1. Introduction
Humanity’s desire for electric energy is expanding continuously with industrialization and global population growth. Around fifty percent of our total energy comes from burning fossil fuels. Still, 1.2 billion or more people around the world do not have access to modern energy supplies. People are adopting renewable energy sources (RESs) to accomplish their energy needs. However, it is reported that the intermittent nature of RESs has an adverse impact on the energy network. Therefore, the high penetration of RESs in the energy hub is inappropriate and challenging [
1].
The world is currently searching for sustainable energy resources to satisfy today’s energy market. The energy resources that satisfy the present demand without compromising the future generation capability are regarded as sustainable energy sources. The term sustainable energy is often used interchangeably with green energy or renewable energy [
2]. Therefore, RESs, such as wind, solar, hydropower, geothermal, and ocean energy, are identified as sustainable energy generation sources. Due to the intermittency of RESs, they can hardly provide a continuous electricity supply. Hence, alternative energy sources are required, which can act as a critical load or baseload supplier during RESs unavailability.
Affordable, resilient, and carbon-free electricity generation is the most vital determinant of a sustainable energy system. Mostly, RESs are recognized as carbon-free energy resources to meet electric demand. Due to the zero direct carbon emissions of nuclear plants [
3], there is a worldwide tendency to move towards nuclear energy. The hybridization between RESs and nuclear reactors could result in a carbon-free, reliable, and innovative energy infrastructure.
Public perspectives on nuclear energy vary widely from country to country. A survey showed that 49% of the U.S. public supports the use of nuclear power, whereas 49% oppose its usage; 2% had no comment on nuclear energy use [
4]. After the Fukushima-Daiichi nuclear disaster in 2011, Germany has announced plans to phase out 10 of 17 nuclear plants from 2011 to 2017 and shut down the remaining nuclear facilities by 2022. It is estimated that the nuclear plant phase-out policy will cause more than 1100 new deaths in Germany due to air pollution [
5]. A study reported that around two million lives could have been saved if fossil fuel-based generation were replaced by nuclear [
6].
A micro energy grid (MEG) is a new form of microgrid (power grid) that supplies both electric and heat energy simultaneously [
7]. MEGs add a combined heat and power (CHP) unit to provide thermal energy. They combine different types of fossil-fired energy generation resources and RES and are located at the consumer end of the supply chain. The “distributed generation (DG)” concept is applied in MEGs. A MEG is considered a potential solution for low-cost energy supply and reduction of greenhouse gas (GHG) emissions. Uniqueness, heterogeneity, interactivity, controllability, and independence are the key features of MEGs. Energy storage systems are included in the MEG to ensure the energy grid’s stability and reliability. The MEG can perform in both grid-connected mode and islanded mode [
8,
9].
Several research studies have been carried to identify the optimal system configuration of hybrid energy systems (HESs) to provide a resilient electric supply. Giatrakos et al. predicted the viability of a photovoltaic (PV)/diesel genset/batteries-based HES [
10]. Mohammed et al. optimized a wind turbine (WT)/tidal turbine/PV panel/batteries-based HES to provide a reliable electricity supply to a distant area in Brittany, France. The particle swarm optimization (PSO) method was used as an optimization technique in [
11]. Ming et al. proposed optimal design methods for a PV/WT/batteries-based HES for both grid-connected and islanded modes of operation. A multi-objective evolutionary algorithm (MOEA) had been used in [
12] to minimize system cost and fuel emissions. An and Tuan proposed a HES optimization method based on a dynamic programming method to reduce system costs for a location in Vietnam [
13]. Al-Masri et al. addressed the advantages of the inclusion of pumped hydro storage with WT for the Jordanian utility grid. Al-Masri et al. reported that emissions and grid purchase reductions were 24.69% and 24.68% for the WT/pumped hydro-based HES of the project location [
14]. Halabi et al. studied different configurations of HES for the area of Sabah (Malaysia) using the HOMER Pro software. The study demonstrated that HES, consisting of PV/diesel/batteries, showed the best result in terms of economic, environmental matrix, and sustainability [
15]. Razavi et al. carried out a case study comprising of electricity-only units, heat-only units, and CHP units to determine the optimal operating point of CHP units. The research was conducted in the General Algebraic Modeling System (GAMS) software based on mixed-integer nonlinear programming (MINLP) [
16]. Hu et al. proposed a modified optimized algorithm, a combination of PSO and a genetic algorithm (GA), to obtain the optimal configuration of a wind/solar/hydro-based CHP hybrid system based on heat-electric coordinated dispatch [
17].
