Chapter 3 describes the detailed procedures of the remote microgrid benchmark and its EMS [10]. The benchmarks information contains the characteristics, parameters, and component specifications and profiles. For instance, these include the efficiency characteristics of the generators, annual load profile, PV profile, battery and generator mathematical cost model, battery throughput and lifetime cost calculation. The PV profile and annual load profile were obtained from a remote microgrid located in North America, a microgrid very similar to the one used in this thesis. This section also describes the optimization problem, followed by the cases studied to analyze and compare the impacts of different types of batteries used in this remote microgrid.
3.1 Remote Microgrid Benchmark
Figure 3.1 describes the benchmark which was used for this study. The remote microgrid benchmark contains two diesel generators, a KOHLER 30 kW (model
30REOZJC) and a 75 kW (model KT75), along with a 27 kW PV battery bank and load. The diesel generators exhibit the characteristics presented in Chapter 2, including a minimum value of 30% of the rated capacity, which means the P (max) for these generators are 30 kW and 75 kW, while the P (min) for these generators are 9 kW and 22.5 kW. These generators operate in isochronous mode. The diesel fuel cost of the generators is assumed $9 per gallon [27]. The capacities of the battery banks are different from each other, which allows them to meet the average load for four hours to improve reliability and fuel efficiency through increased renewable energy utilization. This type of microgrid is a hybrid system which uses a hybrid EMS to control it.
Figure 3.1 Remote microgrid benchmark [10]
The central controller gives instructions to the generators, batteries, and load. In this study, assumed that the voltage levels are the same in the microgrid. Power losses and reactive power are both neglected in the study.
Figure 3.2 shows the fuel consumption curve for the 30 kW diesel generator (model 30REOZJC) and the 70 kW diesel generator (model KT75) according to the manufacturer data sheet.
Figure 3.2 Generator fuel consumption curves [10]
In Figure 3.3 the efficiency of the 30 kW diesel generator is higher than 70 KW generator in a low load situation.
Figure 3.3 Generator efficiency vs loading [10]
0 10 20 30 40 50 60 70 80 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Power output (kW) F uel c ons um pt ion ( g al lon/ hr ) 30kW: 0.0004*P 2 + 0.039*P + 0.475 75kW: 0.0003*P2 + 0.0213*P + 1.1 0 10 20 30 40 50 60 70 80 0 5 10 15 20 25 30 35 40 45 Load (kW) E ff ici e n cy ( % ) GenPower 30 GenPower 75
3.1.1 Load Profile
In this thesis, two types of loads are included. First, a critical load was considered, which is important for residential and commercial purposes such as a health clinic.
Second, non-critical loads were included, for purposes such as water pumps. The annual load profile is presented in Figure (3.4) which was collected from Nemiah Valley microgrid [6]. The loads were given in hourly basis throughout a year. The minimum, maximum, and average loads of the system are 3 kW, 64 kW, and 25 kW respectively. Figure (3.4) shows that the value of the average load is comparatively high in the winter season and low in the summer. The hourly peak load was in January and minimum was in July.
Figure 3.4 Load demands profile [10]
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann
0 10 20 30 40 50 60 70 A ver ag e V al u e ( kW )
Scaled data Monthly Averages
Month max daily high mean daily low min
3.1.2 PV Profile
The hour basis yearly PV profile is presented in Figure (3.5) which was collected from [6]. The average and maximum value of PV power is about 5 kW and 30 kW respectively. The generation of PV power is high in the summer season and low in the winter. The peak value PV is obtained at solar noon which depends on the geographical location. All PV panels are connected together in order to produce 30 kW with the same irradiation.
Figure 3.5 Yearly PV irradiance [10] 3.1.3 Diesel Generator Cost Model
The generator cost modeling, content fuel consumption, generator replacement cost, emission cost and maintenance cost are omitted in this study. The fuel consumption calculated with a quadratic equation. The operational fuel cost of the generator was calculated by multiplying the fuel volume with fuel cost/unit volume. Equation 3.1 presents the daily operational cost of the generator. Both generators have a limited
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 A ver ag e V al u e ( kW /m ²)
Scaled data Monthly Averages
Month max daily high mean daily low min
operation mode of 30% of the rated capacity. So, equation 3.2 then presents the limitations of both generators [10].
