Design Of Hybrid Energy System Research Articles

Thermodynamic and economic analysis of a novel solid oxide fuel cell, two level thermoelectric generator and double effect parallel absorption chiller integrated system

This study presents a comprehensive thermodynamic and economic analysis of a novel hybrid energy system integrating a Solid Oxide Fuel Cell (SOFC) with a Two-Level Thermoelectric Generator (TTEG) and a Double-Effect Parallel Absorption Chiller (DEPAC) for enhanced waste heat recovery. The primary objective is to maximize energy utilization and overall efficiency by leveraging the synergetic effects of these components. Unlike previous studies, this research uniquely evaluates the effectiveness of the integrated system and examines the impact of different Absorption Chiller configurations and the number of TTEG elements, filling a critical gap in the literature. The proposed system achieves a net electrical efficiency of 42.88 %, an energy efficiency of 44.61 %, and an exergy efficiency of 57.16 % under optimal conditions. This marks a significant improvement compared to standalone SOFC systems, with the TTEG effectively harnessing waste heat and contributing an additional 0.680 kW/m2 to the overall power output. The integration of the DEPAC system further enhances performance by providing cooling with a Coefficient of Performance (COP) of 1.351. The economic analysis reveals a positive Net Present Value (NPV) of $1,993,136, a Dynamic Payback Period (DPP) of 6.7 years, and a Levelized Cost of Electricity (LCOE) of $59/MWh, underscoring the system's financial viability. These findings demonstrate that the hybrid system offers a sustainable and efficient solution for power generation and waste heat recovery, aligning with the broader goals of advancing renewable energy technologies and setting a new benchmark in hybrid energy system design and optimization.

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

Hybrid Energy System Design for Enhanced Sustainability in Rigolet, Newfoundland and Labrador

Hybrid Energy System Design for Enhanced Sustainability in Rigolet, Newfoundland and Labrador

Design of small-scale hybrid energy systems taking into account generation and demand uncertainties

The adoption of energy systems powered by renewable sources requires substantial economic investments. Hence, selecting system components of an appropriate size becomes a critical step, which is significantly influenced by their distinct characteristics. Furthermore, the availability of renewable energy varies over time, and estimating this availability introduces considerable uncertainty. In this paper, we present a technique for the optimal design of hybrid energy systems that accounts for the uncertainty associated with resource estimation. Our method is based on stochastic programming theory and employs a surrogate model to estimate battery lifespan using a feedforward neural network (FFNN). The optimization analysis for system design was conducted using a genetic algorithm (GA) and the poplar optimization algorithm (POA). We assessed the effectiveness of the proposed technique through a hypothetical case study. The introduction of a surrogate model, based on an FFNN, resulted in an approximation error of 9.6 % for cost estimation and 20.6 % for battery lifespan estimation. The probabilistic design indicates an energy system cost that is 25.7 % higher than that obtained using a deterministic approach. Both the GA and POA achieved solutions that likely represent the global optimum.

Integration and performance analysis of optimal large-scale hybrid PV and pump hydro storage system based upon floating PV for practical application

The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure.

Design of Intelligent Hybrid Energy Ambulatory Surgical Center Ship in Efficient Way

An Ambulatory Surgical Center (ASC), also known as an outpatient surgery center, is a medical facility that provides same-day surgical procedures to patients who do not require an overnight stay in a hospital. ASCs are equipped with state-of-the-art medical equipment and staffed by highly trained healthcare professionals, including surgeons, anaesthesiologists and nurses. There are many areas along the river where people do not have easy access to any surgery. They have to go far to get this service, for which they do not even take the service. This research is for them. This research will ensure their basic medical services. The contribution of this research, this world-class surgical ship goes to ensure the basic medical needs of poor people in remote areas where good healthcare is not available. Another great contribution that will surprise the world is that no conventional fuel is needed to ensure this healthcare service. So undoubtedly this research has a great contribution in the global energy crisis. To ensure efficient ambulatory surgical center (ASC) ship, conducting site assessment, sizing the hybrid system appropriately, selecting the right components, improving hybrid energy system design, implement intelligent system is important. And these are the scope of our research. A detailed analysis of all possible aspects has shown that using intelligent hybrid energy ships instead of conventional ships improves the quality of service. Long lasting service is achieved with low energy consumption, sustainability and no pollution even without compromising performance. In this research details of ASC will be critically discussed.