Several studies have been conducted to integrate large/medium-scale nuclear reactors and RESs. Suman described an overview of a nuclear-renewable (N-R) hybrid energy system in [
18]. The author indicated the challenges of RESs and nuclear energy while integrating two practically zero GHG emissions sources. The author also discussed the varieties of N-R integrated systems, hybridization features, benefits of the N-R system, and financial conditions.
Ruth et al. classified six types of interconnection process for nuclear-renewable integration: electrical, thermal, chemical, hydrogen, mechanical, and information. The challenges, research, and development aspects of nuclear-renewable hybrid energy systems are presented here. The authors also address environmental perspectives, storage management, security and nuclear fuel disposal sites [
19].
Sabharwall et al. observed three possible cases of nuclear-renewable integration for financial analysis. The cases were: (1) a standalone nuclear generation system, (2) a nuclear/wind generation system, and (3) a nuclear/wind/hydrogen generation system. A sensitivity analysis was conducted by varying the discount rate, depreciation rate, and energy market to compare the net present value (NPV), internal rate of return (IRR), cost of energy (COE), and payback period of the three cases. It was inferred that the nuclear/wind/hydrogen system could be a profitable project for future energy generation [
20].
A comprehensive research and development program on dynamic modeling, simulation, component development, and testing of the nuclear–renewable hybrid energy system (N-R HES) had been articulated in [
21], which provided a useful background to support the analysis of N-R HES. The N-R HES had been categorized into three classes: tightly coupled N-R HES, loosely coupled N-R HES, and thermally coupled N-R HES. The possible advantages of N-R HES involve GHG-free electricity generation, a reliable energy network, and lower COE. The integration of a small modular reactor (SMR) is also viewed as future research. It is expected that N-R HES infrastructure will be demonstrated by 2030.
Baker et al. [
22] quantified the benefits of a flexible nuclear hybrid energy system (NHES) integrated with the grid. A small modular reactor (SMR), battery storage, wind generation, and a desalination plant were studied within a NHES. SMR was regarded as the primary generation source, and the size of SMR (300 MWe) had been discussed in this study. The authors concluded that battery investment was only justifiable for higher levels of renewable energy penetration.
This paper intends to integrate the microreactor (MR) concept with RESs to meet the medium/large-scale off-grid demand. The analysis shows that the integrated MR has the techno-economic potential to replace the traditional diesel genset, reducing the GHG emissions significantly. The paper is organized as follows:
Section 2 develops the diesel genset/MR-based MEG considered here.
Section 3 presents the detailed system modeling.
Section 4 addresses the key performance indicators (KPIs) studied in this paper.
Section 5 formulates the optimization problem and implements it in the HES.
Section 6 presents the results of the study. Finally,
Section 7 concludes with a summary and discussion of this research.
2. Proposed Micro Energy Grid
A typical MEG, consisting of diesel genset, solar PV panel, WT, hydropower, and a biogas generator (BG), is developed in this study. In this study, the diesel generator is used as a surrogate component of conventional fossil-fired generation technology to compare with a MR-based MEG for off-grid applications. Ontario Tech University (UOIT) was selected as the project location since the electric load data are available to the authors for analysis, and the energy demand for UOIT is considerably high. The resource data and load data are collected for the UOIT campus. The schematic of the developed MEG is presented in
Figure 1.
The electrical demand is met by a diesel genset, PV panel, WT, hydro turbine (HT), and BG, while the thermal load is served by heat recovered from the diesel genset and BG. A CHP unit is utilized in the diesel genset and BG to recover the waste heat. The heat-to-electricity (H2E) unit converts the surplus thermal energy into electricity if needed, while the electricity-to-heat (E2H) unit produces thermal energy from excess electricity. The H2E and E2H are inserted to ensure the ultimate reliability of energy supply; however, these units will have the least preference and will be operated only in extreme cases.
Electrochemical energy storage (EES) and hydrogen storage are employed in the MEG to store the electric energy. Thermal energy storage (TES) is also inserted into the system to store thermal energy. The energy management algorithm for the studied fossil-fired MEG is illustrated in
Figure 2.
