Optimal energy management of a rural microgrid system using multi-objective optimization
View Patent ↗Systems and methods are disclosed to improve energy efficiency of a farm with livestock wastes by generating a cooling, heating, and power (CCHP) microgrid model; performing on a computer a multi-objective optimization to improve system efficiency of energy utilization and reduce environmental problems caused by animal wastes; and displaying results of the optimization for review.
1. A computer implemented method to improve energy efficiency of a farm with livestock waste with a microgrid, comprising: generating a comprehensive cooling, heating, and power (CCHP) microgrid model; performing on a computer a multi-objective optimization to improve system efficiency of energy utilization and reduce environmental problems caused by animal waste; displaying results of the optimization for review; modeling, an objective function of system operating cost (OC), including biogas, fuel cost (FC), internal combustion engine (ICE) start up and shut down cost (SSC), equipment operation and maintenance cost (OMC), and cost of electricity bought from the grid (EBC) as follows:
OC
=
FC
+
SSC
+
OMC
+
EBC
FC
=
∑
t
=
1
N
Δ
T
(
F
t
+
F
GB
,
t
+
F
ACC
,
t
)
C
Bio
SSC
=
∑
t
=
1
N
∑
g
=
1
M
(
v
g
,
t
SU
+
w
g
,
t
SD
)
OMC
=
∑
t
=
1
N
Δ
T
(
OM
ICE
∑
g
=
1
M
P
g
,
t
+
OM
GB
P
GB
,
t
+
OM
ACC
P
ACC
,
t
+
OM
AR
P
AR
,
t
)
EBC
=
∑
t
=
1
N
Δ
T
(
C
GriE
P
grid
,
t
)
where ΔT is hourly time step, C Bio is the biogas cost in dollars per liter ($/L), F t,GB,ACC are fuel cost of ICEs, gas boiler, and absorption chiller for cooling in L/h, SU, SD are start up cost and shut down cost in $, OM ICE,GB,ACC,AR are operation and maintenance cost of ICEs, GB, ACC, and AR in $/kWh, P GB,ACC,AR,t is output of gas boiler, absorption chiller for cooling and absorption refrigerator in time t in kW, C GriE is electricity cost of the grid in $/kWh, P grid,t is the power bought from the grid in kW, M is the number of generators, and N is a time t counter;
and optimizing the microgrid based on dispalyed result, start and shut down cost, operating and maintenance cost, and cost of electricity; wherein the steps are carried out by a computer having a processor and memory.
2. The computer implemented method of claim 1 , wherein modeling includes modeling fuel consumption rate of biogas-fueled internal combustion engine (ICE) as real power
F g,t =a g +b g P g,t +c g P g,t 2
where P g,t is the generator g output in kW at time t, and a g , b g , c g are coefficients from the ICE.
3. The computer implemented method of claim 1 , wherein modeling includes modeling hourly available capacity of battery storage P batt,t in time t as:
P
batt
,
t
=
P
batt
,
t
-
1
+
E
cha
,
t
η
cha
-
E
dis
,
t
η
dis
where E cha,t is hourly charging quantity flows into the battery storage in kWh in time t, E dis,t is hourly discharging quantity flows out of the battery storage in kWh in time t, η cha is charging efficiency of the battery storage, η dis is the discharging efficiency of the battery storage.
4. The computer implemented method of claim 1 , wherein modeling includes modeling system constraints as:
∑
g
=
1
M
P
g
,
t
+
P
PV
,
t
+
P
grid
,
t
-
P
L
,
t
=
Δ
P
t
where M is the number of generators g, P g,t is the generator g output in kW at time t, P PV,t is DC output power of a photovoltaic module at time t, where P grid,t is the energy from a main grid in kWh in time t, and P L,t is a load demand in kW in time t.
5. The computer implemented method of claim 1 , further comprising:
incorporating an explicit periodic operating cost minimization criterion applied to infrastructure of the microgrid and maximization of energy output from distributed energy resources (DERs); and
balancing trade-offs between daily operating cost minimization and periodic maximization of energy output from the DERs.
6. The computer implemented method of claim 1 , further comprising providing optimal management of a rural microgrid infrastructure based on multi-objective optimization including economic cost of waste management.
7. The computer implemented method of claim 1 , wherein modeling includes modeling thermal components of a microgrid optimization model with models for cooling demand and hot water demand.
8. The computer implemented method of claim 1 , further comprising using individual objective optimization results as extreme points of a trade-off curve and creating a pareto front for operation management.
9. The computer implemented method of claim 1 , further comprising performing the multi-objective optimization with the CCHP model for a multi-criteria rural microgrid system based on mixed integer non-linear programming.
10. The computer implemented method of claim 1 , wherein modeling includes modeling cooling demand with a combined model of absorption refrigerator, absorption chiller, and heat release from a thermal storage.
11. The computer implemented method of claim 1 , wherein modeling includes modeling of emissions and cost of energy, heat and power, for the microgrid.
12. The computer implemented method of claim 1 , wherein modeling includes modeling of AC and DC power distribution with energy storage, thermal and electrical, for onsite demand management in the microgrid.
13. The computer implemented method of claim 1 , wherein modeling includes modeling of grid-tied or islanded microgrids for revenue maximization and emission minimization using managed sale of energy and reduction of operating cost while providing for local thermal and electrical demand.