Multi-objective optimization of Hybrid Energy Systems based on Life Cycle Exergy and Economic criteria

The present study aims to develop a novel optimal design of hybrid energy systems based on exergy and lifecycle concepts using genetic algorithms. The model consists of both stand-alone and on-grid options with scenarios for exchanging energy with the grid. The objectives include cost minimization or benefit maximization primarily, and lifecycle exergy efficiency, i.e., cost as the sustainability index secondarily. This research considers renewable sources such as solar, wind, hydropower, and hydrogen production and storage in addition to conventional diesel generators. The optimization was performed subject to weather conditions and solar radiation profiles, demand, and environmental or economic aspects. Also, the model contains various modules such as water-heating, waste energy utilization, as well as the options of power exchange with the distribution network and injection of hydrogen produced from excess renewable sources into the gas network. The application was demonstrated in a case study, where specific demands and the climate of Tehran were assumed. The case study considers four scenarios, including standalone, completely on-grid, on-grid with a non-backup generator, and on-grid without an energy sale option. The first optimal objective, the levelized unit cost of energy for the standalone system, is $0.22 per kWh. Moreover, the second optimal objective, the lifecycle exergy cost, ranges from 1.93 to 4.13 in different grid-connection states.

Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications

This paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches Algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases including avalanche due to mountain slope, human factors, weather in the region as well as normal conditions and it has only one control parameter. The advantages of this algorithm are low control parameters, simple structure and also easy implementation. The effectiveness of the SAA algorithm is examined on 23 classic benchmark test functions. Then, the effectiveness of the SAA to achieve accurate results in different aspects is examined and proved on engineering problems including six different cases. The superiority of the SAA to solve the classic benchmark test functions is compared with spotted hyena optimization (SHO), particle swarm optimization (PSO), Aquila optimizer (AO), differential evolution (DE), bat algorithm (BA), dwarf mongoose optimization (DMO), genetic algorithm (GA), artificial bee colony (ABC), and ant colony optimization (ACO). The simulation results provide evidence for the well-organized and efficient performance of the SAA in solving a great diversity of engineering problems. The results demonstrated that the SAA can be more effective than other algorithms to solve the test functions in terms of optimization accuracy and convergence rate. Moreover, the results proved that the SAA obtained more competitive results than the previous methods to solve constrained engineering optimization problems, especially hybrid energy system design as well as economic load dispatch problems.

Design of sustainable offshore hybrid energy systems for improved wave energy dispatchability

Wave energy is a renewable energy source having a highly exploitable potential in several locations worldwide. In the framework of energy transition, the exploitation of wave energy combined with end-of-life offshore stranded gas reservoirs may lead to two positive impacts: the stabilization of the energy supplied to the grid and a better penetration of renewable energy in areas where the grid is not able to compensate the fluctuations associated to renewable energy production. Moreover, in order to guarantee the dispatched energy schedule, wave energy needs to be coupled with back-up systems aimed at valley filling. In the present study, an innovative approach to the conceptual design of hybrid energy systems based on wave energy is developed, entailing an operation strategy that complies with the dispatching needs of grid-connected generation systems. The probability of correct dispatching that the producer assures to the Transmission System Operator is used as a parameter to optimize the design of a Gas to Power back-up system used for valley filling. The approach supports the preliminary design of offshore hybrid energy systems based on wave energy, starting from historical wave data up to the definition of an optimal back-up system valorizing residual reservoir fuels and its operation strategy. The proposed design is evaluated through a Multi-Criteria Decision Analysis, including the technological, economic, environmental and safety aspects, which allows the assessment of the overall sustainability performance of the hybrid system, considering the fluctuations associated to wave power generation during a typical operation period. The methodology was applied to two test-cases in different offshore operating theaters (North and Adriatic seas), in order to test its potentiality. The results highlighted that, in both sites, similar design choices are suggested for the hybrid system. However, the annual energy production resulted 6.5 times higher in the North Sea test-case. The low energy generation in the Adriatic Sea test site caused a levelized cost of energy of 3960 EUR/MWh, much higher than the value obtained for the North Sea case (610 EUR/MWh). In both cases, the gas turbine park impacts negatively on the cost of energy production, but is critical in meeting the design value of the probability of correct dispatching.

