Energy Cost Research Articles

On the Development of a Near-Shore Pivoting Wave Energy Converter

Numerous offshore wave energy converter (WEC) designs have been invented; however, none has achieved full commercialization so far. The primary obstacle impeding WEC commercialization is the elevated levelized cost of energy (LCOE). Consequently, there exists a pressing need to innovate and swiftly diminish the LCOE. A critical challenge faced by WECs is their susceptibility to extreme wave loads during storms. Promising concepts must demonstrate robust design features to ensure resilience in adverse conditions, while maintaining efficiency in harnessing power under normal sea states. It is anticipated that the initial commercial endeavors will concentrate on near-shore WEC technologies due to the cost advantages associated with proximity to the coastline, facilitating more affordable power transmission and maintenance. In response, this manuscript proposes a pioneering near-shore WEC concept designed with a survivability mode that is engineered to mitigate wave loads during severe sea conditions. Moreover, prior investigations have highlighted favorable resonance properties of this novel concept, enhancing wave power extraction during recurrent energetic sea states. This study employs numerical and physical modelling techniques to evaluate wave loads on the proposed WEC. The results indicate a remarkable 65% reduction in wave loads on the moving floater of the WEC during a range of sea states under the implemented survivability mode.

A techno-economic assessment of the viability of a photovoltaic-wind-battery storage-hydrogen energy system for electrifying primary healthcare centre in Sub-Saharan Africa

Healthcare facilities in isolated rural areas of sub-Saharan Africa face challenges in providing essential health services due to unreliable energy access. This study examines the use of hybrid renewable energy systems consisting of solar PV, wind turbines, batteries, and hydrogen storage for the electrification of rural healthcare facilities in Nigeria and South Africa. The study deployed the efficacy of Hybrid Optimization of Multiple Energy Resources software for techno-economic analysis and the Evaluation based on the Distance from Average Solution method for multi-criteria decision-making for sizing, optimizing, and selecting the optimal energy system. Results show that the optimal configurations achieve cost-effective levelized energy costs ranging from $0.336 to $0.410/kWh for both countries. For the Nigeria case study, the optimal energy system includes 5 kW PV, 10 kW fuel cell, 10 kW inverter, 10 kW electrolyzer, and 16 kg hydrogen tank. South Africa’s optimal configuration has 5 kW PV, 10 kW battery, 10 kW inverter, and 7.5 kW rectifier. Solar PV provides more than 90 % of energy, with dual axis tracking yielding the highest output: 8,889kWh/yr for Nigeria and 10,470kWh/yr for South Africa. The multi-criteria decision-making analysis reveals that Nigeria’s preferred option is the hybrid system without tracking. In contrast, the horizontal axis, weekly adjustment tracking configuration is optimal for South Africa, considering technical, economic, and environmental criteria. The findings highlight the importance of context-specific optimization for hybrid renewable energy systems in rural healthcare facilities to accelerate Sustainable Development Goals 3 and 7.

Efficient learning in spiking neural networks

Spiking neural networks (SNNs) are a large class of neural model distinct from ‘classical’ continuous-valued networks such as multilayer perceptrons (MLPs). With event-driven dynamics and a continuous-time model in contrast to the discrete-time model of their classical counterparts, they offer interesting advantages in representational capacity and energy consumption. Spiking networks may also be more biologically plausible, offering more insights into neuroscience. However, developing models of learning for SNNs has historically proven challenging: as discrete-time systems, their dynamics are much more complex and they cannot benefit from the strong theoretical developments in MLPs such as convergence proofs and optimal gradient descent. Nor do they gain automatically from algorithmic improvements that have produced efficient matrix inversion and batch training methods. Most of the existing research has focused on the most well-studied learning mechanism in SNNs, spike-timing-dependent plasticity (STDP), and although there has been progress, there are also notable pathologies that have often been solved with a variety of ad-hoc techniques. While efforts have been made to map SNNs to classical convolutional neural networks (CNNs), these have not yet shown any decisive efficiency advantage over conventional CNNs. More promising research directions lie in the realm of pure spiking learning models that exploit the inherent temporal dynamics (and often leverage recurrency). Metrics are needed; one possibility would be a measure of total energy cost per unit reduction in error. This tutorial overview looks at existing techniques for learning in SNNs and offers some thoughts for future directions.

