*3.2. Energy Simulation Results*

Energy simulations were conducted to quantify the reduced energy demand of the combination of microgrid, renewable resources, and energy recovery measures.

Figure 6 shows the results obtained using di fferent software for the baseline scenario in di fferent climate zones. Maximum di fferences of 6% and 8% were obtained for the heating and net energy consumptions in Vancouver (BC), respectively. This confirmed the validity of the approaches followed in modeling.

**Figure 6.** Comparison between Energy Plus and HOMER Pro software for the current base camp practice (Baseline) for a 150-persons base camp in di fferent climate zones.

Table 5 reports the simulation results for the annual energy consumption of a 150-persons RTC in Brandon (MB) according to different scenarios: Baseline, Solar Hot Water, Microgrid: generators + battery, Microgrid with a Waste Heat Recovery (a counter-flow fluid heat exchanger), Microgrid with a PV array (100 kW PV system to cover peak electric loads excluding cooling), and the scenario with all SHES technologies integrated.

**Table 5.** Simulation results of the annual energy consumption for a 150-persons RTC in Brandon, MB, for the various scenarios with new technology solutions incrementally adopted.


The simulation results indicated that up to 37% fuel savings over current base camp configurations are achieved when all the SHES technologies are implemented for accommodating 150-person in a temperate climate (Brandon, MB) (Figure 7). Considered individually, the most impactful technology was found to be the Microgrid with a Waste Heat Recovery system (scenario 4), as evident in both Figure 7 and Tables 5 and 6.

**Figure 7.** Comparison between different utility systems scenarios: annual energy consumption savings over the current base camp practice (Baseline) for a 150-persons base camp in Brandon, MB.


**Table 6.** Summary of simulation results for the various scenarios for a 150-person RTC in Brandon, MB.

The technoeconomic assessment indicated that the implementation of all SHES technologies significantly reduced the net-present cost (life cycle cost), defined as the present value of all installation and operation costs over the project lifetime, excluding the present value of all the revenues earned over the same interval.

Actual components' costs were used in the analysis. The project lifetime was assumed to be 25 years, while an 8% nominal discount rate, defined as the simple interest rate on borrowed capital before factoring the inflation rates in, was assumed. The inflation rate was also assumed to be 2% over the project lifetime, and it was used in combination with the nominal discount rate to calculate the

real discount rate, which is used to convert between one-time and annualized costs. Finally, the fuel (diesel) cost was assumed to be \$1.00 for each liter.

Results indicated that up to 32% reduction in net present value over current base camp configurations are achieved when all the SHES technologies are implemented for accommodating 150-person in a temperate climate (Brandon, MB) (Figure 8). The levelized cost of energy was also significantly reduced by 25%, as indicated. Figure 8 shows that the SHES acts as the most cost-effective solution; thus, it is the optimal solution that maximizes the use of renewable-generated energy while minimizing the project lifetime capital costs. More importantly, it is evident that the consideration of individual technologies eliminated the benefits gained from the integrated solution, and in fact, most technologies (except for WHRU), although reducing the net-present costs compared to the baseline, produced increased levelized energy costs due to their high initial costs and limited energy savings and utilization of all available energy resources when compared to SHES.

**Figure 8.** Comparison between different utility systems scenarios: net present cost and levelized energy cost over the current base camp practice (Baseline) for a 150-persons base camp in Brandon, MB.

Diesel costs may vary overtime and by the region it is sold depending on the cost of crude oil. Similarly, interest rates fluctuate overtime, being influenced by the economic growth, fiscal and monetary policies, and inflation rates. Thus, it is critical to design an optimal system in terms of life cycle costs at the expected fuel and interest rates.

A sensitivity analysis was conducted to evaluate the proposed system due to variations in the nominal discount rates and diesel costs (Figure 9). As evident, SHES acts as the optimal system design for the widest range of fuel and simple interest rates, assuming a constant inflation rate of 2%. In particular, the baseline configuration is only considered cost-effective at combined low fuel rates and high simple interest rates. It is also evident that the SHES's selected photovoltaics, energy storage, and converter capacities are optimal at the specified fuel cost and interest rates of \$1/L and 8%, respectively. On the other hand, reducing these capacities would act as a more cost-effective solution at combinations of low and high fuel and simple interest rates, respectively.

**Figure 9.** Optimal system design for variations in diesel price and nominal discount rate.

As discussed earlier, only PV capacities up to 100 kW, which sufficiently covers the camp peak electric loads excluding the cooling loads, were considered to reduce initial costs and due to spatial and logistics purposes. Also, various battery and converter models and capacities were considered, and the most cost-effective configurations were selected considering the preselected PV size. Figure 9 shows that the optimal battery capacity is strictly influenced by the selected PV capacity, thus, using a lower PV capacity would imply using a smaller battery capacity except in cases where the interest rates are incredibly high, for which investing in energy storage would not be cost-effective at all. Similarly, the converter capacity is dependent on the selected PV capacity. However, the rate of capacity reduction is higher than that of the battery, particularly at moderate interest rates (8%–12%), driven by high converter capital costs when compared to energy storage and PVs.