Optimum design and techno-socio-economic analysis of a PV/biomass based hybrid energy system for a remote hilly area using discrete grey wolf optimization algorithm

Un-economical grid extension in remote locations has motivated to the creation of stand-alone systems that are inexpensive and reasonably efficient. Renewable sources such as solar energy and biomass are easily available in remote and rural areas which can be utilized to fulfil the energy demands. In the present paper, a PV/Biomass/Battery based hybrid system is considered for an un-electrified village located in Indian state of West Bengal. A novel design of hybrid energy system has been investigated under the technical, social and economic constraints. Further, two distinct resource scenarios have been considered and compared based on system cost. Further, 12 combinations of batteries and PV panels available in market are considered. The Discrete Grey Wolf Optimization (DGWO) algorithm has been used to investigate the optimal sizes and the system net present cost (NPC). Based on the findings, it is found that Scenario-2 (Biomass-solar-battery-based hybrid system) yields the lowest NPC of $159,648.70 at the levelized cost of energy of $0.0712/kWh.

Design of Hybrid Energy System for Railway Application (Case Study of People Mover System in Doha, Qatar)

This paper presents the conceptual design of hybrid energy system used in railway application. The hybrid system with batteries and energy storage double-layer capacitor is a new technology that is used under extreme climatic conditions, especially in daytime temperature up to 50°C, high relative humidity, dust and heavy rain. It is a combination of double-layer capacitors and traction batteries. It draws power both externally and from braking energy. In order to reduce CO2 emissions to the environment, energy-saving drives and energy storage are used. Also, in public transportation, Sitras Hybrid Energy System (HES), hybrid energy storage system for trams, has been developed which combines a double-layer capacitor with a nickel-metal hydride battery. The storage not only allows driving without overhead lines, it also enables braking energy to be recovered. A reliable cooling system is required to ensure that the performance of the battery and the capacitor storage is maintained for as long as possible. The results of finite element model showed the robustness for railway application. The computational model refers to proof of static and dynamic strength in accordance with EN12663. A cooling system for a tram using this innovative technology was designed and qualified for the "Qatar Education City People Mover System (PMS)" project.

Techno-economic and environmental design of hybrid energy systems using multi-objective optimization and multi-criteria decision making methods

Reliable, affordable and sustainable supply of energy is of paramount importance. This can be achieved by designing and implementing hybrid renewable energy systems, also known as micro/smart-grids. The main goal of this study is the techno-economic and environmental multi-objective optimization of a decentralized energy system to provide electrical and thermal loads of a large energy consuming complex with daily average electric and thermal loads of 38.7 and 99.4 MWh, respectively. Pareto-optimal mix of several renewable and non-renewable technologies including photovoltaic (PV), wind turbine (WT), two types of combined heat and power technologies, namely micro-gas turbine (MGT) and fuel cell (FC), grid power, electrolyzer, hydrogen tank (Htank), battery and inverter is investigated. For this purpose, different configuration of these units were simulated in HOMER software, and the exact Pareto set was found by developing a MATLAB code. A total of 1945 optimal non-dominated designs including 11 stand-alone and 41 grid-connected configurations were found, in which the dominant configuration was PV/FC/Electrolyzer/Htank/Battery/Inverter/20%Grid. The Pareto sets included systems with a wide variety of characteristics; the range (average) of net present cost (NPC), loss of power supply probability (LPSP) and carbon dioxide emissions (CE) were 26.9–44.0 (36.1) M$, 0–10 (5.7)% and 8800.9–13649.2 (9790.4) tons/yr for off-grid scenario and 17.2–37.5 (27.2) M$, 0–10 (4.3)% and 11215.1–17968.2 (13476.1) tons/yr for on-grid scenario, respectively. The TOPSIS combined with weighting methods was introduced to choose final design among non-dominated solutions set. At last, sensitivity analysis revealed that the most significant variables on the NPC of the selected micro-grids are real interest rate, investment costs, electric and thermal loads and level of irradiation, respectively.