Environmentally-friendly calcite scale mitigation: encapsulation of CDs@ MS composite within membranes framework for nanofiltration

Scale formation caused by the precipitation of sparingly soluble salts such as CaCO3 is a major challenge in membrane-based desalination technologies, leading to flux decline and increased energy costs. Surface modification using nanoscale coatings is a promising approach to mitigate membrane fouling. In this work, we demonstrate the antiscalant properties of a MoS2-carbon quantum dot (CDs@MS-NF) composite with a nanoflower-like structure coating for suppressing calcite scale formation. The CDs@ MS-NF coating was synthesized via a solvothermal method and applied to a polyamide reverse osmosis membrane surface using an in-situ growth technique. Characterization revealed uniform coverage of MoS2 nanosheets decorated with CDs@MS-NF on the membrane surface. Static fouling tests simulating calcium sulfate scaling conditions were conducted to evaluate the antifouling performance. The CDs@MS-NF coated membrane exhibited a flux reduction of only 11 %, compared to 34 % for the unmodified membrane after 24 hours. Further analyses showed a calcite nucleation induction time of 5.2 hours and a critical scaling threshold of 1.8 times the solubility for the CDs@ MS-NF coated membrane, indicating significant inhibition of scale formation. We hypothesize that the CDs@ MS-NF nanocomposite layer alters surface charge and wettability while also catalyzing redox reactions to hinder calcite crystal growth. This work demonstrates that multifunctional two-dimensional nanocomposite coatings can effectively mitigate membrane scaling through multiple synergistic mechanisms.

Evaluation of a demand response online algorithm on the costs sustained by the user

The Demand Response (DR) algorithm is a technique that allows users to reduce their electricity consumption during peak hours, thereby reducing their overall energy costs. This study aims to evaluate the impact of the DR algorithm on the electrical costs sustained by the user. The research was conducted by analyzing the energy consumption patterns of a group of residential users with and without the implementation of the DR algorithm in their homes. The results of the study indicate that the implementation of the DR algorithm resulted in a significant reduction in the electrical costs sustained by the users. The study also found that the effectiveness of the DR algorithm was dependent on various factors such as the type of appliance, the time of day, and the user's behaviour. Overall, the study demonstrates that the DR algorithm is an effective tool for reducing energy costs and can be optimized through user participation and engagement. The findings of this research can help policymakers and energy providers to develop more effective demand response programs that benefit both users and the grid.

Building Energy Optimization using BIM: A Study on Institutional Building

Abstract: In many facets of the construction industry, including energy management to encourage the development of green buildings, Building Information Modelling (BIM) is one of the contemporary data information platforms and management technologies that is widely employed. The goal of this study was to optimize an institutional building's energy consumption by analyzing and evaluating its energy performance using BIM-provided capabilities. The Civil Engineering block of Sree Vidyanikethan Engineering College in Tirupati, was taken into consideration for the investigation. The 2D blueprint was obtained from the engineering department and BIM technologies were used to create the necessary building model. The Green Building Studio (GBS) tool and Autodesk Insight 360 were utilized to conduct an energy analysis and derive the energy usage figures. The structural elements that are contributing to the high energy demand, such as the area between windows and walls, the width of the chajja relative to the height of the window, etc., were identified, and the BIM model underwent the required alterations to optimize the energy consumption. Comparing the data obtained in the two aspects, it was found that there was a 30% reduction in energy costs and a roughly 29.76% drop in energy use.

A VMD-deep learning approach for individual load monitoring and forecasting for residential buildings energy management

This paper presents a comprehensive solution for Multi-Individual load disaggregation and Multi-individual load Forecasting in residential energy systems. The approach combines Variable Mode Decomposition (VMD) and deep learning algorithms, incorporating feature selection using Random Forest Regressor and parameter optimization through the Salp Swarm Algorithm (SSA). Correlation analysis is used to identify the most relevant Intrinsic Mode Functions (IMFs) for each appliance, while self-correlation is used to determine the past window for forecasting appliances’ usage a day ahead. The robustness of VMD against noise contributes to the improved accuracy of appliance designation. The load disaggregation strategy yields significant results, indicating the high performance of the disaggregation tasks of these models. Specifically, the proposed disaggregation models exhibit an average RMSE and MAPE of 4.10 W and 4.14 %, respectively. Furthermore, we conduct a comparison among three deep learning architectures (GRU, LSTM, 1DCNN) based Multi-Individual load forecasting models, revealing that the GRU-based MILF model outperforms the other two models, it shows an average performance of RMSE and MAPE of 9.54 W and 8.10 %. The research provides valuable insights and a practical solution for accurate load disaggregation and forecasting, contributing to enhanced energy efficiency, cost savings, and sustainable power consumption in residential settings, and give detailed energetic bill with the aim to analyze the occupant consumption behavior, and gives depth insights about power consumption and cost of each appliance and their operation time.