One of the main challenges towards the development of isolated microgrids is the managemen<sup>t</sup> of the various devices and energy flows to optimize their operations, particularly regarding the hourly loads that must be served, and the availability of power produced by renewable energy systems depending on daily and seasonal variations. The SHES combines the existing diesel generators with solar power generation, energy storage, and waste heat recovery technologies, all connected to a microgrid, ensuring uninterrupted electricity and hot water supplies. The reliable, energy-e fficient system helps to manage generator output. By transforming an independently operating system of generators into a demand managed microgrid, SHES provides power only where and when it is needed, instead of completely relying on fuel-burning generators. A critical part of designing SHES was understanding the electric and thermal load and generation profiles to identify the most cost-e ffective energy managemen<sup>t</sup> strategy while maximizing the renewable generation, without significantly increasing the initial costs of system while considering army spatial and logistic requirements. It is crucial to identify the parts of the system that carry these loads at di fferent times of the day and di fferent seasons, particularly at peak loads. The peak electric load typically occurs during the warmest period of the year due to increased cooling loads, while thermal loads during the same period would be low due to the absence of heating requirements. For these purposes, various dispatching strategies were considered for energy managemen<sup>t</sup> purposes of controlling generator and battery operation in periods of insu fficient renewable energy to supply the load, including "cycle charging" and "load following" strategies. A cycle charging dispatching strategy was found to be the most cost-e ffective. The cycle charging strategy implies that the generator runs at its maximum power output when it is needed to serve the electrical loads, while any surplus electrical production is diverted towards charging the battery until the battery setpoint state of charge of 80% is reached. This is accomplished by selecting the optimal combination of power sources, based on fixed and marginal costs, to serve the electric and thermal loads at the minimum cost and excess electricity production, while still satisfying the operating reserve requirements. On the other hand, a load following strategy, which implies that the generator produces enough power only to serve the load while the battery is charged by the renewable sources, would be more cost-e ffective in situations where the renewable generation is comparable to the magnitude of the served load.

The results of the control strategy can be observed in Figures 10 and 11, which show the electric and thermal and the generation profiles for the summer and winter peak demand days, respectively. The electric load served is initially constant and relatively low during early and late hours of the summer days and in the absence of solar radiation (Figure 10). Thus, this low electric load is satisfied solely by the energy stored in the battery while the generators are o ff. As the electric load starts to increase, the generators are turned on to satisfy parts of these loads, while the remaining parts are satisfied using the PV-generated power. To reduce the generator runtimes, at several intervals of the day, the reliance of the generator is reduced and eventually eliminated. At the same time, the loads are still being served by the energy stored in the battery during the previous hours. For the winter day, similar trends can be seen. However, due to lower electrical loads and increased PV generation as a result of a lower sun altitude, after the generators are turned on to serve partial loads and charge the battery, they are turned o ff for extended periods of the day, thus significantly reducing fuel consumption by relying on renewable resources. The generator's runtime was reduced considerably to 4600 h (48% reduction) compared to 8760 h for the baseline configuration. It is also evident that the system could benefit from a greater PV size due to the high availability of solar radiation that is not being taken advantage of in both summer and winter months.

**Figure 10.** SHES dynamic electric load managemen<sup>t</sup> for a 150-persons base camp in Brandon, MB, in summer and winter peak days.

In Figure 11, the thermal loads are very low in summer due to the absence of heating requirements while some heating is only required during night times, which suggested increasing the insulating properties of the tent fabrics. In the winter, the peak thermal heating load is served mainly using the diesel boiler, which is supplemented by the waste-to-heat recovery system that uses the generator's heat to warm up the water delivered to the terminal fan–coil units. This heat is drawn from the generator only when it is running to serve the electrical loads during the winter; therefore, the WHRU system can only serve a part of the daily thermal load, which highlights the importance of considering synergies and di fferences between the di fferent seasonal loads and designing a cost-optimal system in regards to full-year expected loads and availability of resources. It is also evident that a significant amount of excess heat is wasted in the summer months due to the absence of a simultaneous end use, thus, a heat storage system might be beneficial to store the heat and allowing for its use when needed in the colder months or in cold summer nights.

The SHES solution for the camp was also simulated in di fferent geographic locations, to evaluate its performance, feasibility, and expected energy savings outcomes in di fferent climate zones. In particular, the analysis involved the city of Vancouver (British Columbia, Canada), Kanoya (Japan), Churchill (Manitoba, Canada), and Changi (Singapore). Results indicated that fuel reductions from 21% up to 39% are also achievable for extremely hot and frigid climates when the solar collectors' tilt and orientation are optimized for the specific location (Table 7). It is important to note that the solution was not reoptimized for di fferent locations except for the PV and SHW tilt angles, and component and system sizes were kept constant to satisfy the army requirements of standard sizing.



\* The heat recovery system is not required for the Changi climate zone due to the extremely low heating demand.