For electrical energy management, the surplus electrical energy will be stored in hydrogen tanks and EESs. If there is still excess energy after charging the hydrogen tanks and EESs, and there is a thermal energy requirement, it will be utilized to meet the thermal demand through the E2H unit. Electric dump loads will consume the additional excess electric energy. Likewise, during the shortage in electric demand fulfillment, hydrogen tanks and EESs will be discharged to meet the electricity requirement. The H2E unit will be used if excess thermal energy is available and the storage cannot support the deficit electric demand.
In thermal energy management, the excess thermal energy will be stored in TES. If there is any electrical demand deficit, the further excess heat will be supplied to serve the electric demand via the H2E unit. Thermal dump loads will consume the rest of the available surplus thermal energy. Conversely, TES will fulfill the thermal energy shortage by discharging the TES. Any further thermal supply deficit will be met by using E2H. Both H2E and E2H have the least precedence in the energy management algorithm.
To compare the diesel genset with MR within a MEG, the diesel Genset is substituted by an MR. The same energy management algorithm is accompanied in the MR-based MEG. The schematic and the energy management algorithm of the MR-based MEG are represented in
Figure 3 and
Figure 4, respectively.
6. Results
The diesel-based MEG and MR-based MEG are denoted as “Case-01” and “Case-02”, respectively, in this paper. The results of the optimal configuration of Case-01 and Case-02 are recorded in
Table 3. The NPC of Case-01 (
$332.85 million) is roughly four times higher than the NPC of Case-02 (
$79.33 million). Though a comprehensive energy-flow model has been exercised in the diesel-fired MEG to reduce the system cost, the NPC of Case-01 is still considerably higher compared to Case-02. The PSO optimization results suggest three types of gensets, PV panels, WT, hydro plant, hydrogen storage, EES, and TES for Case-01. The optimal system intends to utilize multiple small-size generators rather than using large-scale genset. A single 50 kW generator unit is also added to the optimal Case-01. On the other hand, PSO suggests to include three MR units for Case-02. PV panels, WT, hydro plant, hydrogen storage, EES, and TES are also included in the optimal Case-02. Since hydrogen storage is adequate to facilitate the electric demand, EES’s inclusion is not recommended for Case-02. The results do not insert the E2H and H2E units within the optimal system configuration for both cases. The PSO includes the same size of TES for both cases.
Table 4 lists and compares the KPIs of Case-01 and Case-02. Both cases confirm the maximum resiliency within the defined reliability constraint limits. If
is higher than 100% and remain close to 100%, the system is regarded as reliable. Due to the reduced size of diesel gensets, Case-01 is more reliable in terms of
and
. But,
and
have better values for Case-02 compared to Case-01. Moreover, the LCOE of Case-01 (0.4879
$/kWh) is approximately four times higher than Case-02 (0.1163
$/kWh).
Figure 11 illustrates the PSO convergence plot of Case-01 and Case-02 for the best independent run, among 100 runs.
Figure 12,
Figure 13,
Figure 14 and
Figure 15 demonstrate the electric and the thermal energy generation (excluding storage charging power) and consumption plots for Case-01 and Case-02. The electrical and thermal generation sources, accompanying with the energy storage systems, favorably serve the electric and thermal demand in both cases. A few amounts of energy deficiency or surplus happen due to the allowable defined limits of the
and
constraints.
Figure 12,
Figure 13,
Figure 14 and
Figure 15 validate the effectiveness of the energy management algorithm.
Since both arrangements, Case-01 and Case-02, are dependent on diverse determinants, it is required to carry out a sensitivity analysis by considering the most influential factors. The later sub-sections present the sensitivity analysis in detail for both cases. This sensitivity analysis’s primary purpose is to verify the results obtained from the base case comparison between Case-01 and Case-02. It should be remarked that the cost of environmental impact and GHG emission is not considered in this study.
Table 5 compiles the main idea of the sensitivity assessment carried out in this section.
6.1. Assessment of Sensitivity to Shifting Daily Peak Demand
A sensitivity analysis is conducted in this section to evaluate the impact of shifting the daily peak demand. Although the peak usually occurs at the mid of the day, it may occur at the beginning or end of the day. Therefore, the baseload profile (both electric and thermal) has been shifted by 12 h in this case to reflect the peak variation in system demand. The shifted electric and thermal load profile is presented in
Figure 16a,b, respectively.