Impact of Multi-Year Analysis on the Optimal Sizing and Control Strategy of Hybrid Energy Systems

Grid-connected hybrid energy systems (HESs) represent a very promising option for addressing the problem of power outages worldwide. The selection of a suitable optimization approach and operational strategy are important aspects of the optimal design and operation of these HESs. This study aimed to find the optimal grid-connected PV/battery system sizes to supply electricity for a residential house in Karbala, Iraq, using two control strategies, load following (LF) and cycle charging (CC). The optimization was performed using HOMER software with and without the multi-year effects. The comparison analysis was carried out by considering the techno-economic and environmental performance of the feasible systems. The simulation results indicate that optimal configuration is achieved by using the CC strategy. Furthermore, the multi-year module affects the optimal results dramatically. Under the CC strategy, the multi-year effects increase the required PV size from 6 kW to 7 kW and the required number of batteries from 18 to 20, leading to an increase in the net present cost from $26,750 to $33,102 and a decrease in CO2 emissions from 7581 kg/year to 7379 kg/year. The results also show that the optimization results are highly affected by the variations of some critical parameters, such as solar radiation, average load, and battery degradation limits. The achievements indicate the higher effectiveness of the multi-year effects and control strategy on the optimal design of HESs.

Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system

With increasing grid-penetration of renewable energy resources and a rising need for carbon-free dispatchable power generation, nuclear-hybrid energy systems (NHES), consisting of small modular reactors, are an increasingly attractive option for maintaining grid stability. NHES can accomplish this with a minimal carbon footprint but there are significant uncertainties that are not fully understood. This work describes and demonstrates methods for analyzing the uncertainties of potential NHES designs, including uncertain design parameters and time series as well as variations in dispatch horizon length. The proposed methods are demonstrated on a sample system with 16 design parameters, 3 uncertain time series, and a range of dispatch horizon lengths where the unit capacities and unit dispatch are co-optimized to minimize system LCOE. For the example system, 11 of 16 parameters are uncorrelated with model outputs, allowing for model reduction without decreased accuracy. It is determined that the impact of variation in multiple time series cannot be easily isolated and that the examined sources of uncertainty are of similar importance in terms of overall impact. • Optimize SMR-renewable hybrid energy system design with uncertain parameters. • Quantify design changes with forecasts as stochastic time series. • Investigate optimal dispatch and design with varying horizon length. • Method for sensitivity analysis of combined design and dispatch of SMR hybrid energy systems.

Design and Optimization of a Grid-Connected Solar Energy System: Study in Iraq

Hybrid energy systems (HESs) consisting of both conventional and renewable energy sources can help to drastically reduce fossil fuel utilization and greenhouse gas emissions. The optimal design of HESs requires a suitable control strategy to realize the design, technical, economic, and environmental objectives. The aim of this study is to investigate the optimum design of a grid-connected PV/battery HES that can address the load requirements of a residential house in Iraq. The MATLAB Link in the HOMER software was used to develop a new dispatch strategy that predicts the upcoming solar production and electricity demand. A comparison of the modified strategy with the default strategies, including load following and cycle charging in HOMER, is carried out by considering the techno-economic and environmental perspectives. According to optimization studies, the modified strategy results in the best performance with the least net present cost (USD 33,747), unmet load (87 kWh/year), grid purchases (6188 kWh/year), and CO2 emission (3913 kg/year). Finally, the sensitivity analysis was performed on various critical parameters, which are found to affect the optimum results on different scales. Taking into consideration the recent advocacy efforts aimed at achieving the sustainable development targets, the models proposed in this paper can be used for a similar system design and operation planning that allow a shift to more efficient dispatch strategies of HESs.

