**7. Simulation results**

**5.3. Annual operating costs**

12 Smart Microgrids

be given as follows.

the following equation:

simulation produces the hourly output from the DG as P<sup>D</sup>

*TOC* = *N*∑*PD*(*t*)

capital cost. This gives an annual operating cost of.

**6. Optimization problem formulation**

*PEN* <sup>=</sup> <sup>∑</sup>[*P*<sup>1</sup>

where *P*1 is Power from RER sources, *P*<sup>2</sup>

The only component that has significant operating costs is the DG, which requires fuel, oil for lubrication, and periodic maintenance. Each kWh of output from the DG requires 0.13 gallons of diesel fuel at a cost of \$2 per gallon, with an additional maintenance cost of \$0.05/kWh. The

the yearly energy output. For the entire plant lifetime, the total operating cost TOC can therefore

(0.13 gal \_\_\_\_\_

This quantity can be amortized using the same amortization factor that was applied to the

*AOC* = *TOC* · *CRF* (14)

In this section, the objective function is the total annual microgrid cost *ACS* as described in the

The nonlinear minimization is achieved with either the particle-swarm optimization (PSO) algorithm or the genetic algorithm (GA). PSO is used in situations in which no nonlinear con-

Some of the constraints are simply bounds on the variables. The minimum values for each number cannot be negative, for example, and the upper bounds are chosen to be large enough for the RER to meet a required percentage of the load. The percentage of the annual load that is met by the wind and solar energy is called the renewable energy penetration formed with

> (*t*) + *P*<sup>2</sup> (*t*) \_\_\_\_\_\_\_\_\_\_\_]

The system with DG backup has its own dispatch algorithm, which behaves differently than the grid-connected. This is because the DG on/off cycling incurs a maintenance cost. To avoid this, the rules for determining when to turn on and off the DG are designed to minimize the number of DG cycles. To ensure that there are no power losses, the minimum DG size is restricted to be equal to the peak load, such that the DG is capable of supplying the entire load with no RER assistance, if necessary. A nonlinear constraint is used to enforce a minimum

RER penetration. The optimization requires the GA, and it is summarized as follows:

<sup>∑</sup>*L*(*t*) × 100% (15)

is Power from battery, and *L* is total load.

previous section. This total annual cost is a nonlinear function of the parameters *Ns*

, and evaluating this function requires execution of the dynamic model.

straints are needed, while GA is used if there are constraints.

KWh <sup>2</sup> \_\_\_*\$*

gal <sup>+</sup> 0.05 \_\_\_\_\_ *\$*

(t), and summing this quantity gives

KWh) (13)

, *Nw*

, and *BCAP*

The MATLAB simulation results for the optimization are presented in this section, for two versions of the microgrid: grid-isolated with no backup, and grid-isolated with diesel backup. The hourly RER power is generated in the same way for two cases, using hourly TMY profiles for solar radiation and wind speed. **Figure 10** shows an example of the RER power, for 10 wind turbines and 100 solar panels, relative to the combined commercial and residential load. The upper plot in this figure shows hourly RER power produced for the entire year, and the lower figure compares the RER power to the load for 1 week. When RER power output is greater than the load, the excess production can be passed to the battery. When the RER power is less than the load, the battery must make up the deficit completely for the gridisolated with no backup scenario. If the backup is available, then these elements can be used in addition to the battery.

#### **7.1. Isolated microgrid with no backup**

The dynamic modeling results for the isolated microgrid with no backup are illustrated in **Figure 11**. This figure shows the hourly flows of power from the RER directly to the load, along with the power from the battery to the load. The RER and battery sizes must be large enough to meet the load each hour, and the figure illustrates that the combined RER and battery flows are equal to the load. This constraint forces the RER and battery sizes to be large enough to meet the highest demand in the year, which means that during most of the year the battery is not fully utilized. This fact is demonstrated by **Figure 12**, which shows the battery

charge level. The charge level remains high for most of the year, dipping down to low values

**Table 1** summarizes the results that optimize total microgrid annual cost for the residential load alone, the commercial load alone, and the mixture of the two. For comparison, the sum

When added separately, the sum of the residential and commercial load annual costs is \$91,320. If these loads are mixed, however, and are satisfied by a single, larger microgrid, then the annual cost decreases to \$86,000. This illustrates the cost advantage of combining the two loads into a single mixed load. Separately supplying the two loads would require more solar panels and a significantly larger battery capacity than supplying the mixed load, although the number of MTs remains the same. Because this simulation does not have diesel generator backup, the RER penetration is 100%. For each case, only about 25% of the RER output is transmitted to the load. This low percentage is due to the fact that the RER size must be chosen significantly large to meet peak yearly demand. This means that there will be many