A new perspective for potassium intercalation chemistry in graphitic carbon materials

Potassium intercalation mechanism in graphitic carbon materials is known to be a staging reaction similar to that of lithium, despite their antithetic intercalation trend in turbostratic carbon (TBC) materials. This study clarified the distinctive potassium intercalation behavior of graphitic carbon materials with different local microstructures through a systematic comparative investigation. In contrast to the monotonic stacking sequence of lithium-intercalated graphitic carbon materials, multiple potassium-intercalated graphitic configurations can be formed by potassiation at an energy level similar to the theoretical KC8 formation. Accordingly, potassium intercalation in TBC materials can result in a theoretical low-voltage plateau capacity without long-range ordering. In contrast, the high energy cost for the threshold number of initial potassium insertions significantly hinders two-phase potassium intercalation in well-ordered graphite-like carbon materials, leading to a poor plateau capacity. Hence, potassium intercalation is more favorable in a turbostratic microstructure that includes intrinsic defects, whereas the presence of too many intrinsic defects reduces the polyhexagonal carbon plane for turbostratic intercalation, leading mainly to solid-solution potassium intercalation. Based on these results, the potassium intercalation mechanism in graphitic carbon materials can be classified into three different types such as 1) solid-solution intercalation, 2) turbostratic intercalation, and 3) two-phase intercalation.

On energy complexity of fully-connected layers

The massive increase in the size of deep neural networks (DNNs) is accompanied by a significant increase in energy consumption of their hardware implementations which is critical for their widespread deployment in low-power mobile devices. In our previous work, an abstract hardware-independent model of energy complexity for convolutional neural networks (CNNs) has been proposed and experimentally validated. Based on this model, we provide a theoretical analysis of energy complexity related to the computation of a fully-connected layer when its inputs, outputs, and weights are transferred between two kinds of memories (DRAM and Buffer). First, we establish a general lower bound on this energy complexity. Then, we present two dataflows and calculate their energy costs to achieve the corresponding upper bounds. In the case of a partitioned Buffer, we prove by the weak duality theorem from linear programming that the lower and upper bounds coincide up to an additive constant, and therefore establish the optimal energy complexity. Finally, the asymptotically optimal quadratic energy complexity of fully-connected layers is experimentally validated by estimating their energy consumption on the Simba and Eyeriss hardware.

Synthesis of Optimal Control of Spacecraft Angular Momentum for Spatial Turn Taking into Account Energy Costs Using Quaternions

Background: In this paper, we propose solving the specific original problem of control synthesis of spacecraft attitude. Optimization of the control program is made with the use of a new criterion of quality that combines energy costs and duration of reorientation under restrictions on control (the presence of a time factor limits the duration of slew maneuver). Methods: The construction of optimal control for angular momentum change is based on the quaternion method and L.S. Pontryagin maximum principle. An analytical solution to the problem was obtained on the base of a differential equation relating the orientation quaternion and angular momentum of a spacecraft. Results: Key properties of the optimal solution are formulated in analytical form; the features of optimal motion are studied in detail. The control law is formulated in the form of explicit dependence between control and phase variables. In a case when the controlling torque is limited by the given restriction (at the beginning and end of a turn), analytical formulas have been written for the duration of braking and acceleration. Main relations which determine optimal values of parameters of the algorithm for control of angular momentum are given. Examples and results of mathematical modeling of spacecraft motion formed by optimal control were given. This data in addition to the theoretical descriptions illustrates the process of reorientation in evident form and demonstrates the practical feasibility of a designed method for control of angular momentum during spatial turn. Conclusion: The designed optimal algorithm of control of spacecraft motion improves the efficiency of spacecraft attitude system, and originates more economical performance of spacecraft during flight on orbit.