( )
24(
2)
, , , 1n n diesel n n t n n t n n t t
C
P
C
a
P
b
P
c
U
==
×∑
×
+ ×
+ ×
(3.1) , ,min , , ,max,
n t n n t n t nU
×P
≤
P
≤U
×P
∀ ∈t
T
∀ ∈n
N
(3.2)Cdiesel : Diesel fuel cost ($/gallon)
Pn,t : Power output of nth generator at t (kW)
an, bn, cn : Generator fuel curve coefficients
Un,t : Generator ON/OFF control at t (1=ON, 0=OFF)
Another factor should also be considered— the generator’s hourly replacement cost. The hourly replacement cost of the generator is calculated from the generator’s lifetime hours, which depend on parameters such as frequency of use and maintenance of the generator [28]. According to some current microgrids in operation and manufacturers' data sheets, the lifetime of a diesel generator is estimated at 40,000 hours [27, 28, 29]. The generator's hourly replacement cost ($/hr) is then calculated by dividing the initial cost of the generator ($) by the expected lifetime in hours. The generator's maintenance cost is then estimated to be $8,000 per year. Based on the small size of the generator in the remote microgrid, the startup and shutdown of the generator were neglected based on [30]. The total generator's operational cost was calculated by the sum of the fuel cost and the hourly replacement cost (obj1).
𝑆𝑆𝑔𝑔, ℎ = 𝐺𝐺𝑡𝑡ℎ 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡𝐺𝐺𝐺𝐺 𝐻𝐻𝐺𝐺𝐻𝐻𝐺𝐺𝐻𝐻𝐻𝐻 𝑅𝑅𝐺𝐺𝑅𝑅𝐻𝐻𝐺𝐺𝑅𝑅𝐺𝐺𝑅𝑅𝐺𝐺𝐺𝐺𝑡𝑡 𝑆𝑆𝐺𝐺𝐶𝐶𝑡𝑡, $
ℎ𝐺𝐺 (3.3) = 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡𝐺𝐺𝐺𝐺 𝐼𝐼𝐺𝐺𝐼𝐼𝑡𝑡𝐼𝐼𝐺𝐺𝐻𝐻 𝑆𝑆𝐺𝐺𝐶𝐶𝑡𝑡, $
𝑇𝑇𝐺𝐺𝑡𝑡𝐺𝐺𝐻𝐻 𝐿𝐿𝐼𝐼𝐿𝐿𝐺𝐺𝑡𝑡𝐼𝐼𝑅𝑅𝐺𝐺 𝑊𝑊𝐺𝐺𝐺𝐺𝑊𝑊𝐼𝐼𝐺𝐺𝑊𝑊 𝐻𝐻𝐺𝐺𝐻𝐻𝐺𝐺𝐶𝐶 3.1.4 Battery Wear Cost Model
Determining the battery wear cost ($/kWh) is very important in order to obtain the battery lifetime throughput. So, the battery lifetime throughput can be obtained from the battery datasheet, which is based on standard test conditions. Two types of battery operation conditions were considered. The first is the standard test condition which depends on the battery specification sheet disclosing the discharge rate, rated DOD, life cycle, and so on. The second condition is the actual working condition, which then
includes the high penetration of the stochastic PV system and battery throughput. Schiffer in [31] presented a weighted Ah method to calculate actual battery lifetime and Ah throughput. Both conditions are explained further, as follows.
3.1.4.1 Battery Datasheet Specifications
In order to calculate the battery lifetime throughput and the battery wear cost, the most important parameters for each battery should be presented, as noted in the battery datasheet. Table 3.1 present the speciation of the batteries Battery Capacity, Rated
DOD, voltage and efficiency. This information used to calculate the battery wear cost and
In order to take into account variations in cost for lead acid batteries, five types of lead acid battery are considering: Battery-I PbA [32], Battery-II PbA [33], Battery-III PbA [34], Battery- IV PbA [35] and Battery-V PbA [36]. Similarly, five lithium ion batteries are considered: Battery-I LIB [37], Battery-II LIB [38], Battery-III LIB [39], Battery- IV LIB [40] and Battery-V LIB [41].