When the grid-isolated microgrid is augmented with a DG backup system, this allows the relatively few hours of peak demand to be partially met with DG power. When the system is cost optimized, this leads to several interesting features. The size of the RER shrinks significantly, the usage of the battery becomes more regular throughout the year, and the percentage of RER power that makes it to the load nearly doubles. The trade-off for these improvements

**Figure 13** illustrates the power dispatching for a one-week period in January, showing the intermittent operation of the DG. The DG operates during unusually high loads, which can be

The DG removes the burden of meeting the peak loads from the battery, such that the energy levels in the battery can swing more uniformly over its full range throughout the year. This is illustrated in **Figure 14**, which shows the battery energy level together with the DG operation for the entire year of the simulation. The pattern in the DG clearly shows a seasonal compo-

**cost (×1000 \$) %RER output to the load %RER Pen.**

Renewable Energy Microgrid Design for Shared Loads http://dx.doi.org/10.5772/intechopen.75980 15

seen to occur as shown in **Figure 14** when the battery energy level dips to low levels.

nent, such that it operates more frequently during the heating and cooling seasons.

**(kWh)**

No backup Residential 388 6 621 46.20 28 100

Commercial 428 7 487 45.12 24 100 Sum 816 13 1108 91.32 26 100 Mixed 790 13 959 86.00 27 100

only during a few times of peak demand.

of the results for the residential and commercial loads is included.

hours of energy overproduction that cannot be utilized.

**7.2. Microgrid with diesel generator backup**

**Load** *Ns Nw BCAP*

**Table 1.** Optimization results for the isolated microgrid with no backup power.

is a reduced RER penetration.

**Figure 10.** Combined PV and MT output for N s = 100 and N w = 10 (top). Comparison between total PV and MT output and the combined load for 1 week (bottom).

**Figure 11.** Power dispatching for 48 h in January (grid-isolated no backup).

**Figure 12.** Battery storage level for isolated microgrid without backup.

charge level. The charge level remains high for most of the year, dipping down to low values only during a few times of peak demand.

**Table 1** summarizes the results that optimize total microgrid annual cost for the residential load alone, the commercial load alone, and the mixture of the two. For comparison, the sum of the results for the residential and commercial loads is included.

When added separately, the sum of the residential and commercial load annual costs is \$91,320. If these loads are mixed, however, and are satisfied by a single, larger microgrid, then the annual cost decreases to \$86,000. This illustrates the cost advantage of combining the two loads into a single mixed load. Separately supplying the two loads would require more solar panels and a significantly larger battery capacity than supplying the mixed load, although the number of MTs remains the same. Because this simulation does not have diesel generator backup, the RER penetration is 100%. For each case, only about 25% of the RER output is transmitted to the load. This low percentage is due to the fact that the RER size must be chosen significantly large to meet peak yearly demand. This means that there will be many hours of energy overproduction that cannot be utilized.

#### **7.2. Microgrid with diesel generator backup**

**Figure 11.** Power dispatching for 48 h in January (grid-isolated no backup).

**Figure 12.** Battery storage level for isolated microgrid without backup.

output and the combined load for 1 week (bottom).

14 Smart Microgrids

**Figure 10.** Combined PV and MT output for N s = 100 and N w = 10 (top). Comparison between total PV and MT

When the grid-isolated microgrid is augmented with a DG backup system, this allows the relatively few hours of peak demand to be partially met with DG power. When the system is cost optimized, this leads to several interesting features. The size of the RER shrinks significantly, the usage of the battery becomes more regular throughout the year, and the percentage of RER power that makes it to the load nearly doubles. The trade-off for these improvements is a reduced RER penetration.

**Figure 13** illustrates the power dispatching for a one-week period in January, showing the intermittent operation of the DG. The DG operates during unusually high loads, which can be seen to occur as shown in **Figure 14** when the battery energy level dips to low levels.

The DG removes the burden of meeting the peak loads from the battery, such that the energy levels in the battery can swing more uniformly over its full range throughout the year. This is illustrated in **Figure 14**, which shows the battery energy level together with the DG operation for the entire year of the simulation. The pattern in the DG clearly shows a seasonal component, such that it operates more frequently during the heating and cooling seasons.