Optimizing wave energy harvesting through oscillating water columns in semi-submersible energy harvesting platforms: A comprehensive study on enhancing efficiency and competitiveness

In coastal regions with substantial wave energy potential, significant efforts have been dedicated to advancing both the Technology Readiness Level (TRL) and Technology Performance Level (TPL) of Wave Energy Converters (WECs). Despite their promising energy generation capabilities, the widespread adoption of WEC devices faces a notable challenge in the form of their relatively high Levelized Cost of Energy (LCOE). This economic hurdle hampers their competitiveness when compared to conventional fossil fuels and other renewable energy sources. Oscillating Water Columns (OWCs) stand out due to their noteworthy potential, primarily attributed to their low Operations and Maintenance (O&M) costs. To enhance the energy performance of OWC arrays and pave the way for hybrid offshore energy solutions, the integration of a semi-submersible Energy Harvesting Platform (EHP) is proposed. This integration involves configuring the array with specific OWC End-Stop limitations and geometry to establish resonant conditions during the predominant wave, thereby optimizing constructive interactions and desirable OWC motion with the EHP's heave, pitch, and roll. A Genetic Algorithm (GA) is employed for this optimization process. Additionally, a novel parameter, the In Situ Interaction Factor (ISIF) is introduced, to evaluate the EHP's power capture performance. This metric compares the actual energy harvested from the array of WECs on the platform to the cumulative energy harvested by each device in isolation. Notably, ISIF achieved a remarkable value of 1.99, highlighting the highly efficient constructive interaction achieved through the precise positioning of OWCs. This resulted in nearly doubling the power capture compared to 23 OWCs operating in isolation, underscoring the potential of the EHP in harnessing wave energy. Validation of the analytical analysis was conducted using the open-source Boundary Element Method (BEM) tool NEMOH, alongside reference to the experimental studies.

Optimization of a hybrid system (PV-fuel cell) of energy production for an isolated site

In recent years, there has been a significant shift in the design and implementation of autonomous micro-grid systems, moving away from the traditional reliance on fossil fuels. Instead, these systems are now integrating existing renewable energy sources available in specific geographic locations to promote sustainability and environmental responsibility in the energy sector. One particularly promising approach for energy storage and transmission is through the utilization of hydrogen and solar energy, especially in remote regions where hybrid techniques can effectively meet the required energy demands. Our research focuses on electrifying an agricultural site located in the SBAA region, specifically in the city of ADRAR, southwest Algeria. We propose the implementation of an independent small electric network that combines various renewable energy sources, including photovoltaic (PV) systems and fuel cells. To ensure cost-effectiveness and optimize the system's performance, we aim to carefully control and size the system's components through a meticulous selection process. The primary objective of this study is to evaluate the technical and economic feasibility of implementing renewable energy systems in the targeted region. Our goals are to reduce the total net cost, minimize energy expenses and unfulfilled load, while simultaneously mitigating CO2 emissions. To achieve these objectives, we employ the HOMER Pro simulation program, which enables us to conduct a comprehensive analysis of the proposed system's performance and economic viability. The proposed system incorporates a hybrid configuration of PV panels and fuel cells, leveraging the complementary nature of these two renewable energy sources. PV panels harness the abundant solar energy during daytime hours, while fuel cells provide a reliable and consistent energy source during periods of low solar irradiance or at night. By integrating these two technologies, we aim to establish a self-sufficient and sustainable energy system capable of meeting the energy demands of the remote agricultural site. Overall, the proposed renewable energy system demonstrates positive results in electrifying remote areas while maintaining a conservative cost of electrical energy. By implementing this system, we not only contribute to the establishment of sustainable regions but also promote environmental stewardship by reducing carbon emissions and dependence on fossil fuels.

