utilization rate maximization; (2) using GAMS software to solve for an optimal solution and a worst solution for each optimization objective; (3) processing the optimization objectives by means of a weighting method, uniformly changing a weighting coefficient, and acquiring a Pareto frontier by the GAMS software; (4) acquiring reference satisfaction levels of Pareto optimal solutions according to a fuzzy membership degree, and selecting the Pareto optimal solution having the maximum reference satisfaction level as an optimal compromise solution; and (5) executing scheduling of the micro energy grid according to the optimal compromise solution.

## Claims

### Claim 1

A multi-objective operation control method for a micro energy grid, wherein the method comprises:
(1) establishing optimization objectives of the micro energy grid, which comprises comprehensive income maximization and comprehensive energy utilization rate maximization;
(2) using GAMS software to solve for an optimal solution and a worst solution for each optimization objective;
(3) processing the optimization objectives by means of a weighting method; uniformly changing a weighting coefficient, and acquiring a Pareto frontier by the GAMS software;
(4) acquiring reference satisfaction levels of Pareto optimal solutions according to a fuzzy membership degree, and selecting the Pareto optimal solution with a maximum reference satisfaction level as an optimal compromise solution; and
(5) executing scheduling of the micro energy grid according to the optimal compromise solution,
wherein the comprehensive income f1 comprises an energy service income CSer, an energy trade income CTrade, an operation and maintenance cost COM and a carbon tax cost CCO2:
f
1
=
C
Ser
+
C
Trade
-
C
OM
-
C
CO
⁢
⁢
2
{
C
Ser
=
∑
t
=
1
T
⁢
(
c
e
,
t
⁢
L
e
,
t
+
c
h
,
t
⁢
L
h
,
t
+
c
g
,
t
⁢
L
g
,
t
)
⁢
Δ
⁢
⁢
t
C
Trade
=
∑
t
=
1
T
⁢
(
c
e
,
t
sell
⁢
S
e
,
t
sell
-
c
e
,
t
buy
⁢
S
e
,
t
buy
-
c
g
buy
⁢
S
g
,
t
buy
)
⁢
Δ
⁢
⁢
t
C
OM
=
∑
t
=
1
T
⁢
[
∑
n
=
1
N
⁢
c
d
,
n
⁢
P
n
,
t
i
⁢
⁢
n
+
∑
k
=
1
K
⁢
c
s
,
k
⁡
(
Q
k
,
t
dis
+
Q
k
,
t
ch
)
]
C
CO
⁢
⁢
2
=
∑
t
=
1
T
⁢
c
c
⁡
(
a
e
⁢
S
e
,
t
buy
+
a
g
⁢
S
g
,
t
buy
)
⁢
Δ
⁢
⁢
t
wherein Δt is an optimization time interval, and T is a total optimization time period; Le,t, Lh,t, and Lg,t are powers of electricity, heat and natural gas of a user during a time period t respectively, and a value range of t is 1 to T; ce,t, ch,t and cg,t are prices of electricity, heat and natural gas services respectively provided by the micro energy grid to the user during the time period t; Sbuy e,t, Ssell e,t and Sbuy g,t are a power of electricity bought from an electricity grid by the micro energy grid and a power of electricity sold to the electricity grid from the micro energy grid and a power of the natural gas bought from a gas grid respectively during the time period t; cbuy e,t, csell e,t and cbuy g,t are a price of electricity bought from the electricity grid by the micro energy grid and a price of electricity sold to the electricity grid from the micro energy grid and a price of the natural gas bought from a gas grid respectively during the time period t; N is a total number of energy conversion devices; K is a total number of energy storage devices; cd,n and Cs,k are operation and maintenance coefficients of an nth energy conversion device and an kth energy storage device respectively, a value of n is 1 to N, and a value of k is 1 to K; Pin n,t, Qch k,t and Qdis k,t are an input power of the nth energy conversion device and charging and discharging power of the kth energy storage device respectively during the time period t; ae and ag are respectively CO2 emission coefficients of electricity and natural gas; and cc is an unit carbon emission cost.

### Claim 2 (depends on 1)

The multi-objective operation control method for the micro energy grid according to claim 1, wherein the comprehensive energy utilization rate f2 is:
f
2
=
∑
t
=
1
T
⁢
(
L
e
,
t
+
L
h
,
t
+
L
g
,
t
+
S
e
,
t
sell
)
∑
t
=
1
⁢
(
S
e
,
t
b
⁢
u
⁢
y
+
S
g
,
t
b
⁢
u
⁢
y
+
W
e
,
t
)
where We,t is an generated output of wind electricity during the time period t.

### Claim 3 (depends on 1)

The multi-objective operation control method for the micro energy grid according to claim 1, wherein step (3) comprises:
(3-1) standardizing the optimization objectives f1 and f2, and executing weighting processing to acquire an optimization objective function:
max
⁡
[
w
⁢
f
1
-
F
1
min
F
1
max
-
F
1
min
+
(
1
-
w
)
⁢
f
2
-
F
2
min
F
2
max
-
F
2
min
]
wherein Fmax 1 and Fmin 1, as well as Fmax 2 and Fmin 2 respectively are the optimal solutions and the worst solutions of the optimization objectives f1 and f2; w is a weight of the optimization objective f1, and a value range of w is [0,1]; and (1-w) is a weight of the optimization objective f2;
(3-2) uniformly taking J values of the weighting coefficient w in the range of [0,1], wherein J is a set value; and
(3-3) for each weighting coefficient value, acquiring Pareto optimal solutions of the optimization objective function in step (3-1) by the GAMS software, and J Pareto optimal solutions forming the Pareto frontier.

### Claim 4 (depends on 1)

The multi-objective operation control method for the micro energy grid according to claim 1, wherein the fuzzy membership degree is:
γ
i
,
j
=
F
i
max
-
f
i
,
j
F
i
max
-
F
i
min
wherein fi,j and γi,j respectively are an objective function value and a fuzzy membership degree of an ith optimization objective of a jth Pareto optimal solution, a value range of i is 1 or 2, and a value range of j is 1 to J; and
the reference satisfaction level of the Pareto optimal solution is:
ζ
j
=
∑
i
=
1
2
⁢
γ
i
,
j
∑
j
=
1
J
⁢
∑
i
=
1
2
⁢
γ
i
,
j
wherein ζj is a reference satisfaction level of the jth Pareto optimal solution.