**Table 1.** Optimization results for the isolated microgrid with no backup power.

**8. Summary and discussion**

**Load** *Ns Nw BCAP*

**(kWh)**

*DMAX* **(kW)**

**Table 2.** Optimization results for the isolated microgrid with DG backup power.

**DG on time (hr)**

Residential 214 2 228 35 588 67 21.00 49 82

Commercial 244 1 215 24 756 50 20.13 47 84

Sum 458 3 443 59 1344 117 41.13 48 83

Mixed 404 1 356 50 602 83 39.68 52 82

**DG Starts** **cost (×1000 \$)**

**% RER output to the load**

Renewable Energy Microgrid Design for Shared Loads http://dx.doi.org/10.5772/intechopen.75980

> **% RER Pen.**

17

of 30–40% of penetration.

**Figure 15.** Average cost per kWh versus RER penetration.

mercial costs.

To illustrate the effect of renewable energy penetration, the cost optimization is constrained to produce a result with a fixed penetration. This applies to the isolated grid with diesel backup. **Figure 15** shows a plot of cost per kWh versus penetration, under the assumption that the microgrid's non-renewable power component has a price of \$0.2/ kWh. Plots of the average cost per kWh for all three loads (residential, commercial, and mixed) are shown, where the average cost is computed by dividing the annual cost by the total annual load. As the renewable energy penetration approaches 100%, the cost of power from the microgrid becomes rapidly more expensive, approaching the case 1 result. As the renewable energy penetration decreases to zero, the cost of power approaches the price of nonrenewable energy, \$0.2/kWh. The minimum cost per kWh occurs in the range

**Figure 15** also shows the interaction between load mixing and renewable energy penetration. Below 70% penetration, the mixed load cost is between the residential and commercial costs. Above this threshold, however, the mixed load cost is below both the residential and com-

**Figure 13.** Power dispatching for 160 h in January (grid-isolated with DG backup).

**Figure 14.** Battery and DG operation for microgrid with backup.

**Table 2** summarizes the results that optimize total microgrid annual cost for the residential load alone, the commercial load alone, and the mixture of the two. The sum of the individual results for the residential and commercial loads is included as before, for comparison with the mixed-load results.

The largest effect of adding the DG backup to the microgrid is with the costs, which are about a fourth of the costs for the no-backup case. The optimization was made with the constraint of greater than 80% RER penetration, and it should be mentioned that the costs can be further reduced by lowering this constraint. Another large benefit in having the DG is the increase in the amount of RER power that makes it to the load, which doubles from roughly 25–50%. When comparing the sum of the costs of the residential and commercial loads to the cost of the mixed load, there is less of a difference than with the no-backup case. This indicates that the DG is reducing some of the benefit of mixing the loads.


**Table 2.** Optimization results for the isolated microgrid with DG backup power.

## **8. Summary and discussion**

**Table 2** summarizes the results that optimize total microgrid annual cost for the residential load alone, the commercial load alone, and the mixture of the two. The sum of the individual results for the residential and commercial loads is included as before, for comparison with the

The largest effect of adding the DG backup to the microgrid is with the costs, which are about a fourth of the costs for the no-backup case. The optimization was made with the constraint of greater than 80% RER penetration, and it should be mentioned that the costs can be further reduced by lowering this constraint. Another large benefit in having the DG is the increase in the amount of RER power that makes it to the load, which doubles from roughly 25–50%. When comparing the sum of the costs of the residential and commercial loads to the cost of the mixed load, there is less of a difference than with the no-backup case. This indicates that

the DG is reducing some of the benefit of mixing the loads.

**Figure 14.** Battery and DG operation for microgrid with backup.

**Figure 13.** Power dispatching for 160 h in January (grid-isolated with DG backup).

mixed-load results.

16 Smart Microgrids

To illustrate the effect of renewable energy penetration, the cost optimization is constrained to produce a result with a fixed penetration. This applies to the isolated grid with diesel backup. **Figure 15** shows a plot of cost per kWh versus penetration, under the assumption that the microgrid's non-renewable power component has a price of \$0.2/ kWh. Plots of the average cost per kWh for all three loads (residential, commercial, and mixed) are shown, where the average cost is computed by dividing the annual cost by the total annual load. As the renewable energy penetration approaches 100%, the cost of power from the microgrid becomes rapidly more expensive, approaching the case 1 result. As the renewable energy penetration decreases to zero, the cost of power approaches the price of nonrenewable energy, \$0.2/kWh. The minimum cost per kWh occurs in the range of 30–40% of penetration.

**Figure 15** also shows the interaction between load mixing and renewable energy penetration. Below 70% penetration, the mixed load cost is between the residential and commercial costs. Above this threshold, however, the mixed load cost is below both the residential and commercial costs.

**Figure 15.** Average cost per kWh versus RER penetration.