Reevaluating energy progress: An in-depth policy framework of energy, urbanization, and economic development

Pakistan has challenging energy-related economic concerns, including accessibility, affordability, and sustainability, all of which significantly impact the country's economic development and growth prospects. This study seeks to uncover the intricate link between energy use, power costs, urbanization, alternative energy consumption, and economic development from 1970 to 2018. Utilizing the Autoregressive Distributed Lag (ARDL) approach, Frequency Domain Causality (FDC), and Innovative Accounting Approach (IAA), the research finds that energy use and urbanization positively impact economic development in the short and long term. Conversely, alternative energy consumption declines in the short run, while rising power costs negatively affect long-term economic development. FDC results indicate that rising power costs affect economic development in the short, medium, and long run, while urbanization has a long and short-term and energy use has a long- and medium-run impact on economic development in Pakistan. IAA aligns with ARDL and FDC hypotheses. This underscores the vital role of energy use and costs in economic activity, emphasizing the need for prudent urbanization and strategic alternative energy integration in long-term policy for Pakistan.

Optimized system for combined production of electricity/green hydrogen for multiple energy pathways: a case study of Egypt

Abstract By optimal sizing of a wind/photovoltaic hybrid renewable energy system, trimming the surplus capacity to reduce the fluctuations in the electricity supplied to the grid and using it to produce green hydrogen through electrolysis, a stable output with maximum possible capacity factor is generated to maintain the electricity grid stability. Simultaneously, the trimmed energy is used in a secondary conversion path that minimizes the weighted average cost of energy generated from the entire plant. This surplus power-to-gas conversion allows the use of green hydrogen to produce electricity, methanol, or ammonia subject to the resource availability, site characteristics, and financial feasibility. Based on robust site selection criteria, the best performance is obtained at two sites: Ras Ghareb and Minya achieving the least energy cost with some variance in their performance. For Ras Ghareb site, the optimally sized renewable energy plant provided the grid with a quasi-steady capacity of 423 MW with a capacity factor of 80.04 % and was capable of injecting 2965.8648 GWh throughout the year with the least cost of 2.4355 ¢/kWh. A surplus of 3.9 % of the total energy produced from the plant was directed to produce 1922-ton H2/year, achieving the least cost of hydrogen production of 1.9745 $/kg H2. For the other selected site, Minya, the clipped energy is used to produce 3330.47-ton H2/year with an optimized least cost of 3.5268 $/kg H2. The difference in hydrogen cost was attributed to the number of full operating hours of the electrolyzer in both sites. The cost is mainly affected by the electricity price and the electrolyzer cost. With both tending to decrease, future forecasts show hydrogen cost reductions.

An explainable & prescriptive solution for space-based energy consumption optimization using BIM data & genetic algorithm

Creating energy-efficient buildings is a multifaceted challenge that involves carefully considering architectural, mechanical, and electrical parameters. With the increasing emphasis on low-energy building mandates and sustainable construction practices, integrating energy performance simulation into the design process has become imperative. However, existing methods often struggle to handle the vast amount of design information and to explore optimal alternatives effectively. While recent approaches such as Building Information Models (BIM) and data-driven solutions show promise for energy analysis, they frequently lack transparency and robust design optimization capabilities, limiting their applicability in critical contexts within the Architecture, Engineering, Construction, and Operation (AECO) industry. In response to these challenges, this research proposes a novel prescriptive model aimed at addressing these limitations. The proposed methodology integrates building energy simulation with optimization techniques, utilizing BIM data and a Genetic Algorithm (GA). The Genetic algorithm provides some optimized solutions, including different alternatives of geometry, material, electrical, and mechanical elements to help engineering for data-driven decision-making. This integration aims to develop a model that is both prescriptive and explainable for indoor building design. Leveraging the value engineering method, the proposed approach seeks to strike a balance between energy consumption, functionality, and cost. By doing so, it aims to enhance energy efficiency while providing tangible design optimization solutions.

Inter-array cabling optimization in offshore wind power plants, a new reliability indicator for radial grids

The inter-array grid relates to a significant share of the investments into an offshore wind power plant (OWPP). Optimizing the cable connections regarding costs and reliability is a mathematically complex task due to the high variety of possible wind and component (wind turbine or cable) failure scenarios. This paper presents a novel mixed integer linear programming approach to support investment decisions into OWPPs by trading off cabling purchase and installation costs with power capacity risk (PCR), which is defined as a length-weighed cumulative power flow summation that reflects the consequences of cable failures. Then, quasi-random Monte Carlo simulations assess the optimized collection grids (CGs) to quantify their levelized cost of energy (LCOE). To construct relevant case studies, this work investigates the real OWPPs Ormonde, Horns Rev 1, Thanet, and London Array, which contain 30, 80, 100, and 175 wind turbines. The results reveal Pearson correlation coefficients around 0.99 between the proposed PCR and the expected energy not supplied. Furthermore, this paper’s findings indicate that minimum-cost CGs do not necessarily present the lowest LCOE.