Figure 17 represents the comparison of the NPC for Case-01 and Case-02 for the shifted electric and thermal demand. The sensitivity analysis shows that Case-01 has substantially higher NPC than Case-02 in all scenarios, referring Case-02 is more profitable than Case-01. Since the reliability KPIs are employed as constraints in the optimization problem, the system reliability is maintained in each analysis.
6.2. Assessment of Sensitivity to Shifting of Seasonal Demand
Another sensitivity analysis is conducted here by moving the seasonal demand by six months since it differs from region to region. The yearly peak demand (electric and thermal) occurs around August in the base cases, but the annual peak happens at the beginning of the year (January/February) for seasonal shifted load profile, shown in
Figure 18.
Figure 19 highlights the sensitivity analysis results for Case-01 and Case-02 due to the variation in electric demand, thermal demand, or both. The results affirm that the NPC of Case-02 is still less than Case-01 for all situations. The sensitivity results signify that Case-02 has more financial advantages than Case-01. The annual peak load variation does not widely affect the NPC. Thus, the NPC indicated in
Figure 19 for Case-01 and Case-02 are approximately the same as the base case value.
6.3. Assessment of Sensitivity to Variation in Average Energy Demand
The dynamic behavior of electric and thermal demand may cause the average demand fluctuation due to inclusion or load reduction. Thus, the average electric and thermal demand is altered by ±10% in this sub-section to evaluate NPC’s sensitivity for both cases.
Figure 20 presents the NPC of Case-01 and Case-02 due to the modified electric, thermal, and both electric and thermal demand, sequentially. The results again show the cost-efficiency of Case-02 over Case-01; Case-02 has less NPC than Case-01 regardless of the increase or decrease of electric demand, thermal demand, or electrical and thermal demand. The increment of electric demand forces to include more generators; hence, the NPC increases with the rise of electric demand for both cases, as depicted in
Figure 20a. Due to the CHP capability and less influence of TES on total NPC, thermal variation does not alter the NPC significantly, illustrated in
Figure 20b. Since
Figure 20c simulates both electric and thermal load variation, the system NPC increases with the expansion in system demand. It should be noted that the CHP cost is included within the capital cost of the equipment. The PSO only determines the required efficiency of the CHP unit. Hence, thermal demand variation slightly affects the system NPC.
6.4. Assessment of Sensitivity to Variation in System Equipment Cost
This section intends to investigate the consequence of the component’s cost on the total system economy. This section also examines whether Case-01 has less NPC than Case-02 at any point of study due to the variation of component cost.
Figure 21 shows that the 30 kW genset and 20 kW genset have the highest impact on system NPC; it is apparent since multiple units on 30 kW generators and 20 kW generators are installed within the HES. Due to a single unit installation of 50 kW genset, it has less impact on total system NPC. The EES, electrolyzer, FC, 50 kW genset, and hydropower plant also have a moderate influence on NPC. The rest of the components have a limited impact on the NPC.
Figure 22 examines the details of the most influential cost contributors, 30 kW and 20 kW gensets, in Case-01.
Figure 22 points that the fuel cost of 30 kW genset has the most contribution in the variation of the NPC, followed by fuel cost of 20 kW, O&M cost of 30 kW, and O&M cost of 20 kW, respectively. The capital cost and the replacement cost of the generators are trivial compared to the other investments.
On the other hand, the MR is the primary driver in NPC variation for Case-02, shown in
Figure 23. FC, electrolyzer, and hydro plant also have a reasonable impact on NPC. TES and PV panels also affect the system economy, depicted in
Figure 23. Since MR is the primary driver of increasing or lowering the NPC, MR total cost is viewed in detail in
Figure 24. The different costs of MR, e.g., overnight capital cost, refueling cost, fuel cost, and decommissioning cost, is varied by ±20% of their base prices to recognize the main contributor of MR total cost.
Figure 24 illustrates that overnight capital cost is the primary driver in changing MR total cost, while the other expenses are trivial.
By analyzing the discussion stated above, Case-02 is an extensively profitable system compared to Case-01. The NPC of Case-01 is always higher than Case-02 and not comparable at any point of cost variations.
6.5. Assessment of Sensitivity to Variation in Project Lifetime
Project lifespan is a vital decision-making parameter for HES. Some HESs are profitable for a short project lifespan, but they may not beneficial for a longer project duration or vice-versa. Hence, the project lifetime is used in this section as a sensitivity input parameter to evaluate the impact of project lifetime on NPC.