Technical Control and Optimal Dispatch Strategy for a Hybrid Energy System

Optimal dispatch is a major concern in the optimization of hybrid energy systems (HESs). Efficient and effective dispatch models that satisfy the load demand at the minimum net present cost (NPC) are crucial because of the high capital costs of renewable energy technologies. The dispatch algorithms native to hybrid optimization of multiple energy resources (HOMER) software, cycle-charging (CC) and load-following (LF), are powerful for modeling and optimizing HESs. In these control strategies, the decision to use fuel cell systems (FCs) or battery energy storage systems (BESs) at each time step is made based on the lowest cost choice. In addition, the simultaneous operation of a FC with a BES reduces the operating efficiency of the FC. These deficiencies can affect the optimal design of HESs. This study introduces a dispatch algorithm specifically designed to minimize the NPC by maximizing the usage of FCs over other components of HESs. The framework resolves the dispatch deficiencies of native HOMER dispatch algorithms. The MATLAB Version 2021a, Mathworks Inc., Natick, MA, USA Link feature in HOMER software was used to implement the proposed dispatch (PD) algorithm. The results show that the PD achieved cost savings of 4% compared to the CC and LF control dispatch strategies. Furthermore, FCs contributed approximately 23.7% of the total electricity production in the HES, which is more than that of CC (18.2%) and LF (18.6%). The developed model can be beneficial to engineers and stakeholders when optimizing HESs to achieve the minimum NPC and efficient energy management.

An improved particle swarm optimization for optimal configuration of standalone photovoltaic scheme components

AbstractThe hybrid energy system design problem needs efficacy tools to reach optimal results in remote areas. The metaheuristic optimization methods are the best choice to address complex problems. This article presents an improved approach based on an energy management strategy for optimal sizing and configuration of standalone photovoltaic scheme components. Improved particle swarm optimization for optimization and configuration of photovoltaic panel and battery system is applied using MATLAB and hourly solar radiation, ambient temperature data, and load demand. The objective of the optimization problem is to define the optimal sizing of the system components to meet the load demand to compose a cost‐effective and reliable scheme. The optimized system results from the improved approach are compared by original particle swarm optimization and simulated annealing algorithms. So, the optimum design of the hybrid scheme is analyzed and compared based on different reliability values. The results show that the improved approach finds the optimal results easily with lower cost, faster convergence, and better reliability indexes in different reliability indexes in comparison with the other approaches. In this regard, for different reliability values (1%, 3%, and 5%), the improved particle swarm optimization shows approximately 22.9% cost saving in comparison with the simulated annealing, and improved particle swarm optimization shows approximately 0.35% cost saving in comparison with the particle swarm optimization. Also, show the reliability of the standalone hybrid photovoltaic system.

Sustainable Design of Hybrid Energy Systems towards Carbon Neutrality

As Cornell is transitioning to a carbon-free energy system by 2035, the campus energy system of the future will be based on 100% renewable energy sources. Specifically, the electricity will be mainly sourced from the local electric grid, which is expected to be carbon-free in the next two decades. Earth source heating and lake source cooling will serve as the major source for base-load renewable heating and cooling, respectively. Multiple geothermal wells will be drilled to meet the base-load heating demand. A conventional chiller will continue to provide auxiliary cooling sources for hot summer days in addition to the LSC system. Peak load will be fulfilled by introducing thermal energy storage and green hydrogen. This study addresses the economically optimal future design by developing a multi-period optimization model, to provide insights for the campus energy systems transition. A systematic life cycle assessment is adopted to examine the extent of carbon neutrality based on the optimization results.