Table 3.1 Battery datasheet specifications [26], [25], [43] and [32]-[41]
Battery Type
amp hour ratings,
Ah at C/20
Rated DOD
Life Cycle at
Rated DOD Efficiency
AHI 47.3 0.9 3500 0.85
ZBB 10000 1 4000 0.85
Tesla 6400 1 5000 0.925
Lead acid (PbA)
Battery-I PbA 1110 0.5 1400 0.9
Battery-II PbA 245 0.5 3000 0.9
Battery-III PbA 258 0.5 1000 0.9
Battery-IV PbA 696 0.5 1750 0.9
Battery-V PbA 1104 0.5 1400 0.9
Lithium ion (LIB)
Battery-I LIB 180 0.8 2000 0.9
Battery-II LIB 19.6 0.9 3000 0.9
Battery-III LIB 138 0.8 2800 0.9
Battery-IV LIB 75 1 2000 0.9
Battery-V LIB 100 0.8 2000 0.9
3.1.4.2 Calculate Lifetime Throughput Ah lifetime throughput:
Lifetime throughput is the total amount of discharging energy from a battery during its float lifetime. It was calculated to estimate the lifetime of the batteries and to determine the fuel consumption of the diesel generators. It was considered that the
batteries are expired when the estimated battery throughput reaches the amount of lifetime throughput within the float life. It was also required to calculate the battery wear cost. The average lifetime Ah throughput was calculated in terms of rated depth of discharge (DOD), number of life cycles (Lc,DOD), and battery ampere hour rated capacity
(BattCapAh) by using Equation (3.4) [42]
Ah
lifetime=L
c DOD,×DOD
R×BattCap
Ah (3.4)Average total Ah lifetime:
The average total kWh lifetime throughput is calculated by using equation 3.5.
lifetime,av lifetime vg g ,a BatteryVo Ah lt kWh 1000 × = (3.5)
The information of rated DOD, and Lc,DOD, BattCapAh were obtained from the
manufacturer data sheets.
Calculation battery bank size
The battery bank was sized to meet the peak energy demand of the system (64 kW). The size of the battery bank is different for considering types in the system which depend on the peak load, discharge efficiency, and the rated DOD. It was calculated by using Equation (3.6). There are two factors affect the size of the battery bank discharge efficiency and the rated DOD. For example, increasing the
battery Depth of Discharge or the battery efficiency can guarantee a decrease in battery bank capacity which causes a decreasing in the initial cost.
𝐵𝐵𝐺𝐺𝑡𝑡𝑡𝑡 𝑆𝑆𝐼𝐼𝑆𝑆𝐺𝐺 𝐺𝐺𝐺𝐺𝑟𝑟 =𝑠𝑠𝑠𝑠𝑠𝑠𝑡𝑡𝑠𝑠𝑚𝑚 𝑠𝑠𝑚𝑚𝑠𝑠𝑠𝑠Ƞ×𝐷𝐷𝐷𝐷𝐷𝐷 (3.6)
Battery wear cost calculation
Wear cost ($/kWh) is the key factor to analyze the impact of a batter to the EMS of remote microgrids. It was calculated by dividing the initial cost ($) by the lifetime throughput (kWh) as the Equation (3.7)
, , , initial ba lifetime avg dcr tt batt perk h g W
C
k
C
Wh
η
=
×
(3.7)Number of battery requirement:
Since one single battery is not enough to meet the system load. Therefore,
designing a battery bank is required. That means the battery bank has to contain a number of batteries in order to meet the system load. The number of batteries in the battery bank obtained from equation (3.8). The number of required batteries for the system is required to obtain the battery initial cost.
Battery initial cost:
The initial cost of the battery is very important to obtain the battery wear cost which means decrease the initial cost can reduce the battery wear cost. Therefore, can obtain the battery initial cost by multiply the number of battery required by the battery cost. Equation (3.9) present the initial cost of the battery. Increase the number of a battery will increase the battery initial cost.
Battery initial cost = # 𝐵𝐵𝐺𝐺𝑡𝑡𝑡𝑡𝐺𝐺𝐺𝐺𝐻𝐻 𝐺𝐺𝐺𝐺𝑟𝑟 × 𝐵𝐵𝐺𝐺𝑡𝑡𝑡𝑡 𝑆𝑆𝐺𝐺𝑅𝑅 (3.9)
3.2 Energy Management System
The objective of an EMS is to manage the power between the energy sources and loads in a remote microgrid. The EMS contains two layers (day ahead 24-hour scheduling and real-time dispatch) which are able to control and manage the behavior of the
generators and the operation of the battery (Figure 3.6) [10].
3.2.1 Day Ahead 24-Hour Scheduling
A day ahead 24-hour scheduling layer estimates the power of the dispatchable resources one day in advance in order to meet the minimum operational cost. This layer depends on three factors: available resources, load demand and the forecasting of the PV power.