Figure 25 points out the NPC of Case-01 and Case-02 for different project lifetime. The project duration is varied from 20 years to 100 years. The rate of changes in NPC is very low for a higher project lifetime, implying a good investment for the longer project duration. However,
Figure 25 tells that the Case-02 has lower NPC than Case-01 regardless of the project lifetime.
6.6. Assessment of Sensitivity to Variation in Renewable Resources
The solar irradiance, wind speed, and streamflow may rise or fall at any time. Hence, another sensitivity analysis is handled here by changing the solar irradiance and the wind speed by ±10%. The purpose of this sensitivity assessment is to evaluate if Case-01 is analogous to Case-02 at any point of resource alteration. The sensitivity assessment is not conducted for streamflow since it does not fluctuate much throughout the year for small-scale run-of-river hydro plants [
96].
The optimization algorithm suggests the number of generation components based on resource availability. If any resource availability is reduced, the optimization either chooses another generation source or incorporates more similar elements to fulfill the demand. The PV panel and WT are recognized as the least contributor to NPC in the earlier sensitivity analysis. Therefore, due to either solar irradiance or wind speed’s unavailability, the optimization will either select other high-priced generation sources or add more WT and PV panels. Thus, the NPC is increased with the decrease of solar irradiance and wind speed pointed in
Figure 26. Similarly, if the amount of solar irradiance and wind speed is increased, the PSO optimization will avoid including a large number of PV panels, WT, and high-cost generation sources. Hence, the NPC will be decreased, as illustrated in
Figure 26. Though the NPC is failing with the increase of solar irradiance and wind speed, the NPC of Case-02 is still not comparable to the NPC of Case-01. Case-01 has a higher NPC than Case-02 for all cases, presented in
Figure 26. It should also be noticed that the wind speed variation strongly affects the NPC, compared to the change in solar irradiance, due to the less installation cost of WT.
6.7. Assessment of Sensitivity to Variation in PV panels and WT Availability
This sub-section assesses the variation in the NPC for Case-01 and Case-02 due to the alteration of PV panels and WT availability. The number of installed PV panels and WT depends on the project location’s space availability, user requirements on renewable energy fraction, and the project site’s transport facility. The NPC changes with the inclusion or reduction of the PV panels and WT. Therefore, this sub-section investigates the consequence of variation in PV panels and WT availability for the cases. The run-of-river system is kept outside of this analysis since it is not easy to increase the hydroelectric plant size instantly. The BG is also not examined in this part as the generation in BG depends on the number of available cattle.
The maximum and minimum limits of available PV panel and WT alter the optimization results. Therefore, the maximum limits of the PV panels and the WT have been adjusted from −50% to 500% here to reflect PV panels and WT availability variation. The negative sign implies reduction.
Figure 27 depicts the NPC variety due to differences in PV panels availability, WT availability, and both.
Figure 27a shows that the NPC of Case-02 does not change much due to the changes in PV panel availability since MR is the main contributor in the system economy. It also implies that the optimization does not include more PV panels in the optimal MR-based HES, even if the PV panel availability is extended. The NPC of Case-01 decreases with the increase of PV panel availability. However, the NPC also does not change significantly for Case-01 for the increased number of PV panel availability, signifying PV panel requirement is limited for the optimal diesel-fired MEG (Case-01).
Figure 27b shows that the NPC starts decreasing with the increased number available WT for both Case-01 and Case-02. However, the NPC reduction sustains for a particular range of WT availability; the NPC does not vary beyond 200% changes of WT for Case-01. Due to the lower installation cost and reasonable energy conversion efficiency of WT, compared to solar PV panels, the PSO optimization includes more WT rather than adding the PV panel in this case. Since the increased availability of WT includes more WT and discard PV panels, the NPC is reduced. The corresponding NPC values in
Figure 27b for 200%, 300%, 400%, and 500% changes in WT availability should not differ, but these values fluctuate a bit due to the iterative PSO algorithm.
Figure 27c presents a similar variation, like
Figure 27b, in the NPC for Case-02 and Case-01 since both the PV panels and WT availability have been changed in this case. The degree of changes in NPC is higher in
Figure 27c due to the combined effect of both type changes.
By analyzing
Figure 27, Case-02 is always more competent in accomplishing the demand. Another finding from this study is that installing a massive number of renewable sources, such as PV panels and WT, may not profitable for HES; optimal planning is mandatory for these kinds of systems. This analysis also verifies that the assumptions, made for the variable’s limits in optimization problems, are conservative.