GREENER IS CHEAPER: AN EXAMPLE FROM OFFSHORE WIND FARMS

Abstract Offshore wind farms (OWF) are now in operation and increasingly under construction as scalable, sustainable energy sources. In fact OWFs are currently the cheapest form of new energy projects in Europe. The levelized cost of energy (LCOE) for OWF has fallen drastically due to decades of innovation facilitated by both taxpayer and private sector funding. This emerging industry is experiencing massive worldwide growth with the potential to accelerate the decarbonization of regional and the global economy as well as bring a reliable source of green hydrogen into commercial use, all with minimal disruption to ecosystems and impacts on biodiversity. This paper provides a historical perspective of wind energy harnessing and shows that wind turbines are the oldest, largest and one of the smartest machines. We also highlight the potential of offshore wind energy to provide new solutions to (a) meet clean energy demand for a growing world population, (b) improve energy security of nations through other downstream technologies such as production and storage of dispatchable fuel (such as green hydrogen battery storage) and (c) through supply complementarity improve resilience of nuclear power plants in high-seismic-activity areas. Offshore wind industry can also become a gold standard for future industries, and the paper provides insights into the new green economics and jobs and factories for the future. We show that environment-friendly regulation is driving innovations even further to enhance sustainability of OWF. Examples include material recycling, landfill ban on blade disposal and ecofriendly low-noise offshore construction to protect biodiversity.

Triple effect absorption refrigeration systems for the deep recovery of low grade waste heat

With regard to energy conservation, low grade waste heat recovery technology is attracting significant interest. The absorption refrigeration can significantly utilize waste heat. The current researches have devoted little attention to the recovery of waste heat at lower temperature. This paper presents three triple effect absorption refrigeration systems for the deep recovery of low grade waste heat, which can make use of more waste heat as low as 60 °C. The involvement of absorption heat transformer cycle enables the proposed systems to exploit waste heat at lower temperature. The models of three systems are established using EES software. Energy, exergy and economic analysis of three systems are conducted. The results indicate that the best of three systems can achieve a coefficiency of performance of 0.178 and the maximum exergy efficiency is 0.190. The triple effect absorption refrigeration systems not only generate more cold energy, but also demonstrate better economic performance than the previous cascade absorption refrigeration system. Except for the first system, the other two systems achieve reductions of 8.7% and 15.2% in the cost of unit cold energy respectively. This paper provides three novel approaches for producing high grade cool energy from lower temperature of waste heat.

Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

Optimal design guidelines for net zero energy residential buildings in cooling-dominated climates: Case study of Ghana

Net zero energy buildings (NZEBs) are regarded as an achievable solution for reducing energy use and carbon emissions in the built environment and addressing climate change. However, the ultimate objective of NZEBs is to enhance the energy performance of buildings by changing how they are designed and constructed. Consequently, identifying cost-effective energy efficiency strategies at the early design stages of buildings has become imperative and often required in several countries. This study describes a systematic analysis approach to optimize the design of residential facilities considering both energy efficiency and renewable energy systems. The design optimization is based on life cycle cost (LCC) analysis, which considers capital and energy costs over the lifecycle of the building. The analysis consistently shows that LCC-based optimal designs significantly reduced the building's annual energy consumption, achieving 34–35% energy-saving across the various climates compared to the baseline case. The most cost-effective energy efficiency measures that proved effective across all the climate zones include north orientation of the building at 180 azimuth degrees, natural ventilation, adequate window-to-wall ratio, minimal façade air leakage (i.e., about 5-7 ACH @50 Pa), and high efficient fans, air conditioning, and dehumidification. The integration of the PV system indicates that the net zero energy (NZE) design for the building is technically feasible and cost-effective across the climate zones considered. The sensitivity analysis further shows that the cost of integrating PV systems is even more cost-effective over a more extended period of the building's lifecycle. The study provides an optimal path for designing NZEBs and offers guidelines to achieve NZE across the building's lifecycle.