The EMS algorithm which was developed by [10] contained two objectives: (1) to minimize fuel consumption, and (2) to minimize the throughput of the battery in order to prolong the battery lifetime. Meeting these two objectives are required in order to minimize the total operational cost. In order to achieve these goals at the same time, the EMS algorithm which depends on a goal programming approach is used. The goal programming approach can be presented by equation 3.10 where obj1 represents the fuel
consumption, obj2 prolongs the battery lifetime. W1and W2 are the weights [10].
1 1 2 2
(
)
obj=
W
×
obj
+
W
×
obj
(3.10)These weights decide the priority of obj1 and obj2. The sum of the weights should
equal (1) at all times. If both weights are equal [W1 (0.5) and W2 (0.5)], that means that
both objectives are equal. In the case that one of the weights is higher than the other weight, the objective with the higher weight is more crucial to achieving the overall goal.
3.2.2 Real Time Dispatch
The real time dispatch layer calculated the resource from day ahead scheduling and dispatch. This layer ensures the power balance by compensated any variation from the forecast (this thesis focus on 24-hour scheduling).
Figure 3.6 Energy management system diagram [10]
3.3 Determining Weights W1 and W2
The yearly analysis using a deterministic approach was considered in this study in order to determine the proper use of the generator and the battery through weights W1 and
W2.
The weights (W1 and W2) present the amount of use for batteries and generators in
the objective function. So, any set of weights provides the minimum operational cost was considered to achieve the optimization problem. The generator cost (obj1) includes the
generator’s hourly replacement cost and fuel cost. The battery cost (obj2) includes the
battery wear cost and the float life cost. Dispatch
According to the batteries data sheet, all the batteries lifetime throughput which considers it in this study were designated for a 10-year float life except lithium ion batteries which designated for 5-year. So, the yearly throughput has to be equal or more than the battery designated in order to be utilized. Otherwise, the battery will not be utilized and the battery float life cost will be different between designated and utilized throughout multiplied by wear cost of the battery.
The lowest set of the operational cost which obtained from weights was compared to the other different sets of weights in order to have the best-set of weight.
3.4 Optimization Problem
The objective function with weights W1 and W2 was presented in Equation 3.11
[10] which was obtained by using Equation 3.1 and equation 3.7. The PV operational cost, converters cost and the maintenance cost assume to be neglected since those are constant. The generators or the batteries can be the master unit in the system. In equation 3.11, W1 is the weight, which related to generator cost. On the other hand, W2 represents
the weight of the battery wear cost. The main objective of the optimization problem is to minimize the combined operational cost which depends on the use of a generator and battery. For instance, when W1 increased fuel consumption reduces because more
consideration was given to the generator. When W2 increase, more consideration was
given to the battery cost. That is causing an increase in fuel consumption and prolonging the battery lifetime by reducing throughput. Furthermore, the summation of W1 and W2
equal 1. So, W1 and W2 present the use of the battery and the generator. For example,
generator is low because it takes more weight, in that case, the battery throughput is high which lead to a decrease in the battery lifetime.
Combined objective function:
(3.11)
Where: (t) time, (n) number of generators, (Un,t) on, off generator mode, (Pn ) the
generator power output of n @ time t, (Cn) generator cost, (Cn,hrc) generator hourly
replacement cost $/hr,
3.5 Case of Studies
Batteries are categorized in three family Lead acid, lithium ion and unique batteries.
Simulation Process:
Calculate the key factors for each battery such as Battery wear cost, Battery Initial cost, kWh lifetime average, battery size requirement and the number of battery
requirement.
Validate the optimization model for different types of batteries considering fuel consumption, battery throughput, operation cost and battery Lifetime. This study used IBM ILOG CPLEX 12.1 software to solve the optimization model.
1 1 24 , ,hrc 2 ,24 1 1
min
(
)
N n n n N n t n batt hr t n
obj
W
C
P
U
C
W
C
= = =
=
×
+
×
+
×
∑
∑∑
3.6 Weight Selection Process
Step 1: Yearly scheduling (365 days)
– Calculate cumulative battery throughput
– Calculate cumulative fuel consumption – Calculate float life
– Calculate operational cost
Step 2: Change weights and repeat Step 1
Step 3: Select weight that provides minimum operational cost for each battery
Step 4: Compare the weight in step 3 to decide the best battery for this case study
Step 5: Find the best battery that can provide a better cost effective solution than other batteries.