Artificial intelligence assisted technoeconomic optimization scenarios of hybrid energy systems for water management of an isolated community

Water is an essential resource demanded worldwide and it is quite debatable owing to the economic, political, and energy characteristics of any region. Off-grid water filtration plants are an alternative for communities where transportation of freshwater becomes a real challenge due to a lack of infrastructure for the water potabilization processes. For such potable water filtration plants, hybrid renewable energy systems (HRES) can be a viable solution to meet their energy demand meanwhile providing a sustainable water solution.The main contribution of this work is the unique methodology, which starts with a sizing procedure of various hybrid energy systems using a commercial software “Hybrid Optimization of Multiple Energy Resources (HOMER)” and spreadsheet algorithms, followed by a “Non-dominating Sorting Genetic Algorithm II (NSGA-II)” based multiobjective optimization. Single-objective optimization scenarios contain photovoltaic installation capacity, wind turbines, diesel generators, and battery energy storage systems including Pb-acid (Lead-acid), Li-ion (Lithium-ion), and AGM (Absorbent Glass Mat) technologies as design variables to maximize the cost of electricity or net-present-cost. Multiobjective optimization also involved environmental (CO2 emissions i.e. carbon dioxide emissions) and water cost indices as an additional packet to single-objective optimization scenarios. Afterward, a multicriteria decision-making tool using “The Order of Preference by Similarity to Ideal Solution (TOPSIS)” is applied on the Pareto front to attain the final optimization results. The analysis is further explored in depth by generating digital twins (surrogate or meta model) of HRES data using artificial intelligence techniques (artificial neural network and group-method-of-data-handling). Furthermore, calculus and statistical sensitivity analysis assist in the identification of the significant variables in the design procedure. In summary, the technical contribution of this work can be divided into two sections. The first one is the design of a hybrid energy system for the water management of an isolated community of the indigenous Mayan region of Yucatan, Mexico, which has never been considered before. Secondly, the technical contribution is related to the usage of environmental emissions as an objective function, which is not considered in the traditional design of hybrid energy systems by the software HOMER. Environmental emission as an objective function is not considered while designing a hybrid energy system in commerical softwares like HOMER, in fact, HOMER provides a list of environmental impacts but it is a secondary outcome as a result of technoeconomic optimization.Analysis of results between HOMER pro and spreadsheet has shown conformity, reporting that the optimal case consists of a photovoltaic system, diesel generator, and Li-ion technology of battery storage with capacities of ∼17 kW, ∼5kW, and 44–48 kWh, respectively, corresponding to a net present cost ranging from 70,000 United States Dollars (USD) to 79,000 USD and a cost of electricity ranging from 0.205 to 0.229 USD/kWh. The achievements obtained with multiobjective optimization indicate that the cost of electricity and net present cost can be further reduced by 0.86 % and 0.73 %, respectively, at a decrement of only 0.4% of the renewable fraction as compared to the single objective optimization scenario. It is concluded that multiobjective optimization provides an add-in feature to HOMER by using environmental emissions as an objective function.The design procedure and adapted methodology can be useful to promote sustainable development in the statewide context and can provide a scientific justification to national energy policymakers.

Cost Analysis of Optimized Islanded Energy Systems in a Dispersed Air Base Conflict

The United States Air Force has implemented a dispersed air base strategy to enhance mission effectiveness for near-peer conflicts. Asset dispersal places many smaller bases across a wide geographic area, which increases resupply requirements and logistical complexity. Hybrid energy systems reduce resupply requirements through sustainable, off-grid energy production. This paper presents a novel hybrid energy renewable delivery system (HERDS) model capable of (1) selecting the optimal hybrid energy system design that meets demand at the lowest net present cost and (2) optimizing the delivery of the selected system using existing Air Force cargo aircraft. The novelty of the model’s capabilities is displayed using Clark Air Base, Philippines as a case study. The HERDS model selected an optimal configuration consisting of a 676-kW photovoltaic array, an 1846-kWh battery system, and a 200-kW generator. This hybrid energy system predicts a 54% reduction in cost and an 88% reduction in fuel usage, as compared to the baseline Air Force system. The HERDS model is expected to support planners in their ongoing efforts to construct cost-effective sites that minimize the transport and logistic requirements associated with remote installations. Additionally, the results of this paper may be appropriate for broader civilian applications.